Mica2_TG_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "863c1a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marco\\AppData\\Local\\Temp\\ipykernel_28832\\1865039913.py:6: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  from scipy.ndimage.filters import gaussian_filter1d\n"
     ]
    }
   ],
   "source": [
    "# Importación de paquetes.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as ss\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "from scipy.interpolate import interp1d\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4aad290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado utilizando el filtro Savitzky-Golay en una región específica\n",
    "def apply_savgol_smoothing(y, transition_index, window_length1, polyorder1, window_length2, polyorder2):\n",
    "    y_smooth = np.copy(y)\n",
    "    y_smooth[:transition_index+1] = ss.savgol_filter(y[:transition_index+1], window_length=window_length1, polyorder=polyorder1)\n",
    "    y_smooth[transition_index:] = ss.savgol_filter(y[transition_index:], window_length=window_length2, polyorder=polyorder2)\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bee51406",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado con kernel gaussiano en una región específica\n",
    "def apply_mix_smoothing(y, transition_index, window_length1, polyorder1, sigma2):\n",
    "    y_smooth = np.copy(y)\n",
    "    y_smooth[:transition_index+1] = ss.savgol_filter(y[:transition_index+1], window_length=window_length1, polyorder=polyorder1)\n",
    "    y_smooth[transition_index:] = gaussian_filter1d(y[transition_index:], sigma=sigma2, order=0)\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e822189",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importación de datos TG de Mica-2 pura.\n",
    "df1 = pd.read_csv('./nuevaCarpeta/Mica2_TG.txt',sep='\\t',header=None,names=['Temperatura','TG'], usecols=['Temperatura','TG'])\n",
    "df1 = df1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c800c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1[\"Temperatura\"].values\n",
    "y = df1[\"TG\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abe7612c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7f182170>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrado de datos.\n",
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x[:250], y[:250], linewidth=3, label=\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4adbffaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x[250:]\n",
    "y = y[250:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d73676ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpolación de los puntos del eje x.\n",
    "x_interpolated = np.linspace(x.min(), x.max(), len(x))\n",
    "y_interpolated = interp1d(x, y)(x_interpolated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca862362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.04441676 -0.02220838 -0.02220838 ...  0.          0.\n",
      "  0.        ]\n"
     ]
    }
   ],
   "source": [
    "def calculate_derivative(x, y):\n",
    "    dx = np.gradient(x)   # Calcular el espaciado entre puntos en el eje x\n",
    "    dy = np.gradient(y)   # Calcular la diferencia entre puntos en el eje y\n",
    "    derivative = dy / dx  # Calcular la derivada punto por punto\n",
    "    return derivative\n",
    "#(x_interpolated, y_smooth)\n",
    "derivative_raw = calculate_derivative(x_interpolated, y_interpolated)\n",
    "print(derivative_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2f3e9dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "530"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = np.where(x_interpolated>134)[0][0]\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a44c5060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7f28ff70>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lFyYXbRoa6FITGwJswKd33fpo7MtISi8bseVgl/y0HP8K/nTpen3gO0v11LqdvqepdIMprq2L2l9vUvY5/KlrzWpCDo7Sh0w/NdjBhD2varmNmujHUIRAI0egjGa+eNaqH+i474Wbdx/U3/z4aUnSb5Zt0is3X+prukqrH6YHhSCa/efpfmV3QYzHZtLP+aQeGf5qsIOVvq/PTVnivMYUzzvpx49L1fYn1zFqsIFEKb4o2TLD4xZfLyK1XY8XN+1xPk/XNdgl8w8ZWPiSggig8dcgvIRUYIcKYgN305c3xa4DR/SRnzyjf/j5szpwuDfQcvymmrjYtnEem6u69+pvf/y0frxkXYxLyUh6d5WNghpsIEGSctGKLQe7xusXdj0qTea6hiwhu7xhxPmMkJYc2iD8rPGWPQf1Z7cvUZ+V/vM9izWsq7DrTz8PzkHPk/zxP3fXcv0wG1iOGNihv7ng2ArTFVdShDjDwp6UMR4+f/qtx7V2+379ZOl6LZo2QrPGDI5tWS7aIiTlXlZP1GADCdZ/s6h5YFqjQCPu9Qq7HpVTZOKtwea+lNEIle2Henr1i6c26Im1O+pdlND8HG8f//kyPb1+l57dsEs3/OyZ0l/afNVghz+y//uPa3N/f+vhV3J/b9lzUB/7n2f19QdWHb1WFi/Xbw12ws+8tdv35/5+ZNW2WJflK8Amgq6KGmygrNrf4UP1L4uy4gjSin9ydr2PYr1x1eimGGewkqRa5tt+/4o++5vlMkZ68O/eoCkjB8a6vDDH88Ejvepsb4203HuXb879ff+L3aEaOQbuB7vM+PnL+ujPntM9L2TKNnP0IF0wf3zoX5jcpIjU6NhMQHBLDXZ11GADKFWr+0TMjxBhVyNIikj0XkS4E0Xl7FXpAWfz2d8sl5Q5Jv7lrhedlMGV3j6r6Tf8UnP/8Te64muPlB3PzypXCxz9HMFRcrALhucN7g+uJenHS9dLKj0f/V5j8qfzW9bi46X4c5rP7WrXvaT/IlALBNhAgpT76bXWl6qaddMX94qFzcGuMF3JDdxxo8Q03JOTVMscRGOW2tv3HzuaVrFkzQ5t2X3Qczxfh1tx4Fg8jxhelV5ufD+Bd5jlujjtwl7fNu06qCu+9oje9Z9/1I59h6tPEOBJcO22/YEbRvo5f9NwnYobKSJAgpRctOrVD3ZsL5qpdW8o7lfEdQ12yfyp+akoqbnZtShWkEPtoRXdBZ/3V+l9o5KWKjWzcedg+1lW1PYqYaar1q+/31n+/U+f0ZI1mTz+m3+9XJ+94