diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..87620ac7e74efee566c6ee9d2ed7281ebafb4788
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints/
diff --git a/Entrega.ipynb b/Entrega.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0a01f2bc23fb7b4b4d876374e91065fa49a68c27
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@@ -0,0 +1,1669 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Estudiante: Angie Nicole Hernández Durán - UIS**\n",
+    "\n",
+    "# Numpy y optimizción con SciPy\n",
+    "\n",
+    "Este ejercicio consiste en conseguir una medición de la resolución espacial a partir de una foto del cielo estrellado. Específicamente se calculará la $\\text{anchura a media altura}$ (FWHM), la cual es una medida derivada de uno de los datos que se puede obtener de un ajuste gaussiano, la desviación estándar. Para llegar a esta medición tomaremos los pasos que se presentan a continuación. \n",
+    "\n",
+    "Primero se leyeron los datos correspondientes, en este caso, una imagen en formato jpeg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import leastsq "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sky = plt.imread('data/zapatocaImage.jpeg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(sky)   #La imagen es efectivamente un array de numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(789, 1184, 3)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sky.shape   #Vemos las dimensiones del arreglo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f3cd5f6d208>"
+      ]
+     },
+     "execution_count": 67,
+     "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": [
+    "plt.imshow(sky)  #Y visualizamos la imagen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La imágen guardada en \"sky\" está a color, ahora se deben combinar los 3 arrays para generar una versión a blanco y negro. Una manera de hacer esto es añadir los 3 canales y luego dividirlos entre 3 para obtener un promedio de los valores de cada canal, que darán los valores de gris de los pixeles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sky_bw = (sky[:,:,0] + sky[:,:,1] + sky[:,:,2]) / 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f3cd5ed4e10>"
+      ]
+     },
+     "execution_count": 69,
+     "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": [
+    "plt.imshow(sky_bw, cmap = 'gray')    #Nuevamente visualizo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lo anterior no se ve como las imagenes a blanco y negro a las que estamos acostumbrados, esto es debido a que se suele usar un método que consiste en dar diferentes pesos a cada canal, ya que las diferentes longitudes de onda brindan diferentes sensaciones oculares al ojo humano. A continuación se utilizó dicho método."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sky_bw = sky[:,:,0] * 0.3 + sky[:,:,1] * 0.59 + sky[:,:,2] * 0.11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f3cd5c6b208>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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y2+02gCRiVyHi73w+jyiKnHLX52azmduQo7S09LUKMY5jh2hVeVAx+BSmuu4WQekzrEMX3ayQqvAz+0MVBo2mXdTUepTe2idfuICfK7pnP1RgmV2gBs1nCPr9/pIHQvqkUqkET3CegiDA7u4uUqkUXr16lVBsNObFYhHj8djNHdcDwjBMZFdo5g/niXKi8x5FEUql0pJRJj/TiKkhVoXJtQsfSqY8r/J8rAdJA6Z6gPOi7WkffJ4ojYmGTdSgaX9soTLV7336RPlYs5bIM+QX9SYtv2mdb4px3xnFDdy6RLRWQNJiaUwTWIQ5JpOJU/TFYtG9Nx6PE8hK0YHWTfeU7udsNsPV1RXCMMTW1hYymYxThCcnJ2i326jX607hj0YjxPFit+LFxQUajQbCMHTKXZlMlZx+9zomZlEhoZJRxWZdcyo3tuGLwerzZChFJLZ9/a2pc+pu2pivRYyswypFq9B0XDpvPrrwM1XufFbfU+TGz+32ZF9sUuvwoWq+Ry+Nn7FNImd1pzU7xddv/t3pdBCGt1kMihpt/xkrV8+U9Fm1/Z7vqsKg0mahDGkYZzabJY55oAEKwxA//dM/je9///vodrv46le/iufPn7sFWSJVVf72b23Thkb4nPbZolc7d+RVa2QsP6nyZ6ER1rkJgsCNwfIFDRfnx9LM0lqPKeDOz/F4vNKQsNwpxW3dBqInCoZmccRxjMlkgk6ng2aziXQ6jUKhgHa7jclk4oRBXSydPHWrabkBOPd5fX3dKV/dztvv953BILGDIEC73Ua/38f5+Tl2dnYSxmAVkgXgUKJ+5ysUCnVFWbcy3Hw+d2l2Go6xYQb7v0U1FpVq33S7NJ/lMxbFsG82C4c/q84Z0XeAW9dU0bnWyXjn1dXVUn9VESrSUiBA5WaVWhzHaDabaLfbiOPFwvNwOHRtqkFV1GwNBYBEPXauSXsqAb7f7XZRqVQSc6XzqYqT3oAqPfUU1ENSEKHo2BeKsjxr51Dri+MYe3t7Lgz5/PnzlWlzHLfOF9e1yuVyIrmA7bMtG1r0GVzKhvKresHaZ+vNK1iwqFxlw+okPuMLRdr/NaTKwj0Cb8rjvjOK2w5MFxczmQwKhQKur68TK84AMBqNcHh46JDC63IrFW2pgJCAuVwOuVwuofBSqRTa7Tb29/edAmfMW5l/Npshn8+7rfZ8l+2pkKiyVbRpGUSFj++rG856iH7UODGWTmVn0YsWZVztty+MA8Bt32XfVWFbl5X1aT3A7W5FIjFNu/KFuVRps58aUprNZuh2u65tVS7A8lksqnCtAuMc0zgwHTCKIlQqFWcYiYKpNK0CodJoNpvodDpot9sJT2Q+n7uFTkW5nEfWwe809GCVir6nqFNpoYrKp4SsArMKW+uil0ngonnNh4eHrm7OL+uZTCZu7GrUtC/kF+thU8Z8iQvvvPMOLi4uvKmbFuhwLFa5UpasAVN6WiVtgRf5hwrZtmF5UcHC9fV1Yn3gdeVOZJUok1rG4udcBPSlA1JJURnYkAhwGy+zn7EdhltIOFr6i4sLvHjxwll/Vdz692w2Qzqdxs7OjmMuiyw1k8MKuCp1HZu68KoMFe3YlX6OTZUwcBtjU7edn7MfqVQKlUolEXohnci0DA9ZBaHhDp3TOI4Trnocx3jnnXfQbDYTxk3T07TPqkAo1NZj4HjVMFuUbQUQQEIB8Dsb5uBOwDiOcXZ25sZDhWp5jW2y3Xa7vRSLZRbBdDpFv99f4gUt1iPTo4l1u7y+q3zgU+AqX1QiSjNLD2uQdX6D4DZ0wDnS99UzCMMQ29vbiT0W2ib7oJtylBe4gUXlIpfLodlsolQqLXlOClpeBzJUNjX11uoh9Ur5ntKcz+h7q+Qun8+7lMNUKuVoovWvKncGcSt6YlyIylN3TPnc88vLSxeHViTFgauFtIibQhAEgSPkdDp16G00Grn8Zt2swT4T7cZx7NATd4FaxMq+qPLW+oCkOwgshyU0F5rFMiGwyGLQxSlgsUuQW+jZllp2tsGYID0RzaqJ49gd4arzoG2zHhu7VqF/8eLFksJgfJAozrrTGg+2ylIVJWOvKlCKqrWv6qGo2wsAtVoNrVbL0d2iPKvkfMaEKNP202Zo6HkmDMMpAFHvplwuo9VqJYyzRdXW67Dehs4Z6avtqTyqolN+tqEmNYZ2jaNYLKJareL8/ByXl5eJI0ypAJUfuOhrC+mn45xOp/jt3/5td36PAjQFg1p8Rt/yhSpO6wWxaB+tolVPSGUegJNhesWpVCpxfooPqSfo8Npv/wUW65pwdZwTm8vllqw9cIuYRqORQ8WKmFgfiwoLmZbZJKlUCoPBwO2sPD4+RrvddpkirNe6U7PZzMWlSHBmAwBJ90xDH9ofjccRjTWbTdTrdcfIWo8KkFWOYRi6XVpKB1Xk+mOFl6hGkbIel1osFhO5xJw/694RSSjt+ex4PF7aus53NFyhLjWF0i7E6mYbnW/f7lBV4kovq/CIlC3f2PeUb613ZEMPRFwaDlKazOdzXF9fo1AoLOUy8xl6gapIGMKz+fBadJFMf9v8cl84zSptVbj8XvlR6+Fn19fXuLy8dPzlQ/ZafKcMAsthLr5LGuRyucRpgGrw1KDY+tSbZvs65xaVA8vb/7WoTKknaT0dzdRS70k3VvnKnUDclsF1gY3peTxTIooi97ePgTgRGq/yoVMKHRUuERdRMBeaqtWqMwZEQ5wEvsNyc7YAHj58uHTkqaJRLkCoW0XmoNIIw9DlpSt6se6/joX1W/St/fAxPttgH+gOsi56IWxXz9XQd4jUmR+sC4h8V9tW2ilaC4LALcipMlBviaicAhuGoctzJ+/YzBr2V3lEL51gbFIXhlRxhGGIYrGIXq/njEepVEosbPO3VXyrgIQqX+URnWcqXXokPmNB5K2f833Af7EDee11ipd1qNGxCl6RKA0IlZCCGwUyOgZtS3cv63yTVq/z8IDbTVg671EUIZ/PL6XhqudNnrBxdfUurMHV91TGWTc9VhabXcR+Uq5VHv5IIG5FkBwAFZwiUGDh7uttFD6EYJEpmU0ZVhH02tqaQ7bcHTibLW6WWRVbJ9Ljtvl6vY5Go4H5fJFHTsRrlSYn1cbUFAnyGW7usCEV60HY+KgNTSgyUTqoG6100feCIEgcjKPCC9wiE6WnKlUNE2h4RRnToioiT/3OIln1bsgvg8EgoTR8ClTroZEh2tKNOvYdNYwq7MxgUtrqs/o867Pj0h10XOyiQDPUcn197frqG4/N+1WEDSCxqK9z61OENr5r69b3WKfOh4IgjpFFF775ebVadW1ns1mUSiUXflTgoyEbesmrsi8sgqcxV5poP+08kD6cR/WMSAMLjPieD5Bofyw9+Jl+rgbLV+4M4rZx3VRqsZ1X46u0errhwcYuc7kc4jheij2RwRhL5IYbMkA6ncZwOES73XZnZXAFvFKpuAUmMlCxWMTGxoZDbJVKBZlMBuVy2YV12DZ/s//K1MDydn4gmT4HJLMoOA5VmsrgFDyuDVhBUgVKoVc04AsDKHPSY9CVcxUspsupgNtMAqtUlQ7A7a41hkesorf9Y7GCbN15GhYuOBKFqRFTw08DzTNTODZFqD4ExTY5ZnXZdT6VRtpuvV7HfD53x82G4WKBXpWVomadJ6vQ+IwNW5GGdq7Yb/bPhrPYV1WomjmkvE76+NZy2B9dd2FmivKojpP16uKmzrPWres8tm2dC98cEn0rIKFRZR9UtnXuNDebY9D51aIhUxa7huUrdwJxA8tWnMiOSpQMbHfGKXpSlKfCRQXAsyUqlYpDyPV63WUIaExUlT8Rp1rVfD6P9fV1bG1tYWtrC5VKBWtray7+q0JCa14oFJbGrcKv+cw2RKCx5CiKnIeg/SUTKIKmi63MWygU3BbsOI5Rr9cTcWJFoEpjtk+GJsoiWlBFZMMBPHiLxaI49UZIL+ULKgpm7PAdX138TPttUa8Kog0RWA/w+vo6Ea5iXzT2DiBxANT19XXizG4dJwvHSiWhCoJ7Achr6uGUy2X3vC5qcpy+hTadX867ojqrKGwqqwVXls4cG9vXsCNwy5P6nYISXnzA9uwivNKIvxk60u91zD7FbD1HjYVbMKggRj1THouhxouFfxMMsuhzqqzVwyUN35TDDdwRxM2iE0mmJ/Hs4UfWYlLYuFHDLm6kUimXeZLNZt3ZImR8Khe2rXFF6+7EcYxOp4N+v+92SuoEA7cKjrHeOL490F8tOZlB42I+F5OFzHx+fr40dkWxzHtWASFz04PgWch6mh0zMjSWaZEWUSrr1Dgp+8NCYdejTfm5rUO9BVUawG0Ob6FQcGl4aizYN6WFonTfyY+aJaL9J52U//SUSau4LcKM49i5+hRCm3LJttXIKg988sknTuESAbP0+/0EPwLJmKjmFutzOkby1traGsbjsTvDRovypqWPeg6kQT6fX9ppSFrqPLNYpWhj9xoitbFk8qx6SWr4GWpcdfyxDUXYyyzss9o3egVat9LOyoE1JlTOGu9WmvqAiC13BnGzKDPbyQSSt3Yo0iYBNbacSi22s5dKJTSbTbdo1m630e12nYKnsmC8Mp/PO7SjYRs9HQ9YnLGrjEuCsz6rhJV5rDsGJE8IIy2AZEzYp7DIOI1GY4lZtH+kKcM+1WoVANyir+0jGda3UGIVpCpvi2CpwHyKW40r36WnpQiPO2mZnmcFZWNjA/fu3XN167zqWDhPKnCK7Cw/sQ5bD7BYmNQ27PiskrSGUBGpKu8wDN14x+PxG3fK2qJt8DhXPm/5ptfrOQWnXonyrnqtCgZsKIWeKfvPVFv1CLWeVYac7TIcR29BQZEaSTWKahzIB5x31R2qHDn/Npyj8+dTprrgqs+TJrr5SJW7InstKqdvKnfiBpwwDGO67sCym8P/VcHpiYGqMCw65PkNfH44HDrhJUqYz+cuhqlIiu4YkQ/jolEUoVqtYnd3F7VaDZlMBpeXlwCAnZ2dRBzdMqciMR8zaMZJKpVKLNIpU6rwKl0s+tD0OiDJXHoFF91GRTNKR4sMSCNLc86DRRM6l6SFvs85Yl68LmqqG+yL+wNwYbBCoeDSzpTmetSu0k1PyFOBV/RjkSC/e/r0Kfb29hL04hiYVaS3/Oj41XDbs6H5t164wHlUBKyHpWkfS6VSwrvTeKvOoaUFf6uSsZ4RSy6Xw+bmJl69epWok/Wxz6o0raK3ypt/l8tlt3/CFjXm6vFYNM/vNRymYEaNtWbk+AyhVfxKJxv/p2FXGVSZtetLynNWzp4/f47RaOTV4m9E3EEQ/J+DIDgLguCH8tlaEAT/IAiCz25+N24+D4Ig+I+DIPg8CIIfBEHwk2+qn8UqAN/3HCCZlgxJhlBm5LPcRjocDl02iiqobreLXq+HbrfrVu+BBWOqQGvohYuQQbDY/PPy5UscHx9jPB67kIAibnXhqHisdbdjtS4l+2Fjt1o09qsKztKIwhOGYSJfVDMDdBcq31ekQuHhhcZBkLz1XdGPrx8cj84tj8NctaCkBloZn/M8HA4T295ZLDrX/ugcK5K0KFEL+7K/v58QXD08igbI7pi1CtOiLOVzvkclE0WLRfHt7e0l/lC6UGnHcbyk/FTOeMGvjpX/W4Vj6abG2/ZB3+FzWi+/V9rZNaRVRdcR1LMDkgeasR6t18qe5TEFHYqy+Xc6nUa5XE7wuc0Ksejcjpe04xgVhKoSfxOg/jKhkv8LgD9jPvtrAP5RHMfvAfhHN/8DwJ8F8N7Nz3cB/M0vUT+AZGoT/+dvVQCcDCqZ6XTqUKOdSGWm6XTqXELmhqvFpyGgkh4MBgmCMuSi7/R6PbTbbQwGA5TLZaTTaezv7zsUQsShk6oKSEMenGw9ilWtM7DM0KuEXrNweMt7s9nE2tqaCx/RlVXkwfrZ70KhkLjB3C4KBkGQiKVq333Hq8ZxnFho03mP4+QpenYuOQ8+9K5IxR5KpL+LxeLSVmsNodg6OU41aBRMRf9qjEjP6XSKXq/nQAYvXrACSSNl51rHzP5zzYQ3HCk/+Nxsn2LSv3l0gSJsnSur8BQcTSYTHB0dJdq1IUKt24fwSU8aPfJZu91OGCYqTfaFdeg6hDUa1gtR0OBTiuQFBVRaH/mm3+87L8ouCNuIgeVdHwCwHjTL64wX8CUUdxzH/wTAlfn4zwP4pZu/fwnAvy6f/+14Uf4ZgHoQBDtvagNYTq4HlgXPTrZ937oxJKaiKk1Js6hQEYBF+Nls1lnbIAjQ7/fRarXQ6/Uc0rq6ukK/308od0Xb1pUDFjmsNsaq9LBj9SEzRfYqdKlUCvfu3XPZLJoqOZ/P3W33SjuOl6lyyrQUSkUuNs3QokgbGx4MBiuzazTHVwVIBcF6Dir47L9V8BT24XCIZrOZUCpKTwsebJzahzhJC7brE14ie3sYfxRFzniSB/idGkf9jHOmuyR9ecOWV3SsKif83io5TXnj3GhdWuxalHpCapg5Fnqr/OH7duwK4nyywzCo5Qff2pjlIaWHje9zblSp2uwjPqMGV+liF6tVjm0Yyhpt65n6yu81q2QrjuPjm79PAGzd/L0LYF+eO7j57BimBEHwXSxQuX4GYDleZpURn7HvKTKwFpj1kOHpTlrESkYCbuPhANwNzYxzE02l02m0220Ui0XU63VUKhU0m81EOIDKz24qiePY3R6ujO1DX8pwOm7bjs0zPTw8XDrrheiGn2t826Jv5rLbDRHqgqZSKbzzzjt4/vz50liYM63j0N1tbIvPlcvlhDFRL8LSjhukKNiW0X35sCcnJ0sCZEFDuVxOHCJFL4FbtfU95tNbRMk+sl+cHz6nCFPRrPK6yoHyhSJw6/WwXo3ZWoVjY74ae46iCLVaLbGxyIdClU8U+VraUF5USevhXPq5pryuKqpcNR2QbXBR1xalsdYTx7FbU2E/mCTgy8yxvGJDIL6+EljwrG0N8VJPWa/vTeGS33dWSbyo/cde4Yzj+HtxHP9UHMc/pQxrXTKfgrYLAlQsfMdOvCIiizRYr6I2fkcFxUnsdrtuuzcndT6fO6U+Ho9Rr9dd7FCtuO8iUDKKIj37jCI6nWQqYA3naNyaDKVnlpBWRN58R9E2S71eX1p9f//9953HofFcAO6gL57QBiCBItl3zQZiUfQYhqG7R5Hegio1PYvGniDH+WSaFunnW3CyRXmOc8n4v94er8bTGjD2n0aHv3VdgopOF9UVYdkQgfKoPd2SfbAIlaEFG+JgPVbxsG6lq94ypF6E/vbxK/uk9evfVPKkiSooKm9dG9Ax+xQ761Q5U4XL+phBpWsZ7Lu9OILrO/yxaNsaWF/RVErSTA2YRfQ2o+xNKYG/V8V9GtyEQG5+n918fgjggTx3/+azNxYSUt0QdRdUaXPiddFGNztYtK5hBNbB763wWZcXgEvL4q45G1oB4C6RPT09dYhSz4ZgezaNEcCSYlN6aKiF9ZBR6X6r4LNdzSZRJQ0gccemKgztGxEIcHtsbrfbddkbzF+nu3h5ebmkJBuNRsKg2LCJojbOHy+qmM/naLVaCYObTqdRr9fdnKmRTqVS7uILu0CoQqC8YbN+KIj0EHhqW6/Xczs5raJQviUq5q5cBR+qqCyfsl3r9SidLC8Dt0ZJQ4qkC70pNfZ8RnmP39OziqLFgW3q4fi8V+0/6c92tH/W+2Kc3pe5RB7XjXYWcNnwaRQlEwfIr/ybfWA8X0OW7JvqEmBZ+bN/Wp8N31ovizqF3prWaRedrd5TPbCq/F4V968C+MWbv38RwK/I5/9WsCg/DaAT34ZUVhYfI3Gyaf30WeB2kw7/tjE6Dt6eiuazkrTqFEpddKCl5IQpUmJ/gcVkU9FYZKQK1+a2WqWrVlk3ILA/1r3ns1RQGodW+lE501tg+IbMo4icR2qyL8ViEWtra7i4uHAKrNfrJYyd0gmAuwtRQyz83ocedZzsXxzHePDgQcKr0A1R+p7SUheF6frSC1Ee86UI8reGh0gXRbc+1Eo68Eo7LRqyUL5gn1VBKX9ozFYPomLxhYNU4SufcWw+9K3j02fUmFtvlnOpIRX7rBqOyWSSyM22Hgb7RCWuRtkWOw86Dr5HQ6HKXdux3ofWa1Ex+213Q/rmU2VXDQDpQRlhkoQtpOnryhtj3EEQ/OcAfh7AehAEBwD+1wD+OoC/EwTBXwGwB+Av3Dz+9wD8OQCfAxgC+Mtvqh9IKmv9m/9rPNC6pspkLJx4Vehap9atE64LbUpg/s/2rOINgtsdlrVaLTEZOvlkYIu4tU5tK4qixM5Oa5yU8VQx2NiZCqTGPvU7rffDDz/Exx9/7GKI6m0EwfIVX2zHZpgwZKG0ZXt2jlkX+xiGiw1RL168SMyBZszoAtB8vjgiU881UQRtabcqLqm0sf3juTQ6F/z+TYrExuj5DBVULpdDJpNx6yoqvGp8NbdfjwVgW6SzIl9tT4saf6Wt0o6Hdlk+sUocWD6KlX+rd2WNvXqYasRUFvV7NTA+pW/lncX3vPZHvQVLJz5n+Uqf063zOi6lMwvHYC/IUH57k+K+UxtwfBYwjmN3xi4zOKgogaTgaMwISG6W8Ck+PquKTYlmFaH01xkG3mDB9+7fv4+nT5+6sxe0Xv2tfVtbW0O3210yFGQAjbHpZyqgPhdY09bYtqb36RjJaLPZDKVSycXG9RmGAXS8vDyi1+s5VEIlrgZEaa6ur27S0IWlVCrlNuFYRUnah2GIarXqbq2nktY5UsNJb0Ivj1WeoKfBumy7LOoV6btquGz8VgGI8iDpo3QA4BbMyAflctkZT9KQdeoZN8oTauTYby1Wydnf2l/7veVTBQpKe6WRIk6VUau8ST81QuRjNdZ8R0GAlQe2oSjchkLtWPQ75R32h16c9aJIBwU2elol50oTCHTM/J5j+vTTT3/vG3D+RRUVcP2bBOLGChLGxnBt/q6NhQFIMLG2RUWjMULrtmq/qIzX1tbw6NEjt1tyNpvh/PzcbQQ5OTlJXEulY1WFzttM7HeqdLXPuihorTV/uACpZ2sAy1kGpBtDKHEcJxZgGfrhbffArdtYKBScoidK5gUYFsVrPE+FWRcbdWeqLu7oGDkGjklzpdlXVdhUDBo/Zf027KXGzyfcanCt9+QLoVEh88fSRYGGKpo4jhPnvodh6O6rtOhV+86zeIDklW/MQ7eGyqJ/S2+LoK0hXxWS0bbtWPVz+4zGpu34dM5t6IQhQaWlBUwMfb3uACcLMFifehzqWVl6+PhbQzd6eJsvS8p6SD5v0PVr5Tf/AgsVje04kGQOKlhmRiiDWGZWBlXGYl2qlJURtejzGv/mZE4mE1SrVXfDO1evLy8vsb+/7xSyb1zW9VPDo4jfIn1lQp5DYQ2Btqc33rMtFS6bQ65CQSHQPHj1QprNJn7u534u4S0wL92HogC4zT8A3GIiFakKt14pp3yi80uDwzGrgGk7Fh1zIwzHT4NE5alK1IadrIFk/To/Vglb3tL3dN70x6fg+XwqtTgoTA0UDwdj8bnafF/7qjs7FQyQnmpIfaELvf9S51oNmUXeum5FQ1ur1RKy6QNcpDn7VqvVEp6iyoHyrc/T0AVm0pT80mg0EusqNv+ehlrft3PFYmmrMqyGiN6eNf6ryp1Q3EASFSgD+Fwapt/pJPFZdYmYzmWto0UW1pLyMyCpzFTZc3v2D37wA3Q6HYdAAeDg4MBllhwcHLhVcmVgrVNTgXzt2cIURHuehw9Ja/3A7ZZhVcIaK1Va6HeK1JkZEEWRO8WOaJfMpwukRLo6z0GQvGCWhQKquzB1PmxIiONTYaA7q54JhZdhJjUsOjbWZZGQVc5KJ9/CFwVShdyGSVRhK0+SJupt8QLnIAjwla98BZubm65/AByPacoc++dDqHz3dUi4Wq0mxqtGKQxD1Ot1R1vlVUWTKpt2zFp0r4H2wYISGhLNs9d5UdrSkDNsyVALn1Gems/nqNVqqFQqS+mBrJ9hllXG2BoIDd+uiq9THn288bpyZxS3DRXwb51sFnVPNd5oGd8ypSpe6yrbOmxfNNeZSlu32wMLdHt+fo7RaISrqyucnp46VECGUSRpFZC2ZxlcFYXNZ/UpDTUG6s7qgogKBG8h0fGQWTUvnXQhHabTqUP+8/kcnU5nyfjZTB0Kn7q0ysRbW1sIw9AZO/1ROvAIAvZXPQMdg9LDKn4+z//1uFAf2gduQQH5QbeyE4WqkqFR0YwEO7/Ks2oY1IPgO2dnZzg7O1sZq7WF86jzUalUloym9e5ardaS56AG7PHjx25uV4EMa/C0Pv5ks1lMJhO3XqTvkgbWMGi4QbMz7IKfGhxVuow1KxIeDAauL+RxlSG7W5VIHbhF8fzfFw2wf6uB8xm915U7obhTqRTK5bJTasoIZFYKtmUQPh+G4VLKl+aKqoL0rWSTiWgI9FjXQqHgtumq62cnQZVeHMcoFAruJhPWy9xiTYfiZFlUowLsQyJKizAME9ksmv1gXVztH78j+rXKVpmVKIYbJK6urjCZTHB5ebl0UQLHGMcx1tfXE+2zbm60UeQKIHEZrkVh+sOr3fidKj4qSascLcJW/uG4w3CxCUhppYJIj4+fq+JXhK31Ks+QV/mOxqUt4mf7DEfF8WJNhDt/tc82pKEGzYYxuKBrPU0Nbyg/WSMWRRF++MMfIpVK4cGDB+5zm0pr49Y6F+Rt7iuwC+K6UKfeAeWBckMgZcNybEfj2qrQlUZctOexwb4whfViFYFbmWUdGja09aohn8/n6Pf7bhx6XaCv3ImskkwmE+/sLI40oQI4OjpKIJZ0Oo1qtYrxeOzitsACwTJFigylQvam2JFV3PpZECxW89kW69Gzoim4qpDm8zkKhQI2NzextrbmXM5UKoXj42Nsbm46hGuVk1p6/u9DhxoLjaLIKUEegFOtVpHP53F+fr6kWDjOXC7nFJBFQnoXI+lTLBbddW8A3LneimTYx3Q6jWKx6ASBbXNOZrNZon0W9X4odPxMY/RUwIq42YbSiO9pTJxFgcGXQa5KC+udKa/aebOKy2ZGsS8aY+d7uhCv9fqU2aox6PsaR35T4XG4Fm1rXZqxorRhVgxpb2nD/mod+p2CKBa2QVniAq4Ng7LwEhO+66PJKvrpormNZysf2MwS7YNuEPSdU8NyfX2Nfr+P4XDoDPpoNMLFxQVms5l3ou7EDThEx1G0OOe6UqlgPl8c+E9rHASBO1xHNyLoLiwVpGaziXK5jDAM0e123bt0g4Bbi6eK36ak8UxkfefevXs4PFxsCKUgaJod6zs5OUGn08HW1haCIEC73UYqlcLu7i6ApED70pxUsShD6KSTiezGFA0b6HuK+lRxqIumyk/fm81mWF9fx8/93M/hk08+cZtwKEhra2s4Pj529XW73UQdbJcGgTeJWBeav3mutMaINZSkaNciQlUiKlxcxOP/NjdXi/VW+Lcu6BHtaWiDfavX67i6WpzPls/nXdaMKiWeQ2/PO1HkqspHXXV97k15v1o0zY/3MlIhKR9q2Iifa3qq5Q+dQz37g8WCRE1/07N8FJT4CvWFDe1Yo6TrOXyGninngp/7aKSFvKDhKV+oUg2q0kffJVIn/1CnhWHo4uuvy34B7ojiDoLAZRiMx2OHEuk6cnBWgbHwM5047vIDFkw9mUwScU6NzQG3rpLGzHmWt2YrzGYzl5pl21WXn8iczDGfz1EsFt3pdMrsNn3KurWqbKkcLKrk/7w+itv0tS1FXkASfSni8K0PUHFOJhOXgpfNZp17q24mx0Am1QsBlL7sk/aZ7QdBgF6v5xQt50xRklXYfJd91TNL6HmtQqU++vsEmnVrnNWi7DBc3Eaki2LkBZ0DRd12XCzKp9bDUGOmsVjlHY2TE0WqMlWa8x07XqWZz0AosFHjQ5nSxWwdnyJ3VcR8XnlWgYWCKIv+adwY5uTBTjrPqrRZLF/oXHLjldJ6VeHY1NPQ52n0df2GMfo4Xj6AbWU7dyVU8o1vfAODwQC9Xg+j0SiRnUAC2pVX/lbicMKoWDnxmoLEcwtYVHH63FBgeXFGXVa2a115usVhuDjKkhcM84qxer2+hCA1rcmGdnxGQsM8RHBEO/Z9de+s+63Ftq+x92w26w6SiqII3W7XeRgar9e5IJMqbfhbb6CxhlldaqW9KiRF0GyfQjOfz10+O8et8WTlHdLdptVpm0qfVYIVhslURN4PCSyUA0Nvq86Q980F+6HfUZHr52qAlc5cV1EDyr7avxVxW0MZx4t1ET0OQZWtghkFE6t+K9BRY05aKY3Jgwy/0CgOBoMlpK1/W8OjdNawBce5Ss51w5uukfF9tlUoFFx82sotgEQIkl5nGIYYDocuKkCZuTn/5+6GSqIowsHBgQtlqJuQzWZRq9XcwPQdRaIA3IKXpt9ZxrfulUUtFAh1aRQdax2q4FTJ8XcqlcJ4PEapVHJ9ms1m2Nvbw6NHj1Cr1RJ00AtLtc/atl1M0hLHsWME7aN1Y+3irI5jFYol80VRhE6ng3w+j42NjYRiApZ3punf2ibHZ5GODXkobS3iiePF4T0aMuFl0UGQ3Pln21E3mRcQc354+xGVulUgq+KiFP7pdJpI1bS0tIeP2XFr39TFVkOjc0fvgvWrd6NF+0sDT37wIW5fmiPXJFSuHjx4gP39/cRzOn/sL9vh/HE+yXPlchkAMBgMEn1Sw0GUXSqVEiFNpZn1hnV+FPysor8FisoHPsOmc8RQh/VsrT6JosWBap1Ox7Wtd3baRV1b7kRWSRwvbk23+ZOceD1Zi0RVpMo6lMGBJBpVJlSiKqNygjQkYdGVogWGctSdpSDRCPFz7jK8vr52aWxa5vN54jwQMoT1BizyssynCpbKS9G17wQ6WyxtFfHqyXlUblyH0H4oM6tRJPPrs/Z/nXt9xhot1qlGWuOy1jMhWrK054FQmp1BN5t9tujMRzOLzjOZDOr1emJXn3XbLTrlkQKaW2+9O40xk0dIF9Zh47e6PsA6fi8llUolzk4h/awx0R+folSa8YfH6ZZKJXecL5+j4iewOjs7Q6fTcYpU5dYqPL3ujEid/YyiyHllpDewfBwA/yYNqNhtKIT9tXKr6ZiFQsF5DVxkpTcbhqFbqHxduROI2womkAzyDwaDJSVqGQVYEFtPrWPdqrTCMHShElXyFGr7GfuiDEaBoSJLpVIuDqYbiTi5xWIRtVrNxcvK5bLLNNG6dfzqCmsfOHZ+BiQRhyp3n1JWIbLKSOPJWr+2x34BwOHhoTcGStrwN69J47EFmqrH8AHnJJvNOs+KdWs/NK5OYdZLj5WWykO62KR01r5rSMiOR0MsvmwW0o2XMJBG5XJ5yStZhYRp9DU8SJRpwwH2fUWSahwsatXnOR4qFjW0lncU6VvDeXFxsdQnX2gMuJVrggmLjPlctVpNXK3GdzXMZw21rx7Sm/yul2GQzkxM0IVe5XUfaNCiGSM+2VO55cYh0p56gWhb9YA1CFrujOIuFosAsLRwYFET4E+J47MWqelvYBF6YcqcvqOImUhLt6GrktS/ySyatM++ktG5ALW7u4tsNutS9dg330q9zTLRwhiYGhilA+tl3/R4Ur3tw9KKfU6n04kDjVQp8D22a1O2FD2zrlQq5XLsNb5LQ1Gv1523ZXdTWoXBvmtWhy/WyxAKC8/xoIG27r6vqOegPGgVkaJ7eocEAUdHR84jA5KGjYj85OQkEd7weYbK6zou5TelO39b46JzxEUxAg4+z/Y1nc1Xvz5DviIP6UFeNvZtvSENIwTBInvMGjvWw7lXOupisTU46kGRnuqN6Ng4b2pIVRnb1EZd/LYgTw1DtVp1l5VbY8bNa5lMBr1ez+mK1ylt4I4sTuZyufgnf/InASxQ3MXFhXNrqtVq4oJOLSSsxrVeh0xV6ehEqJVTJWCtt13QostoY+oU1Cha5FfP53OUSiV8+9vfdhfwrjIyPgbRcZMJVXFzRVoZ0ypkK8Bq+FhUoTBXnoqKxaewKLj2cHzWGcexO5+bqXO+udT+2b5qm/ybG0QY1/fVPZ/PE659LpdDv993gq71UhiJwCxy0vQy6yqrIfJ5Oz60TGWisVTti84h6WzrIT9QCfjWHOI4eQSCAppKpeLooSiTBoJj5NjCMMTGxgZOTk5WeoTaNwU5Slu+o4qOf3MRlfPHotkd7L/yjjUw1kvSNlkUhKiCV/nyeSC+Ony52grgVC5JD3qX3MjGO2tTqRQ6nc7dzuMm0mKwnjG6crmMe/fu4cWLFwnLT2Emau33+wm3RusFkltKtWicj3Vbhcb3rCCzz9z+zDxUfp9Op7G+vo7t7W0cHBxgNBphPB47z0LjdYrqFSmrRWdRJiKTTadT1z5pYmP3ilwtY6kiV7SlysMnHEoj1muRLOtWQ2Bzj7UfZHQW276i8UKhgG6365QoBVQzQ2hYSW/OsW+Bln1SxKr0WVtbQ6fTSSAuKi/e4s4sBwom59fnPdlsG19/Xjd+nV/ypkWzmrGj47dpgBwveZLvcPykoXqQqqh93ovyKpUh/7YeohoB61Ww7zTM1nOwhswuGup4FAiosVCZYB2rwkZaVPHb9FCltfIS22w0Gu4OSh2TzSH3lTuxOAnAnfHBbdhRtLjthEib9/5x8SaTyWB9fR1bW1uJUIBFyjqpuk2VBPQha+t+El2zPoYAgIVgDAaDpbsd2Ua/38d0OsWDBw8QhmEi20BXkNm+TpwyjFp4hh70PBIyJ0MNtihyVsFRdMf39HhVHbNVqtYgsl6N0ypS5DuKxGxRw0h6kCY6riBY5Hnb8alLzDEqPXWnpg2xaLqgImrWdXl5mdjopGOYTqfuO/VcVHFYRWY9Kjs//N7yp76jhqbf7yfmkfNglaSCFKb26Tj4jBp84NbQXFxceAGFzq31clVhUTErX/mAkWaNKB/oBijSy3rNvhAS58R6HWG4OJ2QuxZ9IRetSwFWKpVysk1vmTRXXuSY+P58Psfp6enSeG34ZlW5E4o7iiJ3/gIFgJOdyWSwvb3t3HEOmudFEGnRDVa3C0gKAeOrVgGo4CjDsg4yCrd8sz1lPB9Curq6wosXLzCdTtFqtfDRRx/h6upqKRTAwrFRuevkcey07MDt3ZFkaBoYjdXRyHGsOmalFYWn2WyuZDYdqyoHiyg1pqvvq0CTxjoP2iaFiQbbegVWQVBoVAlYoaWg8R17bZQKprrjuv6hqFUVkG6q4LPD4dDRwXpuSnOLVrUNDSdYZaiKQpWxKh5+RtBD2vFzu97BIyQ05GQRLg2hVS5WsWnRnHDKoqJwvu8Lo1Heqdx8SF/f17IK/FhUvLm5mcgu8ZVVxopH7GqdwHLmjo8HSOtcLpe4M/WPxM5JYEEU3d5OxMPbtnkZLxd/GNPM5/MOpReLRXQ6naWtsySUPWxGBZ/PK1NzIqi0idh00dK6Wz6Gnc/nuLq6QqVSwcXFBWq1GhqNhosLK2pSgdOtuRqj1DNErBHQkIi6jWzL9k+fARbnj1hFwt2PrJNpS3Y3HBGshi1s+ARYLBSORqOEAaTBUWXB2KsNM1A56QKVusakn3oiqhSBhTLY2trC5eVlwoVn330LT77wijVIDPP5wlGsYxWSZr/ZBv/3KXeOSzN01BVnWwpa1PhYlKrxWe3fqjCBpbWO1c6VemLWS1OvVo2eolofuNJ5sB4j69K+K0L3AafPP/986blVRY1xOp3GJ598suRJ2XEpLyndlC/q9TrS6bS76cuuVyTo/9oeAgiC4EEQBP91EAQ/CoLgoyAI/t2bz9eCIPgHQRB8dvO7cfN5EATBfxwEwedBEPwgCIKffFMbN+851MiJm0wmOD4+RhzHuH//PjY2NpBOp1Eul10KHrA43nNzc9MtZmrepm1DGUCZQhVdPp9Ho9HA+