1.0.Measuring_Ic_Vc_and_Rn_at_1169ohm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8a8a37e-f395-405f-a07d-0349694b422d",
   "metadata": {},
   "source": [
    "# Measuring the Josephson Current, the gap voltage and the normal state resistance at 1169ohm \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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.986</td>\n",
       "      <td>-62.0989</td>\n",
       "      <td>0.00314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.662</td>\n",
       "      <td>-59.3129</td>\n",
       "      <td>0.00300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.308</td>\n",
       "      <td>-56.4280</td>\n",
       "      <td>0.00286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.991</td>\n",
       "      <td>-53.9660</td>\n",
       "      <td>0.00273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.645</td>\n",
       "      <td>-51.2953</td>\n",
       "      <td>0.00260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0        1        2\n",
       "0 -7.986 -62.0989  0.00314\n",
       "1 -7.662 -59.3129  0.00300\n",
       "2 -7.308 -56.4280  0.00286\n",
       "3 -6.991 -53.9660  0.00273\n",
       "4 -6.645 -51.2953  0.00260"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "df = pd.read_csv(\"./Data/1169_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 =  [-7.986, -7.662, -7.308, -6.991, -6.645, -6.334, -6.023, -5.741, -5.409, -5.141, -4.83, -4.576, -4.251, -4.011, -3.714, -3.432, -3.022, -2.817, -2.796, -2.796, -2.782, -2.754, -2.754, -2.74, -2.712, -2.712, -2.712, -2.669, -2.14, 0.057, 0.064, 0.064, 0.049, 0.064, 0.057, 2.853, 2.874, 2.874, 2.895, 2.916, 2.916, 3.255, 3.566, 3.863, 4.11, 4.357, 4.604, 4.886, 5.141, 5.458, 5.72, 6.002, 6.27, 6.624, 6.878, 7.245, 7.485, 7.81, 8.106, 8.346, 8.135, 7.937, 7.753, 7.57, 7.4, 7.188, 7.019, 6.849, 6.652, 6.511, 6.27, 6.136, 5.946, 5.783, 5.628, 5.444, 5.261, 5.105, 4.95, 4.759, 4.59, 4.456, 4.3, 4.103, 3.976, 3.82, 3.637, 3.453, 3.043, 3.001, 2.987, 2.973, 2.959, 2.959, 2.945, 2.938, 2.93, 2.916, 2.923, 2.916, 2.902, 2.902, 2.902, 2.874, 2.874, 2.86, 2.846, 2.832, 0.106, 0.127, 0.127, 0.113, 0.106, 0.127, 0.12, 0.127, 0.12, 0.113, -2.217, -2.697, -2.697, -2.712, -2.705, -2.697, -2.733, -2.712, -2.754, -2.74, -2.782, -3.192, -3.382, -3.531, -3.728, -3.884, -4.025, -4.223, -4.392, -4.548, -4.71, -4.901, -5.084, -5.282, -5.409, -5.642, -5.811, -5.981, -6.165, -6.362, -6.56, -6.758, -6.941, -7.146, -7.351, -7.549, -7.753, -7.986]\n",
      "I =  [-62.0989, -59.3129, -56.428, -53.966, -51.2953, -49.1212, -46.2131, -43.8214, -41.3588, -39.038, -36.5959, -34.6762, -32.2225, -29.9365, -27.9984, -25.75, -23.7342, -21.586, -19.62, -17.4016, -15.6709, -13.389, -11.7518, -9.8247, -7.9848, -6.1014, -4.1053, -2.273, -0.5785, 1.142, 2.9859, 4.7234, 6.4916, 8.4078, 10.1084, 12.0758, 13.8317, 16.027, 17.7106, 19.7455, 21.8411, 23.8426, 25.8898, 27.9295, 29.9419, 32.3473, 34.2424, 36.6886, 38.858, 41.1289, 43.1734, 45.7227, 47.9786, 50.2891, 52.7218, 55.0643, 57.587, 60.1513, 62.2435, 63.363, 61.8063, 60.5408, 59.0598, 57.4021, 55.8666, 54.6346, 52.9516, 51.6617, 50.1541, 48.6928, 47.3101, 45.9109, 44.3331, 43.0833, 41.631, 40.459, 39.0046, 37.598, 36.149, 34.9245, 33.5834, 32.2136, 30.9482, 29.6902, 28.3395, 27.0093, 25.6763, 24.4539, 23.1748, 22.0785, 20.7735, 19.5156, 18.3361, 17.1655, 15.926, 14.7479, 13.6476, 12.421, 11.307, 10.2633, 9.0784, 7.993, 6.7242, 5.7569, 4.6388, 3.5671, 2.421, 1.2873, 0.3404, -0.8589, -1.8726, -3.0596, -3.9825, -5.0679, -6.2221, -7.4186, -8.4965, -9.6398, -10.7169, -11.9148, -12.9872, -14.2793, -15.3707, -16.7719, -17.8757, -19.2448, -20.3363, -21.6419, -22.9333, -24.2253, -25.4955, -26.8701, -28.0987, -29.622, -30.7619, -32.292, -33.6066, -35.0739, -36.3837, -37.8961, -39.2877, -40.9781, -42.3507, -43.8521, -45.196, -46.9465, -48.3252, -50.0156, -51.4202, -53.0854, -54.5739, -56.3325, -57.7937, -59.4582, -61.1909, -62.6897]\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": [
      "34\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "lower_limit = V.index(0.057) + 5\n",
    "upper_limit = V.index(5.458)\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 2.853 2.874 2.874 2.895 2.916 2.916 3.255 3.566 3.863 4.11  4.357\n",
      " 4.604 4.886 5.141]\n",
      "I =  [10.1084 12.0758 13.8317 16.027  17.7106 19.7455 21.8411 23.8426 25.8898\n",
      " 27.9295 29.9419 32.3473 34.2424 36.6886 38.858 ]\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": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5cfc731650>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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]) > 0.1:\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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7hIeH27XPnz+fhQsXsmzZMqpXr86sWbPo3LkzBw8exMfHJ0fHztwuLi4OX1/fPK9dRERERMQMiYmJhIWF3TYXmx74L1++zMCBA/m///s/Zs2aZWs3DINFixYxdepU+vTpA8Dy5csJCgriww8/5Mknn8zR8TOH8fj6+irwi4iIiIjDud2wddNv2h0xYgQ9evSgU6dOdu3Hjh3j9OnTRERE2Nrc3d1p27YtW7duvenxUlJSSExMtHuJiIiIiBRXpvbwf/TRR+zcuZPo6Ogs606fPg1AUFCQXXtQUBC//fbbTY85d+5cZsyYkbeFioiIiIgUUab18MfFxTF69GhWrFiBh4fHTbe78SsKwzBu+bXF5MmTSUhIsL3i4uLyrGYRERERkaLGtB7+HTt2EB8fT+PGjW1t6enpREVFsXjxYg4ePAhYe/pDQkJs28THx2fp9b+eu7s77u7u+Ve4iIiIiEgRYloPf8eOHdm7dy8xMTG2V5MmTRg4cCAxMTFUrlyZ4OBgIiMjbftcu3aNTZs20bJlS7PKFhEREREpUkzr4ffx8aFu3bp2bSVKlKB06dK29jFjxjBnzhyqVatGtWrVmDNnDl5eXjzyyCNmlCwiIiIiUuSYPi3nrTz77LMkJyfz1FNPceHCBZo3b84333yT4zn4RURERESKO4thGIbZReSnxMRE/Pz8SEhI0Dz8IiIiIuIwcppzTZ+HX0RERERE8o8Cv4iIiIiIA1PgFxERERFxYAr8IiIiIiIOTIFfRERERMSBKfCLiIiIiDgwBX4REREREQemwC8iIiIi4sAU+EVERERE7sShf0HCz2ZXcVsK/CIiIiIiufXLIogeDuvbQ/IZs6u5JQV+EREREZHc+OWfsPMZ6/sqfwePQHPruQ0FfhERERGRnDqwEHaOtb6v8xyEvwgWi7k13YYCv4iIiIhIThx4BXaNs76v+zyEzyz0YR8U+EVEREREbu/AAtg13vq+7vNQb0aRCPugwC8iIiIicms/vwy7Jljf132hSIV9ABezCxARERERKbR+fhlinrW+rzsNwqebWs6dUA+/iIiIiEh2fp5f5MM+qIdfRERERCSrn+dBzCTr+3rTod40U8u5Gwr8IiIiIiLX2/8S7J5sfV9vBtR7wdx67pKG9IiIiIiIZNo/97qwP7PIh31QD7+IiIiIiNX+ObB7qvV9+ItQ9zlz68kjCvwiIiIiIvtmw54/A374LKg71dx68pACv4iIiIgUb/tmwZ7nre/rz4Y6U8ytJ48p8IuIiIhI8bX3Rdj75zj9+nOgzmRz68kHCvwiIiIiUjzZhf25UGeSufXkEwV+ERERESl+9s6EvX/Ord/gJag90dx68pGm5RQRERGR4mXvjGIT9kE9/CIiIiJSnOyZDvtmWN83mAe1nzWzmgKhwC8iIiIixcOe6deF/flQe4KZ1RQYBX4RERERcWyGAXunw76Z1uWGL0Ot8aaWVJAU+EVERETEcRmGdbz+vhetyw0XQK1x5tZUwBT4RURERMQxGQbseQH2z7IuN3wFao01tyYTKPCLiIiIiOMxDOvTc/fPti43Wgg1nzG3JpMo8IuIiIiIYzEM2PMc7J9jXS7GYR8U+EVERETEkWQJ+/+EmmNMLclsCvwiIiIi4hgMA3ZPhZ/nWpcbLYKao00tqTBQ4BcRERGRos8wYPcU+Pkl63LjV6HGKHNrKiQU+EVERESkaDMM2D0Zfp5nXW78GtR42tyaChEFfhEREREpugwDYibBgfnW5cavQ42R5tZUyCjwi4iIiEjRZBgQMxEOvGxdbrIYqo8wt6ZCyMnsAkREREREck1hP8fUwy8iIiIiRUvmmH1b2H8Dqj9lbk2FmHr4RURERKToyHyCbuYNugr7t6XALyIiIiJFx76ZsH+29X3jVxX2c0CBX0RERESKhn2zYO906/tGCzXPfg4p8IuIiIhI4bf/JetQHoAG86HmM+bWU4Qo8IuIiIhI4XZggfUmXYD6c6D2BHPrKWIU+EVERESk8PplEez6M+DXmwl1JptaTlGkwC8iIiIihdPBxbDzz6E7dV+Aes+bW08RpcAvIiIiIoXPobdgx9PW93WmQL3pppZTlCnwi4iIiEjhcvj/IPrP6TZrPQvhs8BiMbemIkyBX0REREQKjyNL4KcnrO9rjoUGLyns3yUFfhEREREpHI6+Dz8+bn1ffRQ0XKCwnwcU+EVERETEfLEfwo9DAAOq/QMaL1LYzyMK/CIiIiJirt9WwbZHwciAqk9Ak8UK+3lIgV9EREREzHP8U9g60Br2Kw+Fpm+BRRE1L+nTFBERERFzxH0G3/cHIx0qDYLm/6ewnw/0iYqIiIhIwfv9v/B9XzDSoOJAaP6ewn4+0acqIiIiIgXrxFew5SHISIUK/eGeZeDkbHZVDkuBX0REREQKzsmvYXMfyLgG5R+GFh+Ak4vZVTk0BX4RERERKRinv4XNvSEjBco9AC1XKuwXAAV+EREREcl/ZzbApvsh/SqUvR9afQROrmZXVSyYGvjfeustwsPD8fX1xdfXlxYtWrB27Vrb+sGDB2OxWOxe99xzj4kVi4iIiEiuxUfBxvsgPRlCe8C9H4Ozm9lVFRumfodSrlw5XnrpJapWrQrA8uXL6dWrF7t27aJOnToAdO3alaVLl9r2cXPTXw4RERGRIuOP72Fjd0hPgpCu0Po/4OxudlXFiqmBv2fPnnbLs2fP5q233uKHH36wBX53d3eCg4PNKE9ERERE7sYf22BDV0i7AsGdofVqcPYwu6pip9CM4U9PT+ejjz7iypUrtGjRwta+ceNGAgMDqV69On//+9+Jj4+/5XFSUlJITEy0e4mIiIhIATv7E2zsCmmXIagDtPkMXDzNrqpYMj3w7927F29vb9zd3Rk+fDhr1qyhdu3aAHTr1o2VK1fy3Xff8corrxAdHU2HDh1ISUm56fHmzp2Ln5+f7RUWFlZQlyIiIiIiAOd3wIYISE2EwLbQ9gtw8TK7qmLLYhiGYWYB165d4/jx41y8eJFPP/2Ud999l02bNtlC//VOnTpFhQoV+Oijj+jTp0+2x0tJSbH7hSAxMZGwsDASEhLw9fXNt+sQEREREeD8LviuI1y7AAH3Qru14OptdlUOKTExET8/v9vmXNMnPnVzc7PdtNukSROio6N59dVX+de//pVl25CQECpUqMChQ4duejx3d3fc3XUjiIiIiEiBu7AbvutkDftlWkC7rxT2CwHTh/TcyDCMmw7ZOXfuHHFxcYSEhBRwVSIiIiJySxf3/Rn2z0Pp5tB+Hbj6mF2VYHIP/5QpU+jWrRthYWFcunSJjz76iI0bN7Ju3TouX77M9OnTefDBBwkJCSE2NpYpU6ZQpkwZHnjgATPLFhEREZHrJRywDuNJOQv+Tf4M+xpKXViYGvjPnDnDo48+yqlTp/Dz8yM8PJx169bRuXNnkpOT2bt3L++//z4XL14kJCSE9u3bs2rVKnx89NuiiIiISKGQeBDWd4Cr8VCqIXT4BtxKml2VXMf0m3bzW05vZhARERGRXEo8BOvbQvIpKBkOHb8D99JmV1Vs5DTnFrox/CIiIiJSBFw6AuvbW8O+X13o8K3CfiGlwC8iIiIiuXP52J9h/wT41YaO68EjwOyq5CYU+EVEREQk5678Zh2znxQHvjWgw3rwCDS7KrkFBX4RERERyZnLR+Hb9nAlFnyqQYfvwDPY7KrkNkx/8JaIiIiIFAHnd8LGbtbZeLyrWG/Q9Qo1uyrJAfXwi4iIiMitnYqEb9v+OfVmA+i8GbzKmV2V5JACv4iIiIjc3LGVsLE7pF2GoA7QaRN4hphdleSCAr+IiIiIZO/AQtj2NzDSoEJ/aPeVnqBbBCnwi4iIiIg9IwN2jodd46zLNcZAy5Xg7G5qWXJndNOuiIiIiPwl/Rr8MAR++9C63PBlqDkOLBZz65I7psAvIiIiIlapl2BzHzj9LVhc4J6lUOlvZlcld0mBX0REREQg+Yz15twLO8GlBNz7KYR2MbsqyQMK/CIiIiLFXeIh2NjV+mAt9wDrzbmlm5hdleQRBX4RERGR4uxcNGzsASl/gHdlaP81+FQ1uyrJQwr8IiIiIsXVyXWw5SFIuwKlGll79j2DzK5K8pim5RQREREpjo59AJt6WsN+cGfotFFh30Ep8IuIiIgUJ4YBP78M2x6zPlCr4kBo+z9w9TG7MsknGtIjIiIiUlwYGbBzHBxcZF2uNR4azAOL+oAdmQK/iIiISHGQngLbBsHxVdblhq9ArbHm1iQFQoFfRERExNGlJkLUA3DmO3ByhXuWQ8UBZlclBUSBX0RERMSRJZ+CDd3g4m5w8YY2ayC4k9lVSQFS4BcRERFxVIkHYUNXuBILHkHWaTf9G5ldlRQwBX4RERERR3T2R9jUA1LOgXdV6PC19cFaUuwo8IuIiIg4mhNfwpa+kJ4E/k2h3f/AI9DsqsQkmoNJRERExJEcWQpRvaxhP6QrdPxOYb+YU+AXERERcQSGAfvnwo9DwUiHSo9B2y/A1dvsysRkGtIjIiIiUtRlpMPOMfDrYuty7UlQfw5YLKaWJYWDAr+IiIhIUZZ+FbY+CnH/ASzQeBHUGGV2VVKIKPCLiIiIFFXXLkJUb4jfBE5u0OJ9qNDP7KqkkFHgFxERESmKkk7Axm5wcS+4+ECbzyC4g9lVSSGkwC8iIiJS1CQcsD5QK+k4eARD+7VQqoHZVUkhpcAvIiIiUpRcOgzftoWUP8CnOrT/Grwrml2VFGIK/CIiIiJFxdWzsKGbNeyXagjtvwGPMmZXJYWc5uEXERERKQrSkiHqfrh8GEpUgHZfKexLjijwi4iIiBR2Gemw7W9wdhu4lYJ2a8Ez2OyqpIhQ4BcREREp7HaNh7jV1qk323wGfrXMrkiKEAV+ERERkcLsl0VwcJH1/T3LIbCNmdVIEaTALyIiIlJYHf8Udo61vm8wHyr2N7ceKZIU+EVEREQKoz+2WsftY0C1p6DWeLMrkiJKgV9ERESksEn81TojT/pVKNsTGr8KFovZVUkRpcAvIiIiUphcjYeN3SDlHPg3hVb/Bic9OknunAK/iIiISGGRlgSbesLlo1CiErT9L7iUMLsqKeIU+EVEREQKg4x02PoInPsJ3Pyh/VrwDDK7KnEACvwiIiIiZjMM2DkGfv8cnNyh7RfgW8PsqsRBKPCLiIiImO2XhfDrYsACLVdAQCuzKxIHosAvIiIiYqbfPrY+SReg4QIo/5C59YjDUeAXERERMUv8Ztj2qPV99aeh5jPm1iMOSYFfRERExAwJv0BUL8i4BuV6Q6N/aq59yRcK/CIiIiIFLfmMda79axeg9D3QciU4OZtdlTgoBX4RERGRgpR2BTbdB1diwbuKdUYeFy+zqxIHpsAvIiIiUlAy0mBLfzi/HdzLQLu14BFgdlXi4BT4RURERAqCYcCOUXDyf+DsAW2+AN9qZlclxYACv4iIiEhBODAfDr2Fda79lRDQwuyKpJhQ4BcRERHJb7H/hphJ1veN/glhfcytR4oVBX4RERGR/HRmE/ww2Pq+xjNQc7Sp5Ujxo8AvIiIikl8Sfoao3ta59sMehEYLzK5IiiEFfhEREZH8kHwKNnSD1ItQpiW0+AAsil5S8PS3TkRERCSvpV6GjT0g6Tj4VIM2n4OLp9lVSTGlwC8iIiKSlzLSYEtfuLAL3AP+nGu/jNlVSTGmwC8iIiKSVwwDop+CU2vB2RPa/hd8qphdlRRzCvwiIiIieeXnuXDk/6xj9Vv9G8o0N7siEQV+ERERkTxxbAXsnmp93/g1KNfL3HpE/mRq4H/rrbcIDw/H19cXX19fWrRowdq1a23rDcNg+vTphIaG4unpSbt27di/f7+JFYuIiIhk4/R38ONQ6/ta46H6CHPrEbmOqYG/XLlyvPTSS2zfvp3t27fToUMHevXqZQv18+fPZ+HChSxevJjo6GiCg4Pp3Lkzly5dMrNsERERkb9c3AebH4CMVCjfFxrMM7siETsWwzAMs4u4nr+/Py+//DJDhw4lNDSUMWPGMHHiRABSUlIICgpi3rx5PPnkkzk6XmJiIn5+fiQkJODr65ufpYuIiEhxk3QCvrkHkn6HgNbQ4Rtw9jC7KikmcppzC80Y/vT0dD766COuXLlCixYtOHbsGKdPnyYiIsK2jbu7O23btmXr1q03PU5KSgqJiYl2LxEREZE8l5r451z7v4NvDWjzmcK+FEqmB/69e/fi7e2Nu7s7w4cPZ82aNdSuXZvTp08DEBQUZLd9UFCQbV125s6di5+fn+0VFhaWr/WLiIhIMZR6GaJ6w8Xd4BFonWvf3d/sqkSyZXrgr1GjBjExMfzwww/84x//YNCgQfz888+29RaLxW57wzCytF1v8uTJJCQk2F5xcXH5VruIiIgUQ9cuwoYucGYDuHhD2/+BdyWzqxK5KRezC3Bzc6Nq1aoANGnShOjoaF599VXbuP3Tp08TEhJi2z4+Pj5Lr//13N3dcXd3z9+iRUREpHi6etYa9i/sBNeS0H4dlG5qdlUit2R6D/+NDMMgJSWFSpUqERwcTGRkpG3dtWvX2LRpEy1btjSxQhERESmWkk/B+nbWsO8eAJ026sFaUiSY2sM/ZcoUunXrRlhYGJcuXeKjjz5i48aNrFu3DovFwpgxY5gzZw7VqlWjWrVqzJkzBy8vLx555BEzyxYREZHi5spvsL4TXD4MnqHQYT341TS7KpEcMTXwnzlzhkcffZRTp07h5+dHeHg469ato3PnzgA8++yzJCcn89RTT3HhwgWaN