viK4x864m9fHu7p01u//oi69xzyWZIMF+cYATg12EBZSb2R10LNGjnGPP/iwMC/8hOW9lIQTdhGWaGk4KD2WoPSgI+7u98+ml2kiPjpCi9wDXbI4WFe4155zuH5XfaDLx19GLrr+U1Vx//0L1/QvkM9Vcd77OXtgYNrvzjFqiPABhIsda9KL/kJNeYc7JArEuRFM/QiUlv+ejiIvxzFkvbsUrwJygXJfjZVSW5xyTx8pIj4WE7B+GV2YrnhfWVqsGvZyLF4JcPMs81nrcDXH1jlozjxnQj80lYdATaQIEmpFYgvRaTxxf2iGXoRyc4jwCycrVWEAz9h8bXTtwwWT1qSJuWjHULQ47psTXXZ3kW8p6tl/bWLh+8Wnztq4y7vnHoX/FRMVFu1pNzL6okAG0iw3EWqxlerZk8RqemLZkryOCEF2w5sQy/utkK1gCvuF80ULqvc+JlvShoh+1xw/mh+pyntNcTXZBW1+rxo9fp9RWVMXFcspBEBNpAgYRvJuBZfikjwxlFRhH6TY5BeRFyvA/etivz1cJDOjRjkeHZag11ShV15WW4ESxEpVxb/Ndj1y8HO57cGuyfGANtPm4ZqSyeFhAAbKCtpP/kijBh6ESn6TA52jXns0iQ8mLrqi9uVkhzsMuXz18ixeN4hehGJuQY7dx7WMAe7ai8iIebZ4jMq66thDbbXolL6DOsUATaQILVuBFhOXMFC8HqQaELXYFcKO4rzTyPniBR9jHOfJywIrKTcZqj3Gqzq3lfnEvhTfByV227+GjkWTl0tsPReTrw52EfjaxcpIr4mqTgPKdy1oc1nhN3jJ/E9JF/rQQ52VQTYQBn1qJEq10Cn1teqtLxoxu/PrcXqmSKS5vtSbOtWw+Pq+4+t1UGPfoiT1g92aQ12+OVWmzaObvqCygXYdUwRKf11K/g8/LYbiTMH209Xh6SAVEeADUSUxnzPBqrorCiO1Yj7ISHWw6lWvYiUrWX0v/xyN3CvB9/SB9N41/OPq7eVDkzYOVMcgEX5RaBKCra/Nzn6WE7hPAOOn11CadmCLy/s9a9kO4Q4DP1W7MQaYPu4xlXtRcRdcRoWATZQRpifFl0vs16xe2yxQvT7T01UWn/X3fSV5g8ndav4Vz5PtkbLD7kcv8d9I+yhkl9ayoznZ12Kfwkqvk75q8EOmCKSN76fmLO/DGF7ESmcxt941Wv24ztS4vyFtXh/eq1GtXVLY8VTUATYQEQuLyNB8w7jEl8Odm0fIMKmiFRS+jOw25VI930pQA12kHSIWm+zBthHxSm6UQKe6qdRDDXYeX/7Oo+jpog42KcuuotMQmBa+gDllSKCagiwgTL8X5hrlwuXNvG/yTHsdP5fle66VrbSJknAvTcSF+X3fFW6o/Oklvm6cev1+WuYv1OkqAa76NtYXpWen7LhY/zcmxxDPsQnpZs+v+LMSPJTiUCKSHUE2EBETmuwS5NJ66JWjRwbUek6REwRScE28cvFqibhVele86/Fy5mCPDD67cbNz1jVXqgSx/bOD3gDPSiX1CIHT/ULW+vtYrsk4nLgoya+ER4y640AGygjzv5Tg6r1z4a1elV63GtVGhhEX2Jx3OK6BrtSrVfk/VLn1qtOarC9+sEOGVSVzNvneJ4Bdg02bZDt57cG249qq+avpjZoFXb+8qtv3P7tX3KN8V2D7V6o642j/O8oSn4F8OoRsFoNNvE3ATYQldsneX8Nk+JXo1el17im0UXjJT9dWEWR6l5EAuVgBxjX95hu1Ou8jFKDHeU6VdLIsQYvmskX5Q2W/itKou/VJLzwyAU//WC7z7pPHwJsoKzgPy1GXmINfnr1o1avSq81v0uv3A920eeI73sordVPw43Jex2S3otIlHYXtXgkDbIMpzXYVV+V7j7cyh/fz/Wov5a7tPcUd6kyJcuskjoT5uE7CWd/6a90paWKM788LQiwgYjSeJ2pVSJBrYNJFzeFkq4UHa9Duhs5Rl8Br3SB0n0Sr0bYDb3FvYiUGS9MP9jFfCWIBM0QCZgiUq4stUz1C7vscOK7Svupia/ayLERTpKYEWADZdSj9Xm52sxaX6tqlYNd6xXzG2AHuqE7XgfuSxlRtmvYQD5KDnYtBOk+syRFpEyh/axKyavSi+fhJ0Uk4JEdtpFj+Ict9zs1VA12AiJTNyki7iVh2wRBgI2m4/oUjTNFpF7i6hGh1usXRw628xfNBPgpv1HesFk2B9vB/vds5Bh9toF4pojUYN9EShHxOZ2fdQuTChF3N33llhPmhWGhU4wcpuVUE28jx6LPHutRbbvGsepJuT/6RYCNpuM3IPJ9QwpflOrzTlkOdrH4f8p3f8MrDS6iz7NoCb6X3WhiSwkqDqrczCbQeEnvpq/8Q0/18UoC7BCN+QIH2Hl/R3lhVJzX8dLeayp/71KsR5uPSoR6XIsa7fJHgI2m477XB4cpImVqnWp9MYsvRaR2NTxe/KeIVJpH4WfXP1s2ehAtlb8RBlm3sjnDPg7OWvdOUzv+T8ziGuwo4Um1hwdfNdhBU0SCVmH3d9PnoAY7LBfdRSbh9HfxoBDHOUKKCJBwrhu91KpnhFqKrTauzikivgPser7J0e3sEiXItgoWjLupwo5y1NckRSTAMnp91mAX86ydr5Ii4md7R+rFJMC4Jb2I+L7eHx3Rb2BcbX+EuTa4SGOLqvjXjzC9iMQRDDfavZYAG03H+XnvMge7+HPdckRqs5ha9yLiYmlha8jKzq9k/g12FwkgrnWLc5N5lbleXSkGCjRL+sH25ud4Lu0Hu2hZMeyA/Dn6+dWif4ywta9u1qA4uA9Tg13/899fDnZNilK4zARsmyAIsIEy/Odqu0wR8f5c68tKbK9KL/4c90/5xZ999lldaf1LXzQTqEhVNdYtxFuUHiuq8XNsujwnvfav17DE1WD7rMn1s62qdtMXRw12foZIDXoRaYz2GUfFmfPvoheROFa90eoeCLDRdPzWKjzwUrfP+UUpTTIF6Q4siHpvK981bQFeNJPmGmfnYsrLDNtbjB/eDbySv89L+8H2W2HgoSRFpHoKQel8A+Zg540fpJFj2F+EXDyUufg1KgmHlr8a7AQUNOEIsNF0/NYq3Pzr5b7Gc3mZScpPYLXqDa60IZZbcbyAxHUtVZzBYdIEelV6mXF93fwDlKmaJL2xLkitpd9cZD/HX8mLHEOkvMfdTZ8p18gxxPL8qrbtknPkBOPnAapqDXYsD9Pu5xknAmw0nST3IpJ2JekVMbdaCZsrWrkXkeh5lpUkKaALy2++b8V5lA0IS2/+cW4x//mnyeqmz28jRz/7pNovWrG8Kj30Tg13fsZx2sV5LteyT3yvy3S1VYvjrExKBZRfBNhoOq5PUacxYrkc7BoHXbW6ePfUuFm4k15ESuYZoUB+FpAicexuK49fKhyeL961d6XDkpaDXdrIMXyqREuV5frLwQ6aInKUv0aOmXFKU7j8Li94SkrV/sEd1IrXg59KhHpULNGLCJBwrq8LtXhVeq3F1sixaHWKa9niXp6bhkzhApey8wvx0o6ki9KgzsWy3KaI+FtmLQRJEfHfyDH4csOkSUXZZMEaOYZbbv50bdWeKHwuu1FrsP1sw6prFkuKSGNdHQmw0YScR9ipE1sjx6LPta7BdtDGsaY9BaRNoBQR3+OVhu3xN3IsHa8mP/pESBEp50hRa0jPdatWg+1jbwXPwc6vUQ4wXcha5PzRWh0F2A0WD+b4Sa2rTzd9jcVZgG2MucgY86IxZqUx5gaP7wcYY36Y/f5RY8z0vO9uzA5/0Rhzod95AmEkuVu1pFyga9bIsc9nv3mhlebr+lEpoAjTg0IQlWppGu0GU8xF+b3OkTjPE79dO9ZCHC9cOdwTfAXDBbERUkR8rHn5Ro5+c7CPjhe2Brt0nmGmqf9ZXlKJ4HGIVNuucaxFAjZNIE4CbGNMq6T/kHSxpHmS3mmMmVc02rWSdlhrZ0v6gqTPZqedJ+lKSfMlXSTpq8aYVp/zBAJL8qutE5MuENeLHIs2Vk9vbVNEXATDzrvpC1AjHndKTexqdId02w92vGUO0tA3yC9LJY0cy2yT4hpsP8sNc15F6kUkSopIqBrscKFR6fU7+LHjd4pY+8H2UTFRl2C3wS5/xkWwYYw5XdInrLUXZj/fKEnW2n/OG+eu7Dh/MMa0SdokaYykG/LH7R8vO1nFeRZbvHixXbJkSeT1CeqHj6/VR376rO/x333qVH3k4rka2tkeY6mie27DLn3vsbX6/YqtWrt9v69putpbdeBIr6aM7NK67Qf0+mPG6MDhXj32ynbNnzhUk0d06Y+rt+vA4V69Ye4YSZkg697lWzR+aKdOmDIsN6+7lm1We6vROXPH5ob1Wenu5zdr+qiBOnb8kLLleHjlNu091KML548rGN7bJ93zwuaCYcdPHqYJwzpL5nHXss0lw4rnt/tAj/6welvZchSP3z/fgR2tOnPO6KrLHNTRqjPmjC4Znj9fr3JWKsOabfu1fNMenTpjpIYPzByDS9fs0Na9h3X+vHFqMd7zvGDeOBkjPbt+l17ddVDnzB2r9lbvi/xdyzbLmMw0/e5bvkVHPALq/vL9fsVW7Tvcm/vc3w/5648ZUzD+/S9261BPX2685Zv2aM22/TrrmDHqbGvRb5/frKGdbTp91qjcevUbMqBNi6eP0O9e7Nak4V1aMGlo7ruNuw7qmfW7dPK0EVq6ZkdJOc+cM1oDO1rLHhcvbtqjV7Ll6GrP3KBXd+/Tii179brZozR4QJskad32A3p+426dMmOkRgxs15Y9h/Tk2p2e26R/W+brL1/xcbt80x517zlUcFzdtWyzOttbctvQa17Dutp13/ItmjisUwsnZ86/R1Zu055DPbpg3jgdONKrh1Zs1czRgzRn3GBt3n1IT63bqZOmDtfYIQNkrfTb5zdr8oguzZswVL99vvLxePrMUfrD6m06dtwQjRzUoT+s3qZjxg3WjNGDdKinT/e/2J27fuRbPG2Elnjsl3LOnTtWz2/crSGdbRre1ZG7Bi17dbcmDe/StFED9ciqTDl6+vq0qntfyTwunD9Oj768XTv3H/G93LOPHaP9h3oLljd4QJtmjR2sp9ft1KKpw9XZ3qpHVm3ToqnDtXXvYd/X1/xldLS25Lb1uKEDtHn3oarT5a/rGbNHa9AA7+O5/9zYvu+wHn+ldJvPHDNIq4u21zlzx+q+5Vs0YVinjvc4jpau2aFt+w6XzKuSC+ePyx1f+SYO69Sruw56jl/telhOR1uL3nCs93kSxYXzx2nvoR49vHKb5owdrJljBumuZZvV1d6qs44ZXXZ5Iwa265QZIyVJR3qt7lu+pWSc184apUdWld5/zp83Tpt2HdSzG3Y5W4/+e5HX/ujXf96eOmOkHn15e9l5nXfcOO07dPTe+fpjxqgze818Yu1Ode/xPpanjhyo4yZk7vv922TyiC7NnzhUowcP0Gf+ZGGUVQzFGLPUWrvY8ztHAfYVki6y1l6X/Xy1pFOttR/KG+e57Djrs59XSTpVmWD6j9ba/84O/6akX2cnqzjPYvUIsF/YuFsXf+mhwNO95/Rp+uRlC2IokTszbvxlw/0kAwAAmsvUkQP14N+/oebLrRRgN3wjR2PM9caYJcaYJd3d/t6859KabaW1Hn58+w9rHJfEPYJrAACA4FwF2BskTcn7PDk7zHOcbIrIMEnbKkzrZ56y1t5irV1srV08ZsyY4q9jFyUI7e2zsb9oI+m+9u5FBZ8vmDdOX79qUcl4X79qkT53xfEFw846Zoy+ftWikn/jh3aWTNv/r5wPvH5WyXy8fPic2bnvb3pT9SYBb188uew8Rw/uKPjuTSdM9JxHfhpDv6tOm6qvX7VIbzx+QtUy3HDx3Nwy3ve6GQXf/cOlx+mU6SMLhlXaTpcsHF/w+SvvOqnqdqs0P0m69PgJGj24o+I4/fPuam8tGP4nJ00qO83MMYNyKTBll71wgr5+1SLdcPHciuP1K7fP+38S7/e5K47X1adNKxj2qcvm68/OLNz+H7vkOJ1QNK0k/dvbT6h6zEpHj9tjxxWmS33pyhMLPg8Z0KaPXFR9HS9eMF5DOtvKfj9/YuGx+H/OnVN1nn69ffFkZ/NyZWBHa/WRGljxtTJf8TEdxLlzx2ryiK7Q0/fzSqOLU7XzLYy3nVz+uB7S2abPv+2Est/PHT+k5B5ZT9NHDYxt3l94xwk6aerwquN98OxZ+uI7TiwYdtH88frU5cnLCCh/JQ3mcUlzjDEzlAmCr5T0rqJx7pB0jaQ/SLpC0n3WWmuMuUPS94wx/yZpoqQ5kh5TpplVtXk2tFkf/ZVGD+7Q5952gt5w7NjqE6TQRQsKA7ZZYwfrogWFQeOYIQN00YIJ2l6Uvzdt5MCScSXpGw+u1qbdR/PzvMYpdvK0ETp/Xmm+dLHF00fqrGw+68ote6uOP2/C0LLLHz+ss+C7lzZ7z2/80E49t2F3wbATp4zQRQsmaOWWvbrzmY0Vy/DaWaN0/OThkqTDvVZ6+Oh3Z84Zoxc37dFjrxzNl6u0veaMHaJM84nsuPPHq6218nP6RQsmaOSgjpL9d3Seg7Vm276CXOl8xhwt0z//ernWbDuar1opD3/S8C4N62ovyW/ONzt7vL20eY9u/vXyiushKbfvi00Y1qln1h/Ndzz3uNJj6fXHjNWyV3dJejk37LWzR2n5pj16en1hruR588b5aqPRf9z+4y+WFQy/YF7heTVm6AC9dtaoqvObPXawlr26W3sO9nh+P25op5a9evRYPHXGSM/xwlgwaZh+tGS9s/m5MGhAm/Yf7q13MWIzYlBHwbUy3/ihnXpG4XJ4p4wcqD0He7R+x4HqI1cwfdQgPbRia8GwIQPatOeQ9/EZ1UULJqitxTjtPnTexKHSUu/vxg4ZoPPnj5N+7P395BFdunhh9ftXrcwaM1ivbAvWXsCv844bp3ue36IntbPieCdPG6HTZhZeyy5aML6krU4SOKnBttb2SPqQpLskvSDpR9baZcaYTxpj3pwd7ZuSRhljVkr6ax1t3LhM0o8kPS/pN5L+wlrbW26eLsrrUtTTcOvew/rTbz3upCyNKEir+OKekxz1pJQph9/x8kb0s/yWAIUsN6rXW8X6Gxf6ae2eP31r0byM8f/WsszyiqePvhOqLT9In9T5+qytul/7W8f7XYvi7devrWg/GHltq9LtZWQ8G4r6Ph6z/y9uFFRSTOtvPxtjKnbbVnyMuuwvPchxWCvFv5ikTU+FHkTayjRg9uOwj55J/PC6JibwMKmo0nFtq3yftF6DOtriyypuMUaHfHQZ2WJMyTGQ1GPCVQ22rLW/kvSromEfz/v7oKS3lZn2M5I+42eeSUOesluVzpPiboniehmKX/5e3xt9fl4vPegP6IL211oSICnYxSnsCxgqiTLHSv1o+zk3+2uq/G6DcuvvFUx7jet3mGtW/tax2iglDwgOi16L7RBUnAFFElSqqS1+aAwiTN/aXryuiUk8TiqpVFxrK3+fsPg61m1vjL8uI2v2kgYH0n31QMOpeD0pqcF2d6b5nVV+kO9rEhc1vB4XtaM12P5fwJD526O2NNCvCMU1sF7j+J5ddvnBxs/n1e1fPz998wZtA1GurF61fV7HZ3E2jTFSu0eKjd994voZs9r8vB7QXCn360A9NVgsF1hPhQfUKMFUnH2HN1qAXemk6rO24n0s7j7Yk8TI+MoIMIq3D3CXCLAjcvlCA1QW589CYeblokbQz/y8Ao/+gM7Pz7j5FyPvtAX/ZfQKEIsFffCJ8ktExbce+jg1j/4E668M5datJPVGxnNblzygGO9fIcKkLFUabq11cr6Ulr/2D7m11Cg38rAqnSNJCGS9jol6/3IZVLUa7Eqr01QBtt9KroiVRLVEgJ0QL23eU+8iJF7xKVSP639BbXCA1/f6mneZ+XnnIYarwS4d3QTajn4CrKRc64KkiPhVLsAuedDxSBExxiPolgmUp+9X8bFkPYaFmm+sD7kJOXDyJLBITlVaP1evC4/C65hNQLECqZyDXbkGO2k52HHzs2uNGue8JMCOyNUD5lu/+ogO9aS3tboLxRcipykiPoOP/LH81WD7L2O5+VUKwPz8rF4QYHvVYAcoo7+APth+ibIfK51+VrZqWXKNHH0WodzqlzRyNN77xm8aRJiUpUr6rFWElNqyy2uQ+1xoSWx4WStJqMH20mj7pFJpbZXGxxUyeFLHfw22xzC3RXGGADsh9hzq0bNFXXU1o0onSmntmcvqsxCT+JgmWO2w9/BKKQT+ymA8/87NK0INtvc4/ucXdPlBZh2k8sfvfMs97PhpvGg8ps884HiVJ9qx7X0TiqGm3OlDbvK4eChJskrHROQa7Jh2aKMF2BVrsKs2cmyeGmy/+7WR9n/KLx/xc3n4N9BxUxelvYjUpRB5y69NiohX8BYksMkf06uRWpALlp97bpx5q8XnW6UlxXFzKt9Nn79g2ivo9uJ7l5QZr3hwtVxPv4rbnNS7HUTcGulmHkal1YuculR0+qV8U5ZVLce60rW8t4kC7CCXvEbppo8AO6JKjawQXKWtWXwS1aUf7IC9iLgINqPe5PMnL+0Hu7RP0crzcl+DHdcpFGS+fh9YyjZybC3drl7bwauRYxR+J7fWTYVi8TZN6H3NmbSvXyWuHy5c9RKT1NSVcqrVYCPDd6VRjX6dc4EAO1GSeZAkldtu+oLPy9ckDoromdYRaA55DwUlvV24yRMvHCcZx7Gfe1fQG5wpc8Vs93jRTMm05RqUxrC54mp8GmeaVhKDjaQcy3GptHauA1lXm7LRdkmlNCN6ITsqSCVXUgPqYgTYCZKkC0cSa+ZjzcEOUQZfvYg4WKZ3ioj/6QtqsCPOy9fy3M4uNOvjTY79oo7nuV19pTR5zzFKl1UV5upvppWW13SNHOtdgnhVuoa6XvVGCYpcq7Te1W6zzbTFjPFXKWIMKSJocAmMr3295CSsMAFWrWpzI6eI5P/tcWFyHUQk5WLnJwe7v6xRA9qSHGyvm4DH9LVLEbFu9nOMN7akHDf5Up+DHfrL4FzV1ibx3lRJpUOo2qo02KpG4vvlWjGXwyUC7IhcnuxJOnDqdWJX2gbF39XjTY5Bp3ER1Hi86C9XK+Ln+MvfTsXFyaQtuP4pOGACS5Da+ADzjeNGXG5/ljRoDFCH7DWu615EMq9Kd/+wl/YANO3rV+kwi1zjnPJN51flbviaKYSuzs8h09JSemQm9VAjwI7IZQ5V2vP9oirePm4bOfp8ei4IVn2kiLjIwXaYItIoP62VE+Rs86pFLjuu3/1fZjyvxoslY5ap1fZcju8a9XLDC7/oC5AuU3F5DubRUJpuhY+KfG0gdpQU7SGtiQ+/shppmxBgJ0iSDpxE5mAXfY7jDXhB+EoRcbBXK7W+D16GJB1l8YrjEC63vb1+ZShJp/CqqY6c/uN/eicPezE+oCXwkpP+HOx6FyCERmsYWOkYqnb+NNaa1kam4sRtql1cCLAjSuJNwYV6rVaQbvpcClNj6GcSF2X2bkCX4ef4S/pFPAnnUNT95N3TS/VhZWuwoxWnRKabvuhzLe2FJqF3NkfSniJSy0aOrs7zJFwvgkj5IVQH3lfWJCLATpAknYhJvIjFmULjd84FdcE12mGeN/mQi270FJEgjHF/HPtNyfDcZUFSVvw2+PE5Pxtg3CBo5NjY0r12yVDpXE7ifTbpGumUJMCOyG0jxwY6cmISZAvU+0TzV4PtvmFZ4fz9lCHv75Lv6n/UxdXIMQ5l37bpkQ7iJ986SNDtXZ4A4zrYeKXHT/R5Jlna16+SyOvexNsuX9of0mrNqHEqigiw4akR8tychoYhUkT8XDhd5HC2FUdvOrrugXsRibGrw6Sx1n/6i98LdLn9WSmN5+gy/KWReE3rgouHvdKGxmk+gtLf8LzS6rl+9HZ1R2m0Yy5KDjZKNdL+J8COyGUYmqTjptl+uvJ/MwmWhB3Xq9LD9iLipZF2db3LWvbFLj4b3fitefG9f/0+GPoftfJ8fNTKh5XEa076GzlG+3Wsopj2Z5Luk340UkDYCLwaOSYVAXZESextA7URND0jLM8eKoKUIe8m2mw/8fs9P6O+5MDrJUh+X0vvnTrivvaQFJHg0h4cNeLqNVyRa5QC1izqn9ToHwE2GlY9GlgF7kUkVGkKefdQkRG0F5HSGsjGuVgFZYzk9z0OviuMy6aIVB83lkaGQbrpi+XXFHcrlcRgL4FFqpmkrnuj1F72i/KQ1mzVd35flV4yzHlJ3CDAjogUkfpxubnCzKuevYgEWbYp+Ls0lSFBh11glQ7TePrB9t5apS+a8Q5nXQfdQaZ3ke7QKI2LXGm0YM4p17+iNMJNJQZpTzNKgqQeWQTYCZKk2sSGaORYh81lyvwdJ68GdLkyBKxGjzOHthaCltd1I8dyvFJESpbh1bOIKR0Wm1hq0NMt7cFRrA8QMc260XZJlPt6o61rVL5uZw20UQiwo4opDrXWqs/v79uxLL9ui64L//0O5+Uz1+hEr/SqdF8pIlUuW428q13tgqgPty1FV1LP7veM+4foIHNz86KZ4s/u1ieJ15zU52CH/K6uElswb1Ee0hJ4StRdkioiqyHAjshlTW//tXzX/iN647//Xmf/6/1asXmPs/mnjcsTLcx9tFY3X69XpQdZcsULfONcqyQFu+EYo5pFbd7HQmk6TuUxgguUKuQkRaRwJmmv4S1+cGomie1FJJ7Zxqap04xi0EjnZAMVNf36z8N/+tULWvbqbq3dvl/v/87SupSFJ2dv9bhURu5FpEKtu2mo+oBg4ugHuxyvHGw/yzEx1GqX42IppZXyaW/kmMBCOdSI/WA3WsCa9ofQWmukc5IAO6I4Ksgef2V77u/VW/e5X4APzdYgJUwvEjVLEanQD7a/FJH8v6vXqqZJrQ5jz9ppj3x315vbd+83chOYNF8jx3qXIF6NuH6NFrBGOe8abFVropGOWQLsBrD74JGaL7MRwut6dNNXME2NLn/ejRxrmxqQVv0BeNRNVKkrxdxnz3GiLdlZqpDv5TXXwZT+HOyIDajroNGOwSjbsRHuw7UW7W5YWwTYEbk8AcrVtv3Lb5Y7XAqiKHhpSx1rsIOUoVKZjRrrIh50k/tuIxE5RcRjlj7ewOnZGDIGVnE1cow8y0RrtNpSlyKvekzbLu3HnF9nHzum3kVwLmw/2Em9hxFgJ0i5APuZ9btqWxAls0V/Mbe5eP7mFfRFMy5E7UWkYjd9Kb5b1epNm5n5VJ+Ryftv4bAoyw0ycsSFecwizceP1AQ12JVysB2veiPcU+IQZTOWm/bcuWN181uOjzDnRtY452RbvQvQ6FxfNPYd6lFPHbvnk6Tt+w5r0afurmsZai3JvYi0eQXYAaZvKXgoKA3wGudyFaymws+5eTRFxH2qRmkwGkftW21ThZqtF5GGOjlCiHX1mjSgLubnvBvY0ar9h3tLhpfbhN9872uiFSqkuHepn+Oxka451GBH5LKbvsdf2a5TPnOP1m7fX7iMGl+oPvDf9em5pJ7CnLNJSBHxI+21jJXU6typ1BA199nrKDO1i+FcLMfXOqVI2muwK13EknrdSGq5oviXK+Ktjf7Ku06Kdf611Ej7nwA7QW66Y5n2eT7F1jbCfuzl7dVHSoB6n2a1OtG9GjmGflV6k+XQVjtz+tc/cjd9Pq+kJbXaUWvOA0zuIlhstF5oPv+2E/SD608LPX0j1ZY1i8bbJdVLPGbwgFhLMKCt1cl8krDtk1AGv0gRiahZ88riEuSG7bYXkeA52LUS9SZfKW/cyNT9l9wapxHHMl8/PYRkUkTcBqiBtp2TFJHwy6+Ht548OdL0aa+hr7R2kY8XGjlG1kSr6lsj7X9qsJEo9Xpg8XvO1uOG69nIMcD0FXs+ScDFKs5dXqv+3D1zsL0zQvyN6Fgm195FDXa1AenSSDfzMBpx/Yp/0Uv6OiShm76Eb6JAvK5jSV0/AuyIanH7fm7Dbm3YeaAGS2osST2pXPN8VXromn6PWtVwxUqVeuX1xVFz7sUqrhrsdB89SQ/eoqpYg53QffvmEyYWfJ42cmCdSuJPEraiq+O43r92St7rkoRyeSHAjqpGNWT/8D/P1mQ5jcRlUOT7jXh1uFp6v2jGvzQFCUHPtgHtbnIPXfDqRaRWgb2rpRTncZOjnF6RD80Ybo1vPmGiujqSc07HzWsXvOWkScHn0yDnadh+sJOKALtB/O7F7noXIdX81tbUJwe7en5vJWl60UxQn33rQl/jxZKD7VHb6/rnzVrnYBcvsJFa9IfRzOuXxDWfPKKr3kUILMox5HVt7mirX9iWhGOikc5JAuyI0hycIBlc1mCXpGA30MUqjLnjh+pXHz6z6nhxbAY/PW5ED+z9jxdLLyKR55hsaV8/NAdX6T5J6Ae7kc5JAmwkSv16EfE5Xh1O79aIZ2l+iUt6sYg2ayfi7kVk3sShIaYKxm9jynp1k+hddx5iPnUqf72kvh/sOMXx0NqAu6NSkWvWqL9BthspIihAN33100DnWSR+XmJSSdprqV2I48HJT5d2RibSDSNIapOL46C0Exp32y2J19K0nzqNtnpJbXhZSZTX0Tfe2savkY4BAmwkSqCbbB3ufknJwQ7ClPlbyqyP667sgs4vytIrTVvv4MirQWO9ymTk5madxG4eEV4Cn2kqqvc57Vq1h944u+l7w7FjHM3dHV8pIjGk2sWFADuiWvWzi3j5TxGpPac52EWzajFGfY4P4Vj7tQ4wbqAHk4g71qtcvmpaapUi4ii4j7MXkSQGT6SIRMCtsar+8yf23GaP4/iGi48LPh8XhYmIbvqaSFJ3LNIjeoCd14uIigMkoz7nNdhOZ1dRpS0T6HXyUQNsHytt5JEDb6L95FnrVKFmaySbdkH23vRRyehvOul1Wje9aZ7vcaud+17f1nP9k7DpSREBfPD8qad2lY6hNGKKSCWtLcbzgv2py+Z7ju/n4h40YI+rkWMtd1Vfn8fyPRoEFpepVjWkzhaTwEayceL54ahPvNn7mlBWLD3zJCPIqyTQNarKyF7r2hIiavNs/9Ggx3YjlZsAO6KkP00nWa26KHOrsfO+S1NEvMe76rRp+sH1p5Xk6fkJnpNyTtTyYc1rlYu3g1eaRmsMvyDEqS3G11QncTM0Um1Z3MYMGaChnW31LYQx6nOd01ZHYY6uML8auTpPk3A2JKEMfhFgAwnnsvbBK+jzSm8wxui0maM0peg1xH5ubkkJGIPVJEW7bHuts5/t0NJSm3Ycrm6wxelKaQ9AG6m2LG4txuhnH3yd/uq8ORo5qKNu5UjK9cWF/utOucOs+IFWyjyUO1l2iGmSsOUbKS2NADuiJBxwjSrqieLy5p7kNzl6NurIHnhBjz/rMUWlmLn4Xuan8igp54Sf9AtXu9MrSO712FjFx1mrMeqtRYDtaE2Lt2k9+qKvJV4Ff1SLMZo9drD+6rxjNHvs4LqUwcjfNahRVEv38LqGhTkmG+VB2Fc/2LGXwh0C7IjoRSS8qA04XN6Q/TYkrM/JXbrU/lqcnoB3G6/Rg9QI+UsRia+bPte9iLS0VK5B8stru3pthpIUkRb3vbh4cXWuxJkikkSNVFsWt4Jd7+eYjeG4jqNb0XrqD3zLrZH3OxCCH5NegXyjHtp00wc0mKhvS4yTV+zfXzsaNB/RK0AOMgc/AXZiaph8XHVd/dzqtVm8aqa9UnSi/OTdP+lHLpqbG3bDxXNLxnN1A2q6FJF6FyBB8gM7r1/CaiVdKSKVv/cKjMM0jG7zrCpP3tHtp0Re15ykHhF1brEAFKrXU3WrxwVo1phBWtW9r2BYUmq0+m8yXmkIlXjdmyrdr3qKusfwlSJSw15EWir88uDnR4nW1mwNdtG4/3LF8frSPSu0Zc9BHen1UWvvmXrjc1iEJ5L+gPdPXzddR3oz++q9r51eMp6r47YkwE57I8eEnO9xqXT+lIybN6qvQzamTZeYB3ifKh3X1TaRqxSRttb65W03swTX2yHtot67XJ7sXjWZ37h6sU6aOrzqeHHzuj739IYNsL1qVcvPozi49JUiEqhE0bRX+OnBT01P//4srhU5Y/Zo/f4jb9C/v3ORr3J4p4j4C7B7Pbr486s9e+PsbG/Vh8+dow+fO0ed7a0l48VXg51u9ayprYX2AIFXkJrT9jB9yflg1Fi97lRTbZt6pS4GeSjqN3bIgJJhYW5lcW/5tLV5IMCOKEXnes1F/XnZZazrdT+YPXawfvbnry0Y1tFW+1PGM/0gG9EFbSAXNAe7OID3s7hanhNerez7+Tk+yuXeGxOs9tK7kWPpeF79ZUcJGHy/hCjAuVJplqU52A4bGifw5nqkJ90XeO/UAW/5wWC1X6na2+LZmcZE+8WnHioe19VSRDxzsIOXYeKwLn343DnBJyzSE6U2wIdKFSaNKF1rg8YSuQbb3UW83I2mOIAYUIcA20tvyBQRzxzsCrM4UnRBrcfNrdIiK12Q/Rwf/QGq1yvkg/CumQ6fNuJXHDckrxrwfsW1Z0bSpOFdzsuQFMXHf9oEOX7yT4lqR2xcgVIj9iIyoK38+RQmRSTMr6gtLUZ/ff4xgZbt5VBPvOdDPSqw4pSutamD7r2H6l2EVKnXLwJ+K3KScgHoCdnI0TMHu9JySlJEAi3O109+XrM877ixub9fM31E2XEr/cTtqwa7zEiDBgRrnuKd2+4mwJ45elDZ7/wGMkFupgMqBNjF28sY6cvvPEknThmu68+aGWAppWr1VssgDsccUNRboBSRlvwa7MrjdrS1xJZP0Gi/Gg/sqPDAWuWY97p+1vM8OXSEADuIdK1NHdy1bFO9i9CwRnm8rMDrYjRiYLvn9APavQ/fYV3e41fiN2cwaM1MpQtGcTk7y1yIvdIg+od1Vbh4e87L44Y6uEIwObjozW1dHdXXf3je/ho1OJP7V24fSlJXe6uGdxUeCze/9XhNHTlQY4cM0CcvW5A378LxisuXz89xUC6QHpTdruWOsWJetVRtHseKV0rHoI7KwfyX33lS2e/83pBGDPT3YpCu9laNG1qar9mv+Pw0xujkaSP08794nT56yXG+llGOV55ovVU6vtKg+HzKV3xtyb8OVTu3hna2a2iI63CxQQMKy9DV0VpyDaq0DmFEfYlO8S9Ala7R/edlufuK13YO+vBfTpD0INfLLmekn33p8XxR6Ve3eiLAjmjKiIHVR2pif3nObL3x+AmSpOMnD9MPrz8t993XrjpZknRjtluxoZ1teu/rZkiSvv2+UyRlasi++I6jAcYHXj9LkjR+aKcuXjDBc5mfumxBLpD5yrsKg5NPXjZfUiaQOGXGSEmZGtIRFS6qrz8m87rwM2aPLglo3r54csFnY6SzsuNPGt6Vm7bf16/KNJprbTH6zOULC75728mTSx46Lpg3TtNGDdSCSUNzw6aOHKjXzhotSXrXqVMr3uwuXVi4jeaMHazjJmTm9ZZFkyQp2zAus16funxBwfh/f+Gx6she/D93xfG66rRpueX93YXHei7zlqsX5/7+xtWZffzld57kWaM8clCH3vGaKfrMnxzdZ1979yKNHjxAD/zd2Xr4hnNy5ZWkz7/t+FytzjevWaxLF07MBYR/fvYs/dvbT5CUqZn7h0vn5abrX9fjJgzN/VQ6YmC73nnKVEmZC3T/8XDu3LG51KCz5ozRlJGZFIj+3jk+fM7sgnV4z+nTdMH8cRo/tFOS9P7XZ2pyF08boRnZ2ud3n5pZztSRR/flZSdOlCR98OzZBcHLh95QOP8Fk4Zp8bRMLf4JU4brgnnjJGWO20rBwC3Zbd9ipM9ecbwk6dLjC4+H844bm9uPw7radfXp0/SpyxbkyvOlK0/UdWdkzslJw7t0/rxxOnNO5tg7q+jY7h9fygRj/dst3/teNyN3zswZOzj39+yxg3XqzFG5bTusq73goWzisM7crxp+087zuy58Y9F6D+tqz23/maMHeZb1jNmjdc7co7+keL0mvPj8ljLXvOLzrrXFaGr2rahvOHaMXjd7lKTMNhziI2g5+9jC5Xz43Dm6+S2F14/+8+vvLzpW33rva0qGS5l17S/HNadP099ccEzu3M83clCH3r54ii5ZOF6StGjqcI3LHt9S5hqb7xNvmqe/v+jocfTe107XJ948L3ccffmdJ+n27DXdr8ED2vRnZ83U316QOV+HD2zXu0+dVnIN+te8a8JHL5mb+/vW9ywu6bJy8ojy6Uz91+b+a1a/02aOrFjOS4+fkDtuRw/u0OUnTcqdo4unjdDowQN01WmZ83/G6EH6+lVHz8ub35rZhydNGa5ZYwblhkuZh+e/y27T/G37ntOn5Zbdf/0q9k9/slAnZ68ZF80fnxt++szMcbdg0lBNGdmV27blfPSSwu338TfO0+ffdkLFaSTpLSdN0ry863axT142X5dnzz/p6DXvQ+fMzlVu9F9n8p0yfWRu33/w7EwsMGJgu06ZXnkf1YtJU6ftixcvtkuWLKnpMt996x/18MptkjLB0JtOmKhNuw6qs6NV27LpI9ZmUklajdGAthZNHN6l5zfu1gsbd2vwgDY9+vJ2X8t65eZLY1uPftZazbjxVwXDvviOE9XaYnTgcK9Wb92nySO6NG5op3p6+zSks12TR3RpVfdejRkyQGOGDNDO/Uc0cXiXVm7Zq0VTh6u3z2rpmh06Ycpwdba3alX3XrW1GE0bNSi3zKfW7dT0UYMKAt2VW/ZoQFtrweu6+/qsnly3Q3PGDdHQzvKB5cZdB7Rz/5GC4Kx/Wc9u2KWJw7s0rKtdT+SVq5zDPX16cu0OnTh1eElNZW+f1RNrd2j+xKFav+OABna0auKwLj2xdofmThjqWTu8fNNuDe1s10SP3NVd+49oZfdezZ84VE+v26lF00aovbVFB4/06ql1OyVlAq78+e7Yd1gvb8t0Jzhj1CCt27FfowYP0IYdB7Ro6vCSmtQDh3v17IZdBd917zmkjbsOaOGkYSV551t2H9SWPYe0YNKw3PJe2bZPJ04ZXraR2+ruvWoxRtPz0hvWbd+vQz29GtDWqr2HenTgSK9mjR6sYdlA6tWdB7T74BHNHV/+wixJ63fs1/7DvTpm3BBJ0u6DR7Ri8x4tmjpCxhg9t2GXxg4ZoLF5AUH/Mbhw0jB1trfoyXU7NXP0oILar0M9vXpq7U6dNHVEwYPU3kM9Wr5xtxZNHaGWlsyr5Z9Yu1MTh3dmt3FmeH85TpoyIvdz+r5DPXo+O23/A4TX9t+695CWb9yjro6W3HrkO3ikV4+/sl0nZo9VP8etJL24aY8Gd7bl8qR7evv0xNqdmjthiF54dbcWTRuhthajp9bt1Iy87bFx1wFt33dY8ycOU1/2GD92/BAN6WzPnQ/F26nfsld3adSgARoxqF1L1+yQrHTs+CF6ees+LZo6Qn3Z7bdg0lANaGvVkle2a372mO7ftrPHDpYx0kub9shKmjt+SMF6b9x1UNZatbe2aP2OA5o3Yag27T6owZ1tGtjeWnJ89vT2aemaHTp+8nAt37RbM0YP0pDO9ty522el5Rt368Qpw/XYK9t18Eivzpg9Rh1tLbrzmVf1ytZ9uvaMmVqzfZ+GdLZrYHur1mzfrxMmD9Oq7n3qaG3RsIHtuWvekd7MNjtxynC9vHWfhg9s18hBHbnjy8rm/j7c26eHXurWxOFdGj1kgPYcPKL9h3vV1d6qlzbv0fyJQzVrzGA9vX6Xhne1a9u+w1o0NbNuz67fpS17DurEKcPV2mL08taj6/3S5j3qam/VqMEdemb9LkmZSo4+K72wcbdOzh633XsOadOug+rqaNGAtlZ17z2UOy+P9PaVPdY27jqg1d37NKSzTQuz14Yn1+3UjLzr+KZdB7V932HNm5g5p1ds3qPuvYc0clCHduw7oj5rNXf8EK3q3qdFU4fr6fW7NHvMYL2ybZ+mjByokYM6ZK0tOV+Lr0H514T1O/Zr36FeHTt+SO7e0tneqj5rNW/CUD3+yg7t2H9Yi6aOUEdri36/cqtmjhlUcK94Zes+PbNhl06bOVIjB3YUnDPDB3Zo76GeXBuLRVMz277/uB3WVbrd+vqslq7doXkThmrQgDYt37RbQzrbC9ov7D/co2WvZq4Vz7+6W+OGDdDYIZlrmNc2kArvPyu37FVHW4uO9FgtnDxMB4/0FtxD+q89S9Zs16KpIzQoe749uW6npowYqNXde3XClOFa9upudbW3qq3V5Lbn0jU79JrpI3P3rP5r7KGePu073KONuw6qo7VFJ2an7z8Hnt2wS4MGtGrjzoNaMGmY2lqNXt2Zuc/0Wen+F7eos71Vr501Kneubtt7SOt3HNDxk4fpqXU7tWbbfl28cLyeWruz4B7c09uX2yb9v5TWgzFmqbV2sed3BNjR5AfY/33tqTrD46mrkpVb9ui8f3vQ17i1CLD7+qxmfvRogD1/4lD98sNnxr5cAACARlIpwCZFJKLozyfJatiTnsctAACA+ogUYBtjRhpj7jbGrMj+f0SZ8a7JjrPCGHNNdthAY8wvjTHLjTHLjDE3543/XmNMtzHmqey/66KUM075AXYCG8EHlqZfNAAAAOohag32DZLutdbOkXRv9nMBY8xISTdJOlXSKZJuygvE/9VaO1fSSZJeZ4y5OG/SH1prT8z+uzViOWsiTHydtKCc8BoAACCaqAH2ZZJuz/59u6TLPca5UNLd1trt1todku6WdJG1dr+19neSZK09LOkJSZM9pk+0qK/STVh8narX0AIAANRD1AB7nLV2Y/bvTZLGeYwzSdK6vM/rs8NyjDHDJb1JmVrwfm81xjxjjPmJMWZKuQIYY643xiwxxizp7u4Osw7uJC1aDoH4GgAAIJqqAbYx5h5jzHMe/y7LH89mkncDh2fGmDZJ35f0ZWvt6uzg/5U03Vp7vDI13reXm95ae4u1drG1dvGYMaV9ksYtakBarpszAAAANKaqPdxba88r950xZrMxZoK1dqMxZoKkLR6jbZB0dt7nyZLuz/t8i6QV1tov5i1zW973t0r6l2rlTAITogo7aeE1NdgAAADRRE0RuUPSNdm/r5H0C49x7pJ0gTFmRLZx4wXZYTLGfFrSMEl/lT9BNljv92ZJL0QsZ2yixqNJq8AmBxsAACCaqAH2zZLON8askHRe9rOMMYuNMbdKkrV2u6RPSXo8+++T1trtxpjJkj4maZ6kJ4q64/twtuu+pyV9WNJ7I5YzPmnrpq/eBQAAAGhwVVNEKsmmcpzrMXyJpOvyPt8m6baicdarTIaEtfZGSTdGKVs9hOqmL2FJIvSDDQAAEA1vcowocjd9yYqvS9aGeBsAACAYAmyH0tAjiO2rdwkAAAAaGwF2RGmr4Y1aIw8AANDsCLAjyg9Hw1RgJ63Su/iBIWnlAwAASDoCbIdCNXJMWARLN30AAADREGBHlLZeN9K1NgAAALVHgO1QqBQR98WIJGXPCwAAADVHgB1R2t7kSCNHAACAaAiwnUpYtBwCNdgAAADREGBHFDUgTd6bHOtdAgAAgMZGgB1R6rrpI0UEAAAgEgJsh0J10+e8FNFQgw0AABANAXZUKYtI6QcbAAAgGgJsh0K9NCZhVdjE1wAAANEQYEdEPAoAAIB8BNgR5df4hqmMTlqNMSkiAAAA0RBgOxQmQyRp8WzSygMAANBoCLAjitqtXdK6xUtWaQAAABoPAbZDYV4a05ewiLa3r6/eRQAAAGhobfUuQKOLmlJhA8zgjM/ep4NHejVoQJuuPWOG3nP69GgL93DwSGGAnbQX4QAAACQdNdgOxZ2DvX7HAW3de1hrtu3Xx3+xLPjCfPjsb5bHMl8AAIBmQYAdUfQa7PDT7j3UE23hHh5asdX5PAEAAJoJAXZEUVOoxwwZEHraT9/5fMSlFwqSrgIAAABvBNgOhUkR6epo1cnTRoRa3g8eXxdqunIO9dDAEQAAICoC7Ihc1PqefcwYByWJjgAbAAAgOgJsh8J00yclp+/pQz299S4CAABAwyPAToCkpD73Jq1TbgAAgAZEgB1RfnDc6H1GJyXQBwAAaGQE2A6FDbCT8rr0ZJQCAACgsRFgR5SU4NgFuukDAACIjgDbobCNHBdOGua4JOEQXwMAAERHgB2Ri6D0nLlj9c5TpkSfUQzef9asehcBAACgoRBgR5QfX4fNwTbG6J/fcrxW/9MlTsoUVvHDwj9cepwuXTihPoUBAABoUATYDkXtRKSlxejZT1ygvzpvjpPyRDF5RJeuO3OmWloavGsUAACAGiPAjsh1w8Ahne36y3PqE2DnN9hs9C4HAQAA6oUA2yFXQWlrnWqNC/r0jlwfDwAA0JwIsCNKU8cbLvLJAQAAmh0BtlONHZXmp7s09poAAADUDwF2VCmqwi6swSbEBgAACIMAO6I0pVUU5mADAAAgDAJsh+oRlH7pnhVauWWPo7kRYQMAAERFgB2R6276+l0wb5yv8b5wz0u64ut/cFIOarABAACiI8B2yGXe8s1vPV6fvnyBr3F37j/iZJnkYAMAAERHgB1RXG0cRw7q0FWnTfNfDgcFoQYbAAAgOgLsiJISlLoI9HmTIwAAQHQE2A7VMyh1n4NNhA0AABAGAXZENkUdYRcE2MTXAAAAoRBgO1TPWl/XKSIAAAAIp63eBWh0H7tknvYd6pEkjRzcUbdyOG/kSBU2AABAKATYEV20YHy9iyDJfe0z4TUAAEA4pIgghxxsAACA6AiwU8JJigjd9AEAAERGgI0cuukDAACIjgA7JdzUYB9FDTYAAEA4BNjIyX9ZDfE1AABAOATYKeGiF5GCOVCFDQAAEAoBdko47wc7+uwAAACaEgF2SrjpBZteRAAAAKIiwEYONdgAAADRRQ6wjTEjjTF3G2NWZP8/osx412THWWGMuSZv+P3GmBeNMU9l/43NDh9gjPmhMWalMeZRY8z0qGVNM+sgR6SwFxFCbAAAgDBc1GDfIOlea+0cSfdmPxcwxoyUdJOkUyWdIummokD83dbaE7P/tmSHXStph7V2tqQvSPqsg7KmlosUEWqwAQAAonMRYF8m6fbs37dLutxjnAsl3W2t3W6t3SHpbkkXBZjvTySda6hWLctNI0dysAEAAKJyEWCPs9ZuzP69SdI4j3EmSVqX93l9dli/b2XTQ/4xL4jOTWOt7ZG0S9Ko4hkbY643xiwxxizp7u6OuCrNrSBFhDpsAACAUNr8jGSMuUfSeI+vPpb/wVprjTFB61Lfba3dYIwZIumnkq6W9G2/E1trb5F0iyQtXrzYTWcajchxN33E1wAAAOH4CrCtteeV+84Ys9kYM8Fau9EYM0HSFo/RNkg6O+/zZEn3Z+e9Ifv/PcaY7ymTo/3t7DRTJK03xrRJGiZpm5/yNiM3L5rhTY4AAABRuUgRuUNSf68g10j6hcc4d0m6wBgzItu48QJJdxlj2owxoyXJGNMu6Y2SnvOY7xWS7rMuuspIKSdbJr+RIxE2AABAKL5qsKu4WdKPjDHXSloj6e2SZIxZLOkD1trrrLXbjTGfkvR4dppPZocNUibQbpfUKukeSf+ZHeebkr5jjFkpabukKx2UFRWQgw0AABBd5ADbWrtN0rkew5dIui7v822SbisaZ5+kk8vM96Ckt0UtX7Nw3k0f8TUAAEAovMkxJV7avCfyPCyvSgcAAIiMADslrrzlj3pl675I8yh80QwRNgAAQBgE2CnyT796IdL0ha9Kj1YWAACAZkWAnSI9fdEysemkBQAAIDoC7BRpa4lW7VxYg00VNgAAQBgE2CnS3hpxdxbkYAMAACAMAuwUaWslLAYAAKg3AuwUaWuJtjvppg8AACA6AuwUiZiCXdRNHwAAAMIgwE6RqH2AFL7JkRAbAAAgDAJs5BT0IlK3UgAAADQ2Amzk5PeDTQU2AABAOATYyClMMSHCBgAACIMAGzmFOdj1KwcAAEAjI8BGnrwUkTqWAgAAoJERYCOHGmwAAIDoCLBTxEbsp6+wFxEibAAAgDAIsFPERuwJmxpsAACA6AiwkcOr0gEAAKIjwEZO4avSibABAADCIMBGTkGCCfE1AABAKATYyCl4k2MdywEAANDICLDhyZCEDQAAEAoBNnIKc7ABAAAQBgF2wv3lObMlSTNHD9JP//z0yiNH7gebXkQAAACiaqt3AVDZX59/jC49foKmjxqkzvZWffryBfrB42v13IbdzpdFDTYAAEB01GAnnDFGc8cPVWd7qyTpqtOm6c6/PDOWZRW+aIYQGwAAIAwCbOQUviodAAAAYRBgI8eSIwIAABAZAXaKRGzjWFSDTYQNAAAQBgF2ymzbe0j//OsX9P3H1gaeNr8Gu5UjAwAAIBR6EUkRa61uumOZ7nxmoyRpxuhBOm3mKN/T9/Yd/buFRo4AAAChUE+ZMv3BtSR979Fgtdh9eTXYLS0E2AAAAGEQYKdYa8AguSDAJr4GAAAIhQA7xYJmefT15eVgkyICAAAQCgF2igUNknt50QwAAEBkBNgpFjRFpLAXEQJsAACAMAiwUyxoLXRvHznYAAAAURFgp0jxi2aC9GVtrdXnf/tS7jO9iAAAAIRDgJ1iQfqy/s1zm3Q4ryNs+sEGAAAIhwA7xYIEyXc+u7HgM72IAAAAhEOAnWJBGioWj7mqe6/bwgAAADQJAuwUsUVJ2FHqoH/93KZIZQEAAGhWBNgN6ozZo6uOE6ShYnE6yYA2Dg0AAIAwiKIalOt+qotTrm+4eK7T+QMAADQLAmx4mjCss95FAAAAaEgE2ClS3A92EMX14bwqHQAAIBwCbEgqDajppg8AACAcAmx4auHIAAAACIUwKkXaixo+BqmDJkUEAADADQLsFHn05e0FnwPlZBfF07wqHQAAIBwC7BTZsPOAs3mRgw0AABAOAXaKBUsRKRzbcTfbAAAATYMAG5JKXzRDDjYAAEA4BNiQVFrbTQ02AABAOATYkFRag+36VewAAADNggC7QbnO4CjOwSZFBAAAIBwC7AZlo7wX3QcqsAEAAMKJFGAbY0YaY+42xqzI/n9EmfGuyY6zwhhzTXbYEGPMU3n/thpjvpj97r3GmO68766LUs406nMcYRdXWNMPNgAAQDhRa7BvkHSvtXaOpHuznwsYY0ZKuknSqZJOkXSTMWaEtXaPtfbE/n+S1kj6Wd6kP8z7/taI5Uyd3r54A2xysAEAAMKJGmBfJun27N+3S7rcY5wLJd1trd1urd0h6W5JF+WPYIw5RtJYSQ9FLE/TcB1gF6MCGwAAIJyoAfY4a+3G7N+bJI3zGGeSpHV5n9dnh+W7Upka6/yo8a3GmGeMMT8xxkyJWM7UcZ0iUtxRHykiAAAA4bRVG8EYc4+k8R5ffSz/g7XWGmPCRn1XSro67/P/Svq+tfaQMeb9ytSOn1OmfNdLul6Spk6dGnLxjSfuFBECbAAAgHCqBtjW2vPKfWeM2WyMmWCt3WiMmSBpi8doGySdnfd5sqT78+ZxgqQ2a+3SvGVuyxv/Vkn/UqF8t0i6RZIWL14cc98ayeErvo4QI7fSvwwAAEAoUcOoOyRdk/37Gkm/8BjnLkkXGGNGZHsZuSA7rN87JX0/f4JssN7vzZJeiFjO1HHei0jxZ2qwAQAAQqlag13FzZJ+ZIy5VpleQN4uScaYxZI+YK29zlq73RjzKUmPZ6f5pLV2e9483i7pkqL5ftgY82ZJPZK2S3pvxHKmDikiAAAAyRQpwM6mcpzrMXyJpOvyPt8m6bYy85jpMexGSTdGKVvaTR7RpWWv7nY2v+I3OdJLHwAAQDhk2jao//fmBU7nRw02AACAG1FTRFAn44d16vlPXqhVW/bphY27dc8Lm/Xb5zeHnl9xON1CFTYAAEAoBNgNbGBHmxZOHqaFk4dp3sShkQLsYsTXAAAA4ZAikhJtrdEi4uJeQ0gRAQAACIcAOyVcB8QE2AAAAOEQYKeE63CYFBEAAIBwCLBTwqvCeee+I6GnpwYbAAAgHALs1CgNiH+4ZJ12H/QXZBf3gx01pxsAAKBZEWCnRLmUjlseWB1qfm0tHBoAAABhEEWlRHEvIP32He7xOX3hZ2qwAQAAwiHAToly4XBx6odfbbRyBAAACIUAOyXKtUn021bR2uLpCLABAADCIMBOiXI11X4rovuKI2wAAACEQoCdEuVrsP1F2D19fQ5LAwAA0LwIsFOibIDtc/pRgwY4KwsAAEAzI8BOibI11T4j7PxGjRfOH+egRAAAAM2JADslovYikp+BPWfskMjlAQAAaFYE2CnhshcROhABAAAIjwA7JVrKRMV+Y2WbV4dNfA0AABAeAXZKlE0RCdMPNlXYAAAAoRFgp0WZmLhczXaxgvg6emkAAACaFgF2SpRrzOg7WM6rwqYCGwAAIDwC7JQo98bG/7h/VeB5+e15BAAAAKUIsFOiXD/YvX3+XoHOi9IBAADcIMBOiah1znTTBwAA4AYBdkpEDYrppg8AAMANAuyUiJo3TQ02AACAGwTYKWEq7Mlv/v7lqtMXdoNNhA0AABAWAXZKVAqJP3Xn81Wnt7RyBAAAcIIAOyWi1joX5GBTgQ0AABAaAXZKRI2Jn12/K29eRNgAAABhEWCnRNRa50dWbcv9/cBLWyKWBgAAoHkRYKdES5UI+7x/e8D3S2eWrtnhokgAAABNiQC7SazcslezPvqrehcDAAAg9QiwU8Jlw0R6FAEAAAiPADslaJgIAACQDATYKUHXegAAAMlAgJ0S1Ro5AgAAoDYIsFPCb3htSbAGAACIFQF2SvitwPbTUx+V4QAAAOERYKeE31el91GDDQAAECsC7CZDgA0AABAvAuwmQ3wNAAAQLwLsJkOADQAAEC8C7CZDiggAAEC8CLCbjFeAfbinrw4lAQAASCcC7Cbj1U3fj5asK/hMJTcAAEB4BNhNxutFM6/uPFCHkgAAAKQTAXaT8arBHtzZVvuCAAAApBQBdpPxysHuaC08DK46bVqtigMAAJA6BNhNxk8vIlecPLkGJQEAAEgnAuwm46cBY1urv9euAwAAoBQBdpPxU4NtRIANAAAQFgF2k/FTg22IrwEAAEIjwEYJ4msAAIDwCLCbjJ93yFCDDQAAEB4BdpPxetFMKSJsAACAsAiwmww52AAAAPEiwEYJ4msAAIDwIgfYxpiRxpi7jTErsv8fUWa83xhjdhpj7iwaPsMY86gxZqUx5ofGmI7s8AHZzyuz30+PWlb400IVNgAAQGguarBvkHSvtXaOpHuzn718TtLVHsM/K+kL1trZknZIujY7/FpJO7LDv5AdDxF9/rcvVh2H+BoAACA8FwH2ZZJuz/59u6TLvUay1t4raU/+MGOMkXSOpJ94TJ8/359IOjc7Pspo8bF1fv7Uq1XH4UUzAAAA4bkIsMdZazdm/94kaVyAaUdJ2mmt7cl+Xi9pUvbvSZLWSVL2+13Z8QsYY643xiwxxizp7u4OU/7U+NwVJziZD48xAAAA4bX5GckYc4+k8R5ffSz/g7XWGmP89APnjLX2Fkm3SNLixYtruuykGdrVXu8iAAAAND1fAba19rxy3xljNhtjJlhrNxpjJkjaEmD52yQNN8a0ZWupJ0vakP1ug6QpktYbY9okDcuOj5hRgw0AABCeixSROyRdk/37Gkm/8Duhzbz15HeSrvCYPn++V0i6z/p7SwoiItUdAAAgPBcB9s2SzjfGrJB0XvazjDGLjTG39o9kjHlI0o+Vaay43hhzYfarj0j6a2PMSmVyrL+ZHf5NSaOyw/9a5XsngWOE1wAAAOH5ShGpxFq7TdK5HsOXSLou7/OZZaZfLekUj+EHJb0tavkQHBXYAAAA4