0.8.Scattering_at_25deg.ipynb

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   "id": "9245ef4f-d761-4766-a5cf-bb1282a7fb0b",
   "metadata": {},
   "source": [
    "** ** \n",
    "\n",
    "<figure>\n",
    "<div style=\"text-align:center\"><img src=\"./img/compton_setup.png\" /></div>\n",
    "<p><center>The experimental setup.</center></p>\n",
    "</figure>\n",
    "\n",
    "# Compton gamma-ray scattering at a 25-degree angle\n",
    "\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": "ad124470-3de7-42ce-9017-bdd83d83e1dd",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad3c5f88-8b0b-4ec2-a251-24d27716989e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e53f6d8-c3aa-44c4-a10c-64f76c6e361e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from numpy import sqrt, pi, exp, linspace, loadtxt\n",
    "from lmfit.models import MoffatModel, PolynomialModel\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import matplotlib.lines as mlines\n",
    "import pandas as pd\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import find_peaks\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7180f1-4fa6-4cb0-8cb7-e8a38e61505d",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "907e6f02-3654-49fb-b3a4-716fe6bd6dd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Load data\n",
    "\n",
    "#Load channel counts of scattering from large aluminum target at 18deg\n",
    "df_25 = pd.read_csv(\"./Data/180912-1322_Cs-137_LgHole_LgAl_025-degrees.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6c9720c-4b60-49e7-9638-b6e399e68f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "   0\n",
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    "df_25.head()"
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   "id": "c0717cd1-b5a5-4ba8-94e0-dca4158d9c14",
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       "    Channel  0\n",
       "1         1  0\n",
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   "source": [
    "#force start the index at 1 and add it as a column to match the channel numbers\n",
    "# Set the index to start at 1\n",
    "df_25.index = range(1, len(df_25) + 1)\n",
    "\n",
    "# Add the index as the first column\n",
    "df_25.insert(0, 'Channel', df_25.index)\n",
    "\n",
    "df_25.head(15)"
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#preview the data \n",
    "\n",
    "#Uncomment the next line to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "\n",
    "#rename the second column\n",
    "df_25.rename(columns={\"0\": \"Counts\"}, inplace=True)\n",
    "\n",
    "#remove unnecessary data\n",
    "df_25 = df_25[df_25['Channel'] <= 700]\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#preview the spectrum\n",
    "plt.plot('Channel', 'Counts', data = df_25, marker = \".\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55c5918c-620c-4d5a-9869-c27dae6a9805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Channel Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>412</td>\n",
       "      <td>637</td>\n",
       "      <td>Range 411-460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>413</td>\n",
       "      <td>664</td>\n",
       "      <td>Range 411-460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>414</td>\n",
       "      <td>674</td>\n",
       "      <td>Range 411-460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>415</td>\n",
       "      <td>732</td>\n",
       "      <td>Range 411-460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>416</td>\n",
       "      <td>789</td>\n",
       "      <td>Range 411-460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Counts  Channel Range\n",
       "412      412     637  Range 411-460\n",
       "413      413     664  Range 411-460\n",
       "414      414     674  Range 411-460\n",
       "415      415     732  Range 411-460\n",
       "416      416     789  Range 411-460"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the channel ranges\n",
    "channel_ranges = [0, 411, 460, 700]\n",
    "\n",
    "# Create labels for the ranges\n",
    "labels = [f\"Range {i}-{j}\" for i, j in zip(channel_ranges[:-1], channel_ranges[1:])]\n",
    "\n",
    "# Use pd.cut() to categorize the data into the specified ranges\n",
    "df_25['Channel Range'] = pd.cut(df_25['Channel'], bins=channel_ranges, labels=labels, include_lowest=True)\n",
    "\n",
    "# Create a dictionary of DataFrames, one for each channel range\n",
    "channel_range_dfs = {label: group for label, group in df_25.groupby('Channel Range')}\n",
    "\n",
    "# Access individual DataFrames using keys (e.g., channel_range_dfs['Range 0-40'])\n",
    "channel_range_dfs['Range 411-460'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "406ce5e6-7458-4a28-b3cc-72d5cd79a401",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#use builtin NameModel, specify prefix:\n",
    "mod2 = MoffatModel(prefix='g2_') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "149f5e88-b6b1-48ce-8677-b0b3436fc431",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#initialize the parameters to be optimized\n",
    "pars2  = mod2.make_params(g2_amplitude=1740.0, g2_center=436.8, g2_sigma=8.17, g2_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbb2b6e9-d208-4358-8c32-73ec778959f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g2_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 53\n",
      "    # data points      = 49\n",
      "    # variables        = 4\n",
      "    chi-square         = 45.0472437\n",
      "    reduced chi-square = 1.00104986\n",
      "    Akaike info crit   = 3.87868352\n",
      "    Bayesian info crit = 11.4459647\n",
      "    R-squared          = 0.99999243\n",
      "[[Variables]]\n",
      "    g2_amplitude:  1646.70445 +/- 9.53631445 (0.58%) (init = 1740)\n",
      "    g2_center:     435.730598 +/- 0.09544312 (0.02%) (init = 436.8)\n",
      "    g2_sigma:      34.0786419 +/- 5.41472712 (15.89%) (init = 8.17)\n",
      "    g2_beta:       2.52527611 +/- 0.69221579 (27.41%) (init = 1)\n",
      "    g2_fwhm:       38.3048275 +/- 0.33218907 (0.87%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     1646.70445 +/- 9.53631444 (0.58%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9986\n",
      "    C(g2_amplitude, g2_sigma) = -0.5686\n",
      "    C(g2_amplitude, g2_beta)  = -0.5390\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(channel_range_dfs['Range 411-460']['Counts'], pars2, x = channel_range_dfs['Range 411-460']['Channel'], weights = 1 / (np.std(channel_range_dfs['Range 411-460'][\"Channel\"]) + (channel_range_dfs['Range 411-460'][\"Channel\"] * 0.0385)))\n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03612b3-eba3-4729-bbfa-a21bf9945e48",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a6ec5ef-37e4-4acc-8a34-fada8924ac6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dd9/h3LlzZcro+sPrfkpLSyt9vBEjRmDfvn1Yt24d3nrrLXz22WcYO3as2fJpaWlYtmwZ+vTpgzp16ljc/7Zt2wAAQ4YMUax/9NFHTfY//+qrr9CmTRv4+PjAw8MDnp6e2LRpE06ePFmmbL9+/RRds5o1awYAZbrp6NZfunRJsb5Vq1aKc9CV6969u6Kvrm69YTek7Oxs/POf/0SjRo3g4eEBDw8PBAQEICcnx2RdTRk0aBA8PT3LrP/ll1/QuXNnBAQEyK/BvHnzFPt1c3PDyy+/jFWrVsnndfbsWaxduxZjxowpNxOQNe+x33//HT4+Phg5cmS553Do0CEMGjQIoaGhcHd3h6enJ55++mmUlJTg77//tup1MCU/Px+bNm3CQw89BD8/P8X7vV+/fsjPz8eePXsUz3nkkUcUy7t27UJaWhqGDx9e5v+lT58+2LdvH3JycpCTk4N9+/bh4YcfVmQJU6lUGDhwoFX1vXnzJl544QXExsbKf7P69esDgNXvB0uEEBg1ahR27NiB77//HrGxsYrtH330EV588UX06NED/fr1w8yZM/HJJ59g+/btiq5uW7ZsQa9evRAZGSmvc3d3x2OPPabY37p161BcXIynn35a8fr5+PigW7ducveWU6dOyV1+DNWrVw+dO3eu8nn37NkTwcHB8nJOTg7+/PNPPProo4pB1O7u7hg2bBiuXLmCU6dOKfYxYMAAxXJ5nxXWdDc8d+4chg4diqioKPl9361bNwBl/96SJJV5H91zzz1WHef8+fPYsmULHn74YXkg8z/+8Q+oVKpKd1cqT0XOCyj7P2fu8/6JJ55QLFfm/5sI4BgHcgFhYWHw8/PD+fPnrSrfsGFDbNy4EREREXjppZfQsGFDNGzYEF988YVcplevXvD09JR/LF3clScqKgpt27ZFUlISPvnkE3zwwQeYNWuWyT7MALBw4UIUFBRYPSg6NTUVABQXKQDg4eGB0NBQxbpp06bhxRdfRPv27fG///0Pe/bswb59+9CnTx/k5eWV2XdISIhi2cvLq9z1+fn5Nnv+0KFDMWvWLDz77LNYt24d9u7di3379iE8PNxkXU2Jjo4us27p0qUYMmQI6tSpg4ULF2L37t3Yt28fRo4cWab+I0eOhK+vL7766isA2vEGvr6+Ft8P1rzHbt26hZiYmHLHzFy6dAn33Xcfrl69ii+++AI7duzAvn375D7K1r4OpqSmpqK4uBgzZ85UvNc9PT3Rr18/AFCMHwDKvp43btwAoA1Sjffxn//8B0IIpKWlIT09HaWlpYiKiipTD1PrjJWWliIpKQlLly7FW2+9hU2bNmHv3r3yhU9VXgcdIQSeffZZLFy4EAsWLMDgwYOtet5TTz0FAIqLsNTUVKvOVff6tWvXrszr99NPP8mvv7n/cXPrKsr475qeng4hhMn/n5iYGEWddCryv278f2YsOzsb9913H/7880989NFH2Lp1K/bt24elS5cCKPv39vPzK5O22tvb2+JxAO1YBiEEHn30UWRkZCAjIwNFRUUYNGgQdu7cib/++sviPqxVmfMKDAxUrEtNTYWHh0eZ19X4fVCZ/28igFmVyAW4u7ujV69e+P3333HlyhXUrVvX4nPuu+8+3HfffSgpKcH+/fsxc+ZMvPbaa4iMjMTjjz+Or7/+GllZWXL5sLAwm9X33nvvBQD8/fffJrMpzZs3D5GRkWXu4JmjCw5u3LihuLtfXFxc5st94cKF6N69O2bPnq1Yb3iujkCj0WDVqlWYOHEi3n77bXl9QUFBhebjMNUqsHDhQsTHx+Onn35SbDc1kFKtVmP48OH45ptvMH78eMyfPx9Dhw61KsWipfdYeHg4/vjjD5SWlpoNHpYtW4acnBwsXbpUvrsOwCb51oODg+U7yC+99JLJMvHx8Ypl49dT938xc+ZMs5mGIiMj5QxMKSkpZbabWmfs2LFjOHz4MBYsWIDhw4fL63WDs6tKFzTMnz8f8+bNk4OBijD8G4aGhlp1rrrX79dff1X8fY0Z/o9b2mdlGP9dg4OD4ebmhuvXr5cpe+3aNQC2/Uw0tnnzZly7dg1bt26V78YDsPm8CKWlpfIg8YcffthkmW+//