2.2.Measuring_Ic_Vc_and_Rn_at_481ohm.ipynb

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  {
   "cell_type": "markdown",
   "id": "d8a8a37e-f395-405f-a07d-0349694b422d",
   "metadata": {},
   "source": [
    "# Measuring the Josephson Current, the gap voltage and the normal state resistance at 481ohm \n",
    "** **\n",
    " * This project uses the lmfit package. Lmfit provides curve fiting using non-linear least squares minimization and was developed by Matt Newville: https://github.com/newville."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bc9cd0-0ca7-4229-bb2d-1414a58bec04",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe638614-0386-4845-b482-4cf89c0a7ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04360f36-197c-420b-8d71-f2df94a17d09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit.models import LinearModel\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0754b065-979d-4761-ae32-c4f0e97c6630",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b466b556-6609-46cb-ace8-4d4f8e8be730",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.142</td>\n",
       "      <td>62.4100</td>\n",
       "      <td>0.00316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.106</td>\n",
       "      <td>61.7223</td>\n",
       "      <td>0.00312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.888</td>\n",
       "      <td>59.9951</td>\n",
       "      <td>0.00303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.718</td>\n",
       "      <td>58.4704</td>\n",
       "      <td>0.00296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.478</td>\n",
       "      <td>56.8291</td>\n",
       "      <td>0.00288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       0        1        2\n",
       "0  8.142  62.4100  0.00316\n",
       "1  8.106  61.7223  0.00312\n",
       "2  7.888  59.9951  0.00303\n",
       "3  7.718  58.4704  0.00296\n",
       "4  7.478  56.8291  0.00288"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "df = pd.read_csv(\"./Data/481_one_pass.csv\", sep = \" \", header = None)\n",
    "\n",
    "#Preview the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f59ccf-9d58-428a-aba2-80e58fb89e31",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0e46b58-f661-4940-a460-22838f6fb379",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V =  [8.142, 8.106, 7.888, 7.718, 7.478, 7.294, 7.125, 6.885, 6.708, 6.539, 6.355, 6.165, 5.953, 5.79, 5.642, 5.451, 5.282, 5.091, 4.922, 4.773, 4.59, 4.392, 4.279, 4.096, 3.947, 3.785, 3.622, 3.488, 3.305, 3.164, 3.051, 2.881, 2.712, 2.57, 2.401, 2.359, 2.344, 2.337, 2.316, 2.288, 2.288, 2.281, 2.267, 2.217, 2.161, 1.179, 0.113, 0.134, 0.12, 0.106, 0.106, 0.12, 0.099, -2.02, -2.041, -2.069, -2.069, -2.097, -2.083, -2.125, -2.14, -2.154, -2.309, -2.464, -2.641, -2.789, -2.938, -3.1, -3.255, -3.411, -3.573, -3.735, -3.905, -4.103, -4.272, -4.456, -4.611, -4.781, -4.978, -5.176, -5.345, -5.593, -5.741, -5.932, -6.165, -6.369, -6.56, -6.793, -6.998, -7.181, -7.457, -7.64, -7.88, -7.669, -7.33, -7.005, -6.624, -6.285, -5.988, -5.656, -5.345, -5.091, -4.781, -4.47, -4.202, -3.933, -3.7, -3.411, -3.185, -2.888, -2.676, -2.436, -2.224, -2.168, -2.154, -2.14, -2.125, -2.09, -1.928, -0.332, 0.064, 0.042, 0.042, 0.057, 2.203, 2.217, 2.26, 2.288, 2.302, 2.429, 2.697, 2.952, 3.192, 3.418, 3.644, 3.898, 4.152, 4.406, 4.675, 4.943, 5.232, 5.515, 5.826, 6.101, 6.376, 6.715, 6.984, 7.351, 7.605, 8.001, 8.184]\n",
      "I =  [62.41, 61.7223, 59.9951, 58.4704, 56.8291, 55.3802, 53.7648, 52.3977, 50.9256, 49.2959, 47.8858, 46.4812, 45.1189, 43.5574, 42.0321, 40.6118, 39.3805, 37.8047, 36.6047, 35.0801, 33.6775, 32.348, 30.9973, 29.5013, 28.3975, 26.8537, 25.7282, 24.256, 23.0998, 21.6849, 20.5061, 19.2011, 18.111, 16.6982, 15.6429, 14.3823, 13.1209, 11.8876, 10.7797, 9.59, 8.5783, 7.3709, 6.3353, 5.1156, 4.0794, 2.8071, 1.7791, 0.689, -0.3295, -1.5015, -2.6543, -3.887, -4.8721, -5.8674, -7.1778, -8.2686, -9.4754, -10.4946, -11.6788, -12.8678, -14.0603, -15.2459, -16.6989, -17.845, -19.2864, -20.399, -21.8759, -23.2109, -24.6381, -25.9117, -27.2944, -28.5728, -30.2189, -31.53, -32.9312, -34.3488, -35.8093, -37.4268, -39.0353, -40.4877, -42.0935, -43.479, -45.091, -46.7234, -48.4493, -49.7911, -51.7312, -53.1631, -54.8993, -56.5685, -58.4063, -60.1493, -61.9004, -59.6138, -56.5453, -53.9926, -51.4324, -48.9937, -46.1067, -43.8815, -41.3165, -38.7236, -36.585, -34.211, -31.9864, -29.6725, -27.4745, -25.0446, -22.964, -21.0512, -18.8607, -16.8524, -15.0494, -12.9149, -11.0601, -9.2387, -7.2849, -5.529, -3.7301, -1.7314, -0.1849, 1.6406, 3.5596, 5.3619, 6.8954, 8.8587, 10.5246, 12.5663, 14.3393, 16.5352, 18.1745, 20.3636, 22.273, 24.5739, 26.5324, 28.7754, 30.8554, 33.1031, 35.149, 37.5618, 39.7066, 42.0915, 44.1066, 46.735, 48.9978, 51.2701, 53.8098, 56.0992, 58.7549, 61.0545, 62.0368]\n"
     ]
    }
   ],
   "source": [
    "#Ascribe the columns to the V and I varibles \n",
    "V = list(df[0])\n",
    "I = list(df[1])\n",
    "\n",
    "#Preview the data\n",
    "print(\"V = \", V)\n",
    "print(\"I = \", I )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "916e74e8-0298-4365-b57c-a2e61a62c345",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123\n",
      "141\n"
     ]
    }
   ],
   "source": [
    "#Function the returns the index of the last occurrence of an item in a list\n",
    "def find_last_occurrence(value, list):\n",
    "    for i, item in enumerate(list):\n",
    "        if item == value:\n",
    "            last_occurrence = i\n",
    "    return last_occurrence\n",
    "\n",
    "\n",
    "lower_limit = find_last_occurrence(0.057, V)\n",
    "upper_limit = find_last_occurrence(5.515, V)\n",
    "\n",
    "print(lower_limit)\n",
    "print(upper_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d68cd360-3b84-4be3-968d-8d77562286e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V =  [0.057 2.203 2.217 2.26  2.288 2.302 2.429 2.697 2.952 3.192 3.418 3.644\n",
      " 3.898 4.152 4.406 4.675 4.943 5.232]\n",
      "I =  [ 5.3619  6.8954  8.8587 10.5246 12.5663 14.3393 16.5352 18.1745 20.3636\n",
      " 22.273  24.5739 26.5324 28.7754 30.8554 33.1031 35.149  37.5618 39.7066]\n"
     ]
    }
   ],
   "source": [
    "#Remove the data above the chosen upper limit to enhance the view and focus on the single forward pass\n",
    "V = np.array(V[lower_limit:upper_limit])\n",
    "\n",
    "I = np.array(I[lower_limit:upper_limit])\n",
    "\n",
    "#Preview the new subsets\n",
    "print(\"V = \", V)\n",
    "print(\"I = \", I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aede1c1b-543e-4e7f-bd18-2cac0aa30847",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Preview the data\n",
    "sns.relplot(x = V, y = I, kind = \"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6909a570-bf72-4f1b-b00d-4e955bfc4a82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Set figure size\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "# Define the data\n",
    "V = np.array(V)\n",
    "I = np.array(I)\n",
    "\n",
    "\n",
    "# Define the window size\n",
    "window_size = 6\n",
    "\n",
    "# Calculate the moving averages\n",
    "V_avg = np.convolve(V, np.ones(window_size)/window_size, mode='same')\n",
    "I_avg = np.convolve(I, np.ones(window_size)/window_size, mode='same')\n",
    "\n",
    "# Calculate the standard deviations\n",
    "V_std = np.std(V)\n",
    "I_std = np.std(I)\n",
    "\n",
