01 - Manipulating fits files.ipynb

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   "name": "01 - Manipulating fits files.ipynb",
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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Read _.fits_ data\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "1q7ADVvKGE2C"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "DATA_DIR = './database/dataset_exoplanets_confirmed'"
   ],
   "outputs": [],
   "metadata": {
    "id": "llHJZAWGIRjt"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Our database, storage on DATA_DIR, can be found on `CoRoT IAS Archive` website or in my [Google Drive folder](https://drive.google.com/drive/folders/1xMlUVc8K5BRd8663Q-UdfkTworL4vDIK?usp=sharing), which for now is structured as ... \n",
    "\n",
    "```Json\n",
    "./database\n",
    "   │\n",
    "   └── exoplanets_confirmed\n",
    "         │ \n",
    "         └── dataset_exoplanets_confirmed\n",
    "               │ \n",
    "               ├── ..._.fits\n",
    "               ├── ..._.fits\n",
    "               ├── ..._.fits\n",
    "               ⋮       ⋮   \n",
    "               └── ..._n.fits  \n",
    "\n",
    "```\n",
    "\n",
    "In `database/dataset_exoplanets_confirmed` has all _.fits_ files of _CoRoT targets with confirmed exoplanets_, obtained in `CoRoT IAS Archive`."
   ],
   "metadata": {
    "id": "DI56awo2xi7Z"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The main purpose of reading _.fits_ data-set is to turn them into a pandas DataFrame. For this, it was decided to use `Astropy==4.2` library."
   ],
   "metadata": {
    "id": "5DeYONXexYYV"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from astropy.io import fits\r\n",
    "\r\n",
    "image_file = fits.open(DATA_DIR + '/EN2_STAR_CHR_0101086161_20070516T060226_20071005T074409.fits')\r\n",
    "\r\n",
    "print(type(image_file), \"\\n\")\r\n",
    "print(len(image_file))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'astropy.io.fits.hdu.hdulist.HDUList'> \n",
      "\n",
      "4\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9x4szMrPIHII",
    "executionInfo": {
     "elapsed": 828,
     "status": "ok",
     "timestamp": 1627495835278,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
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     "user_tz": 180
    },
    "outputId": "bb3fec33-59e3-4d2a-f551-23b5ddda9cf6"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, in the variable `image_file` we have all the information storage into _.fits_ file. \n",
    "\n",
    "It is great to know how this file type works, so in order to know better the data, the `type` METHOD returns that `image_file` belongs to `HDUList` class.\n",
    "\n",
    "> A little breath for FITS: The Flexible Image Transport System (FITS) is a portable file that it is used for store, transmit and process data formatted as multi-dimensional arrays or tables. FITS is widely used in the astronomy community to store images and tables. We use the software _[QFits View](https://www.mpe.mpg.de/~ott/QFitsView/)_ to open this files in our machine and, as soon as it opens, it is ask to choose an extension. \n",
    "\n",
    "So, _.fits_ files can have several extensions, in CoRoT database case, by the `len` command, it only has 4. We are only interessed in the most informative extension with the minimal computacional cost, since we are not interessed in dealing with big-data issues, the third table is the most attractive one.\n"
   ],
   "metadata": {
    "id": "umKhP6zHIyJw"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "scidata = image_file[3].data\r\n",
    "\r\n",
    "print(type(scidata), \"\\n\")\r\n",
    "print(scidata)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'astropy.io.fits.fitsrec.FITS_rec'> \n",
      "\n",
      "[(54236.75758185, 112626.77,   0) (54236.76350826, 112605.61,   0)\n",
      " (54236.76943468, 112771.5 ,   8) ... (54378.80910033, 112496.13, 256)\n",
      " (54378.81502574, 112344.83,   0) (54378.82095114, 112318.5 ,  80)]\n"
     ]
    }
   ],
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    },
    "id": "yg2p9aQHKevr",
    "executionInfo": {
     "elapsed": 227,
     "status": "ok",
     "timestamp": 1627493879406,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "2962b39d-88f1-40d4-ab14-4f8106be2e2d"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "The variable `scidata` contains the information of the third table. We can see that is belongs to the `FITS_rec` class and, to manipulate this type, it is decided to transform it into an array, using `NumPy==1.19.5` library and them plot the raw data, using `Plotly==4.4.1`.\n",
    "\n",
    "> At this point, we come to a problem that is worth commenting on. The original file, when transformed into an array, according to the code: `x = np.array(scidata)` presents the following error when being plotted, using the *Plotly* library: <br /><br /> **ValueError: Big-endian buffer not supported on little-endian compiler** <br /><br /> This error occurs when the data being worked on was created on a machine with a different byte order than the one on which we are running *Python*.\n",
    "To deal with this problem, we must convert the NumPy array to the byte order of the native system before transforming it into a DataFrame or Series. <br /> <br />Therefore, we use the methods *byteswap()* e *newbyteorder*(). <br /><br />Reference: [Pandas Documentation - Byte-ordering issues](https://pandas.pydata.org/pandas-docs/stable/user_guide/gotchas.html#byte-ordering-issues)"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import numpy as np\r\n",
    "\r\n",
    "data_aux = np.array(scidata).byteswap().newbyteorder()"
   ],
   "outputs": [],
   "metadata": {
    "id": "s6VqjtayNcan"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import plotly.express as px\r\n",
    "\r\n",
    "fig = px.line(data_aux, x='DATEBARTT', y='WHITEFLUXSYS', title='Raw Light Curve')\r\n",
    "fig.show()"
   ],
   "outputs": [],
   "metadata": {
    "id": "EJZSmKol5wJF"
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AAEIQAACEIAABCAAAQhAAAIQgAAE5koAkX2uO0NeEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMHsCiOyz3yIShAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbkSQGSf686QFwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMyeACL77LeIBCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGCuBBDZ57oz5AUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKzJ4DIPvstIkEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYKwFE9rnuDHlBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDA7Akgss9+i0gQAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE5koAkX2uO0NeEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMHsCiOyz3yIShAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbkSQGSf686QFwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMyeACL77LeIBCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGCuBBDZ57oz5AUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKzJ4DIPvstIkEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYKwFE9rnuDHlBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDA7Akgss9+i0gQAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE5koAkX2uO0NeEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMHsCiOyz3yIShAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbkSQGSf686QFwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMyeACL77LeIBCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGCuBBDZ57oz5AUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKzJ4DIPvstIkEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYKwFE9rnuDHlBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDA7Akgss9+i0gQAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE5koAkX2uO0NeEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMHsCiOyz3yIShAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbkSQGQfuDPP/eO5zRT/v7LZbH400dxTrJc5p6mzuXA/f+Hlzc/+7XSSa20uDMjjeK6Bfz/34ubfXrhAvfOMO4oa+Kefn9+cnH/pKNbKffx47uN1e/3jfzrZvPzKK9Q79/ejqYEXX3pl85N/OTma9dZd+/Qfx/3/hdOXNv/y7+epd+7xi66BgRLp0bsjsg8sgakemIjsx/Ggnqq+5jYvIjv1Prea3Gc+iOzU+z7ra26xEdmp97nV5D7zQWSn3vdZX3OMjchOzc+xLveVEyI79b6v2jpk3IES6dG7I7IPLIFDFnucC5GdG3ish7UfI7JT72uv8bg+RHbqPdbD2o8R2an3tdd4XB8iO/Ue6+EYjhHZqfljqHOvEZGdenctLLkdKJEevTsi+8ASmOriQWTnBj5V7U0xLyI79T5F3U01JyI79T5V7U0xLyI79T5F3U01JyI79T5V7U01LyI7NT9V7U0xLyI79T5F3Y0950CJ9OjdEdkHlsDYBd01HiI7N/CutbIGO0R26n0Nddx1DYjs1HvXWlmDHSI79b6GOu66BkR26r1rrazFDpGdml9LLXdZByI79d6lTuZuM1AiPXp3RPaBJTDVBYLIzg18qtqbYl5Edup9irqbak5Edup9qtqbYl5Edup9irqbak5Edup9qtqbal5Edmp+qtqbYl5Edup9irobe86BEunRuyOyDyyBsQu6azxEdm7gXWtlDXaI7NT7Guq46xoQ2an3rrWyBjtEdup9DXXcdQ2I7NR711pZix0iOzW/llrusg5Eduq9S53M3WagRHr07ojsA0tgqgsEkZ0b+FS1N8W8iOzU+xR1N9WciOzU+1S1N8W8iOzU+xR1N9WciOzU+1S1N9W8iOzU/FS1N8W8iOzU+xR1N/acAyXSo3dHZB9YAmMXdNd4iOzcwLvWyhrsENmp9zXUcdc1ILJT711rZQ12iOzU+xrquOsaENmp9661shY7RHZqfi213GUdiOzUe5c6mbvNQIn06N0R2QeWwFQXCCI7N/Cpam+KeRHZqfcp6m6qORHZqfepam+KeRHZqfcp6m6qORHZqfepam+qeRHZqfmpam+KeRHZqfcp6m7sOQdKpEfvjsg+sATGLuiu8RDZuYF3rZU12CGyU+9rqOOua0Bkp9671soa7BDZqfc11HHXNSCyU+9da2Utdojs1PxaarnLOhDZqfcudTJ3m4ES6dG7I7IPLIGpLhBEdm7gU9XeFPMislPvU9TdVHMislPvU9XeFPMislPvU9TdVHMislPvU9XeVPMislPzU9XeFPMislPvU9Td2HMOlEiP3h2RfWAJjF3QXeMhsnMD71ora7BDZKfe11DHXdeAyE69d62VNdghslPva6jjrmtAZKfeu9bKWuwQ2an5tdRyl3UgslPvXepk7jYDJdKjd0dkH1gCU10giOzcwKeqvSnmRWSn3qeou6nmRGSn3qeqvSnmRWSn3qeou6nmRGSn3qeqvanmRWSn5qeqvSnmRWSn3qeou7HnHCiRHr07IvvAEhi7oLvGQ2TnBt61VtZgh8hOva+hjruuAZGdeu9aK2uwQ2Sn3tdQx13XgMhOvXetlbXYIbJT82up5S7rQGSn3rvUydxtBkqkR++OyD6wBKa6QBDZuYFPVXtTzIvITr1PUXdTzYnITr1PVXtTzIvITr1PUXdTzYnITr1PVXtTzYvITs1PVXtTzIvITr1PUXdjzzlQIj16d0T2gSUwdkF3jbdkkf2620439zx4sum6Vux4WCGyUwPHdB9AZKfej6neEdmp92Oqd0R26v2Y6l1rRWSn5o+p5hHZqfc11PtAifTo3RHZB5bAVBfRUkX2L3/93Oa//+qFzdU3nUdk/0ceQl2vH0R2aqVrrazBDpGdel9DHXddAyI79d61VtZgh8hOva+hjvusAZGdmu9TL0u3RWSn3pdew8qf/4YRQGQfxm8yoRiRnRv4Gm7gXdeAyE69d62VNdghslPva6jjrmtAZKfeu9bKGuwQ2an3NdRxnzUgslPzfepl6baI7NT70mtY+fPfMAKI7MP4IbL3/DQ2n2TnwbPLgweRnbrZpW6W6oPITr0vtXZ3yRuRnXrfpW6W6oPITr0vtXZ3zRuRnZrftXaW6IfITr0vsW5zzgMl0qN3R2QfWAK5IA91zifZuYEfqtbmMA8iO/U+hzo8VA6I7NT7oWptDvMgslPvc6jDQ+WAyE69H6rW5jIPIjs1P5daPEQeiOzU+yHqbN9zDJRIj94dkX1gCey7wOviI7JzA6+rjTX2I7JX9f7YsycX/67Bez50Otm/pFljjc1pTYjsVb3PaV/IZT/7gsi+H67U6zy5IrLPc1+4Xva3L4js+2NL3c6PLSL7/PaE66T/ngyUSI/eHZF9YAlMddEisve/WUy1V8w7fK8Q2SuGfOVSxWKt1xYi+/r3eK21u8u6ENmp913qZqk+iOzU+1Jrd9e8Edmp+V1rZ4l+iOzU+xLrNuc8UCI9endE9oElkAvyUOeI7NzAD1Vrc5gHkb2qd0T2isUcanMfOSCyr3+P91E3S42JyE69L7V2d8kbkZ1636VuluyDyE7NL7l+++aOyE69962ZOdoPlEiP3h2RfWAJTHVRILJzA5+q9qaYF5G9qndE9orFFLV4iDkR2de/x4eoo6XMgchOvS+lVsfIE5Gdeh+jjpYUA5Gdml9SvQ7NFZGdeh9aQ3PwHyiRHr07IntLCfzzv/5880uXv3Xz1b/9TtFyqosAkZ0b+FS1N8W8iOxVvSOyVyymqMVDzInIvv49PkQdLWUORHbqfSm1OkaeiOzU+xh1tKQYiOzU/JLqdWiuiOzU+9AamoN/UfikszOB2YvsH/vMA5ub7vpI7YI0Jpv432NPPrf5hV+8cvOf/8t/u/j/m6++c3Nycj6anDnO9vKTsC6BHZF93BslAuG4POdwEz5EDojsVd1wDVUsDlF7U8yByL7+PZ6iruY6JyI79T7X2txHXojs1Ps+6mrOMRHZqfk51+fYuSGyU+9j19QU8c6IpZz0JjBbkV2fHLdIXhLZJax7PIvsOo+fPJd/KYZpSWT/r5e/daM2/4fIPu6NEoFwXJ5T3HSnmBORvaobrqGKxRS1eIg5EdnXv8eHqKOlzIHITr0vpVbHyBORnXofo46WFAORnZpfUr0OzRWRnXofWkNz8M+aKOf9CMxWZPcyJJg3CeQayyK7fd1qvOnT7H1Edovuzmmqi4Cvi+EGPlXtTTEvIntV74jsFYspavEQcyKyr3+PD1FHS5kDkZ16X0qtjpEnIjv1PkYdLSkGIjs1v6R6HZorIjv1PrSG5uBvHZV2NwKrF9n1NTES2C2KlzDlr4vxV8XI1qK6PhnvWPFT8lNdBIjs3MCnqr0p5j20yP6Dp082N737dPP0T+ZXZ4js89uTsa8JRPb17/HYNTOHeE/teL9EZKfe51C/h8oBkZ16P1StzWUeRHZqfi61eIg8ENmp90PU2b7nKGmm9HUnsGqRXcK6vlKm6VPsJVTys49F9gce+ebFvvyp+RdOXtxM8b/ynmLeoXN+81sXNv/9Vy9srr31wiLzH7p+/He7Xl5++ZXNyfmXDlYzH/3UixfrVO3c9oxraH57MnaNXHjx5c35F1+eXe2NvU7iraeWb7tr+2z/6T/1f7afXnh589LLr1DvE/08yXV44Ovw9KWNPigD9wNz5/qarOZefmWzOXfAn+G5tri2pqwB/VJJHw6bMgfm5hoYWgMljZS+7gRWLbIbQ9vXxdjOrT7Zfvlbbjrzh0/jH0O1ndp/+Y8Lk/yvH9D/daK5h6z56393/qJ4ec0t03Abkju+0+2ZfmD595MXD3at/dkntoKR2rntO9fQ/PZk7BqR6KhfKo0dl3jrr52p9ljPdP0CXfenvjn8x8mLG/1iqa8f9tTzEmvg3/7jwuaVV16h3hf4DrPEeptDzvqgzM9f4H41h70gh/3X4cUPyZwe7p2VPd3/nh4j46h3ctyfwFGI7FE074Io2vuT7BLq9TUx/oS74+z7n2rUxefrYvinSHW1scb+Q39dzIc/dnpRMFI7N558Xcz6r32+Lmb9ezy3+8rQfK6+afsLdN2f+sbi62L6M+vLGPv5MObrYuazF1wXh9kLvi7mMJyp53lw5uti5rEPXA/D9sFaJ+1uBFYpst/1oU9uJJT7v/j1L+qTYB6/d/3u+756ib2/w90iu7+HPX8qfqoLGJF92I1jqn1j3t32DZG94obIXrFY6/WEyL7+PV5b7SKyU7Nrq+l9rQeRnWtlX7U117iI7NT8XGtzH3khslPv+6irQ8e0jkq7G4HZiuwStfUVLfF/C90WyuPYL/zila8J5dk3f/o8i+xN9llk19xRtD90wXs+RHZu4K6FY2gR2at6R2SvWKy19hHZ17/Ha6tdRHZqdm01va/1ILJzreyrtuYaF5Gdmp9rbe4jL0R26n0fdXXomLtJy3iZwGxFdic49/bQBe/5ENm5gbsWjqFFZK/qHZG9YrHW2kdkX/8er612Edmp2bXV9L7Wg8jOtbKv2pprXER2an6utbmPvBDZqfd91NWhY85dg517fojsA3fo0AXv+RDZuYG7Fo6hRWSv6h2RvWKx1tpHZF//Hq+tdhHZqdm11fS+1oPIzrWyr9qaa1xEdmp+rrW5j7wQ2an3fdTVoWMOlEiP3h2RfWAJHLrgPR8iOzdw18IxtIjsVb0jslcs1lr7UWR//x9v/wjv48+90IwEqSEAACAASURBVPsPSq6VD+ua3zWAyD6/PeE6meeeILLPc1+4Xva3L4js+2NL3c6PLSL7/PaE66T/ngyUSI/eHZF9YAlMddEisve/WUy1V8w7fK8Q2SuGiOwVi7VeW1FkHyJerpUP65rfNTCkTv/p5+c3J+df4pdI/zi/feVaG39PENnHZ0qdzpspIvu894frZ9z9QWQflyf1OQ3PgRLp0bsjsg8sgakufET2aW44U+33sc+LyF7VOyJ7xWKt18W+RfY7P7D9dPz3nlo/y7XWyNzWhcjOtTS3mpxrPojsXCtzrc195YXITs3vq7bmGBeRnXqfY132zWmgRHr07ojsA0ugb8GOZb8Gkf3p57kJj1UPa4+DyF5dK4jsFYu11v2+RfYhguhambOuYddVn5p68sfnNh/77Onm6Z9s5+ST7MPYU7vL4ofIvqz94voavl+I7MMZUofLYYjIvpy94rqq36uBEunRuyOyDyyBqS7OpYvs//1XL2yuufXCXv55+LM/O7fR/2PvzXW3bT/9+eTzfDfy2Gzb4iGyV/WMyF6xaKubpY4jsi93j+++/3Tz2LMnoz9/5l7LfUT2D39s+yxVq3Uhsi+33udel3PMD5Gdep9jXe4zJ0R2an6f9TW32Ijs1PvcanKXfAZKpEfvjsg+sAR2KdoxfOYssn/hwdPNY8+Vb7AWCCWy66V8DBY5hmLr/9w/9LyPiDB0LvzP1g8ie8XD19C+rh9qr2I9FQtE9un3YJe9//7TJxefPdfdtp9n2y45Hcqnz/MRkX2Z9X2oWlr7PIjs1P/aazyvD5Gdms81seZzRHbqfQ31PVAiPXp3RPaBJTDVRTRXkb1NZLBAiMjOA6jPtYPIXtWLryFE9opJn1pagi0i+zL39pivTUT2ZdbsEu6Ha8sRkZ1rZW013bYeRHZqvq1G1jSOyE69r6GeB0qkR++OyD6wBKa6iOYqsreJDB5HZOcB1OfaQWSv6sXXECJ7xaRPLS3BFpF9mXt7zNfm2CL733zn3Obeh3f/2p277z957Tvfl3DNk+Myr/ld9g2R/Xj2epf6WKMPIjs1v8a6rlsTIjv1XlcbS+ofKJEevTsi+8ASmOpisch+z4Pbf57+jW/v/jI65hraRAaPI7LzAOpTd4jsVb34GkJkr5j0qaUl2CKyL3Nvj/naHFtkv/rm8xe/ekd/JLXvNfvFh7ff+f6Xn9t+53tff+z7M4dZd2aI7N1ZUVfrYIXIvo595Hrsto+I7N04UU/z5jRQIj16d0T2gSUw1Q3CInv+btOp8vG8bSKDxxHZ531j9X7OpUVkr+rF1xAie8VkLnU6Vh6I7Mvc22O+NruK7L//R6ebK357+3dTmv7wadd4pWtubj8XlXKkb5nX+Bj7hsh+vHs/Rv0sMQYiOzW/xLrdNWdEdup919qZk99AifTo3RHZB5bAVBcDInv9DVwCvv4fe2+GvPSPncuxxUNkr+r9mIW8Y6l7RPaq3pe058d8bXZ9Pvr5rBaRfZl1vqRrco65IrJT93Osy33mhMhOze+zvuYWG5Gdep9bTe6Sz0CJ9OjdEdkHlsAuRTuGz1JF9oe/vv