RaffvqpTY5X0fMy9fkVGhqK4uJipKWlKYIH4/dBZf6/iQC2OJCLmDBhAoQQGD16NAoLC8tsLyoqwsqVK8usd3d3R/v27eU7uAcPHgQANGnSBG3btpV/LGUtqYgtW7YAABo1alRm2/79+3HkyBEMHz7c6jSXXbt2BaDN1mHo119/LTPRnCRJZbJOHTlyBLt377a6/jVBkiQIIcrU9ZtvvkFJSUmV9+3l5aX4Uk5JSSmTVUnnlVdewe3bt+U7kpbSWRoz9x7r27cv8vPz5QsXc3UFoHgdhBCYO3dumbLe3t4m77zrnmvqbmaPHj1w6NAh3HPPPYr3u+7HuMXKWOfOnREUFIQTJ06YfH7btm3h5eUlZ85aunSp4i5wVlaWyf9La14HAPj666+tPl9zdJ8b8+fPx9dff41nnnnGqufp6DIQGQZOPXr0wKZNmxQX+iUlJWX+R3v37g0PDw+cPXvW7OsHaD+PoqKi8PPPPyuef+nSJUUGLlvx9/dH+/btsXTpUsXrWFpaioULF6Ju3bpo3LixzY+rU5G/d1WsW7cOV65cwUsvvYQtW7aU+bn77rvx/fffl/kctcTce9AW56ULOIzfS0uWLFEsV+T/u6L/M1S7scWBXELHjh0xe/ZsjBkzBomJiXjxxRdx9913o6ioCIcOHcKcOXPQokULDBw4EF999RU2b96M/v37o169esjPz5f7st5///0Wj3Xx4kXs27cPgLbPO6C9SAe0KUh1X/YTJ07EjRs30LVrV9SpUwcZGRlYu3Yt5s6di3/84x9ITEwss+958+YBAEaNGmX1ud9999144oknMHXqVLi7u6Nnz544fvw4pk6dCrVarbgTOmDAAHz44YeYOHEiunXrhlOnTuGDDz5AfHx8hb8cq1NgYCC6du2Kzz77DGFhYYiLi8O2bdswb968Kk+oNGDAACxduhRjxozBo48+isuXL+PDDz9EdHS0ydmCGzdujD59+uD3339Hly5d0LJlS4vHsOY99sQTT2D+/Pl44YUXcOrUKfTo0QOlpaX4888/0axZMzz++ON44IEH4OXlhSeeeAJvvfUW8vPzMXv2bKSnp5c5ZkJCApYuXYrZs2cjMTERbm5uaNu2LVQqFerXr4/ly5ejV69eCAkJkV/TL774Al26dMF9992HF198EXFxccjKysKZM2ewcuVKbN68udzzDAgIwMyZMzF8+HCkpaXh0UcfRUREBG7duoXDhw/j1q1bcuvWhx9+iD59+sjzqpSUlOA///kP/P39LbYiNW3aFA0bNsTbb78NIQRCQkKwcuVKbNiwweTrAABffPEFhg8fDk9PTzRp0gQqlcrkvl955RXMmzcPI0eOREJCgqLLkbe3t9wquGPHDvz73/+WZ4PPz8/H77//jjlz5qBnz56KPvb/+te/sGLFCvTs2RPvv/8+/Pz88OWXX5ZJuRkXF4cPPvgA7777Ls6dO4c+ffogODgYN27cwN69e+Hv74/JkyfDzc0NkydPxvPPP49HH30UI0eOREZGBiZPnozo6Ogqp4g2ZcqUKXjggQfQo0cPjB8/Hl5eXvjvf/+LY8eO4ccff7TpbN/GOnXqhODgYLzwwguYOHEiPD09sWjRIhw+fNimx5k3bx48PDzwzjvvyF2wDD3//PN45ZVXsHr1aqu7rgHm34O2OK8+ffqgc+fOGDduHDIzM5GYmIjdu3fj+++/B6Bs+bL2/7thw4bw9fXFokWL0KxZMwQEBCAmJsbka0IuwE6DsonsIjk5WQwfPlzUq1dPeHl5CX9/f9G6dWvx/vvvy+kHd+/eLR566CFRv3594e3tLUJDQ0W3bt3KzehjSJdZw9SPYQajFStWiPvvv19ERkYKDw8PERAQIO69917xf//3f6KoqKjMfnNzc4VarS6TucYa+fn54o033hARERHCx8dHdOjQQezevVuo1WpFSsmCggIxfvx4UadOHeHj4yPatGkjli1bJoYPH67IzKLLSvTZZ58pjqPLrvPLL7+YfE327dsnr6tfv77o379/mboCEC+99JJinanjXblyRTzyyCMiODhYqFQq0adPH3Hs2DFRv379cjNFlVd/nU8++UTExcUJb29v0axZMzF37lwxceJEs9l9FixYIAAo0lCWx9r3WF5ennj//ffFXXfdJby8vERoaKjo2bOn2LVrl1xm5cqVomXLlsLHx0fUqVNHvPnmm+L3338XABQpdNPS0sSjjz4qgoKChCRJinPZuHGjaN26tfD29i7zPj1//rwYOXKkqFOnjvD09BTh4eGiU6dOilTA5v7uOtu2bRP9+/cXISEhwtPTU9SpU0f079+/TPkVK1aIe+65R3h5eYl69eqJTz75pNzX3dCJEyfEAw88IFQqlQgODhb/+Mc/xKVLl0xmjJowYYKIiYkRbm5uZV4nY/Xr1zf7/2z4P3H69GnRr18/UadOHeHt7S18fHxEQkKC+Pe//y3y8/PL7Hfnzp2iQ4cOwtvbW0RFRYk333xTzJkzx2S2nWXLlokePXqIwMBA4e3tLerXry8effRRRTpbIYSYM2eOaNSokfDy8hKNGzcW3377rRg8eLAiG5g55WVVMv5/1NmxY4fo2bOn8Pf3F76+vqJDhw5i5cqVijKm/veFEPLf1Th1srl6GNu1a5fo2LGj8PPzE+Hh4eLZZ58VBw8eLJP9x9z+LL2vbt26Jby8vMSDDz5otkx6errw9fWVs4ZZm1VJCPPvwaqelxDa//VnnnlGBAUFCT8/P/HAAw+IPXv2CABlMs5Z8/8thDYLX9OmTYWnp6fZLGzkGiQhjGYvIiKXsGvXLnTu3BmLFi3C0KFD7V0dp/bII49gz549uHDhgsksTUT2kJGRgcaNG+PBBx/EnDlz7F0dsqPFixfjySefxM6dOx1uxmtyLuyqROQCNmzYgN27dyMxMRG+vr44fPgwPvnkE9x1111mB/1R+QoKCnDw4EHs3bsXv/32G6ZNm8aggewmJSUF//73v9GjRw+Ehobi4sWLmD59OrKysvDqq6/au3pUg3788UdcvXoVCQkJcHNzw549e/DZZ5+ha9euDBqoyhg4ELmAwMBArF+/HjNmzEBWVhbCwsLQt29fTJkypUyaQrLO9evX0alTJwQGBuL5558vd+4Nourm7e2NCxcuYMyYMUhLS4Ofnx86dOiAr776Cnfffbe9q0c1SKVSYcmSJfjoo4+Qk5OD6OhojBgxAh999JG9q0a1ALsqERERERGRRUzHSkREREREFjFwICIiIiIiixg4EBERERGRRQwciIiIiIjIIgYORERERERkEQMHIiIiIiKyiIEDERERERFZxMCBiIiIiIgsYuBAREREREQWMXAgIiIiIiKLGDgQEREREZFFDByIiIiIiMgiBg5ERERERGQRAwciIiIiIrKIgQMREREREVnEwIGIiMiOdu7ciYSEBHh6euLBBx80u64mdO/eHa+99lqNHY+InAsDByIiBzdixAhIkgRJkuDp6YnIyEg88MAD+Pbbb1FaWmr1fhYsWICgoKDqq6gL0P0tXnjhhTLbxowZA0mSMGLEiArt84033kCrVq1w/vx5LFiwwOw6a+pmTZBh+H4y/Dlz5gyWLl2KDz/8UC4bFxeHGTNmVOh8iKj2YuBAROQE+vTpg+vXr+PChQv4/fff0aNHD7z66qsYMGAAiouL7V09lxIbG4slS5YgLy9PXpefn48ff/wR9erVq/D+zp49i549e6Ju3bpyYGdqnS3p3k+GP/Hx8QgJCYFKpbL58YiodpCEEMLelSAiqmlCCBQV2efYnp6AJElWlx8xYgQyMjKwbNkyxfrNmzejV69emDt3Lp599llMmzYN8+fPx7lz5xASEoKBAwfi008/RUBAALZu3YoePXoonj9x4kRMmjQJCxcuxIwZM3Dq1Cn4+/ujZ8+emDFjBiIiImxxutbRaICjR2vueMYSEgC12mIx3d/i3Llz+Oc//4knn3wSALB48WJ88sknaNCgAYKCguRWgoKCArz55ptYsmQJMjMz0bZtW0yfPh3t2rXDhQsXEB8fr9j//Pnz8cwzz5RZN2zYMDz33HPYvHkzUlJSUK9ePYwZMwavvvoqAGDSpEmYPHmy4nlbtmxB9+7dzZ6D8fsJ0HZVatWqFWbMmIHu3btj27Ztiu28ZCBybR72rgARkT0UFQEf/p99LoLee0WCl1fV99OzZ0+0bNkSS5cuxbPPPgs3Nzf83//9H+Li4nD+/HmMGTMGb731Fv773/+iU6dOmDFjBt5//32cOnUKABAQEAAAKCwsxIcffogmTZrg5s2beP311zFixAisWbOm6pW01tGjwH331dzxjO3YAXTpYnXxZ555BvPnz5cDh2+//RYjR47E1q1bFeXeeust/O9//8N3332H+vXr49NPP0Xv3r1x5swZxMbG4vr162jSpAk++OADPPbYY1CpVOjTp49inVqtRmlpKerWrYuff/4ZYWFh2LVrF5577jlER0djyJAhGD9+PE6ePInMzEzMnz8fABASElKll2Tp0qVo2bIlnnvuOYwePbpK+yKi2oGBAxGRE2vatCmOHDkCAIpBrfHx8fjwww/x4osv4r///S+8vLygVqshSRKioqIU+xg5cqT8uEGDBvi///s/3HvvvcjOzpaDC1IaNmwYJkyYgAsXLkCSJOzcuRNLlixRBA45OTmYPXs2FixYgL59+wIA5s6diw0bNmDevHl48803ERUVBUmSoFar5b+Lv79/mXUAFC0K8fHx2LVrF37++WcMGTIEAQEB8PX1RUFBQZm/rymrVq1S/G379u2LX375RVEmJCQE7u7uUKlUVu2TiGo/Bg5E5JI8PbV3/u11bFsRQsjdnrZs2YKPP/4YJ06cQGZmJoqLi5Gfn4+cnBz4+/ub3cehQ4cwadIkJCcnIy0tTR5wfenSJTRv3tx2la1FwsLC0L9/f3z33XcQQqB///4ICwtTlDl79iyKiorQuXNneZ2npyfuvfdenDx5ssLH/Oqrr/DNN9/g4sWLyMvLQ2FhIVq1alWp+vfo0QOzZ8+Wl8t7fxAR6TBwICKXJEm26S5kbydPnkR8fDwuXryIfv364YUXXsCHH36IkJAQ/PHHHxg1ahSKyhnMkZOTg6SkJCQlJWHhwoUIDw/HpUuX0Lt3bxQWFtbciSQkaLsL2UtCQoWfMnLkSLz88ssAgC+//LLMdt14AOPxLIbBnrV+/vlnvP7665g6dSo6duwIlUqFzz77DH/++WeF6w1oA4VGjRpV6rlE5LoYOBAROanNmzfj6NGjeP3117F//34UFxdj6tSpcHPTJsz7+eefFeW9vLxQUlKiWPfXX3/h9u3b+OSTTxAbGwsA2L9/f82cgCG1ukJjDBxBnz595OCqd+/eZbY3atQIXl5e+OOPPzB06FAAQFFREfbv31/huRJ27NiBTp06YcyYMfK6s2fPKsqY+vtWVXXsk4icF9OxEhE5gYKCAqSkpODq1as4ePAgPv74YwwePBgDBgzA008/jYYNG6K4uBgzZ87EuXPn8MMPP+Crr75S7CMuLg7Z2dnYtGkTbt++jdzcXNSrVw9eXl7y81asWKHI40/mubu74+TJkzh58iTc3d3LbPf398eLL76IN998E2vXrsWJEycwevRo5ObmYtSoURU6VqNGjbB//36sW7cOf//9N9577z3s27dPUSYuLg5HjhzBqVOncPv27XJbmqwVFxeH7du34+rVq7h9+3aV90dEzo2BAxGRE1i7di2io6MRFxeHPn36YMuWLfi///s/LF++HO7u7mjVqhWmTZuG//znP2jRogUWLVqEKVOmKPbRqVMnvPDCC3jssccQHh6OTz/9FOHh4ViwYAF++eUXNG/eHJ988gk+//xzO52l8wkMDERgYKDZ7Z988gkeeeQRDBs2DG3atMGZM2ewbt06BAcHV+g4L7zwAh5++GE89thjaN++PVJTUxWtDwAwevRoNGnSBG3btkV4eDh27txZqXMy9MEHH+DChQto2LAhwsPDq7w/InJunMeBiIiIiIgsYosDERERERFZxMCBiIiIiIgsYuBAREREREQWMXAgIiIiIiKLGDgQEREREZFFDByIiIiIiMgiBg5ERERERGQRAwciIiIiIrLIw94VcBalpaW4du0aVCoVJEmyd3WIiIiIiKpMCIGsrCzExMTAza38NgUGDla6du0aYmNj7V0NIiIiIiKbu3z5MurWrVtuGQYOVlKpVAC0L2pgYKCda0NEREREVHWZmZmIjY2Vr3XLw8DBSrruSYGBgQwciIiIiKhWsaYrPgdHExERERGRRQwciIiIiIjIIgYORERERERkEQMHIiIiIiKyiIEDERERERFZxMCBiIiIiIgsYuBAREREREQWMXAgIiIiIiKLGDgQEREREZFFDByIiIiIiMgiBg5ERERERGQRAwciIiIiIrLIroHD9u3bMXDgQMTExECSJCxbtkyxXZIkkz+fffaZXKZ79+5ltj/++OOK/aSnp2PYsGFQq9VQq9UYNmwYMjIyauAMiYiIiIhqB7sGDjk5OWjZsiVmzZplcvv169cVP99++y0kScIjjzyiKDd69GhFua+//lqxfejQoUhOTsbatWuxdu1aJCcnY9iwYdV2XkREREREtY2HPQ/et29f9O3b1+z2qKgoxfLy5cvRo0cPNGjQQLHez8+vTFmdkydPYu3atdizZw/at28PAJg7dy46duyIU6dOoUmTJiafV1BQgIKCAnk5MzPTqnMiIiIiIqqNnGaMw40bN7B69WqMGjWqzLZFixYhLCwMd999N8aPH4+srCx52+7du6FWq+WgAQA6dOgAtVqNXbt2mT3elClT5K5NarUasbGxtj0hIiIiG9NkCZy7JKDJEvauChHVQnZtcaiI7777DiqVCg8//LBi/ZNPPon4+HhERUXh2LFjmDBhAg4fPowNGzYAAFJSUhAREVFmfxEREUhJSTF7vAkTJuCNN96QlzMzMxk8EBGRwzpwVGD5egEhAEkCBicBiQmSvatFRLWI0wQO3377LZ588kn4+Pgo1o8ePVp+3KJFC9x1111o27YtDh48iDZt2gDQDrI2JoQwuV7H29sb3t7eNqo9ERFR9dFk6YMGABACWL5eoFEcoFYxeCAi23CKrko7duzAqVOn8Oyzz1os26ZNG3h6euL06dMAtOMkbty4UabcrVu3EBkZafO6EhER1bTUdMhBg44QQFq6fepDRLWTUwQO8+bNQ2JiIlq2bGmx7PHjx1FUVITo6GgAQMeOHaHRaLB37165zJ9//gmNRoNOnTpVW52JiIhqSmiwtnuSIUkCQoLtUx8iqp3s2lUpOzsbZ86ckZfPnz+P5ORkhISEoF69egC0Ywt++eUXTJ06tczzz549i0WLFqFfv34ICwvDiRMnMG7cOLRu3RqdO3cGADRr1gx9+vTB6NGj5TStzz33HAYMGGA2oxIREZEzUaskdGsvsHWPdlk7xkFiNyUisim7tjjs378frVu3RuvWrQEAb7zxBlq3bo33339fLrNkyRIIIfDEE0+Ueb6Xlxc2bdqE3r17o0mTJnjllVeQlJSEjRs3wt3dXS63aNEiJCQkICkpCUlJSbjnnnvwww8/VP8JEhER1ZAG9fVBwsjHODCaiGxPEsK4VySZkpmZCbVaDY1Gg8DAQHtXh4iISOGvswKLftN+pb88QkJkGAMHIrKsIte4TjHGgYiIiMpXUKh/XFJiv3oQUe3FwIGIiKgWKDQIHIoZOBBRNWDgQEREVAsUFOgflxTbrx5EVHsxcCAiIqoFCgr1QxbZ4kBE1YGBAxERUS1QwK5KRFTNGDgQERHVAhwcTUTVjYEDERFRLcAWB6rNLly4AEmSkJycbO+quDQGDkRERLUAWxxqDyEE/ve//2Hw4MGoV68eIiIi0K5dO3zwwQdIT083+ZyrV6/iqaeeQmhoKPz8/NCqVSscOHBAUebkyZMYNGgQ1Go1VCoVOnTogEuXLsnb58yZg+7duyMwMBCSJCEjI8Oq+lo69ogRIyBJkuKnQ4cO8nZdUGDq55dffqnAK2daYWEhwsLC8NFHH5ncPmXKFISFhaHQMDUZmcTAgYiIqBZQpGNlViWnlZqaivvvvx9vvfUWunXrhoULF2Lz5s147733cOLECTRv3hx79uxRPCc9PR2dO3eGp6cnfv/9d5w4cQJTp05FUFCQXObs2bPo0qULmjZtiq1bt+Lw4cN477334OPjI5fJzc1Fnz598M4771hdX2uODQB9+vTB9evX5Z81a9bI22JjYxXbrl+/jsmTJ8Pf3x99+/at2AtogpeXF5566iksWLAApuY9nj9/PoYNGwYvL68qH6vWE2QVjUYjAAiNRmPvqhAREZUx/ZsS8a/PtD8795Xauzo1IjMzUwwdOlT4+fmJqKgoMW3aNNGtWzfx6quvCiGE+OGHH0RiYqIICAgQkZGR4oknnhA3btyQn79lyxYBQKxdu1a0atVK+Pj4iB49eogbN26INWvWiKZNmwqVSiUef/xxkZOTIz+vW7du4uWXXxavvvqqCAoKEhEREeLrr78W2dnZYsSIESIgIEA0aNBArFmzRn5OcXGxGDlypIiLixM+Pj6icePGYsaMGYrzKSoqEp07dxYjR44UhYWFJs951apVIioqSly7dk1e989//lN06dKl3NfqscceE0899ZRVr6vudUlPT7dY1ppjDx8+XAwePNiqY+u0atVKjBw5Ul4+f/68ACAOHTokhBCipKREPPvss+Kuu+4SFy5cEEIIsWLFCtGmTRvh7e0t4uPjxaRJk0RRUZEQQogjR44IAGLr1q2K42zfvl0AEEePHq1Q/WqTilzjssWBiIjIyR04KpBq0IPl4tWyd1VrozfeeAM7d+7EihUrsGHDBuzYsQMHDx6UtxcWFuLDDz/E4cOHsWzZMpw/fx4jRowos59JkyZh1qxZ2LVrFy5fvowhQ4ZgxowZWLx4MVavXo0NGzZg5syZiud89913CAsLw969ezF27Fi8+OKL+Mc//oFOnTrh4MGD6N27N4YNG4bc3FwAQGlpKerWrYuff/4ZJ06cwPvvv4933nkHP//8s7zP+fPnQwiBOXPmQAiB119/HbGxsWjevDl+/vlnBAQEoH///nj22WcxefJk+XkrVqxA27Zt8Y9//AMRERFo3bo15s6dK28vLS3F6tWr0bhxY/Tu3RsRERFo3749li1bVuW/gaVj62zduhURERFo3LgxRo8ejZs3b5rd54EDB5CcnIxRo0aZ3F5YWIghQ4Zg//79+OOPP1C/fn2sW7cOTz31FF555RWcOHECX3/9NRYsWIB///vfAICEhAS0a9cO8+fPV+zr22+/xb333osWLVpU4VVwIdUextQSbHEgIiJHlJFZKt77XN/aoPvJyKzdrQ6ZmZnC09NT/PLLL/K6jIwM4efnJ7c4GNu7d68AILKysoQQ+jvrGzdulMtMmTJFABBnz56V1z3//POid+/e8nK3bt0Ud9mLi4uFv7+/GDZsmLzu+vXrAoDYvXu32XMYM2aMeOSRR+Tlzp07i5UrVwohhPjoo49Eo0aNxIYNG8TOnTtF+/bthe6y7cyZMyIyMlJ+nre3t/D29hYTJkwQBw8eFF999ZXw8fER3333naIufn5+Ytq0aeLQoUNiypQpQpKkMnfgDV8Xa1ocLB1bCCGWLFkiVq1aJY4ePSpWrFghWrZsKe6++26Rn59vcp8vvviiaNasmWKdrsVhx44d4v777xedO3cWGRkZ8vb77rtPfPzxx4rn/PDDDyI6Olpenj17tvD395f//llZWcLf3198/fXXFs+zNqvINS4DBysxcCAiIkd09mJpmaDhX5+ViHMXa3fgkJycLACIixcvKta3bt1aDhwOHjwoBg0aJOrVqycCAgKEn5+fACCOHz8uhNBfIN+8eVN+/rfffiv8/PwU+3z//fdF69at5eVu3bqJMWPGKMrUq1dPfPrpp/JyaWmpACCWL18ur5s9e7ZITEwUYWFhwt/fX3h6eop27drJ21UqlUhNTRVCCNGuXTuxcOFCeduOHTvkwCEnJ0d4eXnJ2zw9PUXHjh0V9Rk7dqzo0KGDEEKIq1evCgDiiSeeUJQZOHCgePzxx4WxigQOlo5tyrVr14Snp6f43//+V2Zbbm6uUKvV4vPPP1es1wUOdevWFR06dFB0HRNCCD8/P+Hj4yP8/f3lHx8fHwFALpuRkSF8fX3FN998I4QQ4ptvvhF+fn4uf23HrkpEREQuIjQYkKSy60OCa74uNUncGeQqGZ28bn1OTg6SkpIQEBCAhQsXYt++ffjtt98AoEz2HE9PT/mxJEmKZd260tJSs88x9TxdvXTP+/nnn/H6669j5MiRWL9+PZKTk/HMM88o6lJcXCwPVi4sLIS/v7+8LSAgQH58+PBhNGzYUF6Ojo5G8+bNFfVp1qyZnDEpLCwMHh4e5ZapLEvHNvec+vXr4/Tp02W2/frrr8jNzcXTTz9t8rn9+vXDkSNHygwQLy0txeTJk5GcnCz/HD16FKdPn5ZfU7VajUcffVTurjR//nw8+uijCAwMrNA5uzIPe1eAiIiIKk+tktC7m8Darfp18bHa9bVZw4YN4enpib179yI2NhYAkJmZidOnT6Nbt27466+/cPv2bXzyySfy9v3799utvjt27ECnTp0wZswYed3Zs2cVZRo1aoQjR46gQ4cO6Nq1K2bMmIH77rsPPj4++OSTTwAA586dw8svv4w33nhDfl7nzp1x6tQpxb7+/vtv1K9fH4A2q1C7du3KLVNZlo5tSmpqKi5fvozo6Ogy2+bNm4dBgwYhPDzc5HNffPFFtGjRAoMGDcLq1avRrVs3AECbNm1w6tQpNGrUqNz6jho1Ct27d8eqVauwc+dOfPzxx5ZOkQywxYGIiMjJNfe4gO4bPkSvte/BOz8TIUH2rlH1U6lUGD58ON58801s2bIFx48fx8iRI+Hm5gZJklCvXj14eXlh5syZOHfuHFasWIEPP/zQbvVt1KgR9u/fj3Xr1uHvv//Ge++9h3379inKPPTQQ/Ig7IkTJ0KSJISHhyMsLAxNmzYFoL1QHzFiBJ599ln5ea+//jr27NmDjz/+GGfOnMHixYsxZ84cvPTSS3KZN998Ez/99BPmzp2LM2fOYNasWVi5cqUikElJSUFycjLOnDkDADh69CiSk5ORlpYml+nVqxdmzZpl9bGzs7Mxfvx47N69GxcuXMDWrVsxcOBAhIWF4aGHHlKc/5kzZ7B9+3bFuZkyduxYfPTRRxgwYAD++OMPAMD777+P77//HpMmTcLx48dx8uRJ/PTTT/jXv/6leG63bt3QqFEjPP3002jUqBG6du1a7rHISLV3nKolOMaBiIgc0q5dotTbWwhACEBcqtde/LqiwN61qhGm0rHee++94u233xZCCLF48WIRFxcnvL29RceOHcWKFSsUKT1N9eWfP3++UKvViuNMnDhRtGzZUl42TPmqU79+fTF9+nTFOgDit99+E0IIkZ+fL0aMGCHUarUICgoSL774onj77bcV+01PTxd16tQRs2fPltfdvn1bHsx79epVUVpqeuzKypUrRYsWLYS3t7do2rSpmDNnTpky8+bNE40aNRI+Pj6iZcuWYtmyZWXOE0CZn/nz5yvOc+LEiVYfOzc3VyQlJYnw8HDh6ekp6tWrJ4YPHy4uXbpUpn4TJkwQdevWFSUlJWW2GadjFUKIqVOnCpVKJXbu3CmEEGLt2rWiU6dOwtfXVwQGBop7773X5Ovw8ccfCwBlBlO7qopc40pCmJgJg8rIzMyEWq2GRqNhXzgiInIcvXsD69crViU/8xlafTveThWyn5ycHNSpUwdTp041m8rT0R0+fBj9+vVD165d8corr6Bdu3bw8PDA+fPnMWfOHBw6dAhr1661dzWpFqnINS67KhERETmrU6fKBA0AEL/hWztUpuYdOnQIP/74I86ePYuDBw/iySefBAAMHjzYzjWrvJYtW+Lw4cOIiorCQw89BC8vL3h5eaF169a4desW5syZY+8qkgvj4GgiIiJnZTB5mCH1lZPIOnwGt4IbIjS4dg+U/vzzz3Hq1Cl4eXkhMTERO3bsQFhYmL2rVSVhYWGYPn06pk2bhtu3b6OwsBBRUVFwd3e3d9XIxTFwICIiclbbt5vd9Mf7K7HrvlchScDgJCAxofYFD61bt8aBAwfsXY1qoxscTeQo2FWJiIjIGRUVAbt3y4v77x2JzKA68nL9s9sAaEdML18voMnikEYiqhoGDkRERM7o0CEgJ0devNCgK1Lu6iQvR19Llh8LAaSl12TlqDKOHDmCq1ev2rsaRGYxcCAiInJGRnMAXIzvgoyGreXl4PSL8M1JBaCdWbq2zyTtzP7++2888sgjaNmyJRYsWGDv6hCZxcCBiIjIGZ04IT8s9AtERnAcspu0VhSJvnrozhgHqVYPkHZWN27cwEsvvYTmzZtj3759+P777zFhwgR7V4vILA6OJiIickKaPcehvvM4JfxuQJJQ0FwZOMRcPYSHpz7AoMHB5OTkYNq0afj000/h7u6OKVOmYOzYsfDx8bF31YjKxRYHIiIiJ/PHvlJ4nDouL9+MbA4AuFIcjszAGHl9+M2TDBocSHFxMebOnYu77roLH330EZ5//nmcO3cOb775JoMGcgoMHIiIiJyIJkvgj9U34Z9zW153M+puAMCla8Dt8Mby+tDbZ5CRWVrjdSQlIQRWrlyJe+65B8899xx69uyJU6dO4fPPP0dISIi9q0dkNQYORERETiQ1HQi/cUKxTtfiAACpYY3kx6G3T+N2ao1VjUzYu3cvunfvjkGDBiE6Ohr79+/HwoULERcXZ++qEVUYAwciIiInEhoMhKaeVay7Hd5Efpwapm9xCMi+iaunNTVWN9I7e/YsHnvsMbRv3x7p6en4/fffsXHjRiQmJtq7akSVxsCBiIjIiahVEtoHXZCXi909kanWT/xm2OIAACfXnObkbzXo1q1bePXVV9GsWTPs3LkT8+fPx6FDh9CnTx9IEsebkHNjViUiIiInE5V9Xn6sCa4P4eYuL6eG36UoG3LrNNLS20GtqrHquaTc3Fx88cUX+OSTTwAAH3zwAV599VX4+vrauWZEtsMWByIiIicjzuoDh/TgOABAVATw/JNAelhDlBrc2Q5NO8fJ36pRSUkJ5s+fj8aNG2PixIl45plncPbsWbz99tsMGqjWYeBARETkZMT5c/LjtNAGAICUm8CN2xIG9PNBtipK3t7Y4zJTslYDIQTWrFmDVq1aYeTIkejSpQtOnjyJGTNmICwszN7VI6oWDByIiIicSXY23G/dlBczQuLlx8vXCzSKA/wa15PXBWdersnauYT9+/ejV69e6N+/P0JDQ7F3714sWbIEDRs2tHfViKoVAwciIiJncvGiYjE9JE5+LASQlg54NKgvr/O4qixPlXf+/HkMHToU7dq1w40bN7By5Ups2bIF7dq1s3fViGoEAwciIiInknPqkmI5I1gfJEgStOMZ6ulbHDyvXdJGFFRpqampeOONN9C0aVNs3boVc+fOxeHDhzFgwABmSiKXwqxKRERETiT39BX4Gyxr1HUBaIOGwUmSdjyDQeDgnpcDpKcDnKG4wvLy8jBz5kx8/PHHKC0txXvvvYfXX38d/v7+lp9MVAsxcCAiInIiqowr8uMSN3d5IPRzQ4G60XfufhsEDgC03ZsYOFitpKQEixYtwr/+9S9cv34dL7zwAt577z1ERETYu2pEdsWuSkRERE7E59ZV+XG2Khpwd8eDvSXUjTb4SjcVOJBV1q9fj8TERAwfPhzt27fHiRMnMHPmTAYNRGDgQERE5Fyu6gMHKbYuxj0nITHBqJ99bKxiMe/cVVD5Dh06hKSkJPTu3RsqlQq7du3CL7/8grvuusvyk4lcBAMHIiIiZ3JF31XJK76u6TkaQkNR4uEpL+5dcw0HjnKAtCkXL17E008/jcTERFy6dAnLli3D9u3b0bFjR3tXjcjhMHAgIiJyIsIgcHCvV8dkGU02kBUQLS8HZF7H8vUCmiwGDzrp6el466230KRJE6xfvx6zZ8/GsWPHMHjwYGZKIjKDg6OJiIicRU4OpIwMedE9rq7JYqnpgEdgDIIytKlbVZnX5Dke1KqaqKjjys/Px5dffol///vfKCwsxIQJEzBu3DgEBATYu2pEDo+BAxERkbO4dk2xmB8SAz8TxUKDgStqfYtDYOZ1AHfmeHBRpaWl+PHHH/Huu+/iypUrGD16NCZOnIioqCh7V43IabCrEhERkbNISVEsLtkXZXLsglolIby5sqsSAJy5UK21c1ibNm1C27Zt8dRTT6F169Y4duwYZs+ezaCBqIIYOBARETmJnPPXFctZqiizYxcCm8TIjwNybsG9uNDlxjkcOXIEffv2xf333w9vb2/s2LEDv/32G5o2bWrvqhE5JQYORERETiLvvLLFIVsVJY9dMJYZGKNYDshKMVu2trl8+TKeeeYZtGrVCmfPnsWvv/6KXbt2oUuXLvauGpFTY+BARETkJAKy9C0ORR7eyPdRQ5JMj12QYpTdcFSZ2vERnp61t8VBo9FgwoQJaNy4MVavXo1Zs2bh+PHjeOSRR5gpicgG7Bo4bN++HQMHDkRMTAwkScKyZcsU20eMGAFJkhQ/HTp0UJQpKCjA2LFjERYWBn9/fwwaNAhXDFLVAdqUa8OGDYNarYZarcawYcOQYZCVgoiIyBn4pN2QH2eroiG5SRicJJmcy6EgxLjFQfvcoqLadwFdWFiIL774Ag0bNsT//d//4c0338SZM2cwZswYeHp6Wt4BEVnFroFDTk4OWrZsiVmzZpkt06dPH1y/fl3+WbNmjWL7a6+9ht9++w1LlizBH3/8gezsbAwYMAAlJSVymaFDhyI5ORlr167F2rVrkZycjGHDhlXbeREREVULg8HRUnSU6Vmj7whsFKlYDsi+YbZ1wlmVlpbip59+QrNmzfDGG2/g4YcfxunTp/HBBx8gMDDQ3tUjqnXsmo61b9++6Nu3b7llvL29zWY90Gg0mDdvHn744Qfcf//9AICFCxciNjYWGzduRO/evXHy5EmsXbsWe/bsQfv27QEAc+fORceOHXHq1Ck0adLE5L4LCgpQUFAgL2dmZlbmFImIiGzHIHBwi4lCoKlZo+8IjAuHkCRIQts1KSDrptnWCWe0detWvPXWW9i3bx8GDhyIlStXonnz5vauFlGt5vBjHLZu3YqIiAg0btwYo0ePxs2bN+VtBw4cQFFREZKSkuR1MTExaNGiBXbt2gUA2L17N9RqtRw0AECHDh2gVqvlMqZMmTJF7tqkVqsRGxtbDWdHRERUAdf1YxykqMhyCgJwd4cUFiYv1nW/YbZ1wpkcP34cAwcORI8ePQBorxNWrFjBoIGoBjh04NC3b18sWrQImzdvxtSpU7Fv3z707NlTbglISUmBl5cXgoOV7a6RkZFIuXNXJiUlBREREWX2HRERIZcxZcKECdBoNPLP5cuXbXhmREREFVRaChjcPHOLsRA4AECkvoxPxs1yCjq+q1ev4tlnn8U999yDEydO4KeffsKff/6Jbt262btqRC7DoWeOfuyxx+THLVq0QNu2bVG/fn2sXr0aDz/8sNnnCSEU2RNMZVIwLmPM29sb3t7elaw5ERGRjaWlaYOHOzzrWDF5WWQkcOwYAMDLSQOHzMxMfPrpp5g2bRr8/Pwwffp0vPDCC/Dy8rJ31YhcjkO3OBiLjo5G/fr1cfr0aQBAVFQUCgsLkZ6uTEp98+ZNRN65yxIVFYUbN26U2detW7fkMkRERA7vpvLCvzA4zExBA4oWh7LfhY6ssLAQs2bNQsOGDTF16lS89tprOHv2LF555RUGDUR24lSBQ2pqKi5fvozo6GgAQGJiIjw9PbFhwwa5zPXr13Hs2DF06tQJANCxY0doNBrs3btXLvPnn39Co9HIZYiIiByeUeDw054IHDhqYU4Gg666vhrnCByEEPj1119x991345VXXsHAgQNx+vRpfPzxx1Cr1fauHpFLs2tXpezsbJw5c0ZePn/+PJKTkxESEoKQkBBMmjQJjzzyCKKjo3HhwgW88847CAsLw0MPPQQAUKvVGDVqFMaNG4fQ0FCEhIRg/PjxSEhIkLMsNWvWDH369MHo0aPx9ddfAwCee+45DBgwwGxGJSIiIkeTc+km/A2Ws/0jsHy9QKM4mM+UZNjikJsBFBQADtwNd8eOHXjrrbewZ88e9O3bF0uXLkVCQoK9q0VEd9i1xWH//v1o3bo1WrduDQB444030Lp1a7z//vtwd3fH0aNHMXjwYDRu3BjDhw9H48aNsXv3bqhUKnkf06dPx4MPPoghQ4agc+fO8PPzw8qVK+Hu7i6XWbRoERISEpCUlISkpCTcc889+OGHH2r8fImIiCrr4gFli0NOQDiEANLSzTwBUAQOAIBbt2xfMRv466+/8OCDD6Jr164oLCzEpk2bsGbNGgYNRA7Gri0O3bt3hxDmm1nXrVtncR8+Pj6YOXMmZs6cabZMSEgIFi5cWKk6EhER2ZsmS+DGiZvQJRwtcfNAvk+Q5QndjLIKipQUSHXrVls9KyolJQWTJk3CN998g9jYWCxevBiPPfYY3Nycqic1kctw6KxKREREBKSmA345+taCnIBwCDc3dEksp5sSAISHKxaLb9yGZ3VVsgKysrIwdepUfP755/Dy8sJnn32GMWPGMJshkYNj4EBEROTgQoOBvGx9V6Vcf21A0CHRwoRuRoFDScotuwYORUVFmDdvHiZNmoSMjAy8+uqrePvtt8vMx0REjoltgURERA5OrZIQUapscXiwt1R+awNQNnC4YZ8xDkIILFu2DAkJCRgzZgx69+6Nv//+G//5z38YNBA5EQYORERETsDfoKtSbKtIJCZYCBoAwN9fkUVJ3LxdHVUr165du9ClSxc89NBDqF+/Pg4ePIjvvvsO9erVq/G6EFHVMHAgIiJyAh7p+sDBKya8nJIGJEnR6iBqMKvS33//jUceeQSdO3dGTk4O1q1bh3Xr1qFVq1Y1Vgcisi0GDkRERI6upASemWn65XArAwejsiUp1R843LhxAy+99BKaN2+Offv24fvvv8fBgweRlJRU7ccmourFwdFERESOLj0dkmH68rAwq5+a6ROGwDuPM86n4uxRYV03pwrKycnBtGnT8Omnn8Ld3R1TpkzB2LFj4ePjY/NjEZF9sMWBiIjI0d02GpsQGmrV0zRZAufz9UGGf/YtLF8voMkyP4dSRRUXF2Pu3Lm466678NFHH+G5557D2bNn8eabbzJoIKplGDgQERE5OuPAwcoWh9R0IMdf31XJP+eW5dmmTbh69Sry8vIU64QQWLlyJe655x4899xz6NGjB/766y9MnToVoVYGNkTkXBg4EBERObpKBg6hwUBegL6sX24a3EuLy59t2silS5fQvHlzfPPNN/K6vXv3onv37hg0aBCio6Oxf/9+LFq0CPHx8dbvmIicDgMHIiIiR1fJwEGtktConXIg9UP3plue/+GO0tJSDB8+HIGBgRg2bBjOnj2Lxx57DO3bt0d6ejp+//13bNy4EYmJiVbtj4icGwdHExERObpKjnEAgPr3KIOMlpFpACKteu6MGTOwdetW/O9//8PEiRMxe/ZsREREYP78+Rg2bBjc3d2trgcROT8GDkRERA7u+slbiL7zOM9HjRN/eSAxwconG7dOGAchZhw7dgzvvPMOOnfujGeeeQYA8MEHH+CVV16Bn5+flQcnotqEXZWIiIgcmCZLIOWE/mI/1z+sYpmRjFsnUlMtPiU/Px/9+vVDYWEh9uzZg+7du2PatGmIiYnB6tWrUVJSUpFTIKJagi0OREREDiw1HfDLUQYOusxIapUVO6hE4PDf//4Xly9fBgCUlJRgxYoVWLFiBQAgJCQEPXr0QFgF5pIgotqBgQMREZEDCw0GMnP0F/u5/qGQJFifGakSgcMrr7yC1q1bIzw8HL6+vvDz84Ovry98