fEmR5SkhNBNHwAAQHgE2FBxajs12AAAAOERYKMEATYAAEB4BNggRQQAAMAhAuwUOXik19d41Xo7pAYbAAAgPALsFGlt8RcZV+tNnPgaAAAgPALsFDl/3jhf4/VVrcEmxAYAAAiLADtF2ltbtPxTF+mtiyZXHK+PGmwAAIDYEGCnTGd7qz7/9hMqjnPt7Y9r8+6DZb+nAhsAACA8Auwm9NCKrfqHnz9X9nt6EQEAAAiPALtJ3f385vJfEl8DAACERoANfe/RNQWfSREBAAAIjwC7ie05eESStKp7X8HwFiJsAACA0Aiwm9gX71nhOZzwGgAAIDwC7Cb2zd+/7DmcCmwAAIDwCLBRgl5EAAAAwiPAbnLdew6VDKMGGwAAIDwC7Cb3ms/cU+8iAAAApAoBNkpQgw0AABAeATZKkIMNAAAQHgE2SlCDDQAAEB4BNkoQXwMAAIRHgJ1SLRGiZEMVNgAAQGgE2Cm1cNKw0NNGCc4BAACaHQF2Sn3xypNCT0sNNgAAQHgE2Ck1Y/SgehcBAACgKRFgAwAAAA4RYAMAAAAOEWADAAAADhFgo8AJk8P3PgIAAAACbBT5UoTeRwAAAECAnWonThkeeJrp9D4CAAAQCQF2itl6FwAAAKAJEWADAAAADhFgp5mlDhsAAKDWCLABAAAAhwiwU4z6awAAgNojwE6x+ROH1rsIAAAATYcAO8VuuOi4ehcBAACg6RBgp9iwge06ZcbIehcDAACgqRBgp1yLqXcJAAAAmgsBdsrRUx8AAEBtEWAj572vnV7vIgAAADQ8AuyUC1KBfdOb5sVWDgAAgGZBgI0cY0jYBgAAiIoAGwAAAHCIABsAAABwiAA75SzdiAAAANQUAXbKHeklwAYAAKglAuyU6+nrq3cRAAAAmgoBdsr1UIMNAABQUwTYKdfTR4ANAABQSwTYKdfTS4oIAABALRFgp9yM0YPqXQQAAICmQoCdcp/+k4UaPrBdXe2t+tZ7X1Pv4gAAAKReW70LgHhNGt6lP954ro709qmXfGwAAIDYUYPdBDrbWzWks11Gpt5FAQAASD0C7GZCfA0AABC7SAG2MWakMeZuY8yK7P9HlBnvN8aYncaYO4uGf9cY86Ix5jljzG3GmPbs8LONMbuMMU9l/308SjkBAACAWolag32DpHuttXMk3Zv97OVzkq72GP5dSXMlLZTUJem6vO8estaemP33yYjlhCRDDTYAAEDsogbYl0m6Pfv37ZIu9xrJWnuvpD0ew39lsyQ9JmlyxPKgAuJrAACA+EUNsMdZazdm/94kaVyYmWRTQ66W9Ju8wacbY542xvzaGDM/YjkhyVCFDQAAELuq3fQZY+6RNN7jq4/lf7DWWmNM2H7gvirpQWvtQ9nPT0iaZq3da4y5RNLPJc0pU77rJV0vSVOnTg25+OZAeA0AABC/qgG2tfa8ct8ZYzYbYyZYazcaYyZI2hK0AMaYmySNkfT+vGXuzvv7V8aYrxpjRltrt3qU7xZJt0jS4sWL6ei5AiqwAQAA4hc1ReQOSddk/75G0i+CTGyMuU7ShZLeaa3tyxs+3mTzGYwxp2TLuS1iWZse/WADAADEL2qAfbOk840xKySdl/0sY8xiY8yt/SMZYx6S9GNJ5xpj1htjLsx+9XVl8rb/UNQd3xWSnjPGPC3py5KuzDaERATUYAMAAMQv0qvSrbXbJJ3rMXyJ8rrcs9aeWWZ6z+Vba78i6StRygYAAADUA29yBAAAABwiwG4ipIgAAADEjwC7idDIEQAAIH4E2E2EGmwAAID4EWA3EeJrAACA+BFgNxFelQ4AABA/AuwmQngNAAAQPwJsAAAAwCEC7CZChggAAED8CLCbCDnYAAAA8SPABgAAABwiwAYAAAAcIsAGAAAAHCLABgAAABwiwAYAAAAcIsAGAAAAHCLABgAAABwiwAYAAAAcIsAGAAAAHCLABgAAABwiwAYAAAAcIsAGAAAAHCLAhiTp8287od5FAAAASAUCbGjaqIG67MSJ9S4GAABAKhBgQ5efOEltrRwKAAAALhBVAQAAAA4RYEPG1LsEAAAA6UGADQAAADhEgA0ZUYUNAADgCgE2AAAA4BABNgAAAOAQATbUa229iwAAAJAaBNhQXx8BNgAAgCsE2FAPATYAAIAzBNhQb19fvYsAAACQGgTYoAYbAADAIQJsqJcAGwAAwBkCbGhIZ1u9iwAAAJAaBNhNbkhnm97/+ln1LgYAAEBqUHXZxG5+y0JdvHCChna217soAAAAqUENdhObM26IhnURXAMAALhEgN3ERg7qqHcRAAAAUocAu8l85k8WaPCANr3r1KmaMXpQvYsDAACQOuRgN5l3nzpNV75mqlpbTL2LAgAAkErUYDchgmsAAID4EGADAAAADhFgAwAAAA4RYAMAAAAOEWADAAAADhFgAwAAAA4RYAMAAAAOEWADAAAADhFgAwAAAA4RYAMAAAAOEWADAAAADhFgAwAAAA4RYAMAAAAOEWADAAAADhFgAwAAAA5FCrCNMSONMXcbY1Zk/z+izHi/McbsNMbcWTT8v4wxLxtjnsr+OzE73BhjvmyMWWmMecYYsyhKOQEAAIBaiVqDfYOke621cyTdm/3s5XOSri7z3d9Za0/M/nsqO+xiSXOy/66X9LWI5QQAAABqImqAfZmk27N/3y7pcq+RrLX3StoTcL7fthl/lDTcGDMhSkEBAACAWogaYI+z1m7M/r1J0rgQ8/hMNg3kC8aYAdlhkyStyxtnfXZYCWPM9caYJcaYJd3d3SEWDwAAALhTNcA2xtxjjHnO499l+eNZa60kG3D5N0qaK+k1kkZK+kjA6WWtvcVau9hau3jMmDFBJwcAAACcaqs2grX2vHLfGWM2G2MmWGs3ZlM4tgRZeF7t9yFjzLck/W328wZJU/JGnZwdVtHSpUu3GmPWBCmDT6MlbY1hvkgO9nH6sY+bA/s5/djH6dco+3hauS+qBthV3CHpGkk3Z///iyAT5wXnRpn87efy5vshY8wPJJ0qaVdeMF6WtTaWKmxjzBJr7eI45o1kYB+nH/u4ObCf0499nH5p2MdRA+ybJf3IGHOtpDWS3i5JxpjFkj5grb0u+/khZVJBBhtj1ku61lp7l6TvGmPGSDKSnpL0gex8fyXpEkkrJe2X9KcRywkAAADURKQA21q7TdK5HsOXSLou7/OZZaY/p8xwK+kvopQNAAAAqAfe5OjPLfUuAGLHPk4/9nFzYD+nH/s4/Rp+H5tMZTEAAAAAF6jBBgAAABwiwK7AGHORMeZFY8xKY0y518Aj4YwxU4wxvzPGPG+MWWaM+T/Z4SONMXcbY1Zk/z8iO9wYY76c3e/PGGMW1XcN4JcxptUY86Qx5s7s5xnGmEez+/KHxpiO7PAB2c8rs99Pr2vB4ZsxZrgx5ifGmOXGmBeMMadzLqeLMeb/Zq/Vzxljvm+M6eRcbnzGmNuMMVuMMc/lDQt87hpjrsmOv8IYc0091sUPAuwyjDGtkv5D0sWS5kl6pzFmXn1LhZB6JP2NtXaepNMk/UV2X94g6V5r7RxJ92Y/S5l9Pif773pJX6t9kRHS/5H0Qt7nz0r6grV2tqQdkq7NDr9W0o7s8C9kx0Nj+JKk31hr50o6QZn9zbmcEsaYSZI+LGmxtXaBpFZJV4pzOQ3+S9JFRcMCnbvGmJGSblKmC+dTJN3UH5QnDQF2eadIWmmtXW2tPSzpB5IuqzINEshau9Fa+0T27z3K3JAnKbM/b8+OdrsyfbErO/zbNuOPkoZnX6SEBDPGTJZ0qaRbs5+NpHMk/SQ7SvE+7t/3P5F0bnZ8JJgxZpiksyR9U5KstYettTvFuZw2bZK6jDFtkgZK2ijO5YZnrX1Q0vaiwUHP3Qsl3W2t3W6t3SHpbpUG7YlAgF3eJEnr8j6vzw5DA8v+fHiSpEcljct7gdEmSeOyf7PvG9MXJf29pL7s51GSdlpre7Kf8/djbh9nv9+VHR/JNkNSt6RvZVOBbjXGDBLncmpYazdI+ldJa5UJrHdJWirO5bQKeu42zDlNgI2mYYwZLOmnkv7KWrs7/7ts3+t0qdOgjDFvlLTFWru03mVBrNokLZL0NWvtSZL26ehPypI4lxtd9uf+y5R5mJooaZASWkMJt9J27hJgl7dB0pS8z5Ozw9CAjDHtygTX37XW/iw7eHP/z8XZ/2/JDmffN57XSXqzMeYVZdK5zlEmV3d49mdmqXA/5vZx9vthkrbVssAIZb2k9dbaR7Off6JMwM25nB7nSXrZWtttrT0i6WfKnN+cy+kU9NxtmHOaALu8xyXNybZc7lCmkcUddS4TQsjm431T0gvW2n/L++oOSf0tkK+R9Iu84e/JtmI+TdKuvJ+wkEDW2huttZOttdOVOVfvs9a+W9LvJF2RHa14H/fv+yuy46em5iStrLWbJK0zxhybHXSupOfFuZwmayWdZowZmL129+9jzuV0Cnru3iXpAmPMiOyvHRdkhyUOL5qpwBhziTJ5na2SbrPWfqa+JUIYxpgzJD0k6Vkdzc/9qDJ52D+SNFXSGklvt9Zuz17Uv6LMz5L7Jf2ptXZJzQuOUIwxZ0v6W2vtG40xM5Wp0R4p6UlJV1lrDxljOiV9R5l8/O2SrrTWrq5TkRGAMeZEZRqydkhaLelPlaks4lxOCWPM/5P0DmV6gHpS0nXK5NlyLjcwY8z3JZ0tabSkzcr0BvJzBTx3jTHvU+YeLkmfsdZ+q4ar4RsBNgAAAOAQKSIAAACAQwTYAAAAgEME2AAAAIBDBNgAAACAQwTYAAAAgEME2AAAAIBDBNgAAACAQwTYAAAAgEP/Hw4YERbRSog2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated[:], derivative_raw[:], linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6483a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "530\n"
     ]
    }
   ],
   "source": [
    "end_start = np.where(x_interpolated > 134)[0][0]  # Filtrado de datos\n",