2/ON998g4+Pj5lli4iISHGSeAi+6whJcVCiInRcD96Vza5KJMcK3Tz8eU3z8IuIiMgdu7gfvusEV0+DT3Vr2PcqZ3ZVIkDOc67pN+2KiIiIFErnd1hv0E05ByXrQftI8Lz5xCEihZUCv4iIiMiN/vgeNna3PlzLv6l1Nh7Nsy9FlAK/iIiIyPVOr4dN90N6EgS0hnb/A1cNC5aiq9BNyykiIiJimhNfwsYe1rAfHGHt2VfYlyJOgV9EREQE4PgnENUbMlKgXC9o+wW4eJldlchdU+AXERERObocvu8PRhpUGAD3fgLO7mZXJZInFPhFRESkePv1TfhhMBgZUGUYtPgAnFzNrkokzyjwi4iISPF1YAFsH2F9X30UNHsHnJzNrUkkjynwi4iISPFjGLBnOuyaYF2uMwUaLwKLopE4Hk3LKSIiIsWLYViD/i+vWJfrz7YGfhEHpcAvIiIixYeRAdEj4PDb1uVGi6DmaFNLEslvCvwiIiJSPGSkwQ9DIfYDwGIdr1/1cbOrEsl3CvwiIiLi+NKvwdZHIO5TsDhbZ+KpOMDsqkQKhAK/iIiIOLa0ZNjyEJz8Cpzc4N6PrQ/WEikmFPhFRETEcaVehqj74cwGcPaE1msgtIvZVYkUKAV+ERERcUzXLsLG7nB2G7h4Q7svIbCN2VWJFDgFfhEREXE8V/+ADRFwIQbcSkG7dVCmmdlViZhCgV9EREQcS9JJ2NAZEn4Gj0BoHwmlws2uSsQ0CvwiIiLiOK78Bus7wuUj4FkWOnwLfjXNrkrEVAr8IiIi4hgSf4XvOkFSHJSoBB3Xg3cls6sSMZ0Cv4iIiBR9F/dZw/7VM+Bbw9qz71XO7KpECgUFfhERESnazu+A7yLg2nkoGQ4dIq1j90UEACezCxARERG5Y2c2wfoO1rBfuhl03KCwL3IDBX4RcTinT5+mc+fOlChRgpIlSwJgsVj47LPP8vW806dPp0GDBvl6jrxQVOoUuSXDgF/fsA7jSU20zq/f4Vtw9ze7MpFCR4FfRAq1wYMH07t371zt889//pNTp04RExPDr7/+mi91ZfcLxPjx41m/fn2+nO96FStWxGKxYLFY8PLyom7duvzrX//K8f53UmfFihVZtGhRLisVySfpKfDj47B9JBhpUKE/tFsLrj5mVyZSKCnwi4jDOXLkCI0bN6ZatWoEBhbcV/ve3t6ULl26QM41c+ZMTp06xZ49e+jduzfDhw9n1apVOdq3IOsUyXNJJ+DbtnB0CVicoOHL0PJDcPEyuzKRQkuBX0SKlHbt2jFq1CieffZZ/P39CQ4OZvr06bb1FStW5NNPP+X999/HYrEwePDgbI9z4sQJ+vXrR6lSpShdujS9evUiNjbWbpslS5ZQp04d3N3dCQkJYeTIkbZzADzwwANYLBbb8o1DZTIyMpg5cyblypXD3d2dBg0asG7dOtv62NhYLBYLq1evpn379nh5eVG/fn22bdt228/Bx8eH4OBgqlatyqxZs6hWrZrtG4fjx4/Tq1cvvL298fX1pW/fvpw5c8a27411Zn6LsmDBAkJCQihdujQjRowgNTXV9pn/9ttvPPPMM7ZvFkRM8cdWWNcEzv3459Nz10Kt8aC/kyK3pMAvIkXO8uXLKVGiBD/++CPz589n5syZREZGAhAdHU3Xrl3p27cvp06d4tVXX82yf1JSEu3bt8fb25uoqCi2bNmCt7c3Xbt25dq1awC89dZbjBgxgieeeIK9e/fyxRdfULVqVds5AJYuXcqpU6dsyzd69dVXeeWVV1iwYAF79uyhS5cu3H///Rw6dMhuu6lTpzJ+/HhiYmKoXr06AwYMIC0tLVefiYeHB6mpqRiGQe/evTl//jybNm0iMjKSI0eO0K9fv1vuv2HDBo4cOcKGDRtYvnw5y5YtY9myZQCsXr2acuXK2b5VOHXqVK5qE8kTh/8P1reDq6fBry50iYaQCLOrEikSNC2niBQ54eHhTJs2DYBq1aqxePFi1q9fT+fOnQkICMDd3R1PT0+Cg4Oz3f+jjz7CycmJd99919ZbvXTpUkqWLMnGjRuJiIhg1qxZjBs3jtGjR9v2a9q0KQABAQEAlCxZ8qbnAFiwYAETJ06kf//+AMybN48NGzawaNEi3njjDdt248ePp0ePHgDMmDGDOnXqcPjwYWrWvP3TQdPS0lixYgV79+7lH//4B99++y179uzh2LFjhIWFAfDBBx9Qp04doqOjbddwo1KlSrF48WKcnZ2pWbMmPXr0YP369fz973/H398fZ2dn27cKIgUq/RrsGA2H37Yuhz0I9ywDV29TyxIpStTDLyJFTnh4uN1ySEgI8fHxOd5/x44dHD58GB8fH7y9vfH29sbf35+rV69y5MgR4uPjOXnyJB07drzjGhMTEzl58iStWrWya2/VqhUHDhy46fWEhIQA3PZ6Jk6ciLe3N56enowYMYIJEybw5JNPcuDAAcLCwmxhH6B27dqULFkyy3mvV6dOHZydne3qyM1nKpIvkk/Ddx3+DPsWqD8b7v1EYV8kl9TDLyJFjqurq92yxWIhIyMjx/tnZGTQuHFjVq5cmWVdQEAATk551xdy43h3wzCytF1/PZnrbnc9EyZMYPDgwXh5eRESEmLbL7vj36o9uxoy68jNZyqS587+BJv7QPIJcPW13phbtofZVYkUSerhF5Fip1GjRhw6dIjAwECqVq1q9/Lz88PHx4eKFSvecupKV1dX0tPTb7re19eX0NBQtmzZYte+detWatWqddfXUKZMGapWrUpoaKhdkK9duzbHjx8nLi7O1vbzzz+TkJBwV+d1c3O75fWK5Kmjy+DbNtaw71sTuvyksC9yFxT4RaTYGThwIGXKlKFXr15s3ryZY8eOsWnTJkaPHs3vv/8OWGeyeeWVV3jttdc4dOgQO3fu5PXXX7cdI/MXgtOnT3PhwoVszzNhwgTmzZvHqlWrOHjwIJMmTSImJsbuvoC81qlTJ8LDwxk4cCA7d+7kp59+4rHHHqNt27Y0adLkjo9bsWJFoqKiOHHiBGfPns3DikWuk5EK20fDD0MgIwXK3g9dfgTfGmZXJlKkKfCLSLHj5eVFVFQU5cuXp0+fPtSqVYuhQ4eSnJyMr68vAIMGDWLRokW8+eab1KlTh/vuu89udp1XXnmFyMhIwsLCaNiwYbbnGTVqFOPGjWPcuHHUq1ePdevW8cUXX1CtWrV8u7bMB4KVKlWKNm3a0KlTJypXrpzjOfpvZubMmcTGxlKlShXbTcsieerqH/BdBPz6mnW57jRos8Y6nEdE7orFMAzD7CLyU2JiIn5+fiQkJNj+IRcREZFC5PxOiHoAko6Dize0+ADCeptdlUihl9Ocq5t2RURExDzHVsJPj0P6VfCuCm0/B7/aZlcl4lAU+EVERKTgZaRBzCT45RXrckg3aPUhuJU0tSwRR6TALyIiIgUr5Rx83x9Of2tdrj0Zwl8EJ+db7ycid0SBX0RERArOhT0Q1RuuHANnL2ixDMo/bHZVIg5NgV9EREQKxvFPYNtgSE+CEpWgzWdQKvx2e4nIXdK0nCJS6J07d47AwEBiY2PNLsXhxMfHExAQwIkTJ8wuRRxZRjrETIEtfa1hP7gTdI1W2BcpIAr8IlLozZ07l549e3Lu3DksFkuWp9dm6tKlC/fff3+uj3/+/HmefvppatSogZeXF+XLl2fUqFEkJCTccr9Lly4xZswYKlSogKenJy1btiQ6Otpum9WrV9OlSxfKlCmDxWIhJiYm22Nt27aNDh06UKJECUqWLEm7du1ITk7O9bVkevrpp2863/+JEydwdnZm9erVBAYG8uijjzJt2rQ7PpfILV27CJt6ws9zrcs1x0G7teBe2tSyRIoTBX4RKdSSk5N57733ePzxx2ncuDH169dn6dKlWbaLi4vj22+/ZdiwYbk+x8mTJzl58iQLFixg7969LFu2jHXr1t32WI8//jiRkZF88MEH7N27l4iICDp16mTXW37lyhVatWrFSy+9dNPjbNu2ja5duxIREcFPP/1EdHQ0I0eOxMnpzv8XPWzYMA4fPszmzZuzrFu2bBmlS5emZ8+eAAwZMoSVK1fe9InBIncs4Wf4uhmcWgvOHtByJTRaAE4aUSxSoAwHl5CQYABGQkKC2aWIyB349NNPjTJlytiWX3vtNcPb29u4fPmy3XYzZ840goKCjNTU1Dw578cff2y4ubnd9HhJSUmGs7Oz8b///c+uvX79+sbUqVOzbH/s2DEDMHbt2pVlXfPmzY3nnnvuprVk7rtq1Srj3nvvNTw8PIwmTZoYBw8eNH766SejcePGRokSJYwuXboY8fHxtv0aNWpkDB48OMvxqlataowbN86urWLFisZ777130xpEcu34GsNY5W0YKzGMNeUN49wOsysScTg5zbnq4ReRQi0qKoomTZrYlgcOHEhqaiqffPKJrc0wDJYtW8agQYNwcbH2HM6ZMwdvb+9bvrLr/c6U+dTCzOPdKC0tjfT0dDw8POzaPT09bzrkKDvx8fH8+OOPBAYG0rJlS4KCgmjbtm22x5g2bRrPPfccO3fuxMXFhQEDBvDss8/y6quvsnnzZo4cOcILL7xg237YsGF88sknXL582da2adMmDh8+zNChQ+2O3axZs1t+HiI5ZmTAnmmw+QFIuwyB7aDrdvBvZHZlIsWWAr+IFGqxsbGEhobalv39/endu7fdsJ6NGzdy9OhRuxA7fPhwYmJibvm6/heJ6507d44XX3yRJ5988qZ1+fj40KJFC1588UVOnjxJeno6K1as4Mcff+TUqVM5vr6jR48CMH36dP7+97+zbt06GjVqRMeOHTl06JDdtuPHj6dLly7UqlWL0aNHs3PnTp5//nlatWpFw4YNGTZsGBs2bLBt/8gjj5Cenm73y9GSJUto0aIFtWvbP8m0bNmyuila7l5qIkQ9APtmWperj4IO34BHgLl1iRRzGkQnIoVacnJyll70YcOGERERweHDh6latSpLliyhVatW1KhRw7aNv78//v7+uT5fYmIiPXr0oHbt2re9kfWDDz5g6NChlC1bFmdnZxo1asQjjzzCzp07c3y+jIwMAJ588kmGDBkCQMOGDVm/fj1Llixh7ty5tm3Dw/+a0SQoKAiAevXq2bXFx8fblkuWLEmfPn1YsmQJQ4YM4dKlS3z66acsWrQoSx2enp4kJSXluG6RLBJ/hahekPgLOLlDs7eh8mCzqxIR1MMvIoVcmTJlstxM2qlTJypUqMCyZctITExk9erVWW6wvZMhPZcuXaJr1654e3uzZs0aXF1db1lblSpV2LRpE5cvXyYuLo6ffvqJ1NRUKlWqlOPrCwkJAcjS416rVi2OHz9u13Z9PRaLJdu2zF8gMg0bNowtW7Zw6NAhVq1aBUC/fv2y1HH+/HkCAtQLK3foxJfwdVNr2PcsC52iFPZFChH18ItIodawYUNWrFhh12axWBgyZAjvvvsu5cqVw8nJib59+9ptM3z48CxtNypbtqztfWJiIl26dMHd3Z0vvvgiy7cKt1KiRAlKlCjBhQsX+Prrr5k/f36O961YsSKhoaEcPHjQrv3XX3+lW7duOT7OzbRv357KlSuzbNkyNmzYQN++ffHx8cmy3b59+2jXrt1dn0+KGcOA/XNgz/OAAQGt4N7/gGew2ZWJyHUU+EWkUOvSpQuTJ0/mwoULlCpVytY+ZMgQZs6cyZQpU+jfvz8lSpSw2y83Q3ouXbpEREQESUlJrFixgsTERBITEwEICAjA2dkZgI4dO/LAAw8wcuRIAL7++msMw6BGjRocPnyYCRMmUKNGDdvQHLD2nB8/fpyTJ08C2IJ9cHAwwcHBWCwWJkyYwLRp06hfvz4NGjRg+fLl/PLLL/znP/+5w0/tL5m/HC1cuJALFy7w8ssvZ9kmKSmJHTt2MGfOnLs+nxQjqZfhh8EQ96l1uepwaPwqOLuZWpaIZKUhPSJSqNWrV48mTZrw8ccf27WXL1+eTp06ceHChSwzzuTWjh07+PHHH9m7dy9Vq1YlJCTE9oqLi7Ntd+TIEc6ePWtbTkhIYMSIEdSsWZPHHnuMe++9l2+++cZumM0XX3xBw4YN6dGjBwD9+/enYcOGvP3227ZtxowZw+TJk3nmmWeoX78+69evJzIykipVqtzVdWUaPHgwCQkJ1KhRg1atWmVZ//nnn1O+fHlat26dJ+eTYuDSEfimhTXsO7lCs39Bs7cU9kUKKYthGIbZReSnxMRE/Pz8bFPsiUjR89VXXzF+/Hj27dt3Vw+jkuw1a9aMMWPG8Mgjj5hdihQFp76B7/vDtQvgEQytP4WAlmZXJVIs5TTnakiPiBR63bt359ChQ5w4cYKwsDCzy3Eo8fHxPPTQQwwYMMDsUqSwMww4MB92T7HOtV+6ObReDV6ht99XREylHn4RERG5tWsXYdsgOPGFdbnyEGj6Jjjn/OZ2Ecl76uEXERGRu3chBjY/CJePgpMbNHkdqvwd/pwaVkQKPwV+ERERyd6RJbB9BKRfhRIVrFNuls7+CdUiUngp8IuIiIi9tGTYPhKOLrEuh3SDlivAPfdPrxYR8ynwi4iIyF8uH7UO4bkQA1ggfCbUmQIWzZAlUlTpv14RKRLOnTtHYGAgsbGxZpci+eihhx5i4cKFZpdRfP3+BaxtZA377mWg/ddQ9zmFfZEiTv8Fi0iRMHfuXHr27EnFihUB68Okevfuna/na9q0KT4+PgQGBtK7d2/bU3IzRUVF0bNnT0JDQ7FYLHz22We3PW5aWhrPPfcclSpVwtPTk8qVKzNz5kwyMjJuWofFYmHMmDG5PndO63vzzTepVKkSHh4eNG7cmM2bN+dqfW7t2rULV1fXbB/09cILLzB79mzbk46lgGSkQcxkiOoFqQlQ+h7ouhNCOptdmYjkAQV+ESn0kpOTee+993j88ccL7JybNm1ixIgR/PDDD0RGRpKWlkZERARXrlyxbXPlyhXq16/P4sWLc3zcefPm8fbbb7N48WIOHDjA/Pnzefnll3n99dezbBsdHc0777xDeHh4lnU5OXdOtlm1ahVjxoxh6tSp7Nq1i9atW9OtWzeOHz+eo/V3YtSoUYwfP57du3dz48zQ4eHhVKxYkZUrV97x8SWXks/Ahgj4+SXrcvWnodMmKKFnXog4DMPBJSQkGICRkJBgdikicoc+/fRTo0yZMnZtgwYNMnr16lVgNcTHxxuAsWnTpmzXA8aaNWtue5wePXoYQ4cOtWvr06eP8be//c2u7dKlS0a1atWMyMhIo23btsbo0aNvesycnPtm2zRr1swYPny4XVvNmjWNSZMm5Wi9YRjG5s2bDRcXF+Pq1au2tqNHjxqAERsba7fvypUrjZ49expxcXEGYBw+fDhLTdOnTzdat259y+uRPHJms2GsDjGMlRjGqhKGcezfZlckIrmQ05xrag9/Tr4yHzx4MBaLxe51zz33mFSxiJghKiqKJk1yNxXgnDlz8Pb2vuUrN0NTEhISAPD3v7tZSu69917Wr1/Pr7/+CsDu3bvZsmUL3bt3t9tuxIgR9OjRg06dOt3V+W7l2rVr7Nixg4iICLv2iIgItm7detv1mWJiYqhVqxbu7u52bSVLlqRChQq2titXrjBlyhTmzZtHuXLl8PPzIyYmJktdzZo146effiIlJSWPrlSyMAz45Z+wvh0knwLfWtAlGir2N7syEckHps7Sk/mVedOmTUlLS2Pq1KlERETw888/U6JECdt2Xbt2ZenSpbZlNzc3M8oVEZPExsYSGhqaq32GDx9O3759b7lN2bJlc3QswzAYO3Ys9957L3Xr1s1VHTeaOHEiCQkJ1KxZE2dnZ9LT05k9ezYDBgywbfPRRx+xc+dOoqOj7+pct3P27FnS09MJCgqyaw8KCuL06dO3XZ9p9+7dNGzY0G6bmJgY6tevb9c2e/ZsunbtSq1atQCoXbs2MTExPPjgg3bblS1blpSUFE6fPm33C4PkkdRE+GEYxP3HulxhADR7B1y9za1LRPKNqYF/3bp1dstLly4lMDCQHTt20KZNG1u7u7s7wcHBBV2eiBQSycnJeHh45Goff3//u+6NzzRy5Ej27NnDli1b7vpYq1atYsWKFXz44YfUqVOHmJgYxowZQ2hoKIMGDSIuLo7Ro0fzzTff5Pqa75TlhiemGoZh13a79TExMTzyyCN22+zatcsu8B89epR33nmHffv22drq1q2bbQ+/p6cnAElJSbm/GLm1i/usU25e+hWcXKHhQqg+Qk/NFXFwhWoe/pt9Zb5x40YCAwMpWbIkbdu2Zfbs2QQGBmZ7jJSUFLuvgTXTg0jRV6ZMGS5cuJCrfebMmcOcOXNuuc3atWuznSnmek8//TRffPEFUVFRlCtXLlc1ZGfChAlMmjSJ/v2tQyfq1avHb7/9xty5cxk0aBA7duwgPj6exo0b2/ZJT08nKiqKxYsXk5KSgrOz813XAdbP1dnZ2a63HiA+Pp6goKDbrs+sbf/+/Vl6+Hfu3MkDDzxgW37mmWc4d+6c3WeYkZGR7bcs58+fByAgIODuLlDsHVsBPz0J6UngVQ7u/QTKaIisSHFQaAL/zb4y79atGw8//DAVKlTg2LFjPP/883To0IEdO3bYjRfNNHfuXGbMmFGQpYtIPmvYsCErVqzI1T53O6THMAyefvpp1qxZw8aNG6lUqVKuzn8zSUlJODnZ3z7l7Oxsm5azY8eO7N271279kCFDqFmzJhMnTsyzsA/W4ZGNGzcmMjLSLpxHRkbSq1ev264HOHjwIMnJyXZDrrZt28aJEydsPfyRkZF8//337Nq1CxeXv/7ZiY6OZujQoZw7d47SpUvb2vft20e5cuUoU6ZMnl1rsZaeAjufgUNvWZeDO0PLleChX6hEiotCE/hv9pV5v379bO/r1q1LkyZNqFChAl9++SV9+vTJcpzJkyczduxY23JiYiJhYZpaTKQo69KlC5MnT+bChQuUKlUqR/vc7ZCeESNG8OGHH/L555/j4+Nj6+X28/OzDTm5fPkyhw8ftu1z7NgxYmJi8Pf3p3z58gAsXryYNWvWsH79egB69uzJ7NmzKV++PHXq1GHXrl0sXLiQoUOHAuDj45PlPoESJUpQunRpu/acnDsn24wdO5ZHH32UJk2a0KJFC9555x2OHz/O8OHDc7Q+c0jO66+/zqhRozh8+DCjRo0CrN+4pqWlMXr0aCZMmECDBg3srsvX19d2jI4dO9raN2/enOVGYblDV36DzQ/D+T/vB6n7gvXllHe/OIpIEZD/Ewbd3siRI41y5coZR48ezdH2VatWNV566aUcbatpOUUcwz333GO8/fbbtuX8npYTyPa1dOlS2zYbNmzIdptBgwbZtpk2bZpRoUIF23JiYqIxevRoo3z58oaHh4dRuXJlY+rUqUZKSspNa8luWs6cnDsn2xiGYbzxxhtGhQoVDDc3N6NRo0ZZph691foJEyYYnTt3Nnr06GG4ubkZDRo0MP7zn/8Yvr6+xsCBA42FCxcagYGBxuXLl7NcV0ZGhuHl5WUsWLDA1pacnGz4+voa27Ztu+nnITl0Yq1hfOJvnXLzE3/DOPGV2RWJSB7Lac61GMYNTz0pQMYNX5lXq1bttvucO3eOsmXL8s477/DYY4/ddvvExET8/PxISEiw9SaJSNHz1VdfMX78ePbt25dlSIyYp0uXLjRq1Ii5c+fmyfHeeOMNPv/8c7755ps8OV6xlJEO+2bCvhcBA/ybWMfre1c0uzIRyWM5zbmm/qs5YsQI22wVmV+Znz59muTkZMD6dfT48ePZtm0bsbGxbNy4kZ49e1KmTBm78aQi4vi6d+/Ok08+yYkTJ8wuRa6ze/fubJ8EfKdcXV2zfeqw5NDVs7CxuzXwY0DV4dB5i8K+SDFnag//jVO9ZVq6dCmDBw8mOTmZ3r17s2vXLi5evEhISAjt27fnxRdfzPG4fPXwi4jkj9OnTxMSEsK+ffuoU6eO2eXI2R9hy8OQFAfOntDsX1DpUbOrEpF8lNOca+pNu7f7XcPT05Ovv/66gKoREZHcCA4Ovu3/x6UAGAYcetM6E09GKvhUg9afQsl6ZlcmIoVEoZmlR0RERHIp9TL89AT89m/rclgfuGcpuOobbRH5iwK/iIhIUZTwC2x5EBJ+BoszNJgPNZ/RU3NFJItcBf7s5r3PzurVq++oGBEREcmB3z6GH4dB2mXwDIFWqyDw1k+NFpHiK1eB38/PL7/qEBERuaU9e/Yw76WX6D9gAD179jS7HHOkX4OYZ+Hgq9blwHbQ6t/gGWxqWSJSuJk6S09B0Cw9IiJF25EjR3jhuef496pVuBgGQcHBHDp2DA8PD7NLK1hJv8OWvnB2m3W59kQInwVOGp0rUlwViXn4RUREbub06dP8Y/hwataowcb//Ie3DINdwKkzZ3jrrbfMLq9gnV4PaxtZw76rH7T5HBq8pLAvIjmiHn4RESmUevfqxVf//S9zDIMRgOef7X8HPitZkqPHj+Pj42NihQXAyID9c2HvC9b3Jetbp9z0qWJ2ZSJSCKiHX0REirSH+/Yl1TBoxV9hH2AacCkxkYULF5pUWQG5dgE23Q97nrOG/cpDIWKbwr6I5Jp6+EVEpFDKyMigQd26lD54kO8yMrh+sslxwP95eXH0t98oU6aMWSXmn/M7YPNDcCUWnNyh6RtQZZjZVYlIIaMefhERKdKcnJyY9dJLbMzI4Nsb1k0CjJQUXpo714zS8tfvn8M3raxh37uytVdfYV9E7oICv4iIFFo9e/akRdOmTHF25vqvowOAcenpLH79dX7//Xezyst7pyKtM/FkpEDofdB1O/g3NLsqESniFPhFRKTQslgszJk/n+3p6ay5Yd1YwDsjg5kzZphRWt6L3wJRvSDjGoQ9CG3WgFsps6sSEQegwC8iIoVau3bt6NyhA885O5N+XbsvMCU9nSVLlnDo0CGzyssb53fAph6Qngwh3aDlh5pyU0TyjAK/iIgUenPmzeNAejof3ND+DyDYyYkXnnvOjLLyxsX9sKELpCZCYFvrtJvObmZXJSIORIFfREQKvSZNmvDgAw8w3cWFlOvaPYFpaWl89PHHxMTEmFTdXbh0GL7rBCnnoHQzaPtfcPG8/X4iIrmgwC8iIkXCi7NnE5eezjs3tA8Gqjk789yUKSZUdReuxMH6jnD1NJSsB+3WgquDP0hMREyhwC8iIkVCrVq1eOyxx5jl7Mzl69pdgZnp6Xy5di3ff/+9WeXlTvIZa89+0nHwqQbtI8Hd3+yqRMRBKfCLiEiRMX3GDC5YLLx6Q3tfoIGzM5OffZZC/zzJlPOwoTNc+hW8ykOHb8EzyOyqRMSBKfCLiEiRUaFCBYYPH87Lzs6cv67dCZidns7mrVtZt26drd0wDP773//Solkz3nzzzQKvN4vUS7CxG1zcCx7B0HE9lChvdlUi4uAU+EVEpEiZ+txzpLm6Mv+G9m7Avc7OTJ04kYyMDDZu3Eir5s25//77+Sk6mh07dphR7l/SkmBTTzj3E7j5Q4dI8Klqbk0iUiwo8IuISJESFBTEmHHjeM3JiVPXtVuAOenp7Nq7l0YNGtC+fXtSd+4kEmjl7ExqaqpJFQPp12DzQxC/CVx8oP3XULKuefWISLGiwC8iIkXO+PHj8fD2ZtYN7a2Bh5ycSDtwgE+Bn9LT6QS4GQbXrl0r+EIBMtJg6yNwai04e0K7L6F0E3NqEZFiSYFfRESKnJIlSzJxyhTesVg4esO6TzIy2JeWRh+svf4ArhkZ5vTwGxnw4zCI+xSc3KDNZxDYuuDrEJFiTYFfRESKpH79+oGzMy/lYFs3KPjAbxiw/Wk49j5YnKHVKgiJKNgaRERQ4BcRkSLm4sWLPPfcc9StVYsShkFO+stdgWtXr+Z3aX8xDNg9GQ69CVjgnuUQ1rvgzi8ich0XswsQERHJiatXr/Lqq68yb84crl6+zOiMDCYAOXlclSuQWpBj+PfPgZ/nWd83exsqDSy4c4uI3ECBX0REioTly5czadIk6gCRQEgu9nUDrqWk5E9hN/rlVdjznPV9wwVQ9YmCOa+IyE1oSI+IiBQJf/vb3xg2dCj7gdEWCxdysW+B9fAfWQI7x1jf150Gtcbl/zlFRG5DgV9ERIqEEiVK8O577/HJJ58Q6e1NfWdnonK4rysFcNPub6vgx8et72uOhXrT8vd8IiI5pMAvIiJFykMPPcSe/fupdM89tLdYeB64XZTP9yE9v/8Xtv4NMKxDeBouAIvltruJiBQEBX4RESlywsLC+G7TJma++CJznZxo4+ycZT7+6+VrD//p9bDlYTDSoMIj0ORNhX0RKVQU+EVEpEhydnZm6tSpbPn+e+LLlqWBszMrb7JtvgX+P7ZBVC/ISIFyvaDFMnByzvvziIjcBQV+EREp0u655x527d1Lr379+BvwKJB4wzZuQGpaWt6e+Pwu2NgN0q5AcGfrg7WcXPP2HCIieUCBX0REijxfX18+WLmSFStW8LmXFw1cXNh23XpX4FpeztKTcAA2REBqAgTcC23WgLN73h1fRCQPKfCLiIjDGDhwIDF79xLUsCGtLRZeBNL5c0hPXvXwXz4K33WClLPg3xja/g9cSuTNsUVE8oECv4iIOJTKlSuzeetWpkydynSLhfZOTpwmjwJ/0glY3wmST4JfbWi3Dtz87v64IiL5SIFfREQcjouLCzNffJENGzcSGxTEIvIg8F+Nt/bsXzkG3lWgw7fgUSYvyhURyVcK/CIi4rDatGnD7v376fvQQ5QPC7vzA127CBu6QOIv4FUOOq4Hz5A8q1NEJD+5mF2AiIhIfipVqhSrPvnkzg+Qehk2dIMLMeARCB3WQ4kKeVafiEh+Uw+/iIgUDxmpkHTS+mdOpV+1zrN/7gdwKwXtI8G3ev7VKCKSDxT4RUSkeMhIhaunch74M1Jh88Nw5jtw8bbeoFsqPH9rFBHJBwr8IiIiN8pIh62Pwsn/gbOHderNMs3MrkpE5I4o8IuIiFzPyICfnoDjfz45t/VqCGprdlUiIndMgV9ERCSTYcCOZ+DoErA4Qct/Q2g3s6sSEbkrCvwiIiKZ9jwPv75mfd98CZR/0Nx6RETygAK/iIgIwM/zYP9s6/smb0DlQebWIyKSRxT4RUREfn0TYiZZ3zd4Cao/ZW49IiJ5SIFfRESKt6Pvw/YR1vd1pkLtiebWIyKSxxT4RUSk+Dr+Kfw4xPq++igIf9HcekRE8oECv4iIFE8n18LWAdZpOCsPhcb/BIvF7KpERPKcAr+IiBQ/ZzbB5j7Wp+mW7wfN3rFOwyki4oD0fzcRESlezm2HTfdB+lUIvQ9afgBOzmZXJSKSbxT4RUSk+Lh0CDb3hrTLENQe7v3Y+jRdEREHpsAvIiLFw6VDED0Crl2A0vdAmy/AxdPsqkRE8p0Cv4iIFA97Z8K181AyHNp/Ba7eZlckIlIgFPhFRKR4SDpu/bPOFHArZW4tIiIFSIFfRESKh9RE65+ufubWISJSwBT4RUSkeEhNsP7p6mtuHSIiBUyBX0REiofUS9Y/1cMvIsWMAr+IiDi+jFRIT7K+Vw+/iBQzCvwiIuL4MsfvgwK/iBQ7CvwiIuL4MsfvO3voQVsiUuyYGvjnzp1L06ZN8fHxITAwkN69e3Pw4EG7bQzDYPr06YSGhuLp6Um7du3Yv3+/SRWLiEiRdO3PwO+iufdFpPgxNfBv2rSJESNG8MMPPxAZGUlaWhoRERFcuXLFts38+fNZuHAhixcvJjo6muDgYDp37sylS5dMrFxERIqUVAV+ESm+LIZhGGYXkemPP/4gMDCQTZs20aZNGwzDIDQ0lDFjxjBx4kQAUlJSCAoKYt68eTz55JO3PWZiYiJ+fn4kJCTg66txmyIixdLvn0NUb/CrA11+AhcvsysSEblrOc25hWoMf0KCtQfG398fgGPHjnH69GkiIiJs27i7u9O2bVu2bt2a7TFSUlJITEy0e4mISDGnIT0iUowVmsBvGAZjx47l3nvvpW7dugCcPn0agKCgILttg4KCbOtuNHfuXPz8/GyvsLCw/C1cREQKv8xZehT4RaQYKjSBf+TIkezZs4d///vfWdZZLBa7ZcMwsrRlmjx5MgkJCbZXXFxcvtQrIiJFiO0puwr8IlL8uJhdAMDTTz/NF198QVRUFOXKlbO1BwcHA9ae/pCQEFt7fHx8ll7/TO7u7ri7u+dvwSIiUrTopl0RKcZM7eE3DIORI0eyevVqvvvuOypVqmS3vlKlSgQHBxMZGWlru3btGps2baJly5YFXa6IiBRVCvwiUoyZ2sM/YsQIPvzwQz7//HN8fHxs4/L9/Pzw9PTEYrEwZswY5syZQ7Vq1ahWrRpz5szBy8uLRx55xMzSRUSkKMm8adczVA/eEpFix9TA/9ZbbwHQrl07u/alS5cyePBgAJ599lmSk5N56qmnuHDhAs2bN+ebb77Bx8engKsVEZEiK7OHv0R5BX4RKXYK1Tz8+UHz8IuICN+0hLPboPVqCHvA7GpERPJEkZyHX0REJF/YZunxM7cOERETKPCLiIjjyxzD76pvekWk+FHgFxERx5f54C318ItIMaTALyIiji0jHdIuWd+7KfCLSPGjwC8iIo4tM+yDevhFpFhS4BcREceWecOukzs460nsIlL8KPCLiIhjy7xhV8N5RKSYUuAXERHHpik5RaSYU+AXERHHpsAvIsWcAr+IiDi2awr8IlK8KfCLiIhjS8ucg18P3RKR4kmBX0REHJtu2hWRYk6BX0REHJvG8ItIMafALyIijk2BX0SKOQV+ERFxbLppV0SKOQV+ERFxbKkawy8ixZsCv4iIODYN6RGRYk6BX0REHJsCv4gUcwr8IiLi2FIz5+FX4BeR4kmBX0REHJvtpl09eEtEiicFfhEpck6fPk3nzp0pUaIEJUuWBMBisfDZZ5/l63mnT59OgwYN8vUceaGo1FkgDOOvJ+3qpl0RKaYU+EXEVIMHD6Z379652uef//wnp06dIiYmhl9//TVf6sruF4jx48ezfv36fDnf9SpWrIjFYsFiseDl5UXdunX517/+leP976TOihUrsmjRolxWWgSkXQYjw/peQ3pEpJhS4BeRIufIkSM0btyYatWqERgYWGDn9fb2pnTp0gVyrpkzZ3Lq1Cn27NlD7969GT58OKtWrcrRvgVZZ6GXecOuxQWcPc2tRUTEJAr8IlKotGvXjlGjRvHss8/i7+9PcHAw06dPt62vWLEin376Ke+//z4Wi4XBgwdne5wTJ07Qr18/SpUqRenSpenVqxexsbF22yxZsoQ6derg7u5OSEgII0eOtJ0D4IEHHsBisdiWbxwqk5GRwcyZMylXrhzu7u40aNCAdevW2dbHxsZisVhYvXo17du3x8vLi/r167Nt27bbfg4+Pj4EBwdTtWpVZs2aRbVq1WzfOBw/fpxevXrh7e2Nr68vffv25cyZM7Z9b6wz81uUBQsWEBISQunSpRkxYgSpqam2z/y3337jmWeesX2z4DCuXTcHvyNdl4hILijwi0ihs3z5ckqUKMGPP/7I/PnzmTlzJpGRkQBER0fTtWtX+vbty6lTp3j11Vez7J+UlET79u3x9vYmKiqKLVu24O3tTdeuXbl27RoAb731FiNGjOCJJ55g7969fPHFF1StWtV2DoClS5dy6tQp2/KNXn31VV555RUWLFjAnj176NKlC/fffz+HDh2y227q1KmMHz+emJgYqlevzoABA0hLS8vVZ+Lh4UFqaiqGYdC7d2/Onz/Ppk2biIyM5MiRI/Tr1++W+2/YsIEjR46wYcMGli9fzrJly1i2bBkAq1evply5crZvFU6dOpWr2go1TckpIoKL2QWIiNwoPDycadOmAVCtWjUWL17M+vXr6dy5MwEBAbi7u+Pp6UlwcHC2+3/00Uc4OTnx7rvv2nqrly5dSsmSJdm4cSMRERHMmjWLcePGMXr0aNt+TZs2BSAgIACAkiVL3vQcAAsWLGDixIn0798fgHnz5rFhwwYWLVrEG2+8Ydtu/Pjx9OjRA4AZM2ZQp04dDh8+TM2aNW/7WaSlpbFixQr27t3LP/7xD7799lv27NnDsWPHCAsLA+CDDz6gTp06REdH267hRqVKlWLx4sU4OztTs2ZNevTowfr16/n73/+Ov78/zs7Otm8VHIoCv4iIevhFpPAJDw+3Ww4JCSE+Pj7H++/YsYPDhw/j4+ODt7c33t7e+Pv7c/XqVY4cOUJ8fDwnT56kY8eOd1xjYmIiJ0+epFWrVnbtrVq14sCBAze9npCQEIDbXs/EiRPx9vbG09OTESNGMGHCBJ588kkOHDhAWFiYLewD1K5dm5IlS2Y57/Xq1KmDs7OzXR25+UyLrGsK/CIi6uEXkULH1dXVbtlisZCRkZHj/TMyMmjcuDErV67Msi4gIAAnp7zr67hxvLthGFnarr+ezHW3u54JEyYwePBgvLy8CAkJse2X3fFv1Z5dDZl15OYzLbJSL1r/dCtpZhUiIqZSD7+IOJxGjRpx6NAhAgMDqVq1qt3Lz88PHx8fKlaseMupK11dXUlPT7/pel9fX0JDQ9myZYtd+9atW6lVq9ZdX0OZMmWoWrUqoaGhdkG+du3aHD9+nLi4OFvbzz//TEJCwl2d183N7ZbXW2Rdu2j9U4FfRIoxBX4RcTgDBw6kTJky9OrVi82bN3Ps2DE2bdrE6NGj+f333wHrTDavvPIKr732GocOHWLnzp28/vrrtmNk/kJw+vRpLly4kO15JkyYwLx581i1ahUHDx5k0qRJxMTE2N0XkNc6depEeHg4AwcOZOfOnfz000889thjtG3bliZNmtzxcStWrEhUVBQnTpzg7NmzeVixya79+bNzLWVuHSIiJlLgFxGH4+XlRVRUFOXLl6dPnz7UqlWLoUOHkpycjK+vLwCDBg1i0aJFvPnmm9SpU4f77rvPbnadV155hcjISMLCwmjYsGG25xk1ahTjxo1j3Lhx1KtXj3Xr1vHFF19QrVq1fLu2zAeClSpVijZt2tCpUycqV66c4zn6b2bmzJnExsZSpUoV203LDkFDekREsBiGYZhdRH5KTEzEz8+PhIQE2z/0IiJSTGzpC8c/gcavQY2nza5GRCRP5TTnqodfREQcl8bwi4go8IuIiAPLHMPvpjH8IlJ8KfCLiIjjyuzhdy1pZhUiIqZS4BcREceVqh5+EREFfhERcUyGoTH8IiIo8ItIIXDu3DkCAwOJjY01uxSHEx8fT0BAACdOnDC7lIKXdhmMPx8mph5+ESnGFPhFxHRz586lZ8+enDt3DovFkuXptZm6dOnC/fffn+vjnz9/nqeffpoaNWrg5eVF+fLlGTVqFAkJCbfcLy0tjeeee45KlSrh6elJ5cqVmTlzJhkZGbZtBg8ejMVisXvdc889dsd58sknqVKlCp6engQEBNCrVy9++eWXXF/H9Z5++umbzvd/4sQJnJ2dWb16NYGBgTz66KNMmzbtrs5XJGX27ju5grOnqaWIiJhJgV9ETJWcnMx7773H448/TuPGjalfvz5Lly7Nsl1cXBzffvstw4YNy/U5Tp48ycmTJ1mwYAF79+5l2bJlrFu37rbHmjdvHm+//TaLFy/mwIEDzJ8/n5dfftnuibwAXbt25dSpU7bXV199Zbe+cePGLF26lAMHDvD1119jGAYRERGkp6fn+loyDRs2jMOHD7N58+Ys65YtW0bp0qXp2bMnAEOGDGHlypU3fWKww7p+hh6LxdxaRERMpMAvIqZau3YtLi4utGjRArAG2Y8//pgrV67Ybbds2TICAgLo0aNHrs9Rt25dPv30U3r27EmVKlXo0KEDs2fP5r///S9paWk33W/btm306tWLHj16ULFiRR566CEiIiLYvn273Xbu7u4EBwfbXv7+/nbrn3jiCdq0aUPFihVp1KgRs2bNIi4uzjaEKTY2FovFwscff0zr1q3x9PSkadOm/Prrr0RHR9OkSRO8vb3p2rUrf/zxBwANGjSgUaNGLFmyJEvdy5Yt47HHHsPV1RWAevXqERwczJo1a3L92RVpmU/Z1Qw9IlLMKfCLiKmioqJo0qSJbXngwIGkpqbyySef2NoMw2DZsmUMGjQIFxcXAObMmYO3t/ctX9n1fmfKfCph5vGyc++997J+/Xp+/fVXAHbv3s2WLVvo3r273XYbN24kMDCQ6tWr8/e//534+PibHvPKlSssXbqUSpUqERYWZrdu2rRpPPfcc+zcuRMXFxcGDBjAs88+y6uvvsrmzZs5cuQIL7zwgm37YcOG8cknn3D58mVb26ZNmzh8+DBDhw61O3azZs1u+Xk4JM3BLyICwM3/pRMRKQCxsbGEhobalv39/enduzdLly5l8ODBgDVQHz161C7EDh8+nL59+97y2GXLls22/dy5c7z44os8+eSTt9x/4sSJJCQkULNmTZydnUlPT2f27NkMGDDAtk23bt14+OGHqVChAseOHeP555+nQ4cO7NixA3d3d9t2b775Js8++yxXrlyhZs2aREZG4ubmZne+8ePH06VLFwBGjx7NgAEDWL9+Pa1atQKsAX/ZsmW27R955BHGjRvHJ598wpAhQwBYsmQJLVq0oHbt2lk+i127dt3yeh2OZugREQEU+EXEZMnJyXh4eNi1DRs2jIiICA4fPkzVqlVZsmQJrVq1okaNGrZt/P39swydyYnExER69OhB7dq1b3sj66pVq1ixYgUffvghderUISYmhjFjxhAaGsqgQYMA6Nevn237unXr0qRJEypUqMCXX35Jnz59bOsGDhxI586dOXXqFAsWLKBv3758//33dtceHh5uex8UFARYh+Nc33b9twclS5akT58+LFmyhCFDhnDp0iU+/fRTFi1alOVaPD09SUpKyuGn5CDUwy8iAmhIj4iYrEyZMlluJu3UqRMVKlRg2bJlJCYmsnr16iw32N7JkJ5Lly7RtWtXvL29WbNmjW2M+81MmDCBSZMm0b9/f+rVq8ejjz7KM888w9y5c2+6T0hICBUqVODQoUN27X5+flSrVo02bdrwn//8h19++SXLmPrr67H8eZPpjW3XzxAE1l+OtmzZwqFDh1i1ahVg/0tIpvPnzxMQEHDL63U4esquiAigHn4RMVnDhg1ZsWKFXZvFYmHIkCG8++67lCtXDicnpyzDd3I7pCcxMZEuXbrg7u7OF198keVbhewkJSXh5GTfL+Ls7JwldF/v3LlzxMXFERIScstjG4ZBSkrKbWu4nfbt21O5cmWWLVvGhg0b6Nu3Lz4+Plm227dvH+3atbvr8xUp6uEXEQEU+EXEZF26dGHy5MlcuHCBUqX+CmZDhgxh5syZTJkyhf79+1OiRAm7/XIzpOfSpUtERESQlJTEihUrSExMJDExEYCAgACcnZ0B6NixIw888AAjR44EoGfPnsyePZvy5ctTp04ddu3axcKFC233Ely+fJnp06fz4IMPEhISQmxsLFOmTKFMmTI88MADABw9epRVq1YRERFhewDWvHnz8PT0zHLz753I/OVo4cKFXLhwgZdffjnLNklJSezYsYM5c+bc9fmKlMxZejSGX0SKOQ3pERFT1atXjyZNmvDxxx/btZcvX55OnTpx4cKFLDPO5NaOHTv48ccf2bt3L1WrViUkJMT2iouLs2135MgRzp49a1t+/fXXeeihh3jqqaeoVasW48eP58knn+TFF18ErL39e/fupVevXlSvXp1BgwZRvXp1tm3bZutl9/DwYPPmzXTv3p2qVavSt29fSpQowdatWwkMDLyr68o0ePBgEhISqFGjhu0G3+t9/vnnlC9fntatW+fJ+YoM9fCLiABgMQzDMLuI/JSYmIifn59tCj4RKXy++uorxo8fz759+7IMoZG716xZM8aMGcMjjzxidikFK7IN/LEZ7v0Yyj9sdjUiInkupzlXQ3pExHTdu3fn0KFDnDhxIsvc9HJ34uPjeeihh+ymEi021MMvIgKoh19ERBzVmnKQfAK6REPpJrffXkSkiMlpztV35yIi4ngyUtXDLyLyJwV+ERFxPGlJkP7ng8Y0S4+IFHMK/CIi4ngyH7oF4OpnWhkiIoWBAr+IiDiezDn4XXzASfNTiEjxpsAvIoXCuXPnCAwMJDY21uxSJB899NBDLFy4MP9PdC3B+qeG84iIKPCLSOEwd+5cevbsScWKFQHrw6R69+6db+eLioqiZ8+ehIaGYrFY+Oyzz7KtqWnTpvj4+BAYGEjv3r05ePBgjo7/5ptvUqlSJTw8PGjcuDGbN2+2ratYsSIWiyXLa8SIEbZtpk+fnmV9cHCw3TnS0tJ47rnnqFSpEp6enlSuXJmZM2eSkZGR41pys01O7dq1C1dX12wf9PXCCy8we/Zs25OO803qnzfsajiPiIgCv4iYLzk5mffee4/HH3+8wM555coV6tevz+LFi2+6zaZNmxgxYgQ//PADkZGRpKWlERERwZUrV2557FWrVjFmzBimTp3Krl27aN26Nd26deP48eMAREdHc+rUKdsrMjISgIcftn84VJ06dey227t3r936efPm8fbbb7N48WIOHDjA/Pnzefnll3n99ddzXEtOt8mNUaNGMX78eHbv3s2NMz+Hh4dTsWJFVq5ceUfHzjFbD79m6BERwXBwCQkJBmAkJCSYXYqI3MSnn35qlClTxq5t0KBBRq9evQrk/ICxZs2a224XHx9vAMamTZtuuV2zZs2M4cOH27XVrFnTmDRpUrbbjx492qhSpYqRkZFha5s2bZpRv379W56nR48extChQ+3a+vTpY/ztb3/LVS052Wbz5s2Gi4uLcfXqVVvb0aNHDcCIjY21ta1cudLo2bOnERcXZwDG4cOHs9Q9ffp0o3Xr1re8trt24FXDWIlhbLgvf88jImKinOZc9fCLiOmioqJo0iR3D0aaM2cO3t7et3zdzbCU7CQkWHuN/f39b7rNtWvX2LFjBxEREXbtERERbN26NdvtV6xYwdChQ7FYLHbrDh06RGhoKJUqVaJ///4cPXrUbv29997L+vXr+fXXXwHYvXs3W7ZsoXv37jmuJaf1xsTEUKtWLdzd3e3aSpYsSYUKFQDrtyZTpkxh3rx5lCtXDj8/P2JiYrJcc7Nmzfjpp59ISUnJ+gHmlcybdjWGX0QETV0gIqaLjY0lNDQ0V/sMHz6cvn373nKbsmXL3k1ZdgzDYOzYsdx7773UrVv3ptudPXuW9PR0goKC7NqDgoI4ffp0lu0/++wzLl68yODBg+3amzdvzvvvv0/16tU5c+YMs2bNomXLluzfv5/SpUsDMHHiRBISEqhZsybOzs6kp6cze/ZsBgwYkONaclrv7t27adiwod02MTEx1K9f37Y8e/ZsunbtSq1atQCoXbs2MTExPPjgg3b7lS1blpSUFE6fPm37ZSHPZU7LqTH8IiIK/CJivuTkZDw8PHK1j7+//y172vPayJEj2bNnD1u2bMnR9jf21huGkaUN4L333qNbt25ZfuHp1q2b7X29evVo0aIFVapUYfny5YwdOxawjr1fsWIFH374IXXq1CEmJoYxY8YQGhrKoEGDclXL7baJiYnhkUcesdtm165dtsB/9OhR3nnnHfbt22dbX7du3Wx7+D09PQFISkrKsi7PpGoMv4hIJlOH9NxulozBgwdnmaXinnvuMadYEck3ZcqU4cKFC7napyCH9Dz99NN88cUXbNiwgXLlyt1y2zJlyuDs7JylNz8+Pj5LL/pvv/3Gt99+m6OblUuUKEG9evU4dOiQrW3ChAlMmjSJ/v37U69ePR599FGeeeYZ5s6dm+NacrJNeno6+/fvz9LDv3PnTho0aADAM888w7lz5yhXrhwuLi64uLjw7rvvZhv4z58/D0BAQMBtr/uOqYdfRMTG1B7+zFkyhgwZkuUr30xdu3Zl6dKltmU3N7eCKk9ECkjDhg1ZsWJFrvYpiCE9hmHw9NNPs2bNGjZu3EilSpVuu4+bmxuNGzcmMjKSBx54wNYeGRlJr1697LZdunQpgYGB9OjR47bHTUlJ4cCBA3ZTXSYlJeHkZN9v4+zsbJuWMye15GSbgwcPkpycbPctxLZt2zhx4gT169cnMjKS77//nl27duHi8tc/K9HR0QwdOpRz587ZhiEB7Nu3j3LlylGmTJnbXvcd0xh+EREbUwN/t27d7L62zo67u3uWuadFxLF06dKFyZMnc+HCBUqVytkQjLsd0nP58mUOHz5sWz527BgxMTH4+/tTvnx5AEaMGMGHH37I559/jo+Pj60X3M/PzzYsZfHixaxZs4b169fbjjV27FgeffRRmjRpQosWLXjnnXc4fvw4w4cPt22TkZHB0qVLGTRokF1IzjR+/Hh69uxJ+fLliY+PZ9asWSQmJtoN1enZsyezZ8+mfPny1KlTh127drFw4UKGDh2aq1put01mL/3rr7/OqFGjOHz4MKNGjQKsHTejR49mwoQJtt7+TL6+vrb9O3bsaGvfvHlzlpuE81xmD78Cv4hI4ZmWk2ymxRs0aJDh5+dnBAQEGNWqVTMef/xx48yZM7c8ztWrV42EhATbK3NqOE3LKVK43XPPPcbbb79tW87vaTk3bNhgAFlegwYNsm2T3XrAWLp0qW2badOmGRUqVMhy/DfeeMOoUKGC4ebmZjRq1CjLVJ5ff/21ARgHDx7Mtr5+/foZISEhhqurqxEaGmr06dPH2L9/v902iYmJxujRo43y5csbHh4eRuXKlY2pU6caKSkpuarldttMmDDB6Ny5s9GjRw/Dzc3NaNCggfGf//zH8PX1Nby8vIzAwEDj8uXLWY6ZkZFheHl5GQsWLLC1JScnG76+vsa2bduyve4883kV67ScJyPz9zwiIibK6bScFsO44akoJrFYLKxZs8buyZqrVq3C29ubChUqcOzYMZ5//nnS0tLYsWOH3dRw15s+fTozZszI0p6QkGDrbRKRwuerr75i/Pjx7Nu3L8swFTFXly5daNSoke3egLvxxhtv8Pnnn/PNN9/kQWW38J8ycO0cdPkJSjfN33OJiJgkMTERPz+/2+bcQj1LT79+/Wzv69atS5MmTahQoQJffvklffr0yXafyZMn22awAOsHERYWlu+1isjd6d69O4cOHeLEiRP6b7aQ2b17d5ZpQ++Uq6ur3ZOA84Vh/DWG37Vk/p5LRKQIKNSB/0YhISFUqFDBbpaKG7m7u9+0919ECrfRo0ebXYLc4PTp05w5c4bw8PA8Od4TTzyRJ8e5pbQrYKRb32sMv4hI0Qr8586dIy4ujpCQELNLEREpFoKDgykkIz9zLrN33+IMzl6mliIiUhiYGvhvNUuGv78/06dP58EHHyQkJITY2FimTJlCmTJl7KaOExERsZM5Q4+LD2TzsDMRkeLG1MC/fft22rdvb1vOHHs/aNAg3nrrLfbu3cv777/PxYsXCQkJoX379qxatQofHx+zShYRkcLO9tAt/VshIgImB/527drd8qvir7/+ugCrERERsxmGwbVr1+7uXqzMIT0uCvwiIgCa+05ERAqFjRs30rplS0KCgoiPj7/zA9l6+L3zpC4RkaJOgV9EREz1ww8/0Kl9e9q3b8/V6GjSL1/mpbuZ8//aBeuf6uEXEQEU+EVExCTnz5/nvu7dadGiBWc2b2YNEJ2ezrj0dN584w1OnDhxZwfWGH4RETsK/CIiYoq4uDi+XLuWkUBMejq9AQswBiiRkcHsWbPu7MC2h24p8IuIgAK/iIiYpH79+nTt3JlvnZ3t2n2BienpvPvuuxw7diz3B75+Wk4REVHgFxER87w4Zw6/pKez8ob2kYC/YfDizJm5P6h6+EVE7Cjwi4iIaZo0aULv++9nuosLqde1ewFT09NZvnw5v/76a+4Oqh5+ERE7CvwiImKqmbNmEZueztIb2p8Ayjo7M+3553N3QE3LKSJiR4FfRERMVa9ePfr368eLLi5cva7dHXg+LY2PPv6YPXv25PyAevCWiIgdBX4RETHd9BkzOJWRwb9uaB8MVHFxyV0vv6blFBGxo8AvIiKmq169OoMGDWKOszNXrmt3BaanpfHZF1+wffv22x/IMP7q4S9RBZxc86FaEZGiRYFfREQKhedfeIELFguv39A+AKjl7Mxzkyff/iBpl8HIsL73q6nALyKCAr+IiBQSFStW5O9//zvznZ1JuK7dGZiZns7X337L5s2bb32QzOE8Tq7g7JlPlYqIFC0K/CIiUmhMfe45kp2d+ecN7X2ABn/28huGcfMDXLtg/dOtFFgs+VWmiEiRosAvIiKFRmhoKCNGjmShszPnrmt3AmalpxP1/fesX78+y34XLlwgNjb2uodulcz/YkVEiggFfhERKVQmTppEhqsrL9/Q3h24x9mZqRMn2nr5ExMTmTFjBhXDwujUrt1fQ3rcShZcwSIihZwCv4iIFCoBAQGMGTeO15ycOH1duwVrL/9PO3eyatUq5s2bR6Xy5Zk7cyZhV66QkJBw3ZScJQu+cBGRQkqBX0RECp1x48bh5uXFSze0dwDaOTkxYMAAnps8mX4JCRzJyOAxID09/a8hPerhFxGxUeAXEZFCp1SpUkyYNIm3nJyIu67dAryRkcE44FfD4E2gLOACpGdkaEiPiEg2FPhFRKRQGjVqFL6+vsy6ob02sACodF2bM5CWnq4hPSIi2VDgFxGRQicjI4N169bh4enJqhxMr+mMhvSIiNyMAr+IiBQahmHwv//9j8b169O3b1/qnDnD+lvNu/8nF27o4VfgFxGxcTG7ABEREbDOpd89IoIftm+njZMTUUDrjIwc7euMdQy/ce0CFtCQHhGR66iHX0RECoWUlBRiY2Mp4eTE5IwMWudiX+c//8y4etH6Rj38IiI2CvwiIlIoBAcHs3v/flp37Eg3YDxwLYf7Zgb+dFvgL5Xn9YmIFFUK/CIiUmgEBgby5bp1vPLKK7zm4kIrZ2cO52C/zPGp6SkXrG80pEdExEaBX0REChUnJyfGjh3L1m3buBgWRkMnJ1beZp/MHv60qwnWNxrSIyJio8AvIiKFUpMmTdi5Zw+9+/fnb8Ag4PJNtrX18Gfe46vALyJio8AvIiKFlo+PDx+sXMn777/Ppx4eNHJ2Zmc229nG8GcATu7g7FGAVYqIFG4K/CIiUug9+uij7NqzB586dbjHYmERcP3s/LYhPRmod19E5AYK/CIiUiRUq1aNrT/9xNNjxvAMcJ/Fwh9/rrMb0qPALyJiR4FfRESKDHd3d15ZuJAvv/yS6JIlqe/szHfcMKRHM/SIiNhR4BcRkSKne/fu7N6/n9qtW9MJ+Oef7WnpqIdfROQGCvwiIlIkhYSE8PW33zJ7zhy+dbL+c6YefhGRrBT4RUSkyHJ2dmby5Mls+f57hj/cmPJl0FN2RURu4HL7TUREREyQkQ7XzsPVeEj5w/q6esOff76/J+UP7nngrHXqHg3pERGxo8AvIiIFIyMNUs7+FdizC/J2789hP/lmDjh7QnDHfClfRKSoUuAXEZE7k37tJj3v8dkH+GsX7uw8bqXAPQA8Aqx/ugeAR6B92/XrnN3y9jpFRIo4BX4REbFKS77N0Jkbgnxq4h2cxALupbMJ8AHgHphNgC8NTq55fqkiIsWJAr+IiCMyDEi7covhMteH+j+H1qRdyf15LM7gXuYmAT6bIO/mD07Otz+uiIjkGQV+EZGiwDAg7dLNx75nF+TTk3N/HifXrKH9ZkNnPAKtN8haNOGbiEhhpsAvImIGw4DUizcf+55dgM+4lvvzOLlfF9YDbxPgA8DVDyyWPL9cERExjwK/iEheMDIg5XwubmI9C0Za7s/j7JU1pN8qyLt4K8CLiBRzCvwiItnJSLNOC3m7m1gz266ds4b+3HLxydnYd1uA98r7axUREYemwC8ixUP6tb/mgL/d2Per8X9OIZnLOeABXEvefLjM9WPfPQKsN7s6e+T1lYqIiNhR4BeRoik9JecPcLr6h3W8/J1w88/a236zMfDuZTQHvIiIFDoK/CJSOKQl3Xrs+41BPu1S7s9hcQK30jl/gJN7aXDS/yZFRKRo079kIpL3DAPSLuf8AU5X/4D0pNyfx+Ji7VXPtuc9MGuAdyulOeBFRKTYUeAXkdszDEhNuPlwmetvYM1sy0jJ/Xmc3G4///v1Qd61pGagERERuQ0FfpHiyMiw3pR6qwB//bCalLOQkZr78zh75Gzse+bUki4+CvAiIiJ5TIFfxBFkpMO181mnirzZTawpZ8FIz/15XEpkM9PMLWakcSmhAC8iImIyBX6Rwigj7a8pJG8cMpNtgD/HnU0h6Xv7se/XB3kXzzy/VBEREclfCvwiBSH9Ws7Hvqf88ecc8HfArdQtxsDfeBNrGXB2z9vrFBERkUJHgV/kTqQl337s+/VBPjXxDk5isU4LecsHOF0X5N1Lg5Nrnl+qiIiIFG0K/CIAaVeynyoy21Afb90+tyxO1l71nN7E6lZaU0iKiIjIXVPgF8djGNaHMt1s7Ht2QT49Offnsbjk/AFOHn/OAW9xyvvrFREREbkFBX4p/AwDUi/m/AFOKX9AxrXcn8fJPecPcPIIAFc/zUAjIiIihZ4CvxQ8IwNSzt9mDPz1vfJnwUjL/XmcvXL+ACf3AHDxVoAXERERh6PAL3cvI806LeRtH+D0Z5C/ds4a+nPLxSfnD3ByDwAXr7y/VhEREZEiRoFfsspItfaqZ96gessHOP1h7a2/ozng/XL+ACePAOtTW0VEREQkVxT4i4P0lOxnmrnZLDSpF+/sPG7+Ob+J1b0MOLvl6WWKiIiISFYK/EVRWtKtx77fGOTTLuX+HBYn67SQOb2J1b00OOmvk4iIiEhhY2pCi4qK4uWXX2bHjh2cOnWKNWvW0Lt3b9t6wzCYMWMG77zzDhcuXKB58+a88cYb1KlTx7yi85phQNrlnI19z1xOT8r9eSwu1l71nNzE6v7nFJKaA15ERESkyDM18F+5coX69eszZMgQHnzwwSzr58+fz8KFC1m2bBnVq1dn1qxZdO7cmYMHD+Lj42NCxbmUfArO/XT7IJ+RkvtjO7nm/AFOHoHgWlIz0IiIiIgUQ6YG/m7dutGtW7ds1xmGwaJFi5g6dSp9+vQBYPny5QQFBfHhhx/y5JNPFmSpd+aPLbClb862dfbI+QOc3APA1VcBXkRERERuq9AOuj527BinT58mIiLC1ubu7k7btm3ZunXrTQN/SkoKKSl/9ZgnJibme6035VUe/Jve/gFO7gHgUkIBXkRERETyXKEN/KdPnwYgKCjIrj0oKIjffvvtpvvNnTuXGTNm5GttOVamOXT9yewqRERERKQYczK7gNux3NDrbRhGlrbrTZ48mYSEBNsrLi4uv0sUERERESm0Cm0Pf3BwMGDt6Q8JCbG1x8fHZ+n1v567uzvu7u75Xp+IiIiISFFQaHv4K1WqRHBwMJGRkba2a9eusWnTJlq2bGliZSIiIiIiRYepPfyXL1/m8OHDtuVjx44RExODv78/5cuXZ8yYMcyZM4dq1apRrVo15syZg5eXF4888oiJVYuIiIiIFB2mBv7t27fTvn172/LYsWMBGDRoEMuWLePZZ58lOTmZp556yvbgrW+++aZozMEvIiIiIlIIWAzDMMwuIj8lJibi5+dHQkICvr6+ZpcjIiIiIpIncppzC+0YfhERERERuXsK/CIiIiIiDkyBX0RERETEgSnwi4iIiIg4MAV+EREREREHpsAvIiIiIuLAFPhFRERERByYAr+IiIiIiANT4BcRERERcWAK/CIiIiIiDkyBX0RERETEgSnwi4iIiIg4MBezC8hvhmEAkJiYaHIlIiIiIiJ5JzPfZubdm3H4wH/p0iUAwsLCTK5ERERERCTvXbp0CT8/v5uutxi3+5WgiMvIyODkyZP4+PhgsVjy5RyJiYmEhYURFxeHr69vvpxDCg/9vIsf/cyLH/3Mix/9zIsXR/l5G4bBpUuXCA0Nxcnp5iP1Hb6H38nJiXLlyhXIuXx9fYv0XxrJHf28ix/9zIsf/cyLH/3MixdH+Hnfqmc/k27aFRERERFxYAr8IiIiIiIOTIE/D7i7uzNt2jTc3d3NLkUKgH7exY9+5sWPfubFj37mxUtx+3k7/E27IiIiIiLFmXr4RUREREQcmAK/iIiIiIgDU+AXEREREXFgCvwiIiIiIg5Mgf8uvfnmm1SqVAkPDw8aN27M5s2bzS5J8klUVBQ9e/YkNDQUi8XCZ599ZnZJks/mzp1L06ZN8fHxITAwkN69e3Pw4EGzy5J88tZbbxEeHm57EE+LFi1Yu3at2WVJAZo7dy4Wi4UxY8aYXYrkk+nTp2OxWOxewcHBZpeV7xT478KqVasYM2YMU6dOZdeuXbRu3Zpu3bpx/Phxs0uTfHDlyhXq16/P4sWLzS5FCsimTZsYMWIEP/zwA5GRkaSlpREREcGVK1fMLk3yQbly5XjppZfYvn0727dvp0OHDvTq1Yv9+/ebXZoUgOjoaN555x3Cw8PNLkXyWZ06dTh16pTttXfvXrNLynealvMuNG/enEaNGvHWW2/Z2mrVqkXv3r2ZO3euiZVJfrNYLKxZs4bevXubXYoUoD/++IPAwEA2bdpEmzZtzC5HCoC/vz8vv/wyw4YNM7sUyUeXL1+mUaNGvPnmm8yaNYsGDRqwaNEis8uSfDB9+nQ+++wzYmJizC6lQKmH/w5du3aNHTt2EBERYdceERHB1q1bTapKRPJTQkICYA2B4tjS09P56KOPuHLlCi1atDC7HMlnI0aMoEePHnTq1MnsUqQAHDp0iNDQUCpVqkT//v05evSo2SXlOxezCyiqzp49S3p6OkFBQXbtQUFBnD592qSqRCS/GIbB2LFjuffee6lbt67Z5Ug+2bt3Ly1atODq1at4e3uzZs0aateubXZZko8++ugjdu7cSXR