{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11b2bef2",
   "metadata": {},
   "source": [
    "# Loading A Custom Box Model in MusicBox\n",
    "\n",
    "MusicBox provides two primary ways to load custom box models:\n",
    "* Loading a pre-made example configuration\n",
    "* Loading your own JSON configuration file\n",
    "\n",
    "This tutorial covers both approaches.\n",
    "\n",
    "**Note:** Configuration files define all aspects of your box model, including initial and evolving conditions, simulation parameters, and the complete mechanism (species, reactions, and phases)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e250207",
   "metadata": {},
   "source": [
    "## 1. Loading Pre-made Box Model Examples\n",
    "\n",
    "MusicBox includes several built-in example configurations accessible through the `Examples` class. Each example has a `path` attribute that points to its JSON configuration file.\n",
    "\n",
    "You can load an example using the `loadJson()` method, which takes the configuration path as a parameter.\n",
    "\n",
    "The available examples include:\n",
    "* [Analytical](https://github.com/NCAR/music-box/tree/main/examples/analytical) \n",
    "* [CarbonBond5](https://github.com/NCAR/music-box/tree/main/examples/carbon_bond_5)\n",
    "* [Chapman](https://github.com/NCAR/music-box/tree/main/examples/chapman)\n",
    "* [FlowTube](https://github.com/NCAR/music-box/tree/main/examples/flow_tube)\n",
    "* [TS1](https://github.com/NCAR/music-box/tree/main/examples/ts1)\n",
    "* [WACCM](https://github.com/NCAR/music-box/tree/main/examples/waccm)\n",
    "\n",
    "The code below demonstrates the Analytical example. You can try any other example by replacing `Analytical` with a different name in `Examples.Analytical.path`.\n",
    "\n",
    "To load, run, and visualize an example box model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ab7a11a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                              \r"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time.s</th>\n",
       "      <th>ENV.temperature.K</th>\n",
       "      <th>ENV.pressure.Pa</th>\n",
       "      <th>ENV.air number density.mol m-3</th>\n",
       "      <th>CONC.A.mol m-3</th>\n",
       "      <th>CONC.B.mol m-3</th>\n",
       "      <th>CONC.C.mol m-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000e-01</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.775723</td>\n",
       "      <td>3.979221e-08</td>\n",
       "      <td>0.524277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.752182</td>\n",
       "      <td>3.858465e-08</td>\n",
       "      <td>0.547818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.729356</td>\n",
       "      <td>3.741374e-08</td>\n",
       "      <td>0.570644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.707222</td>\n",
       "      <td>3.627836e-08</td>\n",
       "      <td>0.592777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>576.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.041522</td>\n",
       "      <td>2.129939e-09</td>\n",
       "      <td>1.258478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>582.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.040262</td>\n",
       "      <td>2.065302e-09</td>\n",
       "      <td>1.259738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>588.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.039040</td>\n",
       "      <td>2.002627e-09</td>\n",
       "      <td>1.260960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>594.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.037855</td>\n",
       "      <td>1.941854e-09</td>\n",
       "      <td>1.262145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>600.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.036706</td>\n",
       "      <td>1.882926e-09</td>\n",
       "      <td>1.263294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     time.s  ENV.temperature.K  ENV.pressure.Pa  \\\n",
       "0       0.0              200.0          70000.0   \n",
       "1       6.0              200.0          70000.0   \n",
       "2      12.0              200.0          70000.0   \n",
       "3      18.0              200.0          70000.0   \n",
       "4      24.0              200.0          70000.0   \n",
       "..      ...                ...              ...   \n",
       "96    576.0              200.0          70000.0   \n",
       "97    582.0              200.0          70000.0   \n",
       "98    588.0              200.0          70000.0   \n",
       "99    594.0              200.0          70000.0   \n",
       "100   600.0              200.0          70000.0   \n",
       "\n",
       "     ENV.air number density.mol m-3  CONC.A.mol m-3  CONC.B.mol m-3  \\\n",
       "0                         42.095324        0.800000    2.000000e-01   \n",
       "1                         42.095324        0.775723    3.979221e-08   \n",
       "2                         42.095324        0.752182    3.858465e-08   \n",
       "3                         42.095324        0.729356    3.741374e-08   \n",
       "4                         42.095324        0.707222    3.627836e-08   \n",
       "..                              ...             ...             ...   \n",
       "96                        42.095324        0.041522    2.129939e-09   \n",
       "97                        42.095324        0.040262    2.065302e-09   \n",
       "98                        42.095324        0.039040    2.002627e-09   \n",
       "99                        42.095324        0.037855    1.941854e-09   \n",
       "100                       42.095324        0.036706    1.882926e-09   \n",
       "\n",
       "     CONC.C.mol m-3  \n",
       "0          0.300000  \n",
       "1          0.524277  \n",
       "2          0.547818  \n",
       "3          0.570644  \n",
       "4          0.592777  \n",
       "..              ...  \n",
       "96         1.258478  \n",
       "97         1.259738  \n",
       "98         1.260960  \n",
       "99         1.262145  \n",
       "100        1.263294  \n",
       "\n",
       "[101 rows x 7 columns]"
      ]
     },
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     "output_type": "display_data"
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from acom_music_box import MusicBox, Examples\n",
    "import matplotlib.pyplot as plt\n",
    "import logging\n",
    "import sys\n",
    "\n",
    "# Disable logging to reduce output noise\n",
    "logging.disable(sys.maxsize)\n",
    "\n",
    "# Create a MusicBox instance and load the Analytical example\n",
    "box_model = MusicBox()\n",
    "conditions_path = Examples.Analytical.path\n",
    "box_model.loadJson(conditions_path)\n",
    "\n",
    "# Solve the box model and display results\n",
    "df = box_model.solve()\n",
    "display(df)\n",
    "\n",
    "# Plot the species concentrations over time\n",
    "df.plot(x='time.s', y=['CONC.A.mol m-3', 'CONC.B.mol m-3', 'CONC.C.mol m-3'], \n",
    "        title='Concentration over time', \n",
    "        ylabel='Concentration (mol m-3)', \n",
    "        xlabel='Time (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f14811",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "## 2. Loading a Custom JSON Box Model Configuration\n",
    "\n",
    "Loading your own JSON configuration file works the same way as loading a pre-made example. Simply provide the path to your custom configuration file instead of using the `Examples` class.\n",
    "\n",
    "The example below loads a file called `custom_box_model.json` from the `config` subfolder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f2f2cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Simulation Progress:   0%|          | 0/600.0 [00:00<?, ? [model integration steps (2.0 s)]/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                              \r"
     ]
    },
    {
     "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>time.s</th>\n",
       "      <th>ENV.temperature.K</th>\n",
       "      <th>ENV.pressure.Pa</th>\n",
       "      <th>ENV.air number density.mol m-3</th>\n",
       "      <th>CONC.A.mol m-3</th>\n",
       "      <th>CONC.B.mol m-3</th>\n",
       "      <th>CONC.C.mol m-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000e-01</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.775723</td>\n",
       "      <td>3.979221e-08</td>\n",
       "      <td>0.524277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.752182</td>\n",
       "      <td>3.858465e-08</td>\n",
       "      <td>0.547818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.729356</td>\n",
       "      <td>3.741374e-08</td>\n",
       "      <td>0.570644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.707222</td>\n",
       "      <td>3.627836e-08</td>\n",
       "      <td>0.592777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>576.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.041522</td>\n",
       "      <td>2.129939e-09</td>\n",
       "      <td>1.258478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>582.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.040262</td>\n",
       "      <td>2.065302e-09</td>\n",
       "      <td>1.259738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>588.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.039040</td>\n",
       "      <td>2.002627e-09</td>\n",
       "      <td>1.260960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>594.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.037855</td>\n",
       "      <td>1.941854e-09</td>\n",
       "      <td>1.262145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>600.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>42.095324</td>\n",
       "      <td>0.036706</td>\n",
       "      <td>1.882926e-09</td>\n",
       "      <td>1.263294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     time.s  ENV.temperature.K  ENV.pressure.Pa  \\\n",
       "0       0.0              200.0          70000.0   \n",
       "1       6.0              200.0          70000.0   \n",
       "2      12.0              200.0          70000.0   \n",
       "3      18.0              200.0          70000.0   \n",
       "4      24.0              200.0          70000.0   \n",
       "..      ...                ...              ...   \n",
       "96    576.0              200.0          70000.0   \n",
       "97    582.0              200.0          70000.0   \n",
       "98    588.0              200.0          70000.0   \n",
       "99    594.0              200.0          70000.0   \n",
       "100   600.0              200.0          70000.0   \n",
       "\n",
       "     ENV.air number density.mol m-3  CONC.A.mol m-3  CONC.B.mol m-3  \\\n",
       "0                         42.095324        0.800000    2.000000e-01   \n",
       "1                         42.095324        0.775723    3.979221e-08   \n",
       "2                         42.095324        0.752182    3.858465e-08   \n",
       "3                         42.095324        0.729356    3.741374e-08   \n",
       "4                         42.095324        0.707222    3.627836e-08   \n",
       "..                              ...             ...             ...   \n",
       "96                        42.095324        0.041522    2.129939e-09   \n",
       "97                        42.095324        0.040262    2.065302e-09   \n",
       "98                        42.095324        0.039040    2.002627e-09   \n",
       "99                        42.095324        0.037855    1.941854e-09   \n",
       "100                       42.095324        0.036706    1.882926e-09   \n",
       "\n",
       "     CONC.C.mol m-3  \n",
       "0          0.300000  \n",
       "1          0.524277  \n",
       "2          0.547818  \n",
       "3          0.570644  \n",
       "4          0.592777  \n",
       "..              ...  \n",
       "96         1.258478  \n",
       "97         1.259738  \n",
       "98         1.260960  \n",
       "99         1.262145  \n",
       "100        1.263294  \n",
       "\n",