vo6isWiy0ighdT6fK4pkHQ1ObZ0Oo3d3V0nSDbXlud5s6iRsMxm0bdPgbNPmp6mgs12s9msG4MqT9KGfVDBUwPLeolAVSErsm80GgnBYN/YrnoObM8qVUVt+rcqb5/Q84d0yWQyiVMISQ9F1+re61yr8eTYLOJS+usz/F5/lPdtXTpfvn7QK1V+17bVQ1EFrgupitj13HRVKhYtKr8x3dMWG0bjqZKa46zfK910/KvCL6rgtU39X+Pg1Wo14VH4CmVcN2+tevZ1G6lUz1iaWF7g73R6cTnExsYGNjY2XmtAvkyoZAbgfx7H8dcA/DSAfzsIgq8B+GsA/lEcx+8B+Ec3/wPAnwXw3s3PdwH8zTc1oBOit5UHQeDOth4Ohzg5OcH5+bk7rAkAWq0WWq0W+v2+Nw+TTKshE7YF3Lr1dCWz2SxyuRzW19fdUbMMywBwrruGR1iY8qbCyEUrGoJ6ve7OK2FhX7hjDLiNx7uJEoZUtKQLXMoUNr6mWTcspIU9I8QaNKtI6PWoIdPxkD6MS/M7LtwNBoOEcGndmt5nUw21T9qmol57WJPyGPuiSNRnYPi+XaDVvioN1Xio8ibytspEDasCAS0WabJOGxoA4MIdeuOLDSOxXfK73eCiBs+2EQS34Rfr/TDDSXPHtW+WbtZzYL98fEn6WKPrm0tLY6sc0+k0fvInf9K7SGz7qpk0PmOr8zIejxNGizLB8ahnpJ9rvdR3CqTetHPyx04HDILgVwD8jZufn4/j+DgIgh0A/ziO468EQfCf3vz9n988/wmfW1VnGIZxoVBwSlIFGVjOwODiHHOyGQJQQWOcyGfZb/rlfquhoAteKBQQx7GLrRUKBezu7qLVarmbuLlNmkLSaDRQLBYxGAzc7i8eNlSpVJDL5VxYh2OxZxdwIqPoNs2LC7RkWMZT9XxyRaCknaIRGxLybeqwil1pZI2ehkE0TVK/13q0bk13JMrzGSUqGrtQqryh3s4NL3nHy1AaQ3L6vCpxTbUMw8UZM+12OyFoRNxsn/+r0tcQCQ1sGIaJfGdVRMqXVuHyc6WV8rWiVYtGGeayc8n54uf0eOz36tWpR6PhIJVNnRPSS+XYpuXxWR5VoHOrSs7KhwIxBTubm5vY39/38kmpVMJkMkmcE1MoFPDNb34T/+yf/TPHRxYksR7lazsuvqtjUllclRRBpa9giDz46aefYjgc/v7TAYMgeAzgWwD+GwBbooxPAGzd/L0LYF9eO7j5bKXivql7SemwWIXCBTzeQqPoU0MtiiLUrbUuF9vQSeZ29UKhgFwuh0aj4VzrJ0+eoFwu44svvkC323UMx7TEcrmMDz74wMWx0+nFLTJU+MAtE9NIcIurChMRp56wB9yGM8i0VJ72IgcfqrW0VkFV70SFkgLDmLs+Syav1+tuh6seF8tndD7S6bRTXiz0euxGJBV09lWRrBUgIk8711EUOQOoPzpe5Ru2r3m1VjDtGBXBWoEnLTU1jPF0pSmNMsesrj75QflWTz20CsPST2llEaputtFUOIt2+SwL54P9Zp2ZTAalUgntdjvRF2tgbPurZFN5Utu1xu/8/DzxjM4F5ZPjBxay9OzZs8RnajhVd3B+SKdarbaUA2/nkHyoRQ3AZDJxa3tc0yNgU5rb8qUVdxAEZQD/DwD/0ziOu8bix0EQ/FjQPQiC72IRSnGd1MVDnUBNabKKVhGGMoaib0WHFnmwLqLfUqmEXC6Hi4sLpFIpbG1tucOgjo+P3eQ/fPgQT548wfHxMY6Ojlz/e70e3n33XWxtbSWQJbe5GhogiiKnlDOZTCKlTPtmlZwaOV9MX+enXq9jOByuzBtmPequ8TsaHm6e0Xc1ze7y8nLJIHAelM6qqCxCVyRvXVqde1UcHI8qHs3jVmRn483KO9ofpYFusVe68n0bztHFUI01629VXhraUcXqa8MicWu8lWd8itYumqoxVPmYz+dLpzbqGPXseVVmSpv5fJ5I19TxWG8jCILEURU+j8Om6LHPNlas3jeQXLPR+ki32WzmbgPyPevTGexHr9dLGDh+p6cd2nnN5XKJdZ04jtHv910SBgAMh0MHRFeVL6W4gyDIYKG0/69xHP8/bz4+DYJgJ74NlZzdfH4I4IG8fv/ms0SJ4/h7AL4HLEIl1joq41v3w7pi+kwcxy5lT3NujaFxhGWbuVwOGxsbePToEQ4PD1Gv15HP51Eul/HixQsAcGl6rVYL8/kc77//vtuuzRPhZrMZqtVqIiVOkYTvN5lNj93k94ouVbFQKdo8XHU5bbEoWz+3n/FvejVEikp3zVO2Sk8NrXWr+b0qHEWXGiKxLib7qcrWGjlF+VQsyj/WUyGdWQezTXK5HHZ2dvDixYtEKMTOAf9WlKUCbueO/dCdhNbzYVHFrujPjkV5QF1zFkWFVAoqG0oHBUsM5bHeMAwdCGE7VsFYpEjDasMQVPDWk/BluHCu1EOyIIRtsw7rCekzakToMbDoUcQa7mLR0KDKZhDcHvDlC5uEYegiBQRzDOlqyNW38c6WL5NVEgD4WwCexXH8v5evfhXAL978/YsAfkU+/7eCRflpAJ34NfHtmzbc3+y8Mpl+53tefwNI3ICuWQ6MJ9mJjqIIpVLJbRnu9/sol8vO/R+Px2i324kdeKPRCM+ePcPFxQXy+TyKxSKy2Sx2dnbQaDQSY2GbViiBpIsYx7fXolEQ2R7ddWWwYrG4tIA5Ho8TudsAvDfRaF/sKruNMbJuG2NkXE7d/3v37iXQbBDc5tdToFhvLpdDPp9PGE5dlGGoSs91YQaMhl8self+4ThsiEAzVGxeMkNiw+EQz58/Tyh1GilVKOl0GvV6PYHm+WMXb/ku/+aYOC7rKfjor3xhUTfnV71U1slxEtgoYlX66AYXDdGRbsobnHvyMevUNEXlWVVItk0Wu2bCz6zhXgWG+LfPm7KeidJQvU/2y4Iv8qI+Q0OjvEF6kUb8nrvD+/2+u39AU36n06n3eA9b3rg4GQTBdwD8UwC/C4CU/F9hEef+OwAeAtgD8BfiOL66UfR/A8CfATAE8JfjOP7nr2sjDMOYAqyTYBnGCoG14NZK8X5EHqdKhKPMSqbJ5/NLi1IPHjxAq9XC9fW1O9luNpu5cMpwOHQTyUOZvv71rzvlZZGgIisVfNtvjpX5zvqchid8rrXPUqtLzqKMrTS2gqyx3TAM3W01Oh5dQKzVauj1eol3SONcLucW+lSoqGgUiVEgnj59islkgsPDw4QxW4Xy6eLbMBrHqDxGwSRCUtRpkZpvntSFJo3JH9xTQGOqnoMWpfd8Pkez2US3200oYEV0mpnA9xSNat/UOPM7nhljL9K2IQEfryra1b7xPbtArMjWImmbX697BWyfdLw+z9CO28aUffsAZrOZAwmaDql91mQIrcP2S/UG39fFWvZnPB67DXilUsnFtHO5HAaDgTuIinX1er2VZ5XciUOmwpusEqvQAP82X0USPuakAKrltsiBxMlkMk750oXhQlmj0UCz2UQ+n8fx8TGiaLHDs1ar4f79+7i6ukIqlXLuTxiG2NnZwaNHj1x6oS9OxbipllWr8haxa+HYfCvdZL5yuex2pJImGm/TLeCKPixaUdrZfjJTwvbXhnv4fRwvbn7nJan8jKEXPRdDs18AJK45U34pFAqJ4wQ0tEO66CYfX4jFKgW6zFqPxputItOxUslms1m3kUI9J71kWfla12bogZHGNDBsz8qu5Ru2xfnmQjh5VTeqsW/MilIZY3taj4/fKDs+3rVej43ba5+tovZ5U6SJhtG0r3YxWQ0P61E6si6eKklvQxWyGggfvwDL4JF9oid9dXWFdDrtMpz6/b7js06nkzjT52Zd6vefVfKHXay19CFwdUd1cpSAHLjdKehDmcViMRFaUUVfKBRQLBad8uMi5MnJCWazGR4+fOiu8eLxjMzl9qEr2w/93yoRFSYqJ0U63AJvBUwRQyaTcULI9rLZLDKZTCIbRvui9LeoR+dAi3pAPlRoBWQ6naLb7XpX46fTqcuftbHCMAzxzjvv4PPPP3f0Zd2MxSr6tHS2qWgaL+bn7K8aRH6nC3JKH59HRaTNNLtUKoXRaOTCEEorIJllVCqVEgui7LsaVHoIVAoaa1V6k340gDzLhv2yG5x6vV5iTYDIU4GD8redP513u37AouOwSJ91sx5Lb/3tO76C/WNdCog4XtUp+remHSu4I9CywEaL9Vh1IZx9oiFn3v1sNnNhTF4a7fOkfeVOnFVilbGPKCSAHsgELOeDKsHV+pOxyNRBEDiXVpmFSiAIFgfw6O3TenTpaDTC559/jr29PWQyGRcTz+Vy7oZ3pvhlMhlUKhXXF+umAsubK7RY103dO8C/kUbbUcPAzBfei0h6lUoldx+iVd5KTxoE1gssFm2ZfaL0tUKlikXdZn5HpF2pVNy7GkKZTqd49uwZhsNhwkARnTBEQu9K+UrjqPxcDZM+Sz5TvrOoXBW/9QSpIDhenr9D5WwzH6wi4M32Fo37vC816qQn+dcaLYtYbShDkTH5S+lDGdJ66Z3awjo0bKX8zDEpEuZ7qmg5D0oHy5ukuw3b2L7bDCBrnGgMtc4oitxNNfyct+go/XVudBOhFuoWHhBH3qCR5hrcm5Q2cMcQt7oUVkhIZC68KZrUopPBehg/p4vKdtrtthMStqU508yvZl2ZTAaFQsGl76TTaRwdHWE6neJb3/oWMpkMDg8PcXR0BGARenn48GEilKCLSDqxammVaSzD6v/WwJGG6lL70I664GxH+6fPqXAQya6traHVajnGU6PHwjGoW62IFkimzqmyGw6HS+c2a+xQx8KiQqlrF4qENBapKVsWUZbLZRSLRZydnSXq4xg1/OCLfzJk8Do3mn3QzBy78GcFXxWTbslWxQHcGnDN+OBcsu/6jg2F2frYrp7WRyWn8WFbVGZVuSo91OtQ9Kv9UF63oIL1K7pWVGwBkfKjeioKckg7ek2aiw7AZatRX5An1FBzfL7FTYZmaWTJF/SG1ZtZVe5EjDuVSrkYtwqpCooqBm4ltzdlsyiSiOMYzWbTuZ/dbtctrgHJradxfJtKuLW1hWaziWKxmDgzhavC+Xwe9XodrVYLYRi696JocXnCH/tjfwz5fB6lUslNhl2pZ74sr2hSRtX/gWRcX0MWVnAUWbNY10vrUeVvBUQNmipX4Ba1qgtvBV4VqWVgGztVg6vjJxPzfRpWPmsNEwVRdwJyHcKOl33QsQJw18q1Wi2vYldasX9KJ/bXAhC2p6ESYPl6NDsXq/rJ79SjYn+JEi1vabG8pmOwz5D2QRAklNzrjJRV5pwL3Q+gfGtTFH1hBxoQHTP7orRUxQrAyZyPBsrHtqwCSBp31+c0RGrHomEbYGEA6D3qDTlM8b1Z5L/bMW6bhRCGoctJtkSaz+eJq8Ks+2OVSL/fx9bWFqrVaiL1Rt8B4I5WJAKdTqcolUqo1+tuA0q/30epVMLa2hry+Tw6nY67HIGnFc5mMxcb1zxV4NaS83ONnamQWKShKEK9Ax0DkXSxWEwImFUKFDbfars+Yw2HFtJekZ9Vxj6ko0ZHx6seklX+mpHBd3XhkGMLw8XicBAE7jqwOI4ThpKbnNSQWIHqdrsJpaTAQZWnIlCLUDl3mq3CBUaGhHg70ng8dgundk7s/Fm3nPRXehIc6FVZOm/2tiINXehzlMH5fJ7wgLTYVD9F17ZQKel4lC/JkxqG4DhZFOmr96PGzSpHNbqUCz0i2vK2esVUuj7gw3qtDtINSkobK+c88ZT91wthfLTWcmcUN5CM9zImzKwHHQgRrm6KsK6mTvpkMsHR0RG2t7eXYpt8nuEUMla323Wn2WWzWTx9+hSj0cgh6EqlgtPTUxSLRaytraHX67mLgNPpNJrNJoBbt4ohHp42eHV1lVB4lg5WGVh0RtqQiTSmxtg6v7MuroYcfIhCaapIez6fY3193W1A0ufJmD70rd4NhUURFZ/V79lfNWDab3tNGuPuXOzxISlVckpzi/bVQDCMYT0cXS/RH56hwwO12AfSSNGYxpLV+FCgmYLqy8BQQ638bJWiGhE7VyxKZ0s3vVzXImLLs6qsFRhYj5DK186BKtHXKS4bplKaqneRy+USxxFz3ggE2Cf2hYpf50OLomr1hizf+7xA7bf2kcCgWq1iNpuh1Wp5PRhb7oTiVsEOw0UONS/PZPqXblThBGQyGTSbTUwmE6dMVFkoqmIWBdG2MhoRdrlcxsOHD92Z1KlUCoPBwJ3UVy6Xkc/n3VZXAHjw4AE2NzfRarVQKpVcTrimx6liaDQa+Imf+An82q/9WuJsEfusKgXAH8NlJoVlDCoVGi1V+qSbnm9hEYUWMjkvPOUdeVZx+JhSlR/f0bEqetLnuMLOwvHy93Q6TfCDLpgNBgMXjtAsE3uIURiGS7nDpJ321yppZhhYj4hjottrFZDODYtutFBDVCqVUKlUXD7365SYKh+VI6u0rbFm35R3+HepVEKv11vyhlgU8KjCUwOiHoe+Z+mh4RAfgmY76pGxHhpI7Z/yk/WCVKbYD0s3PmMNj457VV630sUaUFXsuijNcedyOaytrTlwMZlMnOH2lTsR4w7DMOY+fU5IuVzGxsYGgNv81larxQs0AQCNRgP379/H+fm5S9VTwdIUG3V/lCHV3eGKLzcolEolVKtVFItFNBoNbG9vo9Vq4erqyqHy999/H81m060IK+ogM/PvMFwseBYKBQwGA4xGowRzKNOz8G+flbdeAz8rl8sAbo9R1f7odl7SQD0ULWr4lK62H1ScGn5So6CIVeeA7/J5tuUTPhV08oNe6qsKaJXHwiwhGhsrfJoOZtGczqsiaFvPfD5PGBVriIHkpQVMgdTCnaZ6hymf13CS3Rqt8X1rzHVeVFFxnu3hYdYgWe/JZ0z0uVVzR3q9Dixoed1z6iFoG77ndZ3Chl4suPJ5fT450f0YPjkkH+l7Oi+2zwQlBJfPnj3DeDy+2zFuRWjMq1bEdHl5iU6n4xQyd4Dt7+8nLnBlXradKLXaKkQ6YTYuSITe6XRwcXGB3/3d33U3qqdSKTx9+tSlCtqFEt0CzD5zMYIKW70HYPl4SIuGdIFDGYX9IbP4Dvexik9LFEXI5XKJK9OIBlgUZbJdFhtr1rm0Ck+VDtvWwvggi6IpW1cQLNIYeZ4K6ZnL5bC7u4u9vb2l0ADr1uwCOxeq3DgeHZv+1jHG8e1GDp17nTNLe90YxT5NJpMEELA8orytKFEVkmarKP+ri66ejI7byoflN/2e76qy8sWEdd7Iq2p4lOc4LsZ/NSSoc6I0oBLVTBtf/9lnBQ52bjUcyfdtHQC8lyb7aMJ2fAug1sAxlMMLH1aVO6O4rQtFpQwkEQqVMxXh5eWlGzhTa+iO5HI5F/tWxUbm8ykxRR0kNN0XtfBhGGJjYwO1Ws0pc5/7xYUoKmtFnhyTVSb8XBlGkR0nVF15n7un76oLCtweB0pFqWdEK9OqgvOhfu2H/s93KSR6HoiGQrigy0U7Re1Er6oYrbfBTS46ztlshoODAy+643iYn2/RpSo3VV47OzvodDouDMIf3ZHL55XX+L8qTI2jaqiAzxJxqZBb3liFqsmfnE+rsO1c6ji1Do5b5zebzSbmVMEF6azZP3pQE4vyqg0l0CNknz/88ENcXl7i4ODAzZVVqHzfLkLqWNRQ6bscl+2jvYTE0liL1qUyx+wZ5cHXGTQguU9E9zL4yp1R3IpkyJhRdHtkKXDLSP1+38Wh1TqqIAZBgFqt5jI+ptOpOyhKQwY6+ZxIZVwNBWjoZXd3F+vr604hET3wHQqWrrIrQ9AgkKk4druibK22RQf8W/9Xl1q/p1dRKBQSKFn7ZZnLl6vrEyBFYtbAKZICFgxKBahxcJviaYXNl3GhqItjtIpU+6wGw9YTx7dniVtkyTNr+LnSRBf3FFXZMI4qc77rC9kofyiIsGNRkKHKmOed6zzZmK0iaOUbRayq9PieNXSsw4fMCSp84Qv+z01gtsRxjI8//jjh7VhFx/fs2Om1+1CyzgnrZr2+GLi2a42F/UzfsVk4aqSUH1YpZx+va7kzihtYTsCPosi5wMVi0W264X5+64pT8KjkqtUqtra2EhtguPA5nU5xfn6ORqOB+XzuDn/RiSBqX19fd4fBRFHkziPRuxrVnbWLGr4VdOD21hGdQMuMcRyjUqng+vo6EZZRAbMbGMiAbJd0Yh94LKfNj2d9+i69FY5LmVgFRtGOMqb2iXX3+/2Ep0BX2B7Dq0rAKnm2r2EOHzLnhqlVhWlh5BsVXvajVCphOBwmjI+ORz0f63lYnlYDQ9qqS6wejs8gK1/pnPPzra0tDIfDxIYqInj2yXqQ7JeeY670t6Ew7YuOTT0OHuJkeVPr4IYThjYtYKBxfx3y1P4Q7LFN9pGXmFiDriEMDUWy+JSyfqZtrFL2Fs1HUeRoY71iDb34zu/XcmcUt3XTWTgwHuZvEal1afTz09NTVCoVd7lwvV7HZDJBs9nE6ekpnj59ivX1dXzxxRcuzELi8ajO+/fvo1gs4urqCmtra9jY2HDnbatSU4XG/mm+qSrxOI4TQqKozbrInFAbu+MYlV4+V1Fjzapk1K21KFEPdiIzK+ryufraB50Xy/D62yIS9pHZHhra0ef1t3pEGrvkby4esc96hAHnQ3PpOfcMcVHQbJhI+Y7gwqdkFOlaROqLJWtfVVlS0HXx00c/ng3Pwuf1UC6lP9tkHwuFgqOH9WBIA9bJ96zXQ0DCd+wcqletmWCWV0kn8oTWpfS03otFqwosfHxp67NAxhdi0bUYBVnW06T3o3pCEb6OUfth5cqWO5dVAixbNQCJ098sgS3jKHF3d3fdsaxBsAh3vPfee+4y4Ovra7TbbXS7XQyHQ5yeniKOY+zs7ODdd99FKrXYdVcqlRw64mTwzAEyajabRRiGGAwGiVitjUnzM3V1NSShiMwWVfY+gUilUlhbW3NXOKnC0fpet1pumTUMw8Qimn1elYrd3FGpVBLpk6ogNexjUZAKAt9RQbGI24aRisWi25WmStAK5apiY89sT/OmrYe2SgkrndVFV0PB1FM7P/xb6aV0TKUW96/ylEXbPt+zyN8+w76pB6Vzpp+rwuTfNnavhsWG9yxtrMJPpVL4+te/jh/+8IeJODl5TPtsvTI7p7Yt/qgnQA9Es41U2ddqNbTb7cScMftM6WiNCutdJce6kM3P1Lv53d/9XfT7/bubVcLJUkHQCQ2CIJHLrYyhk6/KLAxvV6U1Ts6djfP53LnoGxsb2NpaXJnJnZAMg+h5BsDikP39/X18+OGHDp1yUY0TrotRHJ9FHnEcL6E//U7dOI7HhgPUKquiubi4cAymCmsVwmW7QRCgUqm4DTysj31RBM7CuWI6meaNB0HglLZFwto3LRZ98Dku9mh4hzRkm/quZshoPT6D+LoYrCowvaVE+6tzpfSyXpLOhdJTQ1LkOfJ2KpVyedX1et3Rk+NVD80acy06JzbGbZGwVdJat4a4VO6A29P6bJhAlZPShYudHDPHDwCffvqpC2HY84N4D6j1SqwXp7yq/bBeKosmN+g8VqtVt5uWdVJpW35R+qk3ofTn/PuyTBSUvK7cCcVdKBRw7949jEYjd6A4sLxgae9kVOYDkjHlbDaLZrOJer2Ofr+PYrHoblq/urrC2dkZ4nhxLvQHH3yAQqGA+XzukuB1+/F8PsdgMMDJyQlOTk7w3nvvOQHQiw6sciLTBUGQsM5UmPwslUq5Lc8UViJ9FXBfzi8LjYhlBOCWGRTF6nuKDvQUO2V8i7KVQS2Ssi4s54TFonlr0Hzxe42Z6jio5PSIT3VFfX3U0AGRItE0cHvZAIWX59DoGeo2zq68p7T1/W3XFlToOY9BELgDiRg20rx8NYDc7WsRqBpcVXIazyWt6D0S9fsMu3oB7K8CBtLjwYMHODw8dAbdxsMVPfO3IuLZbOYOd+PnnHflfwU+PjCgfGWNqLbpC/np/9QV5GmdL23HGnRNueS8Wxm0ddjz51eVO3Gs63w+R6PRQLVaTRyhqFYKQEKY8vl8IhtCB8rTt5rNJu7du4fd3V18+OGHeP/9913mwmw2w3g8xqtXr3B+fu4WPAG4fG4+NxqN8MUXX+Czzz5zhOZuSgq+PdI0nU47Y6BCYBU7P9dr0aig1R3m83qkJN+nEOrhWUoTm1NuXeEwDFGtVt3Yr6+v3XZhH1rnOyxUYnoJgG/+gORtIVS41pDou9YjsMUeOERFYGPTnCdbp6JIRfZW4euFE+VyGYVCYYkG2gaQDJGp4VOasv/W02Sb3KilSFzbZLuq5JSWmnqphlfXLYCFh8IFMR+tla7ahi58sv3Ly8uEIlI+UP7Qenw3NFFR05NRJa/8y8L5U71gjafKnral49PnAeBnfuZn3EW+1rtnHJvPW0/N0krfZ/2q3Fd5TLbciRh3NpuNNzc3ASwyHnSzhzKjKh4OXN1mCignbm1tDV/96lfRbDYTlnYymWA8HuP09NSdAPdTP/VTicW6Xq+Hg4MD7Ozs4LPPPnPCw7Mo3nvvPXzlK19xzO5zxdStVAtv3VYguUuR71p0xeNseeOOPu9DWnQ/bWhDFXahUHAGKooid2mE9olFQwW+kAPrV8GkJ6GIxfbVniFikR4AdzMLacNYeq1WS5zFzTY5Tm2DSoB/6wFZFD573K0aJRpT7g/QnZtUShwHQ04UVh2bVV5KE0sDVRLWW7Lzrwaf80cD5psnm+2k80ca6HxqezZkps/z73feeQfPnz9PZLRoO5YPOF47B9YzsyjZhkwsQPL13/ecj38JEjVBwOoc7ZPSgn3j/9b4WtorL6bTafz6r//63Y5xB8EibatcLuP09BRRFC3l0+ogrWutKO3+/fvI5XI4PT1NIC51Q6mQuOuu3++7jTycqIODA5yenqLdbrtbbuI4RqFQQDqdRq/Xc0dm+owLBY4LmRRqRctqhJRZ19bWEnFqRQRUPpYRSCM1PlRM3Kyk6JO00JBQGIZOOXJdwF6/xsuIKfiqmLSv7JNeakCFa+OOvpQ+NbRBELgYI2/H4biVbox36/+j0SjBLzTqzB8mStZFpFwuh3K5jG63672Mwh5Lyv6owBO92XiwKniLvHyGXRU7aa7KlcqazykNbYYSixoSnQcd5yqF7UOC1jCRhhp2s3FojfXb0IOGFBQda3/UAPoyu1SRahtWZ3Bc2pbNZacuUgNraaEnAiqo0fFrqMoaSDXqwJvzuN8YKgmCIB8Ewf8vCILvB0HwURAE/9ubz58EQfDfBEHweRAE/0UQBNmbz3M3/39+8/3jN7URRREePHiAhw8f4uHDh6jX6y70YImrjKUMHUWLCzh3d3cBABsbG7h//z6Oj49xeXmZcHX5/Gw2c8y9t7eHFy9e4PLyEp988gmOj48TSJQ5p4x3qgtoJ5FjUmZm3FozBCzDc8ytVgtAMrOABkcZmf3g51S2zDSwTKCKAwA2NzcThkwFhBs4eKmB7lxTw8HbPvgelSG37Or8xHGcQO30Xkg/q/ytAaQXobS+uZfPGTX2nQJHBa1zaRWuhmvohWQyGXzjG99wNKVL7FMiPld3NBo5D0k/39raWqrHXndHWqTTaXdWjiJupSdDbJYHfQrW9x0NJ4s1nPZzHQtpqPNMOvIMezWsNo7M8Vvv2Sr5IFjk0mvfrHGzCn1Vf+lZqf6wfSM4UOWumSa2D5wHRfOUTaUt17DUKFojw7/ttWy2fBnEPQHwC3Ec94MgyAD4tSAI/t8A/mcA/sM4jn85CIL/BMBfAfA3b3634jh+NwiCvwTgPwDwF1/XAGO3pVIJDx8+dOcUdzqdBKKzrqJ+xtP7jo6OcH5+ju3tbXfC2ieffIIgCNyRq7z5Zjqdupj3+fk58vk8Li4ukEqlnLCMRiNUKhWnAHiAU61WcxPEMWhfqBQU8ehxktatYx3qLvlyZBUZUOh0AYqKc21tDZeXl4lcWd2yDMBt1FBjWK/X3ZGz6j0QvWrbdmMD50T7xjHa+HIcx4n8aSpGn0FTRaVoTXlBz/ZQF9vuYOO8pNNpd0mGzs9sNnNnn7x69Sph9OzlFDpnioA5XjUU7Pf5+fmSx0Qvxobb6GmwqOFSxaHnjWtmyipZU0SowMiGTshLypP0InyxYJvxYy/btehWf1RpW5RPXvEhZ42lq/JU3tB+Kt20LywaovQZL51PzpOOTWXZJhKonFh98bo585U3Ku540SNuPcvc/MQAfgHA//Dm818C8L/BQnH/+Zu/AeC/BPA3giAIYh8lbwoVih5zqTeTa7HEJOp7/Pixy8dmPIrhAm6RPz8/d8xP5DcajRzBeFGqLjRqO/l8HplMBo1GA2trawlrb9G/xmwt8rWMogcu8R3mhGvsVhU1n7uZI1cPDUmtVsPV1dUS3dR9ZG6qMm+n00kgFj7PmLAyF5VeFEXufHTfAo8ys89g6bP6jA0d8H81aAyP+DaYEA1SqVFA2AbTyjgGotfhcOjOcvEV7ZMVYn6vYST+TfStisrnFbE/PK5BDQNdcuuRsB1FkEovpb/1sEhvpR1/K2IkvdUYqqGxnpL1OnXuWR//19AC+6behIIAy0s0kiqzFuxoO5wPn4HQsdu5tXPMMKIaPxbSWfuhNzeFYejWq5RPWHzeiZYvFeMOgiAF4DcBvAvg/wjgOYB2HMeU4gMAuzd/7wLYvxncLAiCDoAmgAtT53cBfBeAS3na39/HbDbD1dXVUm6uToq1yqlUysW0+ZNO394Hef/+fYRh6BaLlLirEDyFmui7VqthbW3NZb7YUI4yMPvFOvm/VeRWODRcQhRFxaPpcHwujm8Pi2chQ/ImdBaLAJTZbJ/ZLplTDZHSS/+eTCbusCj93i5wAsnrx7RNegXFYtGdRa1rBMBt7JT1VCqVRGaDCp0KJ3C76Ym0VRRtc+r18glF1zbO2Ww20el0XKog29fbh5Qeqgx03OpdkK8o1DYWbQ2eKkQaKA29WJRNxK9K3FdsW7PZLGF8rDfBZ7kHQi8KtvF+a7g1rq3j1SMJlIYqX2oc+Ay9Az1WgvVyMx/pYdcBVNdQbny8X61W0e/3l8IkFmBo2yw+gGN1xuvKl1LccRzPAXwzCII6gL8L4IMv894b6vwegO8BQDqdjr///e8nhJkTqRPC7xRV8LoxYCHUDHWcnJzg+voaT58+RbFYxNbWFi4vL3FycuLqsi40PyP6CsMQjUYDjx49Qq1WQ6FQQKlUcouSVCw+heyLhWqxylKRMJWGTizpocJhF84sglNhYXtkVl3pJ40pJBw7x0dBm8/nKJfL7kQ+i6Q03KGKUw0icJt3bMM9qVQK9+7dw9e//nX8w3/4D13GjgrrdDpFo9FAp9NBGIaJXZmqGEkzfh7HcSJPXulrD9nSDBOiKo5JvTEaGh9yUyWraNEidWYQkKfYLzW0Fn1Z5cX6NPxi58WH4FSRcswsGpJhHUEQJHLZreegaFXbVc/R9kVlQ2nD37rgp236vAOltS7aa2gqjmN3HaIFNuyz8qpPhvgMPVr1ttV4+oyUGkwbkrG88rryY+Vxx3HcBvBfA/gZAPUgCDjb9wEc3vx9CODBTUfSAGoALt9QrwtvxHGMWq2G999/H41GIxFGYOHAS6USGo0G7t27h2w2m0DUnCweucrFMpv7HdyEWihAZDR+X6/XXbzcngCoCfb6nqI01h+GIYrFojuS1o6Hk8cFNR9jsU0AboMCwxXq2ukqtmayUFFYoSdjqgFi2/acDkWlOl6OwyovKgWbWcA54v9c0Dw/P8ev//qvu7CLXdGPosgdWRoEQWLRlB4JBVvzjqmwLUpkPfYCXD7HRU1dAOR58Pl8Hufn586l17ErcubYKpUKUqkUCoWCowFj3CrYqnR1rlRZqaLQdi2vaOw8nU67tQD1ovTIAjsnyhvahs6x9pmGQ8MCCnAUzWr/dRyqzNVrskZPjZXWZ+fRxpbtzlTWaZ/TYumtbVEGtE6VXQVRvrqVnqznTaj7y2SVbNwgbQRBUADwrwJ4hoUC/zduHvtFAL9y8/ev3vyPm+//q/gN5kMRdK1Ww4MHD1CpVByj28HxWS4wDYdD9Ho9DAYDd51Zs9lEoVBAtVpFJpNxcXQlMF3ttbU1bG1toVAoIJvNupQ/vS0FSLpNVIqq5FQhqbBTGHjhsJ654kMLDA0EweLscTUQ1opbJgawpPAVzVsh0EIlSAXB/uuiqgq3ok+b2cB21XhQEVghobD3+30MBgOcnp664wjsOBmD5kahfD7v1gNogNlONpt1lwcDSGTaaJ81hGRRrCp9ZhU9ePAAf/pP/2msr6+7ttQI+ARvNps591xRK3B7QqH1THSubQ6xhhM060Tb1v6wUAY0VGTDMbpeYBdDLcpmX7npTb+3gMAiT33feiP87TMYLJxzlTt9RzOelPf5ufK3eloEW6p72D9mbFm9ZJG6/q3eDGmxKt3PAqdV5cuESnYA/FKwiHOHAP5OHMf/ryAIfgTgl4Mg+PcB/DaAv3Xz/N8C8J8FQfA5gCsAf+lNDViUU6/XUSqV0Gq10Gw20ev1XHoaEQ+FjbHQ0WiEdDqNnZ0dlMtlDIdDPHr0CM1mE1EU4eLiArlczrm+FMBqteoIe35+7uKvp6enjhmVqKoIgVulA9zG9vTYRr7Hsra25u6TUzcPWD6PhJ/Z4zYVZSjSI8MpQtbFxCi6XfTU/G0yscbamV6nitm6gmoQ1GiootdxsF8+NGNpTAOhC3tKh2KxiFarhTheHAjG3a9qGHj4Euun0VXXls+q4rWxXUWt2WwWjx8/xne+8x2cnJy47dB27CzkRc6FzjeLTf3S+bS8oYqMaJn3adL4a3xbP/MBBa2fi2XaF6WPfmYVKT3AVXOqykh5SZU79YClj6WZjsWubfBv8hCLxpvV8GkmkwIS9V5sqEaNnPKS9SaUVuqZk58sYCCfUEZfV75MVskPAHzL8/kXAP6E5/MxgH/zTfV63gOwmOTxeIxsNotSqYSdnR2MRiOcnJw415RCzSMsOXmPHz9GoVBAq9XCzs4Onjx5AuD21nMuZBHJfu1rX3NIKooid9BUu912ubz5fN7l2RKRWpfHxkypADKZjEOOwGISmd6lB+yo4rAC0uv1vMhEFbmicUUdFGyNdTIUw36Tlqy3Wq26naVxHLtdYxyDHgbkc5st4mo0Gmg2m2i1Wmi324nQh51/CseDBw+wt7eXGI+N9zI9DAAODw8T3pSGZqbTqWufIQ0KDwVfjQkzZ0hX5lJzB+10OsUnn3zibmaxGQF2Dre2tvDy5UsvYlRhJt1UmepiqoZftC49p53vKC8RJSoCVAOtSl7DRRZUvK4oT9rQhRofa4hIb13EZLvqrapHacMUKnM23EKDpd8p/dQTYF2asWXBjCJl8pB6K1qPhlvZdzt3Wu9wOMTFxQWy2Szq9boLaa0qd2LLeyaTiRn7VRemXq9jc3MTk8kEV1dX6Pf76HQ67r0wDJ0y5tkgROuPHz92QnhycuIOZT87O3Po+2d/9mexubmZ2CTBCdc8ak6CEp/Imt9xCzbR7IpxrswL1mKVM5+37rTG65Thy+WyU/hA8jYOuoC6o1DbWLVoQyFQJW/RCvvImP5kMkEmk8HW1haiKMLV1dXSofkUAL3rk/nVRCo0rvqM3TGq6w9UyETdQRC4q+O4zsBNTtZtZYiF6BhY3k7O8VHZ2YVaq/AY393a2sLBwUGC1j6PS3mAi9WqGO0z5APNzODzmj7Idziflm8tP6hh0XqJzH1Aw3pV1rvxeROqPH3fKV9tbGzg5ORkSYHzPV8YR7/T/5Ue+pnSVj3qVeELn1FQ70LDrYr2qWum0ym63S5arZZbO5nP5/jss8/u9mXBKrCM9/V6PZydneH09NQNzhJIFwK63S56vR76/T4ePHiAdrvtbsxptVro9/vuzOJ6ve7O32ZMlOgYSMa8VCkpwibCY9HNNmqp+T/gP0ub4weWNxXYsAL/t/3ROtLpdMJt1oVK/ra3h/N9NSo+g67IiePhnOhBVpqeR8NZLpexsbHhdqTyOTUUHJ9v1Z99q1QqiXz/MFzsdIzjOHH/JLNuSIt8Pu8MSaPRQLfbXQpdkPbqJbGfqkzUgOj7Ov9sX9dBOp1OYvFUY5+cG+UdGhWfkmH+unoN1nvjYjzngc9Y3vMpbeUtnR/Ohcog61FPz/KWKk71DtUbtZ6kyjo9DyptNRLKn752+LfmeivQsXrFpgwTPKgXYLe4W7RvecJntCaTifNCybvdbhfj8djpwVXlzihudrJeryOVSrkLDpTwNr1sNpslnuHnnU4ncZwk45+lUsnleYdhiLOzM7z77rtL8VRlRuA2xkkGV4TFvlF5kcE0RMG+E8XbQ3RsSqFdiOJn6prZvvJdXdgkTdSN1zge3THGJ5XW1shoHeyPuuLq6nJ8elA8T7kjPW2YSOlFoaCC1Gc1A4MKUG95j+PYoWy+S7RMo/XFF194FYcqSUW1cRwvzak1NhaZsT+6BsFjWclL9Ah1J+fOzg6eP3/uNaysm/1RtG0VzubmJjY2NvCjH/3IIW4qJ14CACQP1VrlBdo8aOspaAqrlR3la6ucaVg0xq/yre+qx2BBE99TebHgw6dAi8ViAgSsUrKsS+ek2Ww6pau6gH1k3/P5fGKTH2k0my2Orj0/P0ccxy7raDKZoFwuu13gq8qdUdz37t1DvV7HxcUFZrMZBoOBU4YquDpxRFaqrEajkcs8GI/HiY0s3FRDwX716hW+8Y1voFarJWK9QPKYzjAM3TGvrAtYHe5QRaaFddEwqaKqVCpotVoJpUy3VDcAqOuoClrdWYtidDysg0rernjzfbrDqsD1NDx+Zt9VYdG/dRGJ4ysWi4lMFralt+2w7zzOlgqH4QwNdVAA1DNaX19Hp9NxiFPprnRUBaFxbo5tVfzTuuzcQUo663qAhnyA29RKPjOfz12IyypSayB8SlSVzfn5ubtHVfmDf6siZd82NzdxfHy8FFbQ9n08b70C6yFYdM7veWSCpija3zbN0nqafE5BgH5m2+X/FuTYOLWGnaxiDsPQKVwWnQOOZzKZYDKZuEV0nrcynU4xHA4T95gywy2dTru1NTtOLXfiPO4gCNDpdHBwcOAyA3K5nEMWQHJrLQ98KpfLTtBYUqkUut0uLi8vMRqNEuEUnk3ChcHxeIzf+I3fcCfPtdtttwAF3G411l10WlQwgaTy1MOf1PDQQJAB4zh23gWVVxzfxomt8rfoQ99RdM0FVZ7PoudMaGxa4/RkdqauaVGBIbMzfZJ9UXc9n887hWbjv/yfW9XZNnC7OKhhG2CxGKlXkV1fXy/NC+lZr9fxp/7Un0IYhm5RUueTxpshFrrBRMEMlbFetvPw4UO3s1O9EB2Thl9SqRQqlQriOHb5+zZUQcVEfuSVc3ZulOesp2cNsr7P+dI29dxtpfHJyUkCILCoYVIP0VfU2On7+pvf64KzenG+UIcaWDWCWiflTdu2gIZtPX36FPl8PjFe67mrl2JpZddpVC6n06kDPdfX1xiNRjg+PsbBwQHa7TZOT09xfn6OwWDg5JFrdGtra05pr6IxcEcQN3DrrgdB4HbnqfVWpqzX66jX61hfX8fBwYFDYdfX1w6pA7fMy4Wb9fV1h2zpnr548QKtVgtbW1uYzWZ48OCB65NFzvytKVc6sfzMKleOS49kta6kdY1tmICeB99V2ihyAhaM1Gw2cXR05Nw0KhLGefk/0ZsaJV/MczaboVKpYDweO6RCw+jzhlRpWCVsFYNtRxXT6xZwORd2bsbjMZ4/f+7qIe04V2pA2FcNg5D3NEslCBbHCLCooAO3x+BqSCUMQ7cIzJvi7Xh07jR8QyNvFaiVB45HBV3Rn/Kk0s0quGKxmLi3Uo25Re1WYal3Ysdm/1fDw7nTnH3lER+KZz2UC5VRXfwnHRVI6JG/+/v7iWwWbV9pp0aLBtLKh4ZYomixRsIxsW0ALg04DEOUSiU3v7VaDeVy2fGQnh65qtwJxU3CMcShN7lYBVkqlZDNZlGr1RAEAXZ3d93mG72AgfVyguv1upu82WzmBCkMQ3fu8re//e3ERh1NK1M3mQzD+pWxiAKsq20nggjPxrN9ShxIrmiTsazQ63Pc2s8MEz6jueaKWNUVVoTM36lUyhlFZWCGVNgvXQzT8elckn5qmOwzVNr5fB5ra2s4PDx0bfC3RXVK5/Pzcxc35Kq9hn5Ig9cpKKtkVWjJrxoiGQ6HiVRLeoxBELijGIjq1ftimzqPfE5DQRrasaBCaavIlP22fKO8EkVRIgtJ511py7+1batUX2csfSDMKmwLfnSebcgCSC6UqidhDQB1CuvjmerKSz7jpvPOPpAGytt8hwuMDJMwnZZHVUfR4kC2wWCAwWDgFDvP/Lf0XlXuRKiErqsuMDEUUq1WnRUkE19cXODjjz/G/v6+UyjcOsz6giBwbm2hUMCDBw8cM2xsbCCXy7l79pgFkE6nsbW15Q6Rsgsu6tare6w5watQkiopjkNj6lSaynBKCyDJkDY2zd/aVhzfHpalTKHGRoVCN0OoIi+VSoldrDRq9XodUXR7GbMqddZBuqoS0LAI54CFNOU4p9OpU3pUWKyHv3V8QRCg3+8jiiJsbGy4BSgaYxobnyusnoyGAzQcxDpIRyoZ1s/+8V0+z7IqBGZj1RwfvSOlkSJ9FXLbto6H87nK8Cn/KQ9qfZwznQdVptzFSqOoWUPkeaW39oU8oTJhi+076W3rsuPR73zeqpUF+6zSUsM6aty0T+Vy2R37TH4jneixKhJnCNBnPFaVO4G42Wn+zmazWF9fRzqddnnbJFy/vzhhlsg5lUqh0+m4PG1bDxddmANar9cxGAyc1WX6HO+6VAtOBlSEGce3C6aaFgTc7rhSxW0RhQqQHnpkkZAujnDjiKb2+dAOkFzhZ3ts347NGhR1z1XZkAZqeIBFloSiDl2s4zjo5Wh4iH+rJ6Uplsw0YH9IC7bBcdoQDwuNMfP6mVJq0SmNIheL8/m8i1HHcezyybkQanNy1Qjr+oCiR9KYz/PCa6W/RdtKSy5a9Xq9xHzyb503fsbfyo/aFscex7HbdWk9DtLYFh9/83/SkIpN5VGRtL5jww7W6Og4gdub2NVbsYpXla0qYDUYKpO2PfKutm2Vvc3q0lIqlTAej5FOpxPhMYYlqW/q9XqC3j9OuROKG1gctUj0xYsOwjBMHL+oli6OFylmL168SCy2KYFHo1FiO/BP/MRPuAt8uTuTbky1WnUXJlCZKbpSoWfRv4MgSKRoWWFSF5O/NdanffeFKzY2NnB1dZXwAixaUPdYaUIh9rnm7B9py3eBWwFgRg3fI1P77nnUupkbT6VgFxFVsNW11qwSNUR8nt4ZvSLOk0V1BwcH2Nvbc4ZD48d8f2Njw7mzjUYDJycn6Ha7jkfK5fJKwdI51blTt1ppG8dxIg1P5015hHPC/F67o5HfW8DD7+fzOQqFQiIc4At/ALebszRLRnmB86AG43WF8833NBzjK6sUuC9coUCDxTcmpVEYhgllr7RXulilr4BI5Yqf67kmvvBmOr242IVpf1Tg6+vrrj0mDlBWbJrw68qdUdzcLcTYkDKUFlWkqiAt+uS7mUwG1WoV29vbKJVKyGQy2NzcRDabxeHhoROW3d1d1Ov1BEK2FpnM7cv3tUJqlakyhl04IYrRz1XJMfZvwzAcp3WJ2R9VjKp4LQpjP/i9CgZwi3I07mvTtHRbvO0L/2Y7Nk+b5yOzqFHTnGOLusvlMqbTaSJWSCNN5Uzemk6nePfdd/Hxxx+7tubzOVqtFhqNBsrlMr7xjW/g8ePHePbsGV6+fIlMJuOOVbDhCTt/Ktg27MFwUhiGS+E3VZLMpuJajQ2bkS904de3VkClr/xhY7KcZ+VL1qVtq9LSd/mdGgTLm1Yp63MacmA/tWgWlPaTHg2NPReWwzBcuvhCjTnHSBDDuVUAo3NmQZGVM80e4jyoN5pKLY574Hqarq0NBgOUy2V3cqkFUdar8ZU7objjOHaLT3rAC0/148KCEk6JDSzH7IJgcdP2N77xDdy/fz9xilihUECxWMTm5qaLOW1sbCzFq+0Eajvq+vFzbdsyIguZlv1WD0LjdRQs0kM3CqgQas6xKn0Ks8Zg2b4KK9+hQGkbfIaHcmkWCfvNUAOzgrQOKnOlBZWXKmfrzagbyvgfv6fxCMPQhc04Lg1rBcFit2SlUsE3v/lN/NN/+k9xcXHhEBi9o/F47C6Lvri4wObmpjsAjOmj1iixz9ZgWo9Dv9M1DR2vzjtprcrJhtEsL3IPAjeiqRHWogrSp5TseLR9BRQqEzYUpEhRQ1/2O86X8oQFC2oUfYofWM5A0nHrc76btHScKj+aWukDSTYWrWPjD40O0/x4+fRsNsP5+blbgykUCgm+ssb0deVOKG7g9uQ0TU/jATrWwgNJxUIGYv7jbDZDNpvFN7/5TTx58iSx0AjcKgrmdKsS8U2yRVUUfrZTqVRwcXGRsJSKWlQwNFuF6ElDMxTG2Wy2tKtLXWkKhgo5kR1PpAuC21tUlEHIfJoZY2N2ahiJZuxuUCB5YFEcxy7Hmfn4rINMTwFTj4R9VWNINMINK+QJVQBWsDhHbP/y8hJxHON3fud33GYjCqdm1ZDO3AmnPKCbn2xoTOdXUakqOqWVejnaZ6Wljs0qJYtwASSup2PR56zS12d0/jY3N3F5ebmUrgokQ2dBELhbiHyelV085t92rkif+/fvo9VquXUn9R74nlXK1jBRZyhvWkSvc2D7oGOwISJrgC3osfF56hMtSp/xeOyupJtMJk5etE0NK64qd+KQqTAMY56BTUGhUuFRrPZwKV9opFarYWNjAwBw7949vPPOOy7jwU4gcDuJehSq0sMypb5jkQoXKlXQfUJqkbuOh88yi4HCQWWvaZI0bKvGZBUvn7Or9tYtJh2UKXWcVHzcAMNV9E6nk3DJLfrmbyL0OI5dXrvGatkPhs5UCbHYDA7f2SukkTUwKhQqpIqkKHzlctntZtWDyHyoS4udexV2y7f2M1VWXKBlW4VCwS3C61hsWppvTvm/ytjr+mNdf/udhh84RuVjnyeiBo1t2rNUNGTI/nMu9XO2ZcM/Shf2wcq+9mlVfFznUceun1lPUGVYw0EcO3BraKm8CVC0bXpn//yf/3N0u10vk92JdECiZR6Er6jr0aNHbiuoMh8zRhQd9Pt9FAoF/Mk/+Sfx3nvvJc6soMJRZH10dLQUarEoAvDHsYMgmfCvCosW3zKK/V9dPlWY8/ncKSOr6OP49qhVIHmWiM/SW4SqdbI/RJ+r3DTtr6J+pob1+31nXLRd3vTCOvP5PNbX1/HNb37T5bVyLnX8FGAVDBUKKm1epqBjUY/BGgf9rYpHjTbROmPnbE95QY2IhqXU+2PdVvBtUT7h8xZ1sW88WTEIbtMDdRy68UflIpVKuVMR7dkk2raG6uj92vAJFaHykOUz0kHftUpbPWzG9JW/tH7bhuUpNeI6P6xP55CfqRyQxvz+dZ9ZBa6ekOoS5RnVO/V6HbVazV3KYfmEY9b0T1+5E4o7DEM3AWTAMAyxvb2NOI7RarVQKBRQKBTQaDTwta99Dffu3XNWS9FDNptFuVx2RFGFQGHv9/v4zd/8TXc2tn6vLjCQZEI+S8WiiIjPsk2bU6tjVWVMRuXkc9HOZproSrieXgfcKg/Np2bdVI5AUsgpNCoUali0KOpRJaU00nY5dl2b4PfFYhFf+cpXUKvVnJGy6Ygq2La9VOr25D01hr6FWwqTje8q3VSoVLGk02kXHmJdqhTVk+OY6U2wXauQWWw/CAL4t+2PzlcqtdhG//Tp00QOPHlSFZS+qyclkr66iB6GoTtmwqc0FWhYI6nyY3lPn7OKdD6fL+XxW/7T/mpd5Bv1npSPLU+zXet9arGG1vaH86yGhTTRebDv6Zk0TFVV/aDzZHllVbkTMe4oihJoAlgg4rOzM0TR4taR7e1tF/Df3d1FHMdOgQ0GA5RKJdy/fx8ffPCBs1hWUQ2HQ/zGb/wGWq0W3n33Xbz33ntL1leF165oK7qlsSE6VhRkQw5c4OJio8bbKehqEFiXHqJkUYs1JCw2xLHqJENFNhbJ2D6Qhhw3jYgymWV4fZ9KLIoinJ6e4u///b/vsjWsS6lhMKWhziN3ayqj09hriIF9pcJQo8I50e/4m3eCWh7iuFRY1TCRVsoHShtVYDqPnKcwXBxVsL29jY8++mjJEPlSVNWA25irtuO7qZx9owzw7AyliT7HsVvXnjyq9alckZb2vTC8PTSM9WvoTL1ICypoKGiMVJnSWJNOGqO3YRMFC0pLH610HvQ57b/OhfKGNX5KXwWsPIr6TeVOKG4OSgUvCAK3aEW3OggCNBoNN8j19XW0221Mp1Pk83l885vfdIf6sA4ywqtXr/DRRx+h1Wq5VMDNzU1sbm4mBNeHJnxWMIoi5+L5irrNXGBVZlYGt8rTnqtAT6LZbOLq6irB3OxjFEVusdQqK6s4WGhIGPe0uaSqXMjg1h1UJcvPrTegAj8cDhO3t1tFrXRifbqtXumv47B/U0it8qThVeOpClrftfNi46b2/Bjtm7rKihJZJxWZnu0NLHbRXV5eLoEF5YXhcOhu39HsIqWJnWurSHyKyypg/uZ8sC+2T6qAFB0rX2h/1EuxoQ3lB5t2q99ZT077quDLLrj7eFOzfazxU2Ng2+HzvowhC7YsDW1bGsLTZ1aVL624g8Wdk/8cwGEcx/9aEARPAPwygCaA3wTwP47j+DoIghyAvw3g21jc7v4X4zh++bq60+nFXZGpVAp7e3sAFoiIV4k9efLExUHr9TparZYTvvF4jEKhgCiK8MMf/hDf+MY3EEUR+v0+hsOhO2yIFynw1LxUKuVWslUx2LAIP9OizAMk7yRUNOqrR11YfqfCRJe4UCg4VGrRhCpU3fTD+ujmK3PkcrnEmSIsVG7qXWxubrpjcXX86s6p+66Lu0EQoNlsutP8hH8S6XpAMlNG0Zm+A9xenkyF2uv1HM19WUDsq41Nsj7NVFHFw01Eioo0TGRRGPusucSqCFQ5Wq/Ol+VEpUy6aaxbw1o+JO8LE7EO0l2VH38UVCigULqxDQUB1sMClhcfuUvU0lHlRr0Ntq19tV6Xtmn7qzSOosiFXzlG8ouG/HQsLHa+1ECo56GARPviA0uqoH1zphksyn+ryo+DuP9dLG53r978/x8A+A/jOP7lIAj+EwB/BcDfvPndiuP43SAI/tLNc3/xdRUz3/rjjz92Zx1sbW3h4cOHbps6B8Mdb8PhEKenpy42GIYhjo6OcHFxgdFo5E5li6IIa2trbpccN2uUSiXU63UnaJrGFEVR4jwLtf4W5fL3KrdYlbh1jyiU+j77QANAT4M3xLN/mj5Iptet4rqYy5vlbTyQ/dMxpFIpdzsMDRzbtcJDIVPkHMdxIkVNDSHRiXVJfd6BpoRpezpHuVwuMS9WsDRkocjRGiR15dWIWCPJeYvjOHFxscZyLVLljxVUn7K1gqqozSoofq9tWfSqz+h6jI7Lp7w4Zxbh2ufYZ6W9yoG9BJnxXVVOhUIhkY+vbeh8WjDE9SpLdx2DHi3A+nTeXmfwVFZ1bkgXpY3tN5/zhXrYBj0OG4pjPdpvX/lSi5NBENwH8N8D8H+6+T8A8AsA/subR34JwL9+8/efv/kfN9//K8HreoDbrBKekcw4zxdffIHPPvsMR0dHTthSqZTL0WUeJBFMv9/H4eEhrq6u3PU/jLPypLlyuYy1tTU0m02sra25PihjWsViDzZX5axKz2el9W/LYGQcH/OkUouzNuyqOZmF/7NPHCPrqtfrS6lSGurg70ql4r5XBGTHomO3CF8VAo2KZsXwh5lALLowqM/RCFm3NwwXmSp8T098A24Vg4YqfO6x0pk/VimqIqQRTKfTePDggTtAyCdcNpxEutAQcgelFuUnNYCqWNU42BRA7bfPxVZ+scZI6WuVo7rvrIdjYV84T1ZBKg35brVaTbQ7m82c18u0OI5NDYIaPx0v21fa2PPhLY051xw3ae7jD32Xz+oagHoPvucsf/h4RefOenN/EIj7/wDgfwmgcvN/E0A7jmP6ewcAdm/+3gWwf9ORWRAEnZvnL17XANNjSqWSY5h+v498Po9PP/0UQXC7t5/3RZKJ0uk0RqORC+pnMhlkMhkUCgV0u13kcjncu3cPcRxjbW0N29vb7jAc3liiC15qiVV4gKTlVGWoTKsuuFUE6qapRVbGIlrge7ZeiwitG85QEdvROLZ6CgASl75qfyuVCgaDQSKFzKIujp9ekCIzFSDWrZcW27ijNXxA8rhZMrI1BhQ0vd+Rhs5mmqjC4rzalDsAbq1AXVca8MePH+OLL77AeDzGfD5P0IYGh/+r4OmBT6rQGAJUD8fOLwvf8ylnRdqagaS8qXOvXo7OrW3P8jUN+3w+X8rqst6J0iSVSjlPzIY/4jhe2uBlx6/0VB7SfkVR5MJq+p6llbZh5ci2oSFERc8qj+pt6PgtIONnlq/Vw+Ezvne1vFFxB0HwrwE4i+P4N4Mg+Pk3Pf9lSxAE3wXwXQAuT3ttbQ29Xg/D4RCpVModvUoBYSof0TMPbOFBPJwIbpWnRX733XdRrVaRSqXcka50z4go1OW2llKVrwq6XY3mBFrErkKhTOILXehhS0Ay/5SK0qICjb9aw8B69HIAAI6urJP9Z98pbDo2bY991H5p/WooLEpXFKWI3Vf0xvUguL01JYoid3qfjUMzfk2a61zoMaka71SDkM/nUSwW3R2R2vZv/uZvOsX1prx339+Wlnac6t6rslB+0QPEtD417Ao+oihy9dsQnvaLf9tYsYYOe70eXr16hWKx6GSJtx2tys5gWEP5n/XbTCmljc+IKJ/zb507C7xYLD9YpEy6WgOksmHptOp/S1traHUBknX7eGKVTABfDnH/LID/fhAEfw5AHosY938EoB4EQfoGdd8HcHjz/CGABwAOgiBIA6hhsUiZKHEcfw/A9wBgc3MzDoIA6+vrDllzYbLb7WIwGCQW1mq1GjY3NxFFEU5OTtziC8/frlarKBQKGI/H2N3dxXvvvediolG0SEnjgVKKApV5Xkc0X6xSUaPGdW/GuhRa4OfAsnumiCKTyeDdd9/Fs2fPEhPNS0itsdD4tzKIDRFcX1+7vF11hYlWuVO13+8nhILvkyHV1dZyfX3tlAX7zLaoBNjnbDbrQmG6k86GXJgHS29EN8jQeGhs3yecnG/mglN56zOz2cwtgLLwICx6ZRYZantWUMMwxObmZuLuSz6nRx7oGO1N8jYmzXesB6j0sApY50HnTOvSNqfTKc7Pz1GpVFz7vV4PUbTYw3B8fIxisYiHDx8uhcsYguDiqOUVRd0+xK6/tfgUvD6rSFYNH59XL8x6RxoGWZXjrf3Rz3y09qF4TYtUmbXK/nVgBvgSijuO438PwL93U+HPA/hfxHH8PwqC4P8O4N/AIrPkFwH8ys0rv3rz/6/ffP9fxT5zYgpDHe12G48ePcL6+robBM995mB4yt9gMEA2m3XnKO/s7GB9fR3lchn9fh+1Ws2dBTCfzzEajXB5eekOfI+i21xdteQWidqsB4tm7DOOuDfxdRvTtK6XMiLjuBSO6XSKvb29JbSiJ6Eputa+6wlrPChKXVIuWFoBZqggCG4vVOVc2OJDhyo8HBuFxGaVcO6twPkKlR7Ho4LC9m1skHFPPXGPPLVKSHU8mr20CklbT8jOJxUpFTLrVXopH6lxUmWofVNviDSwqWfq5rNuu9AJwB2jzIPWhsNhwuPi+K6urtBqtVy7vDDALrKpAVAUruNUWquiVj7T+dV++Ooij1FuVvGj8ivnx/IMFayeA+QDY2pE+L0CEuoTnRM+w/lRnWP7/Dq1+fvJ4/6rAH45CIJ/H8BvA/hbN5//LQD/WRAEnwO4AvCX3lTRcDjEP/kn/8QxOREywx3lcnkJIaRSi228XIC8d+8eqtWqWwDb2tpyyJLI7ODgAL1eD++9955DhGrV9bfPggLJe+eUKdWF9+Wzst/6P7CcLsTFVx2nPSFPwzp8nwzNWDP7yedsrjLbYp06XjKNnpXCmDWZWT0VKnkqGxtCUKZWIVRB8oUe1EAqDRUd8TuL9hTF8Q5TH5rTvli6WI9IN29YROgTNH6Wz+dxdnbmDRFZFz6KIpf5pPQlUrQxUZtdoaElNd4+mrLM53OUSiVnWPb29pxHxmMFNBbPg5J4W5QqODs2nW9V4HzHRzOrkPm5Gmt93sqXDbFYhatzQ7lR5aq0snOjMmU9AkXOaiDUM1K+5P8+4PCm8mMp7jiO/zGAf3zz9xcA/oTnmTGAf/PHqZeXaK6treHDDz9MbH/Xrc+Me/OAoyBYuLybm5sAkgooDBc7KlutFgDg008/RafTwf3799Fut93tE9b1sYuG+h3gF16NAfJZS3wVfv1eUbcKP//XfF+ejeILzVjErwKgOyDVo9DxsD0aMw1PAIvFv1qthn6/n0D7dKmtQlTvhO1a5GKVtKIg/q1HblpUyo087If1bGjkzs/PnYtqc6yJVJX2qkx1rnS9gYXKUYVZxxBFkesnBZ+AwvZXi0+BsA+ak8+QD8GDInX+5jiUdwgu5vM5dnZ20O/30el0nFHmJQ78fzQaoVQqIQxDF6I7PT0FcHvjCxMMCJSY6VWv11EsFhN84VPA6uXyMx4lzCvwKBPWaFIWVJHbObFzbMGAFqtM9TmtX3PQWS/pzWM3tC62a9eg7Fxb/WPLndg5mc/n8Z3vfAeVSsUdhs/sElWQRIE+dKVCH8e3u8uiKMLx8TFOTk6wtraGQqEAIImqFLkCfotnlbmiPWax6PZdVdQWWbzOLVTUqe0R0fqUE79X5abtkUnsphPLnKVSKXGsqZ4rTFd51dZl21cNX6gxUjfSCoKOh58XCgVMp9PERh8iP1/qFvPOWbRtFSqGQFQ5az/sHOhimKIpRf/2fW17lUGzc6a8ozyvPHR9fe28oHfffRc//OEPXXaVNTRWQalisJ4Fz4fO5XJYX1/H1dWV83xHoxFOT0+d5zIej3F5eekuBbi6ukIYhnj06BGm06lLyS0Wi243s9LAl65pEW2/38fx8THK5TKazabLANP3lOZ2PEpvHxpXHlHlr3OjchIEy56rGnPVI3EcuzCjHgqnc2MRvg/wrSp3QnGTYY6OjtDr9fDuu++iVCoBWF7xtgrbPtNqtXBxcYFOp4Pr62tcXl4mXLxyuYzt7W2H2mys2ioln2LxoWQruBZVWzT8ugniu5phooLGvjOswvGR+bgzkHUp0tKxafwuim43pvA9tqtjUeSutCuXy+7QLiI6Pq+KXMNL6lpqLFg9Bb19hkjrdfNlj9VVetu51H5Z91UVexAsdoO22+1E/zk3mo+rc6tCbuOlbIftatzbKh4Njei8DIdDPHv2zD2rXhiftzs0NR7N30Tv3W4X9Xo9cXNQt9t1qZa8AIB18fwdGssoivD8+XOXfppOp9Hr9XB1dYVSqYRer4c4jt0aFflIz40hHeN4cTXh9fU1rq6ukEql3DVyOvdWget86rh13vV9pSl5l7Jnlb1PJ/hk3so7owTKg3zGKvDXGRgtd0JxX19f49NPPwUAl1q0SjFbFKNWtt1u43d+53cQx4sDqEajkcs0ub6+xtbWFnZ3d1Eul5fOcCZDK2OviksVi0V3jgqAREyRqHDVBPhijL5YLpDcGUdFoouQvMGeZ0bb91islQeQOJfE9yzroQtOhtMzx5U2vV4vMVZf3Xa++Jt12M0rvPwgjuMEmrSGyHpe6l0oUtbTExXdsn5V+tbgUGmzPe27HRu9lN3dXRwcHCwdj0qFrvFVRXaag255UPkkiqLEXNgccA35kBYaGkulFrtkW60WarUa6vU6RqMRJpOJOzJiOp2i1Wo55U0DSz4geOD/8/ncbXQrFou4vLx0mV8Mb21vb6Narbq+6YYeyg7vaKxWq+58G176zNTgWq2Gcrmc4CGf96Mo3ip0fYZ9UEVuF/51Lqzn7IsCRFGUOIVUw2Uqjzp2/VlV7oTiHo/HGI/HyOVy2NjYQLPZdN+RAOoaU1GMx2Osra0hk8mg3W7jt37rt9wKeT6fR6PRALBAPt/+9rfdXZO6Mm3zoqmcaBH5PnCryBj7C8Nw6bQ/3+TaGJcN07DY+JYqLEVz/N8qS1UOjIfzeTKej1mJ0kkLRewAnKtHReGL/5Px7I1C7A+/V+Om42WfSUM7BmuM+K56JaQXsHx7STqddvFXBQZUZtaQaUhDFbmm61mlTYPB/l5cXCTQr3WNlQ+sUbBld3fXHedgPTzOo86ZKhXlOeV7ellULHpEQLvddmOyXhbfj+PYZezk83nUajW0222k02mMx2NUq1V3MFqr1XKGuNFoJBSkTRmdTCb44osvXJIB4/ivXr1ygIneWalUSvC48pUu4rP/eoGylT1V8KSnporadRadAw0N6lhUNnwxeOtB8t0/EjHu2WyGdruNn/iJn8D9+/cT5/Pq4Pk/N+lUKhX0+320Wi386Ec/wvHxMVKplNtsk0otto1/9atfxcbGhtvhByCRT8xihV37x/p0pV4FxypsRZSrUDff9yE5IkWN2SrjKCKzCf3ssy6Qad8oeByHdQUplMyJtud/02ipYlMlTAVNmqnRUCXMRScuQPF9CgmARC64rnfwWaU7n6ECUyEhSrMGhd8zdqt8p//r5xYFsy8EBSw0ErZ/5CdF9JxHX5gljmN387wNMTGjR89fUSWhNLMhAwIPKtput+uu85tMJigUCqhWqzg6OnJeF7O4qHDpTWSzWTx+/BjT6dSFKaMocve7lstltNvtpUs7LEIl7zO8EIYhOp2OezcMQ7f5h0dWnJ2duTP4dfFdUS/Bn86/janb44pZhxou3+Khrj/4QhxWKVvD61Parzt5FLgjipvoivEsbke3i3rA7cJMKpXC5eUlTk9P8fHHH2M4HCIMFznQs9kM5XLZHTjfbDYTMUQgmdYH+A2EDwHzfxsmUURpY5kW5VBh2dvD2S8+p4UoWBWBVSA25KDnReuiHJDcIq4IUs8pCYLbbepkcj18S2mjSkeVBY8SYPiA/WR9evKfejkcg8b12VcKvy+MsOq0QIu8qEDVZVUPxSptDcH4cvOVvhr7VmOoismnEDS0YfmC8WFVcPxbUx11vm2M2xrT6XSK4XCIYrGIk5MTjEYjF+ooFApYX19fipnHcZy4GCCfz7vQSCaTwZMnTxBFEfb29jCdTlEoFHB5eYlOp+PCHLwHVS/wJi+FYeg87s8//xynp6eufaZ1FotFNBoNF3rh0c7NZjNhEHVuVDYsUOFvNbwKvBTQaT81Jm/DMFqvtsl3Lf/Y+Wy1Wt7MFpY7o7jn8zmePXuGQqGA+/fvLwk3J/n58+fuHI1PP/0UJycnCWWYz+exvb2Nx48fo9lsugUNqzDU2unE2viYKkhNKbPMQcFmPb4zK+zxp/xO3W+dZCB5upsyjGUUVZpktFwu59ByPp/HcDhcch+DIEjUbV1CSwuiIR4ZoIaE6E/DWvaSCXWR1ZD6lJXSP44Xt+cQiZAfdD5UyTOGrHOmuzIVPfF5K8jKH5z/VeeA8Fntt56NrvFlH5LXZ2xs2s6x9knvIdWwH+dYeZ98oTsZi8Uijo+PEUURarWaAxSj0QhnZ2cutMAz4qMoSnhz7XYbm5ubuH//vgt3VqtVPH/+HIPBAOl02s1bpVLB1dUVut0u2u22O7ytWCy6MbCNVCqFer2O8XiMq6sr5wlsbGyg0+ng5cuX2NnZwXw+R71ed3s+VA7segCNDxdD+ZwqSKWX5QGO24ZMFKwpktc62Q/VC9pPzpVmUN15xT2fL85YzufzePXqFR49euSusz8+Psb5+Tnq9Tr29/fdLREvX750d0bqhDWbTfzsz/7s0kIUFZo9alKVmKJt/d8+RxRiv9d3gWQGhrXael42gKV+sajra5lMEaQqBTIO46FkCLbFPGB1qXWnGJWYzZfVRT8bh1emtehbEaoidzVAPpryeQqTbkTiWElPzcjwCaINWVBB8rQ+TSFU1KyCas8+V0OqISNdqFMgoJ6X9dLYrh07++wLz1l+UQ9PlTj7RhprbD2fz7uQxr1799Dr9Vy2CBcFgyBApVJBEASJ29i5HtLr9dBqtVAul/HRRx+5vuZyORweHro9GhsbG24zDz0IpvkFQYBut4uTkxNUKhU0m03UarWEkdGxXV5e4vLyEtVq1V1xeH5+jo2NDeTzeUcTH52U75QvfDFonYNVXowWfZ/1W31jU4Q1s4RZNm8qd0JxA7e3HQ+HQ/zar/0aGo0GWq0Wrq6u3KmB4/EY29vbmM1maLVaCaVNJkyn0w75AclND0RLynhKXItg1RVSywjcCiEVhn3WPqPKwOfyamFbRMn8zIdSNfxilZ+N2QG3+aXq1tvFU2Vmn0HShSl91xfioQBrDNYqau2jDVXZ/gHJ7BhVaho7tjTlNXPq7tLV5+0/VKLKJ9Y42zi7tmNRGWO0FvkpbZW+vjAbDai9mR1AIq9Z504RuM4b6+VGHca2uWGGG22iaHFhSRzfXg+ofVB+JsDiLVRRFGF/fx/dbhfb29toNBrodrvo9XrIZrNuEZgLl/Q2x+Mxjo+PHVi4vr7G+fm5WzilYZ1MJtja2kK9XsfFxeLA0efPnyOfz6NUKiWydVQZK+hRb0yNruVPnzelcsM5snyv8q2fKY/rPCn/zOdzZ8h88XKWO6O4idL6/T4uLi6wv78PYHEeAt20TCaDnZ0dXF9fY21tLSHkvV4Po9HIXUXGzwF4lRewvIJPZcG/+Z4KkaYHUdjsBCiq4Xvs7/HxsXtXvQFVzOo28jPbXx2TDduoawwkt7ZTMXEc+pzOg9JJFQGQ3Ilps3J8eamqoFXRhOHiQuiTkxO3K07RKDMWrIK3Ck7nRQVWjdx0OnWLbnymXq+j2+0uIVn2bRUdFB3ZRWmdp0KhkECXcRyjWq26NvU9S0Mb9tD5Yd8Y21blZEOB2h/OzcXFBc7Pz7G5uYmzszMX1+YGm3Q67TbU6HoAgKVMn16v586258ab8/NzzGYznJ2duUuhuSbF83EymQy63S4ymYzLDKlWq24zG5E522a/eLja+vo67t+/n9jVSSNgvWgLytTb4f8qS0pLX3jSxritF6bFgj22p/PG57j5kODqdeXOKG668nqsJZEzt1l/+OGH2N7exny+OAO6VCphMpmg0+mgUqng61//Ot5//30AScWmSkMVL5BEdqoMVFnrZNgzg7VQEQLJ2FkURe4eQUVlViFZJE6BVuG2sVjtnyox/h+GoVttt6e08RmL7q2hYKYObwrngiIXAq1brmdnaChA6cZ3Tk5OANyeBkgEPZ/PXbaJTZ+0rifnRYVK0wT5jMaDaRisUVdjp3OiqXJKQ421W6HlLlTtl6JWCq6GpFZ5Hmybx/FaYKEITp+fzxcnLPJMkeFwiHa7jeFwiIuLC7cpjUcmawiPtC4Wiy6UmU6n3fVq5L3r62tcXFy4Y2lLpRLa7bYzWPV6Hb1eD7VazcXM6eG02230+32kUilnLKigmeli4/jz+RwnJyfuQDnGyVVmde5WyavqABvesMZa61XZVU/b8iT/Vh7m+K2sM3uMGTmvQ9vAHVLcwO3gKYTT6RSNRgMPHjxwJ+RlMhmkUins7u6iWq3i4uIC8/kcH3zwAd5///0EIXUSLJMDyYUvFlXadpEBSCJxfqd3GFpm4LP8Xhcn9Hu2zXbVXfYJpw9RadhEJz6VSuGb3/wmPvvsM1xeXiYUOP+290PavjDXXlPzFOHpwhqVHvto48VUXBqSYD3sE5+hMrfzZudD6UWDbuO5iqiiKHJ51hb9qGut88kQgc6FHSdpQ57gWIhUiah8HpePJ9XQa7jNx4v6v9Ln5OQEQRC43Yu9Xs9dTwfAnX/PmD/7oZlGcbxISeSpnaQHQ5gnJycIw8UieKVSQTabRbvdRq1Wc3N7dnaG+/fvI5VKYTQauVj0wcGB23lLj5B8QhpyfEdHR6hUKtjc3MQnn3zi5undd991oM6CLS1Wqfv4SeVN1xas/lDAxP/tRi4WuzaldbG/nU4Hl5eXzjtcNQbgjiluAEuKbXd3F/fu3UOn08H29jaABaEqlQra7TYajQYePnzoUv74rlV81qqucqGA5C3UKihqHRVhaSzZFoukNM5NgVUEtSq2Zl1gZSZVRvZdul3Pnj3DYDBILJiq4VGFr4qb9YzH40ToQXPtVVAsPRVZqhdB5KQhAuYI002+vr5e2pSj46PhsPPR6/US82UNpI5LjQPnxyIpmxvMopkq7D8NNEMyFE7LRxal+4yz0lONPuvQ75U2dlzkAV5TR/qur6+7WLKm+AVB8qTHwWDg5qnT6bj6qaDpLfH39fU1arUa5vPF+Tb1eh3pdBqXl5d48eKFO3WQlzDwaAq2p0aOYySPkbb00Hlu+unpqQujpFKL8+Sr1apLZiiXywleVc+Z85rL5RI7ouM4Rq1Wc2eQ2znRUJnyi75v53kV6mefuG7BS81XlTujuNVFpPDeu3cPw+EQP/jBD5DJZDAcDnF2dobpdOpWld999113g45VBNZi8XsNBWjIwCI6i2BVyGzYg0VRqMZb9T2dPI0Vq6GxdNET4fQ7a7njOHaXSLAdxh5t/JrbkDVmqQqW9NE8Z9sWx2wzOTQ2ynEpGqHwa2hFx0XFoadA6iIQP+NGD+uNaOqedYNZdMwaHiBvEHHpd9ZzsHOhhYpGY+uWDzSkw6MULN8qTwFIeCsWAWp4jAuL1WoVURS5xbtiseiOgojj2CkLhjoYetCUT/ZT51LnlmGQVquV2Ng2nU5xdnbm+ss8bhqHfr+f8NSUN9UTU1A1HA6xt7fnxgzAhX3oAQyHQ5yfnwNYrDV88MEHDuFbhci67WmTQRC481ksv5PuNmxiQyaK6i2QsKHEfD6PZrPp4vxWyWu5M4qb52hTCa+vr2N3dxd7e3vu3JHnz5+7OJ/dnmzdVYuSO50OSqWSy16x7+mzlrhar1ppH8JmfYrWFclRIVL4rEukLqvGfDXXmu2ToRRtc8J1bLoLT0MDymya18znFMnaPur4VaGwXTJfp9NJKEDOdSaTwWg0SrjFKhgWNTKDQY8atQJt+6/j5NyokVGh4u1IanD1Zh411HbRUNcCFF2rcVHPQ3+rIWJ2h45F6am8qIu5qhD4m2l8vOKPl5FUq1WnwPb29jCZTNBsNt3mG84L62cMm9vVOSaeGU70SzoXi0WXJaLHI1he8Xmaavw1/KO05nfq5WpYgx5CHMcutDeZTHB0dISNjQ13RoqvTouWfZ6WGiTOqTXiOj6VUS3kEwUFXCRmptOdR9zpdNqdOVKpVPDhhx8ik8ng+PjYXcrK7deTycQJfq1Wc2cEWyRFgqRSKfzgBz/AcDjEt7/97cRzqwijKHnV94q6FS3o+QZqUVUQqeA4uTYzgf3X3GQyiBoWja0pOrHIQRd8+ZlV7vycoQnevqOhEyou4DY9SwvHyXFRyUbRbaoe51HHpvOlcXcKJQsRIIsKjyphpTXHZQVTDYWGNihQAJa8Kc6zKltVmFw41DZ8KZmK2IMgSOyIVSOohpVeCQEO69FMH9+Wft3PQJpyEYzo/vLyEnEcu/CFHpRGgMG2iJT1YgXyAxcV9Whg9dBIQ9KEilZppIaY/Difz11usyp59otz1ul0kEql3NnfzN7hOSfj8Rhf+cpXEnF8C9wsMPPtD1AZ1uQBvsc503Hbv/V/zpOGVCyQsuVOKG5FM+vr626nFW8ZJ1OenZ05hc2zCngkrLotKqRXV1d4+fIlHj9+7BAuCW3dGiBJVGsMLOLlSjCLojUytE6OFoYzbCyeRV1hRSL6nPZTUatVSnYMypyKFmwcVrNk2Cd1DfU7i+g1rMMxqCLk2Pk9FTtRCmOQ6hmooVK6aPjC0opIVe8sVfqo4dDFRYvmLOJSI8B+6nVovrnSsBQ/0/atd6D9o7JmTFmP4CX9lSfZf+ZlMyT16aefot1uo1Qque+5+AfcHvHKOdD+Aovc8XK57DbqkObpdNodCcuUP1XSNFCsW9c4VnlBmuGzubmJbrfrjrZgFhTrIs+l02l3rrx6a7aovmB7No5N74I0ZIrjfL7ItW40Gk7hKmAgD2l9yrsKJLRYHrM6Q8udUNzz+eKs33v37uHhw4cunPHo0SMcHBzg8vLSnVHw3nvvoVKpoNPpuOMoFdGSML1eD8fHxxgOh/jZn/1Zh1QUaaqVBJKpgcDtZNoNHxo+UOWqMW0iEnVr2b8wDJ0noYqJfSFTqhup/bUTyrxYjWvbOL4dM79Tb4HxQS18huEpPs9DgDhGG9bgWEgLPV/EIizfoUscJ9GRpgta3lGaqAKk68/1AaIm9VxUORNhWWOr73F+uCtVka09Tc7mrNtjB4jS7XEGvlx4zhvvg9SiIRT1QvRdtjcej91iYblcRqfTcRtyGo0GhsMhut1ugibkYQ3/kedKpZJD7LpeoxuReLib8qbdPm6RLxU7jdFwOHQpgrycgfynxpJngGsaJuvjMbUaBrKejnq2wO3hY/P54rTHo6MjDIdD5HI5dDodZDIZ1Ot1NBqNhCK2IRKdQ/WqSDf2Vw3568qdUNxxHGNtbQ3vvPOOSzvjrsl2u+2U3Ne//nWXTcLbp8kATP+K40XK0BdffIFOp4NyueyuQ/MhWxarvFVw1G237rvGPFmsy8X3NdfYulYsjCnaPirK5eRqhgr7TsG3qNKO05Z0Oo1KpZII3Wi/dEt+sVhM7EQko6nCsK4eBZBtad8t0iANbR98fVdjpkiddeuFyBY9s88WeZG2NuTF9ri4pgoGQCLEoP3Ss1FII1XUOm6Gk/SEQ6tU7I5M7Zt6Ctao5XI5vPfee5hMJjg5OUEqlcI777wDANjf38fa2hru37+PTz/91IUZFDTQiPKcexqC+XyRc89dktwwByBh1FWZqYzZeLfyVBzHLkzCy1V4rZrGmNm/bDaLfr+fkDO+3263cXh46Lz0jY0NJ5PD4RD9ft+dn6J9DMPFdW0HBwduJ6cq2ouLC+zu7mJtbS1xAYPyp50r/d6O38qCr3wpxR0EwUsAPQBzALM4jn8qCII1AP8FgMcAXgL4C3Ect4JFy/8RgD8HYAjgfxLH8W+9rv4wXOS9fvbZZ4iiCLu7uzg/P09s9uCK+N7eHs7Pz9FsNpHP59FutxEEAXq9Ho6OjtzEkaFu+u+Itkr4FQW9zj3WhQRlOu7y0i3C6uoRcVrES2FmfFLjhooe7Xs+q6zumo2bqxJXBcvPKKRbW1s4Ojpy86JxUwogc4FVeVh3lKhLx86xqnegAmIFlq6q0oSFi7CKuhTNKy3ovSi6o+Cen58nlIrSysarg2BxlVq/30+ENFRY1aAqPwFwSNbGzZW21rDb51YpALathoFzS0MwnU7R7XYdoDk6OnK7kbmLEgAePnyI4XDotqAzZs0Do7iXgh4YQxPD4RDVatUp0dPT08S8WY/RF6ZQJMznuABKQEPUzUVWnXNdH7Fe2Hw+x4sXL5DNZlEqldyRszxllO3QQPECFiZNaLiNypyG9PDwEJPJBPfu3VsCCXauVFErPZTv31R+HMT934nj+EL+/2sA/lEcx389CIK/dvP/XwXwZwG8d/PzJwH8zZvfK0sURWi1WhgOh9jc3HRHNdKtJLMfHh66MxF4oennn3+OKIpwdXWFyWTits0SaT19+hSFQsEpUVUwFpkCt5evWqSnggDcCgn7p+6kdY9WuWP82yfkdmHS53KxWLec33MRl4rGZ5y0zlar5TZBaD+J4q1yth4JUy2VRnoXp7q1rFMFlihOmZoCZ5U659Iyv45HUaf2S9E4/38d0tH54SFLahzspQoMDfEda/D1xELlERoGZkWowrF8yLa4PdzOJ/vHLJFUKuV2GR8fH6PRaKBcLuP8/NyFFmazmbu+rFQquXtfW62Wa5txZBq7fD6PR48euRCCKnmGp5SnOW/8n3Kk4EHf0XUM7sRMpVIOcVN5RlHkNq5YA8x1qDiO3eFfTDk+Pj52+xsKhQKiKHLeRrfbRRzHLq+a4IzIfjKZuEvHi8Wi295v+ZCF/MLf1sC+CWBq+f2ESv48gJ+/+fuXsLj9/a/efP6340WP/1kQBPUgCHbiOD5eVREnhWcA89D0TCbjLotlTJuxwnQ6jfPzc2ftdaEkjhe5zO+99x7eeeedhJBYa2YF357ap4JnlTCVD7/TsyNYfBYXSKYVcgJVmdk6tKhltgqNTB8Et7vOlAmovFVhqpLQz1mPdf/0cgMfk+nY+J6GedhH5pHzeaIdRdM0jJaGtq/qgajBYbsAnAvPPrTb7aV+Ki00Bqt1qUK2oRG+o16U0jmVWlzuUalU3NkdWifT+NToUbHpxRY0pJpZYb21+CYEcHZ2huPjY9Trdbe4OBgMUCgUXLyXG2SoxIrFogNQ5AXmbnNbOzNQSqUSdnd38ezZM+dR6bGppA3HoQaV80W+tPS1PM+i57grT/ni1mxDjS15YmNjwyHsXC7nYtdsn+sJk8nEoXne+MNF4nfeeQfZbNYdDqXAiV6CAiefR6VrRLbvvvJlFXcM4P8bBEEM4D+N4/h7ALZEGZ8A2Lr5exfAvrx7cPNZQnEHQfBdAN+9+Ruz2Qw7OztushgaYXaJCv7jx49xcXGBVqsFYDEp2WzWpQfev38fpVLJMapaOACuXqYSqpJWRci+SZ8Tf1tlDiTjt/oc+5kgqmFoZWxOsMaKieyUuVeFfhSlW0Vkx8bLJ+xuv1Wum2Z++JA4kNwST8VvY8m26BqAGgQbXtA1BX6nyEyfUTTHdRDOBRUh50gzhFRRcW5YvyoKFs4JFTaVIXlTadZoNPD+++/j13/9150CUm+I7euiJevQhXj2kQctMQurUCgkMi0Y1jo4OHC8WSwWsbm5ieFwiPX1dUynU1SrVZdyee/ePYzHY7cBJZ1Oo1AoAIAzLkwquL6+Rr1eR6FQcIu2egEH6UL6KY/pRi1dV1D50jlRGeX7yus2TKLP0yPkHAGLQ+x4Zn+73XYnDpLPGe5jG+QfevSdTgd7e3tYX1/HxsbG0sKyeg4qs7qD2SZD/EGGSr4Tx/FhEASbAP5BEAQf65dxHMc3Sv1Llxvl/72bzsdhuLiiiIcOcZGAqTh0ge7du4dKpYKDgwNsb2+jUqm4++14gzsX2UgcIm4y2/n5OVKplFPcWnSSSGR120lU1m0PndIJJoNms1l3FgjTh+hK64RaRcvCv3kkpirDVbFOD71XfsY4sXXjFK2rQdFQCo/R5Y3gts9WwPiuboSiUlK0q/Oh/bUGTZGUKj3NcNA5ojLWcIUqWzVaaig1ZKf8YWmufWCbyitxvDizo91uJxYZfYaBoRANL1kPiwppOBy6S3m/+tWvuvp4Ns3a2lpiF2AYLu5rHI1GODk5wePHj931frwQYW1tzYUgyXu6mYjzzC3xhUIBk8kk4dVoOMSGlHTO1TNR2nE+FK1SOVpaK2q38qTzylMbuSB7fX2N3d1dXF9fu5ROvufz5mzG1uXlJVKpFGq1mkPoOiadT9UrPtld9bktX0pxx3F8ePP7LAiCvwvgTwA4DW5CIEEQ7AA4u3n8EMADef3+zWcrSxAEWF9fRxiG6Pf7bvcQb83gkY3r6+vY2dnByckJvvWtb6FYLLp4F123UqmE6XTqYuSPHj1yhJ7NFud4t1otd9iNVXKKpHSC2E8VFhuL1Wu9FDkos9nt3WzfF/NWV5AnuxHdEJXyOTUY1nPQ71SJWcPEfqjCtrFo0kUVogqNdU+1Hs3bVcMGJA9oUhRiPQf7vI356njZF7vV3r