fX3h5eUFSbL9BHJE5PgYOBARETkwtUqCR7FBi4NfKAYnSVZnRoK3N0r9/OGWm6NdtiJw8PDwQI8ePSpTXSKqxRg4EBEROTjv7DT5cfMOofBOqNgdfxEaBlQgcCAiMoWDo4mIiBxZSQncszLkRe+oSszKbNBdSViZVYmIyBgDByIiIkeWng5JGGRQqsAcDqaeI26zxYGIKoeBAxERkSNLS1MuVyJwcAszeA4DByKqJAYOREREjsx4TEJISIV3IRkEDoJjHIiokhg4EBEROTLjC/1KtDhcKzF4TloqDhwprWKliMgVMXAgIiJyZFXsqqTJEjh0Xd9K4V5agrUrM62feZqI6A4GDkRERI6sii0OqelAnq9ytjifnDSkpVe1YkTkahg4EBERObDrJ/XpU4vdvXDgrG+Fnh8aDOT5K4MNv7w062eeJiK6g4EDERGRg9JkCVw+pu+qlOsfiuUbUKFuRmqVhLY9lIFDr+bp1s88TUR0BwMHIiIiB5WaDvjm6rsq5fmFQghUuJtRs3uVgUPjwDQzJYmIzPOwdwWIiIjItNBgQOQatDj4hUCSUPFuRsYpXI0HXBMRWYGBAxERkYNSqyS4uembF/L8gjE4Sap4N6OgIOUyAwciqgQGDkRERA7M16DFoVHrEHglVGJsgrs7ilVB8MjK0C4zcCCiSuAYB3JImiyBc5cE84wTkctz0+hbHLwiKz75m05JkOEkcAwciKji2OJADufAUYHl6wWEACQJGJwEJFbmDhsRkbMrLoZbpka/bDxWoQJEcAhw+ax2gYEDEVUCWxzIoWiy9EEDAAgBLF/PlgciclEZGcrlqgYOOgwciKgSGDiQQ0lNhxw06FQm9SARUa1gfIFfhcABoQbPNZ6NmojICgwcyKGEBmu7JxmqVOpBIqLawIaBg3uo/oNUsMWBiCqBgQM5FLVKQveOynWVSj1IRFQbpBs1twZX/i6Ke7hB0JFuonmXiMgCBg7kcAID9I+73MuB0UTkwmzY4nCtUB90SIWFOLQ3p9L7IiLXxMCBHIo2o5J++cYt+9WFiMjubBQ4aLIE9l1WtlZsWpXGxBNEVCEMHMhh6DIqGTp9HvxiIyLXZRA4CHd3QKWq1G5S04E8X2Xg4JOTxsQTRFQhDBzIYZjKqAQwoxIRuTCDMQ7FgSFls0dYKTQYyPNXtlb45acz8QQRVQgDB3IYpjIqAcyoREQuzKDFoVRd+Q9DtUpCx17KwKFb43QmniCiCmHgQA5DrZIwOEn5JRYZBn6xEZHrMggcigOrMIcDgLs7hCqWGwZmVGl/ROR6GDiQQ0lMkBAUqF/29bFfXYiI7C37qr6r0tXCIBw4WoUxX8apXDmXAxFVEAMHcjjFxfrHRUX2qwcRkT1psgRyr+kDhzzfYCxfLyqfMMLHB8Vevvpl4zkiiIgsYOBADqeg0OAxAwciclGp6YBvnjJwEKJqCSOKVAatDmxxIKIKYuBADqW0VKDIoMWhkIEDEbmo0CAB31z9xX2+bzAkqWoJI4pURrNHExFVgIe9K0BkyDhQYFclInJVao98oETfBJvnH4LBSVKVEkaUBLLFgYgqj4EDORTDbkoAUFhouhwRUa1n1CLQrW8w/BKqlmWuWG3Q4sDAgYgqyK5dlbZv346BAwciJiYGkiRh2bJl8raioiL885//REJCAvz9/RETE4Onn34a165dU+yje/fukCRJ8fP4448ryqSnp2PYsGFQq9VQq9UYNmwYMjIyauAMqaJ0gYPuq7GkFCgu4czRROSCjAIHv+iqpWMFjOaC4PcgEVWQXQOHnJwctGzZErNmzSqzLTc3FwcPHsR7772HgwcPYunSpfj7778xaNCgMmVHjx6N69evyz9ff/21YvvQoUORnJyMtWvXYu3atUhOTsawYcOq7byo8nQtDAEBZdcREbkU4xYB43SqlSDUQfoFjnEgogqya1elvn37om/fvia3qdVqbNiwQbFu5syZuPfee3Hp0iXUq1dPXu/n54eoqCiT+zl58iTWrl2LPXv2oH379gCAuXPnomPHjjh16hSaNGlio7MhW9C1OPh6A7m52haHtHQBP19OAkdEruVMchoaGSwfvxWEu6u4TxEUpF/IyABKSwE35kkhIus41aeFRqOBJEkIMvzgA7Bo0SKEhYXh7rvvxvjx45GVlSVv2717N9RqtRw0AECHDh2gVquxa9cus8cqKChAZmam4oeqny5wKCrWBg0AMOdHVG3SIyIiJ6PJEjiyS9nisOZwcOXncNAxbLUoLQUMvi+JiCxxmsHR+fn5ePvttzF06FAEBuqnFn7yyScRHx+PqKgoHDt2DBMmTMDhw4fl1oqUlBRERESU2V9ERARSUlLMHm/KlCmYPHmy7U+EyqXrlpSu0a8TAli+XqBRHKqUTYSIyFmkpgM+uRmKdXk+wUhLB9Sqyu9XMsrlmnk5DYFqdeV3SEQuxSkCh6KiIjz++OMoLS3Ff//7X8W20aNHy49btGiBu+66C23btsXBgwfRpk0bAIAklb3YFEKYXK8zYcIEvPHGG/JyZmYmYmNjq3oqZIFxViUd3aRHVfnCJCJyFqHBgF+evsWh2N0LxV6+VZrDAQAko3ESC79NQ/tn4pBYxWxNROQaHL6rUlFREYYMGYLz589jw4YNitYGU9q0aQNPT0+cPn0aABAVFYUbN26UKXfr1i1ERkaa3Y+3tzcCAwMVP1T9zDXDV3XSIyIiZ6JWSWgeZjBrtF8wBvd2q3Kra75/kGLZJzcdy9eLqneBIiKX4NCBgy5oOH36NDZu3IjQ0FCLzzl+/DiKiooQHR0NAOjYsSM0Gg327t0rl/nzzz+h0WjQqVOnaqs7VdyBowI79pZdLwFVnvSIiMjZhLnp+2z6RgXbpFUgx1t5B8Y3N11u0SUissSuXZWys7Nx5swZefn8+fNITk5GSEgIYmJi8Oijj+LgwYNYtWoVSkpK5DEJISEh8PLywtmzZ7Fo0SL069cPYWFhOHHiBMaNG4fWrVujc+fOAIBmzZqhT58+GD16tJym9bnnnsOAAQOYUcmBaLIElq83fcerRyewGZ2IXI5I1V/Nu4fapslVXc8ocMhLZ4suEVnNroHD/v370aNHD3lZN6Zg+PDhmDRpElasWAEAaNWqleJ5W7ZsQffu3eHl5YVNmzbhiy++QHZ2NmJjY9G/f39MnDgR7u7ucvlFixbhlVdeQVJSEgBg0KBBJueOIPtJTdeOYzDFzZ1BAxG5HmEwz4IUHGSTfQbHKSeR883LYIsuEVnNroFD9+7dIcxdLQLlbgOA2NhYbNu2zeJxQkJCsHDhwgrXj2pOaLB2HIOpP3lBgYB+LmkiIhdhOEGbDSZ/A4BjF7zQytMXXkV5AIB6fuloxhZdIrKSQ49xINehVkkYnKT/8pIANGmgfWwu0xIRUW0mZdg2cNBkCazaBOT76veVdTmdA6OJyGoMHMhhJCZIiNWOaUe/XkB8rDaQYOBARC5HCLhpbBs46LqE5hkEDr556RwYTURWY+BADqX0zo2voEAJXl7axwwciMjl5OdDKjT48LNB4KDrEprnp9+XT146B0YTkdUYOJBDKSzS/vbyBLy9tY8LCuxXHyIiu0g3agawQeCg6xKa7xskrwso1Jh/AhGREQYO5FB0N9i8PAEftjgQkasyDhyCgmyy28QECQ1a6jMreWanY+ocgQNHOc6BiCxj4EAOpahY+9vTE/Bm4EBErqoaWhx0hFotP/bN004Ax9mjicgaDBzIoRh2VeIYByJyWdUYOOT6KMc4QAjOHk1EVmHgQA6jtFSg+E6Lg2FXpbw88E4YEbmWjAzlsg0DB58o/b7cS0vgVZjD2aOJyCoMHMhh6LopAdrA4a+z2mChpBTsg0tErqWaxjgAgG+UMkLwzU/n7NFEZBUGDuQwdN2UJAA5eQK/b9VvYx9cInIl1/5Kkx+XuLnjwIUA2+3cqPXi6Z7pSOTs0URkBQYO5DB0GZU8PYG0DAnCKEZgH1wicgWaLIGLx/Qfdvk+QVi+wYZdNo0CB59cpmQlIuswcCCHYTgwWjdRkSH2wSUiV5CaDnjnZsjL+X7Btr1xYhQ4lKTyjgwRWYeBAzmMIl3g4KWfqMhQUlewDy4R1XqhwdpxBzp5PkG2vXFiNF6ilIEDEVmJgQM5DF2Lg6en9ndigoSWzfXb128HB0gTUa135gLgk6fvPpTvG2TbwctGLQ4iPcM2+yWiWo+BAzkMw65KgLY/75GT+u0cIE1EtZ0mS2D5egHfXIMWB79gNIqz4UH8/PR3aAAI4wxORERmMHAgh1FkFDikauclUuAAaSKqzXSfe755ysHRNv3ckyRldyUGDkRkJQYO5DB0LQ6FRdq7bhwgTUSuRve555OfIa/L9wu2/eeeQXclN+PJ5oiIzGDgQA7j3CVt88Lla9oJ385cgGKAtCSBkxQRUa2mVkkY3KMY3gXZ8rr4lsG2/9wzCBykzAzb7puIai0GDuQQNFkCx07pl3XjGRrFASp/7bqhD4KTFBFRrZcYq5xXoW7TamhmNeiq5K5hVyUisg4DB3IIprIB6sYz+Hhrl709GTQQkQsw7joUXA2Bg8E+3bMyzJcjIjLAwIEcQqiJ70XdeAYvL+2ybgwEEVFtVpqWplxRzYGDRyZbHIjIOgwcyCGoVRKiI/TLhuMZdFmWChg4EJELKLppdCFvNGGbTRjs0yM7w/b7J6JaycPeFSDS8b7TstC9A9C2pX4QtNziUGinihER1aCi2xnwNlxRHYGDYYtDQa72A1b3YUtEZAZbHMhh5BVof9evq8ycpGtxYFclInIFJbeNWhyqo6uScTDCuRyIyAoMHMhh5Odrf/t4K9czcCAiV1KSmqFcUc0tDgCQdZWBAxFZxsCBHEbencDB10e5Xt9VyWgaaSKiWqjUcJpolQrwqIZexUaBw48L0nDgKD9jiah8DBzIIZSUCLlFoUzgwBYHInIlhoFDdbQ2AMjyVCuWfXLTsXy9gCaLwQMRmcfAgRyCrrUBMNVVSTvegYOjicgVlKRl6BeqY3wDgHQ35X598jLkuXOIiMxh4EAOQTcw2scbcHNTTvTGFgcichUHjgpkXdFfvWd5VU/gEBQbpFj2zcuQ584hIjKHgQM5BN3AaHd3lGkq5wRwROQKNFkCy9cL+OZlyOuu5KmrpftQYN0gxbJvfoY8dw4RkTkMHMghHPlL+8WYkwtMnSMUg/TY4kBEriA1HRBC221IJ883uHq6D7m7ozRQP87hvrvSkZjAoIGIysfAgexOkyWw56B+WQgoBul5cwI4InIBocGAJAG+efpIId83qPq6DxkMvPbK5uAGIrKMgQPZXaqJ7yvDQXq6Foec3LLdmIiIagu1SsLgB5QtDnEJwdXXfchg4LVIzzBfjojoDgYOZHehJu6mGQ7SO3NBGyxkZpftxkREVJskxmXDTZTKyzFNQ6rvYAwciKiCGDiQ3alVEpo10i9LEuRBeposgW1/6rcZd2MiIqpV0o2aYKtpHgcAkAz7QBkfl4jIhGqYjpKo4iLDgZNngKYNgQH36zN76AYLGtJ1Y1Kr7FBRIqJqVJqerryjV03zOACAZBiUMHAgIiuwxYEcQnGx9ndIEBT9eXWDBQ0x1zgR1VaFN2quxcEwKJEyGDgQkWUMHMghlJRof3sYtYGpVRIG3q9fNuzGRERU2xTdNLqAr8YWB0XgkJUJlJaWU5iIiIEDOYhiXeDgXjYgaNfSDR7u2sfPPg7mGieiWqvolp0CByEAjab6jkVEtQIDB3IIuq5K7u6mt+vmcvD2YtBARLVXcWqGckV1Bg7G3aA4zoGILGDgQA5B1+LgaWa4vpduEjjOHk1EtVj6ef3Fe5GHNw6c8am+gxkHJRkZ1XcsIqoVGDiQQ7DU4qCbBI6BAxHVVposgfSLBrNG+wRVb/pp48CBLQ5EZAEDB3IIxWYGR+voWhwKCmumPkRENS01HfDNMwgc/ILl9NPVgl2ViKiCOI8DOQRdi4PZwOFOi0MRWxyIqJYKDQaKDQKHPN/g6k0/zRYHIqogBg7kEOR0rOyqREQuSq2SUFCqz2yU7xtUvemnGTgQUQUxcCCHUGwpcNANjmZXJSKqxQy7KsXdEwKv6kw/7eWFYm8/eBTkapc5OJqILOAYB3II1nZVKmCLAxHVYp7Z+sDBKzyo2o9XpDJodWCLAxFZwMCBHILFFgd5jEM1ZRchIrI3IeCZZXDxXp1zONxRHBCkX2DgQEQWMHAgh2AxHeudid/YVYmIaq28PLgXG3zI1UTgEMgWByKyHgMHcggW07GyqxIR1XbGYwxqIHAoYeBARBVg18Bh+/btGDhwIGJiYiBJEpYtW6bYLoTApEmTEBMTA19fX3Tv3h3Hjx9XlCkoKMDYsWMRFhYGf39/DBo0CFeuXFGUSU9Px7Bhw6BWq6FWqzFs2DBkcBCYQ9G1OJidOZpZlYiotjO+cK+BwKFUrT9GSSoDByIqn10Dh5ycHLRs2RKzZs0yuf3TTz/FtGnTMGvWLOzbtw9RUVF44IEHkJWVJZd57bXX8Ntvv2HJkiX4448/kJ2djQEDBqBEl98TwNChQ5GcnIy1a9di7dq1SE5OxrBhw6r9/Mh6uj+X+a5K2t+aTFTfLKpERPZkh8BB4x4kP85PSceBo/x8JSLz7JqOtW/fvujbt6/JbUIIzJgxA++++y4efvhhAMB3332HyMhILF68GM8//zw0Gg3mzZuHH374Affffz8AYOHChYiNjcXGjRvRu3dvnDx5EmvXrsWePXvQvn17AMDcuXPRsWNHnDp1Ck2aNDF5/IKCAhQUFMjLmZmZtjx1MlAqBEpKtY/NDY6+fE37ZXbtBjB1jsDgJCCxOtMUEhHVtBoOHDRZApfzgnHXnWWf/AwsX1eKRnFu1Td3BBE5NYcd43D+/HmkpKQgKSlJXuft7Y1u3bph165dAIADBw6gqKhIUSYmJgYtWrSQy+zevRtqtVoOGgCgQ4cOUKvVchlTpkyZIndtUqvViI2NtfUp0h0lxfrHpsY4aLIE9iXrl4UAlq8XbHkgotqlhgOH1HQgz09/DPfSEnjmZyONPZaIyAyHDRxSUlIAAJGRkYr1kZGR8raUlBR4eXkh2OjD1bhMREREmf1HRETIZUyZMGECNBqN/HP58uUqnQ+ZV6zvVWayxSE1HTAOEYQAv9yIqHap4cAhNBjI91Er1vnlpyOk+ntIEZGTcviZoyVJ2VwqhCizzphxGVPlLe3H29sb3t7eFawtVYZuYLQEwM1EKBsaDEiSNljQkSTwy42IaheDwEG4u0Py96/Ww6lVEhq0DAZ+0q/r2yoDalVctR6XiJyXw7Y4REVFAUCZVoGbN2/KrRBRUVEoLCxEutFdGuMyN27cKLP/W7dulWnNIPswTMVqKphTqyQkddUvSxIwOEliH1wiql0MA4egO3dMqllsixDFcvOwjGo/JhE5L4cNHOLj4xEVFYUNGzbI6woLC7Ft2zZ06tQJAJCYmAhPT09FmevXr+PYsWNymY4dO0Kj0WDv3r1ymT///BMajUYuQ/ZlafI3AGh7j/4L9NWRHBhNRLWPSKvZWaMBwDPM6Dicy4GIymHXrkrZ2dk4c+aMvHz+/HkkJycjJCQE9erVw2uvvYaPP/4Yd911F+666y58/PHH8PPzw9ChQwEAarUao0aNwrhx4xAaGoqQkBCMHz8eCQkJcpalZs2aoU+fPhg9ejS+/vprAMBzzz2HAQMGmM2oRDXL0uRvgD4dKwD4eDNoIKLaR6SlQ/50q6HAwSOcgQMRWc+ugcP+/fvRo0cPefmNN94AAAwfPhwLFizAW2+9hby8PIwZMwbp6elo37491q9fD5VKJT9n+vTp8PDwwJAhQ5CXl4devXphwYIFcDe4fb1o0SK88sorcvalQYMGmZ07gmqeHDiU0+LgJknw8hQoLAIKCgF/v5qpGxFRTREGF+1STbU4RDBwICLr2TVw6N69O4Qwn1JTkiRMmjQJkyZNMlvGx8cHM2fOxMyZM82WCQkJwcKFC6tSVapGunSspUKbetXc2AVvb+3M0fkFJjcTETk3w8ChhrI/eKp8UeThDc/igjJ1ICIy5rBjHMh1nDyjDR4zs7STu5mbudT7TnelgsKaqhkRUQ1Kr/kxDpIkId/PYIA0AwciKgcDB7IrTZbArgP65fImd9MFDoUMHIioFnLT1HzgAAAF/gbHSkurseMSkfNh4EB2lWri5pa5yd10gUM+Awciqm3y8yHl5+uXazBwKDQMHNjiQETlqFTgcPDgQRw9elReXr58OR588EG88847KOTtYKqAUBPfjeYmd9PNx1fAMQ5EVNvU8KzRhgoDgszXg4jIQKUCh+effx5///03AODcuXN4/PHH4efnh19++QVvvfWWTStItZtaJaFZI/1yeZO7eXtqf3OMAxHVOkZdhHJ8ay5wKFaxxYGIrFOpwOHvv/9Gq1atAAC//PILunbtisWLF2PBggX43//+Z8v6kQsICdL+btEYGPecZHZyN7nFodB8Ji4iIqdkdMH+044gs4kibK04kIOjicg6lQochBAoLS0FAGzcuBH9+vUDAMTGxuL27du2qx25BF161chw0y0NOsyqRES1Vc41ZYtDrm+I2UQRtlaiNmpxKCdNOhG5tkoFDm3btsVHH32EH374Adu2bUP//v0BaGd+joyMtGkFqfbLuzMe0Nen/HLeXtqg4nYaauTLlIiopuRcUQYOeX7BZhNF2FqpYeBQUgJkZVX/QYnIKVUqcJg+fToOHjyIl19+Ge+++y4aNdJ2Uv/111/RqVMnm1aQaj9di4OPd/nlbtzWBgtnLpQ/3wMRkbNRFSojhDy/ELOJImxNBAUpV7C7EhGZUamZo1u2bKnIqqTz2WefwcPDrpNRkxPSBQ7ltThosgSOnNQv6+Z7aBSHcrs3ERE5A99c/cV6sbsnir38zCaKsLVUSRmdHPojFa3r16/24xKR86lUi0ODBg2QmppaZn1+fj4aN25c5UqRa9F1VSqvxaEi8z0