    "# Calculate the z-scores\n",
    "V_z = (V - V_avg) / V_std\n",
    "I_z = (I - I_avg) / I_std\n",
    "\n",
    "# Calculate the change points\n",
    "change_points = []\n",
    "for i in range(len(V_z)):\n",
    "    if abs(V_z[i]) > 3 or abs(I_z[i]) > 0.01:\n",
    "        change_points.append(i)\n",
    "\n",
    "# Mark the change points on the plot\n",
    "for point in change_points:\n",
    "    plt.axvline(V[point], color='r', linestyle='--', alpha=0.5)\n",
    "\n",
    "    \n",
    "# Plot the data again\n",
    "sns.lineplot(x=V, y=I, color = 'orange')  \n",
    "    \n",
    "# Set plot labels and title\n",
    "plt.xlabel('V')\n",
    "plt.ylabel('I')\n",
    "plt.title('Inflection Points Detection')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05975d96-f651-48b2-9092-5704b0f5936c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Preview the inflection points array\n",
    "inflection_points = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc785054-bd2d-4230-86b4-fa6440d296cd",
   "metadata": {},
   "source": [
    "### Clear the graph above to focus on Ic and Vc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1311acd9-0417-4089-a690-7627baaf5f39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vBX4RERERKXNuNmxdN+2KiIiIiJRhCvwiIiIiImWYAr+IiIiISBmmwC8iIiIiUoYp8IuIiIiIlGEK/CIiIiIiZZgCv4iIiIhIGabALyIiIiJShinwi4iIiIiUYQr8IiIiIiJlWIkJ/LNnz8ZisTB+/HjbOsMwmDFjBoGBgbi5udGpUyf2799vXpEiIiIiIqVMiQj80dHRvP/++4SEhNitnzt3LvPmzWPhwoVER0fj7+9Pt27dSE9PN6lSEREREZHSxfTAf+7cOYYOHcoHH3xApUqVbOsNw2D+/Pk8//zzPPDAAzRp0oSPP/6YjIwMPv/8cxMrFhEREREpPUwP/GPGjKFPnz507drVbv2RI0dITEyke/futnUuLi507NiRzZs3X/d4WVlZpKWl2b1ERERERMqrCmaefPny5ezcuZPo6Og82xITEwGoVq2a3fpq1arxxx9/XPeYs2fP5sUXXyzcQkVERERESinTeviPHTvGuHHjWLZsGa6urtdtZ7FY7JYNw8iz7kpTp04lNTXV9jp27Fih1SwiIiIiUtqY1sO/Y8cOkpKSaNmypW1dTk4OUVFRLFy4kIMHDwLWnv6AgABbm6SkpDy9/ldycXHBxcWl6AoXEREREQH4YwW4B0HVdmZXckOm9fB36dKFvXv3EhMTY3uFhoYydOhQYmJiuOOOO/D39yc8PNy2z8WLF4mMjKRt27ZmlS0iIiIi5V12JvzyBPz8sPWVdcbsim7ItB5+Ly8vmjRpYrfOw8ODypUr29aPHz+eWbNmUa9ePerVq8esWbNwd3dnyJAhZpQsIiIiIuVd6q+waRCk7gMsUHs4OHmbXdUNmXrT7s1MmTKFzMxMnnzySVJSUmjTpg3ff/89Xl5eZpcmIiIiIuWJYcCRjyF6DORkgGs1aLsM/LvefF+TWQzDMMwuoiilpaXh4+NDamoq3t4l+9uXiIiIiJRAl9Ih+kmIX2Zd9u8KYZ+Cm7+pZeU355boHn4REREREVOlxFiH8KT/DhZHCJkJjZ4Di+mPs8o3BX4RERERkasZBsQugp0TITfLOhtP2y/Ar73ZlRWYAr+IiIiIyJUunoVtj8GxldblwPsgbCm4VDazqlumwC8iIiIictnpbdapNs/Hg4MT3DUX6o+DGzz4taRT4BcRERERMXLht39DzHNgZIPnHdBuBVQONbuy26bALyIiIiLl24XTsHUYnPzOulxjILR+H5x9zK2rkCjwi4iIiEj5lRQFPw+BzBPg4AItF0Ddv5fqITxXU+AXERERkfInNwf2z4J9M6zDebwbWIfwVAoxu7JCp8AvIiIiIuVLZgJs/j849ZN1ufYwCF0ITp7m1lVEFPhFREREpPxI+B62PAIXkqCCB4S+A3c8anZVRUqBX0RERETKvtxLsOcF+PVV63LFEOsQHp8G5tZVDBT4RURERKRsO38Ufh4Mpzdbl+v9A5q/ARXczK2rmCjwi4iIiEjZdXwNbB0OF1PAyRvafAQ1HjK7qmKlwC8iIiIiZU9OFsQ8CwcXWJd9W0H75dYHapUzCvwiIiIiUrakH4KfB8GZHdblBpOg2SxwdDa3LpMo8IuIiIhI2RG/HH75O2Sng7MvhH0M1e8zuypTKfCLiIiISOmXnQk7x0Pc+9blqvdAu8/BPcjUskoCBX4RERERKd1Sf4VNgyB1H2CBxs9D0+ngoKgLCvwiIiIiUloZBhz5GKLHQE4GuFaDtsvAv6vZlZUoCvwiIiIiUvpcSofoJyF+mXXZvxuEfQpu1cytqwRS4BcRERGR0iUlxjqEJ/13sDhCyEvQ6FmwOJhdWYmkwC8iIiIipYNhQOwi2DkRcrOsN+S2/QL82ptdWYmmwC8iIiIiJV/uJdj2GBz5xLpcvS/cvQRcKptbVymgwC8iIiIiJdulc7DpIUhYbx3C0/w1qD8eLBazKysVFPhFREREpOS6kAQRfeDMdnB0h/ZfQfXeZldVqijwi4iIiEjJdO4w/NQDzsVZh+50/BaqtDG7qlJHgV9ERERESp4zOyGil7WH36MWdF4P3neaXVWppMAvIiIiIiVLQjhsfACyz0Glu6DTd+AWYHZVpZYmKxURERGRkuPIZxDR2xr2q90LXSMV9m+TAr+IiIiIlAwH3oAt/wdGNtR82Nqz7+RtdlWlnob0iIiIiIi5jFzY9Qz8Ns+6XH88tHhDT84tJAr8IiIiImKenIuwdQT88bl1uflr0GCS5tgvRAr8IiIiImKOS+nWm3MTfwBLBbh7MdR+xOyqyhwFfhEREREpfpmnrDfnpuyECh7QfiUE9jC7qjJJgV9EREREildaLET0tD5Yy6Wq9ebcyqFmV1VmKfCLiIiISPFJjoaIPpD1J3jeYX2glldds6sq0xT4RURERKR4nFwHmx6C7PNQqcVfD9SqZnZVZZ7mOhIRERGRonfkU4jsaw37/t2ga4TCfjFR4BcRERGRomMY8Otc2PKo9YFatYZCx/+Ck5fZlZUbGtIjIiIiIkXDyIWdE+HgAutyw8lw1xw9UKuYKfCLiIiISOHLybL26h/90rrc/A1oONHcmsopBX4RERERKVwXU2HjADi1ARyc4O6lUGuI2VWVWwr8IiIiIlJ4MhNgQy84uxsqeEKH1eDf1eyqyjUFfhEREREpHGkHYUNPOB8PrtWs0276tjC7qnJPgV9EREREbt/pbRDZB7KSwbMu3Lve+mAtMZ0Cv4iIiIjcnhPfwqaBkJMBvq2g03/B1c/squQvmhNJRERERG7doSUQ1c8a9gN6QpefFPZLGAV+ERERESk4w4D9s2DbSDByoPaj0HENOHmaXZlcRUN6RERERKRgcnNgxziIfdu63OhZaDYbLBZz65JrUuAXERERkfzLuQCb/w+OrQQs0OLf0GCc2VXJDZg6pGfRokWEhITg7e2Nt7c3YWFhrF271rZ9+PDhWCwWu9fdd99tYsUiIiIi5djFs7ChhzXsOzhDuy8U9ksBU3v4g4KCePXVV6lbty4AH3/8Mf369WPXrl00btwYgJ49e7JkyRLbPs7OzqbUKiIiIlKunT8KkffB2b1QwQs6fA3+95pdleSDqYG/b9++dsuvvPIKixYtYuvWrbbA7+Ligr+/vxnliYiIiAjAie9gyyNw8Qy4+kPntVDpLrOrknwqMbP05OTksHz5cs6fP09YWJhtfUREBH5+ftx55508/vjjJCUl3fA4WVlZpKWl2b1ERERE5BbkZkPMNOsDtS6eAd+W0H2Lwn4pY/pNu3v37iUsLIwLFy7g6enJ6tWradSoEQC9evXib3/7GzVr1uTIkSP861//4t5772XHjh24uLhc83izZ8/mxRdfLM5LEBERESl7MhPg58GQFGldrjcGWrwBjtfOYFJyWQzDMMws4OLFixw9epSzZ8+ycuVKPvzwQyIjI22h/0oJCQnUrFmT5cuX88ADD1zzeFlZWWRlZdmW09LSCA4OJjU1FW9v7yK7DhEREZEyI/En2DwYLiRBBU9o8yHUHGR2VXKVtLQ0fHx8bppzTe/hd3Z2tt20GxoaSnR0NAsWLOC9997L0zYgIICaNWsSGxt73eO5uLhct/dfRERERG7AyIV9r8C+Gdb3FZtC+6/Au77ZlcltMD3wX80wDLse+islJydz7NgxAgICirkqERERkTLuwp/WG3MT1luX7xgJoW9BBXdz65LbZmrgnzZtGr169SI4OJj09HSWL19OREQE69at49y5c8yYMYMHH3yQgIAA4uPjmTZtGlWqVGHAgAFmli0iIiJStvz5M2waBJknwNENWr0Ddww3uyopJKYG/lOnTvHII4+QkJCAj48PISEhrFu3jm7dupGZmcnevXv55JNPOHv2LAEBAXTu3JkVK1bg5eVlZtkiIiIiZYNhwG/zIOZZMHKsQ3faf2UdyiNlhuk37Ra1/N7MICIiIlKuXEyBrSPg+DfW5ZoPQ+v3wUkdq6VFqblpV0RERESK2ZkdsPFvcP4IODhDy/lQdzRYLGZXJkVAgV9ERESkvDAMiF0EOydA7kXwqA33fGV9oJaUWQr8IiIiIuXBpXT45e/wx3LrclA/uHsJOFcyty4pcgr8IiIiImXd2b2w8SFI/x0sFeCuOdBggobwlBMK/CIiIiJl2aElsH0M5GSCexC0WwFV25pdlRQjBX4RERGRsig7wxr0Dy+1Lgf0gLBl4FrF1LKk+Cnwi4iIiJQ1aQetQ3hS94HFAZrOhMZTre+l3FHgFxERESlL/lgB2x6D7HPgWg3afQHVOptdlZhIgV9ERESkLMjJgp0TIfYd67JfR2vYdwswty4xnQK/iIiISGl37jBsGmh9oBZA4+eh6QxwUNQTBX4RERGR0u34N7BlGFxKBZfKEPYpBPYyuyopQRT4RUREREqj3EsQMxV+e8O6XPluaP8leASbW5eUOAr8IiIiIqVNxnHYNAhOb7Yu158Ad70Kjs7m1iUlkgK/iIiISGlycj1sGQpZyeDkA3cvgeABZlclJZgCv4iIiEhpkJsDe2fA/lcAAyo1h/ZfgVcdsyuTEk6BX0RERKSku/An/DwITm2wLtcdDS3/DY6u5tYlpYICv4iIiEhJlh4HG3rBuTio4AGt34daQ8yuSkoRBX4RERGRkur0LxB5H2T9CR61oNO34NPI7KqklFHgFxERESmJjv/HOownJxMqtbCGfTd/s6uSUsjB7AJERERE5Cqx78HG/tawH9ATukYq7MstU+AXERERKSkMA3Y/D9GjwciFO0ZCxzXg5Gl2ZVKKaUiPiIiISEmQcxG2PQbxn1qXm86AJi+AxWJqWVL6KfCLiIiImO1iKmx8EE79CBZH60w8dUaaXZWUEQr8IiIiImbKOAERveHsHuu0m+3/HwT2NLsqKUMU+EVERETMcnY/RPSEjOPgWg06fQe+LcyuSsoYBX4RERERM5yKgKj+cCkVvOtDp3XgWcvkoqQsUuAXERERKW7xy2HrMMi9CFXbQYc14OJrdlVSRmlaThEREZHiYhhw4HXYPNga9oMfhM7hCvtSpNTDLyIiIlIccnNg5wT4/S3rcv1x0PwNcHA0ty4p8xT4RURERIpadiZsHgrHV1uXW8yDBhPMrUnKDQV+ERERkaJ04TRE3Q+nt4CDM4R9CjUHml2VlCMK/CIiIiJF5dxh2NAL0n8Hp4rQ8Rvw62B2VVLOKPCLiIiIFIXk7RDZBy4kgXsN6LwWfBqZXZWUQwr8IiIiIoXtxHfw80DIPg8Vm1kfqOUeaHZVUk5pWk4RERGRwhT3oXXMfvZ58O8G3aIU9sVUCvwiIiIihcEwYM90+OVxMHKg9jDo9C04eZtdmZRzGtIjIiIicrtyL8Evf4fDS63LTf4FTV8Ei8XUskRAgV9ERETk9lxKh40PQeL3YHGEVoug7uNmVyVio8AvIiIicqsyEyCiN6TEgKM7tP8SqvcxuyoROwr8IiIiIrci9QBE9ILzf4BLVet4/cqtzK5KJA8FfhEREZGCStoIUf3gYgp41YNOa8GrjtlViVyTAr+IiIhIQRz9CjY/ArlZUPlu6PgfcK1idlUi16VpOUVERETy67d/w6ZB1rAf1B+6/KiwLyWeevhFREREbib3EuyYALFvW5frjYGWC8DB0dy6RPJBgV9ERETkRi6chp8HwqkN1uW75kDDZzTHvpQaCvwiIiIi13N2L0T2g/NHoIIntF0GQf3MrkqkQBT4RURERK7l2GrY8ghknwfPO6DDGqjY2OyqRApMgV9ERETkSkYu7HsZ9k63Lle71/pALZfK5tYlcosU+EVEREQuyz4PW4bBsZXW5TufhhZvgIMik5Re+tsrIiIiAtYn5kb2g7O7wcEJWi2COqPMrkrktinwi4iIiCRFwcYHIes0uPrBPaugajuzqxIpFKY+eGvRokWEhITg7e2Nt7c3YWFhrF271rbdMAxmzJhBYGAgbm5udOrUif3795tYsYiIiJQ5se/Cj12sYb9SC+ixXWFfyhRTA39QUBCvvvoq27dvZ/v27dx7773069fPFurnzp3LvHnzWLhwIdHR0fj7+9OtWzfS09PNLFtERETKgtxLEP0kRP8DjGyo+TB02wgewWZXJlKoLIZhGGYXcSVfX19ee+01Ro4cSWBgIOPHj+fZZ58FICsri2rVqjFnzhyeeOKJa+6flZVFVlaWbTktLY3g4GBSU1Px9vYulmsQERGREu7Cn7Dpb5AUCVig2SvQ6Dk9TEtKlbS0NHx8fG6ac03t4b9STk4Oy5cv5/z584SFhXHkyBESExPp3r27rY2LiwsdO3Zk8+bN1z3O7Nmz8fHxsb2Cg/UtXURERK6QsgfWt7KG/Qpe0OEbaDxVYV/KLNMD/969e/H09MTFxYXRo0ezevVqGjVqRGJiIgDVqlWza1+tWjXbtmuZOnUqqampttexY8eKtH4REREpRY6tgvC21hl5POtAj60Q1NfsqkSKlOmz9NSvX5+YmBjOnj3LypUrGTZsGJGRkbbtlqu+bRuGkWfdlVxcXHBxcSmyekVERKQUMnJh70zY96J12b8rtFsBLr7m1iVSDEwP/M7OztStWxeA0NBQoqOjWbBggW3cfmJiIgEBAbb2SUlJeXr9RURERK7r0jnY8igcX21drj8emr+mh2lJuWH6kJ6rGYZBVlYWtWvXxt/fn/DwcNu2ixcvEhkZSdu2bU2sUEREREqNc0esQ3iOrwYHZ2izGFr+W2FfyhVT/7ZPmzaNXr16ERwcTHp6OsuXLyciIoJ169ZhsVgYP348s2bNol69etSrV49Zs2bh7u7OkCFDzCxbRERESoNTEbDpIchKBtdqcM9qqBpmdlUixc7UwH/q1CkeeeQREhIS8PHxISQkhHXr1tGtWzcApkyZQmZmJk8++SQpKSm0adOG77//Hi8vLzPLFhERkZIudhFsf9o6v75vS+jwNbgHmV2ViClK3Dz8hS2/85OKiIhIGZBzEXY8DXHvWZdrDoE2H0IFN3PrEikC+c25GsAmIiIiZcOFJNj4EPy5EbDAXa9Cw2c0v76Uewr8IiIiUvqlxEBkP8g4Ck7e0PZzqN7H7KpESgQFfhERESndjn4FW4ZDTgZ41oWOa8CnodlViZQYJW5aThEREZF8MXJhzwuwaaA17Pt3h56/KOyLXEU9/CIiIlL6XEr/62FaX1uXG0yyjtnX/Poieei/ChERESldzh2GyPshdb/1YVqtP4A7HjW7KpESS4FfRERESo9TEbDxQbh4Blz9ocNqqHK32VWJlGgK/CIiIlI6xH8OW4dD7iXwbWUN++7Vza5KpMTTTbsiIiJSshkG/DoHNg+1hv3gh6BrpMK+SD6ph19ERERKrtwc2PEUxC6yLtefAC1eB4v6LEXyS4FfRERESqbsDPh5MJxYA1igxTxoMN7sqkRKHQV+ERERKXku/AmRfSF5Gzi4QNvPoMaDZlclUiop8IuIiEjJkh4HG3rBuThw9rU+ObdqO7OrEim1FPhFRESk5Di9DSLvg6zT4FELOq8D7/pmVyVSqinwi4iISMlw/BvrmP2cTPBtCR3/C27+ZlclUurpFncREREx3+/vwMYHrGE/sDd0iVDYFykkCvwiIiJiHiMXYp6D7WOs7+s8Dh2+ASdPsysTKTM0pEdERETMkZMFW0fCH59bl0NegsbPg8Vibl0iZYwCv4iIiBS/i2chagAkRYClArT5EO4YZnZVImWSAr+IiIgUr/PHIKIXpO6HCl5wz0oI6GZ2VSJllgK/iIiIFJ+U3RDRGzJPglsgdPoOKjUzuyqRMk2BX0RERIpH4g8Q9QBkp4NPY2vY96hhdlUiZZ4Cv4iIiBS9w5/AtlFgZINfJ+iwGpwrml2VSLmgaTlFRESk6BgG7HsFtg6zhv2ag61Pz1XYFyk26uEXERGRopGbDdFPwqEPrMuNnoVms8Ci/kaR4qTALyIiIoXv0jn4eRCc/M4a8Fu+CXeOMbsqkXJJgV9EREQKV+YpiOwDZ3aAoxu0+wKC+pldlUi5pcAvIiIihSftIGzoBeePgEsV6PgfqHK32VWJlGsK/CIiIlI4/vwZIu+Hi2fAsw50Wgve9cyuSqTcU+AXERGR23d0JWweCrlZULm1tWff1c/sqkQETcspIiIit+u3BbDpb9awX70vdNmgsC9Sgijwi4iIyK0xcmHnJNg5HjCg3j/gntVQwd3sykTkChrSIyIiIgWXcwG2PApHv7Iu3/UqNJwCFou5dYlIHgr8IiIiUjBZZyCqH/y5CRyc4O6lUGuI2VWJyHUo8IuIiEj+nTsMEX0g7Tdw8oYOX0O1zmZXJSI3oMAvIiIi+XN6G0T2haw/wT0IOn0HFZuaXZWI3IQCv4iIiNzc0ZWw5f+sY/crNYeO/wX3QLOrEpF80Cw9IiIicn2GAQfesE67mXMBAvtA1yiFfZFSRD38IiIicm252bDjaYhdZF2uNwZazgcHxQeR0kT/xYqIiEhel87Bz4Pg5HeABVq8AfXHa9pNkVJIgV9ERETsZZyAyPsgJQYc3aDtZxA8wOyqROQWKfCLiIjI/6Tsgcg+kHEcXP2gw3+gSmuzqxKR26DALyIiIlYn11tvzs1OB+8G1mk3PWubXZWI3CbN0iMiIiIQ9761Zz87Hfw6QffNCvsiZYQCv4iISHlm5ELMc/DLE2DkQK1HoPN6cK5kdmUiUkg0pEdERKS8yrkAW4bB0S+ty01nQJMXNBOPSBmjwC8iIlIeXTgNUf3g9GZwcILWH8Idj5pdlYgUAQV+ERGR8iYtFiJ6w7k4cKoIHVZDtU5mVyUiRUSBX0REpDxJ2mTt2b94BjxqWWfi8WlodlUiUoQU+EVERMqL+C9g63DIvQiVW0OHNeBWzeyqRKSImTpLz+zZs2nVqhVeXl74+fnRv39/Dh48aNdm+PDhWCwWu9fdd99tUsUiIiKlkGHA/lmweYg17AcNgC4bFPZFyglTA39kZCRjxoxh69athIeHk52dTffu3Tl//rxdu549e5KQkGB7fffddyZVLCIiUsrkXoJfHofdz1uXG0yE9l9BBXdz6xKRYmPqkJ5169bZLS9ZsgQ/Pz927NhBhw4dbOtdXFzw9/cv7vJERERKt4up1ifnJoaDxQFavgl3jjG7KhEpZiXqwVupqakA+Pr62q2PiIjAz8+PO++8k8cff5ykpKTrHiMrK4u0tDS7l4iISLlz/iiEt7eG/Qoe0OEbhX2RcspiGIZhdhEAhmHQr18/UlJS2Lhxo239ihUr8PT0pGbNmhw5coR//etfZGdns2PHDlxcXPIcZ8aMGbz44ot51qempuLt7V2k1yAiIlIinNkBEffBhURwC4CO/wXfFmZXJSKFLC0tDR8fn5vm3BIT+MeMGcO3337Lpk2bCAoKum67hIQEatasyfLly3nggQfybM/KyiIrK8u2nJaWRnBwsAK/iIiUD8f/Az8/DDkZULEpdPwWPILNrkpEikB+A3+JmJbzqaeeYs2aNURFRd0w7AMEBARQs2ZNYmNjr7ndxcXlmj3/IiIiZd7BhbBzHBi54N8d7vkKnNTZJVLemRr4DcPgqaeeYvXq1URERFC7du2b7pOcnMyxY8cICAgohgpFRERKgdwc2DUZDs63Ltd5DFq9Aw5OppYlIiWDqTftjhkzhmXLlvH555/j5eVFYmIiiYmJZGZmAnDu3DkmT57Mli1biI+PJyIigr59+1KlShUGDBhgZukiIiIlQ24ObBv5v7DfbDa0fl9hX0RsTO3hX7RoEQCdOnWyW79kyRKGDx+Oo6Mje/fu5ZNPPuHs2bMEBATQuXNnVqxYgZeXlwkVi4iIlCBGrnWO/SOfgMURwj6FWoPNrkpEShjTh/TciJubG+vXry+makREREoRIxd+GQ2Hl1jDfrsvoMbfzK5KREqgEjUPv4iIiOSDYcD2sXDoA+sDtcI+VdgXketS4BcRESlNDAN2jIPYRYAF7l6qYTwickMK/CIiIqWFYcDOifD7W1jD/mKo/YjZVYlICafALyIiUhoYBsRM+d9sPK3fhzuGm1mRiJQSCvwiIiIlnWHA7mlw4HXrcqt3oe5j5tYkIqWGAr+IiEhJt3c6/Pqq9X3oQqj3hLn1iEiposAvIiJSku2dCftesr5vMR/uHGNqOSJS+ijwi4iIlFT7XrH27gM0fwMajDO3HhEplRT4RURESqJf58Cef1rf3zUHGk40tx4RKbUU+EVEREqaA29AzHPW981egUZTzK1HREo1BX4REZGS5LcFsGuy9X3TF6HxNHPrEZFST4FfRESkpPj9bdg53vq+8T+h6QumliMiZYMCv4iISEkQ+y5sH2t93+g5CJlpbj0iUmYo8IuIiJgt7kOI/of1fcPJ0GwWWCzm1iQiZYYCv4iIiJkOLYFf/m59X3883DVXYV9ECpUCv4iIiFmOfArbRgEG3DkWWsxT2BeRQqfALyIiYob4z2HrcMCAev+Alm8q7ItIkVDgFxERKW5/fAlbHgEjF+o8DqELFfZFpMgo8IuIiBSnoyth8xBr2L9jBLR+Fyz637GIFB39CyMiIlJcjn8DPz8MRg7UfhRaf6CwLyJFTv/KiIiIFIcT/4VNfwMjG2oOgTaLwcHR7KpEpBxQ4BcRESlqJ9fCxgch9xLUGARhHyvsi0ixUeAXEREpSgnfQ9QAyL0IwQ9C22XgUMHsqkSkHFHgFxERKSqJP0JUP8jNgqD+0O4LhX0RKXYF+lfngQceyFe7VatW3VIxIiIiZcapCIjsCzkXoHpfaLcCHJzMrkpEyqECBX4fH5+iqkNERKTs+GMFbB0JOZkQ2BvafwWOzmZXJSLlVIEC/5IlS4qqDhERkdIvNxt2T4UDr1uXA3rBPSvB0cXcukSkXNNAQhERkcJw4bR1jv1TP1qXG06BZq9ozL6ImE7/ComIiNyuMzth4wNw/g+o4GGdY7/mQLOrEhEBFPhFRERuz+FPIPoJ6825nnWhw2qo2MTsqkREbBT4RUREbkXuJdg5CX5/y7oc2BvafgbOFU0tS0Tkagr8IiIiBZWZCJsGwp8brctNXoCm08Gix9uISMmjwC8iIlIQp7fCxgch8yRU8IK2n0JQP7OrEhG5LgV+ERGR/Ir7ALaPhdyL4N0AOnwN3vXNrkpE5IYU+EVERG4mJwu2PwWHPrAuBz8Ady8FJy9TyxIRyQ8FfhERkRvJOGEdwpO8DbBY59Zv9BxYLGZXJiKSLwr8IiIi15O0ETb9DS6cAudK0PZzCOxpdlUiIgWiwC8iInI1w4DfF8LOiWBkQ8UQ6/z6nneYXZmISIEp8IuIiFwpOxOiR8ORT6zLNQdDmw+sT9AVESmFFPhFREQuO/8HRD0AKTvB4gjNX4P64zVeX0RKNQV+ERERgMQf4edBkJUMLlWg/ZdQrbPZVYmI3DY9ElCkhEpMTKRbt254eHhQsWJFACwWC19//XWRnnfGjBncddddRXqOwlBa6pRSwDDgwOuwobs17Pu2hJ47FPZFpMxQ4BcpBsOHD6d///4F2uff//43CQkJxMTE8PvvvxdJXdf6AjF58mR+/PHHIjnflWrVqoXFYsFiseDu7k6TJk1477338r3/rdRZq1Yt5s+fX8BKpUzLPg8/D4Zdz4CRC3cMh64bwaOG2ZWJiBQaBX6REurQoUO0bNmSevXq4efnV2zn9fT0pHLlysVyrpkzZ5KQkMCePXvo378/o0ePZsWKFfnatzjrlDIqPQ7W3w1HV4ClArR6B9oshgpuZlcmIlKoFPhFTNCpUyeefvpppkyZgq+vL/7+/syYMcO2vVatWqxcuZJPPvkEi8XC8OHDr3mcEydOMGjQICpVqkTlypXp168f8fHxdm0WL15M48aNcXFxISAggLFjx9rOATBgwAAsFott+eqhMrm5ucycOZOgoCBcXFy46667WLdunW17fHw8FouFVatW0blzZ9zd3WnWrBlbtmy56efg5eWFv78/devW5eWXX6ZevXq23zgcPXqUfv364enpibe3NwMHDuTUqVO2fa+u8/JvUV5//XUCAgKoXLkyY8aM4dKlS7bP/I8//mDChAm23yxIOXZyLaxrBan7wNUfukZAvX/o5lwRKZMU+EVM8vHHH+Ph4cG2bduYO3cuM2fOJDw8HIDo6Gh69uzJwIEDSUhIYMGCBXn2z8jIoHPnznh6ehIVFcWmTZvw9PSkZ8+eXLx4EYBFixYxZswY/v73v7N3717WrFlD3bp1becAWLJkCQkJCbblqy1YsIA33niD119/nT179tCjRw/uv/9+YmNj7do9//zzTJ48mZiYGO68804GDx5MdnZ2gT4TV1dXLl26hGEY9O/fnzNnzhAZGUl4eDiHDh1i0KBBN9x/w4YNHDp0iA0bNvDxxx+zdOlSli5dCsCqVasICgqy/VYhISGhQLVJGWHkwr6XIaIPXDoLVcKs4/WrtjO7MhGRIqNZekRMEhISwvTp0wGoV68eCxcu5Mcff6Rbt25UrVoVFxcX3Nzc8Pf3v+b+y5cvx8HBgQ8//NDWW71kyRIqVqxIREQE3bt35+WXX2bSpEmMGzfOtl+rVq0AqFq1KgAVK1a87jkAXn/9dZ599lkefvhhAObMmcOGDRuYP38+b7/9tq3d5MmT6dOnDwAvvvgijRs3Ji4ujgYNGtz0s8jOzmbZsmXs3buXf/zjH/zwww/s2bOHI0eOEBwcDMCnn35K48aNiY6Otl3D1SpVqsTChQtxdHSkQYMG9OnThx9//JHHH38cX19fHB0dbb9VkHLoUhpsGQbHv7Yu1x0NLReAo7OpZYmIFDX18IuYJCQkxG45ICCApKSkfO+/Y8cO4uLi8PLywtPTE09PT3x9fblw4QKHDh0iKSmJkydP0qVLl1uuMS0tjZMnT9KunX3vZ7t27Thw4MB1rycgIADgptfz7LPP4unpiZubG2PGjOGZZ57hiSee4MCBAwQHB9vCPkCjRo2oWLFinvNeqXHjxjg6OtrVUZDPVMqw1N9gfRtr2HdwhjYfQetFCvsiUi6oh1/EJE5OTnbLFouF3NzcfO+fm5tLy5Yt+eyzz/Jsq1q1Kg4Ohfd9/urx7oZh5Fl35fVc3naz63nmmWcYPnw47u7uBAQE2Pa71vFvtP5aNVyuoyCfqZRRJ/4LPw+B7HRwD4L2K6FKa7OrEhEpNurhFymlWrRoQWxsLH5+ftStW9fu5ePjg5eXF7Vq1brh1JVOTk7k5ORcd7u3tzeBgYFs2rTJbv3mzZtp2LDhbV9DlSpVqFu3LoGBgXZBvlGjRhw9epRjx47Z1v3666+kpqbe1nmdnZ1veL1SBh18E6L6WcO+X0freH2FfREpZ0wN/LNnz6ZVq1Z4eXnh5+dH//79OXjwoF0bwzCYMWMGgYGBuLm50alTJ/bv329SxSIlx9ChQ6lSpQr9+vVj48aNHDlyhMjISMaNG8fx48cB60w2b7zxBm+++SaxsbHs3LmTt956y3aMy18IEhMTSUlJueZ5nnnmGebMmcOKFSs4ePAgzz33HDExMXb3BRS2rl27EhISwtChQ9m5cye//PILjz76KB07diQ0NPSWj1urVi2ioqI4ceIEp0+fLsSKpcTJzYbtT8GOcdYbdev+He4NB9fim+JWRKSkMDXwR0ZGMmbMGLZu3Up4eDjZ2dl0796d8+fP29rMnTuXefPmsXDhQqKjo/H396dbt26kp6