5nFxPt9xb9cdHKZkpof5WP9LMgWISAuGDLfgNIHGOrilzRPDertdttZLNZbG1t4fPPP3dZWEz348XYzK3m2k2v13O57Xt7e7i4uEA+n8fjx49dH3Uhk+PUlDX1XrLZLA4ODpzMapiKCtcaMd0UpPFulTf1ji14sIhaeUl5U/mO/Hl9fe1u6WHWGlP9gNtt76uAkxog8nOtVsNkMsHe3p4ztsVi0Z03rjyksqghE3peGo57XXmj4g6CoAQgjOO4d/P3fxfA/w7ArwL4RQB//eb3r9y88qsA/p0gCH4Zi0XJTvya+DYJsr29jYODAze4TCbj0v+oCMvlsss/5WSdn5/j5OQEjUbDrXi3222cnp7i3r17mM/nblfX5eUl2u22u2HDulUaouCEqWVXYqpy5rtqWRUh+EIJWtQIqIBTuAeDAV6+fImTkxMMh0P8wi/8AhqNRkIxK5KyFl7HZNMXKTD2QJ84jhMuvnXpNCYXx3FiaziNlEXCLKqMWYfGklXoNOShmTxKdxZ9T4VcL171ISFfPy064mfkJ+UdG1pSdKyFtKE3w3GxkBapVModAnVxcYFsNouHDx/is88+A7BQLpPJBKPRCN1uF/1+PxHSuLi4cHcw8irA2WyGZrPplDDXhQqFAprNpovHHx0duUuDGUZh/UFwu9uR/MRQZKvVcnFfRb5KB/W6LMpWcGDppnPA+bXGOY5jd0iTbu7yKXveWTkajVwYqdPpJEJ6GnrjWGwKIufs+voaT548caCQZ40PBgM0Gg13jAfHqmO/vLx0Z7Yw6WJtbc2lPq4qXwZxbwH4uzeDTgP4v8Vx/PeDIPgNAH8nCIK/AmAPwF+4ef7vYZEK+DkW6YB/+U0NcKswD7iJosit/rZaLayvr7udXnG8WMh8/vw51tbWcHh4iOvra3S7XTx69AjPnz93V5zxONjLy0tsbm7i8PDQWfvDw0O3EgzcpuioJeePIgi7QAUkk/Y5KTbmyjYsQgSSqXsUjslkgk8//RQXFxfuwtQgWMQsW62Wi2Nqzqm2Y5Gv1m/RpTK3DVeQmXXzjN4BahWVRexsV2mkbbIOuuIa4lLBViS3qqjiIxpijNrSRMdq69W29Dfp53P5reJR2ug41GPjdxsbG7i6ugIAfPDBB/j8888BwJ33MZvN8NFHH7n6GZqYz+duRzEvGqBy4TbvnZ0dl1/M+1iZszwcDlGpVNBoNNDpdDAYDNw88GyTp0+fYn19HUdHR+h0Oo4nqOgVRerOSBtOsZuGlFYKjnQu1WDqs8rLRL5BsJw6qp4aFTuvYGMWmz5Dg2SNtip9BXP8vNfr4fDwENVq1S1YBsHi7Jler4cnT544PaNj5xnovV4P/X7f0bXb7aLRaCzRRMsbFXccx18A+Ibn80sA/4rn8xjAv/2merVcX1/j5OQE19fXKBaLmE6nWF9fd7fa9Pt91Go1l3NKxf7ZZ585ZoqiRUpgsVhEp9NBs9nEcDh0d1PyVo5yuYyzszOk02k8efIEX/3qV128VYX0ZiwJRLUqDqdMqczlyxABkotK2o5mCpyenmJvbw/D4RDlchlXV1fY3t7GxcUFnj17hv39fUynU3znO99xhk+VEzM/VEGqQPE5Gkg9Z8OiDoaU5vM5Tk9PkU6nsbW15YygCgsFhMJsD2pSodDdc41GAxcXF85QqLDqHGiGi1W8pKFF8xyDpm6q4dLnVqF59l/nmZ/53FqfF9doNFx2wsOHD3F8fOw2kxCJfvzxx84w6CYWDckw5BdFEc7Pz7G/v+8Oi+JaSqPRwPHxMfb29jCfz1Gr1Vw71WoVrVYLuVzO0TSbzWJvbw/lctnlM29vb6Pf77ssE65hMCRBdEtvMZfLYWNjw+Vvc27J05QzawjtfFgvmJ9ZpK6ySkWpC5/KcxxjoVBAq9VaylhSQ8v2rEFXoGHDgO12G51OJ3EKZCp1ezlCpVLxrqeVy2W3YUj5gHyyqtyJnZO0PMyVVPd4d3cXn3zyCQ4PD/HkyRN3TkKlUsHZ2ZlbvCRBebLZbDbDb/3Wb6HT6SAMQ+f+nZ2duRXhfD6Pd999N3Gint1MASTPgdCFL2UKIh19T7eIq3sZRVFCaZMJ2M75+blDBIxtc7PBaDRyp+SVy2UXb1UFQcPAtnWXl+YOs698Jooid0oeaUoUDMCFbJ48eeKMpypjFvaD7iXLqpDRbDZzt5/YWLr236Ifi4JU+Im27RwS/VlPSutjO/xO481qIMIwTKBMvlcsFt2xqEROV1dXiXjrp59+6vqk94pqmIFhLc4N49Snp6d49uxZIiWTgIcn9/Gzk5MTvHr1Cg8ePEC5XHZgJooi7OzsoNFouB2T3//+911cezqdYn9/3ylKxsWZo020z++osDY2NjAej92NTxxzHMduYZPzw891pyjnxCYJ2PnWNFzLDzzLmmeZcA557gjBii72W4+MPKNeqvKvVcLkD/IWeYRnjs/nt6cWkpZhGLrTCtkv9kHlzlfuhOImc3BB5erqCqPRCKPRCEdHR24BgXG909NTFAqFxKTb8AS3yqtCqVQq7uB1PQWPVpFbYH3pOTYOTeVmv7fMxHreNH6+Q8HmCWyKzC8uLlAoFLCzs4Pnz5/jp3/6pxNWWRUMD+OhsvEVG7vWOLMqtdPTU6dseEj+bDZzx09SMOx8cGxUQqpcSTMWpZ2iJp0LnRPtrxYNl6gitgteWtRL0bxbFU51jck/cRxje3sbrVbLheZevnyZCBucnp66OdDcY1//NcOFKXeXl5fY3993Z2RTNrhuA8BdoA0A1WoV7XbbbT0HFjtHLy8vl0IPvV7PpURycU7vOQ3D0IUfgiBwMqebizKZjAuxkAfr9bqTWVV+ynNUnpwnDT1Zg8rC5zT0pyE+fsZ67ZwRPJEXNblA+8k21MsmMLKgje3b8B6N2NbWlgNXh4eHzoMOw9CdiMqsLIIcpl7ac4O03AnFnclk8P777yOTyWBvb89tAuj3+04xBEGA58+f4+XLl0ilUk6JqwJlClMURe5KMxKSqJiTxo0H3W4XL1++xNraGgAkJoYMS9SgilrdPRU+tc5EvTZ7QJWXul5kjKurK/T7fTx69Aj9fh/7+/vu0tlGo4Fer4enT58mNsIAWNpBxxi4T2kCt0zGlDMATuGwT/v7+/jt3/5td4jS/fv30W63Hf3oAVDYrKK1wrfK/QRukbP+Jo3o6VCRWIOkBpfzaLOC1OiqIVA6aPxav+cYeRgagQGN1Hg8xqeffpoYs1WU/Fu/V1edPDYYDLC3t+eOgZjP5y4/+/z8HEFwe842vatyuezyqMNwsdhGD3IwGDjjy0wsrpV0u13s7Ow4JVssFgEs4uu685b1sp8ESjziYWdnx42J6y7K4/R09PgDjl1lzqbaKS8qDX2KlDyfSqWWUh+5UWhzcxNffPEFxuOx43u9Hk35RPuhnrEahCiKXD1sm7xYKBRQKpVwfHzs1hh6vR7Ozs5cCieR/3y+OG++UqlgOp26M5lWlTuhuIlgPv/8c3fzs56iRkYYj8doNpsYDAaJrau0nJPJxA2WzJVOp118i+EMTvjJyQkuLi5QrVbx3nvvAbh1k7lASGZSpWQRHD8DbiedQqRFGUMVC9vkjq2NjQ00m00Ui0UcHR25jAAatMlk4s4sV2WlSIR0VWXuiyPq2QwcA4Wr3+/jo48+SqzSd7tdtNttbG1todPpoFgsuhRMVYbWiyACsrQiXTmPinRpmIgK9RwKGmrNhtGwhp2b1/1P4eai6/r6Oq6urtzRnYxFAwvjuLe358bHYxaI1NUoqttPunIuiO4VbfIZ7nZkyl2pVEKtVkt4Epubm0in0zg7O8NgMECpVMKjR4+W4qKq5NQL4efT6RQXFxcOPZMfcrmcG68NLwFw6yKsj9fMnZ+fLxleDSFVq1WcnJx4T8PjO5oyyu99oQydd4YyKXu6LsBURipz1rO7u+vOblG6qUIm4GM/aHx0ztSwcD1gPB7j7OwMxWIRx8fHmM/nWFtbc/M+ny/O/CF4DMMQjx49QqPRcGmbryt3QnGPRiN8//vfd3Ey3S7LyxUY59vc3HSK5NNPP3XCHASBQ9ztdjuBDrLZrFu4GY1G7uhKEl+PfAUWE0ehApJbuFnsDindWMP4nxZVYq1WC2dnZ9jc3ES9XneI7fvf/z4ajQYeP37sFnK2t7dRKBRQq9UwGo1criiNApWCdf9t2EaFhP1Op9PubIo4vj2HI5/PY39/Hy9evECr1UIcx7h37x7K5TKur69xdHTklMUf/+N/3B0HS0RCBcX/dVHH0pE0AW43M7GfREH2HBYKKufIhrYoxD5Xm0qTiCiXy6HX62FnZyexws9zpbn4TT6wSAyA4yUdM5EXx0UDw2McRqMRcrkc1tfXcXx87Pg7jmO3e3E+n6NSqWBrawvNZtOdNX14eOjADMMmk8kEa2trDuDU63W3jsNb3Dk3NHBMANCdngRNNJhMvSXd+W6hUHAx5CAI3MIysJyTzPnq9XrI5XJuB6Yqb1XgTOsjULH8qyFByi+BHIDEWS/sBxdIGXuP49h5L+rVK59GUYSNjQ3MZotzuHkRMhW9Kmu+w/NO6OkcHR0hCBYLzcfHx6hUKu5GenpsHA+TK7j58M4vTsbx7R2DZHxgQYQHDx64fFKeFggszp0oFAruvVwuh6997Wuo1Wr43d/9XUwmE7cYwhSbMAwT4RUyYaPRcIhPBZ6CaVfEfYsGGk7hmHQ8ZJD9/X18/vnnKBQKqNfrzlsYDAY4Oztz+bK5XA4nJycoFAp45513HJrZ2dnBixcv8OLFCzx9+tTd9sHJV+XHorFT3cl2cXGBbreLcrmM0WiEH/7whwjDEN/61rdwdXWFk5MThzyKxSKiKHKnwPGcivPzc1SrVaegVWlb+uhvjb8TxSgS96ErVQRqsNQg0cugAuAi5fb2tlsAunfvntv+TYXKlDqdSxuf1bAO+UMVOD28IEheIUclyP7qtV4nJycJpEl6rK2tuc0qV1dX2NnZwfr6uouH7u3tOT7O5/PO+zo7O8P19TXK5TIajYZbGOO8bW9vo1Qq4dWrVy6OzjFkMhk0Gg0cHBy4MRKAkM6ky+bmpjNulF/lf3q6+hkApyxTqZRDporQyUMsGjqjV0De0DPiFaFbgBAEgQNnnENu5+d5LdZzJT/xUDvODc+70aQI9WL0FEOGbG29Dx8+dF7k8+fPnZ6ioo/jOJE+6Ct3QnEDyUObKHSFQsG5K71eD5ubmy4uR+KQoZrNJprNJsIwdAqRKVPccJBOp1GtVt2N0iQ4c8f1/keNZVGxvK7vajnZL22DzMRF1/l8jt/6rd9CPp/HBx98kEAE/X4fk8nExb3m8zl2d3fdjkkyhzIb22H4gIZC82mJPLhQw9uByuUy2u02jo6OnIImQiPaLhQK2NvbQz6fd6mD/X4fJycn2N3ddXTUol4JBYYpaLq4pv2zi00sDHfRKyPKUVeaQvTo0SNcXl66rc3tdhuDwcDR9MWLFwCWT6ljm1Q2aoCogG3IR5U2lW42m0W5XHbKm54e6aqut3pm9JJ4C1Q+n8f5+TkuLy/xySefoF6vOwXNHZC1Ws3tAj48PHQeJkNkzA/nQiMA957e7A7AnUetG68UVaty5OKnhohU4anhI72UH/i9Zg5pSEezKqiA1RsLgsVu6263604NtP1UZVsqlZBOpzEajVxqJHPVNWSli4xxHLt4NOfu8PDQ8R37r/KtCRKWj+mJcSc4T0PlHQSUIe6sVc/UljuhuFUQuBBULpcxm83w/PlzZ/UYByZS0FXXXC6Ho6Mjd8IZL14lY4xGI+Tzeed+qTVut9v47LPP8OTJE+RyuUQqkv5mH1mvTzkrc08mE3S7XVQqFXdiGhUmkIwlb21tJdxePSHv9PQU19fXLvf35cuX+Jmf+Rl37CYLF2VsLFsZmQzJtLKrqyu8evUKL168wGg0QqVScUqLOb39fh/n5+eOoSis3OF1cnKCTCbjFDjbsihGV/r5mWVOKi8KkqIxbjLh+kOpVEKlUnGXapB2FxcXTrHrMbUUaioJXSzTsMzR0RHu37+fEH4+r3FsVSLkDaK7zc1NvPPOOwiCAF988UXigCfdXahrKlQG3CGsMeOrqyu8ePHCZSJw3hlb7vV6qNfr7mLt0WjkNpwxA+j+/fvo9Xp4+fIl0uk0ms0mGo0GXrx44Y4y5W02mp2jSpG8xfRdTeWzcmWNqlWopAMBE4GFPe/DGkryCI+r1fAUvZAgCNBoNNzphgz5zGYzdzclFyWJfi1iJ49qeE4NrV0nIb0s+KNs0oiwrclk4nZwkrfDMESlUknU4yt3QnEDt8qvXq/j6dOnABbpb0Fwm1jPDTka12bhAgCRBVG0HtmocSmNR/Iuy62trUT6ky8mSyXgQwMaJplMJvj8889xenqK7e1tVKtVDAYDfPLJJ4nFPmAR26IQURDa7XbirOX19XUcHh7i5OQEOzs7GI/H7uB66y4qUrRom98R+aVSKTx//twZi0wmg4ODA7x69copuKurKyeUFARgcbLa5eUlut0uHjx4sCSowO3mJCIZi3Lt31SmVBg8wJ5nYfASgPl8cRUVF7FPT08TCp/KmKELi+CJKNXAxfFiV+7V1ZVTgvV63YXJ6Alag81cXF4rVigUsLu7i29961sOkbXb7cTphNpXxt3Vq+Ndq81m0xnuZ8+eOUTMLdE0NJPJxB22xnnVM39odJlDzFP9qNSYhaIehtJMvU+bw24NtAUwHKdeQqHhLsqpKm8aD7tGwvfiOHabiLiQyjklP3U6Hec1cF1A16QIAEgXbtDhOdzsm86T8gt/ayydc8gc7jiOHV9oGjKBULlcdmCRPMI+2XUyLXdGcQML5cqcx1KphGaziePjY5TLZdy7dw+pVAqvXr1y53VzYPl8HrVaze2ObDabDulyCyqFjyvmtL5Uelzs63Q6WFtbS7jh1uJTSdtYLtHUYDDAwcEBfvSjH7mFqcFggG6364RXrfF4PHYpfzs7O26lm8K3sbHhLoio1+u4urrCxcUFSqWSS9/iSroKkeaes8907+h53L9/3x0RwJMWT05OnPDTW1GFQ6bnTTn5fB5f//rXE7F2jXVTwVllYBeC2F61WkU2m0Wn03FnZnCbdhRFOD09TRjtMAzdvZL8X8NC2pZVTOri8p3JZIKrqyuHfjXrRg0P62I4QUM/VKQafmMdmi/Ovtg0VO6OpJJieIzIks+qZ6L3gMZx7DwyTQnd2dlBuVzGwcEBWq0WDg8P3W0werMP61dlyKKxaAUD+p7SmPVw/siTepyCel8Mxel8Wc9MkTf7rnNIeeS7zEGnsWJcmbTzoe9VIIhKml4Jn+MmKOoSyspkMnGLsjyNkQDt1atXzvD3ej23/qH995U7obhpoZvNJrLZLLrdLq6urtxK8ZMnT1CtVvH8+XO3wKTxLO5OIpPzKMtisegyJXjWsOaGk6k/+OADPHz40P0PLCaHCy9q0RWVWKVN5Pf8+XMcHBy4k8h4LgSFR5mQyoMXH+/v7zshnk6nyOVy2NzcdIiIuz/pYhYKBRfD0zQlFTR+dnp6ivF4jJ2dHcxms0TMnMg9l8vh7OzM0ZaFi1wUZKaNESnSVbcxX9Zh3VEKteb0UuCIeIIgcDsO1RW1aZkct7rSRNp2M5BPwcRxjOPjY3diHBfBmbNtlZkqFOB28ZqKczQauXnMZrM4PT11KazqmdmQlvap2+3i1atXuLq6cjv+1AArPcjTQRC4NYowXOwW3trawuXlpfPmmNlBj4IHHCkoYR90bUJ5lbKh4QlV0jrvqjztGNVzodEm0OL86fOsTz/XY2StAdHCsAozc1KpxXVkFxcXzrslf9t4uQUYvnkDbs/oVo+PefMM7XB829vbbqGaO2f5js9DtOVOKG4ALm7W6/WcJacFPD09xf7+vjurm8/HcexSi7jhBrh1y7k5hAiOOxFHoxEymQw2Nzexu7uLarWKi4sLtwD65MkTd0zjvXv33Fng6uLRmuq22n6/7/pKhcGt+VRQGt9UBmcaEReIeMvG9fU1Xr58CQAuPs50Jxqpr3/96yiXywllrcqFRoWpSTw0qN1uu+wQrn5TAPQ89DBcbM1lmheR7fvvv+/OgKbBU+G0GTo2nEQ6qmKl4FgBJe1VsSiit0L2/2/v3WIkzZLzsO9kXbOyLllZVV3V1+mZ6Z7Z2ZmhZ5eExIUJQjBFWSQMrR4EQYQBrSUC+yDqYsOAQcKgBb1JgCBpBRiE1pIl0hAo2TRpEsRChLxegC/cXc6OtDM7PdPT1d3TXZese9Y1656/HzK/U19+df7sbkq7XT3IABKZ+V/OJU6ciDgRceKQdqgt66RUoUSb5+rqatxocnBwEM8vBdpDLt2OqSsManwHBweRnrhioYatQobjz980axweHmJubg4rKyttJgkuvdXkR3qmksPwRa4qFxYWIt53d3cxOjqK7e1t1Ov1eObi6upqW0gc0B4F5ExZ7fpqbvJ7Om5aNvFIoHmMJhP+Jp7dqammNU12ReZPPkDTCMNzyUjVj7a6uoosa8aY0yfCOcBvNaspLrQPwBnjZv+454LhtvRxUeum0sPxpS+MAvXCa9wcYN3t5DZgzQmhiAshxCxfjOckshm5sLa2FgmYdsqhoSFMTU1F7ZLMkFuY6dx0ImQbgLOz7IAmAd25cwdra2sx50q9Xo9x5SxPiUKBS2vioVAoxCXW3NxcfObk5CQe58at1mQGLhDIyJaXl/H+++9H+zTNLbVaLZ5cwhUBl5Xlcjk69uhfOD4+xksvvRQT6nz88cc4PDyMW+B5AgtxRsHjcdbAmdmJKybV5ingVIPWb2UCvmRlPbo5SZ1evMb77CM1bJoaNjY2cPXqVWRZFo/T89A2CkSCMvS9vb3oEKcSoktvLUd/U9Nkcv/T01Nsbm5GmymAOE8AnEvH22icHTrCuUFmXy6Xsb6+HtPC0q/hkRCknZRWqf+JU7VrE98sS8fEGbvWwVzXKdPa6elpWxggwYWHChlVsrTM4+PjaGqkGUPnv678VJHgf2XWrolTwKgp6+joCOPj4zFklv6kw8PDNhMb4/XpWFe6SsGFYNxAu4TXgc+yLGp9ikA6M7hZgYTLU6a5KYIaPAeFUo2hPXTS0UywtraGe/fuoa+vLya1Yp1sozraNJ/uo0ePYvQHd8DxBBHVsFwDUa1FGQvNAMweRkan2fb6+/vjNn/igUCGdnBwgOXl5bjtVzfvkFkRR3Re7e3tta0mOEGZZF8PoT06OsL8/HyMyqGtlysbEjiJWpf8tN8qDShu2E7VMH05S8czr1Hzci1ftXYur/f29qIPgrTENlBLev/993Hp0qU2/Kr/wJf+qljQEa19UwauNK2bdG7cuBHpd2ho6FyyIl9Os86RkRFMTU3FuG0VWEyDrFkHOd4aXqlA3CtjVuVJTTeulftqS8tQIUEtmjRGQccVGcvQVZXTOv+zv+VyOQphnrHJcjmHdVzooORvHydXDDgnOMe0bcwzwudoUmSqiEaj0TbWIYTIhxgZ5asShwvBuKkFEzkubXRpGEJzZ98rr7yCTz75JCK1p6d5EgW3jaukZZ6Hra2tKLnVbKEaDMtkBj49rSPLmtEi8/PzcfflysoKRkZGUK1WYwbCqampGIs9NzcXNR6g/agzoD38TR0idCip9kwbGJ/v6WlmT2SoE9BkTnRiqQ240WimFT06OsLjx4+xs7MTs8CR6Kj10p5aLpdjqkoKt93dXVQqFVSrVTQajZhul0dxMZewTl5ldkB7FIFOasUF204hoEzfVz9Z1tywoLkdFGekG10h9fQ0D4/Y2dmJcf2qYXI7+enpaTSX0YnEMqkApGyrOuaKB46br95Uc+PKj2Gwb775ZpzQDPdkH7laofAjEyAtMfyMDtGJiYkY95xlZ6lEiVtn4MS7MlXVYF0rV2ZNwaaxzmo75jWag2hy05UVx4o0rHhUoaHtOT5uHi4+NjYWI5HUqayRYarVuyDV9qmQ4D1NxcCgB+KUNHF0dBRDlLn5SMujk5L8QvOmXHhTCQkmJbWZnIW5gEmY6sRjJ0nA1Kxv3boVCZq7wYgcmhgYB8uAeDJ65hZ2ItnZ2cEHH3wQbZFk4BofzixgurTmxPBlpGvd/M+lGzcNkdiY36Cvrw/Xrl3D9vY2vve972FiYgLXr1/H7u4u7t69i0qlghs3bkRmyglBR6k7ClmvbvXXXWJAk8j4Lr30lUolOngWFpon1HEDzNTUFCYnJwHg3ERVAqZwUi2ZY62amq7CeJ8mMdV4lGHrRCd9cQXHyBFObJZHgcRJf/Xq1ZjVrdFoRBOYtk/rICNU+tblv9p0CSmTXF9fH4aHh1EqlbC8vIzFxUVsbGzEepg5UuvUOHb1HzBShsLfNx3xW5UI9yuwH2oe8dWRluGrAqWzlD280WhEpUHni58YryYw96MQ6DCv1WptkS8UnP68tkPnq8emA2c7kbmjmNFH3O344MGDeI0mz52dHRweHsaAAiqBGovOvhPnneDCMG5dGjhDc1MJNT8CB71Wq0Un5PT0NKanp5FlGe7evRu16IGBgbijEkAMuRseHka1Wo1IYya2SqXSFth///59PH78OGrElJCqVd2/f7+N4JVIlZjd867Ew6iRiYmJqOlxlxiJpFwuR82Xk3RlZSXa2U9Pmwcj067GCacT0h0kuuyjg4Tto82awoS+Ao7Tp59+2qa1TU1NtY0z+65E6V574kS1Ib7DiayriUajgfn5eVy7dg3FYjFOTNUMWQ4F9crKCrIsi85u2rgZNw8g5kA/OTmJUSEhhHgIAU1ybhJQYGiralD6DBkPc2hreWpGoAmEDkfdm8CVkGqOCro64MpSBZtrw6koEdKj0gbLYb9TgpLlu7DV+1mWte2+5LdGSPncSJkbdR4RNP+L8piUNpsSvil+pCu+UqmEcrkcTTNMpzs+Po7t7W0Ui0W89NJL2NnZifsimCZAfRakY58XneBCMG5Fii9NfCmp365J8dm+vj7UajV897vfbZuUPT098fBUBudz8tAOzeUdbZ7KiOr1OlZXVwGgjakpwrkji9d1aUh7FqET4WVZFjdyMIxra2srZqxjLopbt27F4H4uxxiRUqvVcO/evRg7vrW1FcPCNjc3o0avyzYeVMGwpb29PRSLRVQqFWxvb6O3txfXrl1DpVLBhx9+iI8++iju+mRe5mq1Gh0yxIlqL65BE/SaMvQ8HBUKzQ1B1Wo1hldRw3GhSQaxuLgYhR2Pi9Ix1NhvjvHW1hbq9Tp2dnaiQ1OjQ5ReVct1myjbTyanJi9Nq6DMg0Ahvri4GDd0cAexzh/156hmrQ5snzf6X7VaZbpaR8pUkvqtKwFlToofFcLECaFTm7Tt7K+W6e1JmVS0HucrpFs3yfF5ClKeFbmxsdF2sDJpnyu0k5NmvhwtixEsKvRcKObBhWDcKQkInN+wwWtK1DqItJVxKT8/P4++vj5MTExE+/fKykqbw5If2gI5iWZmZvDaa6+1TfylpSUsLi62bdklcOlEp5ded63RtQsSpmopQDN64OHDh+jt7cXGxkYbk+Ehp9xm3tvbi9nZWSwsLGBgYACPHj2KzsGBgYEYK8o8L8QlmVNPT0/cQr61tdV2Yje3wXMzyebmJnZ3d7G2ttYmqNgn2v52d3cxOTkZ28yDFzh2LN+1IO4apBbKthKoOW1vb2N9fT2m0Hz55ZfbTA064U9Pmyd5r66uxg1cpBkKVMb6Z1kWj+riRNSwwePjYywuLkbbN5mjavl5YY3KzEh3qRBB4vH4+Dg6wJeXl+Nym7v+GDXCMaTGrYLINU6dd2yLa9a8r9/aNvZHmbivLnUOOwPmqkHxonVoG1muMnlX8Fy4qC/J+YXTk7ZD20xFQJ3jLIub6UZGRlAqleKqiY5JKow0VTIskDliFhYW4mq/Xq+3OTO1nXnQma2fdagcQvitEMLHIYSPQghfCiFUQgj/PoRwr/U93no2hBD+aQhhNoTwfgjhi09RflITTTVema0yVd6jHZeOpePjY9Tr9XhSR61Wa2O4NLFQC6N3l3mK6/U65ubmsLq6GrcWK2JJkIzZpFbhWoELHCVk/gfac0UzNnp1dTUSLXOMUPMnAe/u7kbnHPO2UDPTDGwaR04GS+Z5fHyM1dVVDA8P4+bNm/H0ajLbt99+G41GI27kCaEZ1bK+vh5NLdxwwGPVdLlKBsp+qllE7aKc2KpxcuLymxtdeAI6N87QgeYa697eHu7fvx+fY7y25l8ZGRnB8PBwXHGRvijU6/V6dOZub2/H2GmlJ4IKY6UD2rbJ5BnVoqGQpIdGoxn9wvHmUX3Maz02Nobj4+NoN1Umw4+GsxHXzuz4nIZkaqy0zj0F10JVY9T5qWYWxYc6z10YuC+IoLj21QPfVwWIdKdzTJUonUPcnKOCQ+nJfTP7+/uo1WpRIWT0Fn1l+/v7mJ2djWG4zAs0Ozsbz/J0pU5xmRIukb5y77TD1wD8uyzLPofmwcEfAfhlAN/Msuw2gG+2/gPAzwG43fp8FcCvPWUd5ySeamGKdJXgeo0IoAMSQNRmuFWeCCJjIWFx8tJmvb6+jvv372N1dRX37t2LG1/IpDUkzR1BqRUBfzP8KrWM9mViX19fdJxxkEdHR3F8fIwbN27g9ddfj8Lm0aNH2NnZwcTEBNbX16PWzqOuUu1ixjRG9NTrdRweHuL27dvIsqZpgYxuYGAAq6ur2Nvbi1vjDw4OYm5ihgFub29jdXU1vsf+UTMncAI6c+ZEYyIuX35T2HADw8TEBK5cuYLXXnstxpITl4pP+kV0r4BGYzBqg/X4STgA4nmmAwMDWF5ejmPDGG+1F7M/GsOdapczHrdz0sTHzTjcjVkoFGJaAjKdQqEQM/t5O7Q+7rRV+lPaYJufpH2zjcp41PzD590EpsJFmTlB54Qze2XkulIgThkKyp3UqXLZPr7LVTYVI2Wi2gb2R+8fHx/HdMzcaNVoNKIpU1eNq6urWF5ejnH0TDet/iXiNk9xjbSRe+es0WMAfhrAv2g1/CjLsk0AXwbw663Hfh3AX2z9/jKA38ia8G0A5RDC5U51uOQD2jdouPbCzqUklDqveI0hObpEDyFEZ48ijTsGGQ743nvvxZjsVDB+aC2xx8bGYhicL++yLItbopk7mblT4kC0JqnC9vZ2TAHK6JqrV6+iUqng1VdfjTv7GNFBLZTOXIYnqSbL/nqeCOIFQDw1nHbgSqUSj7pirmhqoWQS3PQBNDX+K1euYGRkBMB5JxFhfX09vq/OMhWMKszZxkajgcXFxRiTTIGWZU1zFicl6YDHv7FPbuKi0NHIETqRQjizD9NBSXpglAfNU8ViMdZNhkpGQnB/iEY4UIiqQsKDb2meCiGgVqthZGQkppAdHBzE+Ph43IDGEEoCaZHt4rmhFNjKwFxYufbsK8jUNbadY6B2aaVBn3uElGLG57SNrq3yHdqVacIDzgRMymZNUxnxp7hSDVvHR39T+yZ90dfEjYPMM9NoNFCpVHDz5s3oSPfwPxV0neBpNO6XAawC+JchhP8QQvjnIYQSgOksy6qtZ5YATLd+XwUwJ+/Pt661QQjhqyGEd0MI7+qAcYDUVqfE49qpL6V4Te2Nmv+Yz+7s7EQNnPUQcQyd4u7Czc1NfP/738fi4uI5p01fXx9GR0fbjq9iH1TTKBaLmJqaOqchEXiiCd8dGhrC9PR0DAkbHh7G1NQUisUibt26FaM59vf38cEHH6BarcYdmpyc+/v7cbL7JDs9PY27IQlcgdRqtRiFcu3aNUxOTkZtYXBwMObuBs6InoLtxo0buHXrFq5evRpxS+2b25kZPsjJxbjizc3NONkUN6qlkhF87nOfQ7FYxOzsLI6OjrC2toaVlRVUq9VzoXbb29vxuCzVtsnEqaXX6/W23MzUYE9OTlAqlXDjxg0AZzsXl5aWcPfuXSwtLUVNKk/LNdpvozleU2FDplooNA+dZVpjCmPiiYKLp+SoAKDwo/OawBWNzjMFaqAK7ixz84QLIR3DlLLDMvVaahWteHJNPMXcOL4cW+Udbprk6okmEkZKafn6TV8HceQmKW6cOT09bdt9rQz5x3/8x/Grv/qrcd500u47wdM4J3sBfBHA38qy7DshhK/hzCzChmUhhM4iwiDLsq8D+DoAFAqFTK4DaDeN6H9e887S7kfbtto5dVnuxMDyqPEyxwXtoP39/TGxPe3g7oRizmTP6qVC5uDgALVaLeaV8L7RzqlERHNBT09PzH29urqKd955J0YhPHr0CEtLS3HZrzZDah+pCAH2nwTIlQDbt7CwgOnpaVy5cgXHx8eYmpqKG1W42WZsbCzWyxSVTCOgZgLdPbi1tYX5+fno/OTmg7GxMezu7mJkZKSNSWicro49D9PlAayVSgW3bt3CzMxMPDWGnv+jo6M2Ia0at05I7hTVXXunp6colUrnzDTlcjmaXgDEwzG4CvIVhvZD+6MMRRULNStwoxW1wnq9HhUKvkMzluYa57tDQ0PRmc15QQHvgiZlvnCm56ArOmV07Idqwqo88F3FUcr5qWOvdKG7V7UsrU9xrjyBjJOrpNHR0Zh4K2V/duVQFQwXIsSFO0+zrHlc2je+8Y0YoZSH1ycx76dh3PMA5rMs+07r/2+hybiXQwiXsyyrtkwhK637CwCuy/vXWtdyQTvu0pFEoTY1fusgaYgbQZFNCatxqrpUY7l0CNJ+ODk5iVu3bmF5eRnz8/Nty0bWoUcVAWdmCO0TnWFKuKyTWh0nJrfrcgdWsVjElStXor1MY8eZv4TAXZMUAHpUm08aANGW22g0oumIfanX6/jwww/bhAL9ADyNRo+cAxATbLGOarWK+/fvx7Lv3r2Lzc3NmKN8c3MTIyMjWF9fx8TEBE5PT6MdXelC46qZ24HjvbW1hZ2dHUxPT8fVBlPTTk5OxjS0xJuO/97eXlu0jmppTLLF/vBkm+Pj47jiIcO/c+cOjo+PcfPmzTbaJOjOPWc0SudkBDo+XPnRJLexsREFBkNZGfFDZqaMh+GwSqPqjyGNco7oc7oK0LbyHZ+zCqrNO0PWsWWfWQZ/63Oq3XvZWoY6i/UdN5VwdUDT0sbGRozAKhaLcU+HCiNfQagdnwqA8gZtA9v87rvv4v3334/zxsdf8dkJnsi4syxbCiHMhRBez7LsLoCfAXCn9fkKgL/f+v7d1iu/B+BvhhD+DYA/DWArOzOp5NXRNjjupMtbaur73FGYktJqq+K3epp9aaXEQi2Ujklvhw4UpbwSfd4KQstgFEV/f3/cus3+cDnHSJlGo4E/+qM/QggBr732WpudmEAmTCauk9UJS51L3MXF/4yu0RziZOJkoHrYA/ObjI+PR2F19+5dnJycRLMND5Xlwas8DLpUKqFSqUSTi06209PTKEhpv3/48GHcQvzmm29ieXk5nsTzxS9+MdbH+im0U6exc6OMpoDNsixmdqRWx8gd4pHZ9riaUvzSPk5ntDJsfU5jhbVdatq4dOkSHjx4EFeCNGWUy2UcHh5ic3MTc3NzbQKHbeC4lkolTE1NYWFhoS1uWPHgq1EVIspIXSFyAZTSrp3uXdPns7o61vmmyhzbpmXq7kivw/mK5s+hk5DRZCxLx0qZNt/TeaTKoNbtWrhbAFL40Ho7wdPGcf8tAP86hNAP4AGAv4amffz/DCH8IoBHAP5y69lvAPh5ALMA6q1nnwhOANoJ3ue3ho/pc0o4Ovl5woQShe/U5PtqBqF2e/fu3ZiTQ/NdqO0t1X62l2YNvU7Nhh7+LGvawXkUFwXO8fEx9vb2sLa2Fk+BZv7l733ve0lBUSgUzoUtsr2q1egk02fYPk1oT9OGbokfGBiIJh0yxEKheebn4OAgFhcXsb29jVu3buHk5ARzc3Po6+vD7du34/scGx5ecefOHUxNTaFcLsc+1ev1uNph25aXl+MSd2FhIcan66k88/Pzsc+0XTLMkHjg9+joKJaWlhBCiHZPbmRqNBrROUtmu7q6GlccNHFxV+bU1BQODg6wurqKl19++dz2ch1/4IzpEP+au7yvrw9XrlzBSy+9BAC4fPkyqtUq1tbWcP36dRSLRdy9ezeaeTh+dISrw0wdyM5kKbSUKfrKV2k6xWh8Huq8VUgpM67R5ilvulpyxcmjaVL1MyKHK1bFme/B0PdTDFtXYcyR5Ksp7TPxomYpB8V9J3gqxp1l2X8E8BOJWz+TeDYD8EtPUy7BEe/IdiT48qlVbxvzVEJSpwIHjFLSkQ2cIe/k5ATz8/MIIcTdcqn6leDVLMF6aArhM9TyDg8PMTk5GScdQ/nU1EANlyF5hUIhalRqpnDCZ/tSffOJp/Zv7pwEEDURJU4CBSHzYiiBM/Tu/v37ABAPxmDOFOYcpp11Y2MDo6Oj8bR5js3Ozk48xaVWq8XY9WKxiPHx8Xhaux4g3Gg0Yrw2J6Jqcixbl+Tb29vRlMBIDU5KbsohAyRTpU+CY9zb2xtPXv/CF76Ag4MDfPrpp5ieno75mJVmlFmpNqbMgQy/r68PP/ZjP4aFhQU8evQItVothpIxykgz/tFccPXq1RjhQIeZ067GWKvCwjaSZnRVp7SlChSfc5OCgtKjMydXepQfKJ6UnvWaKnS+amCbSSu+OuaGHZ+/yiNcIJGuNN+68iD9rX1XhS/Vf/a3E1yYnZOqtQLpXVrUiF1r0AGnhqgd151pWmeWZdEhqZtSWIYS697eHqamprC4uNhWr367MySEs3AfoJ1pkoB4qvvg4CAePnwY008CZ/m+eRwSj2dSj38eQbE+1SqJHz7Hd3geZqFQiMe+ZVkWTSDcyUgmT+Im3sj4uexfWlqKce+Dg4NYW1uLp38wQdjAwAAePnwYTSxXr17F0NBQdC5yw8zMzAyyrJluYGtrKx56cfPmTSwtLWFrayuaOjh+S0tLkQFyNyvNFSlbM4UqmSVxRcGiWicZvG9kUZ9ClmVYW1uLDF9Xf8S9j5+ufJxhMRXB9PQ0Hj16FFMfAIgHCI+NjWF9fb0t346aBLQ+pWtnTt4ujq8KOwVXEBR0Jaj98TlN27xeU5woHWt5vtHKhYHjmfdIK1ztkQG7Ld/7oG0gc6djXnmAzi2WlScUnbk77vLgQjDuPCnvyyHXpJ15ankEEp6aQFQbVmcntUVtg0pbhq3xXZ9gbH9Kq3ABw5zWjLjgBpBUWbQzA2caIze9uMmHE8DL0DwhbL/imzhh9MHY2FjcWdjT0xOPk+JvFaLuO2DiI9qBeZ0M4vHjx20aUaFQwNraGl577TWEEOLBv7Ozs3HTS29vL8bGxuJRbTpmdNqenJxEmzRxz92F2memNFhfX0e9Xo+RFxyHgYGBmI2SO3FZJr81pSuF1tTUFI6Pj9HT04NqtRpPF+LYqNPaNW0dA9L+yclJWxri0dHRuM2e57Peu3cPKysrMW88aYSmKY2bVgai5kSlTV+l8Z0U005Byg/kc9hXPD5/eVAIccl5mdLkU4LPtXEXSrxOhUpDj1UJSrVN6031U++5EqX485VPShntBBeCcTuz5jWX1Lq0ce0lpQkA7Yli+JwSr+bt8MHVcCPmqgDOllXaRpeqqeWSDy6X8jp5VXNVDUyZJG3mzNancctss9v73FZJbYwOyGvXrqFarbZtsnn8+HG0l3NLP/NksPzT09OIw+Hh4fhbJ4UKPx6gy5BI7Sfju2/fvh1XNjs7O5iZmYnHrfFACG600agapRNqwdy6rrmTqflrGwDEMwgnJiZinDMAvPHGG/jBD34Q+1UoNENHK5VKPEGmXC5jeHgYu7u7uHfvXsx9TTOP5ihXmkg5+pSela6Ojo5w+fJlXL58GQMDAwghxJXao0ePYjm6YlMmrfStzErpP8X01PmmTCk1b1KM3pURgq4EtX5VUtQM6QJG63ZNV+vQa5p3xN/hPFR8pfiMCgtX0lKr+jym7O3Vtj5JSF4Ixk1wZx+AJw4Mn09JXN735Q6v0YSSIkKGnqlpwJ9JaUy+ciD4hKAm5Als+JxGOCihKSHs7Oyg0ThLzepEp21SBpFlWdSkuXOQOTNOTk7iyebsPxNU0YlFhkEmSCLWQw9cCwIQNVs9/YP2/CxrHthbLpdRqVQwNzeHmZmZuK2+VCphcHAwxm17SgO2jxEkOrEofNScwjHgDlCmSFXckEmPj4/H4+gYgsiYbYYDZlmGTz/9NDpgqSHTsZ3a3MHfqQmvY08B+8knn+Dg4ACf//zn41mrL730Enp7m8fzcZyZK4N0q3MozwHoDNrpNuW/UaGi5fO/b21XZUrfI669TUB7PhKn6dQqu5NJh98aa6/hkyGc7Z50waDKh5edJ8QcUvedl6iw7MS8LxTj9qWNawAu7QkpCejSXAnQvfgpbVs9zK4VOxN8mvaxDU7M+nyWZW2H26qdX/vjeT4ajbNkQroTMoVTXqOtPISA4eHhNsIpFotthAw0VxlMhcuYYZp22F7dVEAtVwVTT09PdEBqe7j6oJbKtlEDPjo6isxaj+0iXnXiE7dqw/YNSKenZ4cn0PkLnGmoGxsb8eDe8fFxzM3NxS3NAwMDKJVKGBoainb3EAIuXboUwxUBYGxsLNbHhPreLmVcHEelNRWE3BjCFYb6Faanp/HWW2/FQ6UVvynmxnngDFFXfTqXlH5TDNtpWH+7mYz9JQ2qpq2mSdalgiZlv+bKiu3Xua5mRFWy6HTe2tqKQp3lu1DR9vl/bae+k/d8ClKKXgihTcFJwdMmmfqRADtJu6faAVOST6UxkafXtEwv/2m0cr+v7znRP0v/UtEzqmnrzkoyIq8baE/KROebgr5PBqd4YQKjUqmEg4ODmHiIeSz43tTUVHRQ0kyiOcfVPwCcTUze1/bS+Uoho8yKZ37euXMn5iDniTsnJycxzSoPy2AECMtQnwjjsk9PTyOzVTrhKoJt1tzWm5ubcTJXq9V40DTHbXR0FKVSKUauDA0NYWJiImpq+/v7MWVCinkA6ThdRqe4My7Lmmlmr127Fm3u7DPNClNTUzg5OUGtVjvHDFzwK86dqbji5B/OD/eh+GoQOEtW5X13fxPvuzDxMvPs7BTAKeapH9bD8FoqBMwjwrLUUap8iHNM5xJxnOIpLjidOft/roA1JW0eXBiN2zVkt3XrNT6jBO7aqz7HclQz17L1O0/b1+cJqpmk6lVbuguHlFbuk0H7ntJy+GwewaoDiDhm/T09PXEDDFNSkqEyLjuEpteceT4GBgbaomSUMN2ZnGVnIXjAWRQHzTspfF2+fBmrq6tYWFjA8PBwjM0+OWkmoac5h47S09NTDA0NtQlxtkU1KG6jZ2QLr2usewhnh/VyxyRw5kTmTtFyuRzjvLk6Ysz39vZ2DP8sl8sYGhpCrVbD4uJidFyOj4+fo03Fn17XKIdCoRBNNUtLSxgcHIzHxnHsjo6OUCwW0d/fH3fMunafR88+93S15/PPFQivw+dqqn51jqZWT2yPauukIbZHTZ2u2OXNWf7WU7V0vvicZRuUhlkuzYl5/UzxFRUkarLT8dYY/zy4MIxbGSgHypGet0NJB88ZaUridWLM3h4t3yHlcPE+6X0Sc957fN61EWXqBBcCKihSz6t5iOWWSiW88sor+O53vxtP6SAhnZ6e4vLlyzg4OGiz6yo+VJtkueoP0PYNDQ3FpDuKX7arUGhmwKOdnUw1hKa5ZmhoCJcuXYobX1jP9vZ2m1bExPRkPI1G8+AHOhGJczp4R0dHYzlk9h6vr+YnauY0WxQKhZgOd2ZmBsPDwyiXy5iZmYlx7iGEeCAGnZRHR0dxFcC66IzTyaw0MzIygvHxcVQqFQBNZ+7a2lqMYKFph0mxNE86/Qk6/gSlc2c2SlcOKaGg9KphhyktX/vuqzMXwkB7fu3Uu2yrr/h0XqSEjGvOat7RNihDp+B2pSElMPibKXdDONtRC5wPa+xkIiFcCMbtTMoHJbWscIneicn6wDgj9We0fLcx+zNKaC5QvM3KrFTIsB6NBHGm7ZPLwZloHrGqJs7TY7j7DzjLfTE+Po5yuYyPPvools1cK759m/X5qknrLRQKGB4ebmPe+hyPk2N7lVnUajVMT09jeHg4OuF4BN3Ozk7UfIrFYjzarVqtxs0+u7u7cTJw3BhtQmHFXCaOS21PoVCItnY6JFnW0dERpqenMTk5GcPZ5ufnY+z58vIyKpUK3nrrrbixi3lNeAiCts8dvUBT0L7zzjsxYyQPquY4MjEaVyFUdNzB5w5GpxtlfCmFKsWoOcZ6zVcROq9pdlCmxZh79YuoNurnoioN9fX1xZ3NSjvujE2tUJ0fqMOb5at5jgeDp5RCfQdozxlOEwjHnGWx/zQJpsxoDheCcacYUYoJAeeT1fg7eQ4gl+QO7gjM0/ZVm81jpK5V+ARJCScFZdTe5tTEYTlaZmrCEDe04e3u7rZt+AkhxO33POPSwwqVyK5cuYL19fVIxG5j135XKhVMTU3hgw8+aNsZSqbrjjkl/NPTU7z//vttE5Rhi5OTk/HwXzLxN954I9p6aecmPnVcKpVKjAoZGRlBlmUxVwrbkHJkc1ON+hUajQZWVlZQLBbj4cK0lfOYqnfeeQchNNPmPnz4EJOTkwAQY7BZj8bYu3IwMTER/4+MjODtt9+OKWW1bYyW0dWRMljdZai0prSnY862aLt4XW3CbktXmkzRkV7nKkvr7KSsaHn8zfLydnr6PMwr19uhceVMg+D4yVMwiXNduXmKAqV/x1UKLgTjBvIllv/2CeigA+TM0+tLaRzalhRRdGLU/t6Tyuczqb4689f+uP1Rn3cHT0qAaNy3a2HcgOM40nZTADCzGplAsViM6U2B9oxty8vLWFtba4tdz7LzO2HZJrX/0QxA4qZTlb8Zz3562jzI99vf/nZkwGyHas6MYS+VSrH8y5cv49KlS3jvvffaVgXOyFTIZFkWc7q8/vrrqFarKBaLePjwYQypDKHpdH311VcxMTGB3d1dLCwsxFwsi4uLuHz5ctSw1aatdKIMQDcDlUol3Lx5EwcHB6hWqzHyh6cUaW4dL9PpTDVxasspBqUrVTf7aXm6D4L31BTBupR55Sk9Xjbxr0KCqyrXWJ1/KG27kqPt1HH3OetZGFO+rtS8ZUjs0dFR29jQTJZy3KbgwjDuvAECzpsO+IxqwJ2kVJ5Tj+V0aos+r4TizDr1Tsqco/W5CSTVfic0bXPqfbf/6bcTHxmnamYu+fVdjTRpNBpYW1tra5vb5pQAyQxZXsoRp3jWo8N0ua8OJE6gcrmM7e3tmGOFO1wZIaCTs6enJ2Z8XF5exvT0dNxAQxv19vb2Oc2RbdR2aVgbNwa9/vrrmJiYQLVaRbVaRW9vL95++21cuXIlhk9WKhVMTk7GU4ucrnR8NKJB6YkMlOPHE4D29vaws7OD1dXVc3Z9x4PTgtO0atgqUHTMXSvWHcV5c1EZqwrBTrTvq4OUZqr/dey1vDze4lE8fN/t3TSNZVkWc9oo8/b32C4Vmnq8HOmKZhLHex5cGMatkJJwKY3XiYnPOqQGRgnB3/NrTtjeLmfgnbRlfd9/pyS248Xr9Wdc404RuGs2qcmQ6psKLz6vgoLxxVqf9zuFS/8uFottOzJ1cjBago6hjY2NeJQa7c6MCmEmQGUkrKOnp5nitVar4fT0FB9++CHq9Xp0xKqQYLuVgfMkmqOjI/T19WF7exvT09M4OTnB5ORkjMSh87LRaMTc3q+++iq2t7cxOzsbQy+VdlyYOa1wku/u7uLDDz/EgwcPYhw9BYHnaXeaSjFqfcZpwn08BN9F7EqS/lah72Pu/XQFxxk+hYQKMzcP6vxIKQpuyqEioLlryGSZl0fLYgSPhriyXrZX+8vVqR5EnYf3J9m5Lwzj9oHMWyoo09F3UwPug5eKOHGNNo+g89qV+u3OH32vU/n6vi+Z8/Ch76e0RO8vAMzMzGB3dzfGMqfw6NdcEKg2oX10B1CqjynBqDikZsrymI6UWgwZJpkyD5lgu7gDslgsRgbG8pjHemNjo83sUavVYqY31tEpzafu+OTkrtVq+Na3voVSqYTl5WWUSqWY+2R8fByHh4dYXFzE48ePcenSJWxsbGB4eDj6HVy4qXKhvhulj2q1GgWCn5fo5jAXwHnaqCtGPkZahvuGUsydZbsDVtvgCoMHB6RoU/0P3h9lzDoXlIbzFC81gzD9w/7+fjxqj+Xv7u5iaGgopjpg/zk3GHnCKCi2mStTVX5UAPm8ScGFYdwqVfMYCAdfGQfBB17fS13XezqYKYLupBU4wSvz0nZ5fV6OSmjXBrSNysy977pEc2atZW5ubp7bKk6mRrumEnwKF3mCSLUVgk5CXyIqA/Ax4qTc2NiIGov2hd8PHz6Mm4cKhbM4WG7eYVuLxWKcSKOjo6jX6ygWi9jZ2Yk2e2d2imMVijreTMZ/eHiIg4MDrKysYGZmBtPT09jc3MQbb7wRQ/ju3LmD9fV1PHz4MB7rtre3Fw+fcKaptm1neJVKBdevX0dPT0/cVJKiMU+I5uPiTEzrUlzkaYOKJzXFpOZAyo6dEho6D4iPPMWF5WiGP3d8ejtSIYXAWaoJLfP4+DjmomGKXL7LSKsU32o0GrFN/O0aP/uuoc5q3sqDC7NzUrVU14jzmLgzTJbTqdNapmp1vJdiivohYaSYvIJr3Km+6IArsSiROvPUPvqE4kT3yQu0pwqlBuDvpCYgkD6RKLVq0H5rGY4v3X7uuGJd1EKUedBEwfzW1LgPDw/jxp5CoRDDADX8ivUyx8fMzEw8i5Fx66pts05PN+rbt3ltdXU1nsR+cHCAer2OUqmEL33pSzEh1PLyMqrVarSxT01NoVQqYXZ2ts1fo+OvdMH/hcLZiTo8VIFjqnZ34jtlM9Uy9Z7TrTNZjo+GufF9VTycLpXpe8SH0kJK6XKhleIHFOzqVFWcpcrUFYDPVe4SDiFga2srrsiYE1/NJ5q6Vz+kT354XTM+so1qztO25cGFYNzOhPUacP40aL3mTgugXSNSzcXtXEDabuxtc804pYnyO9WHFENOMX++p+FrWp+X7d54FyI60fye1stnNC+5M/E8QeS+A7bXVwyuMaUEXh7DoDbCcDEe8KpamW7Y4fmeepI2bczMy1KtVqOpiAmz2GamjWV/vL/Elzt1y+VybNfW1lbMyT0/P4/FxUV88MEH2Nvbi/nXR0ZGcHp6irW1teRmppRGqN/9/f14/fXXUS6XY/pbF5bse4oxan1craX65gzO6SaPWXsdTleqQDmT5nWlL5qUlJZSSp72VSOoOil8Wp/j++TkBJubm1hZWWmLmqKJzE1Y2n5XJLPsLIGU+4M62f4dLoSpxBGq14EnnzJNSEnY1H0fLGfyBNarGjnLz9Ni+O2E5Jqq913rzGuvt101VCcc76Pjwctx4eHvKFGRIN1noPhne3zyOY7yGJTSw8nJSczEd3x8jLW1tXPmCtanm04YAUINlSfV9/f3R2bN49fUmeeaqmtsyqx1zNbW1jA4OBjHhU7LgYEBzM/P4+HDh8iyZuTH8PAwsiyLpyBRw2N5eXU683zrrbewtbWFw8PDuPIIIZwzl6WUBGW4Kadi3srIlQmnLcWf06eXnaI1FRapyIyUcqfjkCrTISU0FLe6AUrLU2Y9ODiIWq3WVqbb/L1PWXa2AS+lED0tPJFxhxBeB/Bv5dIrAP4XAL/Run4TwKcA/nKWZbXQ7OHX0Dx3sg7gv8uy7L0n1BGJjf/1O6UJd9KM/ZoyGJdoPvCKSGdmWmZefT7ZvQwnPHfYKD6csfFdzdng/cvDTwqPKSbvuPKx0AlORsnfDhR6KvBcUGiZKeEJIDonqXGlHK98XzVv2hSzrOmoHBsbQ61Ww8TEBMrlcjRrMBMhc3DrrjiOCyez2vC1L1wOl0ol9Pb2ol6v4+7duwiheWYm47339/fR39+PqakpvPHGGzEUMWU3ds1TtbLT07MDHogXRq+kxo94ZlIuH0tn2k6vypTcTp1Xn4YcuvBhPRQwauLRd11AutLjJj13WGrdapZUXLIO7mDlngA16+g87evri7tj+b6mQWZ5KjzZH/XPaLvz+pQHTzSVZFl2N8uyd7IsewfAj6PJjH8HwC8D+GaWZbcBfLP1HwB+DsDt1uerAH7tia1AOlVqSlr7M/zt2i2vpxiQ15VnQklprW5yUSabInYHZ5Qs00PWdLIoPlI2eNeGfYLkaSlqB2X52iZ9NmUi0rpd+0mNjYIu4VOCks8PDAxgbGwsasRMcqVtYTlu1uC1k5MT7O3t4d69e9GrPzs7i3q9joODAywuLqK/vz/unnTGpXjxe8QBJ6xmNNzd3cX29jYeP36MLMtiTm5uwgCA27dv4/r167k0kvJ3hND0U/zhH/4hfud3ficyGv2kBCSFktOL45x4U7y6Zuj/XZj5/Ez1x4WjzwGPtkgpU+4w9vnI/6o0OH70OjVp4spplsJ+f38/HqxCwaNMmXVy5cXEZL29vTHTJoUDN5Tp+KZ4h8Kz2rh/BsD9LMseAfgygF9vXf91AH+x9fvLAH4ja8K3AZRDCJc7FepIZuNVe9XJkyLIp9HOUwzMmbojTa/5clLBpWaKgFOCIU9A8HnXHlJEr891WsZ6+/x/aqmZGhetV59P4T016V0o5Ak4jjkTQHn/OvWRoBOKE4VnYDLs8OTkBBMTE1HjUscbJ2WWZW2mF23n8PBwdIzRRJNlWdT4j46OcP/+fRwcHGBkZASXLl3CzMxMdHipQzJPADrOuPOuv78fk5OTmJmZQalUivk0PO2ojp2Oq869lH0XOB8BoQLbfRcpBcbHUzVepW+vV2lNQyZdofB54zTk/jCtT9/nodzUqPU5bR8VAb7LDJZZ1h7Bw3DAQqGZIGx0dDQ6Nznuvb29GBwcbNPI2d9O8Kw27r8C4Ddbv6ezLKu2fi8BmG79vgpgTt6Zb12ryjWEEL6KpkZ+jvGQMFKaqS5ZlPj8+ZTtzgeN9eqkcO1btSqdXM4wnAnrdb3nhOaCQ/vp/fdJyH6kvOM6MVKTK2Vz1rK1PtWWFH9ap05knVw6lilB6XWoNkgNm3U/aXJr+YoHgodj8d7AwAAWFxfjhh+mtA3hLDrD+659GxoaigmymACfE3N0dDRGrDCG/MqVK7h9+3ZMM+uQGgunq5GREdy4cQOrq6vo6elpy1XOA2yJz9QYqnbrc0nrTjFKNzWoSSFPyBOo2fp1FQ68pwpIyl+idNBJSfH7KeWDZXPTFvOSEFfaLvZB06/qgeC9vb04OjqKUSkA4tmrfX190Q/BXPjcDOY03gmemnGHEPoB/AUAv5JAThZCeCbrepZlXwfwdQDo6enJHDHOAPRe6pv3U8zXCUf61GaHUg1fmVtKc86rn99P8rJ7+T5ZnTF6Gc4cU4OtbcjTpjtd82WoQ4p5Kjh+8hgvJ6UySb3uW4g5ZkojylC0fd5vMm9+NxrNFKuMFnAHoQsl5kXR/m5vb8elb6lUivHgXCKPjY1hfn4+CqHx8XEsLi7i7t27mJmZwU//9E+3aZNse4pJsY/9/f145513sLOzgz/+4z9GtVqNZiQXLopvvcf/SkMpuvVrPrZucvP55vTtdfIdNzXk0Zq+x/L1IOaUr0jbrPSgwPoZj09NmBuxtC8eJko8MKkZgfSkewrUtMbj//QwB1UU8uBZTCU/B+C9LMuWW/+XQ8sE0vpeaV1fAKBGu2uta7mQp0UB7aFxfo+DpM6LlB0277/blHTgnfl73gMvz/ugWoL3099PSX6fdCli9/amCNOvaZRMaqK5pvM0RO+TSpfceWPHtrjQ9RwawPlcM/6eE7nXnUcDxJ3uOASQXKayDu6m4/uDg4NxYjO0j20+Pj7G6upqjIqp1WpYWlrCxx9/HO3fn3zyCZaWlnLtm536ySO4JiYmcOnSpSTTTWmXWl6KyebhKu+evp+iT/2dp9S4z8L/63jxt9dJAZsnGLx9DhxDOpIrlQqGhoba5qjOnyzLkiGKbIPSFevVIwGZSliP/GNbn+SgfBbG/Qs4M5MAwO8B+Err91cA/K5c/6uhCT8JYCs7M6l0hBRS85YPnQjSrwH5TDP1ng+UDrpqgqm6dJD0Xp6nOwXO8LytKcIkQbmtXd/lhFANKcVg3SyQmgwpzSg1YV0AKc60HF3taJv4nmoxjmNfudBOrfZqoF34+gTU8fCNHMpcsiyLJ+ow7wSZd6FQQLlcxptvvomBgYFYNrXtLGuGAs7NzSGEgJ/6qZ/C9PQ05ubmku1ICWGHQqGAW7duYXx8HFNTU+foTPua2jSjeCONeL15dEhBq+PhihZBr6tGmdce76OC0hHH2bVhLcNpKQXab2ZVZN4a7S9NIRq7rbSrjDzLmlFOzKGjK0b6WDQu3OdyJ3gqxh1CKAH4WQC/LZf/PoCfDSHcA/BnW/8B4BsAHgCYBfC/AfgbT1F+/HabrDLPFKNMTUaXtLwXOy3EnafJ8b4iM6U5ulbj0ImZ+XupZ5Qhe18cN6p5p9qhmmQeTpX5+eQAzrRfZwL+vvZdtSyWkZpMnWx8fioNJ78viwkhnMXiav1aZypiIYTQdham3s+ys7DCQuEshwrvc+NOuVzG5OQkenp64pFnQNNEwpNouD19fX09Hu5L3Liw0OiO1IqSwq9YLGJwcBADAwNxrCmEdOwcWIYLal/9pASdl+nKgF738dfrKlzJFFMmEy/P50xqjqauu6IQwlnOEJqbDg4Oou9C6YC2asUHzR8Mt2T5utPWP76qJzxJ2wae0sadZdkegAm7to5mlIk/mwH4pacpV0ER6rG6wPmsd3zHCSRPO0gRkmqNziy0PUrYXq6X7dq2tjlPayLoBNOyHFzgqH3XNS3XcFJ40WtuJ1YccPIqLtTZpfkWUoL3SREALMf7rThMTVIvh8+p3VM1c+2T9zPPng8gOpOYPrZSqcSDJHj+5Pb2NhYWFiIT527Uer2O1157LTokV1ZWYkbDxcVF7O7uYmRkBLu7u9E2nqJfx0Oj0YjHud28eRONRjMv9cLCAkI4OzJOmUXKdu4KTCfzm/5W5u1KjNJdymfF+6qI+Li7YHa61/qUxthPHTunW2+ztinVZ5ZPR7MyYfo/1AeiZatd2zfgpARcHg3GMep490cIzuSUUF0jIOR1MM9Gq99KVC65FXxJl5KcANq0UtXqdPBca1JCfJJjIlWvCzX/+LLV8eplOyHnaQk+eZTYFUeq6bp24fhWz30e0eaNvU5qbaf23yMZenp64uTr6emJpg0dD2VuHJPt7W1sbm7i+Pi4bcOOnixUrVaxsbGBkZGRqPmfnp7iwYMHWFlZweHhIba2tjA7O4udnR0sLi7io48+wv7+PjY3N7G+vp7Urt0kwA+Thk1NTWF8fBybm5tRa+WBElzGKy583D13uQtfBb7jzl/SQgjhHLMkXlk3mZi+qzSt5h1fiWg9qXaxrpRillKglGZSK0KNKc+yLEYNcXy5UmPCMsWvKn9uFlE85/1PwYXY8g6cPyNRNbuUNqgEB7R3tpPETDEuvuMSMCWRnYBJUCm7mgodlqf/vY3aX63bNQsty/uokJowOuEcn3pfwfHg76aA98k8/QxFb0en1YW3Re+lBJjiT99zzYnml5OTk7gFng4jN+2wfJ7+zuvsAyM6yIxOT0/jEVcAcPnyZYTQPLZsYGAgmkwYQ/748WOUSqV4nQc8pJim4qXRaKBarWJlZQXz8/MxTSzvc9nvkR8ppuvzzekxNQbeHh+fvPdcaGub3bSTGtOUcqHavTJf3f2o72lbUoLAhYy2Q1ctFIz0c6R4i9eZp1mn8JiCC6lx6zVCqpMKLiHzynMi9MmpxOOT3TVXoN3GroPqZbrm6aaXlM3Z68zDSx5jd8eR9yll59O68jTglFBNaTDslzJULyfFIFO4VO0lpWl7GgAHfddpqdFoxO3jHpaVagM/vqW7UGiekDM8PBw3xIyMjGBsbCzGBff29mJ0dBTXrl2Lsb3cPr2+vo56vY6FhYVoZ9eVg69KKDD29vawv7+P9957D/fv329LT9sJ16l5oEqSvqNKRAoXSmMsJzVOqbH1sc+jLWfWPm98HmhbnAacP+icVTrRtmpdTkMUDroCzVMgVMCk+uQ8KgUXQuN2TSAl4VOT/0nS3ZmRIruTZumDohK2k0OhUDi/EUGZNtvhduIU83fC97Zrv1m/l5FyAvl7rjn7ZEs5n3wiK9H7xNW+uFlFJzrrcaHg/fYJx/+aNlPBJ2mKYbmW5QxKBQOv6c5EmgS4fM6yLGrvdGY+ePAAm5ubGBwcRH9/P5aWlnBycoJyuRxzch8dHWFycjIm7WciqhBCTCPa398f28qJz7qOjo6wvLwcT8HRMdNwM6cHx3Vq7jlOec/xxnqUKaVWpDoOWj7phKlP+YxvCWcZ2k6nCa0vRecqkJ0mHDeu1OSBv+emohTtOT94GrgQjFuJKGUnTknrFNP19/R9ZRS8p2kwtQyvD2hnLF6Hl++gA6T9TAmKPOaTMk2k2pPH5F0QpMrT+zpJXOP069pGF5RatwqlFF4JruXpOLmQd/z6dZbly2h1mGoZqsVpkioXFo1GI2rn2pdGo3kWZ39/P8bGxjA0NISpqSnUarW4xX1hYQEbGxvx8Abm0t7c3MTBwQEKhQLeffddvP3229H0AQBLS0u4cuVKW2Kvnp4eTE9PY2NjA729vbmJ/X3888bMca/j69qmj6cL4hSDT81jn/teL7eRp5SZFH3rPPA682hSwa+p4qE05O8pbWj5albJa7/6A1LlOlwIxq2Qkk6dJGIn7SnFgF3q5zFbb48TRqqO1HMurV3ry2PKKQGk5acmV+p5Z2Z5TFvf9/4oIXKi6y7Hp53kvJfCg0KKoabwm8eAFTo5oF2z0vJdQUi1U083cTMEczjX63WsrKygUGjmq9ja2or/6RDlBp79/X1sbGxELZzPMWplbm4uCgO2T52P7uhzAazt4zUdB/52Z7f7ILRsF4Qp4aC4FiTnkwAAEHxJREFUzqM9tqmTMuXj5WPvdSrD1P4qfpw2O/ETVy5SW/edvhRSdeoKT6NO3NTlcCEYN6WZ26t0sBVSxJiSmj5RU1Ka9eu3lpuqx5l1iiCVeDoxB9UIU21OMX9tR2qCOm61H85M87QTfce/9RlnWvqcm29UY6EmldduZx4pZur16nUfwxQTCuHsfMGULV3brdqgt9/bx3K4dZq75CqVSjSJsCwee6b1jo6O4tVXX8Xm5ia2trbiBg6edHN0dBQz2G1vb2N+fh57e3ttkQ6eA8Vx5sLXx84Zmo4VmZbTRB6t6TukGX83T6HQ8XV8uyOQTs7UfEgJKNap4+7tTo0tr/sc1L5rG5UJe7s55ll2Pny1E1wIxp1CIKGT6cQZZkqr03cInTTEFHNXweKD4ASnhKrgRJ36Ttkh/b+2SfvvtmJ/RvvhuHfcer9T2pVnassTkCmtJeVMdeikFfl9x3GKaROXeeURfMlKhvEkwcayvM0hNHM1M5nQyMhINI1oJArf7e/vx+7uLj7++GNsbm5GxyMzGs7OzmJ1dRVjY2O4fPkylpaW2o7OYjtT5gXtrwoj7YsyWD7vDNP7rUyVZSuQjjTOHzhj5kq/au/18XTzndOzb8bTsdB5oDhRhdEd3HlKUIp2U3ShAknBzWt8jrlsFB95cCEYt2qNLtkUfELo4OpvHVQuvVxqel0uBFwTVa3E7d2e9Ijvazk+yFoOr6UIi/e8j4o7JTJljM6stCz/ncfMUrbhFIP3d11Ldebt9aY07BQB+1JeyydOvX3ehhSDz5uwqWV43iR1k4FOyEqlgmKxiJmZmXhiDbMQklYHBgYwPDzcxjjv3buH6elpZFlTg//+97+PnZ2duLGGMeW6SzPllHZ8pkxoviJKCaE8xUod94oXfddXyinBn9I4U3PV56WbylTh8LnM90II5yJ1HCdaFueA4knrVlpQnPC/H0/nh0UojnyeO1wIxk2kpqSM2lf1WX03NbmUebtNmc8qk3Omz2dTDEqPdHKmo+3ydmtdKY0wr94U5JknUgJI/2tbU0vWlN05rw/6nSd0XePJ63dKKOW1TYVCnoDl6kUngmuZ/k4KlwS1waoGmaKDEJonytMkcunSJfT09ODevXtxM06WZRgYGMDAwAB2d3cBAKVSCTdu3ECpVEKhUMDi4mJM1s96aRLZ3d1FtVpFsVhEuVxGtVpta6vTuzNqxZ332ekqxUw5nilTmeImT7ArOB0oQ3QTopeTUkiUkfpc6KRkOB2nGL/jgHVrG70MxReBQsP7n+IPKbgQjJuQcnKkPM2KgJQGxut8NsW0+V8nW8oulpL+zjSUQaS0wTymlGKw2g6CDrpKcy0rNelIQCmPvL7rGpm31bVnbVcnQeROsBQDcDynymIbiDNtU4r5a7a21ORy0Hb5mKs9W/vm2qT2BUCb3XptbQ3VavWcQC4Umg5KRpL09fVhfn4+mlNqtRqyLIthghsbG3GTR6PRwPr6OmZmZjA8PBxNL3kMUvvj9/MEtjPTlBarZWkdzry8PU5nXr//T42jm90ImgAqNbadhIf3P7U61Pb5+Osq3Bm1znWnIW3Tk8wkwAVi3CkzA3+n7jlRpBCf0sRdqjvjTJlVnjRJ/Zw8Jwz+VxuftqUT8/M++QDre46DTpNY8ZCHw5R2y37kCSMnSoWUXVXb7dq89iGF+9RE0Hqo+dGOrKswFbZaR8oc5CszZV5ut2X9tFdScA4MDODo6KjtNJ39/X00Go2YdL9Wq2FoaCiGDfb392NmZgaHh4e4fv16tGeHEOKhtaurqygWizFXiioTqv2lGAT/p5bnjvc8c0AejehvVW5SNKl02InedWy13XnP8nmnxzwlyYWRZ6TkNVe63IdEunDBpLhLCQHnbZ3gwjDuFNJ5nYj2k6uVGFMaJScNgHOT0cEnKXDeyZJnw3PG48SptjFv45P64QIrdU/bkxJWjk99xglKJ7Fq2Vqvj4PjgrgigfvzqbFwJuzM2ycY8eo4JLDO1EEZfN4ZiT6nDFDHjWWnGKMD66PwoJlE8bW/v99Wx+7ubswwx/Sfo6OjMZcJcTE5OYlGo7nd/eDgIPZBHXQpnOpzKfpwHLOtriHryseZaEpzVUXFk5G58vEkjdMFhTM8pelUWSle4fjSe3k8IzX3iZvUPE0pRXye305reXBhGDdwngkoE3R7EJ/LY1AEPsPJpoObksR8R99P/dZ2pkw3+nyeRuz2wTzHn5blwsL/pzRAtjGl0XpbnJH5JHBNO8X4FO+pvudNcmfqKW2P7/thB3lCzLVD/6ZgURxo+1WQsx4yqtQKQjUv1nN8fIz+/n4MDw9jd3c3as2OP/aLjJi7JLMsizsjuY1+dHQ02sLn5uaQZRlKpVK0mTM2XMfNzYpOW3nmp9Q4u8D3sUiZDP08Rm2T4y+PWab8UvqejpGOr9OUChveYzih9lfHxgVBip591a71dqJ7b/eTmPeFYdwphqWd86VtqpM+2G571nf9NyGPOTvkCQ1vs2uDOtgpgtdnUvX6M6lNACqoFA8+KfU976ceXpoipjy8qSBL2Tnz6ktBHtNwbdEZfkoDV1wpLTheUg5NbY+PrwrvlMmB/2kaoe3b+9ff3x/bzbMjqXXzoAWGE16/fj1q2o1Gc3s4k2Q1Go14eHG9XgdwtlHImc7TKA55DFzxwXtUGlz4KY58HmudvhrxOrQNeXZ2H6MU7apQ1+ueCE2fTwkSV8L0nRSutZ2pMp4FLlSSKQVlHK7VONNTAlHGmNIwUwwrT/rptxNjqiwvI9U3EoVqtymt0E0eefVpW1Qb9PLdCavXvOy8icJ7PAHED1NQPPG3tyMPL/o/pdnwnttC87R/bwOdfynnK98l02QfWRavsb8ph5ULtRSzYyIrbT9toWS6zDPCZFV7e3txW3yhUMDy8jJ2dnYwMTGBw8NDLC8vx6O2eCIPy0hpftTqVRMkfrTdfDZlolIhp+Prpid9vtE4O6AgTwFI2cl1bFI08SSGquWxHD3Yl998xlcSSnfeNp0nmtbX54+Wp/hKCdNOSpvChdG4gXztTyWWa1/+vBKeIsiX23zW6/fljDJDfzZvcPi8EnJKM3AGwGWdanLOdL1+9k+1vpSNjeW4vVlx5Ew/xXxU8OQtWQlkHK7V8Z63z9/XZ5XIvV0+Pint1/vlAioljLkHIMWcUszBBY8yTrebO42qA5VlnJ6e4vDwEP39/ejp6cHQ0BB6enrw6aefYnJyMr53fHyMg4ODthNctH8+Rim7vx8BpitVH6M8ge94cCe2j3FKyXJ8s0w34/l8cIHsoZpOUyqQ+UmZGH28UiYgxYPXqWWnQOeujtULw7jzluTA+dA+RU5qcJ5VEqeeS9lHvQ5+52lbbp7xZzuZfrxPXpcTh2pHqWf435Pbpxgm25YSGHrfNTR/joJCiVYFk7bR++1jnhJaOvGdNlJ209SYujBz7VP7USgUYtJ8AG3hd2xHimbcGewCnuXyud7eXpycnETtmRr14OAgrly5gvX1dWxtbWFnZycyIGVC1KqVXvLGmWPg9m/FG5/R911BSNWjjNDpLqXwENcpeuokKNgefTZ16HReu/OUCt5XmvX0CCmTpwsDn9v6jOa7cbx0ggtjKklptvyfkkCKHNXGUhLOB101wVTOapaTQqITTApUc+pUhj7j5iDHTad2qF2Rv1PLNmfYPjF9wqqWoe1wxtlJwKSYeUpI+ET2MU1pMakyU5pfSlABZ6fuaCQGy0vh3NtIJp7SIPm8rhY4ztqvsbEx9Pf3o1AoxBA/bmEvFovx0IXe3l5cv349HmuWZVk0iaQ+fjivKw/eP517xGceXlPv6lxM0VWKUft1ZaCp+e1z1T9eP/HtgiEFzjNYVoo2vI2pPji+nHZ1xep1Pg2Ep+XwP0wIIewAuPu82/FDgkkAa8+7ET8k+Cz3Dfhs96/bt4sPL2VZNpW6cVFMJXezLPuJ592IHwaEEN7t9u3FhM9y/7p9e7HhwphKutCFLnShC08HXcbdhS50oQsvGFwUxv31592AHyJ0+/biwme5f92+vcBwIZyTXehCF7rQhaeHi6Jxd6ELXehCF54Suoy7C13oQhdeMHjujDuE8OdDCHdDCLMhhF9+3u15VgghXA8hfCuEcCeE8GEI4e+0rldCCP8+hHCv9T3euh5CCP+01d/3QwhffL49eDKEEHpCCP8hhPD7rf8vhxC+0+rDvw0h9LeuD7T+z7bu33yuDX8ChBDKIYTfCiF8HEL4KITwpc/KuIUQ/ocWPf4ghPCbIYTBF3XcQgj/ewhhJYTwA7n2zOMUQvhK6/l7IYSvPI++/OeC58q4Qwg9AP5XAD8H4PMAfiGE8Pnn2aY/AZwA+B+zLPs8gJ8E8EutPvwygG9mWXYbwDdb/4FmX2+3Pl8F8Gs/+iY/M/wdAB/J/38A4B9nWXYLQA3AL7au/yKAWuv6P249d5HhawD+XZZlnwPwX6DZxxd+3EIIVwH8bQA/kWXZWwB6APwVvLjj9q8A/Hm79kzjFEKoAPi7AP40gD8F4O+S2b+QkLdl9kfxAfAlAH8g/38FwK88zzb9Z+jT7wL4WTR3gl5uXbuM5iYjAPhnAH5Bno/PXcQPgGtoToz/CsDvAwho7krr9TEE8AcAvtT63dt6LjzvPuT0awzAQ2/fZ2HcAFwFMAeg0hqH3wfwX7/I4wbgJoAf/EnHCcAvAPhncr3tuRft87xNJSQwwnzr2gsJrSXmFwB8B8B0lmU8wXUJwHTr94vW538C4H8CwEQOEwA2syw7af3X9se+te5vtZ6/iPAygFUA/7JlBvrnIYQSPgPjlmXZAoB/COAxgCqa4/A9fDbGjfCs4/TCjN/TwPNm3J8ZCCEMA/i/Afz3WZZt672sKeJfuLjLEMJ/A2Aly7LvPe+2/BCgF8AXAfxalmVfALCHs+U2gBd63MYBfBlN4XQFQAnnTQ2fGXhRx+k/BZ43414AcF3+X2tde6EghNCHJtP+11mW/Xbr8nII4XLr/mUAK63rL1Kf/0sAfyGE8CmAf4OmueRrAMohBOa50fbHvrXujwFY/1E2+BlgHsB8lmXfaf3/LTQZ+Wdh3P4sgIdZlq1mWXYM4LfRHMvPwrgRnnWcXqTxeyI8b8b9xwBut7zd/Wg6UH7vObfpmSA08zL+CwAfZVn2j+TW7wGg5/oraNq+ef2vtrzfPwlgS5Z8FwqyLPuVLMuuZVl2E82x+f+yLPtvAXwLwF9qPeZ9Y5//Uuv5C6kJZVm2BGAuhPB669LPALiDz8C4oWki+ckQwlCLPtm3F37cBJ51nP4AwJ8LIYy3ViR/rnXtxYTnbWQH8PMAPgFwH8D//Lzb8ydo/0+huUx7H8B/bH1+Hk0b4TcB3APw/wKotJ4PaEbS3AfwAZqe/+fej6fo558B8Put368A+C6AWQD/F4CB1vXB1v/Z1v1Xnne7n9CndwC82xq7/wfA+Gdl3AD8PQAfA/gBgP8DwMCLOm4AfhNNW/0xmiulX/yTjBOAv97q4yyAv/a8+/Wf8uluee9CF7rQhRcMnreppAtd6EIXuvCM0GXcXehCF7rwgkGXcXehC13owgsGXcbdhS50oQsvGHQZdxe60IUuvGDQZdxd6EIXuvCCQZdxd6ELXejCCwb/P71cRLkJNMkoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(sky_bw, cmap = 'gray')     #Visualizamos de nuevo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dando diferentes contribuciones a los diferentes canales (30% para rojo, 59% para verde y 11% para azul) se obtuvo una imagen mucho más agradable a la vista y que recuerda más a la imagen original."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahora es necesario seleccionar una pequeña región en la imágen donde haya una estrella. las coordenadas de los pixeles que delimitan la estrella se hallaron utilizando una págia web externa que permite seleccionar manualmente una región rectangular de una imágen. Este método se utilizó igualmente con la selección de las regiones de todas las estrellas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1 = sky_bw[10:19,228:241]       #La estrella es un rectángulo obtenido de la imagen original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f3cd5e589e8>"
+      ]
+     },
+     "execution_count": 73,
+     "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": [
+    "plt.imshow(star1, cmap = 'gray')    #Visusalizamos para ver que la región seleccionada corresponde a una estrella"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El seguiente movimiento consiste en ajustar una gaussiana simétrica bidimensional a la imagen de la estrella,  para lo cual se llevan a cabo los pasos a continuación."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Defino una función que me de los valores de una gausiana bidimensional.\n",
+    "\n",
+    "#tupla es un arreglo donde van los parámetros de la gausiana, la amplitud a, una constante aditiva b, \n",
+    "#la desviación estándar c, x0 y y0 son las coordenadas donde está centrada la función.\n",
+    "\n",
+    "def gauss2D(tupla, x, y):\n",
+    "    \n",
+    "    #tupla es un arreglo donde van los parámetros de la gausiana, la amplitud a, una constante aditiva b, \n",
+    "    #la desviación estándar c, x0 y y0 son las coordenadas donde está centrada la función.\n",
+    "    \n",
+    "    a = tupla[0]\n",
+    "    b = tupla[1]\n",
+    "    c = tupla[2]\n",
+    "    x0 = tupla[3]\n",
+    "    y0 = tupla[4]\n",
+    "    \n",
+    "    exponente = -((x-x0)**2 + (y-y0)**2) / (2*c**2)\n",
+    "    z1 = a * np.exp(exponente) + b\n",
+    "    \n",
+    "    return z1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Hago la malla donde pondré mi ajuste gausiano, esta debe ser del mismo tamaño de la imagen que quiero \n",
+    "#ajustar, la de mi estrella\n",
+    "\n",
+    "x = np.arange(0,star1.shape[1],1)\n",
+    "y = np.arange(0,star1.shape[0],1)\n",
+    "\n",
+    "xx, yy = np.meshgrid(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "z = star1  #Mis datos reales son los valores de gris de mi estrella"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Defino el error, el cual consiste en la diferencia entre los valores de mi ajuste gaussiano y los valores reales\n",
+    "#de mi estrella\n",
+    "\n",
+    "def errormodel(tupla, x,y,z):\n",
+    "    \n",
+    "    #Entra la tupla de parámetros para la función gaussiana, los valores de x,y necesarios para hacer la malla\n",
+    "    #donde esta irá, y los valores reales (z) de los pixeles de la estrella\n",