       "[101 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qV66sakteffVV5NeTTz6parlkKPaKFStUbZOEwv79+6vz9Mcff6jzOH/+fHTt2jXXz5GaGtm0pPy7du3C3r17810WIqKiotFoEJcSh8ikSEQlRWVsyVFZHkcnR2d5LjopWu1LSc+ogW7q2RTLnlmmt5+B4SYfv+SElKzNNcXF1tI8X6NkwsPDVU3JzJkzs4SLzLPbCqkpCAsLw8SJEx94jTQh1ahRA7///nuWcNOmTRtcvnwZkydPxoYNGx752LmR2iOZpK5Tp04oX748Wrduja+++irHz8rs448/VqFLtkmTJqmAIYFhypQpqFixogpVEjg2bdqkq7GSGiqpJZFjyc8yZMgQFaikhim/JNS8++67KsitWrUKI0aMUJ/9/PPPY+rUqarsEphu3ryZ7ykF/P391Tl84YUX8l0OIqJ8X8NSExCeGK7CSURiRNbbpAhEJmbcSlCR/RJWUtNTH/mYFqX0Hy30X4ISToJNnWlb9HLs8x91gZ2VRb4ujvIFrlWrVp6vk5AiateunePz8n7tazKbNWsWGjRooGoW2rZt+0jHzo3U1EhzmNSGSM2QBJQ1a9Y80KcnOwkm0pQlJNy0atUKH3zwAbp06aL2SZCR12hJjY18ptQyifHjx+PQoUNqf0HCjdQISY2PkCA1e/ZsuLm5qRoxIU14UkN0+vRptGzZMs/PkiB3/PhxJCUlYfjw4fjoo4/yXQ4iMl2JqYkISwxDeEK4Ci2yyWMJJnI/QnubFKHuJ6UlPdJxrM2tUca6DJysnXS3jlaO6r7arDJutfu0t7YWtnqfvoLhxghk7ydT2K+vU6eO6iMitTf79+9/rM/KTGaD/uuvv7Bv3z7dvldeeUUFnoeFGwlbWtIUJKTJKPO+xMRE1Y/H0dERFy5cUAEiM6mV+vrrrwtU5szHlUAmM1lnP67QLpcgTXPaDtkSDLU1SUJqfmJiYnDq1CnV7CdBS2qFiMg0A0toQqjaJKiEJWRsWR7fv41PjS/w51uZWcHZxhkuNi4qpMh9uXWycYKzdcZ9CSba/dqQYqgYbvLRNCQ1KPo6dn5Ur15dpWTpHJwXaXYScqGXWoPsZL8EmZzMmDFDvT/7UOn8Hjsnv/32mwogmfvYSFhKT09XNUja8ubWKVdL+y+EnPbJZxWmzMfQHiev427cuFE3Ckq7jIKWjCgTcs5lpJqErwkTJnB5BSIjIn1XguODERIfguCEYITGhyIkIURtElxkvwSWmJSYAn2upZklXG1dVViRQOJq8999FxuXjPvWzrrHJaE2pTgx3DyEfBny0zSkTy4uLqo5ZuHChRgzZswD/VWkhkT6vnTu3Fm9ds6cOQ+Em/Xr1+PKlSuqL0tO5EIsfVikX0nmTrr5PXZOpIZGLubZa2mk6Ug6CEuTT2GRpjipdRo0aJBunzzOLcwVFuksnB8ShiQEyS3DDVHJJ31SJJwExQep8BIUl3ErAUYXZuKDC1TLIrUrbrZuapPgojYbV/VYAor2sYutCxwsHUwqrBRUyb5qU75JuJBmlubNm6u+G9J8kpqaiq1bt6o+IFIrI8Hju+++U31cpJZAwoo02chcNNIs8tJLL+n6seRE+pjIkOXr169n6XScn2MLadqSTsPSh+fkyZOqv8mvv/76QH+dfv36qc+RkUhBQUHo2LEjfvrpJ/X5j0p+PvnZGjVqpDoUy0graRLbtm0bipv8zFLbI81ZMifQ0aNH1bmVc5q9ZoiI9BdcZEizdpMQo72VIBOaGIp0Tf5qhu0t7eFu5w53W3cVVORWHmvvu9llBBoGlsLDcGMkpCOuhAUZtSS1IXfv3lVDo2X+GQkYWhJgZISSvE76gEizkDQtvffeexg3blye/2NJLY103pXam0c5towgMjMz09XaSK1JTh2RZeSRBC9p0pGgJHPByIiqxyETD0r/GunXIp2NZSi4DEGX4d3FzcLCAp999plqepNmOKndkZ/37bffLvayEJkimYvlTuwd3Im7o+ZjuRuXdZNalzRNWr5GBUlI8bTzhIedR46bhBc7Sy7GXNxKaR6nR6gBkg6mZcqUQVRUlKq1yEwu9FIrIRc+mTmWqKTgd5OoYOHlduxtBMYEqgATGBuYEWbuBxoZ8pyf4OJZ2lMFF7n1Ku0FLzuvjPv3b6WpyKwU58ItCdfv7FhzQ0REBiUtPQ334u/hdsztjC32/m3MbRVkZAj0w8iw5XL25VRoKVe6HMqWLouy9mXVY7kvfVvMzdj/zVAx3BARUYmTnJasQsut6Fu4GXMTN6Nv4lbsLV2AedgkczKUWUJLBYcK6laCTHn78upWwou9lX2x/SxU/BhuiIhILySgSFPRjegbCIgOUJuEGAkz0vclrw67MhRawoqEF28Hb939CvYV1H2GF9PGcENEREVKpve/Hn0dN6Ju4HrUdd19qYVJ1eReA2NnYYeKjhVVeKnokHGr7jtWVJ112d+FSmS42bNnj1pF+tixY2qEjazRI6NaciNDd2X0jQwjlinrZfbXDz/8UDflPhER6YfUskhty7XIa7gWdS0jxNzf8uoDI1P8S1jxcfRRAaaSYyX1WG6l3wuHRpPBhRtZpVrW6pFFDvOzaKCEoaeffhqffvqpmhhOhvLKgo+HDx9W85cQEVHxhBj/CH9cjbqKq5FX4R/pr0KMLNCYG+nnUrlMZRVifMr4qPBS2bGyGnXEGhgyqnDTtWtXteWXrOicmYScv//+W03IxnBDRFS4ZCK7KxFX1CYBRrvlFmIszCxUeJEQU6VMlYzNqYqqkeFcL1ScDLrPjUxVLwsPyuRyRET0aGTVaKmBuRxxGZfCL2UEmsgramXp3DrzSoCp6lQVVctUzbh1qqo69MpzRPpm0OFGZpuNjY3Nc8kA6ZsjW+ZJgIiITJUElovhF1WI0d7KaKWcZuQthVKq/0sN5xqo5lQtY3OupmpipJaGqKQy2G+nrCgtK1VLs5SHh0eur5N1jOR1RA8jC3jKQp/ZVz4nMkQy+byshXQ+/LwKMRfDLqr7sphjbvPC1HSuqYKMdpMmJVlNmsjQGGS4WblyJYYOHYo1a9aoRRDzIgsSjh8/PkvNjaxwbYzu3bun1nf6559/EBgYqEJfw4YN1ZpRsvik1oEDB9SilAcPHkRCQoJaW2rIkCFqzaXMK1LLKAVZ2FHWdsq8urWMaJMO3cuXLy/wsTOTkW6Zg6dMpy1rSUnZ2rdvD2P2xhtvqEU779y5A3t7e7VKu6w3ldNaW0T5CTKyoOO50HM4F3YO58POqy23UUrSmVeCTC2XWqjpkhFoZJkBjkwiY2Fw4eb3339Xo6sk4HTr1u2hr5eLs2zG7saNG2plbgkdMrxeVpxOSUnBli1bMHLkSFy8eFG9TobbSzOehBlZQFNeLxfZd999V4Wd1atXZ/kDJ/enTZuGFStWPPaxcyLD+bUrc4eHh6umxueeew63b99Wa4gYK1lUdMCAAahYsaL6uSXode7cWa0flTlgEuXWtHQ29KzaJMzIbU79Y2R9JKl9qe1SG7Vda6tbCTOlLUvrpdxExUajRzExMZoTJ06oTYoyd+5cdT8gIEA9P3nyZM2rr76qe/2vv/6qsbCw0CxcuFBz9+5d3RYZGZnvY0ZFRaljyW12CQkJmvPnz6tbQ9O1a1dN+fLlNbGxsQ88FxERoW7lOVdXV80LL7zwwGvWr1+vzsvKlSt1++TxxIkTNWZmZpozZ87o9vfs2VMzaNCgAh07J9OnT9f4+vpm2Xfr1i11XD8/v1zfJ8eWMsycOVPj4eGhKVOmjGbGjBmalJQUVV5nZ2dVnqVLl2Z53+nTpzVPPfWUxsbGRuPi4qIZNmyY+g5m/9zcLFu2TB3rf//7n6ZGjRoaW1tbzYsvvqiJi4vTLF++XFOpUiWNk5OTZvTo0ZrU1FRNQZw6dUr93P7+/jk+b8jfTXo8iamJmhNBJzTLzy7XTNw1UdPljy6aesvrPbD5rvDVvLT+Jc20/dM0Ky+s1JwOPq3eS2Qs8rp+Z6fXmpujR4/iqaee0j3WNh8NGjRINXnIxH43b97UPf/9998jNTVV1QbIpqV9fZGQa3xKPPRChk7mo5pY/uW/efNm1SxUuvSD/yKTGhXx77//IiwsDBMnTnzgNTJfUI0aNVTNWJ8+fXT7pUbm8uXLmDx5MjZs2PDIx84P6fgtcxfJe2rWrJnna3fs2IEKFSqouY/279+P119/XTW3tWvXTs17tGrVKtX0I/MiyetkTiWZ7LFVq1Y4cuQIgoODVdPmqFGjCvTdiY+PxzfffKNqDmWknszP9Pzzz6syb9y4EdeuXcOLL76ozlvm85gXKZv83LLit7E2mVL+yL8pZNXqk8EncTrktNouRlzMcR0lGa1Uz7Ue6rrVRT23eqqZycaCK8YTCb2GmyeffFL9z5yb7BedXbt2odhJsPm0HPRi6h3A6uHVx/7+/uo8Pqy/hoQUUbt27Ryfl/drX5O9U7b0hdm7dy/atm37SMfOzZkzZ1SfE21wcHBwUMHkYcvZy/B/CRlmZmYqCH3++efq/VOnTtX1tZo9ezb27duHvn37qg7oiYmJ+Omnn3QhbMGCBSrUSV8XT0/PfJVXmttkluyqVauqxy+99BJ+/vlnBAUFqZ+jTp06KrBLk9/Dws23336rmgMl3MjPsHXrVlhZWeWrHGQcUtJSVLOShJlTIadwMuSkmlsmO5mpt4F7A7VJkKnrWhcOVg56KTORITC4Pjf0oLwCYmG8Xi7YAwcOVLU3UkvyOJ+VnVzU169fr+5LTYgEm969e6tw0LRp0zz76kiw0ZJwUq9ePd1j6bfi6uqqamjEhQsX1GzYmWuXpHZF5kqSDtP5DTd2dna6YKM9ro+Pjy6gafdpjysTTcqmdf78edXPRkifG6lZkhpK6WskfaHk/NrY8F/fxio6OVoFmRPBJ3A86LgKNjLHTGYyxLqOSx0VZHzdfdWtzO7Lzr5E+cdwk5+mIalB0dex80FGO8kfvrw67gppdtJe6GV0TnayX4JMTmRUk7w/+zDp/B47N1JTUa1aNd1jmWlajiGzUf/yyy+5vs/SMutEYVKGnPZJeClMBT3um2++mWUepnLl/qsFlA7Tssk5bNmyJZydnVWH7379+hVqmUl/pBbmWNAxFWTkVibJ0yDrPwicrZ3R0KNhxubeEHVc67B5iegxMdw8jPxrKR9NQ/okTTTSn2ThwoUYM2bMA31fZO4W6RMio3HktXPmzHkg3EjtyZUrV/Dxxx/neAzpCyL9U6TZJ3PNRX6PXRBS6yJD1AuTNMVJM6c0AWnLKLUk2matoiLnJz8zaEsNmGyZJ5wkwxMSH4Ij947gaNBRtcl6SzkNw27k0QiNPRqrQCPLFbBWhqhwMdwYCQkX0szSvHlzfPTRR6qPjHS+ln4c0kdEamXkov7dd9+pPijDhw9XYUX6tmzfvh3vvPOO6j+S12zP0o9lyZIlarhy5v4k+Tm2kKat8uXLqz48WvI6mSMnc7OUNN1MmjRJ95qc3ldQ0gQ0ffp01flchl2HhIRg9OjRePXVV/PdJFVYpNOx/JwSNt3d3dWwd+kfZGtri2effbZYy0KPR4ZfS5jxu+sHv3t+aqbf7DP8VneujqaeTdHYs7EKNO527norL5GpYLgxElWqVMHx48fVqKUJEyaofhxy4ZT5VCRgaEmAkf4s8jrpHCydbKVZ5L333lMT7uX1L0ipgZDQoe20W9Bjy8i3zP1kxLlz51C2bNks/VnkPRJo8npfQclny7w7MlFhs2bN1GMZ1TR37lwUN+lTI52zpektIiJChSsZ5SWjvfKabZv0Ly4lTjUvHbp7SG2yBlP2MCMT4zX1aopmns1UoJGZf4moeJWS8eAwITJDsfRziIqKemBEjlzopVZChuSyUyeVJPxu6ocMwZYJ8g7eOYiDdw/iTMgZpGqyDsuW2X2bezVHM69maOLZhGGGSA/X7+xYc0NElMntmNs4cOcA9gfuV01OMSkxWZ6vYF8BLcq2QMtyLVWocbF5eJ8qIipeDDdEZNISUxNViNl/Z78KNNn7zThaOaJl2ZZoVa6Vuq3gUEFvZSWi/GG4ISKTcyvmFvbe3ou9gXtVsMk814x5KXM1v0zrcq3RpnwbtR6TuRnX+yIyJAw3RGT0UtJT1OR5e27vwe7bux8You1V2gttyrXBE+WfUE1OnP2XyLAx3BCR0c4GLM1MO2/uxL47+xCTHJNltexGno1UmGlbvi2qOVXjXDNERoThhoiMxt3Yu9hxawd23dqFo/eOZhnZ5GTtpIJMO+92qpaGtTNExovhhogMlsxkcS3qGrbf3K6282HnszxfpUwVPOn9pNoauDVg3xkiE8FwQ0QGF2guhF/A1oCt2BawLcvoJplET5Y26FCxgwo0stQBEZkehhsiMohAI5Pp/Rvwrwo1gbGBuucszSzVEO2OFTuqQONq66rXshKR/jHcEN0na07JiuQnT57Ud1HofqCRZqYtN7ao7U7cHd1zNuY2aFuhLTpV7IR2FdrB3sper2UlopLl8RbsoRJFFqCUxSBlrSdra2u1knf37t3VwpiZyRpGskCjs7Ozmsq/fv36ao2ltLS0LK+T0SPyfEBAQJb9vXr1wuDBgx/p2DlNpy3rWtWqVUsdy8vLC506dcJff/2lLm7GHKTkZ5bFTOX3ID/z4cOH9V2sEuFyxGV8ffxrPPvXs+j7T18sO7dMBRtbC1s84/MM5j45F7v77Fa3z1Z5lsGGiB7AmhsjcePGDbUyt5OTE7744gsVWFJSUtRikSNHjsTFixfV69auXatW/h4yZIhaQFNev23bNrz77rs4ePAgVq9enWVIrNyfNm0aVqxY8djHzi4yMhJPPPGEWifkk08+UQtaWlhYYPfu3ao8HTp0UJ9pjGrUqIEFCxaoMJiQkICvvvpKrRLu7++vFh01xUn1Nl3fpDb/SH/dfgk0UjPTxaeLGrYtj4mIHkpjYqKioqQ6QN1ml5CQoDl//ry6NTRdu3bVlC9fXhMbG/vAcxEREepWnnN1ddW88MILD7xm/fr16rysXLlSt08eT5w4UWNmZqY5c+aMbn/Pnj01gwYNKtCxczJixAhN6dKlNYGBgQ88FxMTo0lJScnxfdOnT9f4+vpqfvzxR423t7f6DPms1NRUzWeffabx9PTUuLu7az755JMs7wsICND06NFDvd7BwUHTu3dvzb179x743Nzs3LlTnZPNmzdrGjZsqLGxsdE89dRTmqCgIM3GjRs1tWrVUp/br18/TVxcnOZRvpfbtm3L8XlD/m7mJjwhXPP7hd81r/zziqbe8nq6rdFPjTSjt4/WbLq2SROXXLDzSETGK6/rd3asuXkIucYnpCbo5djyr9T8TCwWHh6OzZs3Y+bMmaqZIztt7ce///6LsLAwTJw48YHXSBOS1Cb8/vvv6NOnj26/1MhcvnwZkydPxoYNGx752Nmlp6dj5cqVGDBgAMqVK/fA8/b2eTc1XL16FZs2bVLHlvsvvfQSrl27pn4GqfmRprfXXntNNfe0aNFCHa9nz57qc+X51NRUVaskP+uuXbtQ0CYlqXWxs7NTtWCySVPcb7/9htjYWDz//POYP38+Jk2alK/PS05Oxvfff69Wu/X19YUxk2UOdt/ajf9d/R/2Be7TzUNjVspMLUL5bOVn0bFSR7WeExHRo2K4eQgJNi1+a6GXYx/ufxh2lnYPfZ00ZUgIkz4ceZGQImrXrp3j8/J+7WsymzVrFho0aIC9e/eibdu2j3Ts7EJDQxEREVHg92lJWFm6dCkcHBxQp04dPPXUU7h06RI2btwIMzMz1KxZE5999plqepNwI31/zpw5g+vXr6v+QOKnn35C3bp1ceTIEdUkll/ShCahT7z++uuYMmWKCljSxCQkaMlxHxZuJCz27dsX8fHxKFu2LLZu3Qo3NzcYG/l+nAo5pQLNphubsswULOs2davSDV0rd4WHnYdey0lExoPhxggUtONtQV8v4WHgwIGq9mb//v2P9VmP+z4tHx8fFWy0PD09YW5uroJN5n3BwcHq/oULF1So0QYb7c8lNUvyXEHCjQS9zMeQGhxtsNHu8/PzU/d//fVXvPHGG7rnpLZJGxAlkMnILAl6S5YsUTVA0qnYw8M4LvLB8cFYf3U9/vb/O8tcNJ52nuhetTueq/IcqjpV1WsZicg4Mdzko2lIalD0dez8qF69umq+yq3jrpY02Qi5mLdu3fqB52W/XPBzMmPGDPV+GSr9KMfOTjrNSrAo6Pu0LC0tszyWMuS0T2p4Clvm4zzsuD169FA1R1rly5fX3ZdmvGrVqqmtZcuW6lz++OOPqibIUKWkpWDX7V1Ye2Ut9t/Zj3RNuu67LMO2e1TrgWaezThTMBEVKYabh5ALVX6ahvTJxcUFXbp0wcKFCzFmzJgH+r7IqCQJEjIaR147Z86cB8LN+vXrceXKFXz88cc5HkNqPEaNGoWpU6eiatWqBT52dlLDIk0yP//8M6ZPn/5AvxvpuyJDw2X0VGGQprhbt26pTVt7c/78eVW+3AJdYZDapcw1THmRQJSUlARDJEsgSKCRmprwxHDd/sYejdGrWi909umM0pYP9skiIioKnOfGSEi4kHlqmjdvjj///FMFFamJ+eabb9CqVSv1Ggke3333Hf7++28MHz4cp0+fVsO4pbZA5q2RviLSNJIbqVG4c+eOGjpe0GMLadrKXCshnZAlaEjNhvR/kbAh75W+NI0aNVIBR3tcee/jkI7FMkRdOjAfP35cNRvJZ7Zv3x5NmzZFcYqLi1Mh8dChQ2oOoWPHjqnOz4GBgejduzcMRWJqoupHM2jTIPRc1xPLzy1Xwcbd1h1D6w/F/3r9Dyu6rsDz1Z9nsCGiYsWaGyMhfT7koi2BYcKECbh7965q+mnSpAkWLVqke522s6u8Tvp+JCYmquYQmUhv3LhxeY7Okloa6SQrF+ZHOfbNmzez9ImRz5ML/OzZs1UnXbnQy4R2EkJkvhwZPSTk8+S9j0N+Lgl1MtFgu3btVDmeeeYZNaqpuEnfIGmOk7mDpL+Nq6ur6vMjHbalg3NJdz3qOtZcXqNqaaKSonSjndqVb4cXa7yo5qOxMOOfFiLSn1IyHhwmRGbElYumTBzn6Jh1uKlc6GU0TeXKlVWTCFFJoe/vZkp6Cnbe3IlVl1bB715GZ2lRtnRZvFj9RdX05Fnas9jLRUSmIzqP63d2/OcVEeUqJD4Ef1z+Q23BCcFZaml61+yNNuXasHMwEZU4DDdElOO8NL9e+BXbArbpJtpzsXFRtTS9a/RGWfuy+i4mEVGuGG6ISElOS1arb0uoORd2Tre/kUcj9K3ZF50qdYKVuZVey0hElB8MN0QmTkY4rb60GisvrkRYYpjaZ2VmpWYO7l+7P2q5PNos0kRE+sJwkwMT62NNJvqdvBp5FT+f/xkbrm1Qaz4JWQKhX61+qvnJ2ca50I9JRFQcGG4y0c40K2v92Nrmb3ZgouIg30mRfTbkRwlJR+4dwbJzy9TClVr1XOthYN2BqunJ0uzxjkFEpG8MN9nmH5HZdLXrEcmaQflZlZuoqEgYkWAj30n5bsp39FGkpqeqzsESas6HnVf7SqEUOlbsqEJNQ/eG/K4TkdFguMnGy8tL3WoDDlFJIMFG+90s6CzC6/zXqdmDA2MD1T4bcxv0rNYTg+oMgrfjfwuJEhEZC4abbORfr2XLllUrM6ekpOi7OESqKaqgNTYxyTFqwj3pU6Nd68nZ2ln1p+lbqy/70xCRUWO4yYVcTB61CYBIXyISI1Sg+f3i74hNidXNIjy47mC1xlN+V5onIjJkDDdERiA0IRTLzy7H6surkZCaoPZVLVMVr9d/Hc9UfoadhInIpDDcEBmwoLgg/Hj2R/x5+U8kpyerfXVc62B4g+F4yvsptVQCEZGpYbghMuBQI2s+yaKWwtfdF280eEOtys2RT0RkyhhuiAxIcHwwfjyTEWq0NTWNPRrjrYZvoblXc4YaIiKGGyLDICOelp5ZipWXVupmE2aoISLKGcMNUQkWnRyNFedW4JfzvyA+NWOWYplwb2SjkWjh1YKhhogoB3rtbbhnzx50794d5cqVU3+k161b99D37Nq1C40bN4a1tTWqVauG5cuXF0tZiYqTjHiS5qdn/nwG35/+XgUb6Si8qNMi/NT1J7Qs25LBhoioJNbcxMXFwdfXF6+99hpeeOGFh77++vXr6NatG9588038+uuv2L59O4YOHaom3evSpUuxlJmoKMkyCWv912LxycUITgjWDeke1WiUWiqBgYaIqISHm65du6otvxYvXozKlStjzpw56nHt2rWxb98+fPXVVww3ZPBrSG27uQ3fHP8GN6JvqH3lSpdTzU/dKneDuRknlCQiMso+NwcPHkSnTp2y7JNQM27cuFzfk5SUpDat6OjoIi0jUUGdDD6JL49+iVMhp3TLJMg8NS/XfBlW5lb6Lh4RkcExqHBz7949eHp6ZtknjyWwJCQkwNb2wanlZ82ahRkzZhRjKYnyJyA6AF8f/xpbA7aqx7I0wsA6A9VSCfZW9vouHhGRwTKocPMopkyZgvHjx+seSxDy9uZKyKQ/UUlRWHxqMVZeXIlUTaqaRfj5as+rYd0edh76Lh4RkcEzqHDj5eWFoKCgLPvksaOjY461NkJGVclGpG8yk/DqS6ux6NQiFXCEzCY8vsl4VHeuru/iEREZDYMKN61atcLGjRuz7Nu6davaT1SS7b29F18c/QLXo66rx9WcquGdZu+gdbnW+i4aEZHR0Wu4iY2Nhb+/f5ah3idPnoSLiwsqVqyompQCAwPx008/qedlCPiCBQvw7rvvquHjO3bswOrVq/HPP//o8acgyrtfzedHPsee23t0nYVlWPcL1V+AhZlB/duCiMhg6PWv69GjR/HUU0/pHmv7xgwaNEhNznf37l3cvHlT97wMA5cg8/bbb+Prr79GhQoV8MMPP3AYOJU4cSlxavK9n87/pOausShlgQG1B+AN3zfgYOWg7+IRERm1UhqZYMOESIfiMmXKICoqSvXVISpM8r/Tpuub1NDukIQQta9NuTZ4t/m7qFKmir6LR0RkEtdv1osTFZKrkVfx6eFP4XfPTz2uYF8Bk5pPQvsK7TmzMBFRMWK4IXpM8SnxWHx6MX4+97Ma2m1tbo1h9YdhcL3B6j4RERUvhhuix7Dj5g7M8puFe3H31OMnvZ/EpGaTUMGhgr6LRkRkshhuiB6BhJlZh2dhx60d6nF5+/KY0nwK2nu313fRiIhMHsMNUQHIyKffL/6OBScWID41Xo2CkuYnWQtKlk8gIiL9Y7ghyqdL4Zcw/cB0nAs7px43dG+Iaa2mcXZhIqIShuGG6CGS0pLw3anvsOzsMtVh2MHSAW83fRsvVn9RrQtFREQlC8MNUR6OBx1XtTU3om+ox09Xelr1rXG3c9d30YiIqDDCTWRkJNauXYu9e/ciICAA8fHxcHd3R6NGjdQswa1bc50cMp7h3V8f/xq/XfxNPXa3dcd7Ld5Dx0od9V00IiJ6iHzVqd+5cwdDhw5F2bJl8cknnyAhIQENGzZEx44d1RIIO3fuxNNPP406depg1apV+flIohLryL0jeHH9i7pgI81P63qtY7AhIjKmmhupmZH1no4dO6YCTE4k8Kxbtw7z5s3DrVu3MHHixMIuK1GR19bMPTYXqy5lBPSypcviw1YfonV51kgSERnd2lJhYWFwdXXN94cW9PXFiWtLUU6OBR3D+/vex+3Y2+rxyzVexttN3oa9lb2+i0ZERCiCtaUKGlRKarAhymkklMxZs+LcCmigUbU1H7X5CC3LttR30YiIqDg6FEuNzOnTp+Hr6wsXFxeEhobixx9/RFJSEnr37o3atWs/ajmIit35sPN4b9978I/0V4+fr/Y83m32LmtriIhMJdz4+fmhc+fOqlrIyckJW7duVYHGwsIC6enpmD17Nvbt24fGjRsXbYmJHlNaehqWnVuGhScWqnlrXG1c8WHrD9W6UEREZPjyPQPZe++9p8KMtHVNnToVvXr1UqOlLl++DH9/f/Tt2xcff/xx0ZaW6DEFxgbitS2vqWHeEmxk3pq1Pdcy2BARmVqHYiHNUPv371dNTykpKbCxscHBgwfRvHlz9fzx48fRo0cP3L6d0SGzpGKHYtMkX/MN1zbg08OfIjYlFnYWdpjSYgp6Vu2JUqVK6bt4RERU3B2KRXJyMmxtMxYGtLS0hJ2dHdzc3HTPy33pk0NU0sQmx+LjQx9j4/WN6rGvuy9mtZ0FbwdvfReNiIiKQL7Djbe3N65duwYfHx/1eOXKlWpSP627d+9mCTtEJcGZkDN4d8+7aoi3eSlzvOH7BobVHwYLM648QkRkrPL9F1761AQHB+sed+vWLcvz69ev1zVREelbuiYdy88tx/zj81XfmnKly+Gzdp+hoUdDfReNiIhKSp+bh5F1pszNzWFtbY2SjH1ujF9oQiim7p2Kg3cPqsddfLpgWqtpcLTi75uIyFAVSZ+bnEgH46ZNm6pAI31wiPTN764fJu2dpAKOrYUtJjefrOavYadhIiLTke+h4Dnp2rUrAgMDC680RI8xd83iU4sxbOswFWyqOVXD791+xwvVX2CwISIyMY9Vc1NILVpEjyUsIQyT907GobuH1GOpqZFh3lJzQ0REpodDRsignQg+gYm7JiI4IViFmfdbvo8eVXvou1hERGSo4ea7776Dp6dn4ZWGqAC1hr9e+BVzjs5Ro6GqlKmCuU/ORVWnqvouGhERGXK46d+/f+GVhCif4lLiMP3AdGy5sUU9fsbnGcxoPQN2luzUTkREjxBuEhMTMX/+fOzcuVPNeyOLZmYmyzAQFZXrUdcxbuc4XIu6BotSFpjYbCL61+rPTsNERPTo4eb111/Hv//+i5deeklN2seLChWXnTd3Yuq+qWptKA9bD8x5cg4n5SMioscPNxs2bMDGjRvRpk2bgr6V6JFnG5Zh3otOLVKPG3s0VsHGzZbLfRARUSGEm/Lly8PBwaGgbyN6JDHJMWq24V23d6nH0gQlTVGWZpb6LhoRERnLJH5z5szBpEmTEBAQUDQlIrovIDoAAzYOUMHGyswKn7T5RM1fw2BDRESFWnMjyy1Ip+IqVaqoJRcsLbNeaMLDwwv6kUQPOHjnICbunojo5Gh42Hngm6e+QV23uvouFhERGWO46devn1py4dNPP1Vz3LBDMRX2/DW/XfwNXxz5AmmaNDRwb4Cvn/qa/WuIiKjows2BAwdw8OBB+Pr6FvStRHlKSUvBzMMz8eeVP9VjmWlYVvO2Ni/ZK80TEZGBh5tatWohISGhaEpDJisqKQrjd42H3z0/mJUyw/gm4zGwzkDWDBIRUdF3KJ49ezYmTJiAXbt2ISwsDNHR0Vk2okfpOPzKxldUsLGzsMP8DvMxqO4gBhsiInokpTQFXNrbzCwjD2W/8MjHyL60tDSUZBLAypQpg6ioKDg6Ouq7OCbvyL0jeHvX26rmpmzpsljQcQFqONfQd7GIiMiAr98FbpaSZReICsP6q+vVGlGp6amo71Yf33T4hh2HiYjosRU43LRv3/7xj0omTWr5vjv9HRaeXKged/HpouawsbGw0XfRiIjI1FcFJyqolPQUfHzwY6z