ebWLmI+dzd3YmKiqJGjRo88MADNGzYkJEjR5KZmYm3tzcAw4YNY/78+bzzzjs0btyY++67z252nTfeeIPw8HCCg4Np3rz5Nc/z9NNPM2nSJCZNmkTTpk1Zt24da9asoV69ekV2bZcfCFapUiU6dOhA165dueOOO/I9R//1zJw5k/j4eOrUqWO7aVnKoEvp1l793xcCFmj+GrR6FxycbrqriEhZZDEMwzC7iMv+/PNP/Pz8iIyMpEOHDhiGQWBgIOPHj+fZZ58FICsri2rVqjFnzhyeeOKJmx4zLS0NHx8fUlNTbSFIRETKqIzjEHEfnN0Njm7QdhkEP2B2VSIiRSK/ObdEjeFPTU0FwNfXF4AjR46QmJhI9+7dbW1cXFzo2LEjmzdvvuYxsrKySEtLs3uJiEg5cGandSaes7vBtRp0jVTYFxGhBAV+wzCYOHEi7du3p0mTJgAkJiYCUK1aNbu21apVs2272uzZs/Hx8bG9rpzWT0REyqjj/4HweyDzJPg0hh7boPK1n9cgIlLelJjAP3bsWPbs2cMXX3yRZ1t+pgS8bOrUqaSmptpeV87yISIiZYxhwG8LrGP2czLAvzt0+xk8appdmYhIiVEi5uF/6qmnWLNmDVFRUQQFBdnWX34aZmJiou1BPmB9mM/Vvf6Xubi44OLiUrQFi4iI+XKzYcd4iP3ric91n4DQt3RzrojIVUzt4TcMg7Fjx7Jq1Sp++uknateubbe9du3a+Pv7Ex4eblt38eJFIiMjadu2bXGXKyIiJcWldIi8/6+wb4Hmr0OrRQr7IiLXYGrgHzNmDMuWLePzzz/Hy8uLxMREEhMTyczMBKxDecaPH8+sWbNYvXo1+/btsz2Vc8iQIWaWLlJgycnJ+Pn5ER8fb3YpZU5SUhJVq1blxIkTZpcixSHjOIS3h4S11pl47lkJDSfBDZ7CLCJSnpka+BctWkRqaiqdOnUiICDA9rpyru0pU6Ywfvx4nnzySUJDQzlx4gTff/89Xl5eJlYuUnCzZ8+mb9++JCcnY7FY8jy99rIePXpw//33F/j4Z86c4amnnqJ+/fq4u7tTo0YNnn76advsV9ezaNEiQkJC8Pb2xtvbm7CwMNauXWvX5mYPwLvVc9/MU089dd35/k+cOIGjoyOrVq3Cz8+PRx55hOnTp9/W+aQUOLMT1reGs3uumIlngNlViYiUbEYZl5qaagBGamqq2aVIOZaRkWFUrFjR2Lx5s2EYhtGsWTNj5MiRedodPXrUcHBwML7++usCn2Pv3r3GAw88YKxZs8aIi4szfvzxR6NevXrGgw8+eMP91qxZY3z77bfGwYMHjYMHDxrTpk0znJycjH379tnavPrqq4aXl5excuVKY+/evcagQYOMgIAAIy0t7bbOfTO7du0yACMqKirPtpdfftmoWrWqcfHiRcMwDGPPnj2Gq6urcebMmds6p5Rgx74xjOXuhvEZhvHfJoZxLt7sikRETJXfnKvAL1IMVq5caVSpUsW2/Oabbxqenp7GuXPn7NrNnDnTqFatmnHp0qVCOe+XX35pODs7F/h4lSpVMj788EPDMAwjNzfX8Pf3N1599VXb9gsXLhg+Pj7Gu+++m+9zHzlyxACMFStWGO3btzdcXV2N0NBQ4+DBg8Yvv/xitGzZ0vDw8DB69OhhJCUl2Y7TokULY/jw4XmOX7duXWPSpEl262rVqmV89NFHBbpWKQVycw3jwL8N4zOLNez/2N0wss6aXZWIiOnym3NLzLScImVZVFQUoaGhtuWhQ4dy6dIlvvrqK9s6wzBYunQpw4YNo0IF6wRas2bNwtPT84avjRs3Xve8l5+8d/l4N5OTk8Py5cs5f/48YWFhwK09AO9G554+fTr//Oc/2blzJxUqVGDw4MFMmTKFBQsWsHHjRg4dOsQLL7xgaz9q1Ci++uorzp07Z1sXGRlJXFwcI0eOtDt269atb/h5SCmUmw3bx8LOCYBhnYmn07fg7GN2ZSIipUaJmJZTpKyLj48nMDDQtuzr60v//v1ZsmQJw4cPByAiIoLDhw/bhdjRo0czcODAGx67evXq11yfnJzMSy+9xBNPPHHT+vbu3UtYWBgXLlzA09OT1atX06hRI+DGD8D7448/CnzuyZMn06NHDwDGjRvH4MGD+fHHH2nXrh1gDfhLly61tR8yZAiTJk3iq6++YsSIEQAsXryYsLAwW42XVa9enV27dt30eqWUuJQOmwZZb87FAs1fgwYTdXOuiEgBKfCLFIPMzExcXV3t1o0aNYru3bsTFxdH3bp1Wbx4Me3ataN+/fq2Nr6+vvj6+hb4fGlpafTp04dGjRrl60bW+vXrExMTw9mzZ1m5ciXDhg0jMjLSLlDn9wF4Nzt3SEiI7f3lLxFNmza1W5eUlGRbrlixIg888ACLFy9mxIgRpKens3LlSubPn5/n2G5ubmRkZNz0eqUUOH8MIu+z3pzr6AZtP9PNuSIit0hDekSKQZUqVUhJSbFb17VrV2rWrMnSpUtJS0tj1apVjBo1yq7NrQzpSU9Pp2fPnraeeienm89L7uzsTN26dQkNDWX27Nk0a9aMBQsWAPYPwLvStR6Al59zX7nu8heGq9fl5uba7TNq1Cg2bdpEbGysbRavQYMG5Tn2mTNnqFq16k2vV0q4Mzvg+zZ/zcTjr5l4RERuk3r4RYpB8+bNWbZsmd06i8XCiBEj+PDDDwkKCsLBwSHP8J2CDulJS0ujR48euLi4sGbNmjy/VcgvwzDIysoC7B+A17x5c+B/D8CbM2dOoZ/7Wjp37swdd9zB0qVL2bBhAwMHDrzm1Lz79u2jU6dOhXZeMcHxb+DnIZCTAT5NrOP1PWqYXZWISKmmwC9SDHr06MHUqVNJSUmhUqVKtvUjRoxg5syZTJs2jYcffhgPDw+7/QoypCc9PZ3u3buTkZHBsmXLSEtLIy0tDYCqVavi6OgIQJcuXRgwYABjx44FYNq0afTq1Yvg4GDS09NZvnw5ERERrFu3DrB/AF69evWoV68es2bNsnsAXn7PfasufzmaN28eKSkpvPbaa3naZGRksGPHDmbNmnVb5xKTGAYcnA87JwEGBPSA9l+Ck7fZlYmIlHoK/CLFoGnTpoSGhvLll1/a3chao0YNunbtyvfff59nxpmC2rFjB9u2bQOgbt26dtuOHDlCrVq1ADh06BCnT5+2bTt16hSPPPIICQkJ+Pj4EBISwrp16+jWrZutzZQpU8jMzOTJJ58kJSWFNm3a2D0AL7/nvh3Dhw9n+vTp1K9f33aD75W++eYbatSowT333HPb55JilpsNO8ZB7DvW5bqjIfQtcND/okRECoPFMAzD7CKKUlpaGj4+PrYpAkXM8t133zF58mT27duHg4NunylsrVu3Zvz48bbfOkgpcSkNNj18xUw8r0ODCZqJR0QkH/Kbc9V9IlJMevfuTWxsLCdOnCA4ONjscsqUpKQkHnroIQYPHmx2KVIQ549BZB84u/evmXg+h+D+ZlclIlLmqIdfRESK35kdENkXMhOsM/F0/A9UDr35fiIiYqMefhERKZmunImnYlPo+F/NxCMiUoQ0kFhERIqHYcCBeRA1wBr2A3pAt00K+yIiRUw9/CIiUvRys2HH0xC7yLqsmXhERIqN/qUVEZGilfor/DIa/tyIZuIRESl+GtIjUoySk5Px8/MjPj7e7FKkCD300EPMmzfP7DLMl30eYp6D75pZw76jO9yzChpOVNgXESlGCvwixWj27Nn07dvX9iCq4cOH079//yI7X1RUFH379iUwMBCLxcLXX3990/ouP1n3ZhYtWkRISAje3t54e3sTFhbG2rVr870drE/oHT9+PDVr1sTNzY22bdsSHR1doPNc9s4771C7dm1cXV1p2bIlGzduvKU2+bVr1y6cnJyu+aCvF154gVdeecX2tOFyxzDg2Nfw30bw6xwwsiGoH9z3q6bdFBExgQK/SDHJzMzko48+4rHHHiu2c54/f55mzZqxcOHCm7aNjo7m/fffJyQkJF/HDgoK4tVXX2X79u1s376de++9l379+rF///58bQd47LHHCA8P59NPP2Xv3r10796drl27cuLEiXyfB2DFihWMHz+e559/nl27dnHPPffQq1cvjh49WqA2BfH0008zefJkdu/ezdWzG4eEhFCrVi0+++yzWzp2qXbuCETeDxsHQMZR8KgJHdZAh6+t70VEpPgZZVxqaqoBGKmpqWaXIuXcypUrjSpVqtitGzZsmNGvX79iOT9grF69+prb0tPTjXr16hnh4eFGx44djXHjxt3SOSpVqmR8+OGH+dqekZFhODo6Gv/973/t2jRr1sx4/vnnC3Se1q1bG6NHj7Zr06BBA+O5554rUJuNGzcaFSpUMC5cuGBbd/jwYQMw4uPjbes+++wzo2/fvsaxY8cMwIiLi8tT44wZM4x77rnnhtdRpmRfMIy9LxvGclfD+AzD+MLJMGKmGcal82ZXJiJSZuU356qHX6SYREVFERpasAcLzZo1C09Pzxu+bmdYymVjxoyhT58+dO3a9Zb2z8nJYfny5Zw/f56wsLB8bc/OziYnJwdXV1e7tm5ubmzatCnf57l48SI7duyge/fudm27d+/O5s2b890GICYmhoYNG+Li4mK3rmLFitSsae2dPn/+PNOmTWPOnDkEBQXh4+NDTExMnlpbt27NL7/8QlZW1jWvpUxJ/BHWNoM9/4ScC1CtM/TaA81egQruZlcnIlLuaZYekWISHx9PYGBggfYZPXo0AwcOvGGb6tWr305ZLF++nJ07d+YZO58fe/fuJSwsjAsXLuDp6cnq1atp1KhRvrZ7eXkRFhbGSy+9RMOGDalWrRpffPEF27Zto169evk+z+nTp8nJyaFatWp2+1SrVo3ExMR8twHYvXs3zZs3t2sTExNDs2bNbMuvvPIKPXv2pGHDhgA0atSImJgYHnzwQbv9qlevTlZWFomJibYvC2VOZgLsnAR/fGFddq0GLeZBzcG6KVdEpARR4BcpJpmZmXl6s2/G19cXX1/fIqoIjh07xrhx4/j+++8LXBtA/fr1iYmJ4ezZs6xcuZJhw4YRGRlpC+M32/7pp58ycuRIqlevjqOjIy1atGDIkCHs3LmzQOcBsFwVMA3DyLPuZm1iYmIYMmSIXZtdu3bZAv/hw4d5//332bdvn217kyZNrtnD7+bmBkBGRsb1P8DSKjfbOp/+nn/CpTSwOEC9MRAyE5wrml2diIhcRYFfpJhUqVKFlJSUAu0za9YsZs2adcM2a9euveZMMfmxY8cOkpKSaNmypW1dTk4OUVFRLFy4kKysLBwdHa+7v7OzM3Xr1gUgNDSU6OhoFixYwHvvvZev7XXq1CEyMpLz58+TlpZGQEAAgwYNonbt2vk+T5UqVXB0dLTrqQdISkqy9ejnp01OTg779+/P08O/c+dOBgwYAMCECRNITk4mKCjItj03N/eav2U5c+YMAFWrVr3u51cqnd4G0f+AlF3WZd9W0HoR+La88X4iImIaBX6RYtK8eXOWLVtWoH2KekhPly5d2Lt3r926ESNG0KBBA5599tkbhv1rMQzjhmPWr7fdw8MDDw8PUlJSWL9+PXPnzs33eZydnWnZsiXh4eG2YA4QHh5Ov3798t3m4MGDZGZm2g272rJlCydOnKBZs2aEh4fz888/s2vXLipU+N8/ndHR0YwcOZLk5GQqV65sW79v3z6CgoKoUqXKDa+l1Mg6A7unQtwHgAFOFeGu2VDncXAo2N8TEREpXgr8IsWkR48eTJ06lZSUFCpVqpSvfW53SM+5c+eIi4uzLR85coSYmBh8fX2pUaMGXl5eNGnSxG4fDw8PKleubLd+4cKFrF69mh9//NG2btq0afTq1Yvg4GDS09NZvnw5ERERrFu3Ll/bAdavX49hGNSvX5+4uDieeeYZ6tevz4gRI/J9HoCJEyfyyCOPEBoaSlhYGO+//z5Hjx5l9OjR+W5zeVjOW2+9xdNPP01cXBxPP/00YL1Rd9y4cTzzzDPcdddddp+Xt7e3bf8uXbrY1m/cuDHPTcKlkmHAkY9h1zOQddq6rvYwaD4XXP3MrU1ERPJFgV+kmDRt2pTQ0FC+/PJLnnjiiWI55/bt2+ncubNteeLEiQAMGzaMpUuX5vs4p0+f5tChQ3brTp06xSOPPEJCQgI+Pj6EhISwbt06unXrlq/tAKmpqUydOpXjx4/j6+vLgw8+yCuvvIKTk1O+zwMwaNAgkpOTmTlzJgkJCTRp0oTvvvvO7mbZm7WJiYmhW7duHDlyhCZNmtCoUSNeffVVRo4cSc+ePfH09GTs2LF5Ppvg4GDc3d3tAv+FCxdYvXo169evz/dnXCKd3QvRT8Kff82a5NMYWr0Dfh3MrUtERArEYhhXPTGmjElLS8PHx4fU1FRbT5yIWb777jsmT57Mvn37cHDQrLglSY8ePWjRogWzZ8++7WO9/fbbfPPNN3z//feFUJkJLp2DfS/Cb/8GIwcc3aHpDGgwHhycbra3iIgUk/zmXPXwixSj3r17Exsby4kTJwgODja7HLnC7t27GT58eKEcy8nJibfeeqtQjlWsDAOOrYKd4yHjuHVd8APQ4t/gUcPU0kRE5Naph19Eyr3ExEQCAgLYt28fjRs3Nrscc6Qfgu1PQcJa67JHbQhdCNV7m1uXiIhcl3r4RUTyyd/fnzLe93F9ORfg17mwfxbkZoGDMzR6FhpNhQpuZlcnIiKFQIFfRKS8SgiH7WMgPda67N/V2qvvXd/cukREpFAp8IuIlDcZJ2DnRDj6pXXZLcA6Tr/GQLjqacQiIlL6KfCLiBSzuLg4jh8/TqdOnYr3xLnZ8PtbsOcFyD4HFge48ykImQlOusdJRKSs0ryAIiLF5M8//+SpsWNp2KABXbt04bfffivGk2+GdS2tPfvZ56Dy3dBzB7Scr7AvIlLGKfCLiBSxzMxMZs+eTd3atfnk3XeZmZNDsIMDz02ZUjwFHHwLwtvB2T3gXAlavw/df4ZKdxXP+UVExFQa0iMiUoQOHz5Mx3btSDx1iicNg38CVYGa2dkM/c9/2LhxI/fcc0/RFXD+KMQ8a31fexg0fw1cqxbd+UREpMRRD7+ISBHKzc0lISmJqYbBAqxhH+BhoKWjI5PHjy/aKUFjnoWcTPDrAHcvUdgXESmHFPhFRIpQ3bp1efzxx1no6EjKFesdgNdycvhl506++uqrojl50kb4Y7n15tyWCzQDj4hIOaXALyJSxKbPmMFFJydmXbW+M9DHYmHqM8+QlZVVuCfNzYEd46zv6zyu8foiIuWYAr+ISBHz9/dnytSpvOngQPxV2+YaBvHHjrFo0aLCPenhJZCyC5x8IOSlwj22iIiUKgr8IiLFYNKkSfj6+vL8VesbAaMMg5dmzODs2bOFc7KLZ2H3NOv7pjM0bl9EpJxT4BcRKQYeHh7MnDWLz4HtV217EbiQns7sWfaDfmJjY/nbgw/Sr0+fgp1s30uQ9Sd4N4A7x9xO2SIiUgZYjCKdHsJ8aWlp+Pj4kJqaire3Hi4jIubJzs6mWePG+MXF8VNuLlfeQjsdmOPkxMHYWNzd3Zn54ou8++675Obk4O3pSUp6ev5OknYQvm0CRjZ0WgeBPYriUkREpATIb85VD7+ISDGpUKECc+fNIyI3l++u2vYMUDE3lwH9+1OnVi0+efddXs7JYS6QUZAbendMsIb9wPsU9kVEBFDgFxEpVr1796Zzhw5MqVCB7CvWewKzc3LYt3s3IzIyOJSTw7NAZeDipUtkZ2df+4BXOvEdJKwFBydoMa9oLkBEREodBX4RkWJksVh4bd48fs3OZslV20YAaX89oKvKX+vc//ozMzPzxgfOuQg7J1jf1x8P3vUKq2QRESnlFPhFRIpZy5Yt6d+vHy84OnLhqm2uVy1fDvwZGRk3Pujvb0H67+BaDZr8s5AqFRGRskCBX0SkGB0/fpzhw4bxzZo1VAIu3aR9vgJ/5inYN9P6vtlscNIEBSIi8j8K/CIixSA1NZVpU6dSr04dvvvsMxYaBrtzcvC6yX75Cvx7nodLaeDbEu4YVlgli4hIGVHB7AJERMq67OxsmjVpwh/Hj/McMBXIbx/8TQP/mR1waLH1fcs3waJ+HBERsWfq/xmioqLo27cvgYGBWCwWvv76a7vtw4cPx2Kx2L3uvvtuc4oVEblFFSpU4NG//j3b5ODA6QLse8PAbxiwYxxgQK2hULVtIVQrIiJljamB//z58zRr1oyFCxdet03Pnj1JSEiwvb777urZq0VESr6ZL71EZGQkJ6pXp5mjIx8C+Xnq4eXAf/78+bwb/1gOf/4Mju5w16uFWK2IiJQlpg7p6dWrF7169bphGxcXF/z9/YupIhGRonPPPfewe/9+Jk6YwOMffcQ3FgsfGAY3+hfuutNyZp+HmCnW942ngntQUZQsIiJlQIkf7BkREYGfnx933nknjz/+OElJSTdsn5WVRVpamt1LRKSk8PLy4oMPP2TNmjX8UqkSTRwdWXWD9tcd0vPrXMg4Dh61oMGkIqpWRETKghId+Hv16sVnn33GTz/9xBtvvEF0dDT33nsvWTd4zPzs2bPx8fGxvYKDg4uxYhGR/Onbty/7fvuNDvfdx4PAo0DqNdo5AY4Wi33gP/8HHJhrfd/8dajgVvQFi4hIqVWiA/+gQYPo06cPTZo0oW/fvqxdu5bff/+db7/99rr7TJ06ldTUVNvr2LFjxVixiEj+Va1alZWrV7N06VK+8fCgqaMjP13VxgK4OzjYB/5dz0DOBfDrBMEPFGPFIiJSGpXowH+1gIAAatasSWxs7HXbuLi44O3tbfcSESmpLBYLw4YNY8/+/dRt144uwATgyhH77lf28J+KgKNfWaffbLkALJbiL1pEREqVUhX4k5OTOXbsGAEBAWaXIiJSqGrWrMkPGzYwb948Fjk50dLRkR1/bbMF/tycv6bhBOo+AZVCTKtXRERKD1MD/7lz54iJiSEmJgaAI0eOEBMTw9GjRzl37hyTJ09my5YtxMfHExERQd++falSpQoDBgwws2wRkSLh4ODAhAkT2LFrF66NGnG3xcJLgDN/3bR76EM4uwecKkLTmSZXKyIipYWpgX/79u00b96c5s2bAzBx4kSaN2/OCy+8gKOjI3v37qVfv37ceeedDBs2jDvvvJMtW7bg5XWzh9GLiJRejRs3Zuv27Tw3bRozLBYOXrpERnoK7Hne2iBkJrhWMbdIEREpNSyGYeTn2S+lVlpaGj4+PqSmpmo8v4iUOlu3buWx4cMZ2KkSL3TYCj6NoFcMODiZXZqIiJgsvznX1AdviYjIjd19993s2xEOa2pbH83bYr7CvoiIFEipumlXRKRcSt0PRg74NIaAbmZXIyIipYwCv4hISZd12vqnm2YoExGRglPgFxEp6S4HfhfdqCsiIgWnwC8iUtJl/Wn9U4FfRERugQK/iEhJpx5+ERG5DQr8IiIlnS3wVzW3DhERKZUU+EVESjr18IuIyG1Q4BcRKekuaAy/iIjcOgV+EZGSTj38IiJyGxT4RYpJYmIi3bp1w8PDg4oVKwJgsVj4+uuvi/S8M2bM4K677irScxSG0lJnsTNy4WKy9b2rxvCLiEjBKfCL3ILhw4fTv3//Au3z73//m4SEBGJiYvj999+LpK5rfYGYPHkyP/74Y5Gc70q1atXCYrFgsVhwd3enSZMmvPfee/ne/1bqrFWrFvPnzy9gpaXMxbPW0A/gXNnUUkREpHRS4BcpJocOHaJly5bUq1cPPz+/Yjuvp6cnlSsXT1CcOXMmCQkJ7Nmzh/79+zN69GhWrFiRr32Ls85S5fIc/E7e4Ohsbi0iIlIqKfCLFIJOnTrx9NNPM2XKFHx9ffH392fGjBm27bVq1WLlypV88sknWCwWhg8ffs3jnDhxgkGDBlGpUiUqV65Mv379iI+Pt2uzePFiGjdujIuLCwEBAYwdO9Z2DoABAwZgsVhsy1cPlcnNzWXmzJkEBQXh4uLCXXfdxbp162zb4+PjsVgsrFq1is6dO+Pu7k6zZs3YsmXLTT8HLy8v/P39qVu3Li+//DL16tWz/cbh6NGj9OvXD09PT7y9vRk4cCCnTp2y7Xt1nZd/i/L6668TEBBA5cqVGTNmDJcuXbJ95n/88QcTJkyw/WahTNL4fRERuU0K/CKF5OOPP8bDw4Nt27Yxd+5cZs6cSXh4OADR0dH07NmTgQMHkpCQwIIFC/Lsn5GRQefOnfH09CQqKopNmzbh6elJz549uXjxIgCLFi1izJgx/P3vf2fv3r2sWbOGunXr2s4BsGTJEhISEmzLV1uwYAFvvPEGr7/+Onv27KFHjx7cf//9xMbG2rV7/vnnmTx5MjExMdx5550MHjyY7OzsAn0mrq6uXLp0CcMw6N+/P2fOnCEyMpLw8HAOHTrEoEGDbrj/hg0bOHToEBs2bODjjz9m6dKlLF26FIBVq1YRFBRk+61CQkJCgWorNTQHv4iI3KYKZhcgUlaEhIQwffp0AOrVq8fChQv58ccf6datG1WrVsXFxQU3Nzf8/f2vuf/y5ctxcHDgww8/tPVWL1myhIoVKxIREUH37t15+eWXmTRpEuPGjbPt16pVKwCqVrUGwooVK173HACvv/46zz77LA8//DAAc+bMYcOGDcyfP5+3337b1m7y5Mn06dMHgBdffJHGjRsTFxdHgwYNbvpZZGdns2zZMvbu3cs//vEPfvjhB/bs2cORI0cIDg4G4NNPP6Vx48ZER0fbruFqlSpVYuHChTg6OtKgQQP69OnDjz/+yOOPP46vry+Ojo623yqUWZqSU0REbpN6+EUKSUhIiN1yQEAASUlJ+d5/x44dxMXF4eXlhaenJ56envj6+nLhwgUOHTpEUlISJ0+epEuXLrdcY1paGidPnqRdu3Z269u1a8eBAweuez0BAQEAN72eZ599Fk9PT9zc3BgzZgzPPPMMTzzxBAcOHCA4ONgW9gEaNWpExYoV85z3So0bN8bR0dGujoJ8pmWChvSIiMhtUg+/SCFxcnKyW7ZYLOTm5uZ7/9zcXFq2bMlnn32WZ1vVqlVxcCi87+dXj3c3DCPPuiuv5/K2m13PM888w/Dhw3F3dycgIMC237WOf6P116rhch0F+UzLBAV+ERG5TerhFykhWrRoQWxsLH5+ftStW9fu5ePjg5eXF7Vq1brh1JVOTk7k5ORcd7u3tzeBgYFs2rTJbv3mzZtp2LDhbV9DlSpVqFu3LoGBgXZBvlGjRhw9epRjx47Z1v3666+kpqbe1nmdnZ1veL1lwuXArzn4RUTkFinwi5QQQ4cOpUqVKvTr14+NGzdy5MgRIiMjGTduHMePHwesM9m88cYbvPnmm8TGxrJz507eeust2zEufyFITEwkJSXlmud55plnmDNnDitWrODgwYM899xzxMTE2N0XUNi6du1KSEgIQ4cOZefOnfzyyy88+uijdOzYkdDQ0Fs+bq1atYiKiuLEiROcPn26ECsuQbI0hl9ERG6PAr9ICeHu7k5UVBQ1atTggQceoGHDhowcOZLMzEy8vb0BGDZsGPPnz+edd96hcePG3HfffXaz67zxxhuEh4cTHBxM8+bNr3mep59+mkmTJjFp0iSaNm3KunXrWLNmDfXq1Suya7v8QLBKlSrRoUMHunbtyh133JHvOfqvZ+bMmcTHx1OnTh3bTctljob0iIjIbbIYhmGYXURRSktLw8fHh9TUVFtoEhEpNdbUgXOHodsmqNru5u1FRKTcyG/OVQ+/iEhJZRhw4a9ZiTQPv4iI3CIFfhGRkirrNGSfs753D75xWxERketQ4BcRKanOHbL+6VYdKriZW4uIiJRaCvwiIiVV+l+B36uuuXWIiEippsAvIlJSXe7h96xjbh0iIlKqKfCL3KLk5GT8/PyIj483u5QyJykpiapVq3LixAmzSzHX5cDvpcAvIiK3ToFf5BbNnj2bvn37kpycjMViyfP02st69OjB/fffX+Djnzlzhqeeeor69evj7u5OjRo1ePrpp0lNTb1pXa1atcLLyws/Pz/69+/PwYMH7doYhsGMGTMIDAzEzc2NTp06sX//frs2TzzxBHXq1MHNzY2qVavSr18/fvvttwJfx5Weeuqp6873f+LECRwdHVm1ahV+fn488sgjTJ8+/bbOV+qlx1n/VA+/iIjcBgV+kVuQmZnJRx99xGOPPUbLli1p1qwZS5YsydPu2LFj/PDDD4waNarA5zh58iQnT57k9ddfZ+/evSxdupR169bd9FiRkZGMGTOGrVu3Eh4eTnZ2Nt27d+f8+fO2NnPnzmXevHksXLiQ6Oho/P396datG+np6bY2LVu2ZMmSJRw4cID169djGAbdu3cnJyenwNdy2ahRo4iLi2Pjxo15ti1dupTKlSvTt29fAEaMGMFnn3123ScGlwsa0iMiIoXBKONSU1MNwEhNTTW7FClDVq5caVSpUsW2/Oabbxqenp7GuXPn7NrNnDnTqFatmnHp0qVCOe+XX35pODs7F+h4SUlJBmBERkYahmEYubm5hr+/v/Hqq6/a2ly4cMHw8fEx3n333eseZ/fu3QZgxMXFGYZhGEeOHDEAY8WKFUb79u0NV1dXIzQ01Dh48KDxyy+/GC1btjQ8PDyMHj16GElJSbbjtGjRwhg+fHie49etW9eYNGmS3bpatWoZH330Ub6vtUy5mG4Yn2F9ZZ0xuxoRESmB8ptz1cMvcguioqIIDQ21LQ8dOpRLly7x1Vdf2dYZhsHSpUsZNmwYFSpUAGDWrFl4enre8HWt3u/LLj9J7/Lx8uPyECBfX18Ajhw5QmJiIt27d7e1cXFxoWPHjmzevPmaxzh//jxLliyhdu3aBAfbzwc/ffp0/vnPf7Jz504qVKjA4MGDmTJlCgsWLGDjxo0cOnSIF154wdZ+1KhRfPXVV5w7d862LjIykri4OEaOHGl37NatW9/w8yjTzh22/unsC86VzK1FRERKNQV+kVsQHx9PYGCgbdnX15f+/fvbDeuJiIjg8OHDdiF29OjRxMTE3PB15ReJKyUnJ/PSSy/xxBNP5LtOwzCYOHEi7du3p0mTJgAkJiYCUK1aNbu21apVs2277J133rF9EVm3bh3h4eE4OzvbtZk8eTI9evSgYcOGjBs3jp07d/Kvf/2Ldu3a0bx5c0aNGsWGDRts7YcMGUJOTo7dl6PFixcTFhZGo0aN7I5dvXr18ntTtIbziIhIIcl/N6GI2GRmZuLq6mq3btSoUXTv3p24uDjq1q3L4sWLadeuHfXr17e18fX1tfW0F0RaWhp9+vShUaNGBbqRdezYsezZs+eaNxRbLBa7ZcMw8qwbOnQo3bp1IyEhgddff52BAwfy888/2117SEiI7f3lLxFNmza1W5eUlGRbrlixIg888ACLFy9mxIgRpKens3LlSubPn5+nRjc3NzIyMvJ9vWXK5Rt2NUOPiIjcJvXwi9yCKlWq5LmZtGvXrtSsWZOlS5eSlpbGqlWr8txgeytDetLT0+nZsyeenp6sXr0aJyenfNX41FNPsWbNGjZs2EBQUJBtvb+/P0Ce3vykpKQ8vf4+Pj7Uq1ePDh068P/+3//jt99+Y/Xq1XZtrqzn8heGq9fl5uba7TNq1Cg2bdpEbGwsK1asAGDQoEF5ruHMmTNUrVo1X9db5qiHX0RECol6+EVuQfPmzVm2bJndOovFwogRI/jwww8JCgrCwcGBgQMH2rUZPXp0nnVXq169uu19WloaPXr0wMXFhTVr1uT5rcK1GIbBU089xerVq4mIiKB27dp222vXro2/vz/h4eE0b94cgIsXLxIZGcmcOXNueuysrKyb1nAznTt35o477mDp0qVs2LCBgQMH4uXllafdvn376NSp022fr1Q6p6fsiohI4VDgF7kFPXr0YOrUqaSkpFCp0v9uqBwxYgQzZ85k2rRpPPzww3h4eNjtV5AhPenp6XTv3p2MjAyWLVtGWloaaWlpAFStWhVHR0cAunTpwoABAxg7diwAY8aM4fPPP+ebb77By8vL1pPv4+ODm5sbFouF8ePHM2vWLOrVq0e9evWYNWsW7u7uDBkyBIDDhw+zYsUKunfvbnsA1pw5c3Bzc6N379639+Hxvy9H8+bNIyUlhddeey1Pm4yMDHbs2MGsWbNu+3ylUrp6+EVEpHBoSI/ILWjatCmhoaF8+eWXdutr1KhB165dSUlJyTPjTEHt2LGDbdu2sXfvXurWrUtAQIDtdezYMVu7Q4cOcfr0advyokWLSE1NpVOnTnb7XB46AzBlyhTGjx/Pk08+SWhoKCdOnOD777+39bK7urqyceNGevfuTd26dRk4cCAeHh5s3rwZPz+/27quy4YPH05qair169enXbt2ebZ/88031KhRg3vuuadQzleq5F6CjKPW9wr8IiJymyyGYRhmF1GU0tLS8PHxsU1nKFJYvvvuOyZPnsy+fftwcNB358LWunVrxo8fb/utQ7mSHgf/qQeObjDwPFx1M7WIiAjkP+dqSI/ILerduzexsbGcOHEiz9z0cnuSkpJ46KGHGDx4sNmlmOPyDD2edyjsi4jIbVPgF7kN48aNM7uEMsnPz48pU6aYXYZ5dMOuiIgUIo1DEBEpaXTDroiIFCIFfhGRkkZz8IuISCFS4BcRKWkU+EVEpBAp8IuIlCRGLpw7bH3vpcAvIiK3T4Ff5DYkJyfj5+dHfHy82aXILXrooYeYN2+e2WX8T2YC5GSCxRE8appdjYiIlAEK/CK3Yfbs2fTt25datWoB1odJ9e/fv0jPeeLECf7v//6PypUr4+7uzl133cWOHTuu2z47O5t//vOf1K5dGzc3N+644w5mzpxJbm6urU16ejrjx4+nZs2auLm50bZtW6Kjo+2OM2PGDCwWi93L39//uuedPXu27am+V3vnnXeoXbs2rq6utGzZko0bN95Sm/zatWsXTk5O13yI1wsvvMArr7xie4qx6S4P5/GoCQ5O5tYiIiJlggK/yC3KzMzko48+4rHHHiu2c6akpNCuXTucnJxYu3Ytv/76K2+88QYVK1a87j5z5szh3XffZeHChRw4cIC5c+fy2muv8dZbb9naPPbYY4SHh/Ppp5+yd+9eunfvTteuXTlx4oTdsRo3bkxCQoLttXfv3mueMzo6mvfff5+QkJA821asWMH48eN5/vnn2bVrF/fccw+9evXi6NGjBWpTEE8//TSTJ09m9+7dXP2swZCQEGrVqsVnn312S8cudKkHrH9q/L6IiBQSBX6RW7R27VoqVKhAWFhYsZ1zzpw5BAcHs2TJElq3bk2tWrXo0qULdepcPxxu2bKFfv360adPH2rVqsVDDz1E9+7d2b59O2D94rJy5Urmzp1Lhw4dqFu3LjNmzKB27dosWrTI7lgVKlTA39/f9qpatWqe8507d46hQ4fywQcfUKlSpTzb582bx6hRo3jsscdo2LAh8+fPJzg42O5cN2uzadMmnJycyMrKsu1z5MgRLBYLf/zxh935Pv/8cypVqsSYMWNIT0/n8OHDeWq6//77+eKLL677GRarpEjrn1XamluHiIiUGQr8IrcoKiqK0NDQAu0za9YsPD09b/i60dCVNWvWEBoayt/+9jf8/Pxo3rw5H3zwwQ3P2b59e3788Ud+//13AHbv3s2mTZvo3bs3YB3yk5OTg6urq91+bm5ubNq0yW5dbGwsgYGB1K5dm4cffvia4XnMmDH06dOHrl275tl28eJFduzYQffu3e3Wd+/enc2bN+e7TUxMDA0bNsTFxcW2PSYmhooVK1Kz5v/GvZ8/f55p06YxZ84cgoKC8PHxISYmJk9drVu35pdffrH7AmEKw4BTP1nf+99rbi0iIlJmmBr4o6Ki6Nu3L4GBgVgsFr7++mu77YZhMGPGDAIDA3Fzc6NTp07s37/fnGJFrhIfH09gYGCB9hk9ejQxMTE3fN3oS8Thw4dZtGgR9erVY/369YwePZqnn36aTz755Lr7PPvsswwePJgGDRrg5ORE8+bNGT9+PIMHDwbAy8uLsLAwXnrpJU6ePElOTg7Lli1j27ZtJCQk2I7Tpk0bPvnkE9avX88HH3xAYmIibdu2JTk52dZm+fLl7Ny5k9mzZ1+zltOnT5OTk0O1atXs1lerVo3ExMR8t9m9ezfNmze32x4TE0OzZs3s1r3yyiv07NmThg0bAtCoUaNrBv7q1auTlZVlO75pUn+FC6fA0Q0qtzG3FhERKTMqmHny8+fP06xZM0aMGMGDDz6YZ/vcuXOZN28eS5cu5c477+Tll1+mW7duHDx4EC8vLxMqFvmfzMzMPL3iN+Pr64uvr+8tnzM3N5fQ0FBmzZoFQPPmzdm/fz+LFi3i0UcfveY+K1asYNmyZXz++ec0btyYmJgYxo8fT2BgIMOGDQPg008/ZeTIkVSvXh1HR0datGjBkCFD2Llzp+04vXr1sr1v2rQpYWFh1KlTh48//piJEydy7Ngxxo0bx/fff3/Tz8VisdgtG4aRZ92N2sTExDBkyBC77bt27bIL/IcPH+b9999n3759tnVNmjS5ZuB3c3MDICMj44Z1F7nLvftV24Ojy43bioiI5JOpgb9Xr152IeJKhmEwf/58nn/+eR544AEAPv74Y6pVq8bnn3/OE088UZyliuRRpUoVUlJSCrTPrFmzbGH9etauXXvN2WQAAgICaNSokd26hg0bsnLlyuse75lnnuG5557j4YcfBqxh/Y8//mD27Nm2wF+nTh0iIyM5f/48aWlpBAQEMGjQIGrXrn3d43p4eNC0aVNiY2MB2LFjB0lJSbRs2dLWJicnh6ioKBYuXEhWVhZVqlTB0dExT096UlKSrUf/Zm1ycnLYv39/nh7+nTt3MmDAANvyhAkTSE5OJigoyLYuNzeX6tWr57mWM2fOAFzznoRidTnwV9NwHhERKTymBv4bOXLkCImJiXbjeF1cXOjYsSObN2++buDPysqyG4dbYqbakzKnefPmLFu2rED7jB49moEDB96wzbUC6WXt2rXj4MGDdut+//13u3HrV8vIyMDBwX70nqOjo920nJd5eHjg4eFBSkoK69evZ+7cudc9blZWFgcOHLB9OenSpUueWXtGjBhBgwYNePbZZ3F0dMTR0ZGWLVsSHh5uF87Dw8Pp168fAM7Ozjdsc/DgQTIzM+2GU23ZsoUTJ07YevjDw8P5+eef2bVrFxUq/O+fuejoaEaOHElycjKVK1e2rd+3bx9BQUFUqVLlutdb5HJz4FSE9b0Cv4iIFKISG/gv9+5daxzv1bNwXGn27Nm8+OKLRVqbCECPHj2YOnUqKSkp15yN5lpud0jPhAkTaNu2LbNmzWLgwIH88ssvvP/++7z//vu2NgsXLmT16tX8+OOPAPTt25dXXnmFGjVq0LhxY3bt2sW8efMYOXKkbZ/169djGAb169cnLi6OZ555hvr16zNixAhbm8mTJ9O3b19q1KhBUlISL7/8MmlpabbfEnh5edGkSRO7ej08PKhcubLd+okTJ/LII48QGhpKWFgY77//PkePHmX06NH5avPzzz8D8NZbb/H0008TFxfH008/DVi/hGRnZzNu3DieeeYZ7rrrLrt6vL29AeuQoC5dutjWb9y4Mc9NwsUuZRdcOgtOPuDbwtxaRESkTCmxgf+y/Iz1vdLUqVOZOHGibTktLY3g4OAiq0/Kr6ZNmxIaGsqXX35ZbEPMWrVqxerVq5k6dSozZ86kdu3azJ8/n6FDh9ranD59mkOHDtmW33rrLf71r3/x5JNPkpSURGBgIE888QQvvPCCrU1qaipTp07l+PHj+Pr68uCDD/LKK6/g5PS/Bz8dP36cwYMHc/r0aapWrcrdd9/N1q1bb/jbhWsZNGgQycnJzJw5k4SEBJo0acJ3331nd5wbtXn77bfp1q0bR44coUmTJjRq1IhXX32VkSNH8vbbb9OyZUuSk5MZO3ZsnnMHBwfj7u5uF/gvXLjA6tWrWb9+fYGuo9BdHs7j1xEcSvw/zSIiUopYjKufQmMSi8XC6tWrbU8pPXz4MHXq1GHnzp12Y3X79etHxYoV+fjjj/N13LS0NHx8fEhNTbX17okUlu+++47Jkyezb9++PMNmpGj06NGDFi1aXHcmoIJ6++23+eabb/j+++8L5Xi3bENPSFgPLeZDg3Hm1iIiIqVCfnNuiU0otWvXxt/fn/DwcNu6ixcvEhkZSdu2eiCNlAy9e/fmiSeeyPNEWik6u3fvvuYTfG+Vk5OT3VOHTZFzEZL+ev6C5t8XEZFCZurvjc+dO0dcXJxt+ciRI8TExODr60uNGjUYP348s2bNol69etSrV49Zs2bh7u6eZzo+ETONG6fe2OKSmJjIqVOnCjXw//3vfy+0Y92y5F8gJwNcqoJPY7OrERGRMsbUwL99+3Y6d+5sW7489n7YsGEsXbqUKVOmkJmZyZNPPklKSgpt2rTh+++/1xz8IuWUv78/JWQUYuE6Zb3BmmqdwVJif/EqIiKlVIkZw19UNIZfREq8HzpCUhS0fg/qloDfOIiISKlQ6sfwi4iUC9kZcHqL9b3m3xcRkSKgwC8icgNnzpxh4EMP8d577xXNCf78GXIvgXsweNYpmnOIiEi5psmeRUSu4+DBg9zXsyd//PEH/2/VKgIDA+nbt2/hnsQ2fv9euMEzRkRERG6VevhFRK7hhx9+4O5WrXA6doxfDYN+wJBBg9izZ0/hnijxrwduaTiPiIgUEQV+EZGrvPfee/Ts0YM2586xJSeHusCnhkHdixfp26sXp06dKpwTXTwLKTus7zX/voiIFBEFfhGRK1y4cIGnxo6laW4u/zUMfP5a7wn8JyeHS0lJ9O/blwsXLtz+yZKiwMgFrzvBPej2jyciInINCvwiIldwdXXljXnziAGWXrUtCPgmO5uYHTsYOWLE7T8T4NQG658aziMiIkVIgV9E5Cpjx47lH6NH8w+LhQ1XbWsFfJKbyxfLl/PSzJm3d6Jzh61/Vmp2e8cRERG5AQV+EZGrWCwWFrz5Jp06deJBR0dir9r+N2AmMH3GDL788stbP9HFM9Y/XSrf+jFERERuQoFfROQanJyc+GrVKvxq1eI+R0dSrtr+T2AIMOyRR/jll19u7SSXA7+zAr+IiBQdBX4RkeuoWLEi/123jtOenjzk4MClK7ZZgI+A5jk59OvTh2PHjl3zGLt37+a333679gmykq1/uvgWZtkiIiJ2FPhFRG6gbt26rPrmGzY6OPAUcOVtuq7A6pwcnFNSuL93b86dO2e374cffkhoy5aMfPTRvAc2jCt6+BX4RUSk6Cjwi4jcRMeOHXn3vfd4D3jzqm3VgP/m5BB34AD/N2QIubm55OTkMHnSJB5//HHq5eQQvXMnGRkZ9jtmn4fcv35noDH8IiJShBT4RUTyYeTIkUyeNImJFgtrr9rWFPgiJ4c1//kPEydMYEC/fvz73/9mPrAcyM7JYdu2bfY7Xe7dd3AGR/cir19ERMovBX4RkXx6dc4c7uvTh0GOjuy7att9wOvAgjffJGLdOv5rGIwDmgAVHR3ZuHGj/Q6Xx+87+4LFUuS1i4hI+aXALyKST46Ojnz2xRfUrl+f+xwdSbpq+wRgMbAtJ4def61zANrn5hK14aoZ/TUlp4iIFBMFfhGRAvD09OQ/a9dyoWJFBjg4cOGKbRZgBNDwqn06GAZbtm7l0qUr5vnRDbsiIlJMFPhFRAqoRo0afPPtt+xwdORx7GfuuZZ7gIwLF9i5c+f/VmZd7uFX4BcRkaKlwC8icgtCQkIIbdWKZZBnaM/VWgBuDg724/gvXh7DryE9IiJStBT4RUQKKCEhgU7t27Nz61b+H9apOW/EGQgDNkZG/m+levhFRKSYKPCLiBRATEwMrVu04Pju3WzMzeXBfO53T24uGyMjyc3Nta7QGH4RESkmCvwiIvkUExND+7AwHJOS+CUnh5YF2LcDkJKezq+//mpdoVl6RESkmCjwi4jkk5OTE4EBAfyRm0s/R0c+BbLyue/dQAWL5X/j+K+ch19ERKQIKfCLiORT48aN+S0ujm+//ZbKnTrxKFCzQgVmAIk32dcdCHVwIOryOH4N6RERkWKiwC8iUgAODg707t2b9T/8wK+//soDjz3Ga66u1LBYeATYfoN978nJYeOGDRiGccVNuxrSIyIiRUuBX0TkFjVs2JB3Fi3i+MmTzH7tNTYFBdEKaOvgwArg0lXtOwAnkpKIP3Lkimk51cMvIiJFS4FfROQ2VapUiUmTJhEXH8+qVatwadeOh4E7KlRgNnD6r3btsD6NN2pDOOT+9XVA03KKiEgRU+AXESkkjo6ODBgwgA1RUcTExND9kUd40cmJYAcHHgOOA00qVGBj1AbrDg7O4OhuZskiIlIOKPCLiBSBZs2a8dHixRw7cYJ/vfQSa/38CAFis7PZGLnJ2silMlgsptYpIiJlnwK/iEgRqlq1KtOmTSP++HG++OILmrdqRXpGBoaBxu+LiEixqGB2ASIipZaRC5dSrTPuXDzz15/J/1u+eMY63/7FMzhlneFhjzM8/GwyRtZZLKDALyIixUKBX0QkT3BPviLAX7F89fuLKdZ9C8gCYHGAoH6FfikiIiJXU+AXkbLDFtyv3ct+zQB/G8HdpoIHOFe2zrjj/NfLpfJff161fOU6R5fCu3YREZHrUOAXkZLHyIWLZ286TCZvgE8BjFs/bwXPKwJ5fgN8JQV3EREp0RT4RaTo5OZYe9zzO0wm60whB/cb9bJftc3ZFxydC+3SRURESgoFfhG5udwcuHQ2/8NkLvfKXzzL7QV3rwIOk6n8V4+7gruIiMhlCvwi5YldcC/IOPezFFpwv1ZIv9Y2BXcREZFCocAvUhrZgns+etmv3Hbp7O2dt4JX/nrZ7XrlfcHBqTCuWkRERG6BAr+ImXKz/3dzan6HyWSduf3g7uSdv152u22VFNxFRERKIQV+kcJwZXC/0c2oVwf4wgruN+1lv7JXXsFdRESkPFHgF7lSbrZ1hpj8DpOxDZVJvb3zOnnns5f9yu0VFdxFRETkphT4pWyyC+4FGOd+28Hd5xbmca+o4C4iIiJFRoFfSrbLwf1mN6Neve1S2u2d1xbcK+c/wDtXAgf9JyUiIiIli9KJFI/cS38F9wIMk7l4phCCe8VbmMe9ooK7iIiIlBlKNVIwtuBegGEyhRrc8ztMxlfBXURERAQF/vIr99JVPer5GCaTdQay02/vvE4VCz6Pu3MlcHAslMsWERERKW8U+Eu7nIt5b0692TCZ2w7uFmvveUGGybj4WsO+gruIiIhIsVLgLylyLl4V1vMZ4LPP3cZJrwjuBZnHXcFdREREpNRQ4C9KmQmQ/Mv1e9mvXL7t4F6pYMNkXCpbZ6JRcBcREREp0xT4i9KfP8OmvxVgh6uD+41uUL1im4K7iIiIiFxHiQ78M2bM4MUXX7RbV61aNRITE02qqIDcg6BymwLM414RLA5mVy0iIiIiZUiJDvwAjRs35ocffrAtOzqWop7sKndDj61mVyEiIiIi5ViJD/wVKlTA39/f7DJEREREREqlEj9+JDY2lsDAQGrXrs3DDz/M4cOHb9g+KyuLtLQ0u5eIiIiISHlVogN/mzZt+OSTT1i/fj0ffPABiYmJtG3bluTk5OvuM3v2bHx8fGyv4ODgYqxYRERERKRksRiGYZhdRH6dP3+eOnXqMGXKFCZOnHjNNllZWWRlZdmW09LSCA4OJjU1FW9v7+IqVURERESkSKWlpeHj43PTnFvix/BfycPDg6ZNmxIbG3vdNi4uLri4uBRjVSIiIiIiJVeJHtJztaysLA4cOEBAQIDZpYiIiIiIlAolOvBPnjyZyMhIjhw5wrZt23jooYdIS0tj2LBhZpcmIiIiIlIqlOghPcePH2fw4MGcPn2aqlWrcvfdd7N161Zq1qxpdmkiIiIiIqVCiQ78y5cvN7sEEREREZFSrUQP6RERERERkdujwC8iIiIiUoYp8IuIiIiIlGEK/CIiIiIiZZgCv4iIiIhIGabALyIiIiJShinwi4iIiIiUYQr8IiIiIiJlmAK/iIiIiEgZVqKftFsYDMMAIC0tzeRKREREREQKz+V8eznvXk+ZD/zp6ekABAcHm1yJiIiIiEjhS09Px8fH57rbLcbNvhKUcrm5uZw8eRIvLy8sFkuRnCMtLY3g4GCOHTuGt7d3kZxDzKefc/mhn3X5oJ9z+aGfdflQHn/OhmGQnp5OYGAgDg7XH6lf5nv4HRwcCAoKKpZzeXt7l5u/YOWZfs7lh37W5YN+zuWHftblQ3n7Od+oZ/8y3bQrIiIiIlKGKfCLiIiIiJRhCvyFwMXFhenTp+