    "print(end_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23e19226",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir las regiones donde se aplicará el suavizado\n",
    "transition_index = end_start\n",
    "\n",
    "# Aplicar suavizado en cada región con diferentes parámetros\n",
    "#y_smooth = apply_savgol_smoothing(y_interpolated, transition_index, window_length1=60, polyorder1=2, window_length2=100, polyorder2=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6bf2598",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_smooth = ss.savgol_filter(y, window_length=100, polyorder=2)\n",
    "y_smooth = gaussian_filter1d(y_interpolated, sigma=15, order=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87cbe5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_smooth = apply_mix_smoothing(y_interpolated, transition_index = (end_start-60), window_length1 = 100, polyorder1 = 2, sigma2= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e4a3f8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7f320b80>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated, y_smooth, linewidth=3, label=\"y_smoothed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fd98826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4.09323390e-04 -6.12985133e-04 -1.01864973e-03 ... -2.35405471e-04\n",
      " -1.42038614e-04 -9.49551346e-05]\n"
     ]
    }
   ],
   "source": [
    "derivative = calculate_derivative(x_interpolated, y_smooth)\n",
    "print(derivative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d451fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7f3945e0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated, derivative, linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd715d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7f3fc820>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aplicación del filtro de suavizado con kernel gaussiano en la primera derivada.\n",
    "y_dev_smooth = ss.savgol_filter(derivative,window_length=100,polyorder=2,deriv=0)\n",
    "plt.figure(figsize=(12, 9))\n",
    "# gaussian_filter1d(derivative, sigma=11, order=0)\n",
    "#ss.savgol_filter(derivative,window_length=50,polyorder=2,deriv=0)\n",
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated[:], y_dev_smooth[:], linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e168fc6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "a = np.where(y_dev_smooth < 0)[0][0]  # Filtrado de datos\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d0f4bbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.85\n",
      "-0.0020313313780932533\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[a])\n",
    "print(y_dev_smooth[a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10e2353b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exportación de datos DTG de Mica-2 pura.\n",
    "df2 = pd.DataFrame({\"Temperatura\":x_interpolated,\"TG_smooth\":y_smooth,\"TG_dev\":y_dev_smooth})\n",
    "df2.to_csv('./nuevaCarpeta/Mica2_TG_smooth.txt',sep='\\t',index=False,decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95ef5fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_interpolated[:], derivative[:]\n",
    "df3 = pd.DataFrame({\"Temperatura\":x,\"DTG_raw\":derivative_raw[:],\"DTG_processed\":y_dev_smooth})\n",
    "df3.to_csv('./nuevaCarpeta/ejemploMica.txt',sep='\\t',index=False,decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e85ad3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.where(x_interpolated>114.0)[0][0] #filtrado de datos\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "75bef92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114.1368409243886\n",
      "-0.007375964731427414\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[b])\n",
    "print(y_dev_smooth[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "acba677e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[88.92113529]\n",
      "[-0.11698043]\n"
     ]
    }
   ],
   "source": [
    "min_temp = np.where(y_dev_smooth == min(y_dev_smooth[a:b]))\n",
    "print(x_interpolated[min_temp])\n",
    "print(y_dev_smooth[min_temp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65ba1d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.769527121250362"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Integración numérica con la regla trapezoidal.\n",
    "resultado_original = np.trapz(y_dev_smooth[a:b],x_interpolated[a:b])\n",
    "resultado_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e049ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado desplazado: -4.832838173395284\n",
      "error_integral: 0.06331105214492183\n"
     ]
    }
   ],
   "source": [
    "# Cálculo del error\n",
    "a_e = np.where(x>(x_interpolated[a]-0.1))[0][0]\n",
    "b_e = np.where(x>(x_interpolated[b]+0.5))[0][0]\n",
    "resultado_desplazado = np.trapz(y_dev_smooth[a_e:b_e], x_interpolated[a_e:b_e])\n",
    "print(\"resultado desplazado:\", resultado_desplazado)\n",
    "error_integral = np.abs(resultado_original - resultado_desplazado)\n",
    "print(\"error_integral:\", error_integral)"
   ]
  }
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