+    "\n",
+    "    m = np.ravel(gauss2D(tupla,x,y) - z)  #Estaba teniendo problemas con la salida de esta función cuando estraba\n",
+    "                                          #en leastsq, np.ravel aplana mi arreglo 2D y arregla el fomato de salida \n",
+    "    return m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Para realizar la optimización de parámetros voy a utilizar la función leastsq, que utiliza el método de \n",
+    "#mínimos cuadrados.\n",
+    "\n",
+    "p0 = [1.0, 0.0, 1.0, 0.0, 0.0]   #Esta es mi primera aleatoria elección de parámetros que entrará a la función\n",
+    "                                 #leastsq\n",
+    "\n",
+    "best, suss = leastsq(errormodel, p0, args=(xx,yy,z))  #finalmente uso la función \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = best                    #Extraigo los parámetros que mejor se ajustan a la estrella"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f3cd5f77160>"
+      ]
+     },
+     "execution_count": 80,
+     "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": [
+    "zz = gauss2D(params, xx,yy)      #Utilizo los parpametros obtenidos\n",
+    "\n",
+    "plt.imshow(zz,cmap=\"gray\")       #Y visualizo la gausiana que mejor se ajusta a mi estrella\n",
+    "#plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahora debo repetir este proceso para varias estrellas, voy a implementar los anteriores pasos en una función para que el proceso no sea tedioso."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_param(xi,xf,yi,yf):\n",
+    "    \n",
+    "    #xi, xf, yi, yf son las coordenadas iniciales y finales que delimitan el área de mi estrella en la imágen\n",
+    "    #La función devuelve los parámetros que mejor se ajustan a la estrella, así como los valores necesarios que \n",
+    "    #hacen la malla en la cual va el ajuste gaussiano, esto para fines de graficación.\n",
+    "    \n",
+    "    star = sky_bw[yi:yf,xi:xf]\n",
+    "    \n",
+    "    x = np.arange(0,star.shape[1],1)    #Todas mis estrellas son de diferentes tamaños\n",
+    "    y = np.arange(0,star.shape[0],1)\n",
+    "    \n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "    \n",
+    "    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]      #Dejo la misma elección inicial de parámetros para todas las estrellas\n",
+    "    \n",
+    "    best, suss = leastsq(errormodel, p0, args=(xx,yy,star))  \n",
+    "    \n",
+    "    return best, xx, yy                 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#El siguiente es un arreglo donde coloco las coordenadas de los pixeles que delimitan areas de varias estrellas.\n",
+    "\n",
+    "#El orden es yi, yf, xi, xf\n",
+    "\n",
+    "stars_coord = np.array([[305, 328, 620, 637],\n",
+    "                      [82, 96, 628, 637],\n",
+    "                      [115, 126, 726, 734],\n",
+    "                      [368, 386, 444, 458],\n",
+    "                      [264, 276, 749, 758],\n",
+    "                      [540, 564, 650, 676],\n",
+    "                      [452, 459, 205, 215],\n",
+    "                      [87, 100, 1080, 1092],\n",
+    "                      [177, 185, 1096, 1110],\n",
+    "                      [21, 30, 921, 928],\n",
+    "                      [307, 328, 617, 638],\n",
+    "                      [260, 268, 764, 773],\n",
+    "                      [15, 25, 673, 682],\n",
+    "                      [52, 63, 452, 460],\n",
+    "                      [10, 19, 228, 241],\n",
+    "                      [142, 156,387,400]])  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Pg1048FmeslQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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eONV0/wo4IOmwpP9Q5/ew6FOZbT8h6WeSttsesf1ZSV+SdKftX6rzV8KX6sw4l1ny/5Ok9ZIOdn9//7nWkHPgFHIAKBwfJgJA4ShqACgcRQ0AhaOoAaBwFDUAFI6iBoDCUdQAULj/B7/I4AwBjPoHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARAAAAD4CAYAAAAkarlOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMcklEQVR4nO3dXYwddRnH8d9vd/u2vBSRaqQl0gSDqQRbsyEghkQQA2rghgtINMGY9EYQDAlB77w3Ri+IScOLJiBgCiSEYIEEiDFRoJSq0EKCFaEVLMQA3Ra2u2cfL86pFtmys09n/nNm9/tJNt3zknme6e757cyZM/M4IgQAGSNtNwCguwgQAGkECIA0AgRAGgECIG2siYWOjIzEyEiZbLJdpE4bSh4hK/XzkqTZ2dlitXD8ZmdnNTs7O+cLrakA0erVq5tY9Jy1SilZS5Kmp6eL1RofHy9W69ChQ8VqlQyrsbFGXk7HNDMzU6TO5OTkMR9jFwZAGgECII0AAZBGgABII0AApBEgANIIEABpBAiANAIEQFqlALF9me2Xbb9i+5ammwLQDfMGiO1RSbdKulzSBknX2N7QdGMAhl+VLZDzJL0SEXsi4rCkeyVd2WxbALqgSoCslfT6Ubf3Du77ENubbW+3vZ3rrAJLQ21vokbEloiYiIiJxXyKPYD/qRIg+ySdcdTtdYP7ACxxVQLkWUmfs73e9nJJV0t6qNm2AHTBvFdAiYgZ29dJelTSqKQ7IuLFxjsDMPQqXUIpIh6R9EjDvQDoGD6JCiCNAAGQRoAASCNAAKQRIADSCBAAaQQIgLRGRmnZLjbFreT0ttKT6UpNHpM+fvpY3Ur+zFatWlWs1ooVK4rVkqSpqakidT7u5Fi2QACkESAA0ggQAGkECIA0AgRAGgECII0AAZBGgABII0AApBEgANKqTKa7w/Z+2y+UaAhAd1TZAvmVpMsa7gNAB80bIBHxe0n/LtALgI6p7Wxc25slbZbKn7UKoB2NjLYkQIClgVc6gDQCBEBalcO490j6o6Szbe+1/b3m2wLQBVVm415TohEA3cMuDIA0AgRAGgECII0AAZBGgABII0AApBEgANIaGW05Ozur999/v4lFf8Spp55apI4knXvuucVqSdJZZ51VrNZ9991XrFav1ytWq+R5WbaL1ZL6r7O2sQUCII0AAZBGgABII0AApBEgANIIEABpBAiANAIEQBoBAiCNAAGQVuWaqGfYftL2Ltsv2r6hRGMAhl+Vc2FmJN0UETtsnyTpOduPR8SuhnsDMOSqjLZ8IyJ2DL4/IGm3pLVNNwZg+C3obFzbZ0raJOnpOR7772jL0mclAmhH5QCxfaKk+yXdGBHv/f/jEbFF0hZJGh0djdo6BDC0Kh2Fsb1M/fC4OyIeaLYlAF1R5SiMJd0uaXdE/Kz5lgB0RZUtkAslfUfSxbZ3Dr6+0XBfADqgymjLP0jiXVEAH8EnUQGkESAA0ggQAGkECIA0AgRAGgECII0AAZBGgABIa2w27sGDB5tY9Eds3LixSB1Juvbaa4vVkqSrrrqqWK1t27YVqzU9PV2sVkkRZc8hLTn395g9tN0AgO4iQACkESAA0ggQAGkECIA0AgRAGgECII0AAZBGgABIq3JR5ZW2n7H958Foy5+UaAzA8KvyUfYpSRdHxORgvMMfbP8uIv7UcG8AhlyViyqHpMnBzWWDLwZHAag8WGrU9k5J+yU9HhFzjra0vd329pp7BDCkKgVIRPQiYqOkdZLOs33OHM/ZEhETETFRc48AhtSCjsJExDuSnpR0WSPdAOiUKkdh1tg+ZfD9KkmXSnqp4b4AdECVozCfkfRr26PqB85vI+LhZtsC0AVVjsL8RdKmAr0A6Bg+iQogjQABkEaAAEgjQACkESAA0ggQAGkECIA0AgRAWiOjLW1r5cqVTSz6Iw4cOFCkjiQ988wzxWpJZUcXlhw3OTo6WqzW5OTk/E/qqLGxRl6+C8IWCIA0AgRAGgECII0AAZBGgABII0AApBEgANIIEABpBAiANAIEQFrlABkMl3reNhdUBiBpYVsgN0ja3VQjALqn6mjLdZK+Kem2ZtsB0CVVt0B+LulmSbPHegKzcYGlp8pkum9J2h8Rz33c85iNCyw9VbZALpR0he1XJd0r6WLbdzXaFYBOmDdAIuJHEbEuIs6UdLWkJyLi2413BmDo8TkQAGkLuiZaRDwl6alGOgHQOWyBAEgjQACkESAA0ggQAGkECIA0AgRAGgECIM0RUftCR0ZGotRoy/Hx8SJ1JGnNmjXFaknSaaedVqzWnj17itWampoqVmtmZqZYrSZeS8NgcnJSvV7Pcz3GFgiANAIEQBoBAiCNAAGQRoAASCNAAKQRIADSCBAAaQQIgDQCBEBapUsaDq7IfkBST9IMoxsASAu7JupXI+LtxjoB0DnswgBIqxogIekx28/Z3jzXExhtCSw9VXdhvhIR+2x/StLjtl+KiN8f/YSI2CJpi9Q/nb/mPgEMoUpbIBGxb/DvfkkPSjqvyaYAdEOV4don2D7pyPeSvi7phaYbAzD8quzCfFrSg7aPPP83EbGt0a4AdMK8ARIReyR9sUAvADqGw7gA0ggQAGkECIA0AgRAGgECII0AAZBGgABIa2S05YoVK+L000+vfblzOXz4cJE6knTo0KFitaSyIyCXL19erNbY2EKuInF8RkbK/Y0sOUZTknq9XpE6jLYE0AgCBEAaAQIgjQABkEaAAEgjQACkESAA0ggQAGkECIA0AgRAWqUAsX2K7a22X7K92/YFTTcGYPhVPSnhF5K2RcRVtpdLGm+wJwAdMW+A2F4t6SJJ10pSRByWVO4MNgBDq8ouzHpJb0m60/bztm8bzIf5kKNHW5Y6SxBAu6oEyJikL0n6ZURsknRQ0i3//6SI2BIRExExMTo6WnObAIZRlQDZK2lvRDw9uL1V/UABsMTNGyAR8aak122fPbjrEkm7Gu0KQCdUPQpzvaS7B0dg9kj6bnMtAeiKSgESETslTTTbCoCu4ZOoANIIEABpBAiANAIEQBoBAiCNAAGQRoAASCNAAKQ1MqS01+vp3XffbWLRrSo5h1eSpqeni9VatmxZsVolT7ZczLNxS9ebC1sgANIIEABpBAiANAIEQBoBAiCNAAGQRoAASCNAAKQRIADS5g0Q22fb3nnU13u2byzQG4AhN+9H2SPiZUkbJcn2qKR9kh5sti0AXbDQXZhLJP0tIv7RRDMAumWhJ9NdLemeuR6wvVnSZqnsCUwA2uOIqPbE/kyYf0r6QkT86+OeOzY2FieffHIN7Q2XqampovVKnv07Pj5erNbKlSuL1Sr5x6z070epeh988IF6vZ7nemwh/7uXS9oxX3gAWDoWEiDX6Bi7LwCWpkoBYvsESZdKeqDZdgB0SdXRlgclfbLhXgB0DIdLAKQRIADSCBAAaQQIgDQCBEAaAQIgjQABkEaAAEirfDLdghZqvyVpoaf8nybp7dqbGQ6Ldd1Yr+7JrNtnI2LNXA80EiAZtrdHxETbfTRhsa4b69U9da8buzAA0ggQAGnDFCBb2m6gQYt13Viv7ql13YbmPRAA3TNMWyAAOoYAAZA2FAFi+zLbL9t+xfYtbfdTB9tn2H7S9i7bL9q+oe2e6mR71Pbzth9uu5c62T7F9lbbL9nebfuCtnuqg+0fDn4PX7B9j+1armzdeoAMhlXdqv5FmzdIusb2hna7qsWMpJsiYoOk8yV9f5Gs1xE3SNrddhMN+IWkbRHxeUlf1CJYR9trJf1A0kREnCNpVP0RLcet9QCRdJ6kVyJiT0QclnSvpCtb7um4RcQbEbFj8P0B9X8R17bbVT1sr5P0TUm3td1LnWyvlnSRpNslKSIOR8Q7rTZVnzFJq2yPSRpXf0TLcRuGAFkr6fWjbu/VInmhHWH7TEmbJD3dcit1+bmkmyXNttxH3dZLekvSnYPds9sGFxTvtIjYJ+mnkl6T9IakdyPisTqWPQwBsqjZPlHS/ZJujIj32u7neNn+lqT9EfFc2700YEzSlyT9MiI2STooqfPvydn+hPpb9eslnS7pBNvfrmPZwxAg+ySdcdTtdYP7Os/2MvXD4+6IWCwjMS6UdIXtV9Xf3bzY9l3ttlSbvZL2RsSRLcWt6gdK131N0t8j4q2ImFZ/PMuX61jwMATIs5I+Z3v9YHzm1ZIearmn42bb6u9L746In7XdT10i4kcRsS4izlT/Z/VERNTy16xtEfGmpNdtnz246xJJu1psqS6vSTrf9vjg9/IS1fTm8EKHa9cuImZsXyfpUfXfHb4jIl5sua06XCjpO5L+anvn4L4fR8Qj7bWECq6XdPfgj9keSd9tuZ/jFhFP294qaYf6RwefV00faeej7ADShmEXBkBHESAA0ggQAGkECIA0AgRAGgECII0AAZD2H9f9C5Z3Q15YAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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6leSmJOuTnKu53/9PknRmSynJy5JetL2xt+hySc+22FK/ltP07w9J+mzv8WclPdhiL32zvVlzh8GuSvLm8dRqLVR7B4W/IOlHmvtH+m6SZ9rqZ4kulfQZzW3hPdX7+GTbTZ1kvijpbtvbJV0k6R/bbae5rk7/bvseST+XtNH2btufl/RVSVfY/pXmtsC/2maPH2SB/v9F0mmStvbex/+65PrcpgoA5XCiCgAKIlQBoCBCFQAKIlQBoCBCFQAKIlQBoCBCFQAK+j8skrrvhJBIWwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualizo las areas seleccionadas para asegurarme que correspoden a estrellas\n",
+    "\n",
+    "for i in range(0,len(stars_coord)):\n",
+    "    \n",
+    "    star = sky_bw[stars_coord[i,0]:stars_coord[i,1], stars_coord[i,2]:stars_coord[i,3]]\n",
+    "    plt.imshow(star, cmap = 'gray')\n",
+    "    plt.show()                       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Paso el arreglo de estrellas a la función para obtener los parámetros que mejor se ajustan.\n",
+    "\n",
+    "params = []  #la lista donde guardaré los parámetros óptimos de todas las estrellas.\n",
+    "x_ = []\n",
+    "y_ = []      #Arreglos de los tamaños de mis estrellas, para fines de graficación futuros.\n",
+    "\n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1])\n",
+    "    x_.append(xm)\n",
+    "    y_.append(ym)\n",
+    "    params.append(m)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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0ee6LiD+X9EVJ3+gOtWdOLHxWnYXf0f9A0qcl3SNpn6TvTbWbJdiek/RzSd+KiKOLfzbWfX2Znpe9r6cVHnsl3bno+09020YtIvZ2Xw9IeloLH79mxf7u8+7Fz70HptxPr4jYHxHnI+KCpB9qhPvb9mot/CX8cUT8ots86n19uZ4r+3pa4fGSpLtsf8r2GklfkfTslHpJsb2hO8Ek2xskfUHSniv/qVF5VtL27vZ2Sc9MsZeUi38BO1/WyPa3F65L8YSkNyPi+4t+NNp9vVTPlX09tQnT7ldB/yxppaQnI+Lvp9JIku0/0cLRhrSwlMFPxtqz7ack3a+Ff2a9X9J3Jf2bpJ9J+iMtLIvwcESM5gTlEj3fr4XD6JD0rqSvLzqXMHW275P0n5Jek3Txwizf0cI5hFHu6yv0vE3L3NeMpwMo4YQpgBLCA0AJ4QGghPAAUEJ4ACghPACUEB4ASv4Pgqwai2AqN1oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAADeCAYAAAAU9Eo0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAM1UlEQVR4nO3db4hl9X3H8fens7u4GokVW01d6ViQLVZqDItNtASq3bJNRPugD5QmJDYwT5pUSyAofdQnJdASEmhJWdQoapVglIoQ42IMEkiss7q1umsSa9M4W80aQuIm9U9Xv31w75p1M3/Onb1nzm867xcMc++dsz8+yt3P/u5vzjm/VBWSpHb9ytABJEnLs6glqXEWtSQ1zqKWpMZZ1JLUuE19DJrEU0kkaUJVlcVed0YtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJalynok6yK8l3kjyX5Ia+Q0mSfiEr3Y86yQzwXWAnsAA8DlxTVfuX+TNemShJEzqRKxMvBp6rquer6g3gbuCqaYaTJC2tS1GfDbxwzPOF8WvvkGQuyXyS+WmFkyRN8aZMVbUb2A0ufUjSNHWZUR8Ezjnm+bbxa5KkNdClqB8HzktybpItwNXA/f3GkiQdteLSR1UdSfJJ4GvADHBLVT3TezJJEtDh9LxVDeoatSRNzI0DJGmdsqglqXEWtSQ1zqKWpMZZ1JLUOItakho3tUvI1Y5k0TN8pmZmZmbdjn/kyJHexgZ48803ex1fG5MzaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjVizqJLckOZTk6bUIJEl6py4z6luBXT3nkCQtYcWirqpHgR+vQRZJ0iKmdq+PJHPA3LTGkySNdNozMcks8EBVXdBpUPdMHJQ3ZVqaN2VSy9wzUZLWKYtakhrX5fS8u4BvAduTLCT5RP+xJElHdVqjnnhQ16gH5Rr10lyjVstco5akdcqilqTGWdSS1DiLWpIaZ1FLUuMsaklq3NTu9aF2bN26tdfxZ2dnex3/jDPO6G3shYWF3sYGOHjwYK/jv/76672OrzY5o5akxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1rsvGAeckeSTJ/iTPJLluLYJJkka6XJl4BPh0VT2R5FRgb5I9VbW/52ySJDrMqKvqxap6Yvz4MHAAOLvvYJKkkYnu9ZFkFrgIeGyRn80Bc9OJJUk6qnNRJ3kX8BXg+qp65fifV9VuYPf4WPdMlKQp6XTWR5LNjEr6zqq6t99IkqRjdTnrI8DNwIGq+lz/kSRJx+oyo74U+ChwWZJ9468P9ZxLkjS24hp1VX0TyBpkkSQtwisTJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMmuteHpmNmZqbX8c8666xex7/22mt7HX/nzp29jX377bf3NjbAHXfc0ev4hw4d6nX8Ku/+0CJn1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGddnh5aQk/5rk35I8k+Rv1iKYJGmky5WJrwOXVdXPxnsnfjPJV6vq2z1nkyTRbYeXAn42frp5/OV1ppK0RrruQj6TZB9wCNhTVY8tcsxckvkk81POKEkbWqeirqo3q+q9wDbg4iQXLHLM7qraUVU7ppxRkja0ic76qKqfAI8Au3pJI0n6JV3O+vi1JKeNH28FdgLP9pxLkjTW5ayP9wC3JZlhVOxfrqoH+o0lSTqqy1kfTwEXrUEWSdIivDJRkhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNa7LedRaZ0466aRex5+dne11/AsvvLC3sR9++OHexgbYvHlzr+Mn6XX80T3Y1Bpn1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGdS7q8Qa3TyZx0wBJWkOTzKivAw70FUSStLhORZ1kG/Bh4KZ+40iSjtd1Rv154DPAW0sdkGQuyXyS+WkEkySNdNmF/ArgUFXtXe64qtpdVTuqasfU0kmSOs2oLwWuTPJ94G7gsiR39JpKkvS2FYu6qm6sqm1VNQtcDXy9qj7SezJJEuB51JLUvIk2DqiqbwDf6CWJJGlRzqglqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS41JV0x80mf6g/48k6XX8008/vdfxL7nkkl7H3759e29jz8/3eyuavXuXvdPCCTt8+HCv42tYVbVoOTijlqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDWu0/2ox9twHQbeBI64L6IkrZ1JNg74g6r6UW9JJEmLculDkhrXtagLeCjJ3iRzix2QZC7JfJJ+b6YgSRtM16WP36+qg0l+HdiT5NmqevTYA6pqN7AbvCmTJE1Tpxl1VR0cfz8E3Adc3GcoSdIvrFjUSU5JcurRx8AfAU/3HUySNNJl6eNM4L7xPZQ3Af9cVQ/2mkqS9LYVi7qqngcuXIMskqRFeHqeJDXOopakxlnUktQ4i1qSGmdRS1LjLGpJalyqpn+1t5eQD2tmZqbX8U8++eRex9+yZUtvY7/66qu9jQ3w2muv9Tr+W2+91ev4GlZVZbHXnVFLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGtepqJOcluSeJM8mOZDkA30HkySNdN3c9gvAg1X1p0m2AP1emiZJetuKRZ3k3cAHgY8DVNUbwBv9xpIkHdVl6eNc4GXgS0meTHLTeJPbd0gyl2Q+yfzUU0rSBtalqDcB7wO+WFUXAT8Hbjj+oKraXVU7qmrHlDNK0obWpagXgIWqemz8/B5GxS1JWgMrFnVVvQS8kGT7+KXLgf29ppIkva3rWR+fAu4cn/HxPHBtf5EkScfqVNRVtQ9w7VmSBuCViZLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxqarpD5pMf1BtGEl6G7uP97s0LVW16JvfGbUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcSsWdZLtSfYd8/VKkuvXIJskiQmvTEwyAxwEfq+q/muZ47z8S6vmlYnaqKZ1ZeLlwH8sV9KSpOnqumfiUVcDdy32gyRzwNwJJ5IkvUPnpY/xxrb/DfxOVf1whWP9fKlVc+lDG9U0lj7+GHhipZKWJE3XJEV9DUsse0iS+tNp6SPJKcAPgN+qqp92ON7Pl1o1lz60US219OHGAWqORa2Nyo0DJGmdsqglqXEWtSQ1zqKWpMZZ1JLUOItakho36b0+uvoRMMmNm84Y/5n1aD1nhwbzT3gKXXP5J2T+YbWU/zeX+kEv51FPKsl8Ve0YOsdqrOfsYP6hmX9Y6yW/Sx+S1DiLWpIa10pR7x46wAlYz9nB/EMz/7DWRf4m1qglSUtrZUYtSVqCRS1JjRu0qJPsSvKdJM8luWHILJNKck6SR5LsT/JMkuuGzrQaSWaSPJnkgaGzTCrJaUnuSfJskgNJPjB0pq6S/NX4ffN0kruSnDR0puUkuSXJoSRPH/Pa6Un2JPne+PuvDplxOUvk/7vxe+epJPclOW3AiMsarKiTzAD/yGiLr/OBa5KcP1SeVTgCfLqqzgfeD/zFOst/1HXAgaFDrNIXgAer6reBC1kn/x1Jzgb+EthRVRcAM4w2jm7ZrcCu4167AXi4qs4DHh4/b9Wt/HL+PcAFVfW7wHeBG9c6VFdDzqgvBp6rquer6g3gbuCqAfNMpKperKonxo8PMyqJs4dNNZkk24APAzcNnWVSSd4NfBC4GaCq3qiqnwwaajKbgK1JNgEnM9o4ullV9Sjw4+Nevgq4bfz4NuBP1jLTJBbLX1UPVdWR8dNvA9vWPFhHQxb12cALxzxfYJ0V3VFJZoGLgMcGjjKpzwOfAd4aOMdqnAu8DHxpvHRz03jLuOZV1UHg7xltb/ci8NOqemjYVKtyZlW9OH78EnDmkGFO0J8DXx06xFL8ZeIJSvIu4CvA9VX1ytB5ukpyBXCoqvYOnWWVNgHvA75YVRcBP6ftj95vG6/lXsXoH5vfAE5J8pFhU52YGp3nuy7P9U3y14yWMu8cOstShizqg8A5xzzfNn5t3UiymVFJ31lV9w6dZ0KXAlcm+T6jZafLktwxbKSJLAALVXX0U8w9jIp7PfhD4D+r6uWq+l/gXuCSgTOtxg+TvAdg/P3QwHkmluTjwBXAn1XDF5UMWdSPA+clOTfJFka/TLl/wDwTyWgH1puBA1X1uaHzTKqqbqyqbVU1y+j//derat3M6qrqJeCFJNvHL10O7B8w0iR+ALw/ycnj99HlrJNfhB7nfuBj48cfA/5lwCwTS7KL0dLflVX1P0PnWc5gRT1exP8k8DVGb9IvV9UzQ+VZhUuBjzKaie4bf31o6FAbzKeAO5M8BbwX+Nth43Qz/hRwD/AE8O+M/h42fSlzkruAbwHbkywk+QTwWWBnku8x+pTw2SEzLmeJ/P8AnArsGf/9/adBQy7DS8glqXH+MlGSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMb9H28rPQpMz5i5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMQAAAD4CAYAAACzHkm9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAK7klEQVR4nO3db6ie9X3H8ffHRG2TxkbYH1wi00HxwYSpC26dIzCdRVapPtgDhRZWB3mydnYbFLtnezB8Mkr3qBCiXaFOGdZCka2tULtO2IzxT61JbHGuq8nsYpm1VtTM+N2Dcw++tSe5r/vkXOc65/T9goPnvnKd3/09kk9+v+vf905VIWnJOVMXIK0nBkJqDITUGAipMRBSs3WMQZN46krrXlXlnducIaTGQEiNgZAaAyE1BkJqDITUGAipMRBSMygQSW5I8p0kzyW5Y+yipKlk3vMQSbYA3wWuB44BjwG3VtWRM/yMV6q17q30SvXVwHNV9XxVnQTuA25a7eKk9WBIIHYBL7TXx2bbfkqSfUkOJTm0WsVJa23Vbu6rqv3AfnDJpI1ryAxxHLi4vd492yZtOkMC8RjwviSXJjkPuAX48rhlSdOYu2SqqreSfAz4KrAFuLuqDo9emTSBuaddVzSoxxDaAHxASJrDQEiNgZAaAyE1BkJqDITUjNKXSdNLfuaM4qrarF3jnSGkxkBIjYGQGgMhNQZCagyE1BgIqTEQUjM3EEnuTnIiyTNrUZA0pSEzxN8BN4xch7QuzA1EVX0T+J81qEWanMcQUrNqN/cl2QfsW63xpCkMajKQ5BLgwaq6fNCgNhmYnHe7zmeTAWmOIadd7wX+FbgsybEkfzx+WdI07Mu0Sblkms8lkzSHgZAaAyE1BkJqDITUGAipsS/TaZxzzrj/Vmzbtm3U8c8///xRx3/99ddHHX/s9zjdaWNnCKkxEFJjIKTGQEiNgZAaAyE1BkJqDITUGAipGfLE3MVJHk5yJMnhJLevRWHSFIbcuvEW8BdV9USSHcDjSR6qqiMj1yatuSGNyl6sqidm378KHAV2jV2YNIWFbu6btaO5Enh0mT+zL5M2vMFNBpK8B/hn4K+r6oE5+274J9C92/XMNsPdrituMpDkXOCLwD3zwiBtZEPOMgW4CzhaVZ8evyRpOkNmiGuAjwDXJnlq9vUHI9clTWLuQXVVPQKM2/VKWie8Ui01BkJqDITUGAipMRBSYyCkxkZlp7Fjx45Rx9+7d++o419xxRWjjv/II4+MOj7AwYMHRxv7dLeFOENIjYGQGgMhNQZCagyE1BgIqTEQUmMgpGbIE3PvSnIwybdmfZn+ai0Kk6Yw5Er1m8C1VfWT2bPVjyT5p6r6t5Frk9bckCfmCvjJ7OW5s68N31VDWs7QrhtbkjwFnAAeqqpl+zIlOZTk0CrXKK2ZQYGoqlNVdQWwG7g6yeXL7LO/qvZU1Z5VrlFaMwudZaqqHwEPAzeMUo00sSFnmX4xyc7Z9+8GrgeeHbkuaRJDzjJdBHw+yRaWAvQPVfXguGVJ0xhylulplhocS5ueV6qlxkBIjYGQGgMhNQZCagyE1GzIvkxLn+Eyrp07d446/s033zzq+Lfddtuo4995552jjg9w+PDh0cZ+8803l93uDCE1BkJqDITUGAipMRBSYyCkxkBIjYGQmsGBmDUaeDKJDwdp01pkhrgdODpWIdJ6MLQNzW7gg8CBccuRpjV0hvgM8Eng7dPtYF8mbQZDum7cCJyoqsfPtJ99mbQZDJkhrgE+lOR7wH3AtUm+MGpV0kTmBqKqPlVVu6vqEuAW4OtV9eHRK5Mm4HUIqVnoAaGq+gbwjVEqkdYBZwipMRBSYyCkxkBIjYGQGgMhNRuyL9NaeO2110Yd/+DBg6OOv3379lHHf/rpp0cdH+CNN94Ybey3317+tjxnCKkxEFJjIKTGQEiNgZAaAyE1BkJqDITUDLowN3t89FXgFPCWz01rs1rkSvXvVdUPR6tEWgdcMknN0EAU8LUkjyfZt9wO9mXSZjB0yfS7VXU8yS8BDyV5tqq+2Xeoqv3AfoAktcp1Smti0AxRVcdn/z0BfAm4esyipKkM6dy3PcmO//8e+ADwzNiFSVMYsmT6ZeBLs8+G3gr8fVV9ZdSqpInMDURVPQ/8xhrUIk3O065SYyCkxkBIjYGQGgMhNQZCalK1+ndZbIZbN7ZuHbdl1YUXXjjq+BdccMGo47/88sujjg/wyiuvjDb2qVOnqKq8c7szhNQYCKkxEFJjIKTGQEiNgZAaAyE1BkJqBgUiyc4k9yd5NsnRJO8fuzBpCkMvx/4t8JWq+sMk5wHbRqxJmszcQCR5L7AX+COAqjoJnBy3LGkaQ5ZMlwIvAZ9L8mSSA7NmAz/FvkzaDIYEYitwFfDZqroSeA244507VdX+qtpj31dtZEMCcQw4VlWPzl7fz1JApE1nbiCq6gfAC0kum226DjgyalXSRIaeZfo4cM/sDNPzwEfHK0mazqBAVNVTgMcG2vS8Ui01BkJqDITUGAipMRBSYyCkxkBIjY3KJjL7AJoNO/4Yf2/W+j1sVCbNYSCkxkBIjYGQGgMhNQZCagyE1MwNRJLLkjzVvn6c5BNrUJu05ha6MJdkC3Ac+K2q+s8z7OeFuTm8MDf9e6zGhbnrgH8/UxikjWzRQNwC3DtGIdJ6MHjJNGsw8F/Ar1fVfy/z5/uAfbOXv7lqFW5SLpmmf4/llkyLBOIm4E+q6gMD9vUYYg4DMf17nO0xxK24XNImN2iGmPVy/T7wa1U198ODnSHmc4aY/j3Oasm0CAMxn4GY/j18HkKaw0BIjYGQGgMhNQZCagyE1BgIqRn6gSlaZWtwjn3U8TcrZwipMRBSYyCkxkBIjYGQGgMhNQZCagyE1AwKRJI/S3I4yTNJ7k3yrrELk6YwpHPfLuBPgT1VdTmwhaV2NNKmM3TJtBV4d5KtwDaW2tFIm87cQFTVceBvWGoy8CLwSlV97Z37JdmX5FCSQ6tfprQ2hiyZLgRuAi4FfgXYnuTD79yvqvZX1Z6q2rP6ZUprY8iS6feB/6iql6rqf4EHgN8ZtyxpGkMC8X3gt5Nsy1Jvk+uAo+OWJU1jyDHEo8D9wBPAt2c/s3/kuqRJ2KhMP7dsVCbNYSCkxkBIjYGQGgMhNQZCasbqy/RDYJFPKv2F2c9sVNY/vUV/h19dbuMo1yEWleTQRr4Hyvqnt1q/g0smqTEQUrNeArHR742y/umtyu+wLo4hpPVivcwQ0rpgIKRm0kAkuSHJd5I8l+SOKWtZiSQXJ3k4yZFZm57bp65pJZJsSfJkkgenrmVRSXYmuT/Js0mOJnn/WY031TFEki3Ad4HrgWPAY8CtVXVkkoJWIMlFwEVV9USSHcDjwM0b6XcASPLnwB7ggqq6cep6FpHk88C/VNWBJOcB26rqRysdb8oZ4mrguap6vqpOAvex1Mxgw6iqF6vqidn3r7L0aO2uaataTJLdwAeBA1PXsqgk7wX2AncBVNXJswkDTBuIXcAL7fUxNthfpi7JJcCVwKMTl7KozwCfBN6euI6VuBR4CfjcbMl3IMn2sxnQg+pVkOQ9wBeBT1TVj6euZ6gkNwInqurxqWtZoa3AVcBnq+pK4DXgrI5FpwzEceDi9nr3bNuGkuRclsJwT1U9MHU9C7oG+FCS77G0ZL02yRemLWkhx4Bjs0YYsNQM46qzGXDKQDwGvC/JpbODoVuAL09Yz8JmbXnuAo5W1aenrmdRVfWpqtpdVZew9P//61X1M03o1quq+gHwQpLLZpuuA87qhMZkH8tbVW8l+RjwVZYaKN9dVYenqmeFrgE+Anw7yVOzbX9ZVf84XUk/dz4O3DP7R/V54KNnM5i3bkiNB9VSYyCkxkBIjYGQGgMhNQZCagyE1PwfZOYfqqVRBu4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "#Visualizo las gaussianas que mejor se ajustan a mis estrellas, se ven bien\n",
+    "\n",