16G71g66V8DBY5hl/ic//Q864iwtB58L/04YzIXjE5ZiHvWK4NRPaq3pe058d8bXZ9Pvr5jMi+zBpf0vU411wR2an9udbmvvJCZKfm91Vbc4yLyE69z7Eu++Y0UCI9endE9oEl0Ldgx7Jfqsh+za3b71lFZD+OB9DnHzjZ/PCZ4X8vAJG9qpdjFvLGun/OPQ4ie1Xvc9+rmN8xX5uI7Mus2Vi/HB9mDxHZD8OZep4PZ0T2+ewF18X+9wKRff+MqeP9Mx4okR69OyL7wBKY6iJfqsjuF/Eosj/9/LnNsz8b72bhT8qNvTfOXULK2LHXGO+xZ7f/auGaW4d/dQ8ie1VzxyzkrfE6Ka0Jkb2q9xKfufYd87XZ9fno57Navi5mmXU+1+tvKXkhslP3S6nVsfJEZKfmx6qlJcRBZKfel1CnbTkOlEiP3h2RfWAJtBXovsbXJLLrZft/vLEsxL7uTRc2l19VHqtj65f4uvFd+7uKCLvGX5vfmIITInv1A8uYXNdWc2tZDyJ7Ve9L2tNjvja7Ph/9fEZkX2aNL+l6nGuuiOzU/lxrc195IbJT8/uqrTnGRWSn3udYl31zGiiRHr07IvvAEuhbsGPZr01k1wt3iY1fyEtjdX27+NTFiv1dRYToM9axvnblxnefFhmNNcfYccYUnBDZqx9YxuQ69p4Tr9qnISwQ2cfhOGQPdvE95muz6/PRz2e1fJJ9mXW+y7WBT7XXiOwVC+riOFggsh/HPnM9b/d5ySL73Q+cbvTz2Zf/evhXvVIPy77uB0qkR++OyD6wBKa6gSCy19+4/BI/9t50FRHGnlfxppx71/WMKTghslf1PibXXfcWv2o/9sECkX2/fPexZ4o5xrX5jW+fbO55aFm/UNXauz6j/HweU2TX180989OqZiTex/j72m/iVsxh0Z0FInt3VtTVOlghsq9jH7keu+3jkkV2fn7qtsfHcC0MlEiP3h2RfWAJTHWRTS2y6+td3nH7pUJAm8jgF3G9AOtY/PzSXWLZNFayb4tX59OlP+Ze9/U2XeLsYuO5xXcX/yl82mqhT06I7NW+j8m1zx4cynaMP5TbluvTP9ned+78wKX3sJKv/m5EqX9ffYjsh+U91j762rzyd7bPtl3iXn3z9o+DP/njdgYPPtpus0sOu/h0fUb5ma52rE+y63mseM6bl8T51IX3hLbaE0T2igV1cRwsENmPY5+5nrf7jMhOva/hWhgokR69OyL7wBKY6iKaUmS3kGCRPDJoGpOdX8T1Qmx/v3THOD5uGrNNbnfxyTFK5zF3zVGy2Vef5xbffc0xdty2Wugz36FFdvO2CNQn133bjsl137n2jX/fI9s/lvvxe7qJ36X4Es5vf1+zyNmH4T0PbnO69+HD/dNJRPbl3OdiDbqu9Hz4y7t3q2HfexQrxs7H+rS75vnE53ebJ8cbet41bz+f1fr++k8/P785Of/SmfV2jae8HdNrQGRvrh1zop2GEyL7NNyp9+m4I7JPx566Pzx7RPbDM6fOx2c+UCI9endE9oElMNVFjchefzPJL9xj7ZFf+vcVvylPz90mvDTFOPSYBSflPnRuRPaq3sfkOnRfhvh/98lLResxxLEu12cfhmPk1JcTIntV733ZTWnvulINWkDum0/Xe/0Uddm0lq55+/qMjOYosj/x43Oba245v/nqNy+9TzVxYGyZ1+4h9w2RnRo5ZL3NYS5Edmp+DnV4qBwQ2an3Q9XaPucZKJEevTsi+8AS2GdxN8Vegsj+/adPNt994uyN1i/iesG2+OqX7tJ6m8ZK9urbxacuVuyPuWuOOLbvY88tEWffc40V34KT93lIXET2at/H5DpkT4b4fuaL20+H333/2U/hjiEcdrn++zAcI6e+rI5JZH/Dmy9sLr/qsPfTvvvR1d51pRpEZK/uWZGfr8/IaI4i+xTXfeTEcbl+1sAFkX29e7uG+tzHGhDZqfl91NVcYyKyU+9zrc0+eQ2USI/eHZF9YAn0KdYxbZcgsr/uiu33pOr7j712i8V6wbb46pdu28S2aUx2jz176afM2nxi/D7HMXfN0cd3qK3nlogzNNah/C04eZ+HzIvIXu37mFyH7MkQ3zoBq66/z1xdrv8+DMfIqU/+sj0mkb3LfvXlN5W960prUt3skkfXe/0+6/LGd2+/iqbL98J7jV3z9n5HRojs1f3dPGnXywSRfb17y3Vb3ltE9jIX6mWdXBDZ17mvx3a9DpRIj94dkX1gCUx1wXUV2f/2O+c2X/v7S4XoLnm//qoLm9If+LSQUBJP41h8mb72nduvDPGLuMbsb7tSTk1jX/769tOwf/HZs2JGk09pjq59MXfN0dVvDDvPLb5jxDtEjFgLQ+dDZK/2fUyuQ/dlV/86gbCuv888Xa7/PgzHyKlP/rJFZK/qvS+7Ke1dV6pB1c0uuXS9149Vlz985tzmzz99unn2Z+c2z7z6C/GuOcT1dfXx9RkZIbIvs97j/nPcfQ8R2buzoq7WwQqRfR37yPXYbR8R2btxop7mzWmgRHr07ojsA0tgqhtEV5HdL7S75FnnayFBL9U5bhyzv1rb+kU89tkux9J501idyNDkU5qja1/MXXN09RvDznOLr+Jde9v5nf+w3hj5dIkRa6GLfZPNoUV219CuQlnTWoaOjcl1aC67+tddu3X9febx3jX59GE4Rk5NuZTG1iSy33rX+Yu/rJWIW1prl/0q+c2xz3WlNe1678j3+rp1jlWXjvOeD28/vX7zXecvPq+1Bq2nbv7c3zVv73dkhMjenXPmzvny2CGyL2/PuM6G7Rki+zB+1N+y+CGyL2u/uL7K+zVQIj16d0T2gSUw1YUpkf2aWy9s/udbzl8Uoute6P1Cu0uedb4WEvRSnePGMfurvfzKrSjtF3H12d92/hRdjOmx2OdjiwN57U0+9t2ljblrjl1i7OrjucU3Mt413iH8dsnzhndtBbmc3xpE9js/cLr54J/v9unWyGMXrtF/Dsd1125df5+cu1z/fRiOkVOf/GW7JpE93rtKHLrsV8lvjn2uK60pP5e65tvGy3HGqkvH8bxqfaz1eL62tquP9zsyQmTvzrltHxifP0tE9vnvEdfRuHuEyD4uT+pz3jwR2ee9P1w/3fZnoER69O6I7ANLYKoL9Tvff/m1T3nHl9Wcj19oc3+X8zpfCwl6qc5x4pj93WrML+Lqs38cz/E8lvt1bnEgixlNPqU4Xfti7pqjq98Ydp5bDCPjMWLvK8YuecZ1xrzWILKPVZe7cI0s53Bcd+3W9ffJuQvnPgzHyKlP/rK1yK6vxKq7JvrGjPb7iBnjx+O2ubrsV4w352PXldaUn0td8tbfLzEPxZLPN797bvPgVy/9yreudSl/xfziX5V/wec43ie1PnYOMff7vnKyuf/RS/Np8on+Xl9khMje7YUncuR4ucwQ2Ze7d1x3u+0dIvtu3Ki3ZXJDZF/mvnG9nd23gRLp0bsjsg8sgakuyDWL7B/4s9PNO27fCvh+IS9xtjiQxYwmn1Kcrn0WEfYVvykPzy3Rw0KO+pp8ph6ry/O7T55srrv9dPP4j87ezJVvXGfMH5G9YlXHNfKa+3HdtVvX32c9Xa7PPgzHyKlP/rK1yO7rQWtSzn3j1Nk77pgxd52ry37VxZ5bv+tKa8rPpS65Rn/vzdtuvvQPiCtW17pss/O4a0Ktj51DzL1uv5p8Sv6RESL7eNd2ZM3xPLkiss9zX7he9rcviOz7Y0vdzo8tIvv89oTrpP+eDJRIj94dkX1gCUx10a5ZZI8v8fE4s7Y4oDaONflEu77HFhH2Fb8pH88t0cNCjPqafKYeq8uzbt+Ub1xnzP+QIvt9j2z/oK72OddWzKnv8Vh1U8e1bz5T2uca+PsfnNs88jcnnYXDpty7cO7DMOfaNPdYY4js/X8YHIv9kDiuq7Z7xzvfs/2atx8+c/YT4dFfx8ql7p7YtS7b7DzuedT62DlEJnXXV5NPyT8yQmRfZr3HfeW4+x4isndnRV2tgxUi+zr2keux2z4isnfjRD3Nm9NAifTo3RHZB5bAmDeIb3z7ZPPpe88KxnXx5yayP/bc9kZhkcAv3H4hV6ux2K9j23tc67VPPs4sLA6ojWP2/87j4968Yu6aI86572PPLV5mpr59zzskfl2edfumueI649xDRPYb7tgKWk89314PTz5f1Z/2ONdWzKnvseuyr1+2r+Oa7eZ8nmvAbHL/LmtwrCbfPgzHyKkpl9JYSWS/4V2nm7o/HppjuI6vv+3svdF2ddeZx8ds2+bqsl9j5rPPWK4rranp3lHHJPrrWLnW2XatyzY7j3setT52DpFZ3X41+ZT8IyNE9vZnU2TI8bJ5IbIve/+4/vrvHyJ7f2bU2XKZIbIvd++47qq9GyiRHr07IvvAEhjzYuz6kqo55ySyP/S1rTD58c+fnhGA/TLuVi/sXqP6dFwSFWyvdfr4nodONp+7/+yn/iwOqI37YB+1//DYWZ9o1/c45q7Yff2H2Htu8TIz9Q2JuW/fujzr9k35xHXG/IaI7HUxY3wfO+dYQ997snrg2G6X1jF38Y0+zjHuv36B8A+PjZNnnGtfx7kGzCb37zK/YzX5lhjW2Y+RU13suv6SyK51KZfoc9kVFzZvePOLZ/o03rY+XxM5Xow91rHnUk6lmF32q+Q3xz5zL+1VzLeOSfQ3rzrbrnXZZudxz6PWx84h5l63X/b54EdONw/X7LXi2D8yQmQvXxuRO8frYYTIvp695LrstpeI7N04UU/r4ITIvo59PPbrcaBEevTuiOwDS6DtAvz+091vNH5JLb3Y5nnmJLL7JV2tRQKvJb5Qayz269j2svO67RPH3Bc5xHljv21jzDi+63HMXbF3jbOLn+cWE3NR3y6xDuVTl2fdvimvuM6Y55Qiu9YRc9n12HW5q7/9SlyvuXX7af3HnhknV8+1rzbXgNnk/l3md6wm3xLDOvsxcqqLHfv1KXV/Ur2ryF631rb1+TrT2mIO+zj2XHXXUd0a9pHLvmOau9bUxLaOSfQ3rzrbrnXZZudxz6PWx87B3J58/oXXRHL3ubVP2356PDJqEtkf/vrJ5rtPNP+y3DGdi9ek1n192zFi9J0T+2U8v4buEyL7cezz0DpZkz8iOzW/pnpuWwsiO/XeViNLGB8okR69OyL7wBJoukj0yW69/N33lW4ven5JzS+2pTmOXWT/+rdONr/ya9s/CJdfpP3CrbbE8jNfPNm84/ZuexLZe38cP47t+9hzaz36Xzmob9/zDolfl2eTeBHXGedGZK9+YClxreMWGc7pONeAr6ncv0vOjtXkW2JYZz9GTnWxY3/Me6jIftcHt8+eunuE60Vrizns49hziXkpflx3aXxJfa4rramJbR2T6G9edbZd67LNzuOeR62PnYP24KmfnP0Uet4X+7Ttp8cjoyaR/X/+xvY5/+2Gf6njmM7Ja1Lrvr7tGDH6zol9+R6xNi6I7Mexz2ur2yHrQWSn5ofUz9J8Edmp96XVbCnfgRLp0bsjsg8sgVJRuq/vS5pfUuOLrWPldi4i+y9feWHzujdVYrdFAq/FL79qNRb7dWx7j2ud9olj7jOHGCe/SNs2xrSfWvsqfuxvO7af47fZjznuuZWzuahvzDnGjlWXZ9N1EdcZ80Fkr2q1xLWOW2Q4p+NcA76mcn+fnPVHJN9+y/YT/YrX5FtiWGc/JKe6mKV+M9BYX5H98w+cbK6++fzm6Z9s68Sx6u4RrhetrZTLmH2eS8xLcZ1raWxpfa4rramJbR2T6G9edbZd67LNzuOeR62PnYP2Ieam9eW9sU/cz2deFeavva16VnlcrXw+d9/Jpklkt33MJc9tG/d7TWrd17cdI0bfObEv3yPWxgWR/Tj2eW11O2Q9iOzU/JD6WZovIjv1vrSaLeU7UCI9endE9oElUCpK9/V9SfNLatPLpGPPRWT3y61ardcv4l5LHNdY7Nex7WXnddsnjrnP649x8ou0bWNM+6m1r+eLY03H9nP8Jtuxxzy3cjYX9Y09z5jx6vJsui7iOmMuiOzVDyyR6/2PnmyuuXX7S666eo8c53Kca8DXVO7vk699HavJNzJsstOY46ptsx0yHvPuK7LH6+ajnz157ReVdfcI23dZ0w+eqmpvl/V5LjEv+cd1l8bV99gzJ5sHv9r8lSF1vofsd11pTU1s65hEf/Oqs+1al212Hvc8an3sHMQw5qb1Ra765c7/eGN1H9L4re85v7nlru0vvRTP9t5vtxpDZC9fG2ZGuy4+iOzr2k+uz/b9RGRvZ0QdrYcRIvt69vKYr8uBEunRuyOyDyyBpovPL69qm+w8Vnqx9Vhu5y6yv/7Vr3Lxi7RavaR7jTrXcXxx9wu9feKY+8whxsl8bavWMe2n1r6lsWiXj+3n+Hl8n+eeWzmbi/r2OefQ2HV5Nl0XcZ1xfkT26geWyNW8XJMai9z6HH/63u1XjPzVN/YvZuYacP65v0/+9nWsJt/IsMlOY46rts12yHjMe4jI7nwdT2Jnzst107am62/f1sQn7tl97Z6rrjadZ84xnl93+1as/d5AwT/G1LH+cKzE4dy/67nrSmtqYlvHJPrr+A/+3+lrX4uW+Xmfm+bROtrsPO6c1Po4zhlz0/oiozzmPXWreLZ3n1uN7Utkv+P91byev2trLm18u8bDbvdn09rYIbJTC2ur6bb1ILJT8201sqZxRHbqfQ31PFAiPXp3RPaBJdB0EfV9SSu92Mb4Er/0YnrPg6ebuYvsfoGOrV7EvUb33/vl6lOXfqH3WOnF3TxinPwSbH+1jmk/tfYtjUW7fGw/x8/j+zz33MrZXNS3zzmHxq7Ls+m6iOuM8y9dZH/iR9XXIMV17XIcuZqXa1Jju8SUT9O+7Bqzzi/P5fxzf51/qd++jlWycV9k6L661nHV1tmM0R/z7iuy21frcr7uK90nXDdta2qz09eBtK3dMepq03k2xWmL0eTbNNZl7ib/POa6UtwmtnXrif7xWPEyP+9z0zzKr83O485JrY/jnDmfuPY8Zq5uFc/27nOrsX2J7JrD8/ZtzaWNb9+42LffM9bOCJGdGlh7jef1IbJT87km1nyOyE69r6G+B0qkR++OyD6wBJouor4vaf5u8/hiG+PHeG0i+xM/Pre57rbtpxD7vmh+94mTzdturv7pd8xBx36h9ou4X5aVn8fcF1uNlXxs43XHcx+7dS4xTn4Jtq1ax7SfWvuWxqJdPraf4+fxfZ57buWs/5WD+vY559DYdXnGOs5zxHXGsaWL7F6z9i2ua5fjyNW8XJMa2yWmfJxjvp52jdfkl+dy/rm/KUYes69j5fF4HhnG/tKx46otjY/VF/Negsh+7avPl6eeb64512hdbcZ1l1h+7v6TzWWv/t2PUoyHvnZu87W/3/7ri2d/tv1qmVKcUl/b3PJ58vlzm2d+2rxGx3ZdKW5TvdQxif7xWPHy2rvWZZudx52TWh/rk/6ltSkf96vNuZqrW8X79L0nG329lfvcagyRvVt9ReYcL5cZIvty947rbre9Q2TfjRv1tkxuiOzL3Deut7P7NlAiPXp3RPaBJdB0QfrlVW2Tncf80plfpj0e47WJ7LZ1TMdoav3du22+dS/U8qsbUx4a88u784pzed0eK8Vy/jGOYrhfrf09ZxzTsX0932fv2774f/1bzV+TYT/Hz3H3ee65lbO5qG+fcw6NXZen9zzvm+aL64zzI7JXD77I1bxckxqL3PocN+1LnzhdbPNczj/3d4llG/s6lvtLrRnK9juPNzNzXLWlWGP1xbyXILK79tpqrs0urrvE0v6yK80V/a+5dfu1MvolcylW7ou+ecznsun6lTKxrprqxWvK64n+8bi09q512WbncefkNrPJ+ZiP2jxmX7c5pvvVamwJIvutd51ubvuDeT9z455w3O0eMAUnRPb57s0U9XAMcyKyU/PHUOdeIyI79e5aWHI7UCI9endE9oEl0HTx+OVVbZOdx/ziqRdW98X2PR/afjJd8cYW2fVdt5r/HbefvvaJVucTc9Bx3Qu18qobUyx9IjG/bJuRxr1uz1uK5VxinMzX/jGm/dTa1/M5hxwn+kQ/x8/j+zyPOZuL+vY559DYdXk28Y7rjPPvW2T/wkPbX7S894+rf/3hfXadxHz6HnvNitnXN9tHruY1Rq7Ose06yPnscp7ncv65v09s+zpWk68ZyrZtfx1XbVPMoWMx70OK7Poqo7rcXV+ltXusjV+bXVx3KQ/71+1V9Letc9If5CzFdF/0Vd/nH9jeB/7u+5VftrFvqY11VWJmn5yn+6N/PC6tvWtdttl53Dm5zevO+ThntXnMvm5zTPer1dgSRHbnHNfNcXWdwKI7C0T27qyoq3WwQmRfxz5yPXbbR0T2bpyop3lzGiiRHr07IvvAEmi6QfjlVW2Tncf8EqcXVvfF1i+qije2yO6XZM3xwY+eFRpjDjq2rfN1q7zqxmyTWzNSv9dtm1Is52IWss187R9j2k+tfT2fc8hxok/0c/w83vXcv9C49+FudRHnVs7monV0nTPa/d33zm0e/dvmT+1H+12P6/Js4p33xnPvW2R3Tp7fe1xXQ86ra+v4ilfno6+60Fc8PVxz/dsvcs35akx2t79v+4nex54r30scK7bOUW3s38dxnsu8c3+fue3rWE2+Zthlfx1XbVPMoWMx7yEie64Jnefcoo2+GiyP+9x2pbV7zDVnn9y22cV1Z1+d27+0V/oKkuhvW+WkrzrRWCmm+6Kv+t79geoX2XU27i+1932lenaWmNkn5uk+tbkunZ/azLlrXbbZedw5ufXczi/mpjH3q/343dW67RfbHDOPIbJ3v09H7vs6fvqn2w9d6A8O72uOY46LyD6vej/mWjzU2hHZqflD1doc5kFkp97nUIdDcxgokR69OyL7wBJoKmC/vKqts4t/OM4vnvll2r5+UVW8fYnsysHfDV+XzwOPll+olVd+EXeMutaMNO512zaOuS+zUL/s3K/WtmodM46bo8c8T44TfXRsP8fP413Pu84X43lu5az/lYP6ok3X46H5d52nLs+m9cd1xnmOQWSv4xU56DjamZf3VGOycb/Pc4zSedO+lOyH9OW5nH/u7zOHfR2rydcMZdvGyHHVNsUcOhbzPqTI3nQfcR2V1u6xNn5tdnHdJYb2L+2V90Zj8rVt3N9STPd57ne+53Tj75hXX1yvbezT1L7t5u0vt3KM7BPzjGMx73iseDrXL+Js77XHXD0W25Ld667c/gLi+ttON1f93jZn5+T1unWsnI/7v/hw+ecB+6uti+2xKLJ/6wfnNvd/5dJ/9ab5PWduPZf7vWb1u0/tfY9sfynzN99p/yWzY5hvniPGXdux3yy43wAAIABJREFU91r7tra1zWE9iOz11/Ic9occxt8fRPbxmVKn82WKyD7fveG66b43AyXSo3dHZB9YAnrxqvsn6fklLV/Y19y6fdF9+tU/HOeXuLqXSb+oKu4+RXbn4fZLj1QvpJe9sfqDqB53q7z8cua+ttaMZPfBj5xuPvH56oU9jjmOGZqF+mXnfrW2VVtiaV+PeZ4cJ8bUsf0cP493Pe86X4znuZWzGasv2nQ9Hpp/13nq8mxaf1xnnAeRvXooRq7m5T3VmLi53+eRZd1x077U+ezan+dy/rm/T3z7OlaTrxnKto2R46ptijl0LOa9T5Fd30Hv+tCcOq7L3XaltXusjV+bXVx3KQ/7l/bKe6Mx+do27m8ppvs8d27jej1mn6bW88snxsh/HNZ2mV3MOx4rns/9/fBe+9tv2T6T9VU3pdxspzk97jXF1jnFPh3bx/N7XP2P/l31LwncX2rrYstWY1Fkv/rVX1T83vXVLyxkp/mdS249p/u9ZvW7T6371cb+0nG2zXOUfNbS572ONbOWtc1hHYjs9dfyHPaHHMbfH0T28ZlSp/Nlisg+373huum+NwMl0qN3R2QfWAJNL3/5JS1f2H7x9MujX+J8XmevuGOK7J/6wslGQrrnL7X3fWX7Al8ac5/y8suZ+9paMyrZlca++Ffbl2Ozk19+EYyxSizt6zHPozYzj+f2c/w41ue463w/ePpk8/irX/fhuZWzGed1xxz0NQrX1/xT76H5x3majuvy9FpKvD3mvXH8OYjs//D4yUZfrfGNb1d10vUPLHrPxd5rym0dryY78/Kempv7fZ5jlM6dY2lfSvZD+vJczj/395nDvo7V5GvWsm1j5Lhqm2IOHYt5v+/DFzbXvfPF10Rjj+Uc3O9Wa/nlK188cy9XLTi3Hz6z/SWkhFr7xHHbuXUd5Xk17rESP10jin/fI6eNdorjPDxnbj2P7PJc3huNSch2LNn5OMeL57bJbVyvx6Jf3XHM1TH0xzIV43tPViK47fJ6Yt7xWP7xXPPHtWvc8+XcbKc5PeY1xdY5xT4d2yfO737Hzj75vC627DR2RmS/6ay47lia37nk1jbuj3m5T6371cb+fKx/MfD6q7bXiG3zHNlnTefe61gza1rf1GtBZK+/lqfeG+bfz94gsu+HK/U6T66I7PPcF66XfvsyUCI9endE9oEloBevupe/thc6v3ja3y9xPs83A9srbl+R/ds/PCl+D7dfpjx3U6t8msaVX594imVGpbhebxy74rfPb6655fzmrTdUL+Kyi6yifYml43rMOaiNcXz8uftPN8/8tBKVHN/jfdu2+RwvzhNzNuO8bvupjfaxX8cxbh4b87wuT+dW4u0x743zmYPInvdNv8QQy8eercQz55tb+8o+j/m8jpfH9UsXHUc78/Kempv7fe4YTa1zLO1Lk98uY3ku55/7+8S2r2M1+ZqhbNsYOa7apphxTMKqvvIi9rUdx7yvffVfObnPbc7B/W6dq8/VqhY8d1y3beK47dy6jvK8GvdYiZ/zUGu7h792svnoZ05f+8Wh53AePs+t/WWX5/I8HnOsuM4cL57bPrder/5Yt8eiX91xzNUx3BdzL/UpZsw7HiuHeC7buHaNe76cm+00p8e8ptg6p9inY/vE+dV/9c0XNlf9bvUczn7x/PW/Vv1SJ/ZfjDNDkT2u1Vydt3msufX6Y82seb2HXhsie78X/UPvD/ONvz+I7OMzpU7nyxSRfb57w3XTfW8GSqRH747IPrAE9OKlF5J80T72zMlrL8F+Scs2fqm1v1/ifC77J3987uIn9HRse8XrK7I7ds7BL1Meb2rl2zSusT7xZG8BoBTX6y2Nxb78IhjHIkuv3XE95hxK+/T5V0UWfY2N/Rzf8WL75b8+3bz9lvO1XyEkW8+X845xdBzn8dzK2Yyz/71fPtnc/t6tkBLtm+LmsTHP6/J0biXeHvPeOJ8xRfZP3Xuyedst5y/+4sTx456Yu1vnYhvnXZerY8bWvooZ++NxHS/Z3PPQVuxT7tHOOeRc3e/c4zw+fuon5878gsA5en2220eb53L+ub/P3PZ1rCZfM5RtEyPFcFy1TTE95tjaA/d1aZ33Z750sqkT2WXziXuqPOzj1rn6XG3Mw7nVjV/7zq1o6q9Acx2V1u6xEr+Yhz81b/scy7nUMbKf7PJccZ64tnhcivuZL55s9N3fnju3zjHGL8XJfTFXx3BfzL3Up1gx73jstTtP2cbc1O/5nJO+d/yO95+/+DVsGtecHnOc2Dqn2Kdj++R8st2u55p3bp9kj2s1V6/PPNbcev2xZta83kOvDZH90nemQ+8B8x12DxDZD8ub+p6WNyL7tPyp/3H4D5RIj94dkX1gCejFSy8k8YJ+x+3bfyp/1we3rV/Soo2O/VJrf7/E+Vw27ov2ireryB7/0Kpi+mXK8zS1MZ86uz7xFENrqYtlPnXj7s8vgu5XG1kq/8jRY86htE9xLOfjeLG1jWPHMR87Zs7b4269Dp3HuGac/aNNPHY8tzGu+/bRtuVZ4l2X95gie2mOuCfm49Z7aRvnXYpTx9G+illnU8dL9vZXG+2cQ87V/c69NKfFT4/FOdy3rzbP5fxzf5/57etYTb5mKNsmRorhuGqbYnrMsf11ExasPV7XOm+1r39z/Sd/Yx7RR8fONfarFjync6sbz3Xj8zinY3msxK8uD+foGGqdS+yLx55HdnmuOE9cWzyOsXTssRjXObj1emP8HKd0HmM6hvucu/5Fiudxn2M5N6/Vdvlc9jE3jXs+x/K8jqFzjX3vqYq5x5pax9Mvf5rsdh1TXhLZ9Wl33ZNy3o6bWTkvtbZxX2TjPrXuz6yijY7jPtg2z5F91nTu9btm1rS2OawFkf3sO9Mc9oQc9rsniOz75Uv9zosvIvu89oPrY7f9GCiRHr07IvvAEtCLV37580ui28uuKAtrHre/X+J8rpuC+3Rse7307SqyK1682fhlyvM0tTGfOrs+8RTDL72leF5vaSz2yS6uKY5FlrK58d3VP2/3mHPwy3SMFcdyPtHOx7ZxbPfH1jFz3tFGx16HjmNcM87+0cbHitEUN4+Ned6Wpz5l+fizZ2/8zjvzQ2SvfmH38Ne3YpdYmZdrxdzc7/PSvtrHY67L0nVgG7WfvOd00/W76KNfPM5zOZfcH33aju3rWE32rk3ZNjFSDMdV2xTTYzF2l/j2c95trfLQ3weQX7a99p2X/tJStXDDHdv7nv62RvbRuHPIdePz0to9VuJnZnEu2+dYtlEOP3zmZHPdbec3336sui/YT3Z5rjhP5B6PvTa3HotxnYNb5xjj27+pjTEdw333f+V0o19ye/7SevKY87Gtz5VDzE39nu89H9rWQP7DocpDfvqXVo7TpZXPp++9tNa6+HaxUV4PPVrlZF7ZV2zq2NvW45FNvFe536xsn9u4D7bNc2SfNZ17/a6ZNa1tDmtBZK+/luewP+Qw/v4gso/PlDqdL1NE9vnuDddN970ZKJEevTsi+8AS0ItXfvnzS6Jb2ZQuao/b3y9xPpeP+3Rse730ZZH9+tvOCkB+mbR/bGMufpmK43XHMZ86mz7xFOMdd1wqCjm21+vzulZ2cU3R7v1/crq59+GKTYxpzmbll+kYK45FX80R7XxsG8d2f2wdM+cdbXTsdeg4xjXj7B9tfFzKM8bNc4553pan8tAfEo1zOu/MbwyR/cFHtw+W0hxxT8zHrXOxjdq8J3ENpWP7KmZpXH11vDRmf7USIRVH63jdFWf/yKX69R3xpTXmeb0+98c53JfbLjbZp3Se4ziX3F/yreuzr2PV2anfrGXr/a2zd1y1dTaxP8buEt++zrutvfyq7Z6XBPM6X9eD1xLtNOYcbGcmPi+t3WO2dQy1dfNo3hzLuUS/aON5ZJfnivNE7vE45qVjj8W4zsGt4t76nvOb3357Jf7mOKXzGFMxPntfdS0qtj6p7flL68ljzse2Ptfcce3q17n6Yw62V6v+pvFoG49Lc8XxocfK608/Xv3Ljbr8xUa5lP53Dhq758FLf6Z47NU/IG5mZlWKpb64D7aNc9T5raXf63fNrGVdc1kHInv5Op7L/pDH+PuDyD4+U+p0vkyPQWTXH4jXv3DU36yjFtfJYKBEevTuiOwDS0AvXvnlzy+JbmVTugF53P5+ift44Tt35W97vfRlkT3P4ZdJx4xtzMUvU3G87lh+dWPu7xPPPnWt11s37n7ZKTfd8CVquD+2XnOMae5m5Zdp26qNY/56DceNdj52fMd2f2wd03nHsXgc54lxzTj7RxsfK0aMqeMYN4+Ned6Wp/JoWkPMZQyR3SzMJu5R3BPzcWs726hVbqU4zvnmu7binD+pb1/nYLvY1vGSjf3Vel63ztOt4njMucd5fGx7n8c53JfbLjb2Ufw3/Hol4LpfbY7jXHJ/9Gk7tq9jNdmbtWybGJVybYqrsRi7S3zHc95d27zeJr/Lr9qKmCUf1YpzyHXjc/nZxq3H/vxTW1FT33Pusbp5lGOO5bzlaz/bfPkbJ5vL3lgJsGKrT4PrXh/tFSNyj8fOya3HnL/nj21pzP5NbfSLxzG253fOMd7DX6vuz9FOtvGPsOr8j/78rJhsZnXzql9z1Y3HHOOxfLwvsX+sY+UzpsheylUs4zrMKrKPx5G9bb3eaLfWY6/fNbPWdU61LkT2dQoSU9XTEuZFZKfml1CnY+V4DCK7fyaNfydqLH7Emcf9YqBEevTuiOwDS0AvXn6B803BL7FuZeOx2Pqlzf4+90udbN2nY8fTOCJ7Jbz4RbDpO2PN3QzF1dzFU+eRu+3jmPfCrW1i6/iOHcd87JjO2/25jfPEuIqtsewfbXwsu6a4eSyf33zX6UafmM39Xc7b8mxbQ5yjTmT/q2+cbPT1KdE2H2cWPo97FPfE3N3azjZqNUcpjufOY/Yt7Yd96nhp3P5qHdut83SrOB5z7p4jttE+zxHt4nHMI/aXjh2/NJbj2Db3l3zr+uzrWHV26jdr2TYxkq3jqm2K6bEY2/Hve2R7j/nSI/W16ry7tt7jrvay81qyj3N3TDPxeWntHnMbbUrzlOw0r3PRsf0cyz62MVv9wjPaa9xj+dhrc2u7HNtzqC2N2V8C/xe/vP1l7u//0dmaiH7xOMb2/O77bPjlhPvUZjuzsU2OX8cs2msN2c/jda1q9/rbq0/019nt2q98xhLZ9XVwv/5/qp8NnJNYau1maFbe09xG9rZ1rGy7xnOvX3uzxvVNvSZE9nkICFPXwTHNj8hOzR9TvR+DyN7156lj2ve1rXWgRHr07ojsLSXwz//6880vXf7WzVf/9jtFS714+QXOF5dfYt3KxmOx9UvbZ7+0FV587pc62bpPx46n8SlE9o98qvwpceeo9m03j/cy7vXG+KVj2YmPb/glG3OPMb1v9ovcbR/HclzbuH36+Wq/HNtjsXVM5x3H4rHnU5/zVlz9r7HsH218LLsYU8cxbh6L53XzRJvHf3Ru88SPyj881vnH3JrWEOepE9m7rCXOp5g+V36eI+6JY7q1nW3U1sVxvDyHfRXTNrmt4yW76K8/EKg4nsN5ulUcjzn3PJfOo32cw+sr+TiPJhv7Ob7PY5vj2Db3R5+2Y/s6VpO9Wcu2iZFiOK7appgei7Ed//b3be+LTTGc9z5bryXP4dxz3fi8lLfH3Eab0jz/6zfKDJxLibVj2yayjfYaj2Px2Gtz6zH9rRTHzW2eV+P2/4vPVp8gl5371Zb8cmzP7/7IzX1qS3ZxPM+lOA+FT8JHWx071+yX7Q59rnzGEtnrchdL7Y/rMjKP+6df3uSv9LGtY0f7T33hZPPkj6vnSBxb8rFrzzWz5LXMMXdE9vVdM3OssznlhMhOzc+pHvedCyI79b7vGjtE/KLwSWdnAqsT2T/2mQc2N931kVoAGpNN/O+xJ5/b/MIvXrn5z//lv138/81X37k5OTl/0eQQInt+ifO5LqD4YueXY42PIbLf9cFKLPA8c2q93racZPf4cy+89gJdsvfNKMbs8uIt1ornNsZ2TLd+MZWNY3ssto6lXGJ/PvZc6nfeiut5sn+08bFi1MX9y7ubRcO6eWK8mGPs13Gdf8ytaQ0xnkX2T997srn7gerTwE3z2z/Opz6fX/amik3cE8d0q3XIzzZqYxyPe77SmH0VM9rF4zpeson+zsvr8LlbxfFYKTfPGe3jHF7f3Q+cbr77VMW6ZONYpdbxS2Nej+eybe637/1fOdlce9tp43f/2dex7FtqzVq2TYzk67jOVX0/eGr7ByT/5jsVn6d+sq2TGNvxnVOMkfOyzT5bryXP4Vxy3fi8lLfH3Nrm/kdPNn/w4fpni+z0dwNuerf+gO4LZ55xzs+xHNv5RrZxbzQex+Kx1+Y2jjlubvO8Gre/c7SP7kkeK/nZzm2e32tVDNuoLdnF8TyX4uTcsn2eI45Pdax1RJG9Lg/xMOfc1vm4377mozlzjMgmsrfw7lj2e9+flH9p5PElt15/Haclr20OuSOy11/Lc9gfchh/fxDZx2dKnc6X6ZJF9pvv7PazjX+eUkstzrcWh+xN1Eo57k9gNSK7PmlukbwksktY93gW2XUeP6kuf8dYs8ieX9L9EjmXtm9+vuGX8tdN5u++f/aP0OUX79KDwjHdxtj5xuUXU9k4drbRuWO1vcB6Lvn4WHE9T/aPNpFdzsF2apvyrJvH8R577qxA5n63df4xt7wGj+W8LLI7d8+Rz90fW8eUrfrzufrinjimW+diG7UxjsdLc3rMvs4h2vpYthrPTDQe/Z3XFb9V/iSu4niNnt9zxNZxbOM51NblEm1irHxs/7zex545ufh92jmOc8n9jttlPfZ1LPuW2pif11+yU5/jqrVN7nv6J9u9e9st519j5zziXDGGY7m1/T5b553ncA6Zs89LeXvMrWyefH57T/gfv1quTc0rO+fhVv36FzGv/7XtH3VVv3JybOf7Z5+sxHt9oly/KPNY5ByPvTa3ccy+uc3zatz+MWf1O9dSvjmuzu95qFpD9o/2Oc88b85R49kmxpO9cox9czhWXocS2W++c8veLLynbs0js3e/Wtn6etf5je+69JeRjrfU1uuv47TUdc0lb0T2dQoSc6mvOeaByE7Nz7Eu95XTUkX27z1V/YwYf7YtcfLPm212JV/6lnE/6C8r4xEJrEZk96IkmFsgd19sNZZF9jiuY4370+xZZPe559BLlr6fNf6TYb2YqN+tX8x0U/n+09Wn3tSv/32Dyueyd9+jf1t9VYvsx/gke8zP88yp7Zufb/ilNYhljqcXSfXbz/ugPv8fx3Jc27j1i6nsHNtjsXVM5RP787HnU7+PFdfzZP9oE9daF7ctz7p5HM/zqXVfbOv8Y255DR6758Ftvd/+3i2jLLJ//PNbEc45xHnzsWM6z3wu+7gnjunW3/luG7XycZwP/Fklvnpuj4lBjK+Y+mTkhz9W3QfsU8cr+zuvulZx8vyeI7b2zzlqfXW5ZAaKJ8Hp43ef/ZS5/TWH59QfgdX5tbedf423WTqXUnz5d1mPfR3L85bamJ/XX7JTn+M611Kf4ylPHzuPeB5j5Plsv8/Wa8lzOJfM2edqbePWY/7albf830rwzvHjuXJwHm41no81j+ewf7Rxn9vLrtgK9DqPzJ2v2zhm39zmeTVu/5yDzj1W8sux83n0j2M5zzxvnkvj2SbGk73yjH1zOFZehxDZI0+z8L65NY9o6z63ss3jdfEcd1/tg1+tflYZcw6vb6p1jbmWOcZCZN9P3c5xr8lpu9eI7NT8MV0LSxXZ/ezXzzvxZ9PS3vnnzTa7ki99y7gfZH2U834EENkTL31NjAR2i+gW1fVJd4/FT737xUutbxp6MdG5W4998eHtp6g+ec9ZgdA3KMfyueLFvng8RGSXKKb/Y36OPae2b36+4ZfWIJaleJ+97+Q1YSJy9146ptsY2zZu48NJx+7PrWMpnzwWzz2X+nz8jjsqES/720Zzx7XGmDGW7Jvy9HryPI7n+dS6L7Z1/jG3HNtjmVEW2Z27c4jz5mPHdJ75XPZxPsd0e/1t2+vVNmrlE+N89e+2vxT40iNnx8zXvo4p35yneb3hzZfyzP6OU2oVx7l5/jyXzu1rG8+h1rnkPKONY5b67O859Mci3aeY2cd2ud9zdFmPfR3LvqXWuchWxyUb9zmu2ro+x1OePnYe8TzGcCy3tt9n67XkOZxD5uxztbZx67Ecq+1cOXzgT7fPwphPPtY8eY5o0zSPfjHmcefrNu6Hbbq09s856NxjOd8ucaN/tM955nnzXBrPNjGe7HPMOD7VsfLqIrJHTubtti13rTuuXXPaV+0zP93+7zjR1n1uZZ/Hc7wYe1/H9z68rfG2r33bZX6vb4p17ZLv0nwQ2ZufuUvbT/Jt309E9nZG1NF6GCGyr2cvj/m6TBIppz0JILIHYBLW9ZUy/hS7hiyyP/DINy/250/B+8VL7b+9sP3/mlu2n+hz67GPfHLbr/Yf/6X61J/O5etYPs99cfzjn3v5NXv3e361nstjpTbmVxqfuq9vfk1rFpNSPPnYT21kGDm+4/Zqf8wl237j77e/XNG4jvO4zz2f8nFfqY3z+Nix1WZ/22juuNYc23aOlcd97vXkeTwe47hP7W3v3XL4q69vP1ma/WNucezRv6nyzoxeeumVzQun1SdVnbtziPPn4zifxvK5+uJ8junWOdpGbY5TN+Y68HiOGXM1b9nYz+PZ33FKrXy9xhzH8dTa1zaeQ636NO612y/aNPVdf1sVX3EUL8Z0nA995MLmk5+vbN2v1vHVNq3nn/5ta2tfryv65+NHvtHtWpWf48acct8jf13x8jqdxx/9WTVXjJFzsv0+W+ed53Au5vyrv7ndk6tv2ra5DmRv2xyr7Vw5XHvrNm7MJx+X5og2TfPccEdVU16b27w/TXHimP1zDjr32C5Mon+cL+eZ581zaTzbxHiyf/irFZc4NuWx8vrop87e20v5RE7i/bN/3q7lzj9sX5NYRp6a03umNs8XbfOY7PN4jhdj7+vYe525jDGf1zfFusbIf+4xfn7uxc0rr7xypgbnnjP5nb1nwKMfj5dffmXzHycvUvOv6gTUT7/6WRqvCy++vDk5/9Li6t3Pfv3c0/azxT5/Blnafq813yCRcrgDAUT2ArTS18VIfP+ly996UXSPLvEFTD84638LCG5l8873vLh574e2YstHPvniRv/b95Y7L1z087nGHCv2xeNPFER2+6iN8e2X25hfHpvDed/8fuXNl74sex1xX9ynVpxe/2tbv8jdLJs42sbtN/6+ml/H7s+tY2p9eSyeO0/1+Vit58n+ttH4r/x6lYv8L7/qxc0vv+lsnTlWnDMe181jG8+n1n1qvW916/S4/LyGZ39ydo3Z96WXJbK/VOSQ54+5xHxsF+fXd0ff/O5tHTgftfH//3P9hc0N77qwufGObY7KLce9473lMdeB1+O4Xrdz/cd/e/G1fZWN/d71vm0uf/RnZ3NynFIrX6/RcTxPbO1rG+eoVn0az3lGG8fKffZ1fLXqc79i2ifa6Dj2P/7MlvM3/r6qDcXwvD/+2YWNv6rkn39+6T3Pdn/z7Rc39z9S3VPVH+eNMe0TW+ek1v3u01r+5d+reDr3Oj2H+nwcYziWW9vss73unVUucR7nEHON4z7+1//YMhB79/VtxcDzmKNi5OP7/+rFzevCd65nm6Z5HV82XpvbvD9NceKY/WOeGte5x+K80bfpOPpHu5xnnvfK3zm7B/rFQraJ8XbJLfrv61h5ffTT1TVUN0/kJN7m02VdsrW94svHe6Y2zxlt81ic22M5Xoy9r2PvdeYyxnxe/xTrGiP/ucf4d4nsm82ZGpx7zuRX3edh0Z/Fy69sNhLZYQeDY6iBCy+9clFkX9pa/ezXzzZtP1vs82eQpXFba75R7+S4PwFE9gKzx558bnP5W266KKj7k+wS3vU1MfFT7nL1S5Za/5MS/RNbnbuNNjou/bPu+M+84z+Ltu8ffLj6Y2kaL32S3fOrjfEcI7cWqHL/XM7r+O2Sn5iU4sW+yN0smzhec2u157L3P7FWfjp2jNx6TrV5zOffe/Ls1x3ENXue7G8bj/tcMUvH6mvK03HyPM7RMdW6T21cn8ayv8fjmOdyzKt+t7qGFHNfXxfjHLzPMTfnklvXSck2j5mv4ztWZPKFB7fX9ns+VF3jn7v/ZKM/LOs53Nq/qb3zj6o/8Ov54/742DFs4xzVej9invKLNvr7Eopx1e9t90pjsrGv46tVn/sV03GijY5jv+PFPsVw/rk/niuW7PSd/p4j+rpPbex37Ng6rlr3u09r0fciO57OvU73+Zd4Oo8xFMt/KPTmu6q9t98hW69L+TfNq7Xpq8auv333fMXA85ij2XjuaOO+bBP787Hjq99rc5v3J/vWnds/5ixbnYuJxuO8dXFyv/wdO4598ctnGed5o62ONXeTzZW/07y3Od6hzpX3Ll8X433swvyBR0/OXJfyMXO1ea2Onft1Lvs8nuPF2Ps69l67fvSH3ZXffV+p6mnXub2+Kda1a85L8uPrYqrn+JL2jVx33ze+LmZ3dtTd8tjxdTHL2zOus0v3rCCR0tWDACL7ZrO560Of3EhY93/62hiL6RbZ/T3s8VPuso8vYb5A/dLnNtroWC9FfkHyWDz3S5PieTzG0vgYIrtjz7WNax6ao1iW4sW+yN17Gfcl5yBf26n1i6nsSrFs6zmzv8dzLJ3HuT1P9reNx30e/eOxxmWrPv2vP9776XurP8jpOHke2zu+Wvep9fo8Hv31Rz/dr9ZjniuOxfFvfuulzXM/Oys6RR/Nq7+K/tizVf7OKeZz9wOVwOe5Lr+yyinaejy33tuS7dtuPr+54V3VHOab68jrVo4ei/F07H5zyHl0Off8T/x4W0O3v6+qWfvbJs539wNbwSbmGXNV6IcTAAAgAElEQVSVbbRXLJ3LJu5LnMP9eW22cQyfO16e57tPbOv1gx+tREPFznZP/OhsLXqdytFzqFX/U89X14HG4/+O63w05j75x5rW8WVvvPSTsZ4vxlAcM3njq1/PYrtDt15vrMFSDiXOJbumPjHwPJFjPrZNjHVZ+mR7HIvHV/xWVRtem1szj/Zdjj9+z+nmB0+f3fvop/ilnKNN6TjWRGncfZGP+2Krudtsov1cjpV3V5H95jvPb+798slGf0TZ+9iVue217t+8evss8d/byCyibR7TvUL3+divf3Fx3W3VvdW1ts/We+36yedD5vb6xXZIHHzPPkvMA5G9zMV8aNfHB5F9fXvKdVq/p4js9Wyom+WwsS5KuxuB1YjsEsH1lS7xfwvjQiNxPI79wi9e+Zqwnn0tsMsvi+zqiyJ8fNHyjcMvfW6jjY71MuQXIo/Fcx07lsdjrNdfdVbYsY191MZ4Hl9a+3vXnX2RHZK/mESGjhX7InezbOIoX4mWiiUR0y+mjq1PKDvOdk9OLtp7TrVxPB7HWOp3TLUey/628bjPo78/Oesx2Xper9UcHOf/3nh+c89DZ9cSYyqWY6j1+jxHzNN9bj3mudzvVuOPPbdd/7W3nq2HP/6LSnSP+cRccj5aW87Pc6ltGrOd+TTZesx8zdYxNO48PWYf5+F+n9u3b6tPc5tvnNdxSjl67mivfN0vQcnHjqNz2bzuyktFZs0Rc8i+MUY8jnPmfp+rVexSzP/5lqpmvE7FjL5X/PbW5rtPXfoLmji/1xf7YpwuxzGG4phJF9992ugP0yqfWIOl+eo4l2zr+sTA88Q9y8e2qYvTtV/riv8PYa6cYp4xB80Rz7seS7B1fk0+dfPapyk328yxVd5dRHbXgwVy/YForcf9bWur2/fSvtXZao6mMe/jIVrXg1rNl8+H5OA1iu2QOPievfeYByJ7mYv50K6PDyL7+vaU67R+TxHZ69lQN8ths5u0jJcJrEZk94IO3cYXO7/s+KXPbbTRsez8QuSxW99TiUGOoxuRx+tieVxtvHHl+NHuGI/91RZ57ZFr5G6WTRzla3+9lPrF1HPkeO53K1/Pk9sYS2P2Ueux7G+b+79SfdWM+qK/fylgW8Xy3F6r8/Y8l78qmt73yFkR0jE8h+OYicdjnu5z+8bfOr+5+ubzm/tSzh6Xr/N4e/r04Jv/d/UJ9LhG5yHh8KOfrvZIMbW2nJ/nUts0ZjvzabK98ne3uZmv2TqGfJ2nx2I8Hbu/a16OnVvFMkMd33Rnda+RbSlHzy1756nW/fKLxz6XTZ5f5/pKlZhD9rVP7NenlvXJ8NhXN49iZzvHdCubh7529trwmFpziOuNa1Z8j7XNFePG4xhDscwk2kxx7LVrv5vml92ua3fcOEeMlY+jnX13ab1nam98V/WLuV1iKaeYZ4yh+PG8z7G4vu6K6n5W8n3bLc1705RbKd5c+pR3H5E95921TuqutVL/je+ur5OSvXOKtbbvY9ehWs2Vz4fM7zWK7ZA4+FY/X0UWiOxlLpERx+tihMi+rv3k+mzezyWJ7D98pnq397NfP9P4Z4u6vR7zZ466OehvrrN98zm0prq2+RDZB+6oX67cquDbXvp0Y/LNyX6xjTc297fFlJ0+XSqBVTk0xXfMY2rreESukbtvXHV+Yhd99WCKDyeN53iZt/w1j/bsy39dCXjqi7F0Hn09Jn99xYtztU3OOfrHnGX/h396urn2nec3z/y0qhnn7XliXM91w7vOCj7uV5vn8DpjHo7pNufsfvk6jyyy20ZtjO1cSjHVl/OLcZrGbGc+XWyVu/LJuejrLL76zZONvpbFYzGejt2veeOY8+jaytcMdZxjPfS1S3P03LI1T7Xxq1Fs4zx0Lhuf5zbmkH1tG+O7L9uW5lHsbGd/t202Go9r9XGM+47bzwpajt2nddy6Xyz1iTWW7Sfv2a4r10aO/97/p2fX2Ws/2/Q5j2zzcVsuXecxb31v+tCY8o95xhw0Tzzvc+xro49Ptm3KLdvO6Vx5Tymyf/Qz9b94K3Fq2ivX2iFa16FazZfPh+TgNWpvhsTBt/xMQWQvc6Fe1ssFkX29e8t1e+neLkVkv+GO7QcK9EFE7aOf/frZxz9b1O3vmD9z1M1B/6W1dUgmAyXSo3dHZB9YAvklTN8ZmvvyuW5MvjnlMZ3HG5vH9bLj46bWL0VN8Zv81zqm70wtrS1yjdx1E/vWD6sX1zZfPZjiw0n2OV6Ooblv/v1qX+ONM8ZSf/T12Ote/S7xH7z6cLRN3vvoH9dre7WKaT/n7XlsV9ev8Zh7nkPnGtd32TpWbj137pev85iTyK615HXm3HWu3LX20vrsn1v5qS/62KY0R1vfL7/pwub+r2x/kFKcHMtfVxHn87GuG++t98Hz2cbniitbn+fW/rLLvtk2nmfbG9916TyKne1iDB232Wjca41tjiuxNvfluZrOHXtIjKb4u4wpF+WVa6MU600j/gHNyCAfd8mllF/u86d0cv8u5021K367xJSPr41d/eXXlNuQuPv2Vd5Tiux919e0V762D9H6evG1m8+H5OA1am9KcZ5+/tzmY5893TzZ8LcsSn70bZ8xiOzlZy31sV4uiOzr3Vuu20v3dikiu57x+hnI7z9+9qvPP1vU7W/bzxxP//Tc5s8/fbp5/EeX8qmLSf+8WA2USI/eHZF9YAnkFzTfsHJ/PNeNyTen2O/jeGNzX5e4ttVNqim+7WjPCu+R+198ditKvuWtZ20is7gnejDFh5Ps3vfHpxdfRPUpce1J9C0d++Hy+HMvnLHNvnkeffr31ruqf96e9z76x5xjDoppP3PI89T1K45zV1uaw78tj3PGY88d+3SsWM6jj8iuh/v7/6T6BYbjap5Sfh5vGrONa+KNv3lpfNu4Ve5iUrc+2b35/2xrLM6t4+gTxxy7T2t/tT62v85zjnFur8H7YL9ooz7H8Xhu7S+77Jtt43nJVvlGG8Uu2fWx8ToVO/6f43aZK86bjx07x812hzzX9/Yrr1wb+84hMsjHY+WiuPpDmWOspal2xW/XOXxt7OovP3191mUtXzkzJP6+fMV0SSL7LeGr/TITX9uHaH29qNV8+XxIDq5H7U0pzphzleKvvQ+R/ewzdu37zfrObRDZqfljug4Q2cf9meSYamdOax0okR69OyL7wBLIL1ldhAG9oPglJfvrXGO+yDzeJa5t5dsU33a0ZwX0yP3tLd9/K3ZxT5qEN8dt461906dk/YJre/X7WG0eV1/MJe999I92MaaEen/a3/nmeer6FUdz+P/SHL/e8MsK+eecnZtiOY8+Invpa0c8Tyk/zzd2q9zFpW59cb6Yl46jTxyLPl2P7e82+qkv5xjnrltDtFE8x4mx43H8NH32jXb5uGSrfKOdcizZ9bHxOm949+nm3X9YiUs5bpe54rz52NdJjpvtDn3+pUdOztxHDjF/ZJCPS7W6S05vePOlf4h3lzjyUU4xzxgn12QcaztWTbXZrHVcTA8hsh/iFxC+tg/Rug7Var58PiQH16Pv6TnWmHPl2Mdwjshe/bx4DPvNGhHZqYHjuuYR2cf9mYTrZ5rrZ6BEevTuiOwDSyC/+HYRBvSC4peU7K9zjX3u/n7fExrj/PW3Tjfv+sP2T9lGH47P/nKjyz5GmzbhLf5h2zrW33tyK7S86XfOiv96uEQfvwDHvphLrq17HupXS/LXnHmeun7lER+AMZeYY9Nxztm2iuU8uorsn/pC/Xo1zy75OZ++rXIXm7r1xXgxLx1HnzgWfboe299t9FNfzjHOXbeGaKN4jhNjx2Pb6xcgPo7jdcf6HvA8pnxjn3Jsi9lm43U6rms6x22LY/+6ti5unf2a+yPbfFyq1alZKKeYZ8wn12QcaztWTbXZrHm8i8het/451Ymv7UO0rkO1mi+fD8nB9eh7eo415lw59jGcI7JPIxgcQ23NdY18kp2aV21e9qYLF/+201zrdKy8ENnH/ZlkrH0hTr/70ECJ9OjdEdkHlkB+8evywqeHTPaL53qB8UtM7Oe4mdtQPmLuG3CXfYw2bcJbtK3L0y+2edziu/tLdjH+e//4rCjZt5be8Otbzp/54lmx2nxK85ub2piLc25r63JULM/XVWSvi+Ucrr7xcL+AUu7P/qz6YcM5lNrITcdxHXGs5NvWZ3+30V592rc43y13VYy0hjwu/2ivc8X59L1nay/OE+1LeUTbePybV1e5uF/5+Fitcozx45iPu9jojxDbXnOU1t0ljmOU2rq4Jdu198U9+91rz+7zb7397PkcWKhuY84xp2/8Q1U7sb/Lsf4uQhe7tdp0+ZdjdWvvcy+pizFW/w+e6vcC43vBLq3rUG28T/l8l5j20T1OTMTWfbHNc8cxjttrAJG9nRF1tC5GiOzr2s9dr08/a3f1X4rfnEX2T3z+dPPYcy9cfLb75yc988XWz37tU9vPEm0/B7SNL2UvjznPgRLp0bsjsg8sAT8wxmx1Y/LNacy4xGoW6f1C+cSPugnFfjiZ6x/+ab3AmG3tE9umrziJdtfceqkg0xS/JFLGeHXHOaZqMj+E7RsfQtnPNk1tXb0rlh/6Y4nsTXmMPeY9ff2vNdee5o3cdByZxLFdcrS/2xhDfdq/un9t4R++Yj7yv/adZ+tQcZp+gRj9S3nEnNqOn/nJWZH9f/1mO1+tI+ZQmiOP33DH+c0H//zsdd0lTim2+3ytXHNre872WWubec99nbqeL7tivK+fmft6D5XfEJH9UDl2nedQfwzU145a3VPyue8zu7R+5vrZkGOMOVeOfQzniOwIjsdQ53GNiOzUvOrBz9FYG2s8nqvInp/tfhdTv/bB49on/2xRtz9tPwe0jdfFpX8+94qBEunRuyOyDywBPzDGbHVj8s1pzLjEaha1/EJ523vPiod13Pxw8ng+d7/aprFoVzq+6c6zIl/JZkj8Ujz15ZiqyfwQtm98KGY/2zS1dfWuWH7oL1Fkb1pz29gbfr2qw12Yxvj2jzE9rjHtn23c79Y/fOU9yvb53P5uo3+brX3GbLWOmEMpdh5Xnv5Fie27xLFtqS393YWSHX3N92v4wGeONeD7ZXwm7uPY9yq1ip/Ph8ypNYit7n/+w+0x3phzxbjHcozIPh8R4Vhqbup1IrJT86pBP7P1L3ynrsl9zo/IPu7PJPvcK2LXX4sDJdKjd0dkH1gCfmCM2eoFxi8xY8YlVrMooRdK3Wy7CoDZLp9H3k1j0W7X433EzzFVk+LjF/CYq77Sxg+q7Bft6o7r6l2xPF+TyP7+P6l+EVEXq27uJfTv86szxPhz959u6v4woP5gqfY2c837nM8z1+jfZpt9xzhXHcUcSjHzeCnPLnFKsd33jjvOb95xR1Wv7qdtvj/DBz5LqAHdH/ws3Gfre5XaeH/2+ZC5/cwVb32VUY6V59a4/nXRd544ucQ2+3Y519d2dbFbqg0i+2GukaXWxxrzRmSn5lXXfoYf6jk51bU0V5HdHxrSu43Y+B3H+xGf/W0/S5R+Doi828ajLcfzvD8MlEiP3h2RfWAJ+IExZqsbU53gNeY8xDorWuSHThsfP5xsl8/dr/b1V52dK46Ncdw0967xc0w/cOND2LH9gI4PbY91af0wzrbKwfM1iezRry5WtOG4qkcxznsd+XjfM9fsk89jjHzcxzb77nquOspryLFyXvlc9l3i5LjxXH8T4HUtf5cj2nNc1SosYDH3GojPwn2+OPpelu/PPh8yt5+5Yq17YI6V59b4tbdt/+XVD58ZJpD7b8F89ovD4uSc53SOyD5PQWFONbK2XBDZqXnVtJ/fh3pOTnUdzVVkN38/1/2Oc+O7L/0QXdvPEqWfAyLvtvFoy/E87w8DJdKjd0dkH1gCvmGN2frGNGZMYrWLE/mh08bMDyfb5XP3H6Ldx9w5ph+48QXca4s/MGU/2zS1dTWvWJ6vq8i+y/xNuR37mPc971HmnM+buPWxbYrTZ0x19J4PN3+CPOeVzzWf4mQWffLAtv1eDCMYLbUG4rNwny+Ovgfl+7PPh8ztZ672QPfAHCvPrXHfK9vWf+O7t2J86WtoFKcUO8+/9HNE9nkKCkuvqznnj8hOzas+/Vxve07MuZa75LY0kV37onXFZ3/bzxJtz+q28S4csZn2vjFQIj16d0T2gSXgB8aYrW9MY8YkVrto4ZdJvyy2Mct2+bzNf8zxQ8ztB26pPj9xz8nG/8R7l1zqfH7j/1b7hshesRizdtpi1e173rN83hS3j21TnD5jH/xos8CuWDmvfC6b6247v/d/mdJnXdhOc13AHe6lGjiUeODncL4/+3zIy2l80dY9MMfKc2vc98q29dvuS4+UP6leip3nX/o5Ivu0wsHS62eJ+SOyU/OqWz8z254TS6zxmDMi+3H8wjzu+RqPB0qkR++OyD6wBPzAGLP1S8aYMYnVLgj4ZdIvgW3Msl0+b/Mfc9zfszZmzBxL3836kU+dbN7/J9Uf5Mw2Tz7/wmsv23ls6Dkie3sND2Vc8vd1ke9Lud7zeSmW+/rY2mdo+2v/u51fzktf7TJ0XvzbucMIRmupgUOJB74fq9XLXT5veuF76Gsnm3seKovc8juEyO68c5591pF9l3KOyI7guJRaHStPRHZqXrXk5/yhnpNj1W/fOIjs/X4m6csX+8PcTwZKpEfvjsg+sAT8wBiz9UvGmDGJ1S5iSGDTjTsLbXXssl0+r/Nbc79+cJqaw9Tzr21/xVPXRb4vZc75vInDXMXrPmtoWh9j7fdbGMFojTVwKPEg3o/ve+T0tfuz+tteQM29zi6L7E//ZCuO3HDH2WdBnMv3zrb12y76xjy8rrrxaLvUY0T2wwgES62PNeaNyE7Nq6797Gl7Tiz9GkBkr94Z1/wsX3qdtuU/UCI9endE9oEl4AfGmK1fMsaMSax2QUMvf7rh+CWwL7Nd/frOM2f7h79+sjO/sdbFPrTXeh/Wvi7yfSlzzud95piL7RrWMBeW5DHudQjPZfA8lHgQ78c69rnv100vT66lOpsssn/ynu1XbTm251Icx/C9s239tlMM+8bWsevGo+1SjxHZERyXWru75o3ITs2rdvzs0XPiO0+c29x9f/k5sGudzcVvKSK798PP8vjsb3sGtz2r28bnslfkUX9vGiiRHr07IvvAEog3qLGOfWMaKx5xur2c6+XvCw+dvPZDQF9ufnns67cme31tzdQcpp5/TfuptYinfgjJ96XMOZ8vkcMa1rBE7uTc7RkFp/lzahOZx3qhy/fjX/2N7Vdb+X5dmucHT5/bPPV8JXSUbNQXX7RjzTl2nNsxfO9sW7/tfv+DZXHFsdU69tpaRPb6l/q17TXr2e41Ivvx1PyXHjm9+LeLSrXv54meE9fdvv3l7feeXB+bpYrs199W/e2qtmdw27O6bbxUH2vr+9YPz210PSx1XQMl0qN3R2QfWAJ+YIzZvuWt83+RHHO9c4mllz8/FHbJyS+Pu/iuyWdqDlPPv6a91FrEUz8gXHbF2ftS5pzPl8hhDWtYIndyPnttwWO5PNpEZt1LH3vuhc1f3n26efZnu4sLdT+r+H5deqlTXcW/31KyUV+dyC5fifRxbsfwvbNt/W3zO7baG9+9/cXBEz/anZPzm7p93ZsubC570/aT/4jsy9/PqetpafMjsh9Pzdc9Cx7/UfULXj0n6uyWVtulfJcqssefPfUMLq3NffFZ7b7Yto1H27Uev/2W7c+y+plvzmt88NFzmy//9aX7PVAiPXp3RPaBJRBvSBwv98VYe6cHvh8Ku+ylf2DYxXdNPnBY9nWQa1H7WRJd8j5H8STHWMp5XtNS8ibPdV1z7Ody97NNZNaLln/OULvri5dj5FrRPawuZra9/X1l29L93r4ai3N7Lt8729bvOGrtG1vHVuuY/5+9M22TrKry/ffxS/imP0PffuebFkvobqEAsXFqlEGmKqhiVgTR1lZEUZGhmIoCZBYHEBCqKKpAaH3u7eFeKjML7z33+WfUj/OPlXufKU5ERmTufJ581t5rXmuvvePEihMnLtu/MdMHEq5/u8bELfulyb57Go7bVW/LZrc02XdPzXNux9cCf13RGL5vHNiofvfGzsrPTm2y/+zQevWHo5MfTffX6tR500ZPyew0HDUe98KyxenXJ+7bjC3SXS9emuwzlgCFWeDqvilm7XQY8qIArg/83MWTu676yOxEXl5UdmJsuzEmradfHJODnbjOOzEm1qvA1X+NKmu4/Gv4bz+bNM7v/OHkeuCtk1ubB1xnCPobGsb6wFLfHGKeguiINaE7psV/1Q0b1b5bN6oXX5m8IRYu8mqe0p0675EVzW0jz9nZ9mYSPTnb6BZEp3jb9OLHskLiln+lyb51TyzruhW/xlmr0mQfJ4+rUI+c2/HM1pxzUGP4hNN5vwqxdfVxJzbZWT+tm/Lgr9WpvLTRUzI7DUeNx72wbHGyL6NfM7ZId714abLPWAIUZoHL/+a3bY10GPKi0MZb6Pn1jo8VKbnK52oVcqN9wcWV+8vFg+NWfbwTY1r1NSn+r/b5sRvXT29UOEtSb664zrh030Z14oOtzRdyFt/w+Bwd8DqM5zVyzsMYmsMoD6+gaG4buaZ44RF0XY5njG5BdGIXnlWExC3fS5N9a82v4poWn7uvY2myd8/VqtcV53Z87bv7vvp536LBp7NR5/2qx+3+r2KT/bVj079JF9eE6wKtm2L112qPnXEbHb6dDKnxuBeWKeaDt9f7Mvo1Y4t014tvabLf88snqr/5273V0ePvfZyc53/zWvWJT35q81/08ldngAvnAle/EXDO5zeqa28pd6OXWl79Wh57DZ98Ybo5Mrb+ZdHHBdGy+DOrH7qbdVYdRb6cB6UG+tWA3qhwlqTeXPHmU3lN0cl3fMPjc9cBP5A3w8x5pjlzh66TcZR3ftHcNjJN8cIj6LoczxjdguiUTCpPyCwjfPzZterkv9eNNeKWr6XJXuel69pde9N6dfVNO6sR1zX2ncBXmuz9a37WdX/jnVPV48/U32SaVV9XeT+3b//BevXmienHi3CeO5/O+676V4FvFZvsvh5ao7gmXBeIT2vgr9WpNWmjp2R2Go6cLvP1Cz5qzWP+625nGQ3JwJYm+7W3/qjSP3//6z//u9pz0bWbTfe1tY3qwq/fXKnpXv4mGeDCucB+b0JLvkq+Sg2sTg1wsbTT18wvNnZCrLtl3XbCWpUYVuc8bFsrvVHhLLnq4Hr1+tvTDRbfl/HN1zmfr/OgH4q76uBGpWZJfPPjOqI/vBl2/NH3phvc0KJezVPy8IvmtpEn3hgPdMG3Tk7fKec0xugWRKdsN+lFdlngH45Ocn31DXXjiPzJx9Jk31rPbWvn+WvjXXX6A4fXqteO9c/RMsddmuyLX88rrp+8lryZeP2YZ634uc2+ffTptanXDZ3nzqfzfp4+LVp3abLX1wk7bW371BI1vszXL/iovRpjK73e2TIw1WRXQ/3v9lwy1URXQ12NdTXY9Rfns5lffWleQAqs3xiWXJRclBpYXA3oBfLt9z+cukNw7Pzf9r3dcUe0X2yMncPt0EfDajtsF5uLOwNKrpcr13qj4mdJfJPp+zK++fK1hC/KSz8052csnYyBOf74pkrzlLzrcV3IE2+MB3pKr9MYo1sQnbLdpBfZZYHkT/7jE/nTvDTZ+zccPX/kdCfCVO3shDhLk71/zc+67pyfiz47scueFdR5ztmuuXxyPtFmjXeZ5EuTvb5G2Wlr26fOqPFF78EhPmpfRrnV79JubwRbmuzctY5b8c52PUZGPGrIl79qyxsZf1Ep4+V641vWo6zHTqwBvYjrhXGesf39P5XamWd+56Xb39TMy0bRW/ZGqYHpGrj/8bXGBoLvy/jmy3MJX+pNKjTnZ3zzHfUzNsHl+OObKs3lE3Ip6LqQ7/JmMupF1iG6BdEpH2KeXGbZxsTJa7P8I48alyZ7/4aj52/Z1ntMf1K1M6b+7dJVmuz9a37WteL8XPTZiV32rKDOc852zfV7JP9yVX3zjmizxrtM8qXJ3r3Jfuzdyevj/lvrD6WXaS1n8YW9sOg92MdnfNS+jHKlzztbBjo12f057KXJPp1wfxEp4+k3miUfJR+lBuZfA984MLk4Lbmef65XKcc33lF+yHmV1qv4unP2b2wQxwaCNxvimy+vA/iivN4IQXP+pnGOP76p0pxGX06f60KeN2oxHugpvU5jjG5BdMqPJr3ILgskf/Ifn8il5rulya7nQb/9/jjNRc8fOd2JMFU7OyHO0mQfZx/0qQXOz0WfndhlzwrqPP/uPVs//IXnwO0b1afPO12ddd7WRl+fmJeFtzTZ62sUrX3TuuzUM08xsxcWvQeb8h1p+Ki9GGnTHc8y65uBqSZ7fOZ6nEu5HhejR8qUO9knqeYFosCd8wa5rGVZy1WqAS5gVsnn4us4e+xr12xs3hGUyqd+bEq1kaIV3Dj5L3kseUzVgPadv3HR/OmXJnvx4SPTd/TFN1+uz/fvmyemmzROc5ncOMcf31RpzpveLrpu++56dd2tGx/HG+Nx/VGv0xi7n1+6rL7TsUkvsssCiVM1gE/kUvPd0GTX649ivvy6OgfkYgj0/A2RXxWZx5+dnBOfPvd09e5fpvf8qsSQ8rM02Re/lrwGLfrsVLOc/erw7z+XxjuPxqn6SeEu2z95fVjGfVKa7KXJrprdrj2Y2i85HD6m9l7fpnLhn87AVJNdJN21zjPYUw31+PiYaXW7bxZfHMq824toyVPJU6mBcWpATQm9eJZ8jpPPVcqjLo5o6LjfwqsmvGHl9DLefbVS1nxxa659529cNP76NZOGgGi+L2MDxNcpxadGxWf2Tu4MdN62sety3tQbr3sfbP5wrqlZEuNx/fGschrjnJ9NepFdFkicnMPyi5xrvBua7KkczLI+nr9Z9Cy7rB6jQayrVPNteS1N9pTx51wAACAASURBVN3TZKd+h8K2WoLOa+wy7pNVaLKf/HP9uqS1Ip+sm16LybXg9bdOziZe13itjnzItNHhG/u1Ar3LAMnpMtYo+cFHrfvv3pg+p3ZfV3fciLc02aVejfRPfPJTm/9qtPOnsfCOg7ZbIYdRgYt7A1tyXXJdaqCuAS5wSk7qnOyWXOjiiAtUjzleBDutjHdfnZQ1X+ya60z2Ny6ef9G+/YN0I+2hw5O7f+HnTarmvEmDdvOdzY1w+ICuC5wgb7Qc5nhdLje+75HpN+auN55VTmOcs0388C0zJE7OYflKvjQuTfbpN/Jd1tLz14V/WXlO/vup6ooDG9VTL6Zz4OfGrDWvO4r37P0ouccXnZ/SZE+v9zzXgVqatY76+sheHQq72tuu+Lr4twpNdl6nWCfyyVyvxR4rdEHhea2OfMi00eHDD/SC3wmQnC16D/bJHT5q3eNa7tbe7lhxTzXZ9QiY793z8Fi6d4UeDqMCF/smtuS75LvUwKQGeFEs+dh9e0J3vemZt3HtuVjlIjfSy3z31UpZ88Wtufadv3Hx3H/ha3WDXXh/8xVlfP/Ch67ICz4HXZfzpN6M5XhdLjfWYy5SOoXjzTSyke/QkekPGeATJP4os4xz4uQclo/EonFpsvdvOHr+lnHNu/qUqg2X9X09a80vU85Kk71/zXtdDBlTS7GOHji8Vl26f6M68ef5+ETdDYVdY83F11V+nnw7ucmudVXuuE4QTOWyjY5M25kI3yrCZa5R8omPWte4lruikTvHILc02fW89b/5272VfuC0/LVnYOiLSJFb3BvekuuS651cA7wo7uQYS2zpPcza7/3ydONOF026gOIit+Qvnb+Sl5KXedWAHunSRbc3QPzNjmR9/+rZ1o8/U9+9HnnbbOX4f3L/9Bvk4x98WH3n7tpOm94UnTdvwGtu2qgeOLy+pcl+9N21qTfnOR9lw/OE3mWFqaYBeZLPpcnev7nn+fN1f/tPp6pL952uDj05XUvOs0zjVG24f74HZq35XM7c3qLGpcnev+ZnXRtqKdZRDj+rPeSpu6EQPW1w3nG02W+ilyZ7/f6D9ym5fLWdiTm5VcAva43qN1N0TaYc4qP2a1yr9i5o4WjKwFSTHUY9l12PhREsf80ZGPoiUuS6vQEteSp5KjXQXAO8KJY8NedpJ+aHtfeLJMWpuS6eRN+JcZeYdl+t79Q1Zw9rv5517kdT+zXu3/O+VH+YFvd8W35y/JwVsv/gE5PzoumZ6212RJcu/v0NNGN0aH74ufXNuyqPvz/9Zg8eoHjRueyQOD23xCHfS5O9/1p6/nz9U7l2+jKMT/z75JsMB2+vH+/29/90utI+i/75Pp215nM5izYXMS9N9v41P+u6UEuxjnL4We0hT90Nhehpg/OOo81+E32nN9n33bpeXXTJ5HrEr2E8J1y/5OjwrsIZjq994bLWqG580P585a3p6664Vs0d0EJty0CyyS4hPTpGd7XzI6htinYrfeiLSJErTYJSA6UGxqgBXhTH0FV0rFZNsvZcyLF+mutiUHRwBa7W2pb12j3r9eQLa9VZiTvfb/1u3VSP9aDnLUdc0zyeEfAKf+y9+nEm4GeB/kbU30AzRrfm+OVj6A5Fd73LPCZOzmH5SiwalyZ7+1r+7KH16nd/rPk8f772qVw7Xd/M8Hkcv/PBqer1t+d7F7z7yDgXD/tBdPFGf/vMczb66BiLtzTZZ1vLIetALcU6yuGH2EjJUHdDYUpnCjfvOFI2u+JWocke3x+QT9aN9xfEHOk5PvjRH/VAB3ImSj+4nQLJWdyD2x2f+8VY66nf+nnYvhW2W3u7Y8WdbbJjgLva+SFUQTXf1YQvf9XHF84cNgX2e+NX8lXyVWpgthrgAqbkcbY8LkP+uNjpeicpa48cMWiuizgucsEXuPo1UtZw561hbp92PQe61ESuKa+zgje5XfR04fE3kOhO2RGNs8vHKRuiu95lHnvM+ElMmpcme/NatuWPnAqmeKHf/+jkGf+PPzv9SCTogpftn3yQpcfOOH7MsfvI2OvBbbEfRBev0+L4m99b37wbXj+mGmma52ykeGfFXXvzJI/60CKlqzTZ03lJ5WosHLUU6yiHH8sudTcUdvVj3nF09SPFV5rs9fsP3qek8nTk+bXq8LOTm4G0nimeVcYta426X4y1X7nm5Bwvfd7ZMpBtsnMne2moNyd46ItIkdt5b5TLmpY13Y4a4AJmO2wXm+PWvC52nnzhVPXg49OPjcjlmbX3iyTxaq4L01zzLqev4Mddz5LPks8uNbCd+1Rvqm74dv6O+S7+Rx5/U0xTUWcSY/g15+xSDhhDd3jNjflGqdtbhjFxcg7LJ2LReDc32d86eap69y/NDUfyp5zRQPb8+RrD67mGzr4SBBchNSc9kTbW3H1knIsHf0Rv8wneHF/OxlhxuZ42X0qTfX715evg49ya5PAuO8uYuhsKu9qedxxd/UjxlSZ7/f4jnr80cPVaoBrhBgCtZyqXq4xb1hp1vxj7fuU1pbkDWqhtGUg22bl7/dpbf9Qmv+vpXpRlXN5QlxooNbDoGuACZtF2i73xa52LzJd/3033Y89s/eEarQt6aDKUteqWz92Sp8+eX/KxTGt96f6dtR68KdbdwY89M7mbWGdSbDBqzhs8nVWMU2vDmYbuZYbE6T4Tk/zerU32Y+9OmirfOJBvpugH2cifcqaxcub587WH9+wLtj7fnNc/rpFcjjE1h522x8sg1wfio2wxzsWDPx57zha8+B75cjYi3xjzNl9Kk7002anHNti1HttqrqueefCVJnu6yc43XuI5r5rQes5jLbZT57LWqPvF2Pclrym7vsk7YwKmmuzcvf43f7u3Onr8vRlV7w5xL8rdOubrJbs1/hL3zmoQrNp68mK4an4Xf7fuGy4y25rs4tOPFHLxGC+S0EOToeR6a653c05KXZR6mGf9/+Cn69Vl4YMDnUmxwag5Z5dqknHKN840zrxlhsTpPhOT/N6tTfZUXnwdX3xl8tgAfWuBfElGPMydX2N0ig4vPJxzguAc8oOkyN774MTuMy+P94z2t9//sLr3UP04BPdXdvFHd3f+4Wi9H/AJegqyX2Lc8EqH2wA/D9jmS2mylyY79dgGm+pTv5/AndBtNdekZ9600mRPN9l9zeJZKNq812XR+j3eRdpmj+Rs4te3vp++7uI1ZXd0cucX5ZYm+/fueXh+1nag5rYXi91Ajwflboi5xFiaFMtSA7wYLos/xY/he4OLzP/47+bHxcDHBRQXTOQeOk0G8AUOX5udlLtSF6UOFl3POpPitaLmnF2qScYp3zjTOPOWGRKnfD767trmPzHJ793YZL/iQP1Iotxaci55HcTrm7ju5Fr5hRce9AmCcxhlU/xH3ztVHX+/f4N03y3rle7Yv+am6bjdpnzGn8uvm5xJX7q85tfzisUjHfA5JE8xbni85sDNC7b5Uprs/Wto1rXKrUkOP6s95Km7oRA9EeqbUdKp31EQbd5xRPt95qkmuz7U66NjHrysiXLHeec4xoJ+bp74c51v54l87jP6XY+vWTwLRXP5nTD2eBcVD6+1TY12/IpryZzXlB3Ytl1oSFNN9oVa3iHGKMjdDONBuZtzUWIvzYtF1wAvhou2W+yNX+tcZP7vUx9Vf/mPvH74uGiLF0zQucgta5XP5W7MTamLUg+LrnudSfFaUXPOLtUk45RvnGmcecsMiTMVj/zejU12z4WvpR4bcOk+NcjXPm76/OMX6v35xHNr1fEP5nMnO+uketOYc1GQ+qIWmXeFyMW43aZ40Od8yOKP5wt+QWSk0/GM0cN8nrDNl9JkT6/RdqxJ21rN6hN1NxTmfoSYvcN+mHccs+QhNtmvODD5NsuQD+xm8SPKsibKHeeL4xgDP33u6Ur/mpNvaEA/L90e+p2ODq0l64ke0Vx+J4w93kXF08UmPOQ+Ql5TdkirdtvC2NJk1/PY4+Ninv/Na9UnPvmpzX/Ry1+dgViYu3EeD8q2HPAjF218Y9D1ifcYeoqO+k1PycVy5YIXw7Iuy7UuQ9aDi0w12f/rw3w88HHRFi+YoHORO8SXsWQu+Eo+jrFsFD39crzIuljk632pg351sMh86UyK14qaUx+qyXiOuX+caZx5ywyJMxWP/C5N9rqZwll01Q3r1WcvaK/fV49OmpW6K1R3iv/0ga2PllGOH3pi7eMf9JWNVL2wTqozjfHF+anBlHwTDjmvAY3dpnjQ4XzI4o9oDx5eq3752PRjbJCRTvQ4RI/j5jVu86U02dNrNK/1kN7cmuTwY/lC3Q2Fvv/cJ/aO/G+Kz2V0TszjdxbcRmocm+zzznnKhxSONZE/8VG/+AhPV3jpvvo8d5ucX76e6NRasp7gWFfXsepjcpo7o+cRXxeb8JD7CPG37naW0ZAMbGmy68dO/QdP9Zz2PRddu/mM9rW1jerCr99cqele/iYZiIW50+bfOPPpayquC7668fHXKFP0HE6HCodvjmcs/KLsjOVv0dP+JqvkaDpHvBiWvEznZRXzwUXmTmqylzN4+epykWvSdjG/ivu0+Ny/plUH8U2151E12VQrnI3zeFM6tk7iTMUjW6XJPmnK6FE6fc8irneQ8xxDU45jbaXWmHUSr8boFIQfPcy7QuT8R6blq9sUD/o8DmTxx2nwn/jg1Md3mO6/Nd3kQg8y84T46Gvg9kqTvTTZqcc26PvPa4i9o1oTvq3mxIMt17OI8So02ckN8MtXDL8pMbXvOb98PbElftYTHOu6iPUZw4Y+yJXvT74w/eGn6+5So84/xriLTXjIfYT64GTfzRul1TtjBqaa7PzwqTfRNVZjXQ12/cX5jPZXXjwW5rLO9UNUQ3zjcNQnnvFTT2g6FB59enLYdLEhfg7fNn6/QG3jTdG72knJFlz/N9IlZ4vPGRc3JfeLz/3YOeciszTZV38tx66NMfUt8nWx7WJ+zLiKruXdN1+5svkNvGqyqVY4G8d4EzpvHTQPUvHIdmmyb1R6TIz262fO61ezXO9whnmOoSnHfhaI19dc71dePz7d5JEsOp0fPS7fZYycQ/lKbYBHl8cBDX+cBj80eD32627dqA4/W78nQ2aeEB/dD7dXmuylyU6ttkHff15D7B3VmvBtNScebLmeRYyXscmuH3QmH+SO+azw7AvrDwzJL2eUryd2tJasJzjg0y9Nn9foWzaYii/6SJ5z52LkH2PexSY85DwHV75Ju80BbGmyc9c6fsU7248ef2/zznY15Mtf9fGBlSvQZcHnDrM2//xwjJvSaVH/xZfm31DpEOFwarKv54A9cHh983mM1982/EOCJhuF1u8NTsnX8uWLF++yNsu3Nn3X5KqDk4tLmuznXHi6OivRhNBZ7Bdj8WyG3uWc7etjX/5l8KGvzzudv21NVD8XXTLOfoq1efD2/LXBTs97iS9fU6rJWCueL840P/eWdcz1cCoe+bzbm+y6tm/6lqyvexxzvcMZ5jmGphy7nHiplXf+fUKbPAe+5pMsupwfPch3hcg5lH5qAzz6sA1eMBUj/NDgJ3b0uz5k5gmxhx/RVmmylyY7tdoGff95HXltC99Wc+LBlutZxHgZm+zkSznxMTmaFb782lr1m9fqOueMki1yjg2tJesJDphbf3QsCyS+Jn/Jc+5cnEcsXWzCQ85zsPR5Z8tApya7P4e9NNmnE54rzGXD5w6zNj8f+1X9NZirbph+c+wHS9T/+DPrmz9mlNKvQ4XDKUUH5wdz1A9PG+xip01HoeffGJfcbH9uePEua7H9a5FbA547nKODf/cvkwtUmuxcgEEH+tkonnjBBH0Zzr9l8IG8FTjZI21rcuWBjS01NTR3sTZVr0N1FbnlPeNmXRvVZKwV18mZxpm4zJDr1VQ88nu3N9l9XfuOud7hDPMcQ4tnjHh/dmi9+v2b040d1kk+6IdX0SV+6gv/mLfBN0+cqo6+92HyjJP+R8K3ftGHbewJpmL8yQPrm/qhwU/sxOT6sDFPiD38cFvH/jQ58++6u262Ob2M68bkmLnIrUkOP5ZtanIo9P3nPnltC4/+VM0hBw/zecFrb96orrutru+xmuy/++Op6uv7Tlevvz17jbDuyomPydFYkBzfeMekZyRb4LChNWM9wQFz64+O7YJ6fMrjz9Y9Mc7gJn/Jc1ONjh1PF5vwkPMcnO54llnfDEw12eMz1+NcyvW4mL/bc0lV7mSfpDpXmMuGzx1mTX6mDgX90BAyfrC8dqx+wRNdhwYHEPzAJho8gn4wD/FfOnI+uJ0y3rlv3HfD2rJPd0Osqxrjd37Y7Zs4XGzFJnt8VJfuAoRXkAumT5/30eb5zNm5DOffMviwqnWD3+d8vn7dBTdLXttk9bxfagp7Q2HUo3odqqvI7dzXatVkrBVfb840P/eWdcz1aioe+Vya7MPr+KkX16qrb1ivLruubuBQJ/rmFzUBTpB1EGRthPexf8Pm4Lc2qiuuP10dea4+q9Dr8IZvT3zQs+UPPble3f79bq/z7pt+mFENui9fnj/j8R857ZV4hnMd6DHB7z7Pa4yP+OF28Cn3A4nOW8azN1PJYW5NcnjkZoXU3VCo2k75cOT5yeNO5L/o6E/VHPLwMJ8XjHbGarKzz3M56RMP646v84LyiceByYbsvnVybfPHm7GpNeNcAOew6TnnfWIewnv0jK/I6ttP+ErtidZlbch5U41iZyzYxSY8nvPUuG9TufBPZ2CqyS6S7lrnGeyphnp8fMy0ut03SxXlMuI4IPr4ljsU0BEPfd+0fgDBD2yiwSMofRw6ffz/l6vqi1UOQddbxsPf4JTcLV/u2Kf7b6vrvqzTcq2TziE/H3Prw3kXm+xRljWHH/qd/zZ5k8/ZuQzn3wOH6+dA5uJ2vB4l4PNFjMnfImwNsZHyr89rYrTZVBc/f3hyp07KZtTTZR71qGa7yBWexe+D7cx52xmpOuK8W3bI3oy1r/zK953eZNc3YPlRONYtlYsh9dZ0dpHfeMZgW5C1Ea+P4XGfdGcq81TNIYNP8cNwZJsgsikeaNiBR3ho4BSLfPSYoKV8HxuHj/jh+vGpqcn+rTMfUKjB5bJlnG+66xsTTflhTdSw1A1yv/r19Gt7aq2a9HWlUXdDoWr7yPOT6wQ9ggS7PGJKcQmH/qY44EHHvGC0s5ub7Hfftz51Dmm9rr5x+gPIyEP+gKqB3Fr94mH9pkZdFzm+ofhrbpr4+upba9XbZ76Fs2fv5HqM2pNuzuAmX9mDTTU61M+cXBeb8JDvHNx9Xd1xI97SZJd6NdI/8clPbf6r0c6fxsI7DtpuhbnCXCa8mhZc5PTxK3cooCMeLL5p/QCCH9hEg0dQ+jhE+viPH3qWsb4C6jrLeHe9cd8N680+5QV/N8S8ajFqbTiXmnznvBvaZP/lY5PzTnf6dT1nm/wZg9bn7Ja97ajjW787/QZgjLjH1BFrR/OueY2ybTnmPEnJDYkp6lFdDtFTZHb2a3fbGak64nxcdsjejLWvGpbvO7nJ/s4Hk/1Nw5l1S+ViyJ5ue33QneG5M0Y+sDay7eM2/1I1h8x3fzz89QMdqVwQa+QRHhpynNseE7SU72Pj8BE/XD8+NTXZm+RdVxlP6vvQkUnNPfhEvhlJTqkVzZU/8Km1GiO/1N1Q6PWtMT7h92fPn5yj6G+KAx50zAtGO2M12YnZ8zA0BnTh67yg7LDnZUPzaFvxOE/0RXR9a4nznJiRkT5wY0N8lS3s4Z/blY/CN62N6xrbz5y+LjbhIa4c3K293bHiTjbZx1K+G/TkCnOZ8NpM8aDo4p9kUpsY2Xiw+KaVHAcQ/MAmGjyC0of9Pv7jh+z3kXPbZbyz39Av8/pSv119ZJ/m9ltXPYVvfjWvtemyrpx3Q5vsqoXHnlmr3n5/cnYvQ030PYO3w+c2H7V2//SF+q7GeeyVpvqINM3feKfbB8hRVr7ncvzZC+rHLSCX423Kgf8GAXrgV40/+cKp6td/6OY/cgXO73xahtyqzj5z5m6xlD+qI87HZYecJ7H2FZd838lNdmJnDZWDN96pG3vgh8Irrm8/h0+e+XHTaEO+uH8+PveLzXpTNZda32hzljlnb7QjPDT0Kxb5ePmZx+iAF0z5PjYOH/HD9ZPnVWyy69FpyqEeJ+QxbfeY9RfM+cKawKu5eMGn1iqnqw/ea2/I+Ns/2KiuumFrAxO/pZOaYpzzD/vX3pTPU062Dx47yJQme30zhdbN1065Uk36GpI/4G3fm5zHVxyY3nfISB+5Hhviq2xhD7/cLvuqyx6UnrH9zOlz/9t4iCsHd0Mfd54xTjXZ9Zx13cWuZ7Hn/kQTT3km+yRDucJcJjyHQl+f+BG+uEl1h7h0xYPl8LNr1eXXnf74k0cOoGhX+nI0f/Ysfos/HnRRp885YGSjj5zrKOOd/YZ+mdeX+u3qIy/euT3VVU/hm1/Na226rCtn7SxNdnQ0nbOLXOu+Z/B21HGbj1q7Lus3S16b9Eea5lrfW+9qv4MyysrHXI6Fp36uOjjR7c8l7hqf64/20S/YVV/hm9/ZtCy5jXUS/aLmvX6Wdcx5kopJPu+mJvsXL5s0S7izPa7rPOY5W1oP1kZ2fdzmR6rWUuvbpqcP3c9RlxM+0hRLLu6U72PjyIX8iLrJ8yo22fFd8cW4tnPO+gvm/GBN4CUG8Km1yunqg/danXXs8eG3dLIujHP+YZ/Yc3yz4rGDnt3eZNeHGuREufe1E17r6msILxD+uG7IRDx5HwNiW7aw537xQe937p68tnmNRvuuK9LmNe9iEx7iysFcL7jgu2VgS5NdP2qaeyQMj4spP3xaJzdXmE34rsXdpKMPTfa0mfvIiDd3AOB/08EiWdGl58qDG9XPD9V3rTlN9Mv2128g0S38H9+pn7kVD7q7frRR7b81feeJLuQk33aIi6f8lxwsWw34Hujim/ZG3FNd5ArP4mpfZ1GXdeXMzTXZuZv66MnpN7LophbQwxm8nWsdz+42X7bD5zYflV9y3OZ/V76opynuqFMfZmuN2/yWjSgrXM6W8NQOsM3G3i9t3UeuP9pHr2DMQZlvzWXJySQnqiOvnWUes2di7Wst5fduarIvU/1qPVgb+dX0zYnod6w33YSUWt8oN8vcz1HXI3ykHTpSv8dyXo2j7/OYkwvlV/o/s/ejisd6kPPSZJ++bptlHVh/wZwe1gRezcULnrWK8vc+tF794Vj9/jvS2+ax/maZe3z4LX3UFOOcT9gm9hxfDq8fvZQO9RtyPMJjB56+TXa+fYo8kJg9D9D6QnTh66Kg7EbbisfXMPoCv6DH6TL6NqfTxhpjW7bcnnwUDTqwaW3gkZ6x/GvT08UmN8vGvMd53e0soyEZmGqyo4BmOj+AqrvXNVbzXT+MWv7qDMSC7DJnA3ThHYNH9rQp++rKbWT8bzpYJCu6bMbD1GnQ8Q3dmrt9DjrR73lgvTr5562HHzrc7lsn+8eNnkVD/4r9om0Xe8vT3PA90GVdePGm7rvIFJ7x1vtr16Q/7PMca226rCtnXmyyX3PTxIaedwuPQ3RTC9CWoSY4uz0fTePt8LnNR+WXHLvv195c360DPsUHrQk2xS2dR99dq+65f73Sjwq+/vakDvS8ynj3YvyBqZQ/OVvCUzvAttz484i/fPl6te/W6UZWtI9ewaZ89KHl4snp+McvbFS3fXd9S+5y/Cm8vs5+XeaD/hR/wc125qqOvHaWecyeibWvGpDfpck+Wy0M3UtaD9amr45Yb37jUF9dXfmvOpi+tlAc8cxrappE3+cxp9aVX/0wITHKFjkvTfb09Zuvx76b9SOR6bPuM+edrvQba+Jn/VOv2ehjTeDVXDTwWhd4gfo9H60dvOAFj3/wYXX5/voHVJ3mY9Z+DOjx4bf0UlOM3b6P8cHjeeSpSYzPvtzepMWOy7t+xthh3qfJzm8pPfL0Vn88Zl3rob8N6r3Cj+/b2FwzeF0X/i4Cym60rXUltykf4BfEf8Eoc+T5rTlz/iFjbMtWtCcadKDXqNvTjw0Tm/Q4bZ5j/GqyiV9tsO52ltGQDCSb7CjyH0Cl4Q6twEkG2go0RWcDpGjzwMmeNnRX3eLXD4bmDgH8b9rAkuWFPR6mTpNP8GmMbo3dPgcdsYgGLsaFPkHxRfqyzj32ZfWx+DX/N4d964B9SN3v5DW66JL5579P/pTzLuvVlY8zLzbZwecgz2F988T0ub0MNZE7p3N53g6f5aMu1nM+aY1T66z1ePRX0432FF9Or+Ob4pbO3NpHOfGdfeFHH8eS8ifK4Ifw0U7b+umDbPQhj4xsv/DKdF5dP3ZnhdjvqufeQ5M4yc15X+x/rihG4uxqt/D1zzM5a9oDXlPLMKYuUq9X8i/XZL/rR+tL9+znvvkkdtZt2eBQ/2Ie+p45Q/LA+RRl9+w9Xd3147qBE+lxHn2fxxxfY15ki5yXJnt7o421S60RNEE14gV5zU3xsyY3fHvCyxkKXuvicmricrMXvE5nbZtsit/9nHV81t6PNj8Ml17/IImakn6NH31qvdK1iPvrvng8XeOQ/P2PTa5fXD7acDvQujTZ9Y2BV99a23L9hA5B1oo8Cvf0S2vVsXfrWPUbFPrQzx/xG2P81a/XNr9Zgh6H0YbTxhhLf7ShJxzo96Ny+uEX9Hz4uku2rRZdtusY27IV7YkGHZjzAbr8lJ6u9uHTByW5G6vgSUHsuk19+3XfLXUuc3mP+NLrnS0D2Sa7372uO9jVcN+Nf3r2vB6Po7v7U3+xIDU/+4L6DW6KzgZI0eaBkz1txK664U9tXuHw3zdwitcPeT+oxAtNPvkY3cK7TuTdN3AxLvQJSkekL8P87Au3XiB77LP6KF2fu3irjVn1FvnhTYKYOx79EfF964B9SN1HfTtprg//like5bzLenXl48zr22Q/9t6Hm81e5IH3PbJ9+dLdwtec+bGpLmt29oWTbf6AeQAAIABJREFUvXXoyemmdZts6gfrHnqinw7lL/d6IvtaY+42cn+U5yjXpR5cB+Om/SudrGmEUS76lPInyrgPUX+MD16g+NEn6PbxG15B1+/4tvHN38mvKfbbdEDnzShyQOhdoPLSlpsuegpPt9dUasnrZ1nHTXUhn3NNdmphWePq4ldT7MS3nXCof4pddxHTqBhyZvSNO3V299Uh/i7rNisPvsa8SC85L0329kYb65taD2gOle8Ur3CsCfycoeC1LsiyRpEXuiBr22RTfOgYE57z+enXCff3J/dPrnP16Fn3130h9j5xiJdcuXy04XagtTXZ8V96m/KKfXKppqvGl+6rY03JOy73Q9DojDbAjwWlP2UjhcMmNEFyKkje4GurRZftOsa2bEV7okEHpnzQN0/xUVB6utqHD3nmXSF+uc2oi3kbTPU9C657BpJNdj0SRo117l73hnuu2dzdZD9O+dLU4BctPsKGx90oBo8jZ/no8feqv/nbvZu8yPDc+SFNdgo8V7xt9JxcF3xKt3DanF3kxQN/bkNjwzdwitcPeT+oxAtN9nyMbuFdJ/LRt/hVedfHwSfc2P//ctVsOj1OfEvhoPWF0jWmvr7258W/k2LKxaLHGKTyl+NnH/o+SsnvBBznQJ9YcnnroyPHq5x30d+VjzOvb5MduQiH5Euxps7VXA5y+Kdfqt/85Xj2nD/5QPq8L21Ub51c2/zR7HhxmpMFH/N/tKVhjhyQnDXlSjZSdMlGfPQHO21Qj4LJ8Ugnfkb4wu+nL+ZF19e60ZXyx88KfT38xjua74q78cydcOh0KHvoE9ScnOB35CeG146tVYefrePWM5L9Ocleh9hwXYybaPA4xD5yQOe5499qvxzPWDESJ7gCZ7suasoftcTaLTNsikN+lyb7/OqkKfeiXWlf4W/jdTofWFOHqTPD+ZdpvIi9wutMzItsc06WJnt7o426Sa0ZNIfKd4pXONYEfmoXvNZF334UHVzkdd2sLTaPvXdq8xF2fGgNLzrmCakpt0F8utNb+Mefra+NoMnHGAd+pyB5cfkUH35Am1eTnbjdn1Q8jkMGHyMkxogfc556rGaTXWj6ZgU5FYyxUIvOM+sY27IV7YkGHZjyIcpp3tcv8t9XDr/cZtTFvA3m+qYF3y0DU012GspqNKea6TSvab53MzGMC1vyJdVk54MA0WOTXXP85wOClA48U5P9f+y5pBKMf+QEfZGeKlB9df/g7fm7iHWhkZIbA8fB6rq04bRJHRfHbErh4c9tbHh9A6d48UXQDxzxQpM9H+d0Ix99A+/xoE+wLW6Xaxv7ncfyA1/b5FL0lGwKl5LtgpvVvy42toPnxjuaGx/b4dNQm6n1Fo76jXpT/OJhH+bkop5VnitWPU/x3PAtjVxuFGsTTXemzZIP5bxL3nWx3+SHfLjWvsq33U32LjE15S2+HuR4lZM/HJ1+xE18fcjJgo95lXzqdQH+CPG1SUY29CGAy372gsnFf5TT15Y/fV7zt9lcD2P54bHouba8MREeP1PQ5aCj12ngfH3Fz1wQ+QiR9a9sC5eSJyf4jSz8Od3g4Xff8RGaQ+w5rmmMHXQCXSaFc7pscleZfxjgPGU82/nq+aOWWLtlhu53HMvvndpk1zNob2n4xknMxSrNOQ+oQ+arEMMi9gpndcyLbHM+7/Qmu24QaHu8w+vHm5tt1FNqzaA5VL5TvMKxJvBTu+C1LqwNPEB4XTdri804hxcd84Qpv/HZ/cIH0Z584dTmY1acjs85SK7QnePDDvShTfajJ3WTRF0j2Ec/cbs/xKM7+VWD8gGc5A5+q7nnFG1ga96wya7TyKkg8eOb4nT6GGNsy1a0Jxp0oPugD5z0KJ8op3lf34ixrxx+uc2oi3kbjD3PMu+XgS1NdjWj1ZjO/YkmHjWfF/GnhnlTg1y02GSPfone9MFAnyY7TXd8ShXoUy/Uz9hK0blTbx5vzPxgxbY2nDYp8xRkU4oGf25jw+sbOMWrHxXRBw7H358+cMTrfvo4p5sDK/oG3mNCn2Bb3C7XNJYdtyU/8LVJLke758zX25x+xfXpF8I95/d/o3rNjd3usHX7qzDWGvCjNavgb5OPqfoRjvqNsil+8SgncU9F2VWcp55nS6wxF7mcKe7rbs1/MNMk1yVnkv+5/chXSkY8Wp/oc+SFT7zb3WSf9bE8isH/Y6zM1ZRxPsZ91mXfLdPrKx1+VmMrB7EpqEfcwPfPX6/HWjvRoQmCi7bE17bWrodxlNOcHzHFlnCpf7cHHb2X7q/jAOf5FT9zr0H0AJF1W8Kl5MkJfiMLPzqB0OPcbeEjvA6x57imMXbQCXSZFM7psik9r7/d/gGay5Vx/+sZcsa65eCxP6X3R45/Xnj8TUHZ3IlN9r57MJWbZcb5eaA1PNDSuFqmWFJ1ftXByetmvAs5xdsFx1nteVIOJEtt7PQmu+LV+/pcvl47NrmG4DF6KT7qpokGj6DyneIVjjWBn9dj8FoX1gYeILyum7XFZpzDi455wpTf+Ox+4cNXrpxcB319X/0eizjwOwXJFbpTPMJhB/rQJnvUg33wxO3+EO+5F09eW/WtG3CSizrQBWyjw7dI6D6RU0Hix5cua+jyXcbYlq1oTzToQPly8s+Taw89+1xz/xaF5tLTxTY8fzyxtaagtUH8cpvkC1nmbTD2U8u8Xwammuz9RBfDPUaTXQ1xmuIpr+PjYnhUjHhpqutOdu6K97vaUwWqwvYDLvJQ5GyESJ9lnrIrO7LZpFc/uAQdfvyMEL99A0eeOBcv+kVzP32c04189M3x6EefoGyNcTEsO9iSHfmBr9jtCuWX60IOv5kDh9iRfuT07DZ0zRNefGndzJHta2+qa6qv3a9cUetyWcWlNSU2p63aOBWDcKk6UPMvhVfM5CRHX7W84G8qP8QaaU2x+15AN7BJDp4mKHnV4/2PT9/l7DLwRJ+dR2P4pG/7m+z1eR397DJXDP6fklE+nMfHumBNyaRwyEHTXGvOXPBz/5w/A5EXZI0k7zrw1XXefOdk7Z1PdNfj/G3jKKc5urEvXOofv7EvHuypiRLr3OfiZe41GO2gz21hL8pHv5GFP6cbPPy3f79+DcEGNIfYc1xu7LlEJ1AyfLtFOD1KJ36Ig17ZxN+YE3gKzO+7IbnxnJN7oO4AlE79tgM4/b7D79/a+k0Z6MC3/3SqOvJcvZ7gh8Km2DZ1/mWtOvzUXys9K9dtIOe4VRn32YPEuUrQz4hHnsq/3i9jTLGGdOMTZ1bTnopyTXP0eZ6UC8lQG7uhyU7MqVyRB38NinzUz4kPTlVqCD90uD6/oDlUvtHxTjhPWBP4sQte/uATPEB40X3oyHq178wNK9hkrZnDi455wpTf+Ox+RR/EA100fM5BcoXuyHf4uUn+sQN9Xk12Hivo/ng88kPzsy+oX3uJAR8jbKNH/kXM3ad7H1yvXnlrrbrywHq17+bp3oBiJedDoH6PKcphO7U/RIMOVD7EKz3gLgs3t0CPtnJzr+8cTw6PD26TNUOGeRtM9UwLrnsGdnyTXQ1xb5p3SY0a8tz5TpP9iWd+u4mLd82nCvTF325UP/xZfcBFnv/43xuV/i8PmzDyDZmn7MqO7DXpk8/6Fw/8+Bkhfos/0nJzdEu/eNxPH+d0Ix99c7x06y6CZ389iePJ57baaspBE012sCU++YGvTXIpmuJ1XfA8/5vpmvnMmTvYh9iRfuQ8v9iaB8SedGusdR5qJ+ez4pJetzXURk7un78+WYd5fNPEbaZiEC7GTi4jHl3kJEeHD3j9rcPXBR2LgKn8EGukNcXueyH63SQXeVNzyaseZSNFFw6e6HPkh0/61jb+Wp3a+Oumbs2H/jf5Fe373OX2fjkfm8v4OPrrNMbUdeRlDl8bjPyau/+Sl61nXpo+X4XXHkdekDWSvOvAV/eF9YJPPK++MdGHHudvG8v+/ltqHz0O7LuvPnZ74LGnuXxlLuhzpxMTOhwi77aEy8k/9tRG9cfjEzqy8LtejaGDZ67cXnVwQpdvN905HYfzMXZInGed+9HHvzPguYQOlCzxybb8YX1dr8bQxYNM5Cnz9HoNzYvnnFoB+jrBhx14cvDqGyd+nnh/+jzI8bfhsZuCkqXeBF0X/I5blbHnnzh2EmTNVjGmWEOKgetb9krk6TvnDIx5kh5q4/L90/XuNpAfyx/XPcu49r39OozayNnrogsdDz4+ed1TXtAHzSFnCPwv/b7mJ6fwowu8/InrFXnfeHujuveB6XMcm8jqcST4KIiOecJDT2y1Q3z4BXQ/xON49zs1JlfoFs/jT+v/dPXokxMfHjJf0LHx0f+tPlz76OO8oIf69lrAH6D8RQ9yxEDzXHjsX3XD1lzA3wVGG11k5s0TfYpz7Ctn5KovPPjNM6/7f5rWgS2tEeuEPdGgA0VjXR2HjNO7+uh2u8rAhw/4JDy+wMO8DXbpmRaefAZ2dJNdDXb9oGnqWev5lFSb/HsuunbzLnaa7Hr2e6pZnyrQ3776UfXjX+SfyapPOPV/xZmvlaR0NOGa5FJ2xS97TTrls/7FAz9+Roh98Udabo5u6ReP++njnG7ko2///eFfK71BevmVj6r/+Z8fVf/14SS37ofrb8pBE0328YEc4WuTXIomf1wX+uSz8+P3EDvSjxx6XPc8xtjLxdPHZs5nxaU8ua0+ett4pRfdQMmcfWF+P7fpzNFdPzzCxdiFi3sGfkFycv+j3XyMtee6lmmcyg+xPvncR9U1N9bxxpx5HJJJ6RJPk5zryI0lr7Vpyik8OR/QDZ/0ffTX/1ed1kX6mdeKobDJL+ym/HK5FB1Zh7xpFy7663yMpTfy+Ry+v//cZJ2vPDD9Rg86Mj53/4WXrYgDj7wgsYrX+fEVG4KsF3zwuB7nbxvjB3zMr7npdHX4V82vtfgtWeRcj3xlLuhz8TMnJnQ4RN5tCddFHln4Xa/G0MEzV27dN8bQgawBcyD88hkeXyfoQMkRn/jlD3LoBEIXDzLQCkzv1Vnz4jmnVoC+TvBhD54cZP2Qy/F1wbsf2HcoHdSboOuEz3GrMm6Lm9hWFbJmq+h/rCGPYYyal372UMyTaNTGNxpe85Efy58Y89A5vsu/Nh3kNcfXRRc6yKPbhRbhf/2f9JlCTuFHF3j5gx14gOJ578/1azN4Qcno/faPfl7Tjzxbv/d23nmNU34THzSg+yAex+fWCjy5Qrfw6EMPUHjkdA2/2WhP9HuUK2rh4kvrNUjpwT42HTq/4/uOm2z01TUWf/QpzrGjHJDzvhCdWguXdTzrhD3RoANFQ4fjkHG622kau90mvhQNH/BJPPgCP/M22NQrLbT2DEw12b2hrKZy6t8fldKufnaOoY+LGdpgl8dqyscmu/yQTu5wJ7LU8371FY349R0vZL6uwVc6nNZlHOWOPF9/dfHXf6jH6BK/bDJPQfnM11Pgx88Ise9fRYk8cY5u2RbN86PxA4fXq0eerp9tGnUj3+ZbtKv5vQ9tzUkqB0042ccH8elrj+ShSS5FU7yuSzzEpV/SRoYcDbEj/cihB73zgtjzeKKtr11df6IaaT7P+ay4tKZuy+VmHUsvuoHS+dSLs9dQ9C0Vo2xGvHBxz7gufiRHj4VwfG4cay/HNwZeX8Ubqsfzjw7WX/nwOPwMhBcovpQu0WOukekK9bXr6EuUxeeUD46TL9Kl/7EeF3O0Q/7v+LfpPalmuef2Oz+apsf4mLsMcQDhcajYoacgvOLTj4mxVnocBDRBZMFp7r4ILx2Ou/LM3dHRB55Tq2enOj982BD09frGgfXqvkfq9fPHr7lM0zgVB7g2KP/QDa/mWkvNyR08Pne6x4QeoB6jIn1uS/q6yPMBjH48HH0O8Qscc60Bvgoyhg70tQInCL98hoe1jH4jR3ziFw9y0IHQxYMMtAKn9+hY+fCcK++/fGytOvzc5HEKvk4a+1y8Tf+sX9QfZVTH/OAxtDfembzu3nTm8VHUXC5myf3rTyb79ZY71zcfCfHyaxP/XObNE/VjIrC1zNDz7XHslHHbui5znN//6eSxW3okUlyntprvWnPsoZgnyWMzPi7msWcm19W/f6s+Q8fyp6vfbXz4rvjaeKmBHF8XXeggj24XWoTkUXjJYZ81gR9d4OUPduABigd/wQHPvnByfvG6Ljx2czLIYpv5UJjym/igAd2GeBwvf8lXCuIvusWDPvQAhUeHmpm6a5g5esQjfs8T8kD0nPj32hY2HTq/4/uO3be+svPijz7FOXaVA3LcF6Iz1gB46fZ1kk3RoAOFR4fj8NHpXX10u11l4MMHfBIeXzTmufHgmiC9zgKHZWCqyd6mgmeXL7LRPqTJLv9Sd50Tn3Q6/cHHn5+6292f4c4HD8QsWW+0c8id+8X6Ta4KG3yqeONGSPE04dhA8MTNCB4oft9k4B1KB3rgx88IsS/+SMvN0S2b4vH8aIwczz99/e1p3Wog6gX94DfrFy1k2qCakLLrP2Tnscfx5//ldJV6npbHoGfF6pe8o2yXueJ1XZKRPcWhpt1ZZxrt4rn/0bXqmZfXqsuuq+uriw3Jsk6e6y6ykUc/ThhxqTn2RKOGIp/zRJrP3WeXUVzKk+Ncbtax9KIbKJ1xvWa1o4YVjWGND3xz8iZINj122RFOMUe8aHfdXe8d8bhffgHs+LFjcd1xHH2K9Ka55x8+1l96PQ4fwwsULaVL9FROkesC5Uf0Jcrhc/ThnIumG5byBX1jNdmlr61JHnPg8chnngEZ42J+9gUfVVccmPh+xYGNzedYEwcQXofSDT0F4YUPPwWPnqw/9EIWfs1jPUiH49CFbnTo2cx6vr7r0D6696FJfNgQ/MXDzU0wbLhM0xgf4GHeBSqOKMcPMUkeX665aWOzqffHE2vVb147VT33u0kM0AXb7GHriefWPv6xpzb5Nnr0nbnWzGUZQwf62oIThN/X39ccOlAyxCedykVON3TxIOO2y3jY9VFT3jznyju8cZ3E5+smeupfDfqn7PUh6peMzho9G/m532w9c9wudaUP3PArBSVDkx266k945oIpX8SzrP+eb49jp4zP/eL49bzo3KjO4jqNVWecgX6WKj7VKzZjkx1eQeTH8kcfUqlJOet+wXf2d5M+1lM8unHs4SPTr6dddKGD3LhdaBHCK7zG+EhO4UcXePnjsvAJigd/HZ8bY7dNBts5PV3xKT3CKXZiArpO8The/pKvFMQOusWDPvQAhUfHrE32625rz7/bxachkBiHyM5LJvrEY3KiPeWAnDvssvexEWsAvHTHehYNOlA+ocNx7it097Fp7Hab+FI0fHCb+PKm/aAquCZI37TAYRno1WSXidTd3MNMN0vJTryTnka3JNXsdro/FkZNcqdp7PTYZI+2vIkem+yy7c9s55CjqFWsKmzwqeJlU7hMii+Hi3JxMx74Zv2GWzrEL5spfV+9cmOzwas7BdEDP35GiH3fwJEnztEtH0Tz/Gjs/Lr70udjjd2HVC7AyR/3T3jJunyKB/k2KFnXpXz6D9ak8vviK/UbvKj/pjum1xt/0RNjAS/4QoNe6fnRL6Z9FW7P3ulHD+APejXXWOsGDeg84FLQfXYZ5U16HZeSH4qTXnQDpcvXa6hu5H76wHr10it1XmUH/Rp77JIRTjFHvGh33ze9d7CBnMcADVvMx4D6Ecj4gzTSm6qBrvZyvkun/j0OH0e7oqV0iS/KdfUNvpQv0ICyIb7og9bTcZqjb8wmuz6g1PrgT4Sxrjy38i9+Q8J9li7N8TsHo019sHToyeYmNTLox0/yBB2bV9+0XukDQc3VLIcuKB2+1uhCNzocwu886NQHZM6bGmMDGcEvfG3rWQ0dHXEOvgnKxyY5fCF3UVcb3fmxRV2Lpm+hyb7/SJvLPParCT33wUT0nblsuG+MoQNZK+ZA+OUzPL6e0IGSS8V39Q0ble46Fp0PL/94on5zjgx2C5xPQ9JrTvVFnvWGmvUVTmOfey36GHnWL+oXL7UBlIzrwA51hS50RyjZ0mSfT33EXJf5dJ5Vw9QruUnVvNd31zF17/tENiSPzaYm+5j+8PqvOza7+p/jw3f2d45PeGKIY2TQpdcQfWinm6igAdFx3pcmr+luF1qEN99RX98p//pmzO/+uPWaE12slfyJ64Vu8eAvuCYoPYqhTQbbTbq60HJ69pxff7iueou6JOcxy19yn4LYEYSOTvQAhYdn1iZ7rqmMbUG36/i+Y2LsK7cM/L4u5J5vAOimH3ApSNyxBsArv7GeRYN+iX0zHx3QYm6gp/xI4dxuit6Ewwe3iT99a6a5U1uobRno3WT3R6m0Kd8NdAqWolYhq7Dffv9UpV9EprAdsjlcxumMc/SIj5sRn1yPbDJ3eOhI/SKPHunHxxTEvm/gFJ/j0C3bwruPGjvvvMbug+cgjuWP+ye6ZF0+xRP15OaSdV37b5vOdy6/0Sf0p/DSj55IBy+oXKPn6hu21muMW7zIIyeoppnjo254nQdchOd9cfriwWXkj3x2XJSfZS696AZKn69XSv+FX51+M5PiAffmO5MY0Ck7Po7rdfl1k3WKePzy/YINQY/F8dhy3KzjXzyyVj2deKSO11cfG3oT4vlHVr4Tr8fhY/G6XdFSusQX5bDTFaZ8kew59kgT2RBf9EHr6TjN0Tdmk106m+KMdeX88k/znx2qzwZ9e8LzAw++p6Dza/y7N+p1TPELhwz68ZM8Qc/Je8zS4fPnfzdp+l5z4/TZ67rgx7775Hy58ZMvnNq8A/b2H0zypcdJEAO+O0QPOOZdoHxsksMuuYs62+jOjy3lx/Ftdw4de3ea32Wj78xlw31jDB0oPr0G/eMX6jyIBr+vv68ndKBkcvF5Pfh1k+JABn8K7P562CdXXnP+4Z/wrI/0xfmN307vc2yzfq6f+qQ2gJKBJohd6gpd6I5QMrHJfucP1yudF86b8sXtLtuYPHgMZTyffTA0r6rhuE6abz526dn6veCQ2qLufZ/IT+nCZmyyc2OGy4h3iH2XwR570ml9x310sS6y4WPN9c0yvr0KTTDG6zSNPYZIS809l5F+lm5uOFLfsCXbOX7ZJfaoJzWXHsXZJkOdpHSMhSOmlC3hoMtefC2P9YEOQWj4iR6g8PAMabJf8NXp6xfs5KDbzfF0wRNjF95l49n7pdOVHjdF3gWpQV8zpzMmbvGDEwSv/KKLuEWDzg0XoqEDGvxA6G6naex2m/hSNHxwm/jRt2Z2Qx93njGWJvuM2aVgKWrfbCp+CtvpbArHwecwR4/4uBnxCV3id18iHn/QAz/4CO/84eSF4Oi73S/K0C3b0uc+ahxtzGPuPpADwcv2TV8Iyx/3TzySdfkUj+tsGkvWdcVYWV/xOC36hA3w//SFOg7Jogc6/OBZZ/CyxRgoPe6r8Kw/PIJ6JA96NY+64YVHcP+t0806eOTvI0/VF4LIiE5OHIecv+CB6wulF91A7Dbpct7Id+y9Oq++nuRVssJfdXCjevjJtana07dS9MxX0eM64pfrdNvSm/ILu84765h1iXrkG7g+6/Odu6cb0OjAjvTq2zfCK0aPiXy6jOfB/XA5+PtAco8e6X7lzfq3JaQLn90H4bWejtMcfcvWZPd1jHVIvvE9Bcmp7jBra8Yijx30MydP6IQ/QtZEfNJBvWgeeVNz5LEvnjabKT3uN2P0OEQWHPMuUD42yfHG+ueH0q/ZarJIXh+Wtdnjm1NvnZx+fWqTa6JH35mrXsiZIGPoQPaYbIATvO+RSVzX3jw5Y3Un/eHn6n2GPqBkyKXrxPc9e/Vor/rNNnhk3HYZ19cjY+WCNdFrZdT59WtqnPj07zyslUPorB/6nYfaAEomRZcO4f31Bf0OxROb7PqAyHk0TvnidpdtHPMd4ynzrWu86JyohuPNEL5us9QUe8j3ieKTTmzEJjvxu8wYdY899uQscfXRRTyy52OfgwfGeMEDiUGPUAXXBD2XTXyi/fDn0+83nF92id3xubHs+lrn+ProzOlow5MDatL5hYMuPPmV78f+NPkwRGP+0eF86NONExq7PuTUZD/5wXrF7yKd8/n69Un8nocud61j06EeW+vzoWP3f6iO7ZTT6yd5FyS3vmZOZ8zaih+cIHjlJX74rTi/ZtcaxK295LLggdGG20uNiUHyKXoTDv/dJn70XesZW6S7Xrx3kz0+k3y3Z5CCpahVyKnC/vnDddOQzeEyqQtzp7NBBCM+bkZ8Qkb8sskcCB5/0BPx0GeB6JZt6XEfNZ5Fd1dZ94EcCEa8nr/r/sEjO8iJ7jyp9YM3Qsm5zeg/6ysep7k91ym+n9y/vvns9s/98+SFXDj0RDnwrDO6PD5w0qN/5oLShw7wknWceFL64BGMetEVZfXc1M+eP3lEDR/soAcZQcedc2H6kTbOnxpLB3q+feYOVPHlfEXHXXfXF1DCeT14HjTmH52yB07Qf6iXXAivMfaA0uGy+uHcz+ydvJmT3lQzArvoGAPiR9Ql34RTPshr5Ek981+xpvixQ8z6ACI+KoCvCGJHMq7Lx025cD50EQtz/ECPZITz56Djc9QXY/S1HrvJfrLhB5RiXcl/7hLl8SvCEfNLr05eyz593mSPETO5SEFkU7QcDr/Qz5w8telkTcSHjjYZ9wV5ZEXrI48u95sxeh5+qt7T8OvHV/fd3O81UT6iEz0Rvn68uYGuH8WLMouaR999Ts4EGUMHssfkLzhB4R99eq06/n76EXToA0qGXLrOtjwg47bLePymImvSlm/VcvxdndQaskboQ7/zUhtAyaTo0iE8OnNQPLHJnuJN+eJ2l20sf1NxFNz4+2BoTlXDfm0qPb5us9QUe8j3ifRLJzZKk31rLcR9HtdWeX3iue57i3WIelJz8d714/oaJPKwbhGfmmvdfa3hif700YmOvpAajLalRzjozOU318j6/Q3N9ftv+26p3w9KTnj9R39cHzzX3TKhcrlHAAAgAElEQVRZa70fSfGPkYfU43CirS5z978L/zLykHdBcutr5nTG1If4wQmCV5yfPX/rnk3FLxndFOKyzhdtuL3UmBikI0VvwuGD28SXvh/o7PYe76zx92qyb8cPn84a4LzlOZwoahVyqrC1IShyNofL6E2B3/EqXqcjm8LHzYhPyDz6q3zDE18E0SO7jh9jHHW7jxqPYaNNBz6QF6Dk9GNVen5fvGtYTUvx6S5I8SEjnz2GJ19Y63whJDn3JfrNuovHaS/8ftLcOvfi6UPf+VyWsfsp/8ELSr8uuvkkmPiA0u2+Ci996IBPehwnHuF+/epadaF9BQ4ebPun++hClrn0/Md//rV64rm6LtEDj6DjfOw8jKWTsUPJIau4fex8cRxzgpz43JbG/JNX8YIDIk8uhNdY+vRsYB6LIB3IAF2vGtDI4TN05mNA/Ii65JP2VLz48Dd60T/pEI4cuE7sECvQY5Ks8MiJ5rp87HLwA50PnKDjo33RwMGHz8zRFWOET/JjN9nxCdsOY/7hjfDEB/Vd6G8cr7/67DFHGebYY94F4hf6mQtKvk2nry06dM6p9rrYRx7ZLjZTet1vPStVfp97cf1haFscKZ0RJx/H0BP1Lmoefde1EM889/yxJvADfe+AE3R8KhbXjRy5bJN1fTz2AB2rBv08XmbfWRPWKOcr6+p0Xy/G0NGHfuiC6AIio8aX06VDc+g5KJ7SZJ++hs3lquDHzVOsYeVXNU+eVZtD/9lD0Yb0YWMZmuxX3zi5jj7W8be/8J393ZQfzyPjV96afLjNPMJ45kS6vqFzZeKbO5FvyFwxXXF9vsaIvYturbuvNTLUBfM+OpHpC6nBaFt6hIOu+ecuPj11o47oigOb6ADvNHhcH/VxxXX5vIpf3ypEfruh+7/dvgy1T94FqTFfM6czZm3jHgTfx5d/uGiy3lzbR1m3cfD29eofLppcs+vDHfxxSAzS4/guY/x3m9GfrvN591B3uv6pJjs/8hl/NNTn/uOjOz05XeLjcKKoVbipwtbGoKjZJC7DHbrwCDq9CR83Iz4hgz3mQOmHJoieiHeeoeOo233UeKjePnL4QPzAlA78i74ho6/fp3igN0HJ4Uuq4cO6iyflG/Rv/etadem+05vP/4fv3kPr1RUHJvmEjx+dwyfwgsgB4QHKB3wFJ//RIRx6HOd5czzjlIzrlz/MNd44/X+rv/xXXSfogQc/mKfo0ARdv+Mlx93f+mAFPcrBWefm745XvNfcVDe69t1S3x0iW3oh1d3BGvNPXmUDHBC7nkeN5asgdOlABihbl+7fqA49OckXcsSJXeZjQPxwXTEufBaPxrzZeeDw1otNj9F1YodYgR7TTx+cxI2caNG20xhH6DJOc3y0Lxo4+PCZObpijPBJfhmb7MQFJOceM7QI9SFk27Mvowx1i37mguIlj1GOOf6JDx3QukA9DkWyeoMLf5tN+BxGv0WjFuSjzpt9t9Znm8t2HaNP/nWVWSa+prx6/nxND36rPm+FJx50CToeukP9NoC+NcA3NyRDLttkXQ8+uu3tGJ99YZ2TJvt7vzzNR8xNMstAY03a/L3yQP36i9++XoyhoQ/90AVZWyAymjtdOjSHnoPiKU32fAMql7eCn0/OVPPkVrU59J89FPeJ9GHDm+y6+Qu7LpPag319wh570uXxs6sddMnXeA2P3jfP/Ag28ej564w5J5hHGP2I9HnO9f6TfKTseOwpesTpru0oE/VHetQxxpx6iralWzjo2NJNdvglutYVGjoE9cgev1aAx/VJ9uqb6tqGx6H4XcZp2zFeJl+Gxs9eFIxr6TQfs7biT+GH+pKScxtOd7z7QAzidXyXcSout9ln3KUPWnjyGZhqsufZakr54dM6FxrRwLzOni/tm4Zi1sbwseZshIO3b30TL17oyAEjPm7GeGCyKZEHSg80QfREvPMMHUfd7uODh2drMHT1CR+IH5iSxz9Bp7tMigd6E5QcvqRyzfqKx20z1gWB9DPPQX1w8/NDE/9/9eu16itXTt5Yoz9lO/qND/5ID/mPDvH/5IGJDcd53hx//W0T37HtNGwjy1zxLarJ/q1/nV5v/CMP+BThj39ZP4ohfushtz5NNaAcyAa5kA7HRb9yNlxO669HUQgX/Z91nsoPa4xv+Cxb0HS3KnlwHx56Yvq55tCwg04gOtDrMYoWbbs+xhG6jNMcj31deOuDDRr8wsOHz8zRpfV0HHyS3WlNdvLUB1LvypHkmAtqTh5zOqkJ8aEjx5vD6xsYTmuz6byMo9/Cs+6+5vAPgeiTf0Pkt1umKa9PnWkC6fnqvqbkVbKeR3RFfFuMyJFL19km676gZzug5ydnX/ERIzxxDn7ZIGsyxN/UGhIf+tDvvKxt/Jq1ZMQHnTk6c1Aypck+n4ZxLucFn8+3nxle933H7CEgOZcebHiT3fnYQ5JJ7cG+vmCPPeny2O1qB10537hJBCg+/fYH8Ss22WceofvxiD0+LvJtx9xj72JfuY0y5Bv5SAc/JqSeom3ZEA46Nt1vjVPrxTeyUw30PXvrG7Akm7KLLUF9803vNR23nWPlo83n7fSvi23f49QYa+k0HxOz+FP4Lna78rgNl3G8+0AM4nV8lzFxffN769VbJ5u/SeO+pMbTHc8y65uB0mTvm7HAr4L/44npN3++aSha8flYczYCL8LOI17oyAEjPm7G+AIivVE3+qEJokf6HT/GOOp2Hz1fY9jK6cAHxX73L+oXuBQ//vnaiI810DjFA70JSg5fUrlmfcfOC3d38azxlO3oNz4Qq+ga46Pm8DhOPOQVvO5kjXFDk56LL9uodCfE0fem61V6ujTZv3tPfXee640xae5r6XR8BqKHGHN3s0NHzvU7zsf6sVjZ5hmATiPfnkfHRb9cNo5dDtp9j6xXt3yn3gOegyFj4ndZ+Yg9QXzWGy397gE0agJZ7uqFH7wgdpAFqsmtHzd8/rf1hx3IScZ1MdYHo9E2MoLwaazHjJxlz7qHD/spiDw+M0dW63L5dXXNwiddpclen6/Km3IS65g8pnIvnK8tOnK8XfFtNlN6ot/ioRZ8zVOyXXHok39dZZaJr2tefU3Jq2Q9j+iK+LZ49fiPZ16uP9xznW2y7ovbX/TY85OzfeXB6ddv8Xn95OSWAa/49CO9Q/xNrSExoS+15rm1lYx0Qtf8tQ4/TqjXqqtvrM99fIgw5UsqBu6iTdEWietSezHGMs83vxeVG1+3rjWXqiv2EBD/xYuNoU32Pxxd27xOfuXN6WZYyg+3xx51PvzrGiu+Kx6NuUv96hum9z/ngPh8rG+4vvZ23XQnL0D347L9218P+CXosTs+N1ZuowzfWEXm2Lv1+2hwgrd/f6Pym7mc1ndM/llrlxcOOnj3W2PWGLrDlE6nq9baeJx/GcbKx6r5HPPme5waVEyOj2Ni9j04r/VzG+674/9wbG3zZi1dRxCDeKPfTXM11l2/HoPsupzWZRxanmXaMwOlyd4zYZGdYvci9k1DEYvPx5qzwXXAoQceQeiOS+Fl7/7H66/j8wKir4LxrG23j754ABFDxOPbLDDqxkf54vmaxUabLD5gkzyk5PDP10Z8LpPigd4E9Sw2fEnlmnWfR170KBH8TtmOfuMDMqJrjI+aw+M48ZBX8OLTv2SwDc1xyAmnf827NNndx5vP/Oo7OiL0tbzokvrCFttA/CNGl3OdTkcWOvMU1C+Xv/rW1jcOrx07tfn4Ge6Kl6y+jSCd+pptyq+UfuHIi6+J8PowAx9nhcTvelhj/Mr5TE0gi5/4DV4QO+hsgp8583sKktGbO/S4H9E2PILwMZYtfVClD4vga7KPPD4zR1bxeYzwSecim+yPPF3HI9+aYnIauVNcjh9rTG4EpTPOyWPOHv6Jbywf22ymfIl+i+eK6yf1+OaJ+kOhlGxXnNdWV5ll4uuaV19T8ipZ3zvoiviu8ZJL19km6764/UWPj7/ffKbrelC/00GM+McH8MyXFfr69/VRa6jrn+Mf1D+Ciw7ykVrz3NpKRjqha44e9KZgFx7JpXyJdagPHMR76Eh9vRV5FjWfZW1SeSq4+rp0nrmgfmWjS83l6om6BuKz+KmNoU12fBR0+zRD9e1ex2NPv6nleI3xr0usepyYvkFFLJJBt/RIn/sGHzjNdeaCT0H3A99SfNuBI9autuW/y7BeyH/6zO/hMHcoXs+b0/qO0ZPKp3DQXe+//WzSnBT9nvunG5XOl9LpdK8xxy/zWPloi2uZ/Zdv+oF79jo1qJh0TQTeoX/Q7XtwXuvnNjyXjqcuBYlBvO532ziuo+auy213GceeZ5n3y0BpsvfL1xZuCt6L2DcNRSw+H2vOZtCGQg88gnoUzY9/uV7ddff0gY8cvG5Petiot3+/1uv2kZMe7AoSQ8Q7z9Bx1I2P8iX6P9RGmxw+YJM8pOTwz9dGfC6T4oGeg4+d+RFafEnlmvWdV17wO2U7+k1ukBFdY3w8yy5iwYnHfQcvXIwb2g9+tlb95rXpF0N8kQ99muzSib979p6uUg13X0t8kD3iBULzePDLodORhc58TJjyK6efXAhGnseemT5b9px/urrrR9M44khB+eGPSHGem+6YPl9yPlMTyOInfoMXTOU5xsTc7R15vn6j5Pho223BJ5zG6BWEz3FxjDw+M0dW8XmM8EnPoprsugCNOYhx5ObIxdzk+PviyY2gZOOcPOb04p/4Lts/vX45mTZ8m82UfPRbPPrwTM+pT/EPwXltDZHfbpmuefU1Ja+SFZ4Y0BXx0NsguXSdbTLui9sfMh76NXI+sGmyKT8VCzHC63kFt4xwFj8VNzGxnszJR2rNc2srGemBroYeetCbgl14JCdfjp6s6xqfHWKbdXXaosezrE0qTwXX3JwdKz/UkPQ99eLw1yTqGoh/qkNqI9dkv+GO+iaIpj0Y6xy9sik7Dz6xXh19V7C+3ov7AP9SdiKvfvOKOAQlwweS2PT8wZvCQYvQ/cC3yLNd8z5xyEf5z5pofvd9k3rCf3KWilO2+tpDbw5++Yq6ruCR7ZQdfMrRXZ5xCvpNUCn6MuKUD+JfRv+6+KQY2L/U4PlnfntGv1UHDejx+h4U3WldbHfh8bPV+d02dSlIDOLF5y4w+q6563LbXcZbmp4F0SsDpcneK11bmSl6L2LfNPrxND5Jo6CR4cXaDwd4BNHDxoOmTXPg1o8qfs1Yj6tBp+Avznzy/kB41jnyQOlxOWKIeOcZOo66PSbiHKq7qxw+kFvykJLHP18b8blMigd6DhIrvqRyLZzk4U35NwsOv1O2o9/YQUZ0jfHRdYCLvoNXPHrGseg8+9xp2ALii+ZDm+zyVfLoAjoOH0TDNpAf1zvx5/rNLjoE9W0RQRocyLl+x401xucuNcLakYvog8cjve6701Jj+NHpPNFezmf2ArLI4Td4wS7x4ovbcxtX3TDZX68dm5ybasDuuyV9MY7tXJzYSkG3LzpzdOoNiMfosS2qyS6/PDfyLRVLCodczE2KdwiO3MR6YE4ec7rxT3x3/Xj6tS4n04Zvs5mSj3GkeGbFeW3Nqms75Lvm1deUvEpWePxGV8RDb4Pk0nW2ybgvbn/IuIsufHT9+Oi4OJZu8UV53eEdeTW/7Xvr1Q3f3no2pngXgfP172tPcSMTc0U+UmueWw/JSE+Ojq0IsRXxcc7XvuNduvjutllXpy16rOcMxxjKfLpJu4z58PrV3bxD64a6BhKr9LFvc012eAWb9mCsc/TKJt8w5JocnTEe/Dvy3OQ8uO62/LUBvOiSPXCC0u35gy+Fgxahx4vuyLNd8z5xyEf5z5portcV5Qj/yZmuff1RiaKf+CCdS2TnDbvmvgufbj6bt79j6tc6d4lrTJtj61IM7HWvQdlxGjwer+9B0Z02lp9+trpOt81+E/QY8LkLjL5r7rrcdpfx1q5nwfTJQGmy98lWgpei9yLOPSORgkZGzXd9LUlf3wUHjyCbj43H5hH8f1VVvfvnU9Xrb0832NGTenYdug+EH59EhhikH9xYMOomJo9zLFs5PfiATfKR4sc/Qae7TIoHeg6ypviSyvWtd61tNm+5QHH7Y4zxO2U7+o09ZETXmMcduA6NkSdOyYN3HHqbaOgSb98muz411l0o+mFNyaML6Dh8EA2/miA6BCXrX1NzOfgcN9YYn1M5jTZYO8FI0xw/BS8/c8ev45rG8sN1Om+0l/OZr/zqTkDJI4ffrrNLvPjj9iSHHt1F/PCT0+em0+FDXvNcnNhKQeTxmTn6JeMxwid8abLXuYn1wNzzmMq/rykyKb4+uDabKV36/QPJHXoyvf9SMn1xXlt9ZZeBv2te45oi53sHnKDju8ZJLvvI+j52+0PGXXTho+snPj1z3fE+Zh9Eeck6H2Pxd/EH/nnDWXzxGMkV/pKPuOZNjZ9zv5hvsqF3FohPgvgbIfkQjLTUXK+1+j0Uf8+R4mvC8Zs+rx+ffg3F31liLrKLb85RQ8p91zpK1QfrD2Qtxat9pTlNdhrc8DiMe1DynGnRP/TKpsfh+qKv+Mf1puaRhzm86JM9cMil7KZw6IhQP5Kasxd5Fz3/3MX96lE5+VHi987wm5wRL3hB4frkzWUXOWb9F2lz3raU91WPSzFQV5wL5M1p8Hi88cxxGjpmhfjwdnikn9sWj+wIegz4nIN6BN7RdyfnSPRdc9fVN45E27OgemRgqsn+v/7zv6u/23NJ9YlPfqrxXzziLX/Vlk2tgs5tBIo7RxceHkE2HxuPzSOoJvufzvygaZM+p6H72HsfbtrRj6w4nY3YFIPz9xlH3cTkcfbRN4QXH3Sngxpt5COlC/8Ene4yKR7oOcia4ss8cu3+psb4nbId/UYeGdE1Zu46NEaeOCUP3nHobaI9dHitOvzs5IUj12Q//yu1zZRP2MEvoPCM8UFz+JsgcoKSzfHCl6PPgsfnVE6jXv2gn3xRPiNNc/wURJ/jmsYxfueVXbf33G/WNn/YyL8V4HTWT1B45q4T/1wuN/7GwUlt6OJDcuhJ8TsdPnKseS7OlC5wyOMzc/SL754H6oYYfMLPq8l++Ln16twv1m+cZCvGjv9tELmYmza5rnTWP9YDc89jSif+iQ+ZFF8fXJvNnK74bbMc31C819ZQHdspx3Nr23yIa8p6CI8sOEHHQ2+D5LKPLLXqtoeO1QBtk8VHoPiJK/qiHxRHH/vA5ZCFx6H4oz7WyvkWNY6+9LGr/MBPrpiTD19z/WCY6FdcX5/R8AOvuXGjij/sB21WiE+C+Bsh+RB84fdr1b6bp69Xj/2p3heSdf6oq8v89eP56zn8nTXuIl+/Pi8iF1ffUNd3U6211QfrD8T31+1H/Giy60dDoUfoexCb8Kh+wQmKVzTZpLbhBcYPlKJ/mrtOH0de2QNHkz51HuZ8wSeHOkOwiW6nr9JY/hODIHERg+NEAy+oeZ+8uWwZz3ZmKO+s26rmUjFQb5wLxOI0eDzeeOY4DR2zQvnw1snJDzi7LrctHtEEPYYXX0m/dycW9GkefdfcdcHbFZY+72wZmGqyz6Zqd0pT5BSxChpchHqx16dYEe9zL3w2HxuPzSM4pMmuZ4U9+tTkIHr6pfVKF8xuu0sMzt9nHHUTk+Ilzj76hvBGH8h1Shf+CTrdZVI8n7cf0YTXIbqiL+AXAfUsYPn0/Z9Oxybb+MpXLvGHWEXXj2wx93qnPsXjawrecehtosEjuKXJfuaHLLWf0JHyCR3EpQ9X+IYAOOQ1h78JIico2RwvfDn6LHh8TuW0r178FESf45rGv3x0+sUfXj5V7+ML6ycoOeZ//0+nq3+5anIRiX9d9L77l1Mff7ovOXxLyTp9z96PKn31nRxLLq5zky7081gavckUzvVJXjj5SNPbY5tXkz36obnHjl/Ct/0jF3PTJteVzvrHemDetgb4Jz5kutrO8bXZzMnNG++1NW9b89D/P/97o1rb+GvnmmNNWQ/fO+AEeVRfH5/Jpetsk6dW3fbQsWw1NXalFx+5q1g4fIy+wCse9oHjkBWM/+Jv0hf55z3Xox2G2tD1L7Lkijn58DUHB8+iIfYF8Rf44iunqgcO148b0xrBTwzfODDJlW6qQY61FATXFf7h6OS6kUchYAd57C86T8Xe1n07NCepWmN92yDrD3Qfvn7NpBYv+OrkbnanxXGsK71PhUd1qzOdc128oun9ytfO2IAXqMeyuu/RP831vsB5GEde2Ys47MwCiXkWHcsgq9/A4kMH5Yk84pvjRAMvqDnnk+PLeLz9ncul8j6Pus7ZmwfeX9M4F7Cj80Hv6ahHQY+X/QfdaeiYFX72gsk6XhR6RG6b+hf0GDw2fJQ/+qBPc3yLcQmvWFwXvF3h7uzsjhf1VJNdd6fvueja6ujx98azsMM1UfAUsQoa3BDohc/me+53k4vbA9+avEmQjSFN9jZ/xoohZSfq5jBRvMSZkhsT986ZO8T0LDjpJdcpG/gXDzeXSfG0Hc7YivkAv92Q+HQHlH8dOMbK3OvdY/c1Be844myiwSMYm+z6RFg/2ikaOlI+oYO4mAuCQ15zp+fGyAlKto0vR58Fj8+pnPbV6/Gg7/5H1zYfg+O01Djagifiu8xZP0Hx+3zWeBVXk29OZ02xKTlwxNGkCx59Nf/Vo/WbN9cnefjAk3vhF9lk1xtW4uFOM3xrguQs5qZJpg/N119ycY7POZ34Jz5qKsfbFR8/fOwqN28+asjrat42x9Q/jyb7ky/Ue6+Pr+TS92ObPLVJTc4CZatNn/uILXyMsvCKj33gOOEli54bv72++ag1+Jv0IQNUg4WGGrhlhOQK38iHrzk4eBYNsS+Iv0CnyS+tEThiiHPJspbUAfq6QD9Psely2Ft0noq98Zpw5168tdZ8jZvGrD+vkUPXhfrFluuhfoUTPdak8zKOMeEn9C+e+aaP7q7HJjDy6rnvEYeeWaB0PmQ/1jqLrmWRVUzkEZ8cJ9rN35n0NM7a+9Emr68vMgWOt79zuVTe+9T1/luHf9id82FWvL+mpc4Fxec9DY83njlOm9WvNnm3fet303ey07N6/reTa9qfPTR9wwA2tKei71/42mxrtcNbuHMPr7HJXpru7fnnRYRNrQIHNwSyWQR980mX21i1Jjs/rKXnZCsWfzGNcQ7JW1cZv2OBXKdk8c8PbvG5TIonHnDwA7HlawluGWD0E59irPc/Pvngx+v9bnsWn68pOXEcepto8AjGJrvT0IGP7hN8qbjAIa85/E3QLzBStpBFP/MxIT6nctrXDn4KRn1OS42jLXgivsuc9RMUv8/5kaTc71100d/km+KGjn1yLHxc52deXqteemX6Dqk2H1yfdMIP3nO/yCa75/quH3ePiZzF3BDXrNDX331kfXgzn7ODf8o1Mjnernh98yB+/byr7Dz5qCGvq3naG1v3PJrsQ30kl74f23RRq8q///PIEcc1jVXTspXSh1+SZ+w1jo9RFl7JsQ8cJ7xk8Us60SH429dPfXx3Irb1TchnX55cAyAnKP6o2+mMz75wOk/gFwXJFfbw2dccHDyLhtgXlL+PP6sbCybfHHOa/PK869rs0v0bFc3DXz5Wf9vM15UcdIVea9iUrO6Y14+z4tOi81TsjbuXutZD5Btr/b/1r+ubv1+i11rVla8v9SvcG+/U74mdJzV2X3N+cva++lZ9g1HUVeq8e60pz+SdPDpONNZT0OfwF9g937PkSvnP7YuUXvFz/Z2ibweOGlIdxdcq9+ftM49Q83j9dV/yTnPZeYzdNvoVyy13Tp99NNb1206iwyt/fTy27+1d0MLRlIHSZG/KTgeaCtw3tQoc3BDIZhH0zRdtrFqTXf7rubT6gQaN/ZCIcQ7J2xAZcp2SxT9Bp7vMa2+fqq69eaPSHfLwtB1w8Clm6Zq1XtA3FvT4XGfMR8p/eKTD15ScOA7dTTR4BJepyU7sbet39gWnq89eMLlDw2MZY9w1b11sseZx3STrtNQ46ocn4rvMn/71WnXZ/o2PH6lFPQnqsUCPn2kwdNGV4lHD6+Y70uezr6nsSZ4cKyaNUzr74FyfdCIL3vfHdjXZiR3fmiA5GyM3KTu+/qLHuXD6tkBKVjj8U677xJXTt8x4asjrapn9jb4tU5P9ujN3ab0Rftgx+uxzalP59/8c3nkY605GNZakNyXna8xYNf7IU+vVfY9sbaSiF17N2QeOE1424ZdO7MOvH8vUt1zEI1liRwYo/qgbmsMuPM4/9jj6jz+KXTR9kw/c2La76nP7v3ik/kBD/kEDet7BYYc1lJzGwjuOXLRBP09dB3YKnN77q5qPtjrI0WPdDY3/0+d+tFmj+nH6qIP6FV71qHMv8qTm8vnam9Yr/choig7Ofw9DMuAdjhWn69yJY+WJWiE+x4nGenIeMYe/wMWcKVqLPnWtdTryfPNeWvTaUUOKJb5WuS+8xnu84KhXp7nsPMay/eDh9YrfSpSNu3689e5z9oYgY/HKZ/zqu47INcEObdDC0pCB0mRvSE4XEpuSTR1fRKB3hV7sceNLx0/uX69+89qplXtcTIzfD4lUnJF/HnNyndKNf4JOb5IRX9vhjK6x6gV9Y0F9Ov2ZvXUDEL0xHyn/4VGOfE3JiePQ20SDR3DWJrueXcbzy9DLWuKD5tCaILGLX7JNvPOi4XMqp31tkgc1of0DI+mB5pA7GI79qX7WKzbhYz4LpJ7iHpxFZ07W1xR75FgxjbHOrk868eXNE2vV3fetf9xgE37RTfYHDk+aLw9mfhwXXx2SszFy43oZx/WPc/hyEP+Ua9Y0x7vqeK+tVYxlmZrsyp9/461LPh9/ZrJ/zr5g0iDiHKRmmQN5bi3zWJ9R7u771qqXX6vf1LLeqfPfZcUXn2mseJDHvuOkEx3uFzjJkhPkgeKJuqE51DdmfL7ocfQfnxX7I09P8vwPFy2mwZGLHZ8i3c81eDzv4JAT7ZGn1qp3PliSedwAACAASURBVPiwuvOHdd5v/Ha9juSjCbpd6ZZe/fYKdgrc3noZK/9a56Y6yNFi3c3qj+or6nCc/OxqM9Zu1Mv8qRfrevbH6EEX7GrTZXbjWHmiVojfcaKxnoI+h7/AxZwpyn2futZ6dd1Ti1pD+aQnJuiRTk2+iRbjBSd8pM3bf/22imzwGyoap9bC9wpj8f70gfqcnIfvXfqghSefgdJkz+emE4VNqTdlKngehwK+L5QO/uPGd12reCe7+++HRFOcLjP2mDyn9OKfoNObZMTH4bjn/MmP8dx8R30AShZdilnzeNEBfdlgzEfKf3gUl68pOXEc8elHIfU1KOY52KXJ/sRzk1zrbuicHsezlvjn6+N8cUzs27l++JzKafS3bU4eUnzQHD72q/VKd52n+J/77VqWluJvwlFPcQ82yQyl+ZpijxyPtc6uTzqbfF10k12+vHWy35trcqa4mmIZSnv1rbXNr6Ty2wt96wH/lGvWdKgvyy7ntbXsvqb8m7XJ7jo5qxy3iPGb79Sv//hAzTIH+nrpw1//cUr5yuPY4Md/PuBEPnX+u032JnrYB/pg757762sT6Xed6IBfdHDoFA69QPHoGph5DqIrR583nnxiJxU7tO2C+BTtX3WwXjd4lE/GX/nG9F1wB26fzKkd9ImfPHSBfp6io8D2Wl+1HKXOlC71Qf2NFW/qjHCc/Oxq05vnTf4dvL3eW27LZbradJndOPbzhfgdp5oix4I+h7/AxZwvyn2futZ6LdvrgXzSzUqqGf8wOdYQ55vHC45zzmlRfuw5e6DNJnyCjOWLy/Vdxy6xdGqEFqZsBkqTPZuabgQ2paDuFvn1q+0NQ5eJYy/6uPGdtzTZ+zWEPHeMlWvdUcbcIYeYoOMlozcrjvMxB54geF9TcLxAOR+0ZYQxHyn/4VG8XrvkxHF9Y+zSZJd+NSv4yn2bDdYF/zRvkxGd2MW/XeuHz7PklFjJA3OH0ByOYdNt5Ma/fnVt89P9RTwD29eUPU+Ox1pn1yedubiF344me5M/KRo5W9Qe4HxhfVI+OQ7/lOuuMi6/SmOvrVXyG193QpNdsbAO9z60Vh06Mv1myM9Q+JrOFucnT0DkU2cx+0Ty19w0uX7RoxI0j/sAG+67dKLD+cHJNn4gDxSP7gB9+qX6jlBoDtHluEWOo/+ez+32jTycdV57gwW/5TNj5LtA8tAF+nnaRXfhaV+/VI6GrGNKz1Bc6kzpUh9j+53ah4678uCwmm/Ki38Q5bZcpsu+dP7dOlY9UDfkwHGikWNBn8Nf4LAzpG/elPs++1frtWyvB/KJemqKhfPNeR5+aq1662R945jT+uayLz+/O9Fmk9j0+2Tf+n79YaDL6UYpn/f1JcXfrRNauHIZ2NJk/7s9l1Sf+OSnGv/Fox9FLX/Vxy8ivJjMCr3IOQxSOkuTffYme1MzlgNN0PN/4oPm5wBzwAki52sKjhco54O2jFC5+vmhterEmWcgp/wnZ4rXa5ecOK5vjF2b7H306tm7+ooW/snvLvLELv7tWj98niWnxEp9MncIzeEYNt3GMox9Tdnz5HisdXZ90tkUd2mybz3fOV9Yn6b8iZZa0zaZVaV7ba1iDDutyc4ZSc1yfn71qsmbZq3X9bdtfca5rx0yqbOC9caOy2HzpjvrR1CBi3sHG5J3nfpRwW8cXK90xzu60SE+cMigx/XfeMf0HdXwCKLLcYsc4z82iUP53G7f8KkLxG/5zLiLHDzkoQtcpbwQ3yrCIes4ZpypM6VLfYztN7+N4bHtu2X6TJlnw7vU+2wNXtUDdcMaOk40cizoc/gLnG0NuuZPue+zf7VeOie66l8En3yinppi4XxL8VCvKdq8Y2izSWzRj0uurmvktu+t9VrHqCvOv/PD9dLmnTEDU032GXXtSnE25VjQi5zDIKW7NNm3NmFSeRqK40AT7KODg1IQOV9TcMfPPF5ITV5wqwR5gfU4yZni9dolJ47rG+s8muz44F9vB9cEiV1xevxNMmPTxsgpPlGfzB1CczjLOrruZRr7mrLnyfFY64y+A9/cqPQYqab4V6HJrg/e9Bz/+x+tG3FNMc1K43xhfdr0pda0TWZV6dSWanUVYxizyX7s3cmPrG9HHlgHzkhqlvMTuuAfT0zepKqZlPIVmdSaogc7Ls8PBurHMsHjR9w72BBfk07R0SE+9CKDHtcPPzSHTTTnm9cY/6N+5XO7fYs+Nc3Jv3xm3MQfaeShC1ylvMQ4V2k+ZB3HjC91pnSpj0X4ffYFdUNpzJhTukq9z5Zr1QN1Q34dJxo5FvQ5/AXOtgZd86fcd92/+raHfhhc50RX/YvgUw1RT02xcL6leKjXFG3eMbTZJLZ5++H6tc7lb7YMlCb7bPn7+EWEzTkr9ALnMEjpLE325WyyX75/691pvqa+lot4DIbbG3PMC6xeGNDrLwJeu7x4OA6ZrnCeTXa9kLBGXfwhdsl4/F1kx+IZI6f40hQ7NIezrCM2lw36mqqO5R85VuxqJs/qM/q65G8Vmuyz5qOvPOcL69Mmn1rTNplVpVNbqtVVjGHMJvt2xs86sMepWc5P6ILyU9+My/mLTGpN0YOdqOPNE9N68SPuHb1h5vF3bTrRge+yiQy+6tnH+AI/NIdNNOeb1xgfo37lc7t9iz41zcn/UJ/JQxc41EaT/4W2tYnGmm5XbnJnSluNbLffY+er1PvW2uyTY9UDNYOc40Qjx4I+h7/A2daga/6U+677l7WSTFf9i+I756K6/5KzyfmWilcx9clFzsYQfMof18Necdw8x1cemOzJGVuku158qsmuR8Dsueja6ujx9zYTE+dCiiae8riYSe2wKceCvmk4DFK6S5N9+k1kKkez4DjQ/AWliz6tmdbQLyZ8TbvoWAWeVJzkTPF67eqwFu6oPfOsb4xNTfbHn1nfbILO8qEFa9TFL2KXjK9zF9mxeHhB9jwP1d0UOzSHY9gc6uu85HxN2fPkWD/M++rR2c+bF15Zq669eaPTbwaUJvvWfB95fvKc58PP1o28pnpIrWkT/yrTqFXt01WMY7c22ZvWys/cyMd6dz2LeW3mbIv6NG/TiQ7xIY+MfI2vv/B7HIybaPDME/LYu2hD+dxu36JPTXPyP9Rn1rELHGqjyf+dRjvn89OPMxkSH2s6RHYMma5nSqyZ7fZ7jNhdx/9n783bLauqq/Hv45fwnzy/j5D4n/9ErTRGJbHNm4aAtFYhlIJETexek6jY0otQIEgjtiGCoadExfiqMaHuvUXB/j2jDqPOPOus3a527z3qeWDtZq015xpzzLnXGffcfT9p3ntsr+t4mPALPpAjxAyY8hrar9+5+Wx426nNnk41Zhi2xDNGe9up/S8V2XndVzIhRoyh7VfTcVctYn3z9eG6fPdSr6/P5lve+UrWH2rc85BE9hg/IZDIHogikzJWaxOZxcA3t0T2fRHGh9PUa3zY2wfKkLkQM8QQBZP9bUx5be6tb53EDOt1uYtvi4esuUtkD5mXYxkjnne1XLsb564xse+d+Nhmc4p354bOfeLGo+bkJ7d8tfMRF9u6sbX953psY8qc56anxHolsofz2hfTufKzz29y1T53+sbUdH8pIvtPnztovnbn9kPoIz/a/GDoov+z+fA8Jk625rqxuvojGzHv9Ot/I8W9757f89DGj+89tvXN7UPf2uodn++WYxwDX9352J/r+Levb/8Yqr1n/9gg+6ZuuUbXDq5b39z7tZ0TOxuHMT66Mes6nxMuYzCI2TcGRlNjGWsdzI0uLth7eG0c/mhgab9jrV/zxBF6wQfyhJgiP3gNLbjzre9sr8XIH9pSOyyOzPcv37b9Y5oWO8Tx7u9sn91Pn97uzW2/mo67ahHX6+uDvzv3rfunvXqtpvXH8AV7WeSo/oUhIJE9DL+dB4Z9eEw9tsnBYuCbK4XIDju333vY/Pin2yLqsx3jmn2Ydq0zhq0pc3z19s0DZ+i3JmkDa0EMfRsMXGe/JbTA6Pv/sf1WacqYphbZj130SnPsL4fFhzF245w7ps93vGogli+2HvG4xnwNXa+NKT8IcBNWYr0S2cOfQb6YhvKk1vHkqn3u1Oqrz6+pIvtbX/92j2/Omq7Z+Ax9brDeos2xFvrYVu/4fLcc4xifj+zPdTxrXstm79k52Dd1e/93D5qvvL7Hs7awduubvVfz8VQMx/AqBy7v+MBWGHrLu7bHNWNvfQNGx68P+zb71FhaP0KO2/K/jSuXX7tZ799cFrbuEJ81tr5cAY/JGcYH+cFrvjZHjaEvajecYb6jtZi8/X2bfEYcec/GFPGz/Ws67qqhXG/X86VrfE3rTOkL8zNQIl39cInsgRQgEWO1NmlYDHxzpxLZfbZSXLMP0651prA9dM6fPr8VkIeO8T2MbEyHzjPHfiljmlpkH4M3Y4y4upuOMfPMoa/lLo9rzdcQPG1M+UGAG60S65XILpF9DJ/J1bnWo6ki+xiMSva18Rn63GC9RZvD9w+9Lg622eLz3XKM6/L5yP5cB+blsb1n5+B92/bdt32HHv/tlX5BELXe+jZ0vtL9QjD62Ke7hS/yIQcuWMclJ7biTi5cY63tq3cctPIHa/vz198Z3LUu9Dt+/TixPiT+ri9j9zsxbbu+6Lw+8XxoTMAL1o53vH+zDuQZr/naWHk41Ef1O9s89tRmr/2TZzbfVv+T1/+48Kf+bVOHb/jnwypF9r94/YcAY2PI+tY1TjVtu+cMlEhXP1wieyAFfA+KkGs28VkMfPNJZA8XYXy4xriGXxe3vw1gYxpj/lrnsBukLu5O8V8iexm+W+7yOHZsp/Ah9hisievjBwFutEqsVyJ7ON99MY3Nm1rmI1ftB9tafBvix1pE9us/tfkNuas/2i02ADPWI7RDMEzdh893y7GrXn9tjc9H9uc67Jq+8/3tr5+Tu+zntn333f5DzvmaFbcvasYnZvgu5hCMbDy7OOTG08Wu7/wt7zzbfOBS/ysJbvj05jp8oZ1Pf8H/g5A+O0Pv23cN0+bQsW398PqLp392cP5vBLl9sLYhcbLxGOrXkHldf9rOx+53Ytpu80nX5ye2Wx6TI+BzyhojngznCZ6Bd9y7+0XCnzy9/SGzjRX30jamiOMV1x01Mf4OxZS4kVNjx2It+C3wrnFT5+6ac273mKeBEunqh0tkD6QAiRirtYnYtdmRyB4uwsSKWd88jKn7gOobN7f79gNBF3enrEsiexm+k7u2jR3bKXyIPQZr4hq5ueRGq8R6JbKH890X09i8qWU+cnWuz5i1iOyoLfjGGMS4Pu6wHqHt65vjPp/vLsfop+sDvmzAe8cu2qyB5+hrOcvrvpb9fPdiX0PNyGkvlv8hPmPsE88eNE+Zd+26scQ54z/VZ4zH3xDw+cq5cY/HbKfa6xsHW/htVeTil2/dcrVvXNd94mbfYcz+sOdbO++z/cHj29qA91Xzelc7ZN6u8fbe2P1OTNvWDx0PF0xrxAq8YD588eYNjx97cstt3rNt6pyvEadSPrXlOWOAlrHhXtrG1L2Xex1T6w7+/lifr1Pn7pt3TvcZ30CJdPXD90T2Nx27uHnDG9/c+R/6/OZ3v189eACARIzV2iTsmlMie/fDugu73PcYU98DKrcvKe3x4Yz1tj3Ap9qvSWTHGtYSU67TtrFjO5UTMcdxE4l1cnPJjVaJ9UpkD6/vvpjG5ExNc5Grc33GrElkH8obW3OHjknZj893l2P002ebvGQNtX15jy3vuW3ffbd/yPn7Lz1q3vrO+YlbsTDyxZDXGP+p+LrPVTsP58Y6eMyW/fDNyz9797DYdL1vl/PBFteG1rXHfmParvlgj3H6/JcPm8uu8Ys9dg4cP/3idr/Z5svHPzdMjG8bb68zV10/2s65JjuHjofxdMk42fzCD7LwR8DbOMTrMXJwyZjGXFtbnjMGaBkX7qVtTN178A01+p0f8Ne1mL5jrql1Z8i4IX1ir6em+a68bht7Cb1hCOyI7GFTrXM0C02s1iZa15wS2cNFmC58Y95jTH0PqJh2Ss/FhzPW2/YAn+qjRPYyfCd3bRs7tlM5EXMcN5FYJzeX+GYf/tp8TDtD55LIHs53X0yH4j+3fvxQMNdnjET2fb7bmlsDH/l8dzlGP30+kpd8Zti+vMeW99y2777bf43nsTDyxZDXGP+p+PK56vOVc+Mej9nSHs5tTb/u4+1iDm2w5Ry2xT2uDa1rz/Ydctw13/EbjpofPH5wQRjCOuxaOP/X7tiKG9a3r3r+SC/HoIXvXWu1ffuO4dephw6aD9+4i4/1xx7Hstvnl+7PS7h388Fypu04NAfFkeEcQZ774sAYoLX3UZsefWx/jK1jGHPZh9vrcsz4TK07+K26mH4scS7LjXUqu/FWvSOy49vpx957vHn6uRfjWVj4TLYIxTi2Cds1n0T2/WLfhVfJe4zplE1HSb/H2ubDGeu1RXrsPL7+EtnL8J3ctW3s2Prinfuau1HMbd+1J5E9nO+1xdSNccxzfuCY6zNGIvs+323NjcmVqXPx+e5yjH765iUv+cywfXmPLe+5bd99t/8az2NhhBji24hve9f+K4oY/6n4Yjzm9/nKuXGPx2j5rnbYxLmt6ZiLvvztFbvCDm2wZT/b4p7l7Ne/uf07Abbf0GN3PryCh2Nph/64+cB+vM7+bO262de2wIZz4zref2/vjzmGLc5l/bnr/oPmuV+caZ58frdWse8YG+o7PT5zwQ68IH+Htsz9uaxxzn7a3LbxYQzQ2uttx7Y2YQzHA5vTL21r9FCs3vKuVwbVLtWduDXE/o0Sy42FS7jJlyeRPRDitsIz9botRF1zSGTf3eh1YVX6HmM6ZdNR2vcx9u3D1RbpMXO09ZXIXobv5K5tY8e2LeY5r7sbxZy2fbYksofzvbaY+uIc6xo/cMz1GbMWkR3vKR8ac1tzh45J2Y/Pd5dj9NNnm7zkM8P25T22vOe2fffd/jqf/uH7zm9vXzvixpPxn4ovxmNOXzw5N+7xGC2PYRPHtqZjLggy+KGAOyfP2WK8KzzjnrvGuwe+A92HgW8+9qMdfKMd/vKc99kyT3ifrV03+9oW2Ni14thiZ/v2HcMW56I//IEBf5iBuekb+/bNq/vT83KO2IEX5MjQdipn54hPaZ+Z225sGAOb424fe25rE8bcZP6+BfqNXefQenLTrdsfYo61of67tei+R7a/ZQVsLDcCJdLVD5fIHkgBW2xiHNvk75pPInu4CNOFb8x7jOmUTUdMP1LPxYezW6Rj2JXIXobv5C7at73+DYMXXirjSwwetc3hbhTb+uW6LpE9nGO1xTQld/jBZK7PmLWI7PbDSx8fGFPU3r6+Oe7z+e5++OYzwufD7acOGgiLvPfsz880z/x8k9tcH1vOw/aK6zaCL+5DmOR1temwYIyBMWOGP1b6oeuPzv8Xgj1544s37eIej9HyGHZxbGs6/UPrznn8+i136LMdi2tXf3TLS87l9uHYIS184DxsIezjG4I8d1vgevVHtj/YaKsPfX4BG4uBxXGI77bPZ760nYv+/NO/bH1EX7tWa9fOo+N0eToHbC1HXN63ndt8n8Ma5+wjc9uNBWOA1r3nO/+Pp7ZiN8bYWoX+YzEaUk9gw9oZa0P9d2sTsLS428/4gRLp6odLZA+kgK/ohFyzyd81j0T2cBGmC9+Y9xjTKZuOmH6knosPZ6wXRTumPYnscfEcGhv77a/YMR3qQ45+dsM2dHOZ0i+J7OF8ry2mKfnCDfJcnzES2ff5zpjieZqSO0Pn5vPdrY/c3wydh/1uO3XYnLhhV0jlXBDjmb/AwWLBPmp3PyjHxgNx+uodh82lJ3ZfxdJn5x3v9/cnb3yxJLdwj8doeQybOCYncE4eoXXnZD97ndfovx3PY7cPXptjRXCO9bWwxXnGtHaNsO8b6/oF+3ZtmMOe49jO6/N3yDX64+tLP61dXz9dS5unteILXpAjQ9sYnK0Vj5r8uvSao+b5X/prDX4IfsXJw+aJ54f/ParjH9v8EA7xs7UKcR+77iH1BDasnbE21H+3JiFOxP2+R3Z5ESiRrn74nsj+pmMXN29445s7/0MfvL9d/5rRD5G+h41N/q6+Etl3C0EXVqXvMaZTNh2lfR9j326QuDkfM76rr0T2Mnzngxccjh3Trnjnvmc3bOBxbvuuPYns4XyvLaZujGOeM0/n+oxZush+y90HzQevOdvgW8FD486YovYOHZOy32NPbT4wf/Pbux++ub+ZatvuGzDXn75ns17mL3CwWNCe2t0PyrHxQDyn4N42hs/Vbz1w0Fxx3VHzs/860xy/fiPI33Zq821IjCUf0PIYa8MxOYFzyzfXJvvZ6+h/4mNHzT0PHZ5/t7gdz2OOI5YYP/S9wujLeca0do1twpf1i2u6877db5DyOnzHsZ2X6xnbwi7W4hvHNVq7vn66ljZPa8UXvCBHhrYxOFsrHjX5NTQeQ/sxbmhtrcL4sev++Od2f2vGNx42rB1fH10bXncQJ9Zx1nzGXjpvGAJ7Irv+8Ok4QEnEWK0tDF1zSmQf/mG1C8cc9xjTKZuOHP7FssEHLdbrFupQGxLZy/CdD94UMQ3lRMzxdsMGHsece8pcEtnD+V5bTKfwYOgY5ulcnzFLF9mHxtH2Y0xRe+312o4hDOIPIk71y+4b/vGz29rL/AUOFgvup9QO/xA9BSvEcwrubWPanqtPnT64IJhgLPmAlsfwH8fkBM4t3y778AaLex8+aCDis5/1xfZvO+Y44oXx6Mtz277zr3e/sc++bXO3Xf/O9w+aS04cNT/+z90fXtn+1i+8ugWvT3rul1u/gI1dK47tGOv3mGPM0bZ++mftjplbfdPmb2l8p+SDzfeh/iMPP3jNbi4OHbvWfszdWC3j5qvRQzG+/NqzzX++cGan5reNRV2KUd/a5l/bdfDgqpObH27gh9+WF+MUUfV2EZDI7iIy8tySMcaxTe6u+SSy7xaCLqxK32NMp2w6Svs+xj4ftFgvN+djxnf1lchehu/2A1TsmHbFu8Q95il4XMK+tSmRPZzvdhNeQ0xtfGMfM0/n+oyRyL7Pd8YUdSk2X2qaz+4bbJ4yf4GDxYJ1Wm1akQ4cmYJ72x+ks7F1+WdjzW+14xvnLjfYz/7xUHcunLOf9d/Xz73GceQWxqMPz21r58Z19nXnjHFu/bLz0R/gZP3BsR3DfmNbzPHt7/rXTz+s3bHzq3/aHC6J75R8sPk+1Pd7HjzYqRNDx82h399fvf3hwVveFY8rzN1YLeOG1tYdzD8U58++/rnrrvs3Yu/l127X7s4BG9aOe1/nw7nSl6cjJVF1dxCQyO4AMvY0VpHiPLY48Jqvlci+/6HUh1MN1xjTvmJWg68hPvBBi/XiARgylztWIntcPF18287tB6jYMW2zWeo68xQ8LuUD7UpkD+e73YTXEFPGNkXLPJ3rM0Yi+z7fGVPUpRScqWVOu2+wecr8BQ4WC9Zptx3Sxx2j8/YP4+DHFEwZNxdbG1uXexwDe7jHP7zmcgPXMS+++e3OYc/tfPTD3m87fu6XL5+f/wOXbgSey6/b2PHh4F6j721zh1znerAWOw/xQWv9wbEdQwzGtpjDzmvH04+2+7avjtvzLDU29u8qpbZl5wcvyJGhLfnszvOO9+/ixzXxtz984+wccz22uWWP7XrGiO/3Pby/zxgam65+xB+trTsY88yLLzf3PLR9tRV9t3/M/KqT+++I55zsb1vYsHbsPR3v5kofHn15OlYTVf9dBCSy7+Ix+qyr8Ey5ZxOia7xE9jQPiy7Mp95jTPuK2dT5axlnH4p4AMb0SyJ7XDyHxsZu7GLHdKgPufoxT8HjXDbb7EhkD+e73YTXENO2WMe4zjyd6zNGIvs+3xlT1KUYHKl1DrtvsHnK/AUOFgvWabcd0scdo/P2D+TgyxRMEbe/eN/+txBtbF0u2ljbez5u4PUyeE+67ece2/kYY7dP2/kTzx40z/7iTPOt7xw2Tzy3sWP9wHx4ZYuLDc7b5gy9btdj56JfaK0/OH7yhX1hi1gMbWHXzmvH/fT1P4zYdt/21XF7nqXGplR8YNdydcgx+WwxwTzuGp5/6UyDP47NOX3j7BxzPbbrtsd2PW3XbR8eE6/YLV4x8uVbD5unX3x5R/ymHdYv+oHW+s1+tmVMbT+OR/33zcn7aofXm0tPdO8vR4uiGrCDwI7IvnNHJ4MQsEUhxrEtDl3zSWTfPmC7cKrhHmOKh0UN/qTygQ9FrBcPwJh2JLLHxXNobOwGJ3ZMh/qQqx/zFDzOZbPNjkT2cL7bTXgNMW2LdYzrzNO5PmMksu/znTFFXYrBkVrnsPsGm6fMX+BgsWCddtshfdwxOm//MA6+TMEUcbMxJcY2ti4XbaztPTtP13g7Bsd2Ptp3+4w5t3586PrN/sDFBudj5hzT167HjqNfaL9y2+GFd1Pjm6Hox3HEYGyL8e463Tn+7sr9H6i4fXTenmepsfnnf+3/Q5IpfJiSD+Qz/MEfwEaLeVwO2hzAsR2XYi2l5rTrvvI6f5790+fbf5jGGNB/F7cU57bmcP7Hntr30a6N/WzLmNp+5AP6WTtcX22t63tt/l1z42Hz70/u7z1tHAYJoerUioBE9lZoht2wZIxxbJOwaz6J7N2FoQu73PcYUz7wctvPZY8PRawXD8CYdiWyx8VzaGzsJiF2TIf6kKsf8xQ8zmWzzY5E9nC+2014DTFti3WM6x/66OYD2FyfMRLZ9/lua28MjtQ6h9032Dxl/gIHiwXrtG2v/ujur6nbe6WO+3wu5ddQu/h1/ilrQNxsTGnPxtbloo21vWfn6Rpvx+AY33SF3auv3wqMbp8x59YPjnOxwTnvxW778LHY4Jv4/ON1T50e/k5kxsm2fK7YazouJ5iPxZ6cHDsuRn/aHpMLzLNPfn5bzzHPlSe3AjNej+LOyXEx/K5pDruu//T8Zgpxb4tr8AAAIABJREFUuPVbB81ff3Bb67gGxgCv13n7+/Zx4/iYLWsVfLDzwhf6hdae23485tptP45DH2vHzlvT8R2n9n+4UJN/xLqrHaaEqlcbAjsi+29+9/vm3Zfc0KBt+zekT9vYJV7vIueUezYBu8bPXWS3BRLHXWud+z3GlA+8ua+nzX8+FLHe2DGVyF4mR+wGJ3ZM23hU6jrzFDwu5QPtSmSPw/eaYsrYpmhZe+f6jJHIvs93W3tTcKaWOcld5KqtvdwjAgeLBXPatjffdVDdh+4+n63/SzpG3GxMuTYbW5d7Ntb23pdv2wpHXePtGPeY9t3rY87tejjOjS/OeS9224YP/erChutXOx9xPFasyMlY842Zh7bH5ILlM193dMV1u785glxw5+QfS7b+fej6XVHX3pvLMfGAv1iz67fFwa1H6DslBnbOKcesVfSZc7j+2XP2sS3XzpZrJx+sHd6rrYWPvlen1eKnxbvteIm6bc417Ynsbzp2cfOGN7658z/06RLicy6gtK02Yk69bpOvaw6J7PsP2i68St5jTEs88HKu2z4MfRuhEF8kspfhu90IxY5pCB9SjGWegscp5h8zp0T2OHyvKaZj4j+2L2vvXJ8xEtn3+W5r71g+zKk/uYtctbWXH6KBg8WCOW1brJf97fWSx30+l/QtpW3EwcaUtmxsffy89Z6D5jHnV9fxjezrPr4Ry/rG++bENdpvuz/k+s13bcV+9nfji3Pei92S266NL9688eu+R/brB33g+tVKZM/JAZer5GNXy7rBXH/69MGFnGK+IRd8c9x2apujWCfnyrnmPltjBVes4fgNR83H/+/mM4k7v8WB+Ng+U2Jg55xyzFoFP+x41z+cX3XysOHrt2xfHDN+bLmuz94U51VYnC9lCyzcdae0N3ZuF3PfeWmNde7290R2fZN9XEh9pAy5ZpOkax6J7P4HbRdmpe4xpiUeeDnXbB+GbRuhqf5IZC/D95QxncqFVOOYp1hzKhtD55XIHofvNcV0aOyn9GOezvUZI5F9n+/2w9kUTsxlDLmLXLW1lx/WgYPFgjltW6z1iefq+tXsPp+t/0s6RtxsTLk2G9sx3ORcU8fT/hibbl/LRd5z44tz3ovd+uzDBn4I4f5gwrXN9auVyD6UA+/4wFGD10UN7e/rNyUfunL9+Ouvfnr8mf1nJTjPHKEvnIvnNbTA5M/eO/wb9m7Nc9dgc92tR+g7JQZ2zinHNg52vOtfn2+MH9rrPn7Y/OlfbfhITKwdF5dazuGju+5afIMfNj5tx+MUUfV2EdgR2d2bOu9HoI2YU6/bBOyaQyK7/0HbhVmpe4xp30OllH+x7PKhiPXi4RJrXswjkT0unkNjkzKmQ33I1Y95yk1cLrs+OxLZ4/C9ppj64hzrGvN0rs8Yiez7fLcfzmLxpMZ5yF3kqq29/BANHCwWzGnbcl28dvHVR81lHx4uZnBczLbP55i2apoLcbMxpW82tozXkJZzTR1P+0NstfWxXGQfN744573Yrc/+UBtcf1frrqWrr+6Fic858SMnXZtXntz91rd7H6/fAr/c62POaXsoT9GvL9fxXvK2+Zgj9JFz8dzX5uY97I2x6dY8dw0WC9+8uGb75Di2cbD2PnjN7vO4zzfGjxhwfTy3dlxcajmHj/S7Fp+sHzY+bcf9Kqh6dCGwI7LjFTB6XUwXXPv32og59frQBJDIvv+hdCrmqccxpn0PldR+pJ6fD0WsFw+XmPYkssfFc2hsUsZ0qA+5+jFPuYnLZddnRyJ7HL7XFFNfnGNdY57O9RkjkX2f7/bDWSye1DgPuYtctbWXH6KBg8WCOW1brov9MPanz5f9Zjt9sX6u4RjY25hyzTa2jNeQlnNNHU/7Q2y19bFcZB83vjjnvRTt5dceNXhtzdi5uf6u1l1LV1/dmy6yv/VdZ5vLrz3b3J7gDyJeft2uiIk4kZMP/eCgue6T2/vkc1sscR88w/uv2/r0XaftMXwNyXV3TQ882l//r//nLSZ964lxH5gw19h2zevWPLevxdY3H67ZPjmObRysPXsd6+jz7dlfvNxccd1R88SzGy5yfcTEnc/FpoZz+Ei/a/DH9cHGp+14X/XUlTEI7Ijs7kCI7sfee7x5+rkX3Vs6fx2BNmJOvW6ToGsOiez7H0q78Cp5jzHte6iU9DGGbW6QsF48XGLMyTkkssfFk7j2tSlj2mc7933mKTdxue1bexLZ4/C9ppja+MY+Zp7O9RkjkX2f7/bDWWy+1DQfuYtctbWXH6KBg8WCOW1brof9uP+wc9v+OY7pSw5bNdkA9hb3v7l8I2Tdcar9W6iMn6/9zvc3Ytmph6eNv/LkUXPihjCxyXKRPrrxxTnv1dQO4Ya7liFj1Ge82G45EhM/zuvOyevg45du3YrO5LPbH+df+MY2z7r6+cbaa9b20Hxg3bDPgaFjXV9x/qfv6Y4R7Vm/Yx//7ev1D/Oeeviw4Q9DPv3F/h9guDi4vllsgLd7/+t3jv+hnJ1zyrGNgx1vr8PPsfzg+jjOnc9dew3n8JF+1+CP64ONT9uxxN4wBCSyh+EXfVNlk6CN9LgukX3/Q2kXXiXvMaZ8OJT0JaVtu2HBwyWmLYnscfEcGpuUMR3qQ65+zFN3Y5vLvrWTUmSH6MC1WptLPOY6a4hpSnyZp3N9xkhk36/v9sNZSu6UnpvcRa7aPOWHaOBgsWBO25ZrYD/uP+zctn+OY/oy1Nbb39ctCA2dp3Q/YG9xx/ELL+3zmzGbQ2u5SH/d+OKc92pqh/DBXcuQMeozPl8tR6bgZ+PkHoNz7pzWnr1PPrv9cc7aif5d/TjW+sFraF3bQ3Linoc2wvNNt44Xh11fcd7mG/20dYrXYrfWB4vBENvoY8e4vtl71g772VjavqmPad/awd+OwPW3v3+TN2P5wfVxnBtv2qypHcLBkv7a+LQdB0qkqx8ukT2QAm3EnHrdJlzXHBLZ57NpZ0z5cOiK65zv2U1D7Ie7RPYyfE8Z09q4zjx1N7Yl/EwpstuYllhbTps1xTTluhnTuT5jJLLv13d+qASHU3Kn9NzkLtZpay8/RAMHiwVz2rZcA/tx/2Hntv1jHLf9YcC/uXwjFNEX2nLPeZ1t3332q70F9hZ3G1PGaW6t5SJ9d+OFc96rqR3CF3ctQ8aoT70iO2uTy0m8rgb3yGdfDFk7weGufhz7ic9v6t01N26+vHHtP27r35Q8wLe98Qd9x451fcV5H69tneJ6bBvj73qc/IT/Sy19tuGHWzutbzi2GPnWamNp+6Y+pp+unVMPHTSnHtz8RoXLTbeve84vB3GcG2/arKkdwsGS/roY+84DJdLVD5fIHkgBHylDrtlC2TWPRPbxD+EuPFPeYxHlwyGlrZJz201D7Ie7RPYyfE8Z05Jc9dlmnrobW1/f1Nckssfhe00xTckZ5ulcnzES2ff5PnQvmJJXOeYmd5GrtvbyQzRwsFgwp9/yrq3IRT/Zj/sPOzfHxWppy52PNt377rk7ru++27/Wc2BPDOCjjSnjNLfWcpG+u/HCOe/V1A7hibuWIWPUZ1t/hmJhOTJ0jO1n4+Qeg3Pse8+D3UI3+cz+tmXtxHxt/WD79Etnzr8vm7lu8xx/OPWJ57avncmRD/htGXcdFiN7j8f0nedu27Z+t589f8s7t7x48PsbIZz3LQ4+2+/8wNnmCvNHaS2mNr6++fAO+ss+fLb5+6u2or6NpbWd+tjnH23iByjw8/Z7d7+lz/ttLfFCTNFnSmzoV64WPvZxMJcvPjttWNvrgRLp6odLZA+kgCVjjGObkF3zSWTf/1DahVfJeyxufDiU9CWlbT4Esd7YD3eJ7GX4njKmKbk4ZW7mqbuxnTJX6BiJ7HH4XlNMQznRNZ55OtdnjET2fb4P3Qt28WIO98hd5Kpbe796x2Hzg8cPvB9UP/OlrZjAdX7ldrzzdvvh3c7NWhCrtfHBnPiWKFqIPd/45kHzwPe270DGdbe/6wf+IKJ7bY7n2PtZ3N2YMlZzaino2PrqxtPeq2ltH//cRnDt4pK7lq6+ujc9Ty1HpuBo4+Qeg3Oc08dXy0neZ3/b4h77tvWz62Cu15LnxAW+89iuzx7Td3vNHret3/Zxj2nz8g+fbX77PxtBmH2IK1qfbeLK/i6mvM7Wzsdj2kcfG0vez9F2+TfVPvEiRlNiQ79ytUM4mMsXn50hsQiUSFc/XCJ7IAWGkHRMH1sgu8ZJZN9uBLpwquEeixsfDjX4lMIHPgSx3tgPd4nsZfieMqYpOBgyJ/PU3diGzDl1rET2OHyvKaZTuTBkHPN0rs8Yiez7fB+6FxzCj5r7kLvI1bbaSyzYsi/zu219dm72jdVaX+iP68fzvzzT3PKtjdiOb+912X7mxZebu+7fFea7+td6D3s/i3tbTF2saj6noIOY0083/vYe+9TQ2li0cQa+v+2ibn62jZ3TdTdmuX23HJli2/rvHoNrqDfP/eJMc/pXZ5rLrz1q7n7Q/21y8tn68Cd/tYn/sz/fPotsv/f9w5Yfdh3kVy15Tlzg+1OnD5o/fX1ddq08pu88d1u7fvcez6/+6OGFenfpie0PU1OK7MjVYxe9cqEW2TrD9cM/+G/v5TomNjHtMVbk3pDY0I9SLXy08Sjlh8/urfdsv4zQFadAiXT1wyWy91DgN7/7ffOmYxc3j/zwcW/PLnJOuWcTsmv8kkT2rnUu4R4LHB8OS1iTbw18CGK9sR/uEtnLbJZSxtTHoZLXmKdYc0k/YFsiexy+1xTTlJxins71GSORfZ/vQ/eCKXmVY25yF7naVnuJBVv2ZX63+WnnZt9Y7de+edhcemL7bfo23+Eb//Bnl22uoavPHO5h72dx78KFa669paBj66vlIuJi79W0HhsLy59Pf/HoQpzgu7se23cpx6XXaDnShql9DRb62Hd6W//tD+3svEO4Rz5bH9rmuOK67atnPvpPm3p38pPbPTL5VUueEyN+Bj358W2NtuvFMX13r/PchxPvoSXWnOef/uXwQh5dce30b7LzXfAuprTNtdG+bbl+9O3qZ8fEPr7mH48aYBFzXmJMnvbFhliVbOGjjUdJX1zbQ7nhFT51cTACOyI7BeU3vPHNTdd/EJ3Rt8Z/N91yb3P8xi+0uoZ76GP/QUC36333JTc0BwdH57sQE4ns+x8CQwqoLZAh88xhLIsbHw5z8HmKj3wIYr1DC/hQOxLZ4+bfUNxTxnSoD7n6MU/djW0u+9aORPY4fK8ppja+sY+Zp3N9xkhk3+e7/XAWmy81zUfuIlfbai+xYMu+zO+29eD1MewTu8UeZ4jv1rcuH9ivq88c7k3BhWuvteXnFVtfLRcRF3uvpnV8/U5/DoC75C98d9dTgmtvTfxt+tJrtBxx8b3zvsPzP4yjjxTbf/r8Vqjjvb/6u7PN0z87aDDmPPdObH/DYgj38F7sS04cNVed3ArQ1jc7h4/79j45hNZeL3VMjOA3feAaXMzpu3ud53jvPI99LefnPGhp/0vfeGXU62Ku/shR88iPN795YOejDbT0wa7N3scx7aNvVz93XO3nLiZtMSVGNbTw0cZjrE//57Jtfo4d29b/T99ztrn0mqPBf1zYaqU6Ho/Ajsg+fng9I6xQ7hPZIaxTSHdFdpxTRIe4DpGdc0hk3z6oYhZhWyBjzlvjXCx2KLY1+hfLJz4Esd7YD3eJ7GnysC/2KWPaZzv3feYp1pzbtmtPInscvtcUUzfGMc+Zp3N9xkwR2WM/Y2LGI8Zc9sNZjPlqnYPcRa621V5iwZZ9r7juqPnQ9e37KohJqV7BAv5Z3/GKhj6MWY98Lcf67s3pmotLW0y53jm0WBNiYOur5aJ7r6Y1IQc+9ul9oR1xIX+xFnc9uTn3wPcPmm89sO9nTD9irfFP/vKVC4LnGP9gn9xwx/F5Rh9tX3sN42xOPX36TPPCgNpDu7Zl/DGntWf7+Lhv73MO65O9n/uYWBFP2OcaXMzpO65/89uHzXsv3rwS5zNfPGzw+i6MdcfYc66N86ClfSuy4w/R4m90sD9ajuF81l/eQ2vH+Pra+zimffS1c7r95nbuYtIWU2JUQwsfbTzG+hQyts0WfrA2Jvb1qLzz9GQxIjvhh2BOgZzXbOv7Jru9j2PMwW+zuyI7z2ljDFmH9LVJ1dVfr4vp/0DThV/Oeyx2iG1Ou7lt8SGI9cZ+uEtkL8P3lDHNzc8+e8xTd2PbNy7FfYnscfheU0xT8IRz3n7v5l3OEB15bU6tRPZ9vg/dC84pzj5f7TOmrfYSi09/cSvCtfX12WAdiNlij2N999l1r3XZZ9+uPnO45+IyJk7EoLYWawL2dg9/w6cOmz9+h/891bX5j289u9z59ye3/MW6mGNuv1znwIw4p7LZtca/eN/wb412zdPlO8aRG24/rB33OLfta69hXKycsvXL2qOPbPHKmNtP7YrEvHffI5vc+Nqdu4Iw7+duiRXxhP02XmH9D/7gTHPzXRvfOdbi68bpqo9secK1EUe0nMOK7OxnW47h/NZf3kNrx/j62vs4pn30tXO6/eZ27mLSFlNiVEMLH208xvoUMtZnC/M98dz+XrOLC64+qvNxCOyI7BSQa34dTN/yYojsENApohMTfNOd33Lnt97hSxc5p9yzSdU1XiL7uELRhWXqeyx2iG1qWyXn50MQ6439cK9VZMc7WUtintp2ypim9n3s/MxTd2M7dp4Y/SWyx6nvNcU0Bi+65oBo0nW/5nsS2fdjN3QvWHNch/hmnzFttZdY2A+tbX19NlkHYrbwxfrus+te67LPvl19artHYfKP37H9Zq+LS+y9IHHK2WINwN63h2dMfPdy+thnC3/Q8srX368Nn9Gf/IXvzDGuJ3dL/1Pa7Vpj1z3XpzF97Vj8RgHX+dZ3bX9Agz7ME86Nln3tNfQdU/s4h69l/DFnyGeZZ3+x//zy2ctx7eQnNyL4T5/f/lCA+Wtj4cOROFt8OebEDUfNN765mZPXuB7iiJZzDBXZ+Vqg/3hq66+djzbQ0i65Yu/xmPbRt6sf+8+ldTFpiykxqqGFjzYeY30aMpa4DJkb842Nd5/mqvvdCOyI7OhqX7vC16vMSXQPFdmxfrteiuz3Pvij899ux/z236//+7CJ+d8HzR9S6poXInvX/drvPfLD7TeSavc11D8WP8Q2dK6ax9/9ne1P+BHfmL6+8sprze9ePht1zhD/GNN/+cqyY/ovX00X0xD8U4y9ENOvlo/pmcNzzcuHryThu41pChxrmrOmmNaES22+/P7lV5qjV17t5bvdN8R+xtSGydC9YG1+j/XH1iMc+8YTC8Sc/dv6+sazDoS2f/6erTBmfRm6t+uyT79tn3+4evv8tddrOea6GRP4ZXHhOdc21xZrwlq4XrsOxsJ3z/ar5Zj+wh/GDb7zmPdzt8QnxO5ffGCbn7558N5r33VcAwZt99zrbl/8RoMrmtsxb3nnxq9nTm8/F7l4g2PAgHOjJSa89snPb3y85a7tPfaZ0lofHvze1rcpc9U8hvlrY4JjrN/6TZztdY7xXeNY4oiWc9x087kGX5ZhH7flGPS/98FdP37y5GFz2TVnm6de2I0JfSFX3DlxTvvo29XPN7bma8QLLfxsiykxKt2iJrz02914jPXJxrJtLHFx73/g0v16hvnGxtjqnToej8CeyO5OQZGZgjtaK0K7/Uufh4jsENj/4A8vap5+7sULy7Dr96378OyrTcz/8BepmSxd88JBfLu3q0/N9/79J+cGrbPmNQz1jfFEbIeOmWM/G1Mcx1zDq6+91px9pR6+v/8fNt/cwkYq5jprmwvrI39jx7S2tXKdNcT03KuvNa+cey0Jt2xMa4tBbH9qimnstS1pvrPnXm1efbWf7ymfMbXhOXQvWJvfY/2x9ait9n73h+eaqz/ySvPywasN+7f19dlnHQhtYfMv/2azRwcX4RfmHLq367JPv/HhHP/h3PK9a2ype1w3YwI/4LN7zrXNtWUcuF67DmLvu2f71XJMf+EP43T9J1+5cMz7uVvi878Hr17Yc471ATHAf23jDo7a5yYWbWPtddcGY2/72GPM/fv/3f384toDx4AB5+ac9hr6/Pyl3XmI25TW+kD7U+apfQzz18YEx1i/9Z3Y2+vEyF7jPBxr+3COr9x6rnnlXHusOMbGmfO1tbTbFSvaR9+ufm02ar1OvBiHtpgSoxwtffLZuvZjr+zks69P37UTN7TXMo71+XD73ecu1BH2QzuGa+TBBTFUB5MQ6BXZ7awQsJcqsvsEdqydIjvWjj58VztxGfurF339+esh+BWirr56XUw9v57WFSfcY5Gb8qs6fXPXdN/++lbsX1Or7XUxzFP8qlZNMYjti/1VtNgxje1r6HzM0xpiqtfFxKnvNcU0lJ9LHq/Xxezznc8YcHjJsbfPmCG1l/2H9CVurAN4tzCPp7Swybjgecg9z9C9XZdN+uq2+MOuEN27xpa6x3UzJvADmLjn7prmdv7CS6+/LsbzekBiTyxqXxv9hZ+ME1oe836O9oqTR809Dx409z+6fVUG/KLtK0625+vb37efE4gB/uN4t8Xcb7tofxz69a3/re/avhLJtcHYu/Z4jrldXtAexn799deQoA/n5pz2Wuw9OH2An7Hndtdb8px1Guu85ENHzQev2XDEjQuxt1gQI9uXceWabB/OMfR1MTbOnK+tpV3rn9uX9pcWU4sx1mxjSlxyt/DjypYaxbjYeAzx75kXz5x/LRFed4Q/Wt03hrjYfsDGZ3cM1+g/tU610xDoFNnxXnL7DXZXYJ5mMu2oKd9kd18RYz2kyI4++If5LQ4kYqyWidGXDBLZ9z+UxopB7HlY/PpiGttu7vnsQ69rEzDFL4nsZfhuH+CxYzqFBynHME+x5pR2hsydUmTnH8nEeof4Muc+NcV0zjim9l0i+359515w6Xlq9w1Dai+fSUP6kre2DvB4SgubjAv8Pt0hvtK2bbts2n7uMW12jS9xD2IVfGVM4ANwcc/d9czx/MkXDprnf7mfp8R9Lvt7+mvjhnjdevf+H0dl35jtfY9s7bTxgPZwn8du++kv7IvpiEFXrtCeOxfOLWd996091wZj7xvHuWmbLe1xLK9zbnud12LvwekDfIw9N9dTQ4u1MTbA8tTDGw7efNduPl/++m9B4A8F029ihJbXOBfPbR/GKqXITru+lvaXFlOLMdZtY8p45G7hB/2ytu2XZK//VPsPCu0YHrsx5fW21mcf2FgecOyXb91y2LXTdm71UB2PR2BHZKegTGHdisnjp847AiI4/WZLYRye8Fv4vGdfC+P+MAF9eJ+Y2LnQn9i0EXPqdSYG2q45JLLvPhy7sCp9jwWuL6al/Qy1bx96OA6dz46XyB4XT4tt17F9gMeOaZfdEveYp1hzCfvWZkqR3eaptbnE45piukR8Y61JIvt+fedeEByOhXON89h6NKT2fvHmzYfWUw9thZC+ddk6wOMhLf7Y3aXXHDU3fmZjE/4xLnwe4ptnL/xqP34+n7ps+vrzGm12jS9xDz9kgI/uPsE95zqW2BL3uezv6a+NG+Jl85B9UrTWThsfaBf3eey28PnpF1/euY8YXPZh/zfVMZ723Llwbjnbdp993Hxk7H3jODdts7Vz8Rpazs05cY1/zPOp08PqjJ2v65g+wEfWs67+c71nOUdcn/P8wAx1/KfP7WL8jbs2gvwtd2+fN4wz8SCOaPEHZHFfIvsujsRqamsxxhw2poxH7hZ+0C9r282lUw93C+38mw2Yw8XHzus7pv1/+ND2N95gn3XEjnH9cm35zvMqucuz1imyQ0zWv24EfKQMuXbpNZsNAh8EbXNJZI9bwNtwjnGdRa4vpjFslZzDPvSmFPMu3yWyl+E7H+DgcOyYdsW7xD3mKdZcwr61KZE9Dt9riqmNr4534yuRfRcP8MN+SFoyX+y+YUjtPf2rM83dD46r0bYO8HhIS3/4HETLuEx5HnbZ7IoxbXaNL3GPPhOfy6/dxIXn8GkKTpx3Di1xn8v+nv4CW8YJrc1D9knRWjtt8cVvDDz9uqBsBSjrD3zGeL76A/cYA9vPHtOevcZjYsFzt8X9Rx/bCK6f+eKuaNZnl77SPlra41jewzlsu9d5P2ZLH2BvyXlqOTcFV/cHuuQGY0Ec0fJYIvv+noZ4TWmJK1qMtzFlPHK38IN+Wdu+XLrt1EGDH9TgtyVOfmL3t3DsHC42dl7fMWzdfNdh8/APD5rHnzlovn7n5odBrCN2jM8v15573q2A6