0tNmlSAFo3rw577//PtWrV+fMmTPMmjWLli1bsn//fkqXLm12eflGgf8uWSwWu2XDMLK0iUjRN3LkSPbs2cOWLVvMLkXyUY0aNYiJieHixYt8+umnDBo0iE2bNin0O6i4uDhGjx7NN998g4eHh9nlSAHo1q2b7X29evVo0aIFVapUYfny5YwdO9bEyvKXAv8dKlOmDM7Ozll68+Pj47P0+otI0fb000/zxRdfEBUVRbly5cwuR/KRm5sbVatWBaBJkyZER0fz6quv8q9//cvkyiQ/7Nixg/j4eBo3bmxrS09PJyoqisWLF5OSkoKzs7OJFUp+K1GiBPXq1ePQoUNml5KvNIb/Drm5udG4cWMiIyPt2iMjI2nZsqVJVYlIXjIMg5EjR7J69Wq+++47KlWqZHZJUsAMwyAlJcXsMiSfdOzYkb179xITE2N7NWnShIEDBxITE6OwXwykpKRw4MABQkJCzC4lX6mH/y6MHTuWRx99lCZNmtCiRQveeecdjh8/zvDhw80uTfLB5cuXOXz4sG352LFjxMTE4O/vT/ny5U2sTPLLiBEj+PDDD/n888/x8fGxfaPn5+eHp6enydVJXpsyZQrdunUjLCyMS5cu8dFHH7Fx40bWrVtndmmST3x8fLLck1OiRAlKly6te3Uc1Pjx4+nZsyfly5cnPj6eWbNmkZiYyKBBg8wuLV8p8N+Ffv36ce7cOWbOnMmpU6eoW7cuX331FRUqVDC7NMkH27dvp3379rblzLF+gwYNYtmyZSZVJfkpc8rddu3a2bUvXbqUwYMHF3xBkq/OnDnDo48+yqlTp/Dz8yM8PJx169bRuXNns0sTkTzy+++/M2DAAM6ePUtAQAD33HMPP/zwg8NnN83DLyIiIiLiwDSGX0RERETEgSnwi4iIiIg4MAV+EREREREHpsAvIiIiIuLAFPhFRERERByYAr+IiIiIiANT4BcRERERcWAK/CIiIiIiDkyBX0RERETEgSnwi4jIHevZsyedOnXKdt22bduwWCzs3LmzgKsSEZHrKfCLiMgdGzZsGN999x2//fZblnVLliyhQYMGNGrUyITKREQkkwK/iIjcsfvuu4/AwECWLVtm156UlMSqVasYNmyYOYWJiIiNAr+IiNwxFxcXHnvsMZYtW4ZhGLb2Tz75hGvXrjFw4EATqxMREVDgFxGRuzR06FBiY2PZuHGjrW3JkiX06dOHUqVKmVeYiIgACvwiInKXatasScuWLVmyZAkAR44cYfPmzQwdOtTkykREBBT4RUQkDwwbNoxPP/2UxMREli5dSoUKFejYsaPZZYmICAr8IiKSB/r27YuzszMffvghy5cvZ8iQIVgsFrPLEhERwGJcf5eViIjIHXr88cdZvXo1CQkJHDt2jPLly5tdkoiIoB5+ERHJI8OGDePChQt06tRJYV9EpBBRD7+IiIiIiANTD7+IiIiIiANT4BcRERERcWAK/CIiIiIiDkyBX0RERETEgSnwi4iIiIg4MAV+EREREREHpsAvIiIiIuLAFPhFRERERByYAr+IiIiIiANT4BcRERERcWAK/CIiIiIiDuz/AeuqsCFZqEVTAAAAAElFTkSuQmCC",
      "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]) > 0.1:\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": [
      "[-7.986, -7.662, -7.308, -6.991, -6.645, -6.334, -6.023, -5.741, -5.409, -5.141, -4.83, -4.576, -4.251, -4.011, -3.714, -3.432, -3.022, -2.817, -2.796, -2.796, -2.782, -2.754, -2.754, -2.74, -2.712, -2.712, -2.712, -2.669, -2.14, 0.057, 0.064, 0.064, 0.049, 0.064, 0.057, 2.853, 2.874, 2.874, 2.895, 2.916, 2.916, 3.255, 3.566, 3.863, 4.11, 4.357, 4.604, 4.886, 5.141, 5.458, 5.72, 6.002, 6.27, 6.624, 6.878, 7.245, 7.485, 7.81, 8.106, 8.346, 8.135, 7.937, 7.753, 7.57, 7.4, 7.188, 7.019, 6.849, 6.652, 6.511, 6.27, 6.136, 5.946, 5.783, 5.628, 5.444, 5.261, 5.105, 4.95, 4.759, 4.59, 4.456, 4.3, 4.103, 3.976, 3.82, 3.637, 3.453, 3.043, 3.001, 2.987, 2.973, 2.959, 2.959, 2.945, 2.938, 2.93, 2.916, 2.923, 2.916, 2.902, 2.902, 2.902, 2.874, 2.874, 2.86, 2.846, 2.832, 0.106, 0.127, 0.127, 0.113, 0.106, 0.127, 0.12, 0.127, 0.12, 0.113, -2.217, -2.697, -2.697, -2.712, -2.705, -2.697, -2.733, -2.712, -2.754, -2.74, -2.782, -3.192, -3.382, -3.531, -3.728, -3.884, -4.025, -4.223, -4.392, -4.548, -4.71, -4.901, -5.084, -5.282, -5.409, -5.642, -5.811, -5.981, -6.165, -6.362, -6.56, -6.758, -6.941, -7.146, -7.351, -7.549, -7.753, -7.986]\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": [
      "39\n",
      "59\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",
    "#Function for detecting the first occurrence of an item in a list\n",
    "def find_first_occurrence(value, list):\n",
    "    for i, item in enumerate(list):\n",
    "        if item == value:\n",
    "            first_occurrence = i\n",
    "            break\n",
    "    return first_occurrence\n",
    "\n",
    "low_limit = find_first_occurrence(2.916, 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": [
      "[2.916 2.916 3.255 3.566 3.863 4.11  4.357 4.604 4.886 5.141 5.458 5.72\n",
      " 6.002 6.27  6.624 6.878 7.245 7.485 7.81  8.106]\n",
      "[19.7455 21.8411 23.8426 25.8898 27.9295 29.9419 32.3473 34.2424 36.6886\n",
      " 38.858  41.1289 43.1734 45.7227 47.9786 50.2891 52.7218 55.0643 57.587\n",
      " 60.1513 62.2435]\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      = 20\n",
      "    # variables        = 2\n",
      "    chi-square         = 18.0313012\n",
      "    reduced chi-square = 1.00173895\n",
      "    Akaike info crit   = 1.92753857\n",
      "    Bayesian info crit = 3.91900312\n",
      "    R-squared          = 1.00000000\n",
      "[[Variables]]\n",
      "    slope:      8034.89231 +/- 96.8211357 (1.21%) (init = 8024.464)\n",
      "    intercept: -2716826.95 +/- 438440.564 (16.14%) (init = -2646578)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(slope, intercept) = -0.9495\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 * 0.01817))\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': 8034.892306692875, 'intercept': -2716826.948653902}"
      ]
     },
     "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 =  0.000124 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 = 4.73517611 K\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "Weak Coupling Formula\n",
      "-----------------------------\n",
      "delta_t = 2.7386619475277844e-22 J\n",
      "Gap Energy = 5.477323895055569e-22 J\n",
      "Gap Voltage = 3.4186268225287533 mV\n",
      "Josephson Current = 20.929103726833482 uA\n",
      "______________________________\n",
      "______________________________\n",
      "------------------------------\n",
      "Strong Coupling Approximation\n",
      "------------------------------\n",
      "delta_t = 2.156743932971374e-22 J\n",
      "Gap Energy =  4.313487865942748e-22 J\n",
      "Gap Voltage = 2.6922281025731793 mV\n",
      "Josephson Current = 15.779533732456965 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 = 4.73517611 #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': 21.8411, 'Ic_weak': 20.9291, 'Ic_strong': 15.7795, 'V_gap': 2.853, 'Vg_weak': 3.4186, 'Vg_strong': 2.6922, 'Temp': 4.7352}\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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