+    "for i in range(0,len(FWHM)):\n",
+    "    zz = gauss2D(params[i], x_[i],y_[i]) \n",
+    "    plt.imshow(zz, cmap = 'gray')\n",
+    "    plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En la lista llamada 'params' están los parámetros, por columnas, a, b, c, x0, y0 que mejor se ajustan a cada una de mis estrellas. Con el parámetro c, la desviación estándar, puedo calcular la FWHM mediante la expresión:\n",
+    "$$FWHM = 2\\sqrt(2 \\ln 2)c\\approx 2.355 c$$\n",
+    "\n",
+    "Extraigo este parámetro para hacer el cálculo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Obtengo la FWHM usando la formula anterior\n",
+    "\n",
+    "desvesta = [np.absolute(row[2]) for row in params]         #Extraigo el tercer elemento (c) de cada fila\n",
+    "\n",
+    "FWHM = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tengo muy, muy pocos datos (16), sin embargo voy a derivar de estos algunas medidas estadísticas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Primero, un histograma \n",
+    "\n",
+    "n, bins, patches = plt.hist(FWHM, len(FWHM), facecolor = 'gray', alpha = 0.5)\n",
+    "plt.title('Histograma del ancho a media altura de mis estrellas seleccionadas')\n",
+    "plt.xlabel('FWHM [pixeles]')\n",
+    "plt.ylabel('frecuencia')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En el anterior histograma podemos ver que la mayor parte de las medidas se encuentran al rededor del valor de 2 pixeles. A continuación calcularmos algunas otras medidas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Ahora algunas otras medidas\n",
+    "\n",
+    "average_FWHM_bw = np.average(FWHM)    #Calculo el promedio,\n",
+    "median_FWHM_bw = np.median(FWHM)      #mediana,\n",
+    "std_FWHM_bw = np.std(FWHM)            #desviacón estándar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El promedio de las medidas es: 3.432858986192058\n",
+      "La mediana es: 2.260080688320251\n",
+      "La desviación estándar es: 2.369829780288601\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('El promedio de las medidas es: ' + str(average_FWHM_bw) + '\\n'\n",
+    "     'La mediana es: ' + str(median_FWHM_bw) + '\\n'\n",
+    "     'La desviación estándar es: ' + str(std_FWHM_bw))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Para los diferentes canales\n",
+    "\n",
+    "Ahora es necesario seleccionar las mismas regiones de los diferentes canales, para lo cual se modificó un poco la función get_params para incluir la posibilidad de seleccionar los diferentes canales de color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_param_RGB(xi,xf,yi,yf,channel):\n",
+    "    \n",
+    "    if channel == 'R':       #Agrego estas condiciones para tomar los diferentes canales R,G o B\n",
+    "        n = 0\n",
+    "    elif channel == 'G':\n",
+    "        n = 1\n",
+    "    elif channel == 'B':\n",
+    "        n = 2\n",
+    "    \n",
+    "    star = sky[yi:yf,xi:xf,n]        #Se elecciona de la imágen original, el rectángulo del canal que corresponde\n",
+    "    \n",
+    "    x = np.arange(0,star.shape[1],1) #Todas mis estrellas son de diferentes tamaños\n",
+    "    y = np.arange(0,star.shape[0],1)\n",
+    "    \n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "    \n",
+    "    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]  #voy a dejar la misma elección inicial de parámetros para todas las estrellas\n",
+    "    \n",
+    "    best, suss = leastsq(errormodel, p0, args=(xx,yy,star))\n",
+    "    \n",
+    "    return best, xx, yy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Obtengo los mejores ajustes repitiendo lo hecho anteriormente, no guardo el tamaño de los arreglos\n",
+    "# ya que no voy a visualizar más\n",
+    "\n",
+    "params_R = []\n",
+    "params_B = [] \n",
+    "params_G = []\n",
+    "\n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'R')\n",
+    "    params_R.append(m)\n",
+    "    \n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'G')\n",
+    "    params_G.append(m)\n",
+    "    \n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'B')\n",
+    "    params_B.append(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extraigo los valores de la desviación estándar c\n",
+    "\n",
+    "desvesta_R = [np.absolute(row[2]) for row in params_R]\n",
+    "desvesta_G = [np.absolute(row[2]) for row in params_G]\n",
+    "desvesta_B = [np.absolute(row[2]) for row in params_B]\n",
+    "\n",
+    "#Uso la formula para calcular el FWHM\n",
+    "\n",
+    "FWHM_R = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_R)\n",
+    "FWHM_G = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_G)\n",
+    "FWHM_B = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'frecuencia')"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualizo los histogramas\n",
+    "n_bins = len(FWHM)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize = (15,9))  #Hago mi canvas\n",
+    "ax0, ax1, ax2, ax3 = axes.flatten()\n",
+    "\n",
+    "ax0.hist(FWHM_R, n_bins, histtype='bar', color = 'r')\n",
+    "ax0.set_xlabel('FWHM [pixeles] (Rojo)')\n",
+    "ax0.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax1.hist(FWHM_G, n_bins, histtype='bar', color = 'g')\n",
+    "ax1.set_xlabel('FWHM [pixeles] (verde)')\n",
+    "ax1.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax2.hist(FWHM_B, n_bins ,histtype='bar', color = 'b')\n",
+    "ax2.set_xlabel('FWHM [pixeles] (azul)')\n",
+    "ax2.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax3.hist(FWHM, n_bins, histtype='bar', color = 'gray')\n",
+    "ax3.set_xlabel('FWHM [pixeles](blanco y negro)')\n",
+    "ax3.set_ylabel('frecuencia')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En los histogramas anteriores podemos observar que la mayor cantidad de medidas del FWHM se encuentra cerca al valor de 2 pixeles, también se puede apreciar que hay valores bastante alejados de este, incluso mayores a 10 pixeles, derivadas de un par de estrellas que ocupan un area considerablemente mayor a las demás."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "También obtengo las otras medidas estadísticas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B&amp;W</th>\n",
+       "      <th>canal R</th>\n",
+       "      <th>canal G</th>\n",
+       "      <th>canal B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>promedio</th>\n",
+       "      <td>3.432859</td>\n",
+       "      <td>3.355606</td>\n",
+       "      <td>3.436947</td>\n",
+       "      <td>3.518292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>media</th>\n",
+       "      <td>2.260081</td>\n",
+       "      <td>2.234073</td>\n",
+       "      <td>2.261304</td>\n",
+       "      <td>2.295926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>desv estándar</th>\n",
+       "      <td>2.369830</td>\n",
+       "      <td>2.180836</td>\n",
+       "      <td>2.395190</td>\n",
+       "      <td>2.592449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    B&W   canal R   canal G   canal B\n",
+       "promedio       3.432859  3.355606  3.436947  3.518292\n",
+       "media          2.260081  2.234073  2.261304  2.295926\n",
+       "desv estándar  2.369830  2.180836  2.395190  2.592449"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_FWHM_R = np.average(FWHM_R)\n",
+    "median_FWHM_R = np.median(FWHM_R)    \n",
+    "std_FWHM_R = np.std(FWHM_R)\n",
+    "\n",
+    "average_FWHM_G = np.average(FWHM_G)\n",
+    "median_FWHM_G = np.median(FWHM_G)    \n",
+    "std_FWHM_G = np.std(FWHM_G)\n",
+    "\n",
+    "average_FWHM_B = np.average(FWHM_B)\n",
+    "median_FWHM_B = np.median(FWHM_B)    \n",
+    "std_FWHM_B = np.std(FWHM_B)\n",
+    "\n",
+    "#Las acomodo en un dataframe para su fácil visualización \n",
+    "\n",
+    "medidas = pd.DataFrame([\n",
+    "  [average_FWHM_bw, average_FWHM_R, average_FWHM_G, average_FWHM_B],\n",
+    "  [median_FWHM_bw, median_FWHM_R, median_FWHM_G, median_FWHM_B],\n",
+    "  [std_FWHM_bw, std_FWHM_R, std_FWHM_G, std_FWHM_B],\n",
+    "],\n",
+    "  index = ['promedio', 'media', 'desv estándar'],\n",
+    "  columns = ['B&W', 'canal R', 'canal G', 'canal B']\n",
+    ")\n",
+    "\n",
+    "medidas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voy a acomodarlos en una tabla para que se vean bien"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos observar que las medidas son diferentes entre todos los canales, lo cual tiene sentido, ya que cada canal aporta diferentes valores a la imágen final, en el caso de la imagen a color, si cada canal aportase el mismo valor, no se conseguirían los diferentes colores que se requieren. Así mismo, son diferentes a las medidas de la imagen en blanco y negro.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Teniendo en cuenta la incertidumbre\n",
+    "\n",
+    "En los cálculos realizados anteriormente no se tuvo en cuenta la incertidumbre de las medidas, en esta sección se incluye de la siguiente manera: se obtiene la raíz cuadrada del valor de cada pixel, y se divide el error de cada pixel por este valor, en la función en la cual se define el error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Redefino el error \n",
+    "\n",
+    "def errormodel_uncert(tupla, x,y,z, sigma):\n",
+    "    \n",
+    "    #Entra la tupla de parámetros para la función gaussiana, los valores de x,y necesarios para hacer la malla\n",
+    "    #donde esta irá, y los valores reales (z) de los pixeles de la estrella. Adicionalmente la incertidumbre en\n",
+    "    #un arreglo llamado sigma\n",
+    "\n",
+    "    m = np.ravel((gauss2D(tupla,x,y) - z) / sigma)\n",
+    "    \n",
+    "    return m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Redefino mis funciones para obtener los parámetros óptimos, añadiendo la obtención de sigma y cambiando la\n",
+    "#función de error que uso por aquella que incluye la incertidumbre\n",
+    "\n",
+    "def get_param_uncert(xi,xf,yi,yf):\n",
+    "    \n",
+    "    star = sky_bw[yi:yf,xi:xf]\n",
+    "    \n",
+    "    sigma = np.sqrt(star)               #Añado este cálculo\n",
+    "    \n",
+    "    x = np.arange(0,star.shape[1],1)    \n",
+    "    y = np.arange(0,star.shape[0],1)\n",
+    "    \n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "    \n",
+    "    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]      \n",
+    "    \n",
+    "    best, suss = leastsq(errormodel_uncert, p0, args=(xx,yy,star,sigma))  \n",
+    "    \n",
+    "    return best, xx, yy  \n",
+    "\n",
+    "def get_param_RGB_uncert(xi,xf,yi,yf,channel):\n",
+    "    \n",
+    "    if channel == 'R':       \n",
+    "        n = 0\n",
+    "    elif channel == 'G':\n",
+    "        n = 1\n",
+    "    elif channel == 'B':\n",
+    "        n = 2\n",
+    "    \n",
+    "    star = sky[yi:yf,xi:xf,n]   \n",
+    "    \n",
+    "    sigma = np.sqrt(star)                  #Igualmente incluyo este cálculo\n",
+    "    \n",
+    "    x = np.arange(0,star.shape[1],1) \n",
+    "    y = np.arange(0,star.shape[0],1)\n",
+    "    \n",
+    "    xx, yy = np.meshgrid(x, y)\n",
+    "    \n",
+    "    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]  \n",
+    "    \n",
+    "    best, suss = leastsq(errormodel_uncert, p0, args=(xx,yy,star,sigma))\n",
+    "    \n",
+    "    return best, xx, yy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Continúo con los pasos previamente realizados, hallando el mejor ajuste y calculando el FWHM y los medidas estadísticas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Obtengo los parámetros que mejor se ajustan a cada estrella\n",
+    "\n",
+    "params_bw = []\n",
+    "\n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1])\n",
+    "    x_.append(xm)\n",
+    "    y_.append(ym)\n",
+    "    params_bw.append(m)\n",
+    "    \n",
+    "params_R = []\n",
+    "params_B = [] \n",
+    "params_G = []\n",
+    "\n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'R')\n",
+    "    params_R.append(m)\n",
+    "    \n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'G')\n",
+    "    params_G.append(m)\n",
+    "    \n",
+    "for i in range(0,stars_coord.shape[0]):\n",
+    "    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'B')\n",
+    "    params_B.append(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extraigo los valores de la desviación estándar c\n",
+    "\n",
+    "desvesta_bw = [np.absolute(row[2]) for row in params_bw]\n",
+    "desvesta_R = [np.absolute(row[2]) for row in params_R]\n",
+    "desvesta_G = [np.absolute(row[2]) for row in params_G]\n",
+    "desvesta_B = [np.absolute(row[2]) for row in params_B]\n",
+    "\n",
+    "#Uso la formula para calcular el FWHM\n",
+    "\n",
+    "FWHM_bw = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_bw)\n",
+    "FWHM_R = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_R)\n",
+    "FWHM_G = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_G)\n",
+    "FWHM_B = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'frecuencia')"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Grafico los histogramas\n",
+    "\n",
+    "n_bins= len(FWHM)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize = (15,9))  #Hago mi canvas\n",
+    "ax0, ax1, ax2, ax3 = axes.flatten()\n",
+    "\n",
+    "ax0.hist(FWHM_R, n_bins, histtype='bar', color = 'r')\n",
+    "ax1.set_xlabel('FWHM [pixeles] (rojo)')\n",
+    "ax1.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax1.hist(FWHM_G, n_bins, histtype='bar', color = 'g')\n",
+    "ax1.set_xlabel('FWHM [pixeles] (verde)')\n",
+    "ax1.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax2.hist(FWHM_B, n_bins ,histtype='bar', color = 'b')\n",
+    "ax1.set_xlabel('FWHM [pixeles] (azul)')\n",
+    "ax1.set_ylabel('frecuencia')\n",
+    "\n",
+    "ax3.hist(FWHM, n_bins, histtype='bar', color = 'gray')\n",
+    "ax1.set_xlabel('FWHM [pixeles] (blanco y negro)')\n",
+    "ax1.set_ylabel('frecuencia')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Estos histogramas no presentan diferencia respecto a los anteriores, las diferencias introducidas por la incertidumbre deben ser más finas y no evidentes en un histograma."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B&amp;W</th>\n",
+       "      <th>canal R</th>\n",
+       "      <th>canal G</th>\n",
+       "      <th>canal B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>promedio</th>\n",
+       "      <td>3.442413</td>\n",
+       "      <td>3.362915</td>\n",
+       "      <td>3.548846</td>\n",
+       "      <td>3.515920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>media</th>\n",
+       "      <td>2.213994</td>\n",
+       "      <td>2.197444</td>\n",
+       "      <td>2.216574</td>\n",
+       "      <td>2.192405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>desv estándar</th>\n",
+       "      <td>2.406436</td>\n",
+       "      <td>2.223734</td>\n",
+       "      <td>2.421031</td>\n",
+       "      <td>2.609954</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    B&W   canal R   canal G   canal B\n",
+       "promedio       3.442413  3.362915  3.548846  3.515920\n",
+       "media          2.213994  2.197444  2.216574  2.192405\n",
+       "desv estándar  2.406436  2.223734  2.421031  2.609954"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Calculo las otras medidas\n",
+    "\n",
+    "average_FWHM_bw = np.average(FWHM_bw)\n",
+    "median_FWHM_bw = np.median(FWHM_bw)    \n",
+    "std_FWHM_bw = np.std(FWHM_bw)\n",
+    "\n",
+    "average_FWHM_R = np.average(FWHM_R)\n",
+    "median_FWHM_R = np.median(FWHM_R)    \n",
+    "std_FWHM_R = np.std(FWHM_R)\n",
+    "\n",
+    "average_FWHM_G = np.average(FWHM_G)\n",
+    "median_FWHM_G = np.median(FWHM_G)    \n",
+    "std_FWHM_G = np.std(FWHM_G)\n",
+    "\n",
+    "average_FWHM_B = np.average(FWHM_B)\n",
+    "median_FWHM_B = np.median(FWHM_B)    \n",
+    "std_FWHM_B = np.std(FWHM_B)\n",
+    "\n",
+    "#Las acomodo en un dataframe para su fácil visualización \n",
+    "\n",
+    "medidas_uncert = pd.DataFrame([\n",
+    "  [average_FWHM_bw, average_FWHM_R, average_FWHM_G, average_FWHM_B],\n",
+    "  [median_FWHM_bw, median_FWHM_R, median_FWHM_G, median_FWHM_B],\n",
+    "  [std_FWHM_bw, std_FWHM_R, std_FWHM_G, std_FWHM_B],\n",
+    "],\n",
+    "  index = ['promedio', 'media', 'desv estándar'],\n",
+    "  columns = ['B&W', 'canal R', 'canal G', 'canal B']\n",
+    ")\n",
+    "\n",
+    "medidas_uncert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B&amp;W</th>\n",
+       "      <th>canal R</th>\n",
+       "      <th>canal G</th>\n",
+       "      <th>canal B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>promedio</th>\n",
+       "      <td>3.432859</td>\n",
+       "      <td>3.355606</td>\n",
+       "      <td>3.436947</td>\n",
+       "      <td>3.518292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>media</th>\n",
+       "      <td>2.260081</td>\n",
+       "      <td>2.234073</td>\n",
+       "      <td>2.261304</td>\n",
+       "      <td>2.295926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>desv estándar</th>\n",
+       "      <td>2.369830</td>\n",
+       "      <td>2.180836</td>\n",
+       "      <td>2.395190</td>\n",
+       "      <td>2.592449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    B&W   canal R   canal G   canal B\n",
+       "promedio       3.432859  3.355606  3.436947  3.518292\n",
+       "media          2.260081  2.234073  2.261304  2.295926\n",
+       "desv estándar  2.369830  2.180836  2.395190  2.592449"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "medidas    #Visualizo las medidas calculadas previamente para hacer una comparación visual de estas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos ver un pequeño cambio en las medidas estadísticas, todos los promedios aumentaron al tener en cuenta la incertidumbre, así como la desviación estándar, la media disminuyó en todos los casos."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Una vez realizado todo este procedimiento, podemos decir que la resolución espacial de la máquina utilizada para capturar la imágen es de 3.4 pixeles."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Entrega.md b/Entrega.md
new file mode 100644
index 0000000000000000000000000000000000000000..5025be84abbc89dc317be63bb125596b65518cdd
--- /dev/null
+++ b/Entrega.md
@@ -0,0 +1,1072 @@
+**Estudiante: Angie Nicole Hernández Durán - UIS**
+
+# Numpy y optimizción con SciPy
+
+Este ejercicio consiste en conseguir una medición de la resolución espacial a partir de una foto del cielo estrellado. Específicamente se calculará la $\text{anchura a media altura}$ (FWHM), la cual es una medida derivada de uno de los datos que se puede obtener de un ajuste gaussiano, la desviación estándar. Para llegar a esta medición tomaremos los pasos que se presentan a continuación. 