Pi4mJ2KVKE9HMuP/SzLh/0cy4/9LMuH/Rzvr4yf9OuiIiIiEh5ph5+EREREZEyTIFfRERERKQMU+AXERERESnDFPhFRERERMowBf7b9M4771C7dm1cXV1p2bIlGzduNLskKWRRUVH07duXwMBALBYLX3/9tdklSRGYPXs2rVq1wsvLCz8/P/r378/BgwfNLkuKwKJFiwgJCbE9nCcsLIy1a9eaXZYUsdmzZ2OxWBg/frzZpUghmzFjBhaLxe7l7+9vdlkligL/bVixYgXjx4/n+eefZ9euXdxzzz306tWLo0ePml2aFKLz58/TrFkzFi5caHYpUoQiIyMZM2YMW7duJTw8nOzsbLp378758+fNLk0KWVBQEK+++irbt29n+/bt3HvvvfTr14/9+/ebXZoUkejoaN5//31CQkLMLkWKSOPGjUlISLC99u7da3ZJJYqm5bwNbdq0oUWLFixatMi2rmHDhvTv35/Zs2ebWJkUFYvFwurVq+nfv7/ZpUgR+/PPP/Hz8yMyMpIOHTqYXY4UMV9fX1577TVGjRpldilSyM6dO0eLFi145513ePnll7nrrruYP3++2WVJIZoxYwZff/01MTExZpdSYqmH/xZdvHiRHTt20L17d7v13bt3Z/PmzSZVJSKFJTU1FbAGQSm7cnJyWL58OefPnycsLMzscqQIjBkzhj59+tC1a1ezS5EiFBsbS2BgILVr1+bhhx/m8OHDZpdUolQwu4DS6vTp0+Tk5FCtWjW79dWqVSMxMdGkqkSkMBiGwcSJE2nfvj1NmjQxuxwpAnv37iUsLIwLFy7g6enJ6tWradSokdllSSFbvnw5O3fuJDo62uxSpAi1adOGTz75hDvvvJNTp07x8ssv07ZtW/bv30/lypXNLq9EUOC/TRaLxW7ZMIw860SkdBk7dix79uxh06ZNZpciRaR+/frExMRw9uxZVq5cybBhw4iMjFToL0OOHTvGuHHj+P7773F1dTW7HClCvXr1sr1v2rQpYWFh1KlTh48//piJEyeaWFnJocB/i6pUqYKjo2Oe3vykpKQ8vf4iUno89dRTrFmzhqioKIKCgswuR4qIs7MzdevWBSA0NJTo6GgWLFjAe++9Z3JlUlh27NhBUlISLVu2tK3LyckhKiqKhQsXkpWVhaOjo4kVSlHx8PCgadOmxMbGml1KiaEx/LfI2dmZli1bEh4ebrc+PDyctm3bmlSViNwqwzAYO3Ysq1at4qeffqJ27dpmlyTFyDAMsrKyzC5DClGXLl3Yu3cvMTExtldoaChDhw4lJiZGYb8My8rK4sCBAwQEBJhdSomhHv7bMHHiRB555BFCQ0MJCwvj/fff5+jRo4wePdrs0qQQnTt3jri4ONvykSNHiImJwdfXlxo1aphYmRSmMWPG8Pnnn/PNN9/g5eVl++2dj48Pbm5uJlcnhWnatGn06tWL4OBg0tPTWb58OREREaxbt87s0qQQeXl55bkHx8PDg8qVK+venDJm8uTJ9O3blxo1apCUlMTLL79MWloaw4YNM7u0EkOB/zYMGjSI5ORkZs6cSUJCAk2aNOG7776jZs2aZpcmhWj79u107tzZtnx5POCwYcNYunSpSVVJYbs8vW6nTp3s1i9ZsoThw4cXf0FSZE6dOsUjjzxCQkICPj4+hISEsG7dOrp162Z2aSJyC44fP87gwYM5ffo0VatW5e6772br1q3KY1fQPPwiIiIiImWYxvCLiIiIiJRhCvwiIiIiImWYAr+IiIiISBmmwC8iIiIiUoYp8IuIiIiIlGEK/CIiIiIiZZgCv4iIiIhIGabALyIiIiJShinwi4iIiIiUYQr8IiJyy/r27UvXrl2vuW3Lli1YLBZ27txZzFWJiMiVFPhFROSWjRo1ip9++ok//vgjz7bFixdz11130aJFCxMqExGRyxT4RUTklt133334+fmxdOlSu/UZGRmsWLGCUaNGmVOYiIjYKPCLiMgtq1ChAo8++ihLly7FMAzb+q+++oqLFy8ydOhQE6sTERFQ4BcRkds0cuRI4uPjiYiIsK1bvHgxDzzwAJUqVTKvMBERART4RUTkNjVo0IC2bduyePFiAA4dOsTGjRsZOXKkyZWJiAgo8IuISCEYNWoUK1euJC0tjSVLllCzZk26dOlidlkiIoICv4iIFIKBAwfi6OjI559/zscff8yIESOwWCxmlyUiIoDFuPIuKxERkVv02GOPsWrVKlJTUzly5Ag1atQwuyQREUE9/CIi8v/bsYMaAGIYgGEDVybjr1IYiXucIhtFlI/ce8/unpkR+wA/4vADAECYww8AAGGCHwAAwgQ/AACECX4AAAgT/AAAECb4AQAgTPADAECY4AcAgDDBDwAAYYIfAADCBD8AAIQ9Em4a96kxSs4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Set figure size\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "# Define the window size\n",
    "window_size = 6\n",
    "\n",
    "# Calculate the moving averages\n",
    "V_avg = np.convolve(V, np.ones(window_size)/window_size, mode='same')\n",
    "I_avg = np.convolve(I, np.ones(window_size)/window_size, mode='same')\n",
    "\n",
    "# Calculate the standard deviations\n",
    "V_std = np.std(V)\n",
    "I_std = np.std(I)\n",
    "\n",
    "# Calculate the z-scores\n",
    "V_z = (V - V_avg) / V_std\n",
    "I_z = (I - I_avg) / I_std\n",
    "\n",
    "# Calculate the change points\n",
    "change_points = []\n",
    "for i in range(len(V_z)):\n",
    "    if abs(V_z[i]) > 3 or abs(I_z[i]) > 0.01:\n",
    "        change_points.append(i)\n",
    "\n",
    "# Annotate inflection points on the graph\n",
    "for idx, point in enumerate(change_points):\n",
    "    if idx in [1, 5]:\n",
    "        plt.annotate(f'Inflection Point\\n(V={V[point]:.3f}mV)\\n(I = {np.interp(V[point], V, I):3f}$\\mu A$)', \n",
    "                     xy=(V[point], np.interp(V[point], V, I)),\n",
    "                     xytext=(V[point] - 1.2, np.interp(V[point], V, I) + 3),\n",
    "                     arrowprops={'facecolor': 'red', 'shrink': 0.05})\n",
    "\n",
    "    \n",
    "# Plot the data again\n",
    "sns.lineplot(x=V, y=I, color = 'orange')  \n",
    "    \n",
    "# Set plot labels and title\n",
    "plt.xlabel('V')\n",
    "plt.ylabel('I')\n",
    "plt.title('Inflection Points Detection')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85392e7-7fdc-4476-805f-665973b49608",
   "metadata": {},
   "source": [
    "# Estimating the normal state resistance (Rn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59c0fa22-6aaf-4859-b923-3ff6aa7fe6de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.142, 8.106, 7.888, 7.718, 7.478, 7.294, 7.125, 6.885, 6.708, 6.539, 6.355, 6.165, 5.953, 5.79, 5.642, 5.451, 5.282, 5.091, 4.922, 4.773, 4.59, 4.392, 4.279, 4.096, 3.947, 3.785, 3.622, 3.488, 3.305, 3.164, 3.051, 2.881, 2.712, 2.57, 2.401, 2.359, 2.344, 2.337, 2.316, 2.288, 2.288, 2.281, 2.267, 2.217, 2.161, 1.179, 0.113, 0.134, 0.12, 0.106, 0.106, 0.12, 0.099, -2.02, -2.041, -2.069, -2.069, -2.097, -2.083, -2.125, -2.14, -2.154, -2.309, -2.464, -2.641, -2.789, -2.938, -3.1, -3.255, -3.411, -3.573, -3.735, -3.905, -4.103, -4.272, -4.456, -4.611, -4.781, -4.978, -5.176, -5.345, -5.593, -5.741, -5.932, -6.165, -6.369, -6.56, -6.793, -6.998, -7.181, -7.457, -7.64, -7.88, -7.669, -7.33, -7.005, -6.624, -6.285, -5.988, -5.656, -5.345, -5.091, -4.781, -4.47, -4.202, -3.933, -3.7, -3.411, -3.185, -2.888, -2.676, -2.436, -2.224, -2.168, -2.154, -2.14, -2.125, -2.09, -1.928, -0.332, 0.064, 0.042, 0.042, 0.057, 2.203, 2.217, 2.26, 2.288, 2.302, 2.429, 2.697, 2.952, 3.192, 3.418, 3.644, 3.898, 4.152, 4.406, 4.675, 4.943, 5.232, 5.515, 5.826, 6.101, 6.376, 6.715, 6.984, 7.351, 7.605, 8.001, 8.184]\n"
     ]
    }
   ],
   "source": [
    "#Preview the main dataset\n",
    "print(list(df[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78ae52f8-aafc-41de-a28e-b7e91c22e3e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "150\n"
     ]
    }
   ],
   "source": [
    "#The dataset includes points from two complete sweeps\n",
    "#Identify the index of the approximate limits of the normal state on the positive side of the graph \n",
    "\n",
    "def find_last_occurrence(value, list):\n",
    "    for i, item in enumerate(list):\n",
    "        if item == value:\n",
    "            last_occurrence = i\n",
    "    return last_occurrence\n",
    "\n",
    "low_limit = find_last_occurrence(2.302, list(df[0])) #the second change point above \n",
    "up_limit = list(df[0]).index(df[0].max()) \n",
    "\n",
    "print(low_limit) \n",
    "print(up_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b864c5cd-63be-4c90-ba23-6d975b3d44c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.302 2.429 2.697 2.952 3.192 3.418 3.644 3.898 4.152 4.406 4.675 4.943\n",
      " 5.232 5.515 5.826 6.101 6.376 6.715 6.984 7.351 7.605 8.001]\n",
      "[14.3393 16.5352 18.1745 20.3636 22.273  24.5739 26.5324 28.7754 30.8554\n",
      " 33.1031 35.149  37.5618 39.7066 42.0915 44.1066 46.735  48.9978 51.2701\n",
      " 53.8098 56.0992 58.7549 61.0545]\n"
     ]
    }
   ],
   "source": [
    "#Select data from the first pass\n",
    "V_n = np.array(df[0][low_limit:up_limit])\n",
    "I_n = np.array(df[1][low_limit:up_limit])\n",
    "\n",
    "#Preview the data\n",
    "print(V_n)\n",
    "print(I_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c19d83-70af-4459-979f-865a26a9e202",
   "metadata": {},
   "source": [
    "## Define the model and fit the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14e579a1-d903-42ee-9b29-f3b31a16d046",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    Model(linear)\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 7\n",
      "    # data points      = 22\n",