QETkdg7v8JapgjHvezWyiCFvSZAmc1oQo1h3akYY/9pVW+7GJyPlUKnC4cOECSkpKyqwvKCjAlStXqlwpci1yV6VyWhxCg7WpWg3VVDM+EVG1MwgcREhIjbWkpqYD+UapX31z07F+O8eSEVFZFepXtGLFCvnxunXroFar5eWSkhJs2rQJ8fHxtqsd1XqlQiDfisHRapWEgQ8IrFivXS5vvgciIqdjMI+DCKq5OyKhwUCun7LFwTcvXW7RVatqrCpE5AQqFDg8+OCDAABJkjB8+HDFNk9PT8TFxWHq1Kk2qxzVfoWFgJAfC8DffCDQ7h437NxXitR0oEtboFFcjVSRiKj6GbQ4SDXYlKpWSbivdzDwgX6db246W3SJyKQKBQ66uRvi4+Oxb98+hIWFVUulyHXsTdY3hX/xLTA4SZTbr9frzuzRO/YBf+wXGJyEGukHTERUnUR6OnSfZG5hIeWWtbXOnXxQ5OMPz/wcAIBfXhpbdInIpEqNcTh//jyDBqoyTZbAxj/0y7pMSeb61WqyBK7ftL48EZHTSNV3VSoNDKrxw4sgfbByb1w6b8gQkUmVzp26adMmbNq0CTdv3pRbInS+/fbbKleMar9UExOUltevtrzMSuyHS0ROSwhFi8Pu88EIPFp+66utlQYFAymXAQDeWUxXR0SmVarFYfLkyUhKSsKmTZtw+/ZtpKenK36IrBEaDBh/LZbXr7ai5YmInIHmehbcSorl5Vy/0BpvTTVscTAcqE1EZKhSLQ5fffUVFixYgGHDhtm6PuRC1CoJ7dsI7DmoXbaUKUmtkjDgfoGVG/XrkrpyAjgicm6aC2lQGyzn+QbVfGtqqEHgwBuARGRGpVocCgsL0alTJ1vXhVxQfKz2oj8iFBj3nGSxaf7eVm4I8Ncvr98OHDjKMQ5E5LyCS5V3+PP8Qmq8NVUKCdIvsMWBiMyoVODw7LPPYvHixbauC7mg4jvzCPr7WddyoMkSyM7RL3OANBE5O1WB8g5/vl9IjWc1cjfI5CTY4kBEZlSqq1J+fj7mzJmDjRs34p577oGnp6di+7Rp02xSOar9dBOQu7tbV54DpImo1jG6UH/8mVCoajirkXuovnlDyswEiooAo+92IqJKBQ5HjhxBq1atAADHjh1TbJMk9jcn6+kCBw8rA4dQE033HCBNRE7NqGuQKja0xqvgFmZ0zIwMIDy8xutBRI6tUoHDli1bbF0PclHFdxKJWNvioFZJSGgicPSUdtnSgGoiIodnPKYg2A53QkKMJp1LS2PgQERlVHoeByJbqGhXJQCIryfh6CmB2BjgsYEMGojIyRkEDkX+gfD0sMNXs6nAgYjISKU+nXr06FFul6TNmzdXukLkWorvzB1obVclAPC886718mQqViKqBQwu0ktUwbDLyAIGDkRkhUoFDrrxDTpFRUVITk7GsWPHMHz4cFvUi1xESbE2G1KFAoc736rFxeWXIyJyCgYX6cXqkHIKVp+jN4ORYLB8/nAq4vvbpSpE5MAqFThMnz7d5PpJkyYhOzu7ShUi11Jcia5KuhaHIgYORFQbGLY4BNV84KDJElh+IEQROJzcm4aQLMFWXSJSqNQ8DuY89dRT+Pbbb225S6rlKjPGQdf9t6jI9vUhIqpxBoGDsEPgkJoOFHj6o9hd30nKNycNaZzOgYiM2DRw2L17N3x8fGy5S6rlKpqOFdB3VWKLAxHVCorAIajGDx8aDEhuEvJ89UGLb14art7gxJpEpFSprkoPP/ywYlkIgevXr2P//v147733bFIxcg36rkrWN4fruipxjAMR1QqGgUNozc/hoFZJSOoqkOcfClX2DQCAX04qft0OJDRldyUi0qtU4KBWqxXLbm5uaNKkCT744AMkJSXZpGLkGoor0+LAMQ5EVFvk5Wl/dOwQOABATKSEXD+DFofcNAgBpKUDapVdqkREDqhSgcP8+fNtXQ9yUfIYhwq8Exk4EFGtYZT21C3UPlmVQoOBa376ied889IhSUCIHeaiIyLHVaVZZg4cOICTJ09CkiQ0b94crVu3tlW9yEVUZoyDbnB0aSlQUiIq1M2JiMihpKYqFt3C7dPioFZJKGoUChzXLvvmpmFwEifYJCKlSgUON2/exOOPP46tW7ciKCgIQghoNBr06NEDS5YsQTinqScryWMcKjBM39PgXVtUXLGMTEREDsWoxcE9zD4tDgAQ1kgftPjnpyExgUEDESlVKqvS2LFjkZmZiePHjyMtLQ3p6ek4duwYMjMz8corr9i6jlSLVaarkodBWQ6QJiKnZtTi4BFpnxYHAIrZo71zM/Qf0EREd1SqxWHt2rXYuHEjmjVrJq9r3rw5vvzySw6OpgqpTFclSZLg6SFQVMxxDkTk5IxaHHL9Q+Btp6oYBg6SEBBpaZDYg4CIDFSqxaG0tBSenp5l1nt6eqK0tLTKlSLXUZmsSgAngSOiWsKoxeGLpcE4cNRO8ycYZXRKO5dqpiARuapKBQ49e/bEq6++imvXrsnrrl69itdffx29evWyWeWo9iuuxMzRADMrEVHtUJCivzjP9wlEiZsnlq8X0GTZIXgwChyWLky1XxBDRA6pUoHDrFmzkJWVhbi4ODRs2BCNGjVCfHw8srKyMHPmTFvXkWqxksoGDncavDjGgYicWcF1feCgm7lZN39CTcvyVuZe9clJtV8QQ0QOqVJjHGJjY3Hw4EFs2LABf/31F4QQaN68Oe6//35b149qucqMcQAMuioxcCAiJ+abq48Qcv21d/ztNX9CmnsoDOd688tN5SRwRKRQoRaHzZs3o3nz5sjMzAQAPPDAAxg7dixeeeUVtGvXDnfffTd27NhRLRWl2oldlYjIleVfM2xxCIYE2G3+hKB4ZVclv5w0TgJHRAoVChxmzJiB0aNHIzAwsMw2tVqN559/HtOmTbNZ5aj2q3RXJQYOROTkNFkCeVf1gUOufygEgEZx9qmPOtIfpd4+8rJfbiongSMihQoFDocPH0afPn3Mbk9KSsKBAweqXClyHZXtqqSTyb63NqPJEjh3if2ZiWpKajrgm2MQOPhp7/jbY3yDjluYvtWhoYqTwBGRUoXGONy4ccNkGlZ5Zx4euHXrVpUrRa6jMulYDxwVOH9Z+3jdNsDXR/DLrYoOHBVYvl5ACG3/6sFJ4GtKVM1CgwT8cg26KvmF2r9rUGgocPUqAMArK81CYSJyNRVqcahTpw6OHj1qdvuRI0cQHR1d5UqRaxBCVLirkiZLe4FriFk/rGPYomD8WBc0ANqMLnxNiaqfGtlwL9X3t8wNCLN/1yCDlKzuGZzHgYiUKtTi0K9fP7z//vvo27cvfHx8FNvy8vIwceJEDBgwwKYVpNqrxGCuQGsDh9R0yBe4Osz6Ydmfh0qxalPZ9ZIEdErka0pkF7dvKxZ7DAiFn71b+gxmj/Zg4EBERioUOPzrX//C0qVL0bhxY7z88sto0qQJJEnCyZMn8eWXX6KkpATvvvtuddWVahldawNgfVel0GDtxa7hha7dm/YdnCZLmAwaAO3ruHN/2fV8TYlqgNGs0X51w+xUEQMGLQ6emQwciEipQoFDZGQkdu3ahRdffBETJkyAuHP1JkkSevfujf/+97+IjIyslopS7WM4eVt2rkCw2vKdNrVKwuAkYPk6AV3sYPemfQelyRJITQdyci13OYoIBW7euUbQjnHga0pU7YwCB+OZm+3CoA5eDByIyEiFZ46uX78+1qxZg9u3b+PPP//Enj17cPv2baxZswZxcXE2r2BcXBwkSSrz89JLLwEARowYUWZbhw4dFPsoKCjA2LFjERYWBn9/fwwaNAhXrlyxeV2pYg4d11/QTv9GO0DXGokJEvr20D6uF8NBvKYcOCowdY7A/J8Ffl5VfllJAoLV+uXXnuVrSlQjHDFwCNO3engU5gG5uXasDBE5mgoHDjrBwcFo164d7r33XgQHV1+fhn379uH69evyz4YNGwAA//jHP+Qyffr0UZRZs2aNYh+vvfYafvvtNyxZsgR//PEHsrOzMWDAAJQY9pWhGqXJEli/Xb9c0QG5YSHaC9vCouqonfPSZAkc/atUMdjZkqSugGHRoiIwLStRTTAOHMIcq6sSgLJ1JCKXVqGuSvYQHh6uWP7kk0/QsGFDdOvWTV7n7e2NqKgok8/XaDSYN28efvjhB9x///0AgIULFyI2NhYbN25E7969q6/yZFZVBzn73hmbn5tv+7o5K8OUquV5fBCwZIV+ef12INhgTscvFwACgmlZiaqbweDoUg9PuAUE2LEydxgHL7dvA7Gx9qkLETmcSrc42ENhYSEWLlyIkSNHQpL0FzNbt25FREQEGjdujNGjR+PmzZvytgMHDqCoqAhJSUnyupiYGLRo0QK7du0ye6yCggJkZmYqfsh2QoMB48vRigzI9fPV/s7Ls2m1nJZxStXyHDDKqCwEkKYxWDZYz7SsRNXI4G5+SVCo9kPQ3owDB7Y4EJEBpwocli1bhoyMDIwYMUJe17dvXyxatAibN2/G1KlTsW/fPvTs2RMFBQUAgJSUFHh5eZXpThUZGYmUlBSzx5oyZQrUarX8E8s7LjalVkno0Ea/XNEBubrAoagYKCriha2pFhxzTp+3fr+6ViAiqgaGgUOwA4xvAMp2VTJKGUtErs3huyoZmjdvHvr27YuYmBh53WOPPSY/btGiBdq2bYv69etj9erVePjhh83uSwihaLUwNmHCBLzxxhvycmZmJoMHG6tXR8LugwKRYcCwRyqWxcfbC3BzA0pLtd2V1OYnNHcJptLU2kpIsD5DU2gwmG2JyFYMLspL1A4SOJjqqkREdIfTBA4XL17Exo0bsXTp0nLLRUdHo379+jh9+jQAICoqCoWFhUhPT1e0Oty8eROdOnUyux9vb294e3vbpvJkUkGh9rc6sOIXo5IkwdtLIC8fuHlbuPzFrFol4YH7lAPObWXjHwKHT2iDEo57ILKd3Cu34Xfn8YWCMOQfFfb/31KrIdzdIemSh7CrEhEZcJquSvPnz0dERAT69+9fbrnU1FRcvnwZ0dHRAIDExER4enrK2ZgA4Pr16zh27Fi5gQNVP13g4O1V8eceOKoNGgDg+/8Bf+wrLf8JLiAo0HKZykg+rm/J4LgHItvQZAkUpejv5uf4hznG/5YkKborld66ZcfKEJGjcYrAobS0FPPnz8fw4cPh4aFvJMnOzsb48eOxe/duXLhwAVu3bsXAgQMRFhaGhx56CACgVqsxatQojBs3Dps2bcKhQ4fw1FNPISEhQc6yRPZR2cBBNxDY0Lptrh08HDhqeb4GW+G4B6KqS00T8MvRBw65/mGO879l0F2p8Bq7KhGRnlN0Vdq4cSMuXbqEkSNHKta7u7vj6NGj+P7775GRkYHo6Gj06NEDP/30E1QqfU7P6dOnw8PDA0OGDEFeXh569eqFBQsWwN3dvaZPhQwUFmov/r0qOD7B3EDg9duBhKau123JVCBlip8vkGuDLFQVyX5FRKaFeuXAs7hAXs71D3OY/61s3zDovkGvHE+FxhG6UBGRQ3CKwCEpKQnCxJWir68v1q1bZ/H5Pj4+mDlzJmbOnFkd1aNKklscvCv2hRRq5ou1IvNA1CbWZlSySdCAimW/IiLT1AXKsQO5/qEO8b+lyRK4WhCC5neW/XJS8f16gUZxTIxARE7SVYlqp8p2VVKrJPTuVna9o9ytq2m6jEo1oVULoFEcZ5YmqjKjbEV9Hw13iLv6qena8RY6/tm3HKcLFRHZnVO0OFDtVJXB0V3auQEoxbpt2mVXvhOuVkkY9IDA8vX6dfc0A46crNh+JOgnfzMnQwNMnaOfaK5zW4GOia75uhNViVHg4F8/wk4VUQoNBq4YBA5+ObchQSAkmP/jRMQWB7KjwjuBg1clAgdAGzw0jtc+7tHJtVOENm2oP/fXRwH/6O+GB3tL5bZESBLQKVG/7G7FbYTzl5Xdonbu1wYSB46y9YGoQoznRzCeP8FO1CoJ8Ynh8rJncT4eui+XNweICABbHMiO5BaHKkze5n8nCbqHh2t+qekmZkvL0F64BwYAIcHa+wGJCRIaxWm7GIQEA2cuaFOp6udjkHDjlv6Cv7i4cnXQpWhlH2iiCnDQwAEAYu9Rtn60jkwF4GKDx4jIJAYOZDf6wdGV34cuO29RJS96ndmBo0IOBHQys7Xrda0vapUkDxZPTIAikACg6N5UFa46MJ2o0gxnjfbygbufXzmFa5hxEHPrFhAXZ5eqEJFjYVclshvdBG5FRZXv5uKpCxyqsA9npEvBaiqbUnmTSKlVEuLracckWJuNyRquOjCdqNIMJlYrUofWXIYDa4SHK5c5CRwR3cHAgeziwFEhpwf9YSkq3Ufe8043p8p2s3FW5V30W5sBxZbZmDonspsSUYUYXIwXB4eXU9AOjAMH425VROSyGDhQjTOesEzXR74y6T11YxtcrauSubksAOvv/qtVEgYn6QdQSxJMprm1RodEBg1EFWIQOJSEOFjgYKqrEhEROMaB7MDU3fLK9pH3ctExDmqVhCYNBE6dK7ut+V3W3/03HkCtVklYt620UvUhogow7KoU7DgDowEAfn4o8fGDe36udpmBAxHdwRYHqnGmushUto+8PDi6qOr1cjZ1okxfrJ84jQq13hiOe7CkczvgmSFW75qIzChO0V+M/50d4XApjYsMu0+xqxIR3cHAgWqcWiUhqat+WZcatDJ3rXVjHFytxQEACgpNX2hUZZZXSwHHrv2Aj4ksWEJou5pxRmkiyzRphfDQpMnLOf7hle6uWV1KDFtB2OJARHewqxLZRfO7JKzbJuDhDrz2bOVnHtZlVXKlwdG6uRuysk1vr0qGo1QLAYcQQG5u2fV/Jgus2Qx5joikrgIxkRJC73R/0tVZt0zkyg5svY2eBss5AWEOl9JYMe6CgQMR3cHAgeyiuET729OzaheSni7WVcnU3A0JTYFjp6CY2K2yr6muG5m5jE2SBISHSQCUBXRBA6D9vW4bAAhIEtD8LoETf2ufoa2fa8/yTa5NkyVwYuctReCQ6x/ucCmNRbjBJHAMHIjoDgYOZBe6FgIP96rtx5W6Kpmbu6FOFNC7m6QY4FxZ2kxLUBxHFyYYBiUShCJ0KC817PG/lcvL1nGWaXJdqemAX47yQjzHP9zxUhobZla6edN+9SAih8LAgeyi5E6Lg3sVAwdXmjna3NwN5y8DndtKNuviYJxpCUCZoERyA0TFky/J9hwQ6N3dgS6SiGpIaDAQYBw4BIQ7XkrjSIMWh8xMaG7nQx3mY7/6EJFD4OBosgtdVyWPKoauni4SOGiyBHJyTd/W//tsxbIoWcMw05KprEtuVbzG2XnA9nUmcgZqlYQ2kcosRd0HRThWawMAtwjl3BJzv7jpcJmfiKjmscWB7ELX4lDlrkouMDja1LgGQwKONajSGo42EJSoJoUU6rv+CA8PtOrkQIMb7jibF45WBst+WTexfH1ddjMkcnFscSC7KLZV4KAb41BLB0ebG9dgyNEGVVrDGetMZCv5V/RdlaSIiLIT29iZJktgz9UIxTr/nFtVSvVMRLUDAweyC10LgbuNuiqVlAIlpbWvGd3cuAZDSV1r/g6gpTpZUpXMT0TO7MBRgfTT+haH3MCIckrbR2q6dsC2If/smwz4iYiBA9mHrVocDMdI1MbuSqZm2TbWrmXNXoBrsgRKKjEwOiZS+zuhKdOxkmvStSAGZOsDh2ul4Q433ic0GMgNMA4cbtnlJgURORYGDmQXts6qBACp6Y715WsLapWEgfeXXyY3v2bP29IkceYEq7W/CwttVxciZ6JrQfQ3CByyAyIcrvuPWiWh38AAFHj5y+saB9xCl3a8ZCBydfwUILuw1TwOh47pH3+1ELUy60eTBuXf4Zs+t2bP25pWEFO8vbS/c3K1d17PXRIOd6eVqDrp/ncMA4ecgAiH7P6TmCDBM1rf6hBZyrkciIiBA9lJiQ3Sseqa/XWE0E5cVtsuRjVZ5W+v6fPWThInycGDJAG9u1kOJqLvdFVKzQCmzhGY/7PA518L/LGvChNCEDkRtUrCg90L4ZuvkdfVb+N4qVh13AzncuAkcEQEpmMlO7HFGAdTA4drY5rP6zctBwQ1fd7Gk8SpVRJ8fYTJGad1YqO1a/Lylftatw3ILyhFg3oSQk3MfK3JEkhNh8ltRM7GeA6HuvdE2qkmVojQBw5utxk4EBEDB7KT4mLtJWVVxjjomv2NgwdHbPavrANHBVZutFzOHtlOtJPD6ZdNzTg9bY6ALtlVSJD5fW3bA2zboy3Yu5tAQlMJqenAtRsC67dr/8aSBAxO4sBqcnLGd+4jHThwMKib++0bdqwIETkKBg5kF7ZocdB2mQGWrVNGDmcuAIkJld+vozDuiqXj5wPc1x5GF9SOkd7UOJiQJCE3O5w4bd0+1m0D1m0re966LlmcgIqc2g2jC/AIx0vHKjMIHDxTb+g/cIjIZTFwILuwVValRnFl19WWi0tzczj4+gJd2rkhoalQdBVydKaCoIqqjV3RyMUYtzg4SeDgVlQIZGYCarUdK0RE9sbAgezCVvM4mEoNWlsuLs11xQrw0/42vrvv6Ko6aZzO2UsCAlUf82Bp7ATHVlC1MA4cwsNNl3MExt2obtxg4EDk4hg4kF3osypV7YLM1MV1bZndVK2S0K29wNY9yvWBThQsGDIVBFWGbjxEVcY8HDiqH8htaj8Hjgq5CxzHVpBN+fsDTZpoL8JLSrRNiI7KVODQuLF96kJEDoHpWMkubNXioEsNquNI/f2r6sDRskEDAPj61HxdKs3gz5DU1ba7FkI7vqWiaWh1Y0d0QYxxOltNlsByg3EztSHNL+fNcCBjxgB//QWkpwOpqfauTflMBQ5E5NIYOJBd6CaAq+oYB0B7JzgyTPv4wd61486wuYHRAODpRO2EpQZTNKzfDtwVZ/tj7DlQsYvh8tL4ytthfntl2evifd+RUkz9WjtvxtQ5olZOkuhMFO8DT097V6d8RuMv8i6