+
+Primero se leyeron los datos correspondientes, en este caso, una imagen en formato jpeg.
+
+
+```python
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.optimize import leastsq 
+```
+
+
+```python
+sky = plt.imread('data/zapatocaImage.jpeg')
+```
+
+
+```python
+type(sky)   #La imagen es efectivamente un array de numpy
+```
+
+
+
+
+    numpy.ndarray
+
+
+
+
+```python
+sky.shape   #Vemos las dimensiones del arreglo
+```
+
+
+
+
+    (789, 1184, 3)
+
+
+
+
+```python
+plt.imshow(sky)  #Y visualizamos la imagen
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f3cd5f6d208>
+
+
+
+
+    
+![png](output_5_1.png)
+    
+
+
+La imágen guardada en "sky" está a color, ahora se deben combinar los 3 arrays para generar una versión a blanco y negro. Una manera de hacer esto es añadir los 3 canales y luego dividirlos entre 3 para obtener un promedio de los valores de cada canal, que darán los valores de gris de los pixeles:
+
+
+```python
+sky_bw = (sky[:,:,0] + sky[:,:,1] + sky[:,:,2]) / 3
+```
+
+
+```python
+plt.imshow(sky_bw, cmap = 'gray')    #Nuevamente visualizo
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f3cd5ed4e10>
+
+
+
+
+    
+![png](output_8_1.png)
+    
+
+
+Lo anterior no se ve como las imagenes a blanco y negro a las que estamos acostumbrados, esto es debido a que se suele usar un método que consiste en dar diferentes pesos a cada canal, ya que las diferentes longitudes de onda brindan diferentes sensaciones oculares al ojo humano. A continuación se utilizó dicho método.
+
+
+```python
+sky_bw = sky[:,:,0] * 0.3 + sky[:,:,1] * 0.59 + sky[:,:,2] * 0.11
+```
+
+
+```python
+plt.imshow(sky_bw, cmap = 'gray')     #Visualizamos de nuevo
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f3cd5c6b208>
+
+
+
+
+    
+![png](output_11_1.png)
+    
+
+
+Dando diferentes contribuciones a los diferentes canales (30% para rojo, 59% para verde y 11% para azul) se obtuvo una imagen mucho más agradable a la vista y que recuerda más a la imagen original.
+
+Ahora es necesario seleccionar una pequeña región en la imágen donde haya una estrella. las coordenadas de los pixeles que delimitan la estrella se hallaron utilizando una págia web externa que permite seleccionar manualmente una región rectangular de una imágen. Este método se utilizó igualmente con la selección de las regiones de todas las estrellas.
+
+
+```python
+star1 = sky_bw[10:19,228:241]       #La estrella es un rectángulo obtenido de la imagen original
+```
+
+
+```python
+plt.imshow(star1, cmap = 'gray')    #Visusalizamos para ver que la región seleccionada corresponde a una estrella
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f3cd5e589e8>
+
+
+
+
+    
+![png](output_15_1.png)
+    
+
+
+El seguiente movimiento consiste en ajustar una gaussiana simétrica bidimensional a la imagen de la estrella,  para lo cual se llevan a cabo los pasos a continuación.
+
+
+```python
+#Defino una función que me de los valores de una gausiana bidimensional.
+
+#tupla es un arreglo donde van los parámetros de la gausiana, la amplitud a, una constante aditiva b, 
+#la desviación estándar c, x0 y y0 son las coordenadas donde está centrada la función.
+
+def gauss2D(tupla, x, y):
+    
+    #tupla es un arreglo donde van los parámetros de la gausiana, la amplitud a, una constante aditiva b, 
+    #la desviación estándar c, x0 y y0 son las coordenadas donde está centrada la función.
+    
+    a = tupla[0]
+    b = tupla[1]
+    c = tupla[2]
+    x0 = tupla[3]
+    y0 = tupla[4]
+    
+    exponente = -((x-x0)**2 + (y-y0)**2) / (2*c**2)
+    z1 = a * np.exp(exponente) + b
+    
+    return z1
+```
+
+
+```python
+#Hago la malla donde pondré mi ajuste gausiano, esta debe ser del mismo tamaño de la imagen que quiero 
+#ajustar, la de mi estrella
+
+x = np.arange(0,star1.shape[1],1)
+y = np.arange(0,star1.shape[0],1)
+
+xx, yy = np.meshgrid(x, y)
+```
+
+
+```python
+z = star1  #Mis datos reales son los valores de gris de mi estrella
+```
+
+
+```python
+#Defino el error, el cual consiste en la diferencia entre los valores de mi ajuste gaussiano y los valores reales
+#de mi estrella
+
+def errormodel(tupla, x,y,z):
+    
+    #Entra la tupla de parámetros para la función gaussiana, los valores de x,y necesarios para hacer la malla
+    #donde esta irá, y los valores reales (z) de los pixeles de la estrella
+
+    m = np.ravel(gauss2D(tupla,x,y) - z)  #Estaba teniendo problemas con la salida de esta función cuando estraba
+                                          #en leastsq, np.ravel aplana mi arreglo 2D y arregla el fomato de salida 
+    return m
+```
+
+
+```python
+#Para realizar la optimización de parámetros voy a utilizar la función leastsq, que utiliza el método de 
+#mínimos cuadrados.
+
+p0 = [1.0, 0.0, 1.0, 0.0, 0.0]   #Esta es mi primera aleatoria elección de parámetros que entrará a la función
+                                 #leastsq
+
+best, suss = leastsq(errormodel, p0, args=(xx,yy,z))  #finalmente uso la función 
+
+```
+
+
+```python
+params = best                    #Extraigo los parámetros que mejor se ajustan a la estrella
+```
+
+
+```python
+zz = gauss2D(params, xx,yy)      #Utilizo los parpametros obtenidos
+
+plt.imshow(zz,cmap="gray")       #Y visualizo la gausiana que mejor se ajusta a mi estrella
+#plt.colorbar()
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f3cd5f77160>
+
+
+
+
+    
+![png](output_23_1.png)
+    
+
+
+Ahora debo repetir este proceso para varias estrellas, voy a implementar los anteriores pasos en una función para que el proceso no sea tedioso.
+
+
+```python
+def get_param(xi,xf,yi,yf):
+    
+    #xi, xf, yi, yf son las coordenadas iniciales y finales que delimitan el área de mi estrella en la imágen
+    #La función devuelve los parámetros que mejor se ajustan a la estrella, así como los valores necesarios que 
+    #hacen la malla en la cual va el ajuste gaussiano, esto para fines de graficación.
+    
+    star = sky_bw[yi:yf,xi:xf]
+    
+    x = np.arange(0,star.shape[1],1)    #Todas mis estrellas son de diferentes tamaños
+    y = np.arange(0,star.shape[0],1)
+    
+    xx, yy = np.meshgrid(x, y)
+    
+    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]      #Dejo la misma elección inicial de parámetros para todas las estrellas
+    
+    best, suss = leastsq(errormodel, p0, args=(xx,yy,star))  
+    
+    return best, xx, yy                 
+```
+
+
+```python
+#El siguiente es un arreglo donde coloco las coordenadas de los pixeles que delimitan areas de varias estrellas.
+
+#El orden es yi, yf, xi, xf
+
+stars_coord = np.array([[305, 328, 620, 637],
+                      [82, 96, 628, 637],
+                      [115, 126, 726, 734],
+                      [368, 386, 444, 458],
+                      [264, 276, 749, 758],
+                      [540, 564, 650, 676],
+                      [452, 459, 205, 215],
+                      [87, 100, 1080, 1092],
+                      [177, 185, 1096, 1110],
+                      [21, 30, 921, 928],
+                      [307, 328, 617, 638],
+                      [260, 268, 764, 773],
+                      [15, 25, 673, 682],
+                      [52, 63, 452, 460],
+                      [10, 19, 228, 241],
+                      [142, 156,387,400]])  
+```
+
+
+```python
+#Visualizo las areas seleccionadas para asegurarme que correspoden a estrellas
+
+for i in range(0,len(stars_coord)):
+    
+    star = sky_bw[stars_coord[i,0]:stars_coord[i,1], stars_coord[i,2]:stars_coord[i,3]]
+    plt.imshow(star, cmap = 'gray')
+    plt.show()                       
+```
+
+
+    
+![png](output_27_0.png)
+    
+
+
+
+    
+![png](output_27_1.png)
+    
+
+
+
+    
+![png](output_27_2.png)
+    
+
+
+
+    
+![png](output_27_3.png)
+    
+
+
+
+    
+![png](output_27_4.png)
+    
+
+
+
+    
+![png](output_27_5.png)
+    
+
+
+
+    
+![png](output_27_6.png)
+    
+
+
+
+    
+![png](output_27_7.png)
+    
+
+
+
+    
+![png](output_27_8.png)
+    
+
+
+
+    
+![png](output_27_9.png)
+    
+
+
+
+    
+![png](output_27_10.png)
+    
+
+
+
+    
+![png](output_27_11.png)
+    
+
+
+
+    
+![png](output_27_12.png)
+    
+
+
+
+    
+![png](output_27_13.png)
+    
+
+
+
+    
+![png](output_27_14.png)
+    
+
+
+
+    
+![png](output_27_15.png)
+    
+
+
+
+```python
+#Paso el arreglo de estrellas a la función para obtener los parámetros que mejor se ajustan.
+
+params = []  #la lista donde guardaré los parámetros óptimos de todas las estrellas.
+x_ = []
+y_ = []      #Arreglos de los tamaños de mis estrellas, para fines de graficación futuros.
+
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1])
+    x_.append(xm)
+    y_.append(ym)
+    params.append(m)
+
+```
+
+
+```python
+#Visualizo las gaussianas que mejor se ajustan a mis estrellas, se ven bien
+
+for i in range(0,len(FWHM)):
+    zz = gauss2D(params[i], x_[i],y_[i]) 
+    plt.imshow(zz, cmap = 'gray')
+    plt.show() 
+```
+
+
+    
+![png](output_29_0.png)
+    
+
+
+
+    
+![png](output_29_1.png)
+    
+
+
+
+    
+![png](output_29_2.png)
+    
+
+
+
+    
+![png](output_29_3.png)
+    
+
+
+
+    
+![png](output_29_4.png)
+    
+
+
+
+    
+![png](output_29_5.png)
+    
+
+
+
+    
+![png](output_29_6.png)
+    
+
+
+
+    
+![png](output_29_7.png)
+    
+
+
+
+    
+![png](output_29_8.png)
+    
+
+
+
+    
+![png](output_29_9.png)
+    
+
+
+
+    
+![png](output_29_10.png)
+    
+
+
+
+    
+![png](output_29_11.png)
+    
+
+
+
+    
+![png](output_29_12.png)
+    
+
+
+
+    
+![png](output_29_13.png)
+    
+
+
+
+    
+![png](output_29_14.png)
+    
+
+
+
+    
+![png](output_29_15.png)
+    
+
+
+En la lista llamada 'params' están los parámetros, por columnas, a, b, c, x0, y0 que mejor se ajustan a cada una de mis estrellas. Con el parámetro c, la desviación estándar, puedo calcular la FWHM mediante la expresión:
+$$FWHM = 2\sqrt(2 \ln 2)c\approx 2.355 c$$
+
+Extraigo este parámetro para hacer el cálculo.
+
+
+```python
+#Obtengo la FWHM usando la formula anterior
+
+desvesta = [np.absolute(row[2]) for row in params]         #Extraigo el tercer elemento (c) de cada fila
+
+FWHM = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta)
+```
+
+Tengo muy, muy pocos datos (16), sin embargo voy a derivar de estos algunas medidas estadísticas:
+
+
+```python
+#Primero, un histograma 
+
+n, bins, patches = plt.hist(FWHM, len(FWHM), facecolor = 'gray', alpha = 0.5)
+plt.title('Histograma del ancho a media altura de mis estrellas seleccionadas')
+plt.xlabel('FWHM [pixeles]')
+plt.ylabel('frecuencia')
+plt.show()
+```
+
+
+    
+![png](output_33_0.png)
+    
+
+
+En el anterior histograma podemos ver que la mayor parte de las medidas se encuentran al rededor del valor de 2 pixeles. A continuación calcularmos algunas otras medidas.
+
+
+```python
+#Ahora algunas otras medidas
+
+average_FWHM_bw = np.average(FWHM)    #Calculo el promedio,
+median_FWHM_bw = np.median(FWHM)      #mediana,
+std_FWHM_bw = np.std(FWHM)            #desviacón estándar
+```
+
+
+```python
+print('El promedio de las medidas es: ' + str(average_FWHM_bw) + '\n'
+     'La mediana es: ' + str(median_FWHM_bw) + '\n'
+     'La desviación estándar es: ' + str(std_FWHM_bw))
+```
+
+    El promedio de las medidas es: 3.432858986192058
+    La mediana es: 2.260080688320251
+    La desviación estándar es: 2.369829780288601
+
+
+### Para los diferentes canales
+
+Ahora es necesario seleccionar las mismas regiones de los diferentes canales, para lo cual se modificó un poco la función get_params para incluir la posibilidad de seleccionar los diferentes canales de color.
+
+
+```python
+def get_param_RGB(xi,xf,yi,yf,channel):
+    
+    if channel == 'R':       #Agrego estas condiciones para tomar los diferentes canales R,G o B
+        n = 0
+    elif channel == 'G':
+        n = 1
+    elif channel == 'B':
+        n = 2
+    
+    star = sky[yi:yf,xi:xf,n]        #Se elecciona de la imágen original, el rectángulo del canal que corresponde
+    
+    x = np.arange(0,star.shape[1],1) #Todas mis estrellas son de diferentes tamaños
+    y = np.arange(0,star.shape[0],1)
+    
+    xx, yy = np.meshgrid(x, y)
+    
+    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]  #voy a dejar la misma elección inicial de parámetros para todas las estrellas
+    
+    best, suss = leastsq(errormodel, p0, args=(xx,yy,star))
+    
+    return best, xx, yy
+```
+
+
+```python
+#Obtengo los mejores ajustes repitiendo lo hecho anteriormente, no guardo el tamaño de los arreglos
+# ya que no voy a visualizar más
+
+params_R = []
+params_B = [] 
+params_G = []
+
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'R')
+    params_R.append(m)
+    
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'G')
+    params_G.append(m)
+    
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'B')
+    params_B.append(m)
+```
+
+
+```python
+#Extraigo los valores de la desviación estándar c
+
+desvesta_R = [np.absolute(row[2]) for row in params_R]
+desvesta_G = [np.absolute(row[2]) for row in params_G]
+desvesta_B = [np.absolute(row[2]) for row in params_B]
+
+#Uso la formula para calcular el FWHM
+
+FWHM_R = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_R)
+FWHM_G = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_G)
+FWHM_B = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_B)
+```
+
+
+```python
+#Visualizo los histogramas
+n_bins = len(FWHM)
+
+fig, axes = plt.subplots(nrows=2, ncols=2, figsize = (15,9))  #Hago mi canvas
+ax0, ax1, ax2, ax3 = axes.flatten()
+
+ax0.hist(FWHM_R, n_bins, histtype='bar', color = 'r')
+ax0.set_xlabel('FWHM [pixeles] (Rojo)')
+ax0.set_ylabel('frecuencia')
+
+ax1.hist(FWHM_G, n_bins, histtype='bar', color = 'g')
+ax1.set_xlabel('FWHM [pixeles] (verde)')
+ax1.set_ylabel('frecuencia')
+
+ax2.hist(FWHM_B, n_bins ,histtype='bar', color = 'b')
+ax2.set_xlabel('FWHM [pixeles] (azul)')
+ax2.set_ylabel('frecuencia')
+
+ax3.hist(FWHM, n_bins, histtype='bar', color = 'gray')
+ax3.set_xlabel('FWHM [pixeles](blanco y negro)')
+ax3.set_ylabel('frecuencia')
+```
+
+
+
+
+    Text(0, 0.5, 'frecuencia')
+
+
+
+
+    
+![png](output_41_1.png)
+    
+
+
+En los histogramas anteriores podemos observar que la mayor cantidad de medidas del FWHM se encuentra cerca al valor de 2 pixeles, también se puede apreciar que hay valores bastante alejados de este, incluso mayores a 10 pixeles, derivadas de un par de estrellas que ocupan un area considerablemente mayor a las demás.
+
+También obtengo las otras medidas estadísticas.
+
+
+```python
+average_FWHM_R = np.average(FWHM_R)
+median_FWHM_R = np.median(FWHM_R)    
+std_FWHM_R = np.std(FWHM_R)
+
+average_FWHM_G = np.average(FWHM_G)
+median_FWHM_G = np.median(FWHM_G)    
+std_FWHM_G = np.std(FWHM_G)
+
+average_FWHM_B = np.average(FWHM_B)
+median_FWHM_B = np.median(FWHM_B)    
+std_FWHM_B = np.std(FWHM_B)
+
+#Las acomodo en un dataframe para su fácil visualización 
+
+medidas = pd.DataFrame([
+  [average_FWHM_bw, average_FWHM_R, average_FWHM_G, average_FWHM_B],
+  [median_FWHM_bw, median_FWHM_R, median_FWHM_G, median_FWHM_B],
+  [std_FWHM_bw, std_FWHM_R, std_FWHM_G, std_FWHM_B],
+],
+  index = ['promedio', 'media', 'desv estándar'],
+  columns = ['B&W', 'canal R', 'canal G', 'canal B']
+)
+
+medidas
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>B&amp;W</th>
+      <th>canal R</th>
+      <th>canal G</th>
+      <th>canal B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>promedio</th>
+      <td>3.432859</td>
+      <td>3.355606</td>
+      <td>3.436947</td>
+      <td>3.518292</td>
+    </tr>
+    <tr>
+      <th>media</th>
+      <td>2.260081</td>
+      <td>2.234073</td>
+      <td>2.261304</td>
+      <td>2.295926</td>
+    </tr>
+    <tr>
+      <th>desv estándar</th>
+      <td>2.369830</td>
+      <td>2.180836</td>
+      <td>2.395190</td>
+      <td>2.592449</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Voy a acomodarlos en una tabla para que se vean bien
+
+Podemos observar que las medidas son diferentes entre todos los canales, lo cual tiene sentido, ya que cada canal aporta diferentes valores a la imágen final, en el caso de la imagen a color, si cada canal aportase el mismo valor, no se conseguirían los diferentes colores que se requieren. Así mismo, son diferentes a las medidas de la imagen en blanco y negro.
+
+
+
+## Teniendo en cuenta la incertidumbre
+
+En los cálculos realizados anteriormente no se tuvo en cuenta la incertidumbre de las medidas, en esta sección se incluye de la siguiente manera: se obtiene la raíz cuadrada del valor de cada pixel, y se divide el error de cada pixel por este valor, en la función en la cual se define el error.
+
+
+```python
+#Redefino el error 
+
+def errormodel_uncert(tupla, x,y,z, sigma):
+    
+    #Entra la tupla de parámetros para la función gaussiana, los valores de x,y necesarios para hacer la malla
+    #donde esta irá, y los valores reales (z) de los pixeles de la estrella. Adicionalmente la incertidumbre en
+    #un arreglo llamado sigma
+
+    m = np.ravel((gauss2D(tupla,x,y) - z) / sigma)
+    
+    return m
+```
+
+
+```python
+#Redefino mis funciones para obtener los parámetros óptimos, añadiendo la obtención de sigma y cambiando la
+#función de error que uso por aquella que incluye la incertidumbre
+
+def get_param_uncert(xi,xf,yi,yf):
+    
+    star = sky_bw[yi:yf,xi:xf]
+    
+    sigma = np.sqrt(star)               #Añado este cálculo
+    
+    x = np.arange(0,star.shape[1],1)    
+    y = np.arange(0,star.shape[0],1)
+    
+    xx, yy = np.meshgrid(x, y)
+    
+    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]      
+    
+    best, suss = leastsq(errormodel_uncert, p0, args=(xx,yy,star,sigma))  
+    
+    return best, xx, yy  
+
+def get_param_RGB_uncert(xi,xf,yi,yf,channel):
+    
+    if channel == 'R':       
+        n = 0
+    elif channel == 'G':
+        n = 1
+    elif channel == 'B':
+        n = 2
+    
+    star = sky[yi:yf,xi:xf,n]   
+    
+    sigma = np.sqrt(star)                  #Igualmente incluyo este cálculo
+    
+    x = np.arange(0,star.shape[1],1) 
+    y = np.arange(0,star.shape[0],1)
+    
+    xx, yy = np.meshgrid(x, y)
+    
+    p0 = [1.0, 0.0, 1.0, 0.0, 0.0]  
+    
+    best, suss = leastsq(errormodel_uncert, p0, args=(xx,yy,star,sigma))
+    
+    return best, xx, yy
+```
+
+Continúo con los pasos previamente realizados, hallando el mejor ajuste y calculando el FWHM y los medidas estadísticas.
+
+
+```python
+#Obtengo los parámetros que mejor se ajustan a cada estrella
+
+params_bw = []
+
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1])
+    x_.append(xm)
+    y_.append(ym)
+    params_bw.append(m)
+    
+params_R = []
+params_B = [] 
+params_G = []
+
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'R')
+    params_R.append(m)
+    
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'G')
+    params_G.append(m)
+    
+for i in range(0,stars_coord.shape[0]):
+    m , xm, ym= get_param_RGB_uncert(stars_coord[i,2],stars_coord[i,3],stars_coord[i,0],stars_coord[i,1], channel = 'B')
+    params_B.append(m)
+```
+
+
+```python
+#Extraigo los valores de la desviación estándar c
+
+desvesta_bw = [np.absolute(row[2]) for row in params_bw]
+desvesta_R = [np.absolute(row[2]) for row in params_R]
+desvesta_G = [np.absolute(row[2]) for row in params_G]
+desvesta_B = [np.absolute(row[2]) for row in params_B]
+
+#Uso la formula para calcular el FWHM
+
+FWHM_bw = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_bw)
+FWHM_R = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_R)
+FWHM_G = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_G)
+FWHM_B = 2.0 * np.sqrt(2.0 * np.log(2.0)) * np.array(desvesta_B)
+```
+
+
+```python
+#Grafico los histogramas
+
+n_bins= len(FWHM)
+
+fig, axes = plt.subplots(nrows=2, ncols=2, figsize = (15,9))  #Hago mi canvas
+ax0, ax1, ax2, ax3 = axes.flatten()
+
+ax0.hist(FWHM_R, n_bins, histtype='bar', color = 'r')
+ax1.set_xlabel('FWHM [pixeles] (rojo)')
+ax1.set_ylabel('frecuencia')
+
+ax1.hist(FWHM_G, n_bins, histtype='bar', color = 'g')
+ax1.set_xlabel('FWHM [pixeles] (verde)')
+ax1.set_ylabel('frecuencia')
+
+ax2.hist(FWHM_B, n_bins ,histtype='bar', color = 'b')
+ax1.set_xlabel('FWHM [pixeles] (azul)')
+ax1.set_ylabel('frecuencia')
+
+ax3.hist(FWHM, n_bins, histtype='bar', color = 'gray')
+ax1.set_xlabel('FWHM [pixeles] (blanco y negro)')
+ax1.set_ylabel('frecuencia')
+```
+
+
+
+
+    Text(0, 0.5, 'frecuencia')
+
+
+
+
+    
+![png](output_53_1.png)
+    
+
+
+Estos histogramas no presentan diferencia respecto a los anteriores, las diferencias introducidas por la incertidumbre deben ser más finas y no evidentes en un histograma.
+
+
+```python
+#Calculo las otras medidas
+
+average_FWHM_bw = np.average(FWHM_bw)
+median_FWHM_bw = np.median(FWHM_bw)    
+std_FWHM_bw = np.std(FWHM_bw)
+
+average_FWHM_R = np.average(FWHM_R)
+median_FWHM_R = np.median(FWHM_R)    
+std_FWHM_R = np.std(FWHM_R)
+
+average_FWHM_G = np.average(FWHM_G)
+median_FWHM_G = np.median(FWHM_G)    
+std_FWHM_G = np.std(FWHM_G)
+
+average_FWHM_B = np.average(FWHM_B)
+median_FWHM_B = np.median(FWHM_B)    
+std_FWHM_B = np.std(FWHM_B)
+
+#Las acomodo en un dataframe para su fácil visualización 
+
+medidas_uncert = pd.DataFrame([
+  [average_FWHM_bw, average_FWHM_R, average_FWHM_G, average_FWHM_B],
+  [median_FWHM_bw, median_FWHM_R, median_FWHM_G, median_FWHM_B],
+  [std_FWHM_bw, std_FWHM_R, std_FWHM_G, std_FWHM_B],
+],
+  index = ['promedio', 'media', 'desv estándar'],
+  columns = ['B&W', 'canal R', 'canal G', 'canal B']
+)
+
+medidas_uncert
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>B&amp;W</th>
+      <th>canal R</th>
+      <th>canal G</th>
+      <th>canal B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>promedio</th>
+      <td>3.442413</td>
+      <td>3.362915</td>
+      <td>3.548846</td>
+      <td>3.515920</td>
+    </tr>
+    <tr>
+      <th>media</th>
+      <td>2.213994</td>
+      <td>2.197444</td>
+      <td>2.216574</td>
+      <td>2.192405</td>
+    </tr>
+    <tr>
+      <th>desv estándar</th>
+      <td>2.406436</td>
+      <td>2.223734</td>
+      <td>2.421031</td>
+      <td>2.609954</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+
+```python
+medidas    #Visualizo las medidas calculadas previamente para hacer una comparación visual de estas
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>B&amp;W</th>
+      <th>canal R</th>
+      <th>canal G</th>
+      <th>canal B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>promedio</th>
+      <td>3.432859</td>
+      <td>3.355606</td>
+      <td>3.436947</td>
+      <td>3.518292</td>
+    </tr>
+    <tr>
+      <th>media</th>
+      <td>2.260081</td>
+      <td>2.234073</td>
+      <td>2.261304</td>
+      <td>2.295926</td>
+    </tr>
+    <tr>
+      <th>desv estándar</th>
+      <td>2.369830</td>
+      <td>2.180836</td>
+      <td>2.395190</td>
+      <td>2.592449</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Podemos ver un pequeño cambio en las medidas estadísticas, todos los promedios aumentaron al tener en cuenta la incertidumbre, así como la desviación estándar, la media disminuyó en todos los casos.
+
+Una vez realizado todo este procedimiento, podemos decir que la resolución espacial de la máquina utilizada para capturar la imágen es de 3.4 pixeles.
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