1X6sev17vdYxpPEZ1IiYiIioMDDdUbGKTYzFh9wQcuHNAhZn3WryHl2u+rO9iERGRkdH7P5cXLlwIHx8f2NjYoEWLFvDz88vz9fPmzUPNmjVha2sLb29vvP3222rGZCrZQuJDMGTLEBVsbC1s1YzDDDZERGR0NTerVq3C+PHjsXjxYhVsJLh06dIFly5dgoeHxwOv/+233zB58mQsXboUrVu3xuXLlzF48GA1Smvu3Ll6+Rno4W5E3cCb295EYGwgXG1csbDjQi6lQERExllzI4Fk2LBhGDJkCOrUqaNCjqxXJeElt9mR27Rpg/79+6vans6dO6vlIB5W21Nc4pJSEZ2You9ilChnQs5g4KaBKthUdKiIX579hcGGiIj0X3PTqFGjfE+odvz48Xy9Ljk5GceOHcOUKVOyzKHTqVMntbxDTqS25pdfflFhpnnz5rh27Ro2btyIV199NdfjJCUlqU2rqCYaPHUrEmNWnkBDbyd83bdRkRzD0OwL3KdmHU5ITUBd17qqxsbV1lXfxSIiIiOXr3DTq1evQj9waGiomvBPFt/MTB5fvHgxx/dIjY2874knnlDDiVNTU/Hmm29i6tSpuR5n1qxZmDFjBopamkaD2xEJCAiLR8fanujhWw6mbMO1Dfhg3wdI1aSidbnW+OrJr2BnaafvYhERkQnIV7iZPn06SgJZ8kFWI//2229VHx1/f3+MHTsWH3/8MT744IMc3yM1Q9KvJ3PNjXRELmyNKzpj5FPV8M32K3h/7Rk083FG2TK2hX4cQ/Dbhd8wy2+Wut+tSjd83PpjWJpb6rtYRERkIh65Q7E0KV24cEHdr1u3rmq6Kgg3NzeYm5sjKCgoy3557OXlleN7JMBIE9TQoUPV4/r16yMuLg7Dhw/He++9p1saIjNra2u1FYfRHaph96VgnLodhYlrTuHn11rAzMx01keS2rTFpxfj25PfqscDag/Au83e5Rw2RERUrAp81QkODkaHDh3QrFkzjBkzRm1NmjRBx44dERISku/PsbKyUu/bvn27bl96erp63KpVqxzfEx8f/0CAkYAkCrhEVpGwNDfDV30awsbSDPv9w7DswA2YinRNOj4/8rku2LzV8C1MajaJwYaIiIpdga88o0ePRkxMDM6dO4fw8HC1nT17VjX3SNApCGkuWrJkCVasWKFqgUaMGKFqYmT0lBg4cGCWDsfdu3fHokWLsHLlSly/fh1bt25VtTmyXxty9K2Kuz3e61ZH3f9s80VcuhcDYydrQ32w/wP8cuEX9Xhy88kY4TuCq3oTEZFhNEtt3rwZ27ZtQ+3atXX7ZBi3TMYnQ7MLok+fPqq2Z9q0abh37x4aNmyoPl/byfjmzZtZamref/99dcGU28DAQLi7u6tgM3PmTJQkr7SoiB0XgrDzUgjGrTqJdSNbw9qiZISvwpaSloJJeydha8BWmJcyx8dtPkb3qt31XSwiIjJhpTQFbM9xcHDA3r17VRDJ7MSJE2pRzaIaaq2PJdMfR3BMIp6ZtxfhcckY3q4Kpj77Xxg0FklpSWqo957be2BpZokv23+JDhU76LtYRERkhApy/S5ws5T0t5ERSnfu3NHtk1oUWQZB+t1QBg8HG8x+ob66//2ea9h7Jf/9kQxBfEo8Rm0fpYKNtbk15neYz2BDREQlQoHDzYIFC1R6khmCq1atqrbKlSurffPnzy+aUhqoznW98ErLiur++NWnEBb732SChr4A5ohtI3Do7iHYWdhhUadFaFO+jb6LRURE9GjNUkLeIv1utJPtSf8bmVnYEBRXs5RWYkoaus/fhyvBsehQywM/Dmpq0B1to5Oj8ebWN3Em9AwcrBxUsPF199V3sYiIyMhFF+D6/UjhxpAVd7gRF+5Go+fC/UhOTceMHnUxqLUPDDXYvPHvGzgbdhZlrMtgydNLUNvV+PoSERGRYV+/H2kSvyNHjmDnzp1qzhuZmyYzrs79oNplHTG1ay18+L/zmLnxAlpUcUEtr+IJVoUlKikKw7cOx/mw83CydsIPnX9ATZea+i4WERHR44cbWf5AhmLXrFlTDdnO3MRiyM0tRU1qa/ZcCcWOi8EY/dsJrB/1BGytzA0m2Az7dxguhF+As7UzfujyA2o419B3sYiIiAqnWUoCzWeffYbBgwfDEOmjWUpLOhQ/8/VehMQkoU9Tb3z2UgMYQrAZ+u9QXAy/CBcbF1VjU925ur6LRUREJia6KIeCy6R6bdpwZMyjcLW3xtd9G0IquFYdvYV1JwJhCDU2EmxcbVyxtMtSBhsiIirxChxuZD4bmY2YHk3rqm4Y0yEjILy39gyuhcSiJIpJjlGjoqQpSmpsfuzyI6o6VdV3sYiIiAq/WUo6EHfr1g2XL19Wyy5YWlpmef6vv/5CSabPZimttHQNBvxwCIeuhavOxmvfag0by5LT/yYuJQ5vbH0Dp0JOqc7DEmzYx4aIiIy2WUoWx5SRUjVq1ICrq6s6UOaNHs7crBS+7tsILqWt1DDxmf9cQEmaefitbW+pYONo5YglnZcw2BARkfGvLSWrckvtjSEqCTU3WrsuBWPwsiPq/sL+jdGtQVm9rxU1cttIHL53GA6WDljSZQnqutbVa5mIiIiKvObGxcVFLblAj+/Jmh4Y8WTGuXz3j1O4qsf+N7K6tyyCKcFGllRY/PRiBhsiIjJIBQ43H374IaZPn474+PiiKZGJmfB0DTSv7IK45DS89ctxJCSnFXsZ0tLTMGXfFN0imAs6LkAD95I/TJ2IiKhQmqUaNWqEq1evqvWlZPHM7B2Kjx8/jpKsJDVLaQVHJ+LZb/YhNDYJLzQujzm9fYttQsR0TTqm7Z+Gv6/+DQszC7W69xPlnyiWYxMREZWI5Rd69epV0LfQQ3g42mBB/0bov+QQ/joeiOY+LujbPGM18aIkAfUzv89UsDErZYbP233OYENERAaPC2eWIIt2XcVnmy/CysIMf41ojXrli3b02bcnv8WiU4vU/ZlPzESPqj2K9HhEREQlpkOxieUfvXmjXRV0qu2hVg8f8esxRMYnF9mxfr3wqy7YTG0xlcGGiIiMRr7CTd26ddXw7+TkvC+2V65cwYgRIzB79uzCKp9JMTMrhTm9G8LbxRa3whMwduVJNeFfYdtwbQNm+2X8jt5q+Bb61epX6McgIiIq0c1S27dvx6RJk3Dt2jU8/fTTaNq0KcqVKwcbGxtERETg/Pnz2LdvH86dO4dRo0Zh6tSpJXZCv5LcLKV1/k40Xli0H4kp6RjdoRomdK5ZaJ8tI6LG7hiLVE0q+tfqj8nNJ3M1dyIiKvEKcv0uUJ8bCTCrVq3C3r17ERAQgISEBLi5uakRVF26dMGAAQPg7OyMkswQwo2QRTXHrTqp7n//ahN0ruv12J95MvikWggzMS0Rz1Z+FrPazlIdiYmIiEw23BgDQwk3Ysb/zmHZ/huwt7bA36PaoKq7/SN/1rXIa3h106uITo5WI6K+6fANLM2yDuMnIiIyyRmKqfhMfba2GhYem5SKN34+pm4fRXB8MN7c9qYKNg3cGmBO+zkMNkREZLQYbkowS3MzLBjQCJ6O1vAPjsWE1SeRXsAOxjHJMRixbQTuxt2Fj6OPmn3YztKuyMpMRESkbww3JZyHgw0Wv9IEVuZm2HIuCF9vv5Lv9yanJWPcznG4HHEZbrZuWNRpEZxtSnafKCIiosfFcGMAGlV0xqcv1Ff3JdxsPns3X8sqvL/vffjd81MLYX7b8VtUcKhQDKUlIiLSL4YbA/FSkwp4rU1ldX/86lO4eC86z9d/ffxrbLqxCRalLPDVU1+htmvtYiopERGRfhV4bSmRnp4Of39/BAcHq/uZtWvXrrDKRtlMfbYWLgfFYJ9/KIauOIr1o56AS2mrB163+tJqLD27VN2f0WYGWpdrrYfSEhERGUi4OXToEPr376/muck+ilwmg0tLSyvM8lEmFtLBuH8j9Fy4HwFh8Xjr12P4+fUWquOx1t7be/Hp4U91sw9zWQUiIjI1BW6WevPNN9UMxWfPnkV4eLiaoVi7yWMqWk52VlgysKma++bQtXBM+/usLmReDL+IibsnIk2TpkLNmw3e1HdxiYiIil2BJ/ErXbo0Tp06hWrVqsEQGdIkfnnZcTFINU3JyPD3u9XGc43tMOCfAQhOCEZzr+ZY3GkxLM05lw0RERmHIp3Er0WLFqq/DelXh1qeapI/MXPTSQze+KYKNlXLVFUdiBlsiIjIVBW4z83o0aMxYcIE3Lt3D/Xr14elZdaLaIMGDQqzfJSH15+oDP/gaKy7OwuB8VfhaOmEhZ0WwtHKcGukiIiIij3cvPjii+r2tddey9KRWFq32KG4eMn5dqu4HZax56FJN0fq3cGw0rjpu1hERESGFW6uX79eNCWhAlvnvw4rzi9X9x1iB+BuiBeGrjiClcNbwdbKXN/FIyIiMoxwU6lSpaIpCRXI0XtHMePgDHV/eIPh6FZhCF5YdACnbkdh7MoTWPRKE5ibldJ3MYmIiAxjhuKrV6+qvjedOnVS25gxY9Q+Kh63Y27j7V1vIzU9FU9XehojG45EFXd7NURc1qD693wQZv5zQd/FJCIiMoxws2XLFtSpUwd+fn6q87Bshw8fRt26dbF169aiKSXpxKfEY8zOMYhMikQd1zqY+cRMmJXK+DU283HBly/7qvtL91/H8v1sQiQiItNT4HluGjVqhC5dumD27NlZ9k+ePBn//vsvjh8/jpLMkOe5kcUwJ+yagG03t8HVxhUrn1sJr9JeD7zu213++HzzJUir1HevNsXTdTz1Ul4iIiKDmOfmwoULeP311x/YL6Onzp8/X9CPowL47tR3KthYmlli3lPzcgw2YkT7qujX3FtN8Df69+M4cTOi2MtKRESkLwUON+7u7jh58uQD+2Wfh4dHYZWLstkesB3fnvpW3f+g5Qdo6NEwzyHiH/Wsh/Y13JGYko7Xlh/BtZDYYiwtERGRAY2WGjZsGIYPH45r166hdeuM1ab379+Pzz77DOPHjy+KMpq8yxGXMWXfFHV/QO0BeL768w99jyym+e2Axui35BBO347CoGV++HNEa3g42BRDiYmIiAyo5uaDDz7AtGnTMH/+fLRv315tCxYswIcffoj333+/wAVYuHAhfHx8YGNjo5Z2kI7KeYmMjMTIkSNRtmxZWFtbo0aNGti4cSOMVVRSFMbtHIeE1AS08GqBiU0n5vu9pa0tsHRwM1RytcOt8ARVgxOblFqk5SUiIjK4DsWZxcTEqFsHB4dHev+qVaswcOBALF68WAWbefPmYc2aNbh06VKOTVzJyclo06aNem7q1KkoX748AgIC4OTkBF/fjFFCxtShWDoQj9o+CnsD96K8fXms7LYSTjZOBf6cG6FxeHHRAYTFJaNtdTf8OKgZrCweaRYAIiIivSjI9fuxws3jkkDTrFkzVfMj0tPT4e3trebQkdFX2UkI+uKLL3Dx4sUH1rTKL0MKN9+e/BaLTi2Ctbk1fu76M2q7ZiyU+ShO3YpE3+8PISElDT18y2Fen4Yw4yR/RERkIApy/c5Xn5vGjRtj+/btcHZ2VkPBpcNqbvI7FFxqYY4dO4YpUzL6kggzMzM1KeDBgwdzfM/69evRqlUr1Sz1999/q87N/fv3x6RJk2BunvNyA0lJSWrLfHIMwe5bu1WwEdNbTX+sYCN8vZ2w6JXGGLriKNafugNnO0t82KNunr9LIiIiQ5SvcNOzZ0/Vv0V7vzAuiKGhoWqRTU/PrHOwyGOpmcmJdGLesWMHBgwYoPrZ+Pv746233kJKSgqmT5+e43tmzZqFGTMylikwFAHRAZiyNyP09avVD92rdi+Uz32ypgfmvOyLcatOYsXBADiXtsK4TjUK5bOJiIhKCr01S925c0f1mTlw4ICqjdF69913sXv3bjXrcXbSeTgxMVEt3qmtqZk7d65qqrp7926+a26k6aukNkvJDMQDNg6Af6Q/Gnk0wo+df4Sl+aM1weVmxYEbmL7+nLo/o0ddDGrtU6ifT0REZFCT+FWpUgVhYWE5jmKS5/LLzc1NBZSgoKAs++Wxl1fOk9PJCCkJOJmboGrXro179+6pZq6cSI2TnITMW0klOfOjQx+pYONm64Y57ecUerAREmbGdaqu7kvI+ftkYKEfg4iISF8KHG5u3LihmpOyk9qR27dv5/tzrKys0KRJE9WXR0s6FMvjzDU5mclIKWmKktdpXb58WYUe+TxDt+byGvxz7R+YlzLHF+2+gLude5Eda2zH6hjUKmOF9wmrT2Hb+awhk4iIyOgn8ZPOvJkXz5SqIS0JOxJKKleuXKCDy6R/gwYNQtOmTdG8eXM1FDwuLg5DhgxRz8swcWm6kn4zYsSIEWpk1dixY9WIqitXruDTTz9Vq5IbunNh5zDbL2O9rrGNx6KpV9MiPZ70m5revS6iElKw7uQdvPXbcSwb3AxtqrkV6XGJiIhKTLjp1auX7qIogSQzGZYtE/HNmTOnQAfv06cPQkJC1KSA0rTUsGFDbN68WdfJ+ObNm2oElZb0lZFg9fbbb6vVyCX4SNCR0VKGPlGfLIiZkp6CDt4dMLju4GI5rgwF/7K3rxoevuVckBpJ9cvQ5mhSyaVYjk9ERFQiOhRL7cyRI0dUnxlDVNLmuZGJ+sbsGIPdt3ejgn0FrOq+Co5WxVuupNQ0DPvpGPZcDoGDjQV+H9YS9cr/VzNHRERk1B2KZaSSoQabkmjZ2WUq2FiZWWHuk3OLPdgIawtzfPdKEzT3cUFMYioGLvXD5aCM2aeJiIiMfuFMIf1iZLi2NBtlH6VkDP1fisuJ4BOYf2K+uj+lxZTHnqjvcdhamePHwU0x4IfDaqHN/ksOY+XwlqjmYa+3MhERERVLs9SJEyfw7LPPIj4+XoUcFxcXNSGfnZ2dWvNJJtoryUpKs1RkYiR6b+iNe3H30K1KN8x6YlaJmC04Mj4Z/ZYcxoW70fBwsMaqN1qhsltpfReLiIhMXHRRNktJZ97u3bsjIiICtra2OHTokFq8UoZ1f/nll49TbpMhefKD/R+oYFPJsRI+aPlBiQg2wsnOCr8ObYGang4IjklC/yWHcDMsXt/FIiIiyrcCh5uTJ09iwoQJahSTTKYn89vIKKbPP/9crdRND/fz+Z+x6/Yu1c/my/ZforRlyaoZcSlthV+HtVBNUnejEtFvySHcCmfAISIiIw03MuxbOzxbmqGk342QqqJbt24VfgmNzJmQM/jq+Ffq/jvN3kEtl1ooidzsrfHb0Bao4lYagZEJDDhERGS84UZWBZeh4KJ9+/Zqjppff/0V48aNQ7169YqijEYjJjkG7+x5B6npqXi60tPoU7MPSjIPRxv8NqwlfFztcDsiAX2/Z8AhIiIjDDcyI7AsdyBmzpwJZ2dnNXOwTMb3/fffF0UZjaafzccHP0ZgbCDK25fHh60/LDH9bPLiVcYGK4dndCqWGhwJOOyDQ0RERjNaSl4qTU/SHGVjYwNDpK/RUuuvrsd7+95T60at6LoCvu6+MCRB0Rl9b66FxKFcGRv8PrwlKrmWrL5CRERkvIpstJSEm2rVqrFvTQHdjL6JmYdmqvtvNXzL4IKN8HS0wcphLVHVvTTuRCWiz3eHcD00Tt/FIiIierxwIx2Jq1evjrCwsIK8zaSlpKXg3T3vIj41Hk09m+L1eq/DUEkfHKmxqe5hj3vRiXj5u4OcyZiIiAy/z83s2bPxzjvv4OzZs0VTIiOz8ORCteK3LKswq+0smJuZw5B5OGQEnFpeDgiJSUKf7w7ibGCUvotFRET06DMUSwdimZ04NTUVVlZWaiK/zMLDw1GSFWefG7+7fhj671BooFHrRskIKWMhMxkPWuqHU7ej1GKbK15rjsYVnfVdLCIiMlIFuX4XeG2pr776yiBG+ZSUyfok2LxQ/QWjCjbamYx/GdoCry0/giM3IvDqD4fxw6BmaFXVVd9FIyIiE1fgcDN48OCiKYkRCkkIUbcdvDvAGDnYWKoam2E/HcV+/zAMXuaHRa80RodanvouGhERmbAC97mRJReCg4Mf2C+djOU5+k9kUqS6LWNdBsbKzsoCPw5qhk61PZCUmo7hPx3D3ycD9V0sIiIyYQUON7l10ZE1pqQPDj0YbpysnWDMbCzNseiVJni+UXmkpmswbtVJ/HTwhr6LRUREJirfzVLffPONupX+Nj/88APs7e11z6WlpWHPnj2oVatkrpOkryHgcSkZ88A42xh/R1tLczPM6e2LMraWWH7gBqb9fQ5R8SkY1aEa+2gREVHJDDfSkVhbc7N48eIsTVBSY+Pj46P2U4ao5Izh0WalzOBg5QBTYGZWCtO711EB5+vtVzBn62WExSVj2nN11HNEREQlKtxcv35d3T711FP466+/1JBwyl1EYoS6lfltJOCYCqmlefvpGnCys8SM/51XtTihsUmY87IvrC3YJ4uIiIpega+6O3fuZLDJB1Ppb5ObIW0q4+u+DWFpXgobTt9VQ8ZjElP0XSwiIjIBBR4KLv1rli9fju3bt6tRU+np6Vme37FjR2GWz2BFJUWZdLgRPRuWh0tpK7z58zE1VFxWFF82pJma5ZiIiKjE1NyMHTtWbRJy6tWrB19f3ywbZYhIymiWMuVwI9pWd8fK4a3gZm+Fc3ei8eKiA7gaEqvvYhERkRErcM3NypUrsXr1ajz77LNFUyJjq7mxMe1wI+pXKIM/R7TGwKV+CAiLVwFnycCmaObjou+iERGRESpwzY2MjKpWrVrRlMYIOxSbes2NViXX0irgNPR2QmR8Cgb8cBj/nL6r72IREZERKnC4mTBhAr7++utcJ/Mj05mduKDc7K3x+7CW6FzHE8mp6Rj523F8v+cqv0tERKTfZql9+/apEVObNm1C3bp1YWlpmeV5GSZO/zVLOVtzZFlmtlYZsxl/vCFjmPinGy+qpqoPe9RVEwESEREVe7hxcnLC888//9gHNnbsUJw78/uT/VVwtsXMjRfw6+GbuBkejwX9G6sJAImIiIo13CxbtuyxDmhqNTdslsp9sr+hbavA28UO41aexN4roaqj8dJBzVDR1U7fxSMiIgP2SO0Aqamp2LZtG7777jvExMSofXfu3EFsLIf4Zu9zYwrrSj2OLnW9sObNVvB0tIZ/cCx6fbsfR2+E67tYRERkSuEmICAA9evXR8+ePTFy5EiEhISo/Z999hkmTpxYFGU0OGnpaYhOilb3WXPzcPXKl8HfI59AvfKOCI9LRv8lh/HHsdv6LhYREZnSJH5NmzZFREQEbG1tdfulH47MWkxAdHI0NMgYAcRwkz9eZWyw+o1W6FLXE8lp6Zi45hRm/nMeaekcSUVEREUcbvbu3Yv3339fzXeTmawKHhgYWNCPM+omKQdLB1iasYNsftlZWWDRgCYY0yFjHqUle6+rNamiErgmFRERFWG4kbWkZOmF7G7fvg0HB4eCfpxR4hw3j87MrBTGd66JBf0bwcbSDLsvh+D5b/dzyQYiIiq6cNO5c2fMmzcvy6gX6Ug8ffp0LslwX2QiOxM/rucalMMfb7ZG2TI2uBYSh14L9mPb+SB9F4uIiIwx3MyZMwf79+9HnTp1kJiYiP79++uapKRTMbHmplA7Go9qg2Y+zohJSsXQn45i3rbLSGc/HCIiKsx5bipUqIBTp05h1apV6lZqbV5//XUMGDAgSwdjU6YNN5zA7/F5ONjg16Et8ck/5/HTwQDM23YFZwOjMbePLxxt2J+JiIgKIdyoN1lYqDAjGz2I4aZwWVmY4aOe9VRNzvvrzmLbhSDVTLX41Sao4cl+XkRE9JjNUrNmzcLSpUsf2C/72CyVdXZihpvC9XJTb6x5o1VGP5zQOPRcsB9/n+QIPSIiesxwI7MS16pV64H9sojm4sWLC/pxRikiketKFRVfbydsGP0EnqjmhoSUNIxdeRLT/z6rVhknIiJ6pHBz7949lC1b9oH97u7uuHv3Ls9q5mYpG4abouBqb40VrzXH6Pvz4aw4GICXvzuIwMgEfReNiIgMMdx4e3ur0VLZyb5y5coVVrkMGpulimdl8Qmda2Lp4KZwtLHAyVuR6PbNXmy/wOHiRESmrsDhZtiwYRg3bpxaHVzWmZJN+tu8/fbb6rlHsXDhQjWc3MbGBi1atICfn1++3rdy5Uo1z06vXr1QkkQksVmquHSo5Yl/xrRFgwplEBmfgtdXHMWsjReQksZmKiIiU1Xg0VLvvPMOwsLC8NZbbyE5OVntk1AyadIkTJkypcAFkCHl48ePV/11JNjIBIFdunTBpUuX4OHhkev7bty4oRbqbNu2LUoSjUajWzST4aZ4eLvYqZXFZ228iOUHbuC7Pddw5EY45vdvjPJOnJ6AiMjUlNLI1fgRyPw2Fy5cUHPbVK9eHdbW1o9UAAk0zZo1w4IFC3TLO0jT1+jRozF58uQc3yPLP7Rr1w6vvfaaWusqMjIS69aty9fxoqOjUaZMGURFRcHR0RGFLSY5Bq1/b63uH33lKKzNH+280KPZfPYu3vnjNGISU1HG1hKfv9QAXep66btYRET0mApy/S5ws5SWvb29CiX16tV75GAjNT/Hjh1Dp06d/iuQmZl6fPDgwVzf99FHH6laHZk8sKQuvWBrYctgowfP1CuLf0ZnNFPJgptv/HwMH6w7i8SUB9dDIyIi41TgZqm4uDjMnj0b27dvR3BwsKppyezatWv5/qzQ0FBVC+Pp6Zllvzy+ePFiju/Zt28ffvzxR5w8eTJfx0hKSlJb5uRXlDiBn/5VdLVT61J9+e8lfL/nGn4+FAC/69JM1YiT/hERmYACh5uhQ4di9+7dePXVV9WQcOnQW1xiYmLUcZcsWQI3N7d8Tzo4Y8YMFBeGm5Izq/HUZ2ujTTU3TFh9EpeCYtB9/j683602XmlZqVi/t0REVMLDzaZNm/DPP/+gTZs2j31wCSjm5uYICso6fFcee3k92E/i6tWrqiNx9+7ddfu0NUeyJIR0Qq5atWqW90gnZ+mwnLnmRvr0FBUumlmytK/hjk1j22HCmlPYczkEH/x9DjsvheCzFxvA3YHNhkRExqjAfW6cnZ3h4uJSKAe3srJCkyZNVBNX5rAij1u1avXA62Vm5DNnzqgmKe3Wo0cPPPXUU+p+TqFF+gNJx6PMW1HShhtna+ciPQ7ln4SY5YOb4YPn6qganR0Xg/HMvD2cE4eIyEgVONx8/PHHmDZtGuLj4wulAFKrIs1MK1asUKOvRowYofr1DBkyRD0/cOBA3RBzGXIuHZgzb05OTnBwcFD3JSzpG2tuSiYzs1J4/YnKWD+qDWp5OSAsLlnNiTN17RnEJaXqu3hERKTPZqk5c+ao5iHp9CsT71laWmZ5/vjx4wX6vD59+iAkJEQFJlnaoWHDhti8ebOuk/HNmzfVCCpDoR0txaUXSqZaXo5YN7INvtxyCT/su47fDt/Efv9QzOnti6Y+hVMjSUREBhZuimI24FGjRqktJ7t27crzvcuXL0dJwg7FJZ+NpTnef64OnqrlgXfWnEJAWDx6f3cQw9tVwdudaqjniYjIhMLN9OnTi6YkRoLhxnDISKrNb7fDR/87jz+O3cZ3u69h58VgzH25IeqVZ7MiEZHJhBstmXxP+siIunXrolGjRoVZLoPFcGNYHG0s8WVvX3Su46n631wOikXPhfvx1pNVMapDNVhbsBaHiMjow41M3Ne3b1/VXCSdeYUsfyAjlmQhS3d3d5gyXbhhnxuD0rmuF5pUcsa09efwz+m7mL/DH/+eC8IXvRugQQX+LomIDEmBe+rKmk8ymd65c+cQHh6utrNnz6r5Y8aMGQNTJst06ToUs+bG4LjaW2Nh/8ZYNKAx3Oyt1MR/z397AJ9tvsjlG4iIjHnhTFm0atu2bWpdqcz8/PzQuXNnVYtTkhXlwpnxKfFo8VsLdf9Q/0MobVm6UD+fik94XDI+XH8O60/dUY+ruJXGpy/UR8sqrvouGhGRSYouyoUzZZK97MO/hezLvs6UqYlKilK3lmaWsLOw03dx6DG4lLbCN/0a4ftXm8DT0RrXQuPQ9/tDmPLXGUQnpui7eEREVJjhpkOHDhg7dizu3Mn4F60IDAzE22+/jY4dO8KURSRF6JqkuHaR8fTF+fft9ujXvKJ6/LvfTTw9dzc2n72rmiGJiMgIws2CBQtU1ZBM4CfrOMlWuXJltW/+/PkwZZyd2DiVsbXErBfqY+XwlqjsVhpB0Ul485fjGPbTUQRGJui7eERE9LijpWT9JpmFWPrdXLx4Ue2rXbs2OnXqBFOnbZZytuG6UsZI+ttsGtsWC3b447s9V7HtQjAOXN2N8U/XwODWPrAwN5yZtImIjNkjzXMjTS5PP/202ug/EYn/NUuRcZLZiyd2qYmeDcupeXGO3IjAJ/9cwJ/HA/FJr3pqODkREelXvv+puWPHDtSpU0c1P2UnPZdlIr+9e/fClGlrbtgsZfyqezpg1fBW+OzF+qrZ6sLdaLy46ADe/eMUwmKT9F08IiKTlu9wM2/ePAwbNizH4VcyNOuNN97A3LlzYcq0fW6crfmvd1NZabxPs4rYMaE9Xm5aQe1bffQ2OszZjV8OBSAtnR2OiYhKdLg5deoUnnnmmVyflzluZEkGU6YdLcWaG9Ob/O/zl3zx54hWqF3WEVEJKXh/3Vn0XLgPR2+E67t4REQmJ9/hJigoKMf5bbQsLCwQEhICU6ZtlmKfG9PUpJIL/jeqDT7sXgcO1hY4GxiNlxYfxNurTiIoOlHfxSMiMhn5Djfly5dXyyzk5vTp0yhbtixMma5ZiqOlTJaMmBrcpjJ2vvMk+jT1hkx3tPZEIJ76che+3eXPZRyIiEpSuHn22WfxwQcfIDHxwX+BJiQkYPr06XjuuedgyrTrSrFZitzsrfHZSw2w7q02aFTRCfHJafh88yU8/dVubDzDCQCJiErE2lLSLNW4cWOYm5tj1KhRqFmzptovc90sXLgQaWlpav4bT09PmOraUi1+bYH41HhseH4DKjlWKtTPJsOVnq5RtTefb7moJgAUzX1c8MFzdVC/AoMwEVFhX78LtHBmQEAARowYgS1btuj+5Slz3nTp0kUFHJmpuKQrqnCTnJaMJr80Uff39d3H2ht6QHxyKhbvvobv91xFYkq6arJ6vlF5TOxcE+WcbPVdPCIi0ww3WhEREfD391cBp3r16nB2Npw+JkUVboLjg9FxTUeYlTLDiVdPqFuinNyJTMAXWy6p2hxhbWGG156ojBFPVoWjTe6d9omITFl0UYcbQ1ZU4eZyxGW8uP5FNcfNnr57Cu1zyXiduhWJTzdewOHr4bqVyMd0qIb+LSrByoLhmIjoUa/f/Ata2MPAbTgMnPLH19tJLcb5w8CmqOpeGuFxyfjwf+fRce4urDsRqPrqEBFRwbHmppAkpSXhbuxdpKSnoLpz9UL7XDINqWnpWHnkFr7efgUhMRmdjmt5OWDSM7XwZE131beNiMiURbNZSj+jpYgKo9Pxsv03sHjXVcQkpap9zXycVafjFlVc9V08IiK9YbjJA8MNGYKIuGQs2n0Vyw/cQHJqutrXtrobJnSuiYbebPokItMTzXCTO4YbMiT3ohIxf8cVrDpyC6n3++B0qu2JcZ2qo155TjdARKYjmuEmdww3ZIhuhsWr/jhrT9yGtp9x5zoScmqgTjl+j4nI+EUz3OSO4YYMmX9wLL7ZfgX/O30H2v9zn6nrhTEdqzPkEJFRi2a4yR3DDRkD/+AYfL3dHxsyhRxprhrVoRr75BCRUWK4yQPDDRmTK0Ex+GZH1pAjHY+lJqeZj4u+i0dEVGgYbvLAcEPG6GpILL7deRXrTgYi7X6nHBlC/taT1ThPDhEZBYabPDDckDG7FR6Pb3ddxZ/HbiM5LWMIee2yjmrdqm71y8LcjCGHiAwTw00eGG7IVIaQ/7jvGn49fBPxyWlqn7eLLYa1rYLeTbxha2Wu7yISERUIw00eGG7IlETGJ2PFgQAsP3AdEfEpap+znSVebeWDQa0qwdXeWt9FJCLKF4abPDDckKku6/DHsdv4Ye913AyPV/usLczwQuPyeP2Jyqjm4aDvIhIR5YnhJg8MN2TKpLPx5rP38N2eqzh9O2MleyGdjoc+UQVtqrmy8zERlUgMN3lguCEC5H/7Izci8MPea9h6IUg3jLyGpz0Gt66M5xuVZ78cIipRGG7ywHBDlFVAWJxaiXz10Vu6zsdlbC3Rt5k3XmlZCd4udvouIhERGG7ywHBDlLPoxBSsPnILPx0M0PXLkZHjHWp5YmCrSniimhvMOJSciPSE4SYPDDdED++Xs/NiMJYduI79/mG6/ZXdSquanJcaV0AZO0u9lpGITE80w03uGG6ICraG1c8HA/Dn8UDEJqWqfTaWZniuQTkMaFFRrWPFDshEVBwYbvLAcENUcBJs1p0IVEHnUlCMbr/Mfiwhp2fDcnCwYW0OERUdhps8MNwQPTr5c3H8ZgR+PXQTG87cRXJqxhIPtpbm6NagLPo190bjis6szSEivV6/zVACLFy4ED4+PrCxsUGLFi3g5+eX62uXLFmCtm3bwtnZWW2dOnXK8/VEVHgktDSp5IK5fRrCb2pHvN+tNqp52CMhJU1NEvjiooN4+qs9WLLnGkJikvRdXCIyUXqvuVm1ahUGDhyIxYsXq2Azb948rFmzBpcuXYKHh8cDrx8wYADatGmD1q1bqzD02WefYe3atTh37hzKly//0OOx5oaocMmfkGMBEVh55BY2nL6DxJSM2hwLs1LoUMsDvZt6q0kCLc1LxL+liMhAGVSzlASaZs2aYcGCBepxeno6vL29MXr0aEyePPmh709LS1M1OPJ+CUkPw3BDVLTDydefvIM1x27j1K1I3X43e2vVL+fFxhVQpxz/vyOigivI9dsCepScnIxjx45hypQpun1mZmaqqengwYP5+oz4+HikpKTAxcWlCEuan4KEA7ePZNyv0UW/ZSHSE0cbSzVcXLbLQTFYc/QW1p4IRGhsEn7cd11ttbwc8FKTCujRsBw8HGz0XWQiMkJ6DTehoaGq5sXT0zPLfnl88eLFfH3GpEmTUK5cORWIcpKUlKS2zMmvSARfAH57GXCtxnBDpJZycMB73erg3WdqYc/lEPx5/Da2nQ/GxXsx+OSfC/h04wW0qeaGXg3Lo0s9L9hb6/XPEREZEYP+azJ79mysXLkSu3btUv1vcjJr1izMmDGj6AtT2j3jNi6k6I9FZECkr03H2p5qi4xPxobTd1XQOXEzEnuvhKrtvXVn8HQdL/TwLYf2NdxhZcH+OURkoOHGzc0N5ubmCAoKyrJfHnt5eeX53i+//FKFm23btqFBgwa5vk6avMaPH5+l5kb69BS60m4Zt4lRQGoyYGFV+McgMnBOdla6ZitZ02rdiTv4+2QgroXG4X+n7qjN0cYCXeuVVc1WLau4wpxLPhCRIYUbKysrNGnSBNu3b0evXr10HYrl8ahRo3J93+eff46ZM2diy5YtaNq0aZ7HsLa2VluRs3ECSpkDmjQgPgxwLFv0xyQyYJVcS2Nsp+oY07EaTt+Owt8n76jRVsExSVh19JbapCNy13peag6dZj4uDDpEZBjNUlKrMmjQIBVSmjdvroaCx8XFYciQIep5GQElQ7yleUnI0O9p06bht99+U3Pj3Lt3T+23t7dXm96YmWXU3sQGZTRNMdwQ5XvuHF9vJ7W91602/K6HY/2pO9h09q7qiPzzoQC1uTtY49l6Xuhan0GHiEp4uOnTpw9CQkJUYJGg0rBhQ2zevFnXyfjmzZtqBJXWokWL1Cirl156KcvnTJ8+HR9++CH0yi5TuCGiApPA0qqqq9o+6lkX+/1D8c/pu9hy7p6aFHDFwQC1udlboXNdL1WrI01XnEOHiErUPDfFrUjnuVnRA7i+G3hhCdDg5cL9bCITJss87PMPwcYz97D1fBCiElJ0z5WxtUTH2h7oUtcL7aq7w9bKXK9lJSITn+fG6HDEFFGRkNFTHWp5qi0lLR0Hr4apZqt/zwUhLC4Zfx0PVJuscdWuhpsaeSWzI7uUZsd+IlPEcFMUI6YYboiKjDRBtavhrrZPemlw9EY4tpwLUk1XgZEJ9+8HQbrkNK3kgk51PNCptiequOuxTx4RFSuGmyIJN6H6LgmRyfTRaVHFVW0fPFcb5+5E49/zQdh2Pgjn70bD70a42j7deBGV3Uqr2hxpwpIOyeynQ2S8GG6KpFmK4YZIH6Ou6pUvo7bxT9fA7Yh4bL8QrProHL4ehuuhcbolIBysLdC2hhuerOGhFvX0cOQyEETGhOGmMLHPDVGJUcHZDoNa+6gtJjEF+66EYvvFYOy8GKz66UjnZNlE3XKOKuRIh+TGlZxZq0Nk4BhuCnsouGC4ISpRHGws1fw4sqWna3DqdiR2XQrBrkvBOHU7SjVnybZw51W1xlXrqq5ofz/seLvY6bv4RFRAHApemMKuAvMbA1b2wNTAwv1sIioSMlHg7ksh2HMlRK1zFR6XnOX5Sq52aFvdDU9Uc1fz78jQcyIq2ddvhpvClBgNzL6/btXUu4AV/8VHZEikVufsnSi1ivmey6E4fjMCqen//YmUEVj1KzihTVVXPFHNTTVh2VhyXh2i4sBwo69wI6fyEw8gLRkYdwZwqli4n09ExSo2KRWHroZhn3+oqtm5FhKX5XlrCzM0ruismrGkVqdBBSeuaE5URDiJn76UKpXRqTg6MGPEFMMNkUGT/jed6niqTdyNSsB+/zAc8A9VgUcW+Tx4LUxt2Ao1iWBTH2e1JETLKi6oX55hh0gfGG6KYq4bbbghIqNStowtXmpSQW1S6X01JC4j3FwNxaFr4aq/jvTbkU3YWJqhSSVnNPdxRbPKzmjk7czlIYiKAcNNYeOIKSKTmVenmoe92l5tWUn117kcHKOasQ5fD1ebhB2p6ZFNWJqXQv3yZdQkgk19XFTw4RIRRIWP4aao5rqJZ80NkSkxMyuFWl6OahvcprKq2bkSHKtCzpHr4fC7Ho570Yk4fjNSbd/tuabeV9W9tFomQoKOdFCu4lZafRYRPTqGm8LG9aWI6H7NTg1PB7VJzY6EndsRCSrsHAsIx5EbEfAPjlVNW7KtOnpLvU+Gmjeu6KQ6Kjeq6IwG3mXgaMPh50QFwXBT2Li+FBHlEnZkQkDZpM+OiIhLxrGACBwNiFDDzk/dikRUQgp2XgpRW8b7gOoe9mjo7YSG3s7w9S6Dmp4OsOAsykS5YrgpbFxfiojyybm0VZbRWMmp6bhwN1oFnRM3I3HiVgRuhSfgclCs2lYfva3rqCx9d3wrOKF+hYxbmWxQAhQRMdwUPq4vRUSPSIaN+3o7qW1Im4x9ITFJOHkrEiekZud2JE7fikJMUqpq1pJNy9HGQs2zIwuH1r+/ebvYMvCQSWK4KbLRUqy5IaLH5+5gjafreKpNyKisa6FxKvCclrBzOwrn70YjOjFVzb0jm5b035FFQSXwyG3dcmVQ2a00zNlhmYwcw01RdiiWGYv5ryYiKkQykko7BF3bd0easy4HxaigcyYwCmcDo3DpXozqv3PgapjatOyszFHLywF1yjmiTtky6lb68HD+HTImDDdFFW7SkoDkWMDaQd8lIiITaM6S2hnZtLSBR4JOxqrnGTU88clpuuHoWvJvsMqupVG7rAxld0Ct+7cVnNmsRYaJ4aawWZUGLO2AlPiM2huGGyIqIYEnLV2D66GxOH83BufvRKuwc/5OFEJjk1VTl2z/nLmbZfmJGp72qKnm73FAdbnv6QBXe2s9/VRE+cNwU1S1N5E3M/rduFTRd2mIiBTpa1PNw0FtPXzL6fZLp2UZpXXxXjQu3I1R96+GxKqFQ7PX8gg3eytU95A5fOxR3dNBDVWXW862TCUFw01RjZjShhsiIgPotOzu4I52Ne6P9gSQkpaO66FxuHgvBhfvRt8fjh6Dm+HxqqYnNPb+gqGZuJa2QlUJOvf7BGk3L0cbNm9RsWK4KQocDk5EBs7S3Ew3w3LmWp745FQ1s7J0WJZbCTwSfAIjExAWl4yw+0tNZCadmKu4l0ZVd3tUcbNX92XUltzaWfEyRIWP36qiwMUzichISRiR+XRkyywuKVU1ZUng0W0hsbgZFq86MZ8NjFZbdlKrI0GnsgQe14zQ4+NWWs3RY23BEVz0aBhuinLEVHzWKlsiImNV2jrn0CPNW9KUdfX+OlrXQmJVx2Vp8pJV02UxUdmyN3HJVDzlnGzh41pazb4stxVd7dT9ii52rPGhPPHbURTYLEVEpGvekuYo2bKLjE9WgefG/bBzPSwO10PiEBAWh7jkNLXQqGz7/HPuJyQhp+L99boq3b+VGh9PBxuurG7iGG6KAlcGJyJ6KCc7KzSpJJtzlv2ygrp0Wr4RlhF85DYgLF7VAMmtTE4oI7xkk4VHs7MyN1Nz9FRwscu4Vdv9+062cLO3Zvgxcgw3RYErgxMRPTIZWZUxgssazXxccqzxkaCj3W7dDz23IuJxJzIRyWnpunl7ciLhp5yTjQo85Z1sVfOXPC7vbKsee5WxYX8fA8dwUxS4MjgRUZHW+MiWvX+PSE1Lx92oRBV0bodLs1a8rnlL7kv/Hgk/N8Li1ZYbmctHQk/ZMjYoW+b+re6xDTwcbNREiVQyMdwU5Wip+FBZ5U4Wg9F3iYiITIKFudn9vjd2QNUHn5cOzveiEtXQ9cCIBHV7R+5H/nc/MSX9/lw+yWq9rtxI85ZXGWt4Odrev7WBh6ONupXaH+n742hrwTl+9IDhpiibpdJTgcRIwO7BalUiItJPB2dd+MmB9PeJiE9RIUdqgLS396IScEfdZmxS+xMam6S2nIa4a1lbmMHT0Qaejtaqtkea2jzu3/fIdN/J1pL9gAoRw01RsLAGrMsASVEZw8EZboiIDILUssgyErJlXpcrewDSDWOPSlThJ+j+/aCYJARJAIpOVB2fk1IzhsLLlhcLs//6GUmNkLu9NdwcrO7fWsO1tDxnpZ4rY2vJ2qCHYLgpKqVdM8KNjJhyq67v0hARUSGRYCGLh8pWt1zOAUgkpqSpEV0SfIKiM26DY5IQHJOo9gdHZ9yXmqLUdI0KSbI9jKV5RgCTwCPBx+1+GFNlUrdWuudd7K1Q2src5MIQw01RdioOv8bh4EREJsrG0jzPJjCt5NR0hMVlhB0JPdLUpbuVpq+YZN39mMRUpKRp7oelJOC/RdxzZWVhpkKPs11G8JFbbe2Us50lnO8/p7bSlupWym7IGG6KCkdMERER8hc+MkZk2T70tVIbpNbwik1CmOr0LCEoGeFxSff3y/37z8clq2YxCU/5rRXSsrE0UyFHRqVJAHJSm5XqG6Tu21qhjLrN2C9NZbLJ+0pCLRHDTVGxc824ZbghIqJCIjUqMhePbPkRn5yqCzzh8cmIkNu4ZETE37+NS9Htl32R95vIZMRYQQORNqhJyGnk7YTvBzaFvjDcFBUuwUBERHpmZ2UBOxeLhzaNZe4sHZOUisi4FBV2ZJOO0RJ6tOEn43EyIhNSEBWfknGbkIK0dI2qJZImNdmnTww3RYXhhoiIDEypUqXgaGOpNlmoNL8kFMl6YBJ6JOiY63lYO8NNUeHK4EREZEKhyN7aQm0Vsi4VphecOreocPFMIiIivWC4KSpsliIiItILhpuiDjfx4UB6mr5LQ0REZDJKRLhZuHAhfHx8YGNjgxYtWsDPzy/P169Zswa1atVSr69fvz42btyIEsdWu+SCJiPgEBERkWmEm1WrVmH8+PGYPn06jh8/Dl9fX3Tp0gXBwcE5vv7AgQPo168fXn/9dZw4cQK9evVS29mzZ1GimFv8F3DYNEVERFRsSmlk/JYeSU1Ns2bNsGDBAvU4PT0d3t7eGD16NCZPnvzA6/v06YO4uDhs2LBBt69ly5Zo2LAhFi9e/NDjRUdHo0yZMoiKioKjoyOK1ILmQOgl4MUfAe/mRXssIiKiksLcGnDwLNSPLMj1W69DwZOTk3Hs2DFMmTJFt8/MzAydOnXCwYMHc3yP7JeansykpmfdunU5vj4pKUltmU9OsY6YknDz5+vFd0wiIiJ9q9AcGLpVb4fXa7gJDQ1FWloaPD2zpjt5fPHixRzfc+/evRxfL/tzMmvWLMyYMQN6Uf8l4O5pIF2/MzUSEREVK3Mr6JPRT+IntUKZa3qk5kaavYpF09cyNiIiIjKNcOPm5gZzc3MEBQVl2S+Pvby8cnyP7C/I662trdVGREREpkGvo6WsrKzQpEkTbN++XbdPOhTL41atWuX4Htmf+fVi69atub6eiIiITIvem6WkyWjQoEFo2rQpmjdvjnnz5qnRUEOGDFHPDxw4EOXLl1d9Z8TYsWPRvn17zJkzB926dcPKlStx9OhRfP/993r+SYiIiKgk0Hu4kaHdISEhmDZtmuoULEO6N2/erOs0fPPmTTWCSqt169b47bff8P7772Pq1KmoXr26GilVr149Pf4UREREVFLofZ6b4las89wQERFRsV+/9T5DMREREVFhYrghIiIio8JwQ0REREaF4YaIiIiMCsMNERERGRWGGyIiIjIqDDdERERkVBhuiIiIyKgw3BAREZFR0fvyC8VNOyGzzHRIREREhkF73c7PwgomF25iYmLUrbe3t76LQkRERI9wHZdlGPJicmtLpaen486dO3BwcECpUqUKPVVKaLp16xbXrXoInqv847nKP56r/OO5KhieL/2fK4krEmzKlSuXZUHtnJhczY2ckAoVKhTpMeSXyS9//vBc5R/PVf7xXOUfz1XB8Hzp91w9rMZGix2KiYiIyKgw3BAREZFRYbgpRNbW1pg+fbq6pbzxXOUfz1X+8VzlH89VwfB8Gda5MrkOxURERGTcWHNDRERERoXhhoiIiIwKww0REREZFYYbIiIiMioMN4Vk4cKF8PHxgY2NDVq0aAE/Pz+Ymj179qB79+5q9kiZ/XndunVZnpe+69OmTUPZsmVha2uLTp064cqVK1leEx4ejgEDBqiJn5ycnPD6668jNjYWxmbWrFlo1qyZminbw8MDvXr1wqVLl7K8JjExESNHjoSrqyvs7e3x4osvIigoKMtrbt68iW7dusHOzk59zjvvvIPU1FQYk0WLFqFBgwa6CcFatWqFTZs26Z7necrd7Nmz1f+L48aN0+3j+frPhx9+qM5P5q1WrVq653musgoMDMQrr7yizof8Da9fvz6OHj1aMv/Gy2gpejwrV67UWFlZaZYuXao5d+6cZtiwYRonJydNUFCQxpRs3LhR895772n++usvGYGnWbt2bZbnZ8+erSlTpoxm3bp1mlOnTml69OihqVy5siYhIUH3mmeeeUbj6+urOXTokGbv3r2aatWqafr166cxNl26dNEsW7ZMc/bsWc3Jkyc1zz77rKZixYqa2NhY3WvefPNNjbe3t2b79u2ao0ePalq2bKlp3bq17vnU1FRNvXr1NJ06ddKcOHFCnX83NzfNlClTNMZk/fr1mn/++Udz+fJlzaVLlzRTp07VWFpaqnMneJ5y5ufnp/Hx8dE0aNBAM3bsWN1+nq//TJ8+XVO3bl3N3bt3dVtISIjueZ6r/4SHh2sqVaqkGTx4sObw4cOaa9euabZs2aLx9/cvkX/jGW4KQfPmzTUjR47UPU5LS9OUK1dOM2vWLI2pyh5u0tPTNV5eXpovvvhCty8yMlJjbW2t+f3339Xj8+fPq/cdOXJE95pNmzZpSpUqpQkMDNQYs+DgYPWz7969W3du5AK+Zs0a3WsuXLigXnPw4EH1WP6QmpmZae7du6d7zaJFizSOjo6apKQkjTFzdnbW/PDDDzxPuYiJidFUr15ds3XrVk379u114Ybn68FwIxfanPBcZTVp0iTNE088oclNSfsbz2apx5ScnIxjx46p6rfM61fJ44MHD+q1bCXJ9evXce/evSznSdYIkSY87XmSW6mmbNq0qe418no5n4cPH4Yxi4qKUrcuLi7qVr5TKSkpWc6XVJdXrFgxy/mSamFPT0/da7p06aIWrTt37hyMUVpaGlauXIm4uDjVPMXzlDNpSpGmksznRfB8PUiaTaQpvUqVKqq5RJqZBM9VVuvXr1d/m3v37q2a3xo1aoQlS5aU2L/xDDePKTQ0VP3BzfzlFvJYftGUQXsu8jpPciv/02RmYWGhLvjGfC5lpXrpE9GmTRvUq1dP7ZOf18rKSv0hyOt85XQ+tc8ZkzNnzqg+DzLj6Ztvvom1a9eiTp06PE85kPB3/Phx1a8rO56vrOTCu3z5cmzevFn17ZILdNu2bdXK0zxXWV27dk2do+rVq2PLli0YMWIExowZgxUrVpTIv/Emtyo4UUn8V/bZs2exb98+fRelxKpZsyZOnjyparj++OMPDBo0CLt379Z3sUqcW7duYezYsdi6dasa3EB569q1q+6+dFqXsFOpUiWsXr1adYilrP8IkxqXTz/9VD2Wmhv5u7V48WL1/2NJw5qbx+Tm5gZzc/MHetDLYy8vL72Vq6TRnou8zpPcBgcHZ3leRh1I73pjPZejRo3Chg0bsHPnTlSoUEG3X35eafKMjIzM83zldD61zxkT+Rd0tWrV0KRJE1Uj4evri6+//prnKRtpSpH/hxo3bqz+RSybhMBvvvlG3Zd/RfN85U5qaWrUqAF/f39+t7KREVBSW5pZ7dq1dc14Je1vPMNNIfzRlT+427dvz5Jw5bH0CaAMlStXVl/ezOdJ2qWlnVV7nuRW/pDIH2itHTt2qPMp/6IyJtLnWoKNNK/IzyjnJzP5TllaWmY5XzJUXP6QZD5f0lyT+Y+F/Itdhlhm/yNkbOQ7kZSUxPOUTceOHdXPKrVc2k3+tS19SbT3eb5yJ0OSr169qi7k/G5lJc3m2aeruHz5sqrpKpF/4wu1e7IJDwWXHuHLly9XvcGHDx+uhoJn7kFvCmSEhgyHlE2+WnPnzlX3AwICdMME5bz8/fffmtOnT2t69uyZ4zDBRo0aqaGG+/btUyM+jHEo+IgRI9SQyV27dmUZhhofH59lGKoMD9+xY4cahtqqVSu1ZR+G2rlzZzWcfPPmzRp3d3ejG4Y6efJkNYrs+vXr6nsjj2V0xb///que53nKW+bRUoLn6z8TJkxQ/w/Kd2v//v1qSLcM5ZbRi4LnKuvUAhYWFpqZM2dqrly5ovn11181dnZ2ml9++UX3mpL0N57hppDMnz9f/U8g893I0HAZw29qdu7cqUJN9m3QoEG6oYIffPCBxtPTU4XBjh07qnlLMgsLC1NfdHt7ezWccsiQISo0GZuczpNsMveNlvxBeOutt9SwZ/kj8vzzz6sAlNmNGzc0Xbt21dja2qo/yvLHOiUlRWNMXnvtNTW/hvy/JRcO+d5og43geSpYuOH5+k+fPn00ZcuWVd+t8uXLq8eZ523hucrqf//7nwpz8ve7Vq1amu+//z7L8yXpb3wp+U/h1gURERER6Q/73BAREZFRYbghIiIio8JwQ0REREaF4YaIiIiMCsMNERERGRWGGyIiIjIqDDdERERkVBhuiIiIyKgw3BCR3g0ePBi9evXS2/FfffVV3WrHD9O3b1/MmTOnyMtERI+OMxQTUZEqVapUns9Pnz4db7/9tlpMVFZlLm6nTp1Chw4dEBAQAHt7+4e+/uzZs2jXrh2uX7+OMmXKFEsZiahgGG6IqEjdu3dPd3/VqlWYNm1altWFJVDkJ1QUlaFDh8LCwgKLFy/O93uaNWumaptGjhxZpGUjokfDZikiKlJeXl66TWo6pCYn8z4JNtmbpZ588kmMHj0a48aNg7OzMzw9PbFkyRLExcVhyJAhcHBwQLVq1bBp06YHalW6du2qPlPeI81NoaGhuZYtLS0Nf/zxB7p3755l/7fffovq1avDxsZGfc5LL72U5Xl5/cqVKwvtHBFR4WK4IaISacWKFXBzc4Ofn58KOiNGjEDv3r3RunVrHD9+HJ07d1bhJT4+Xr0+MjJSNS81atQIR48exebNmxEUFISXX34512OcPn0aUVFRaNq0qW6fvHfMmDH46KOPVA2TfI40Q2XWvHlzVa6kpKQiPANE9KgYboioRPL19cX777+valCmTJmialEk7AwbNkztk+atsLAwFVDEggULVLCRjsG1atVS95cuXYqdO3fi8uXLOR5D+tmYm5vDw8NDt+/mzZsoXbo0nnvuOVSqVEl9joSdzMqVK4fk5OQsTW5EVHIw3BBRidSgQQPdfQkgrq6uqF+/vm6fNBeJ4OBgXcdgCTLaPjyyScgRV69ezfEYCQkJsLa2ztLp+emnn1ahpkqVKqpm6Ndff9XVDmnZ2tqq2+z7iahkYLghohLJ0tIyy2MJIJn3aQNJenq6uo2NjVV9YU6ePJllu3LlygPNSlpSEyQBRWphtKQ/jzR7/f777yhbtqyqIZJaJGn20goPD1e37u7uhfxTE1FhYLghIqPQuHFjnDt3Dj4+PqqzceZNmply0rBhQ3V7/vz5LPtl9FSnTp3w+eefq2avGzduYMeOHVk6LleoUEGFIyIqeRhuiMgoyLBsqVHp168fjhw5opqitmzZokZXyaionEjNi4Siffv26fZt2LAB33zzjar1kT45P/30k6odqlmzpu41e/fuVR2aiahkYrghIqMgnXz379+vgowED+mfI0PJZWJAMzOzPOe5kX41WvL6v/76S428ql27tpr/Rpqo6tatq55PTEzEunXrVMdmIiqZOIkfEZk06VQstTIywWCrVq0e+vpFixZh7dq1+Pfff4ulfERUcKy5ISKTJiOfpOkpr8n+MpNOzfPnzy/ychHRo2PNDRERERkV1twQERGRUWG4ISIiIqPCcENERERGheGGiIiIjArDDRERERkVhhsiIiIyKgw3REREZFQYboiIiMioMNwQERERjMn/AdHQ+NBbLnEXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from acom_music_box import MusicBox\n",
    "import matplotlib.pyplot as plt\n",
    "import logging\n",
    "import sys\n",
    "\n",
    "# Disable logging to reduce output noise\n",
    "logging.disable(sys.maxsize)\n",
    "\n",
    "# Create a MusicBox instance and load the custom configuration\n",
    "box_model = MusicBox()\n",
    "conditions_path = \"config/custom_box_model.json\"\n",
    "box_model.loadJson(conditions_path)\n",
    "\n",
    "# Solve the box model and display results\n",
    "df = box_model.solve()\n",
    "display(df)\n",
    "\n",
    "# Plot the species concentrations over time\n",
    "df.plot(x='time.s', y=['CONC.A.mol m-3', 'CONC.B.mol m-3', 'CONC.C.mol m-3'], \n",
    "        title='Concentration over time', \n",
    "        ylabel='Concentration (mol m-3)', \n",
    "        xlabel='Time (s)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "musicbox",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}