m2KkiROQonOgShGqTYhtMAGfI/06/f3c35w8aAPMDowGgqLhm61JZmiyhOAchtBmvbO2P/UCHRO2BrBmTEGqmG9vVGwLxd+aSMFbV7m+WukaZYosxFposgRXr9cvMTGVflXkf2FVwMErdPeBWov3Q2bPuJgKThGPXmYiqFQMHsgtbZVXS0XXfyS+wzf7szdzAaADYmwzERDr+l7fJgevVdKxlawXOXjI934PxBbhaJSEsROB2mnIf67YBCU0F1CoJdaIErt65uVrV7m/mukaVd/FelQtMw/O9dNV0WtvakDzA2VTmfWBvmhwJkn8EAjOvAQD8s244fJ2JqHoxcCC70HVVquoYBx0fb+3vvFoQOOgu/JK6ai9mTXGGL29zA9fNtaRUZfD0mYv6x7oLssgwgWOngF37tQGL4QV4YaHp/Vy+KqBuKsHLoAfJc0OButGmgxBrVHSG84peYBrW6cwFKJ5rSm1JHuBsnHGm+9R0wEcVicDMa9Co6yDHP9zh60xE1YuBA9lFiY27KukCh/x8AcWIXCdjfKfZHGf48tZN0Gd4Pi2aAEf/0peRYHhRL2HdNoG8/KofWwjg60Vl1y1fL9CwvkB2jpnn3XnNDVtLPO9k/qpsK0BFuj5psgSOnSp74W/49zYMFI7+JbB+W8VacmpL8gBn44wZ4EKDgTX3v4OMwLq4FttO/lDSdesjItfDwIHswlZZlXR8fLSXoI7eVam8O9am7jQD+gDCmS44dBITJDSK0170enoKzFms3C4ADBkI1IuRcOYCFEGDjw+Qb4MgQnE8AWz/Eyg1caUtAQgOBH7fUorMbP36wycE2reB1a0AprpGNWkgcOrcneOY6fpkGJiYEhJsuYw1jCdN5HwVNUOtktC7m8DardplZ8gAp1ZJiH3pYZwwavlcv13frY+IXAsDB7ILW2ZVAgBfXYuDAwcOlu5YmxsQLQcQUN6dd5Yvbd1EdecuAcLECQb4as/DOIuUcdCgO39Ae/Fb2YHW+w6bXt+8cdlWCkA7+Do60nQrwPFTAnc30V9wm/sbR0dADhxeeAqIiSw/aDTl6F8C67dXfS4Mw5Yqpxus6+QMA1JbTYhY3bTvVWVlnaHFk4iqBwMHsgubtzjcCRyyc4Bzl0SF755qsoQ8kLReHdtflFvTb91cth8dw7vzzhI0GCqvq0Z5WaR0jM9/8bJSnDxTsTq0vQfYf6Tsel8f4PjfZo4rtEGLqTEYv28F1m4TGJykDWbM/Y0NA1o3g8xfuvfdzduWz9/ceJeKMGypcsbBus5MkyWwa79ynTO83s7YxYqIqg8DB7KLoiLt7zwbjUnQBQ4XrwLzf67YrMKGswRrCfTuJtClne2mObFmYKRaJaFhfYGzF8s+XyfA1zmDBsD0mAd9y4l1t19156/JEhUOGgDTQQOAcsdVSACC1EDHRJS58AP0F9z9e5r/G+ca7D8nV/u77Puu+nVM1F+kOuNgXWdmMsuYE7zeapWEvt0F1mzRLjtbiycR2RYDB6pxB44KZN+5eFq0DBhsg7zgJUad1q29e6rJMn3xpr27W2qz4MHSAFldP/PgIABmAofacJfPcMxDiEGrkK7/d3l31Q3P39RFWGVYk8kpKgKYs7j8ckIAqzaZ3padK5CZpV/OyTX/vqtuHu7aY6tV1TNfBZnnzK93+zYS1mzRvl9feBKIiWLQQOSqGDhQjTKeEdlW3SP+MnH32Zq7eeVdgNpyAKBaJSE+VuD8Ze2y4V07awe8JnV17C4N1tKNeTCmDdJKFf34zY3rKG+eC2vd3RhofTew8Lfyy12/WfljAMDPq5TLObnA7grOdm0r2/8EduzVdq0yhXeSq49aJSE6QsjvJ2e6c+8mSfD3FcjJA9zcHb++RFR9GDhQjaqO7hGaLIFDx8uut+ZuXnnjCszVS9c64OUpUFgkWRxPoSsf4K9f99JwIDJMMjsoNixE+Vr17gabdp1yVF3auSGhqZBbJICyrROAvttTVe7aR0dIkNzMPz/AH2bTtlbFqfMCZy/Yfr/GzAVWQmhfN1PpfiPDBDRZ1s3ATRXn56v93bU9cG8r5wgadPx8gZw8IDfP3jUhInti4EA1qjoG2plrNbj7LsvPVaskPNjb9AWoJGlTiJ67pA8wdh8Q2HXAsP7aB+bGRJhrTfBwN9/PHABupwHPPwkUFUllLpprO+MWCXMBpa7b054DAjsPWN/6EBIEpGVoB0RHhpkfX2E4CZwt1UTQAGgvThvWk5CdK8q0egCmXy9tVintBmZZsj3dIPlYJ0xw4OcHII2BA5GrY+BANUqtktCjo8DmXdplWzTXm+u2cuxv4PhpYfHip9XdwLJ1Zde3bKa8kCqP8ZgIXbac5etNX6Dl5mnrXV6LR1GRxEmWLFCrJPTuLqFDoraVoqBIYJGFrkdpGdrf128K3NvKDeb+vrpyzmr7n0C7lkBIsPWDzw3pWiYiwwTqRtf+1q6aUHBnxnJvL/vWozJ0rSUMHIhcG78NqMbFx2ovhgMDgHHPSVW+o6nttiKZ7Hqhu/g5+lcpNFmmL5503VHc3IC+3bWPg9VA8omK1WP9dm3AcOCowNQ52ru85u6C30oTOHdJICtbOw+AKc4waNJRqFXaIMvL0/r30oGjwB/7SquxVval62pXVXMWaVvOqOp0LQ66LHDOxP9O4HDlujD7WUpEtR9bHKjG6VJTqgNt1wVH121l9g9CTndp6OdVgCSZbn3QTcqk8gfy7nyxp2sqXgchgMvltDIY+m0tYOku8JkLQGJCxevhysy1PjVuAPx9TrlOCGC9DeZGsLU6UcDVlIo9p1sHbQuDuTkyqkLAOeYbcHSaLIG8O3frnbHFQXMnM9ih40DyCcstuURUO7HFgWqcrqnbz8e2+1WrJAQFmt+uy+BkeLdMkyVw9C/tsrc3sG1P1eqQk2e7GWGN60qWqVUSWjZXrmt1N1AvxnR5R3x1r90AurWHyRY0cxrWU7a6GXYBtDSxoDVs1XrhqnStkCV3GrhOnXXEd555miyB0+f1y6Y+S4nINbDFgWqcHDj42n7fhpmLTDHMlGQ8AdfN21U/vrlc/pXhDJNDORpNlsBhoy5mh08AA+8vW7ZTIrD7YNlAT9diIUna7mslJdVX3wG9yr5nhAAa1pfQrpX273/1hnXzW8TXMz9HRue2AjtNTF4HAA3qAeculV9PZ5lvwBGZypz2+1ageWPbpHquCc46eR0R2R4DB6pxuXnab1BfG7c4AGX7DpvL4FTRCbiefgT4/n82qqSVeLFWcebS/RYVly27+yDQsrk2sDCcyVp38e3pKe4Mjrcd42HK5gJN3YW/WqUNCBKaCly+KiAkQJMJea4L4+QC5ubI6JgoYdcB03OFnLukDZBKyxnu4SzzDTii2jBDd2hw2fcuP5+IXJNDd1WaNGkSJElS/ERFRcnbhRCYNGkSYmJi4Ovri+7du+P4cWVC/4KCAowdOxZhYWHw9/fHoEGDcOXKlZo+FTKQd2eMg5+f7S9EfI0ChwG9lMu6C6CK9vvOy6+ZiybdUZxpcihHohvjYEiSoJi5WUcIbdDw3FBg5BBJHqivG2hdWGT7197aUPXMBeWyWiWhRVM3JDRxQ5d2bhj3nKSosyXlJRAAyg8aAO2cJeyWUjnOPGO0jloloVcX/TI/n4hcl0MHDgBw99134/r16/LP0aNH5W2ffvoppk2bhlmzZmHfvn2IiorCAw88gKws/VXCa6+9ht9++w1LlizBH3/8gezsbAwYMAAl1dn/gMqlG2QnVUMPc+MWh4b19V9sDerrB/Ndu2H62JIENGlQdv3p8zVz0SQADBlom2xTrsj4AlmStDNu7zpgurwQ+rS3xhdBpoIQQ5KknZhv5BAJ45+XEKK20UnAcv9xXXBTkQu3xARtoKHLHFYRP68Cps4RzK5UCdoZo5XrnPGiu1NbfX3HPM2B0USuyuG7Knl4eChaGXSEEJgxYwbeffddPPzwwwCA7777DpGRkVi8eDGef/55aDQazJs3Dz/88APuv1/byXnhwoWIjY3Fxo0b0bt37xo9F9KOKzh7Uft4ww7A30/Y9AvI20vZoH4zVb9N3LmrqskSWL/d9POTuprOtFPR1KxVEeDrfBcVjkSXYUvX11/bVcR8oGjuzq9udmrD/umd2wEtGpuemC8nT3mMbh2A7XvKtjKYm9HZUHV1ZVGrJNzdBFi7zXS3JUt1YnalyjHOotQozi7VqBJPDwn+vuJOAgj+/YlclcO3OJw+fRoxMTGIj4/H448/jnPntDkVz58/j5SUFCQlJcllvb290a1bN+zapZ1d7MCBAygqKlKUiYmJQYsWLeQy5hQUFCAzM1PxQ1WjGyRoyNaZOS5dU+5r5z7DDErAuUvaidlMXTT16gLERFasHaQimW+s3Z8zdWFwVIZ35M21HFjT3UJ3l17XqtCnmxvqRruVuduvyRLy5F462/8EkrpB0frxYO+yLSK9u5nuXlVd7wNTrTLhIdY9l9mVKsc4vbOztt6o72StO3WOXdeIXJVDtzi0b98e33//PRo3bowbN27go48+QqdOnXD8+HGkpGgTnUdGRiqeExkZiYsXtbe0U1JS4OXlheDg4DJldM83Z8qUKZg8ebINz4Z2mxicacs7q5osgZNnlOsuXtU/TssA5v9s/ssuPhYICrTujjAAdGkLhIVKZTKmVBb7DVcPcy0HHdtY91qbG3BsyFzWmTqREsY9VzbTkXH2I18ffeadmngfGLfKrNkscCtNu6289z8D24orKRFy90wdZ229Kb3zvtj0B7B5J+dyIHJFDh049O3bV36ckJCAjh07omHDhvjuu+/QoUMHAIBkdKtOCFFmnTFrykyYMAFvvPGGvJyZmYnY2NiKngLdockynQ7SlhciVZ3oKipcgreXhKSu5ae/1OmQqL24axQH/LBU4MYt0+W6tQe271VmwQEqfyFLFWd8oWzr19nUxHO697apwMN4XXXXzxTDOnh46Cv+0nAgN1dCTp7ATyv15XXjRbT/Z86TStTedu43HYU5W2YlTZZAyk39srMGP0RUNQ4dOBjz9/dHQkICTp8+jQcffBCAtlUhOjpaLnPz5k25FSIqKgqFhYVIT09XtDrcvHkTnTp1KvdY3t7e8Pb2LrcMWc/cRX3nRNt96ZibNdhaXp7a3zGRxokHy3qwtzIFZt0o84GDYU7+8u46U/WypuWgKvs2bNWoTKtBddbPEsP/mfBQCW5hElJuAYb/B/c01aWB1a7r3FagYyKD3fJosgQ27jC9zdlabziXAxEBTjDGwVBBQQFOnjyJ6OhoxMfHIyoqChs2bJC3FxYWYtu2bXJQkJiYCE9PT0WZ69ev49ixYxYDB7Itc/3MOyTa7qJD13e7snStUJay6Tw2oGzzvMbMEBjDu87G/eIrkxmHHJfheAhny4plmI41K1v723ielcMnlQHGzv3O21e/pqSmm74F4YzdEmtDWlkiqjqHDhzGjx+Pbdu24fz58/jzzz/x6KOPIjMzE8OHD4ckSXjttdfw8ccf47fffsOxY8cwYsQI+Pn5YejQoQAAtVqNUaNGYdy4cdi0aRMOHTqEp556CgkJCXKWJaoZxhf1uoGitv7iTEyQ8PyTlXuubrBfeQGIJAGxdYyChiyBMxdNl3W2iwOqGmcNBtMy9I91wcBfZywHBLruKhwoa5qpi21AO3eIMwWWwJ25HDrrl/n5RuSaHLqr0pUrV/DEE0/g9u3bCA8PR4cOHbBnzx7Ur18fAPDWW28hLy8PY8aMQXp6Otq3b4/169dDpdK3m06fPh0eHh4YMmQI8vLy0KtXLyxYsADu7u72Oi2X1boFsGyd9vELT+m6BNmeduKuil/ITJ2jH+yn63O+54DAzgOmZ+nVMdcNa0h/oEVTfqmSY9NkCVw36ru+bJ2wOmMYu6uYp1ZJaNFY4Njf2mXdZ0jdaOf8XOhyr4RNO7WfrU8+CDRp6JznQUSV59CBw5IlS8rdLkkSJk2ahEmTJpkt4+Pjg5kzZ2LmzJk2rh1VVKFBusrwkOr7wqnsWAfjwX5qlYTe3SV0SBTljkUwNzDWuGWCyBGZC3yt/f9hd5Xyhd5JddusIdD/fue+Q3/YYD6bRb8Bg3vbdh4eInJ8Dt1ViWqXggLtb3c3wKMaQ1bjPPUA0KE1YM3Xm6k89Za6n5jKi88mfHIW5c1zYQnf65bl3/nciwh37iQIxvPwCLCbGpErcugWB6pddF+g3t5l0+jamqn0llERQpECtVsH7SRdplJo2uJ4RM7AXEYoABbnKBn3HIMGS/Lytb99vZ37ddLOwK5cx25qRK6HgQPVGF3g4FNDWW6tyZUfrLbdxFv2TKdJVBXmAt9GccDhEwIbzKQUZdBgmfy551N+OUdX3lwlROQ6GDhQjanpwMEUR5h4i8gRmZuorqDQfJNDSYmAu3vZLGOp6doLTVf+f9K9Dtm52mV7fu7ZgqkZ2NlNjcj1MHCgGpN/Z3C0o32BsqWAyDRNlsCOvea35xcA/n765QNHjVvwnC/tqC0Yvg46xvNiOKPEBAm+vgI/LgMCA1zzb0vk6jg4mmpMgcEYByJyfKb6tRvad0S/UTd4VlfeVed4MH4ddIqLa8frUCdCGyxk5wKlFU1dR0ROj4ED1Zi8O4GDLwMHIqdgaRb1zTv1EyeWN3jWlZgLtnSDpJ1dQID2PVFaCuTk2Ls2RFTTGDhQjcnU3Xlk6zaRUzCV2tiQEMDlq9r/a1OzJLvi4Flzwda1GzVfl+rg7ibB31f7+OoNtjgQuRoGDlQjDhwV2JusfXzwqHaZiBxfYoKEcc9JeGyA6Qvin1dr/5/VKgltEpTbkrq63gBptUpCl3Zl1+86APyxr7TmK2RjB44KecD3ot/4WU7kahg4ULUznjgIcM2+z0TOSq2S0KKpmzy/gyHDsQzR4crt67e75oVlsNr0+vXb4dSfe/wsJyIGDlTtdu4vO1DQFfs+Ezm7RnGm1+v+nzMyRZn1rnhh6e1ler2zf+5xHAsRMXCgaqXJEth9oOx6V+z7TOTsUs1cIOr+nzMyy25zxQtLNzfT3bOc/XPP1PgNZz8nIqoYBg5UrXYfMH2nsXOi6/V9JnJ25gb+6sYyuJn4RnHFC0tdBqXwEP3rVdWZ6R2BPFjeYJ2znxMRVQwngKNqo8kS2Lnf9LYOifyiIXI2pmYPDg4CurTTRgylRmN/a8PFcmXoAoe60cDwf0i1amb6xAQJJSUCKzcCMVGcBI7I1TBwoGpjrltDhza14wuUyBUlJkhoFAccPSmwbjvgafAtkl+gLHtvK/PjImqzvHxtVOXrUztnpg8PlQAIeVJPInId7KpE1cZUXncA6NKOQQORM1OrJDSK1/4f5+Tq1+ffudOuGxz85yFg6hzhcpmVdAGUj0/t/Kzz99P+zs2zbz2I/r+9ew+OqszzP/45gSSQ0LSBSC5AIBokSAJqwkCAAhndxAsj6MzKzo+dlXIuiwKDg1vl6NaOulUr1u6yWzO1Aw5qoY7MD5mfiszqGEAhiA4CAYYAiiB3hpiRSxIuJjH9/P5ou5NOutOdW19Ov19VqaTPefr00093znm+57kh/Agc0C2nz7r04S6XTp9tPz+502Epd7jvtj4JtDYAdtC68uhyuQMDz+rwDY0t6eJxZiVPhTqlX2Tz0Vs8C8Bd/Upqbo6fzxUAXZXQDa//0aW9B1oe3zTWpe/e6RuLpvRv8yRiBsAWPP/bxkjVfzVKTZGuXPGf1hjp0BGj9MHulkinw1JtvdG5Cy2P7aC23ujkGaMv/up+7DJGdjzp9W91Xv/ir0bZmfZ7jwD8I3BAl5w+6xs0SNLeA9LYG1xKSrS8lYHLbSoSzc3hyyOA3tP6/3/FbyWp4zvPf3jPncaypPE3Gv35oDugcA+gjv1BtpVVRuvKfcvgnfelpEQT8++trT37W/5+brU0q9R+7xGAfwQO6LTaeqPyCv/7Vr8peSoHs0qlS37uQFZWcZEBYpm/FYRDZYxv0OHpypQ3MnZbHk6fdWlduf99sf7e2mr72dvh8wMQOgIHdIq/u2r+eC4miX6+YVxkgNjmbwXh7vAsEhftsw/5617lPicGfk6svLdQdbR6tF3eI4DACBwQstp6o7dCCBo8jJEam/xv5yIDxC7PQnA9FTzEwiJxlVXGu36Fp0U1b6SCtrzEwnvrDH+fvd3eI4DAmFUJIftTpQnSizk0XGSA2OZ0WCqd1vXnD81o+TtaFomrrTc6etL/7E+e7jmeyrKnRfXkGdNh8GQpOt5bT/KuHt3qLXlWDgdgf7Q4ICTuaVe7f5xoqSQA6J7sDPciYF0xcrh05gv33w98T0ofJB09acI6w1LrbkdHjqtda0LrcVj+AgRjpKsdrGMwY5JUNN6e57qiQksXao0qtrsfb9gq9e/H2DUgHhA4IKhgfXg74yf/RxqWxcUFiHWBFniUpNumSu9tC7z/Yl3L30eOSS//v8CV9t7QuttRW57WhIx0o8YmS3/5IvBkEP/7nv/tluwbNEjuoGvrxy2PGSANxA+6KqFD3Zk9xZ+mJi4qgB04HZZml/n+P1uSZpdZunls6P/n23apXReg3lwsrm23I3+MkX6zWlq1NnDQIAVub7H7kmgdDZAGYG+0OKBDHc2eMmG8NH2Su4Kwc69Rxcf+03kwtgGwl6JCS3kjpVNnjIwl5WS33GWfXSa/FfTEvtJXDYGP2duTJ/T0jFCB2HkCCAZIA/GLwAEd6qg7QuHolkpCUlLwmUUY2wDYj9NhyZnf/v/aE1ScvyAlJhrt3i/t/LPU9LX0+YnAx2tbAe3MCtOhpO3pGaH8sXsl2j1AWj5Tc3N+B+IDgQM65HRYGn2d0aGjvttbXxhr6402ddCfWXLPusHAOSC+OB2WnA6ptl7atc9/TT1ziFRd0/K4dJq7VUAyQQctt7Zrn0vrN7i7CXWU1j0jlG8XpJvHSnsOtEvaJfFyk6So0NIXXxr9qVIaP4bzOxAvCBzQocoq/0FD6wtjKE3/G7ZKhfnG9hdTAO11dI5oHTT07SttqJD8Tfzc0QBc97iFwGnbzqC0YavvsfPzQgsccodLx051nOb+u6UCPy0wdpSe5p5Zy996PQDsicABPlpfYCX/ixu1nRkplKZ/Fn0D4leo3YO+/rrj/YHOI+f8DMr1pD1yPPhg6NNnO35dj+OnO95vWdLwofERNEhSaor79+Urkc0HgPAhcIhjbfsDt10ZdXKR/wt925mRPP1dO7o4273PL4DA/PWJ7yp/5xF/Y7Esyz224q0NwQOWD3aE9tr+juNZzSJeuii1NuCbwOESgQMQNwgc4lTbIKF0mtGGrb7TIn64K/SZM1oPhBzkd0Gl+LqgAvCVN7JnjvOnSqOSopbuSp4bIElJUmOjO43nnNPYJJlenkJp9h1S2kBLg8K4eF208LQ41NVLVZ+6lDOU8zxgdwQOcajtPObGKOBc5WPypIOH3X8HCwA8AyElqahQPoEEFxMgvvnrTtQVH+6SPtplNKvM/dhfS+fPfiSlOd1jG3p7BiVJys2Jz/Pb4ePugv26WVr7v5JkNLuMgdKAnRE4xKHOzGPuCRok6Wc/dt9ZC1XrQAJAfOtoaufOMnJ3ewoUFCQlus9TToeliTcZbd/Tc6/d1pvvuls14q2yXFtv9M777bezgjRgb6wcHYc8AxU7K6ErTwIA+V9pursC3QDZ2Wrq14EDQj/eqNyunRt7e7XraBSoBYkVpAF7o8UhDnV1oCKzIgHojrYrTct4urj0rPc/lPJGuNTYZHVqqtDEvtKjP7G8i9at/F1orbPxOGtcoBYkJsIA7I0WhzjVlWZ1LgYAusvpsFSQn6DC0QnKGWr5vcM/fZI0tbhrd/8ld0V+5Wpp1VqjLdtDf94n33TNzM2xNCwrQbNKW/JnWdLsMkv/ONc9k1Jr8VhZDtSCxEQYgL3R4hCnLta5OpV+oIM+qwB6lr9VnCVp68fuO/+Titx38s980T5NMG0bCgpGS/2TpV1VgVsRjHxbDtrOFuc+B1qaVWaYNU4t5bN6ndHZL6TbpzIwGrA7Aoc4VfPXzqWvq3cPhovHiyOA3pOd4VkJoYWn609ujnuChdwcS4X5RhV/Mtq5r2uvk5Qo3VOaoOklpsOuSGe+MD6zJPmb5MF/QBGfnA5LQzPcgUOzK37LAYgXdFWKUwM6MWDQY3tlfA3+A9D7/E3W4K/rj9Nh6Z7SBN1a0rXXGZDachxPV6TSae3TbdiqkAY6e44Tz0GDh2chuMuXuUYAdkfgEKeSkzp/sfuwMrQLKgCEyj1Zg+9YgkBdf2rrjSo6MWahta0fuxe+bM3d2uGLWYE6LzXFXY6sIA3YH12V4pS/mUbGjZGyhshnBenW4nHmEAC9L9SuP51Zg8aftmsMeFo7Wh8zHgc6d5enNefcBbq0AnZHi0Mcqq03Onaq/dU381pp6oQEPfoTS3NmMnMIgPAJpetPdxeRa9ua0JnWDgR2ptp9PfniS2nZStOuZQeAfdDiEGcqq1pmA5GkPglS8zcTLG3cKqX0d6+A6sy31NDEzCEAoofTYalseudnWPLwd/ODgc7dU1tv9OGulsfGsHo0YGcEDnGktt43aJBaggbJPa9J6xM+F1QA0WbqhARJLm+XSsuSSqdJ1ziktW8H7spkKfDND38zJyE0/rqP0a0VsC8ChzgSSv/gtid8LqgAos3UCQkqzDftbmq0bSX1BBTGknKyaTHtDYwTAeILgUMc8XeCb4sTPoBYwPoK0cE9TkRaV+6+sNCtFbA3BkfHEfcqrb7bBl0jBgYCsA3WVwi/okJLmenuv2eXsXo0YGe0OMSRXftc2tBmUOHo66QpEyzu0AEAumygQ6r+UjKGawhgZ1Hd4rB06VJNmDBBDodDQ4YM0ezZs3Xo0CGfNPPmzZNlWT4/kyZN8knT0NCgRYsWKT09Xampqbrnnnt0+vTpcL6ViDt91qW3NrgHQLdWf5k7dACA7vGs5XDpcmTzAaB3RXXgUFFRoQULFmj79u3auHGjvv76a5WWluryZd8z0x133KGzZ896f9555x2f/Y888ojefPNNrVmzRtu2bdOlS5c0c+ZMNTc3h/PtRExlldFvVvvft/9Q+9VUAQDojNQU9+9zF42OnjSqree6AthRVHdVevfdd30er1q1SkOGDFFlZaWmTWvprJ+cnKzMzEy/x6itrdWLL76o3/72t7r99tslSa+++qqGDx+uTZs2qayszO/zGhoa1NDQ4H1cV1fX3bcTEbX1Rm+Vd3wCZ85tAEB3DEi1JBnt2S/t2W++GTPHeAfAbqK6xaGt2tpaSdKgQYN8tm/ZskVDhgzRDTfcoB//+Meqqanx7qusrFRTU5NKS0u927Kzs1VQUKCPPvoo4GstXbpUTqfT+zN8+PAefjfhce5C++5JbbVdTRUAgM44c9b3SuNZCI6WB8BeYiZwMMZoyZIlmjp1qgoKCrzb77zzTq1evVrvv/++li1bpp07d+rb3/62t7WgurpaSUlJSkvznWM0IyND1dXVAV/v8ccfV21trffn1KlTvfPGetngEKZWZQpWAEBX1dYb7fu0/XZuSgH2E9VdlVpbuHCh9u3bp23btvlsnzNnjvfvgoICFRcXa8SIEXr77bd13333BTyeMUaWFbgJNTk5WcnJyd3PeIQdOR48zZQiuikBALrmXIDggJtSgP3ERIvDokWLtH79em3evFnDhg3rMG1WVpZGjBihw4cPS5IyMzPV2NioCxd8z2w1NTXKyMjotTxHg9p69yqqwUwqImgAAHSNZ3HRtkqncVMKsJuoDhyMMVq4cKHeeOMNvf/++8rNzQ36nHPnzunUqVPKysqSJBUVFSkxMVEbN270pjl79qz279+vyZMn91reo8G5C8FXiZ5dxjSsAICuc68ebfkED5OLpakTorqKAaALorqr0oIFC/S73/1Ob731lhwOh3dMgtPpVP/+/XXp0iU99dRT+u53v6usrCwdP35cTzzxhNLT03Xvvfd60/7whz/Uo48+qsGDB2vQoEH6p3/6JxUWFnpnWbIrz10gf8HDlAlSyS0EDQCA7isqtJQ3Unrh/xpdrJNGjeTaAthRVAcOK1askCTdeuutPttXrVqlefPmqU+fPqqqqtIrr7yiixcvKisrSzNmzNBrr70mh8PhTf/f//3f6tu3r+6//35dvXpVt912m1566SX16dMnnG8n7JwOS7eWGG1uNXkUAQMAoDc4HZbSB7kDh7pLkc4NgN5gGdNRZxZ41NXVyel0qra2VgMHDox0dkL2ly+MVvzWKKWf9PADBAwAgN7z5rsu7d4v3Vwg3TaFaw4QCzpTx6UDos25XO7fSUkMUgMA9K7LV9y/9+yXlq00qqzi3iRgJwQONucJHBL4pAEAvai23ujQ0ZbHxkjrylkEDrATqpM210zgAAAIg0DrOWyvJHAA7ILqpM3R4gAACIfBARZ7+7BStDoANkF10uY8gUMfPmkAQC9yOixNKW6/3RjpfIDWCACxheqkzdHiAAAIl4LR/rcnJtLiANgB1UmbY4wDACBcGpv8z97XFGA7gNhCddLmaHEAAITL4DTJahMjWJY0KMD4BwCxheqkzTHGAQAQLk6HpVmllk/wMGMy6wgBdkF10uZocQAAhFNRoaXSaS2P3/9Q2rbTFbkMAegxVCdtzhs4cLMHABAGtfVGG7b6biuvIHgA7IDAwea8g6P7RDYfAID4cO6CewrWtjZsZT0HINYRONgcLQ4AgHAKtBAc6zkAsY/AweZc39zcYYwDACAcnA5LZdPbb2d2JSD2UZ20OQZHAwDCbeqEhHbBw6xSi9mVgBhHddLmmpvdv5mOFQAQTlMnJGhysfvvwnz3bEsAYhvVSZujqxIAIFLS09zBQlNThDMCoEdQnbQ5uioBACIlpb/79+Wrkc0HgJ5BddLmXN80OdBVCQAQbqkp7t/1l6SjJw3TsQIxrm+kM4DeRYsDACBSUr9pcbhYJ61aa2RZ0qxSxjsAsYrqpM0ROAAAIqXZ5dvCYIz01gZaHoBYRXXS5poJHAAAEbLnQPttLAQHxC6qkzZHiwMAIBJq640+2uV/X2IiLQ5ALKI6aXMtgQP9SQEA4XOug1aF36yWKqsIHoBYQ+Bgc7Q4AAAiYXCaZHVwz4qxDkDsoTppc54xDkzHCgAIJ6fD0qzSwJEDYx2A2EN10uZocQAAREpRoaV/nBt4/6C08OUFQPdRnbQ5b+DAEAcAQAQMy0rQ7DL/F6Ejx8ObFwDdQ+Bgc57AoU+fyOYDABC/ArU8MM4BiC0EDjZHVyUAQDRobGrf6mCMdOoMgQMQK6hO2hxdlQAA0SDQLEtr32ZqViBWEDjYHCtHAwCiQaBZloyR1pXTZQmIBVQnbY6uSgCAaFFUaGnQNf73ba8kcACiHdVJm3N9cx4mcAAARFptvdH5i/73bdslWh2AKEd10uZocQAARItzQRZ8o9UBiG5UJ23OxcrRAIAoEWiAtMe2XVLVpy5aHoAoRXXS5hgcDQCIFoEGSLe29n+lZSsNMy0BUYjqpM3RVQkAEE2KCi3dP7PjNMy0BEQnqpM2R1clAEC0yRlqddhlyYMxD0B0oTppc7Q4AACiTShdliTpw0pmWgKiSd9IZwC9i8ABABCNigot5Y2UKv5ktHOf/zTGSOcvSJLRuQvuwdVORwhNFQB6BYGDzTE4GgAQrZwOSwX50s59gVsVznxhtOr37iDCsqRZpe6gA0D4UZ20OVocAADRLNgUreUV7qBBcv9+awODpoFIoTppcwQOAIBo5hnvEMpgaal19yUA4UZXJZtjViUAQLTzjHc4cMjoj1uCpz/4mdGgNPdK1EmJRo1NFuMfolhtPWNU7ILAweZocQAAxAKnw9LY0dIftwTvhrR9r7R9b+t07r+nFBsVjFZIgUSwymxvVHYjXYHu7ut35fmVVUZvbTAxN0altt7o5Bn39ypnqEXA8w0CB5sjcAAAxAqnw1LZdKPyiq49/8Nd7h9PIFE23WjqBPcF0FMRvHJVqv6rVFnVfsC1p2J89ITR1o/dRwlU2e1s4NG6Ai25g5ySIt8K6emzLp04I40YKg3Lan/hbluZldSuchsoX92twLd9fuk0o+wMy9vi07blx5PXt8o9n0bLwn55I9VhXjuj9eea0r99Jb+j1+iorNaV+wamxeOMrsvxH0SE8hp2aRmzjDHBQ3uorq5OTqdTtbW1GjhwYKSzExJjjH6xzP3xPvQDKTuD6AEAEP227XR1OXhoKz9PSkqU9n3ScbricS3BhD+3TXFXDNOc7nS79vk+N/PalsetAxNJGpcv7fvU/3GnFEslRZY2bTPae6Bl+403SHfNsLyVzrav6c/YG6SDn7VU1KdPkjLSpStXpbffa9nu8Y9zpQu17v2Su+Kd5mxpsZHcgcm5C9J7H3b82h6WJY2/UfrzwcBlWXCDuyw9wZnkLsPrctyv78lT6/wkJRpdqHWn9Ww7esKo4uP2xy+bLk2dkOAn2JGcDvexT/7F9zsxLl/KGSpd/Sr4ey2b7j6OJNXWSxu2trxXT5n72+cxfZJ0XU5LGUe6G1dn6rgEDiGKxcBh559dWr/R/bf77oIVE82DAADU1hudOmN0+SvpyHHp0yORzhFiyfUjpc+PRzoXneMJeMKtM3XcuLoFvXz5cuXm5qpfv34qKirSBx98EOks9ZraeqM/bGp5zBR2AIBY4l7jIUETb0rQ3NkJKh4X6RwhlsRa0CC5px7etM0V6Wx0KG7GOLz22mt65JFHtHz5ck2ZMkW/+c1vdOedd+rgwYPKycmJdPY61Lr/XkeufiVdviKlprh/t21L8kxh52leAwAgVtxa4h4nQD8J2FnFdqlfsisiLQ+hiJuuShMnTtQtt9yiFStWeLeNGTNGs2fP1tKlS4M+P1JdldoP0Ok6y5Ie/QkzAwAAYlPbAcbTJ0lXrkg7g/T9B2JJuOtrnanjxkWLQ2NjoyorK/Xzn//cZ3tpaak++ugjv89paGhQQ0OD93FdXV2v5tGf2vqeCxokz6AgggYAQGzyrPdw/oI0qNVg0kFpLp9BqDdcJ312NHL5BLojmnuIxEXg8OWXX6q5uVkZGRk+2zMyMlRdXe33OUuXLtXTTz8djuwFdK6HV8YcmkHQAACIbU6H1a5CNXVCggrzjU9A0XpwtfTNVJ3Zlqo+NX5nuglmTJ706ectzxs5TDp+2jfN+DHuWYKqPm1//DF5Usa10tBM6cQp6cPKzudBkm7IlT471vJ4SLpU82Xnj9MZnpmO/Mkb6R68HgrLCv09D8uSTp8NLe30SdL1OZY+DzDLkj/jx0ij86S0gdLFWnm/J5K763dzs2Rc0tYdXfucusOy3N/jaBQXgYOH1WY9e2NMu20ejz/+uJYsWeJ9XFdXp+HDh/dq/toa3INfmmj+EgIA0F1tAwqnw5Izv/01fuoEyxtkfH7CtKsYtp1OtPWshLX1/oMTY7mDEk8LSOk0d7rERKOmJsundUSS8q+XJhW1pPFUXDsKblrn4/RZl06ecU8fOiwrQdt2trS4eNJd/SrwMfJGyiffknzy68lT6/fV+jUdA6x25bC90vgEQ8OypDPVvnnytBa1Pn7aQHnLyJMPf8Gfp2za5rV12ebmWJpwU/uAsXVw4DlO689jWFbg79WEm1o+c6ml3GrrfKdaLZsuFea3lEvbz3DKBKnkFvdrbq802rbL/+t5yipae4jExRiHxsZGpaSk6Pe//73uvfde7/bFixdr7969qqgIPll0LI9xsCTNKmMqVgAA2vIEA20rom2DhGjJV7D0rdN19hg9ledoKcPeFuz9dbS/bdApte+CFy6s4+DHxIkTVVRUpOXLl3u33XjjjZo1a1ZUD46W1K65NRDPrEoDUqR+/dzb/EXWAAAAgMTgaL+WLFmiH/zgByouLlZJSYlWrlypkydPav78+ZHOWlCBmlsBAACAcImbwGHOnDk6d+6c/vVf/1Vnz55VQUGB3nnnHY0YMSLSWQMAAACiXtx0VequSHZVAgAAAHpDZ+q40bksHQAAAICoQuAAAAAAICgCBwAAAABBETgAAAAACIrAAQAAAEBQBA4AAAAAgiJwAAAAABAUgQMAAACAoAgcAAAAAARF4AAAAAAgKAIHAAAAAEEROAAAAAAIisABAAAAQFB9I52BWGGMkSTV1dVFOCcAAABAz/DUbT113Y4QOISovr5ekjR8+PAI5wQAAADoWfX19XI6nR2msUwo4QXkcrn0l7/8RQ6HQ5ZlhfW16+rqNHz4cJ06dUoDBw4M62vHE8o5fCjr8KGsw4NyDh/KOnwo6/CJZFkbY1RfX6/s7GwlJHQ8ioEWhxAlJCRo2LBhEc3DwIED+ccNA8o5fCjr8KGsw4NyDh/KOnwo6/CJVFkHa2nwYHA0AAAAgKAIHAAAAAAEReAQA5KTk/Xkk08qOTk50lmxNco5fCjr8KGsw4NyDh/KOnwo6/CJlbJmcDQAAACAoGhxAAAAABAUgQMAAACAoAgcAAAAAARF4AAAAAAgKAKHKLd8+XLl5uaqX79+Kioq0gcffBDpLMWcrVu36jvf+Y6ys7NlWZbWrVvns98Yo6eeekrZ2dnq37+/br31Vh04cMAnTUNDgxYtWqT09HSlpqbqnnvu0enTp8P4LqLf0qVLNWHCBDkcDg0ZMkSzZ8/WoUOHfNJQ1t23YsUKjRs3zrtIUElJif74xz9691PGvWfp0qWyLEuPPPKIdxvl3TOeeuopWZbl85OZmendTzn3rDNnzujv//7vNXjwYKWkpOimm25SZWWldz/l3X0jR45s9522LEsLFiyQFMNlbBC11qxZYxITE83zzz9vDh48aBYvXmxSU1PNiRMnIp21mPLOO++Yf/7nfzavv/66kWTefPNNn/3PPvuscTgc5vXXXzdVVVVmzpw5Jisry9TV1XnTzJ8/3wwdOtRs3LjR7N6928yYMcOMHz/efP3112F+N9GrrKzMrFq1yuzfv9/s3bvX3H333SYnJ8dcunTJm4ay7r7169ebt99+2xw6dMgcOnTIPPHEEyYxMdHs37/fGEMZ95YdO3aYkSNHmnHjxpnFixd7t1PePePJJ580Y8eONWfPnvX+1NTUePdTzj3n/PnzZsSIEWbevHnm448/NseOHTObNm0yR44c8aahvLuvpqbG5/u8ceNGI8ls3rzZGBO7ZUzgEMW+9a1vmfnz5/tsy8/PNz//+c8jlKPY1zZwcLlcJjMz0zz77LPebV999ZVxOp3mueeeM8YYc/HiRZOYmGjWrFnjTXPmzBmTkJBg3n333bDlPdbU1NQYSaaiosIYQ1n3prS0NPPCCy9Qxr2kvr7ejBo1ymzcuNFMnz7dGzhQ3j3nySefNOPHj/e7j3LuWY899piZOnVqwP2Ud+9YvHixuf76643L5YrpMqarUpRqbGxUZWWlSktLfbaXlpbqo48+ilCu7OfYsWOqrq72Kefk5GRNnz7dW86VlZVqamrySZOdna2CggI+iw7U1tZKkgYNGiSJsu4Nzc3NWrNmjS5fvqySkhLKuJcsWLBAd999t26//Xaf7ZR3zzp8+LCys7OVm5urv/u7v9PRo0clUc49bf369SouLtbf/u3fasiQIbr55pv1/PPPe/dT3j2vsbFRr776qh588EFZlhXTZUzgEKW+/PJLNTc3KyMjw2d7RkaGqqurI5Qr+/GUZUflXF1draSkJKWlpQVMA1/GGC1ZskRTp05VQUGBJMq6J1VVVWnAgAFKTk7W/Pnz9eabb+rGG2+kjHvBmjVrtHv3bi1durTdPsq750ycOFGvvPKKysvL9fzzz6u6ulqTJ0/WuXPnKOcedvToUa1YsUKjRo1SeXm55s+fr5/+9Kd65ZVXJPG97g3r1q3TxYsXNW/ePEmxXcZ9I/bKCIllWT6PjTHttqH7ulLOfBaBLVy4UPv27dO2bdva7aOsu2/06NHau3evLl68qNdff10PPPCAKioqvPsp455x6tQpLV68WBs2bFC/fv0CpqO8u+/OO+/0/l1YWKiSkhJdf/31evnllzVp0iRJlHNPcblcKi4u1jPPPCNJuvnmm3XgwAGtWLFC//AP/+BNR3n3nBdffFF33nmnsrOzfbbHYhnT4hCl0tPT1adPn3ZRZU1NTbsIFV3nmbWjo3LOzMxUY2OjLly4EDANWixatEjr16/X5s2bNWzYMO92yrrnJCUlKS8vT8XFxVq6dKnGjx+vX/7yl5RxD6usrFRNTY2KiorUt29f9e3bVxUVFfrVr36lvn37esuL8u55qampKiws1OHDh/le97CsrCzdeOONPtvGjBmjkydPSuJc3dNOnDihTZs26Uc/+pF3WyyXMYFDlEpKSlJRUZE2btzos33jxo2aPHlyhHJlP7m5ucrMzPQp58bGRlVUVHjLuaioSImJiT5pzp49q/379/NZtGKM0cKFC/XGG2/o/fffV25urs9+yrr3GGPU0NBAGfew2267TVVVVdq7d6/3p7i4WHPnztXevXt13XXXUd69pKGhQZ988omysrL4XvewKVOmtJsq+7PPPtOIESMkca7uaatWrdKQIUN09913e7fFdBmHezQ2QueZjvXFF180Bw8eNI888ohJTU01x48fj3TWYkp9fb3Zs2eP2bNnj5Fk/uu//svs2bPHO63ts88+a5xOp3njjTdMVVWV+f73v+93SrRhw4aZTZs2md27d5tvf/vbEZ8SLdo89NBDxul0mi1btvhMQXflyhVvGsq6+x5//HGzdetWc+zYMbNv3z7zxBNPmISEBLNhwwZjDGXc21rPqmQM5d1THn30UbNlyxZz9OhRs337djNz5kzjcDi81zvKuefs2LHD9O3b1/zbv/2bOXz4sFm9erVJSUkxr776qjcN5d0zmpubTU5Ojnnsscfa7YvVMiZwiHK//vWvzYgRI0xSUpK55ZZbvFNbInSbN282ktr9PPDAA8YY99RzTz75pMnMzDTJyclm2rRppqqqyucYV69eNQsXLjSDBg0y/fv3NzNnzjQnT56MwLuJXv7KWJJZtWqVNw1l3X0PPvig95xw7bXXmttuu80bNBhDGfe2toED5d0zPHPYJyYmmuzsbHPfffeZAwcOePdTzj3rD3/4gykoKDDJyckmPz/frFy50mc/5d0zysvLjSRz6NChdvtitYwtY4yJSFMHAAAAgJjBGAcAAAAAQRE4AAAAAAiKwAEAAABAUAQOAAAAAIIicAAAAAAQFIEDAAAAgKAIHAAAAAAEReAAAAAAICgCBwBAj7IsS+vWrYt0NkIyb948zZ49O9LZAICYQOAAAOiU6upqLVq0SNddd52Sk5M1fPhwfec739F7770X6awBAHpR30hnAAAQO44fP64pU6bommuu0b//+79r3LhxampqUnl5uRYsWKBPP/000lkEAPQSWhwAACF7+OGHZVmWduzYoe9973u64YYbNHbsWC1ZskTbt2/3pvvyyy917733KiUlRaNGjdL69eu9+5qbm/XDH/5Qubm56t+/v0aPHq1f/vKXPq/j6UL0n//5n8rKytLgwYO1YMECNTU1edOMHDlSzzzzjB588EE5HA7l5ORo5cqVPsc5c+aM5syZo7S0NA0ePFizZs3S8ePHe6dwAMDmCBwAACE5f/683n33XS1YsECpqant9l9zzTXev59++mndf//92rdvn+666y7NnTtX58+flyS5XC4NGzZMa9eu1cGDB/WLX/xCTzzxhNauXetzvM2bN+vzzz/X5s2b9fLLL+ull17SSy+95JNm2bJlKi4u1p49e/Twww/roYce8rZ6XLlyRTNmzNCAAQO0detWbdu2TQMGDNAdd9yhxsbGni0cAIgDBA4AgJAcOXJExhjl5+cHTTtv3jx9//vfV15enp555hldvnxZO3bskCQlJibq6aef1oQJE5Sbm6u5c+dq3rx57QKHtLQ0/c///I/y8/M1c+ZM3X333e3GUdx11116+OGHlZeXp8cee0zp6enasmWLJGnNmjVKSEjQCy+8oMLCQo0ZM0arVq3SyZMnvWkAAKFjjAMAICTGGEnuWZOCGTdunPfv1NRUORwO1dTUeLc999xzeuGFF3TixAldvXpVjY2Nuummm3yOMXbsWPXp08f7OCsrS1VVVQFfx7IsZWZmel+nsrJSR44ckcPh8HnOV199pc8//zzoewAA+CJwAACEZNSoUbIsS5988knQKUwTExN9HluWJZfLJUlau3atfvazn2nZsmUqKSmRw+HQf/zHf+jjjz8O+RihpHG5XCoqKtLq1avb5e/aa6/tMP8AgPYIHAAAIRk0aJDKysr061//Wj/96U/bjXO4ePGizziHQD744ANNnjxZDz/8sHdbb7QA3HLLLXrttdc0ZMgQDRw4sMePDwDxhjEOAICQLV++XM3NzfrWt76l119/XYcPH9Ynn3yiX/3qVyopKQnpGHl5edq1a5fKy8v12Wef6V/+5V+0c+fOHs/r3LlzlZ6erlmzZumDDz7QsWPHVFFRocWLF+v06dM9/noAYHcEDgCAkOXm5mr37t2aMWOGHn30URUUFOhv/uZv9N5772nFihUhHWP+/Pm67777NGfOHE2cOFHnzp3zaX3oKSkpKdq6datycnJ03333acyYMXrwwQd19epVWiAAoAss4xntBgAAAAAB0OIAAAAAICgCBwAAAABBETgAAAAACIrAAQAAAEBQBA4AAAAAgiJwAAAAABAUgQMAAACAoAgcAAAAAARF4AAAAAAgKAIHAAAAAEEROAAAAAAI6v8DnnUAy1NRtxIAAAAASUVORK5CYII=",
      "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",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#plot data and models\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 0-411'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 411-460'], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(channel_range_dfs['Range 411-460']['Channel'], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 460-700'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "plt.annotate('gamma@661.657keV', xy=(453, 1400), xytext=(500, 1650),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "\n",
    "\n",
    "# Create legend entries for the blue and red lines (data and model) and display the legend\n",
    "blue_line = mlines.Line2D([], [], color='#7c8bfd', label='Data')\n",
    "\n",
    "red_line = mlines.Line2D([], [], color='red', label='Moffat Fit', linewidth=2.5)\n",
    "\n",
    "plt.legend([blue_line, red_line], ['Data', 'Moffat Fit'], loc='lower center', bbox_to_anchor= (0.5, 1.005), ncol=2,\n",
    "            borderaxespad=0, frameon=False)\n",
    "\n",
    "plt.title('Cs-137 gamma ray scattered at 25deg from an Al target ', y = 1.07)\n",
    "\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2b1027-4a59-4481-a1c1-3fbcebed488d",
   "metadata": {},
   "source": [
    "# Estimate the measured energy using the calibration curve (prepared in notebook 5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24a2edda-9e8b-4159-ab0a-3abaf36f6a0c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r1_c0   -27.70990     4.87254\n",
      "p_r1_c1     1.45191     0.12099\n",
      "\n",
      "Linear Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.0038505734658223\n",
      "r-squared =  0.9999951130945643\n",
      "\n",
      "Quadratic Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r2_c0   -35.38041     7.32770\n",
      "p_r2_c1     1.71396     0.22612\n",
      "p_r2_c2    -0.00061     0.00046\n",
      "\n",
      "Quadratic Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.00411106030073\n",
      "r-squared =  0.9999959265220606\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "x_r = np.array([29.3812658, 70.4034958, 100.839961, 102.324468, 254.677094, 350.450834, 638.540552, 738.972592])\n",
    "y_r = np.array([14.4136, 80.998, 122.060, 160.605, 356.005, 511.006, 661.6570, 1274.54])\n",
    "yerr_r = np.array([0.0003, 0.008, 0.010, 0.015, 0.017, 0.008, 0.003, 0.008])\n",
    "\n",
    "#use builtin NameModel, specify prefix for each:\n",
    "mod_r1 = PolynomialModel(1, prefix='p_r1_')\n",
    "mod_r2 = PolynomialModel(2, prefix='p_r2_')\n",
    "\n",
    "#set initial parameters\n",
    "pars_r1 = mod_r1.guess(y_r, x=x_r)\n",
    "pars_r2 = mod_r2.guess(y_r, x=x_r)    \n",
    "\n",
    "#fit with weights=1./yerr\n",
    "result_r1 = mod_r1.fit(y_r, pars_r1, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.198))\n",
    "result_r2 = mod_r2.fit(y_r, pars_r2, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.186))\n",
    "\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r1.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r1.redchi)\n",
    "print(\"r-squared = \", result_r1.rsquared)\n",
    "    \n",
    "print()\n",
    "print(\"Quadratic Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r2.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')    \n",
    "\n",
    "print()\n",
    "print(\"Quadratic Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r2.redchi)\n",
    "print(\"r-squared = \", result_r2.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a66c1b87-e6c8-46d9-ae50-3016f4ca2a0a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "435.7305978891946\n"
     ]
    }
   ],
   "source": [
    "#Print the new position\n",
    "varys = result2.params.valuesdict()\n",
    "\n",
    "position = varys['g2_center']\n",
    "\n",
    "print(position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95593d9f-fdd4-4e12-b3c7-0f72c9fd9f06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear model estimation\n",
      "----------------------------\n",
      "619.1055192381854\n",
      "\n",
      "Quadratic model estimation\n",
      "----------------------------\n",
      "563.6446730792231\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(\"Linear model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r1.eval(pars_r1, x = position))\n",
    "\n",
    "print()\n",
    "print(\"Quadratic model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r2.eval(pars_r2, x = position))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e571bf-5dac-4f6f-a945-31abb057a1ef",
   "metadata": {},
   "source": [
    "## Creating a new table row by plugging the result into the Compton formula\n",
    "\n",
    "![](./img/Compton_formula.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f53e2625-65c8-4845-a2ac-1019beb123cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 0.09369221296335006, 'y': 1.774167392618017}\n"
     ]
    }
   ],
   "source": [
    "#Extract the energy from the model\n",
    "energy = result_r2.eval(pars_r2, x = position) \n",
    "\n",
    "#Calculate the variables (to be compared in the last step) according to the Compton model\n",
    "x = 1 - np.cos(np.deg2rad(25))\n",
    "y = 1 / (energy / 1000) #convert the energy to MeV\n",
    "\n",
    "#Add the data to a dictionary\n",
    "Compton_data = {\"x\" : x, \"y\" : y}\n",
    "\n",
    "#Preview the created row of data\n",
    "print(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9c5178-3039-4201-8966-2087be5445d7",
   "metadata": {},
   "source": [
    "### Append the values to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a99b305e-ea0b-4d10-8523-972be7cc20a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open(r'./Data/Compton_data.csv', 'a', newline='') as csvfile:\n",
    "#     fieldnames = ['x','y']\n",
    "#     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "#     writer.writerow(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8203f67b-6889-4733-9265-b0407b6b7939",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "251a54f2-8889-46e1-a0ed-8a8bccb4c899",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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