      "    # variables        = 2\n",
      "    chi-square         = 20.0389890\n",
      "    reduced chi-square = 1.00194945\n",
      "    Akaike info crit   = 1.94602216\n",
      "    Bayesian info crit = 4.12810707\n",
      "    R-squared          = 1.00000000\n",
      "[[Variables]]\n",
      "    slope:      8343.81750 +/- 71.6540968 (0.86%) (init = 8180.933)\n",
      "    intercept: -4215107.85 +/- 274324.408 (6.51%) (init = -3457501)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(slope, intercept) = -0.9309\n"
     ]
    }
   ],
   "source": [
    "#Define the variables and convert to Volts and Amps\n",
    "x = V_n / 0.001\n",
    "y = I_n / 0.000001\n",
    "\n",
    "#Use builtin NameModel, specify prefix for each:\n",
    "mod = LinearModel(prefix='') \n",
    "\n",
    "#Set the initial parameters to 'guess' mode\n",
    "pars  = mod.guess(y, x = x)\n",
    "\n",
    "#Fit with weights=1/yerr\n",
    "result = mod.fit(y, pars, x = x, weights= 1. / (y * 0.01673))\n",
    "\n",
    "#Print the fit results\n",
    "print(result.fit_report())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2b31af-3b9f-41ff-bab6-429b46e63cd5",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b562e985-269d-4d96-9741-543a4bb1a329",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set the figure size\n",
    "plt.figure(figsize = (9,6))\n",
    "\n",
    "#Plot the data and the model\n",
    "plt.scatter(x, y, linewidth=2)\n",
    "plt.plot(x, result.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('V ($V$)')\n",
    "plt.ylabel('I ($A$)')\n",
    "plt.title('Normal State IVC')\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bf727c-9b28-4da8-b6c6-4fb66078c772",
   "metadata": {},
   "source": [
    "# Calculating the theoretical Ic and V_gap\n",
    "\n",
    "![](./img/Ic_formula.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c3f054f-0b07-4f8f-b3de-a8fdd96668ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'slope': 8343.817495594772, 'intercept': -4215107.852248299}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Access the model parameters\n",
    "varys = result.params.valuesdict()\n",
    "\n",
    "varys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b00cd57a-cc33-4faf-a5ee-3ae6e148bd15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal state resistance: R_n =  0.00012 Ohm\n"
     ]
    }
   ],
   "source": [
    "#The slope is equal to 1 / R \n",
    "R_n = 1 / varys.get(\"slope\")\n",
    "\n",
    "print('Normal state resistance: R_n = ', round(R_n, 6), 'Ohm') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9447a90f-624a-41a4-9975-06e5b6d0a2b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature = 6.83519217 K\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "Weak Coupling Formula\n",
      "-----------------------------\n",
      "delta_t = 2.0029001911042982e-22 J\n",
      "Gap Energy = 4.0058003822085963e-22 J\n",
      "Gap Voltage = 2.5001874810938682 mV\n",
      "Josephson Current = 12.879776293231668 uA\n",
      "______________________________\n",
      "______________________________\n",
      "------------------------------\n",
      "Strong Coupling Approximation\n",
      "------------------------------\n",
      "delta_t = 1.8221605699157733e-22 J\n",
      "Gap Energy =  3.6443211398315466e-22 J\n",
      "Gap Voltage = 2.2745731742800817 mV\n",
      "Josephson Current = 11.129773194634131 uA\n",
      "________________________________\n",
      "________________________________\n",
      "________________________________\n"
     ]
    }
   ],
   "source": [
    "#Define the terms for the strong and weak coupling formulas \n",
    "t_c = 9.25 #critical temperature\n",
    "delta_0 = 2.25288819E-22 # from 1.764 * k_B * t_c,  [check eqn. 1 on the report paper] \n",
    "t = 6.83519217 #from the calibration curve in notebook 2\n",
    "e = 1.6022E-19 #elementay charge\n",
    "pi = 3.14159265358979323846264338327950 \n",
    "t_red = t / t_c #the reduced temperature\n",
    "k_B = 1.3807E-23 #Boltzmann constant\n",
    "R_n = 1 / varys.get(\"slope\") #from the slope of the IVC\n",
    "\n",
    "\n",
    "#weak coupling formula\n",
    "\n",
    "delta_weak = delta_0 * 1.74 * math.sqrt(1.0 - t_red)\n",
    "\n",
    "E_weak = delta_weak * 2\n",
    "\n",
    "V_weak = (E_weak / e) * 1000\n",
    "\n",
    "#The Ambegaokar-Baratoff formula\n",
    "Ic_weak = (pi / 2) * (delta_weak / (e * R_n)) * math.tanh(delta_weak / (2 * k_B * t))\n",
    "\n",
    "\n",
    "#strong coupling approximation\n",
    "\n",
    "delta_strong = (delta_0) * math.sqrt(math.cos((pi * t_red**2)  / 2 )) \n",
    "\n",
    "E_strong = delta_strong * 2\n",
    "\n",
    "V_strong = (E_strong / e) * 1000\n",
    "\n",
    "#The Ambegaokar-Baratoff formula\n",
    "Ic_strong = (pi / 2) * (delta_strong / (e * R_n)) * math.tanh(delta_strong / (2 * k_B * t))\n",
    "\n",
    "\n",
    "#Print the results\n",
    "print('Temperature =', t, 'K')\n",
    "print('-----------------------------')\n",
    "print('-----------------------------')\n",
    "                         \n",
    "print('Weak Coupling Formula')\n",
    "print('-----------------------------')\n",
    "\n",
    "\n",
    "print('delta_t =', delta_weak, 'J')\n",
    "print('Gap Energy =', E_weak, 'J')\n",
    "print('Gap Voltage =', V_weak, 'mV')\n",
    "print('Josephson Current =', Ic_weak, 'uA')\n",
    "print('______________________________')\n",
    "print('______________________________')\n",
    "print('------------------------------')\n",
    "print('Strong Coupling Approximation')\n",
    "print('------------------------------')\n",
    "\n",
    "print('delta_t =', delta_strong, 'J')\n",
    "print('Gap Energy = ', E_strong, 'J')\n",
    "print('Gap Voltage =', V_strong, 'mV')\n",
    "print('Josephson Current =', Ic_strong, 'uA')\n",
    "print('________________________________')\n",
    "print('________________________________')\n",
    "print('________________________________')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ef1804d8-51d9-4eea-ac5c-b806b800994a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Ic': 14.3393, 'Ic_weak': 12.8798, 'Ic_strong': 11.1298, 'V_gap': 2.203, 'Vg_weak': 2.5002, 'Vg_strong': 2.2746, 'Temp': 6.8352}\n"
     ]
    }
   ],
   "source": [
    "#Add all the collected results to a dictionary\n",
    "Josephson_data = {\"Ic\" : np.interp(V[5], V, I) , \"Ic_weak\" : round(Ic_weak, 4), \"Ic_strong\" : round(Ic_strong, 4), \"V_gap\" : V[1], \"Vg_weak\" : round(V_weak, 4), \"Vg_strong\" : round(V_strong, 4), \"Temp\" : round(t, 4)}\n",
    "\n",
    "#Preview the newly created row of data\n",
    "print(Josephson_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29b131a-d29f-4f75-ba01-4e28fbbc0548",
   "metadata": {},
   "source": [
    "## Export the results to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "565be433-d8b5-44ea-a793-bbf072ae75c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open(r'./Data/Josephson_data.csv', 'a', newline='') as csvfile:\n",
    "#     fieldnames = ['Ic','Ic_weak', 'Ic_strong', 'V_gap', 'Vg_weak', 'Vg_strong', 'Temp']\n",
    "#     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "#     writer.writerow(Josephson_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f9e1ba-ea28-4854-8b64-a7ac34c3ddf8",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  }
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