m4fAjsiu9vZ/XY4v73t9lvzuUvI0HMmW9+DQCJ73AIeGreu8SxyfTHtmmMO9+xDb0ox71qjRPYyfGc9Aodjx7Qr3iXuMU+x5hL2rU2J7HH4XlNMbXx1vBtfiey7eIAf9kPSkvli9w2pai/rwAPf276qgte6Wvrz/Z9sxt16z8GFuEx5HnbZ6oqx5ULXHLnv0efbT21Exx88vuHxGvcNc9nfkyOIHeOE1uYh+6RorR3yp6/1+cHcxKs93vnXu19O8/XHNdrx3ScWvnu4RnuYw64B9xj7IWPpA+3ZeXGPuc452T9FSx/g95R6lsKnFHPaeMXAlXGmr8QRLY8lsu/vaYjXlJa4osV4G1PGw22/+I3dH4a590PP4Qf94lwQ0/vW546x5+5YztvWtuUt6wjG8W+68AeXro2u8zXruzHW3imyuwYour/p2MXn/yCoe3+N513knHKPydb3IJi7yI5fsWXRmILTnMZwnX0xndOafL7ah15b4feNG3JNInvcDcsQzNGH9QgcHjpmrv2Yp1hz6TVIZI/D95piWppTNduXyL7Pd/shqebYhfpm9w2pai/rAHzl8ZDW5881N25+LRt/lGzs2rtsds1ludA1R+57bT7bfUPsvWCbzVLXiflc9vf0F3jhG+NfvWPzB/ae/cW43OA8Y1ub70Nj5rNhc5P5wRj4+uMa7flaIAHcAAAgAElEQVTuW87a+/wmvbVn14C+fXa/dPP+npL27Lzwz10LfU7R0gesYcl5auPFWIXgiW8NP/bktv4TR7Tf/feD5rIPHzW//f255rf/0/7aX44Z4889Dx2e/9Zyl+/kz9JiSrzQYv02pjZf7fGQPrb/2GP4Qb84tis2vOeOsefsw5bztrVteWt58PHPHTZ33tcv/tOmbdeo68Zcc6/ITmGd7zJHq2+0b0NgyRjjmMnWV3jnLrIDKxaNGLjVPAfX2RfTmtcwxDf7QGsr/EPm8fWRyL7d0PnwSXWN9QgcTmWjlnmZp1hzaZ8kssfhe00xLc2pmu1LZN/nu/2QVHPsQn2z+4ZUtRfvND75yU1dZ00Y0sb2x9p84OFzDUSTGz512Hzo+nYxBvhaLtg5ph7/8V9sv/k7dQ6Ma4u93TfE3gu22Sx1nfjNZX9Pf314xeYZbdnW5rvPB981O57HNjfpN2NAYZx92XJuntuWnH3bu3bf6f6J/7v5Nqy1Z9eAOWjXzmePfTnw3ddfPfONb+6KX+5a6HOKlmuGrz4fU9gsMaeNF2MV0w/8QUlgeI/5OyEvH8YX2Yf4TP4sLabkKvPQxtTmmj0e0sf2H3uMeNAvjh0SI3fMPQ9uv3Hvjue8bW1b3loe3Pvw/v7StdN2vlU7dTQFgT2R3fdHQCWqt0PbRsyp15l8fQ8CiezTi8bU2Ewdx+LIh8PUeWofZx9obYV/6hokspfhO+sRODw1dnMZV1OeSmSPw/eaYjqXPCjh51CRHb4xprGfMSXW3WXTfkjq6jf3e3bfkGOPhFe+kEN9bWx/rL1XX3tt8DPVcsHOMfWYny/uun/74X7KXG3cs/uGpecpcSOmbZjUcp3++vyJzTPasq3Nd58Pvmt2PI9tbtJvGwP2sy3nttd4TM5yLve6tWfXgH60+8nP+/NpTA589Y7NHJyTPqdouWasYYyPKXxJOaeNVypcH39md88qkX0Xj9D44jdtrv7IYfPU6c0PpWxMmatuO6SPO4bnfMUKz30t1sQcesu7zjZXf6T7h+XEgGMwJ/nI+dmHLa/b9thFZ5uP/NNRc9XJ9i+E8Q9A33p3ex/a6Grb1U/dGYLAjsj+m9/9vsGrYPQ6mCHQbfp0kXPKPSYfE69tDonscQt4G84xrrM4IrYx5qt1DvtAw3FMPyWyx8VzaGxYj8DhoWPm2q+mPJXIHofvNcV0rnmRw2+J7Pt8xx6Q/M0Rg1I27L4h1x6JuPa1sf2x9moQ2RFz69PY4zbO2H1D7L1gm81S199/yfa3Akr5MMYuY+wbY2sO+4W0l1931OBVKXYOm+8+H3zX7Hge29z8mkeYZj/bcm577W0XbeJHzroY8Lq1Z9eAufh53b1OO2Ny4Nmfb3ISv+FCf1O1XBv8HONjKn9SzWvjwlilssV5JbLv72mITYzWjemV1+3WGXL6hZem/SaYWweYy7bFOphDtj70rY9jMBf5yHndsbxu26G5ij8S78439ny4GqqePgR2RHZfB13rRmAsYfv6M/mYeG39JbKHF482bGNfZ3EcU4Rj+5BjPvvQG/oQGOqXRPYyfGc9AoeHxmqu/WrKU4nscfheU0znmhc5/JbIvs93+yEvRwxK2bD7hlx7JNaFvvaU+fX/GPhYe1NE9j9/79kGr8Kw3LBzDj22ny+GjvH1a8PE7hti7wXbbJa6zlhYTEv5MsQu4+jry7WwT2hLG3YeCD/41ieu8X5fa8fz2NYK1hAbA/bjPWuP9+44ddDc+9DmdU3sZzHAMbnss8d5aJdz8Drb536xX9/71pzjPtcGP+F7DpslbNi4MFap/ZDInpZPbkztOfj8p+/Z1heb08zJvnbIGHDoiWcPmhM3HDZjBG3mHX7Ah1fGYR764/KS122bM1e7FVDd7UNAInsfQj333YQIPWfy9T0IJLKnLeChcbTjWRztJs3eX8qxfcjFfghIZC/Dd9YjcHgpPG1bR015KpE9Dt9rimkb73T9TCORfZ/v9kPekjli9w259kisC26L9zG/4wPbdzLHxt3amyKyc19luWHnHHL8Z+892zzwve17oIeMaevTho/dN9Dntr5zv85Y9H1mq2WdjKXPH66FfUJb2uA8D31/yzveG9JyPHiL1yTg3NYK1hAbgwtjHt0XsHjP2rZz8D7mI5d99mw/zMU57HW+3sLaquWYa4O/S85TGxfLkZRx6BPZv/+TzWvLPvH5uL+xYHN4TTG1MQafbb4ev/6wGfL6F+YtWoujvc7jEB7929c3sbdzcF6Xk7xu25xx7ZFAdbsHAYnsPQD13XYTIvScDz2bfL45lyCyo+jhP9/6lnSNxdEW/SWtj2uxD7nYDwGJ7PsiDHFP2bIegcMp7dQwNzdVNeSpRPY4fF9L7a0hf0J8kMi+z/fj16/vdTGx9w1tnLTPNdYItLjO50CKZ561FUNk/9S/7v+KvLWB43d8YMOjd//d5ocHLsZu/yHnN9910Nx5X7tgavF17bXFZK7XyZe+z2y1rI/x9fnDtbBPaEsbnDdUZMd85BZazo8Wf3zykR9vOUnfwb9nXny5efKF7T0cP/nCbs1FP4yhrzz22WNf2mDs265bP2s65tqwDvhek28xfbFxYaxizu+bq09kxxi8ysQ3NuSa5e+aYmpjDD679QGYMl+HtBZHX/9QHuH1Nl83f/SYNtzY87ptc8a1TwPV/W4EJLJ349N7102I0HM+9PoSeAkieyhWcxnP4ugr+nNZwxA/7UMu9kNAInv8zdiQmLIegcND+s+5DzdVNeSpRPY4fGftjV2P5szzGn2XyL7P97XU3pT7hjauW2xZI9DiOp8DKZ551lYMkb1tHdYOP0twXW4ttH2HHrtzuDhbv/r6umPndk5ciXPt/l//qcPmY5/eFajpM9cylAdd/SwenDelyM41sKVvQ/mHfhhDX3lMLqPl3OxLG1xr23WOq63l2rCOoTjVtoYh/ti4MFZDxoX0GSKyh8zfNtbyd20xZT6itflKrOz9vmOLo69vbB7RBn1ly+u2zRnXXhFUHToRkMjeCU//TSZCrJYPvb4Elsi+/6E0Vgxiz8Pi6Cv6sW2VnM9uZGL7IZG9DN9Zj8Dh2DGtbT5uqmrIU4nscfjO2ptzU1obr+fgj0T2fb6vpfbafUOuPLXYskagxXU+B1I886yt3CL7bacOmktOHDUv/HKXa9anIcdXnNyKjW21xeKbK6ZtvqS+Tr70fWZL7UeM+bmWITzo62Px4LwS2XdzL0bMQuZYS57aZ4zlZQh2fWNrENn7fJzzfV9MbU0Ct9312fs8xqtkeGxb1ix7Dcf4ISXa2DyinSE+53ym9qug6tGFgET2LnQG3HMTIvScD72+BJbIXtdmpSvuLJ6+ot81bm737EMvtu8S2cvwnfUIHI4d09rm46aqhjyVyB6H76y9OTeltfF6Dv5IZN/n+1pqr9035MpTYsuazzqB69/45uZ9qSmeebSDNobIDrxO3HjUXHbt9tVC1gaO+z5LuP37zofUE+KLuXLFdIhfKfqQQ304p7Ade06upY8DXfevOrkvQnHeqSI7Xq1w4saNaEZuoe1a/9vft8mJrj723gu/OnP+h1C3fGub//DbZ8/WLGDB2PM6zoEDXqtkbdR2zLUtPU8ZFxur1LEoJbLbmKZeY8n5fTG1dclXH+x9Hlu8eA0ta5a9dtmH8Tzbf596DBxox52L122Ltbv9Up0PkEHVpQMBiewd4Ay5FZvYTHg+tNvml8ier8i0xWDodRZHX9EfOscc+tmHXmx/JbKX4TvrETgcO6a1zcdNVQ15mkNkX9Pfw8i5Ka2N13PwRyL7fn1fS+21+4ZceUpsUfPv/PZhc+0/bgQ5XKc/fXvwKXnFvSDaWCI7/aDf1gaO+9bxtou2f+jVHes7p72ulvhifK6YdvmT8h73DX04p/Qh1txciy/uQ67942f9+cN5p4rsdn3kFlp7PeYx1wq/ffbcXGPseZ3nMX1KMRfXtvQ8ZVywzlyxkci+v6eJyWFfTJm3aH31wd7nsc0BXkMLnthzHMN/2o3NI9pyMeJ1tl+5LV3dc23jXP/CEJDIHoZf9Ic8E74vgSWypy3gvmIz9RqLo6/oT52zxnF8+GC9sf2TyF6G76xHKWIamyOh83FTVUOe5hDZ+54xoXjWMJ61twZf5EN7DZPIvo/NWmqv3TfgOEeeEFvWQJ6z9qNu8F5Mf1iP0I4R2R9/5kxzy93bb8XyWWXxsjhaO33raBtn57DHQ/AgnhhnfRwydm59GIs+nOewLq7FxnvMMeL+s//avNv88uuOLuQy55XInqe+DeXaWvLU1rhceSqRPS3XfTG1tYrPcpsL9j6PbQ7wGlrWLHsNc9FubB7RjvUXx7zO1r2f+jxQIl39cInsgRSITfCHf7j5VZR//tfun1ZJZE9bwGPGlcXRV/Rj2ik9Fx8+WG9sXySyl+G73YDEjmlt83FTVUOeSmSPw3fW3tq4Jn924yuRfRcP8CPl87Qm/tl14jiHb3yu8YMyz1n777zvoHnwB1tRO5ZPrEdox4jsrn0+qyxeFkdrh2t05+B52zg7hz3muK6WeGKc9bFrzFzvMRZ9OM9hfVyLjfeYY+aPu1bOu3SRffsDhu7P7y4+pc7Xkqe2xuXKU4nsaZ/lvpjaWuWrRfY+j20O8Bpa1ix7DXlKu7F5RDtuLeB1tu791OeBEunqh0tkD6RAaoK3zS+RPW0Bb8N9ynUWR1/RnzJfrWP48MF6Y/sokb0M3+0GJHZMa5uPm6oa8lQiexy+s/bWxjX5sxtfiey7eIAfKZ+nNfHPrhPHOXzjc+3SE5tv2/I8de1nPUKbWmT/+6s2r4HBHzvtwtTib/1rO+6ai/eIJ+bIFVPazt1y3xBbdMm9DtjjWtpi33e9LX84bwyR/eEfb76Idus98X8IRsy5Tvj9g8fPNHgn/PMvbWuTmzNzjf1a8tTGK1esJLJv84V5FbP1xZR5i9ZXi+z9t79v83y0OcA6hX72mOPgP+3G5pG1YXHidbb2Xo7jQIl09cMlsgdSIAfJfTYksqct4D7Mp1678uTm3V73Pjwfn6eslQ8fPAymjO8aI5G9DHfsBqQrPku4x02Vb3OWe30S2ePwvdTGNDdf5m5PIvs+31M+T2vii10njnP4xufa526ap8iO33g9fv3ut2Utjqh71338qPnp82ea53/Zjak7jjWzrR0SH+KLOXLFdIhfKfpw3xBbdEnha9+cXEtb7Puut+2dOG8Mkb1vDTHuc51tMXVzpq1fDF9SzrGmPO2LaWycJbJ3P3dC8bY5yPy7+qOHzVUf2fzhZV8tIgdwjzXJ5gCvoZ895jj4TLu0GboOjrc2eA0tr7O193IcB0qkqx8ukT2QAjlI7rMhkT1tAfdhPvXaCy+daW47le5bF1P9ij2ODx88DGLPLZG9DN/tBiR2TGubj5sq3+Yst68S2ePwvdTGNDdf5m5PIvs+31M+T2vii10njnP4xucaa/1Tpw+aqz961Dz94stJ7bMeXXr8KOib7D6MLI5cl6+fe82Oo39drTved058MU+umPr8yHGN+4bYoksO310bXEtX/LvutfHug9dsvjXa9wMf159S51xjW0zdnGnrV8r/oXbXlKd9MR2K2dB+EtnTPsttDtr8wyubvnL7YfPsL/btkwPgPWudzQFeQz97zHGIPe1am0M50dXP2rD9eJ2tvZfjOFAiXf1wieyBFMhBcp8Niez7BdSHk67lw4kPHzwMYuNem8jOBzPa2GutaT6uM0VMa1onfOGmqoaYSmSPU7dKbUxr43bt/khk3+d7yudpTXyw68RxDt/4XMtd61mPUojsp3+1/dbbmHVZ/PHtd/rY1g6JD/HFHLliOsSvFH24b4gtuqTwtW9OrqUt9n3X23j31Okzzfd+dK556bfz+LIR19kWU+YM7n/tjsNmrr+lvKY87YtpX26MvS+RPe2znDmIuLblqRszcgC8Z61z52Ef3uc5WszH/kNtuj60nT99+kzzxHP7mOFLmq4PbXOkuB4oka5+uET2QAqkIPWQOSWy7xejIbipTzrc+PDhwygm1hLZ08WtK052E97Vbwn3uKlq+6CYc40S2ePwnZvTnLGTrfGxk8i+j1nK52lNHLXrxHEO3/hcy13rWY9SiOzAbcq6iD+efxxPP9G+9aLNt5B5bUh87Dy5YjrErxR9uG+ILbqk8LVvTq6FsR7b3nJ3u4j+yrnXFimy92Fa8/015Sm5nCtPJbKnfZbzuYW4Do0pOYBvu7PWYR4es+Wc3/7umeaOezd/AwLXmMu5f7D2nr/fPoPpQ642UCJd/XCJ7IEUyEV0145E9rQF3MVb5/1424debLwksvfjHxtzzPfMz7c/RU8xf01zcoOFDx6l/UopsmNt2CR+97H2D8Sl1x/LPjfVsebTPGnq0BiR/c/es/m27dLFu5TP05p4bNeZK6YUl3LXetajuYjs4ImNjxUaujhEfNE/V0y7/El5j/sGtCnt5Jj7G3cdNJdfd9ic+Njmvcbk69C2K9YS2dM8O0N4saY8JYdz5alE9rR8t8+loTElB5AzrNuYh8ds0e+qk9t6/s1vHzQp/9ByXw5bv/r6xr4fKJGufrhE9kAKxCb00Pkksqct4EPjoH7bONiHXmxcJLJvcY6Nbd98jz520Pz7k+Xs9/kX6z43MrmFF5//qUV2n80lXrOb6iWubylrGiOyM0+7BJ0l4MLnKda7hPW0rYHrRK7miinFpdy1nvVIIvty9hOsR0vKU5uT5OyQtit/JbLXx3nWwZy1t+05kPo6+ZsrTyWyp+W7rVFDY0oOgGus25jn8ms33xT/xOfb/2hqan52zU9f4X9XvxT3AiXS1Q+XyB5IgRSkHjKnRPa0BXxIDNRnNwb2oRcbm9pE9kd+vPkVsrm+izF2fJYwHzcyuYUXH3YS2Xdriw+jIdfspnpIf/WJg/tYHCWy7+PO5+nQD5BjMa+lP9eJXMVxDr8oLuWu9axHEtnzxDkHlxjTJeWpzUmub0jblb8S2evjPOsgYtsVuxx5lNoG+ZsrTyWyp+W7rVFDY0oOgGsYQ97j75k8+cLBhdel5d4X9HGfvsLfvr6x7wdKpKsfLpE9kAKxCT10PonsaQv40Dio3zYO9qEXG5faRPbY69N8Wx6VwoIbmRo2WBLZ4/DBbqpL8Up2+2MpkX0fIz5Ph36AnCvPuE5+4M2xDopLuWs965FE9n2+54h7ChuM6ZLy1OYk1zekxbg2jJcksmONl1971Nx2qvyrBdvwHnKddRCx7YrdkLlq70P+5spTiezttSAGV2yNGhpTcgD2r7xu8611COz0h/mQe19A+20t1md9b+uX4nqgRLr64RLZAymQgtRD5pTIvi2MQ/BSn/R42YdebLwlsqePX+yYzW0+bmRq2GBJZI/D9+98/6B54HvLf/f83HLN9Vci+z7f+Twd+gHSxXQu51xnTqGn1IdpflCuVWTHH4S76dbD5rIPnz3/Xm5wyMYH/g/hFfHNGdMhfqXow5guKU/dmHONfS3GtWG8NJG9bZ1zuq48bedraBwlsqfDFrGxNWpo7cUfMr393vYfjDEf0IbGP+Z4fjZF/Y0575C5AiXS1Q+XyB5IgSEkTdFHInvaAp4iZkuf0z70Yq9VIrv4HptT7nzcyNSwwZLILr67/FzyuUT2fb7zeTr0A+Rc+cF14gMkjnOso9SHaYqUNYnswBt/1O2xp/zYP/Hs9o+fD/2QT3xzxjQHb3w2GNMl5anNSa5vSNuVvxLZ/fnl41Sua8rTdDGRyJ4OW+SHrVGxai/zAW2uHBxih59NUYOH9I/ZJ1AiXf1wieyBFIhJ5jFzSWRPW8DHxEJ9t7HgRjw2JhLZtxjHxlbzbbDlRqaGDZZEdvF9TXkpkX2f7/wQGesDZK184jqxd+gS6WL6X+rDNPdHtYnsfdjece/mb9AM/ZBPfHPGtG8Nqe4zpkvKU5uTXJ+v/eN3bP5gIO915a9E9v0an4qTQ+dVnqaLiUT2dNiC37ZGxaq9zAe0Q3MoRz9+NkWdzWHP2giUSFc/XCJ7IAUsGXMeS2RPW8BzxnJJtrjZjr0miezie2xOufNxI1PDBksiu/ju8nPJ5xLZ9/nOD5GxPkDWyh+uE3uHLpEupv/f/PZGNL79VN5XSXF/NDeRHdjT9yFxoFiRM6ZD/ErRh7gsKU9tTnJ9vvZfvrb94UtfrCWy79f4FHwcM6fyNF1MJLKnwxYctzUqVu1lPtTwGdDmMT+bosba6zmOAyXS1Q+XyB5IgRwk99mQyJ62gPsw17V+zLkRj42VRPZ+7GNjvrb5uJGpYYMlkV18X1P+SWTf5zs/RMb6AFkrn7jOPpEutv/P/XIf89g23Pm4P5qjyP6t+w+bb9w17IcSFCtyx9TFO8c5Y7qkPLU5yfXZ9q77D5v/fOFgR+jqi7VE9vz1po//ytN0MZHIng5b8NrWqFi1l/lQw2dAm7v8bIoaa6/nOA6USFc/XCJ7IAVykNxnQyJ72gLuw1zX+jHnRjw2VhLZ+7GPjfna5uNGpoYNlkR28X1N+SeRfZ/v/BAZ6wNkrXziOvtEulr9H+MX90dzFNnHrJNixZpiuqQ8tTlJztoW98EHtx+v+7gikX2/xvtwynlNeZouJhLZ02Hr1p5Ytfe+R840l5442zz9s2E/TM6Vq/xsihqcyybtBEqkqx8ukT2QAiRi7lYie9oCnjueS7HHjXjs9UhkF99jc8qdjxsZiezimssNnaflhET2fXwpYMX6AFkrh7lO7B26RLpa/R/jF/dHqUT2+x89aD54zVHz3C/2+TTGz9C+Eu/K4h8aP5uT5KxtmaduP1732ZfIXh8nlKfpYiKRPR22qC+29ix9j8TPpqjBvtqa8lqgRLr64RLZAymQktxdc0tkT1vAu7DXvXbsuRGPjZFE9nbMY2O91vn4gUMiu7i21hwotW6J7Ps5xw+RS/8AyXVi79Al0pXiZky73B+lEtlj+hoyF5+la4ppDfuGkJjZsTYn/+SvXrnwPn7yl3lq+/XFWiL7fo23mJc4XmOe5nqeSmRPy3dbe3LFtESOwqZE9kChtuBwieyB4JdKOonsaQt4qbjO3S434bHXIZFdfI/NKXe+L926+SNedz9Y/lcF9boY8d3l55LPx4jsV508PC/6nH5p2Rzhh8ilf4DkOrF3ePp0+dqbMs+4P5LIvpzcZUyXKLKj9lghlmtFziJPbO7iHq/7ckgie32ct7Htip0vnnO7Ru7mep5KZE/Ld1t7csW0FOexPvI3tw+BEunqh0tkD6RAbsLTnkT2tAWcOKsdh3OqB4FE9nFxEG/H43X6V2ca/EGvGrCTyD4+fjXETT5Mi9sYkX1NGN9y90HzyI+WLTzbD8tLjy33RxLZp9WJGvnBmEpkl8heIz+7fJLInq4OSWRPhy04bfcNS6q9vnyVyB4o1BYcLpE9EHxfQuS4JpE9bQHPEcMl2uAHjthrk8guvsfmVM3zSWQX32vmZ2zfJLKvl+/2w3JsXtU2H/dHEtmXw3fGdElCz1OnN7/Vd9mHj5pHHztoLjlx1Nz42c1vEGG9yFnkls1de92Xd/ome32cl8ieLiYS2dNh69aeJdVeX+2UyB4o1BYcLpE9EHxfQuS4JpE9bQHPEcMl2uAHjthrk8guvsfmVM3zSWQX32vmZ2zfJLKvl+9WqIvNq9rm4/5IIvty+M6YLk3oueehg+anz2/j5BNkbe4Ch6d/1v5bNxLZt1jWUpd8Ma3Ft9h+ME9zvVpEIntavtvas7Ta63JfInugUFtwuET2QPDdZMh1LpE9bQHPFcel2XnyhYPmmZ/Fj41E9viYLo17S1qPRHbxfUl87luLRPb18t1+WO7jydzvU+iRyL4cvjOmSxd6fIKszd0bP9P9qj2J7PVx3hfTudfYNv+ZpxLZ6+NhW8y6rtvas/TaK5E9UKgtOFwieyD4XUUg5T2J7Mt4UKTkyJLmlsguvi+Jz31rkcguvvdxZEn3JbKvl+/2w/KSOO1bC4UeiezL4TsFkKULPT5Blrk7RLiUyF4f530x9dWtJVxj7c2Vp/ome3q+545pqTzgMwbrze1DoES6+uES2QMpkJvwtCeRPX0BJ9Zqy2Mtkb18DJQH+WIgkT0f1uJ1eawlspePQak8oFBX4gNk7jVTFJDIvhy+UwDJJd7l5izt+QRZ5q5E9nny2RdTxntpLWtvrjyVyJ4+J3LHtFRO8BlTYo8UKJGufrhE9kAKlEo6iezpC3ip2Mrufmwlsu9jIp4sFxOJ7MuNrfJ2P7YS2fcxWQtPKNSV+ACZG2OKAhLZl8N3CiC5xLvcnKU9nyB7+ldnmitOHjb3P9r+LnaO1zfZ6+O8jSnjtNSWtTdXnkpkT8/33DEtlRt8xpTYIwVKpKsfLpE9kAKlkk4ie/oCXiq2srsfW4ns+5iIJ8vFRCL7cmOrvN2PrUT2fUzWwhOJ7MuLvRXvEN8lc5kCSC7xrhSWoTGVyF5fHtiYluJVLru5BVmJ7On5njumubjq2uEzRiJ7oGBbYLhE9kDQ3WTIdS6RPX0BzxVL2emPpUT2fozEo+VgJJF9ObFUXvbHUiJ7P0ZL5ZFE9uXF3op3EtmXEd/QmEpkr48HNqZLfb5wXbkFWYns6fmeO6bkUu5WInugUFtwuET2QPBzJxvtSWRPX8CJtdryWEtkLx8D5UG+GEhkz4e1eF0ea4ns5WNQKg8ksi8v9la8k8i+jPiGxlQie308sDEtVf9z2c0tyEpkT8/33DHNxVXXjkT2QKG24HCJ7IHgu8mQ61wie/oCniuWstMfS4ns/RiJR8vBSCL7cmKpvOyPpUT2foyWyiOJ7MuLvRXvJLIvI76hMZXIXh8PbEyX+nzhunILshLZ0/M9d0zJpdytRPZAobbgcInsgeDnTjbak8ievoATa7XlsZbIXj4GyoN8MZDIng9r8bo81hLZy8egVB5IZF9e7BbtldAAACAASURBVK14J5F9GfENjalE9vp4YGNaqv7nsptbkC0tskOYzYVtKTu5Y1pqnRLZA4XagsMlsgeCXyrpJLLXt2EpxYU12JXILr6vgedco0R28Z1cWEMrkX29fJfIvrzYW/FOIvsy4hsaU4ns9fHAxnTp+4zcgqxE9vR8zx3TUjkikT1QqC04XCJ7IPilkk4ie/oCXiq2srsfW4ns+5iIJ8vFRCL7cmOrvN2PrUT2fUzWwhOK7Gv65t2lx4+aV197bbHfNLTinUT2ZeR2aEwlstfHAxvTpT9vcguyEtnT8z13TEvliET2QKG24HCJ7IHgl0o6iezpC3ip2Mrufmwlsu9jIp4sFxOJ7MuNrfJ2P7YS2fcxWQtPfvZfZxp8WD5+/eFiRWfGkqKARPbl8J0CCARLxnmJrRVkp/zgRCJ7fZy3MV0iZ+2aWHtz5alE9vR8zx1Ty6ecx3zGYL057cKW/oUhIJE9DL/shGeCSWRPX8CJtdryWEtkLx8D5UG+GEhkz4e1eF0ea4ns5WOgPEgfA4oCEtnTY52LzxRAcol3udbl2rGCrET2ZfDXxtSN99LOWXtz5alE9vQ5kjumpXKCzxiJ7IGCbYHhEtkDQS+VdBLZ0xfwUrGV3f3YSmTfx0Q8WS4mEtmXG1vl7X5sJbLvYyKeLA8TigJLF9n5CiCsd4ogOyfuUwDJJd6VwsYKslNiqm+y11fPbExL8SqXXdbeXHkqkT0933PHNBdXXTt8xmC97r3U54ES6eqHS2QPpEBqgrfNL5E9fQFvw17X82MvkT0/5uJ5OcwlspfDXrzPj71E9vyYi+f5MacoIJE9P/ap+E4BJJd4l2odffNaQVYi+zL4a2PaF/+532ftzZWnEtnT50jumJbKAT5jJLIHCrYFhktkDwS9VNJJZE9fwEvFVnb3YyuRfR8T8WS5mEhkX25slbf7sZXIvo+JeLI8TCgKSGRfTmwpgOQS70rVBSvISmRfBn9tTEvxKpdd1t5ceSqRPX2O5I5pLq66dviMkcgeKNgWGC6RPRB0NxlynUtkT1/Ac8VSdvpjKZG9HyPxaDkYSWRfTiyVl/2xlMjej5F4NH+M3n/J2fN/5FUi+/xjyXykAJJLvKPd3K0VZCWyL4O/Nqa5+ZTbXm5BViJ7+hzJHdPcnKU9PmMksgcKtgWGS2TvAf03v/t986ZjFzeP/PBxb08mQe5WInv6Ap47prLXHlOJ7O3YiDfLw0Yi+/Jiqjxtj6lE9nZsxJvlYMMPyxLZlxdTiezdMdU72bvxKVHnJbKni4lE9nTYMlcksqfH2Ct86uJgBKoX2W+65d7m+I1faF0Q7qGP79/Tz73YHHvv8QZCedc/9PuDP7yoecMb33zhPwjrGCeRPX0Ss2CqFdZtHJDILm60cWOJ1yWyi+9L5HXbmiSyi+9t3FjSdYnsy+M5YyqRvTu2Etm78SlR5ySyp4uJRPZ02DJXJLKnx7hLO9W9fgSqFdnxzXGK3j6RHcI677siO4Vx3KdY3gUFRPY/OnZxg9b9x7n0Tfb0yczCqVZYuxyQyC5OuJxY8rlEdvF9yfx21yaRXXx3ObHEcwqy+ib7cvh+6TVH518BJJG9O6YS2bvxKVHvJLKni4lE9nTYMlcksqfH2NVEdT4OgWpFdi4j1zfZh4rsFN0p/DPZc7d6XUz64pI7prLXHlOJ7O3YiDfLw0Yi+/Jiqjxtj6lE9nZsxJvlYCORfTmxZF5SqJTI3h1biezd+JBPOVtyt8S7nnOuE7ZyC7IS2dPznc/TpdderrNEnlKLVTsNAYnsTXP+G+z2dTH22+8U1fFN9oODo+bdl9yw8372l3570JT4DyL7rwrZLrFe2SzDs1pwP/vKq81v/ueoSK7VgoH8WE8OvHxwroHQrpivJ+ZrjvXv/vdsc3j2nPiuPd2iOXDpic23nj944qh59bXXFr1WiloPfX/ZNfzzX9nEFO2SazjXibhOiem5c681v/7vw1lgRO4iX9cS0yWvE2tjTHPl6Zmjc81/v3w2O3+Yp0vnLmLK52mumJbKEa4THM7twzRpWaOIgER2ImFafEsdYjpEdYrs9z74o/PX3FfT4KfzJf6DuyXsymaZeK8d99dea5pzrwr7tfNgLeuHAIP/1rJerXPdsUZtR40XD9bNg6XH/8prXzkv9qBd+h6eotZ/PHFu0Xn9lVvOnY8p2iXzl+tEXKfEFF8Mm8sentxFnq4lpkteJ9bGmObKU+xnXi3wmZV5unTuIqbHP7qO2st9AzicO0+NNKrDCQhIZPeAZv9gKkX2tve75/6VJ9rT62LS/yoSsVZbHmu9LqZ8DJQH+WKg18Xkw1q8Lo+1XhdTPgbKg/Qx4K99L/2d7OASRa0HHk2Pa0nu3nnf5luyX7vzsCnpR2rb9tUiU2IKcQjfwkztZ4z5yV3ka4z5ap3DxrRWH2P5xZjmerWIXheTvu6Tv7liGouLY+fhvgEcHjs2tL9HItWlEQhIZPeA5RPZ8Q12vDKG33DnsFACTx0vkT19AZ8aG42LHxuJ7PExFU/rxVQie72xUd7Ej41E9viYiqf1YcoPyxLZ64tNSL6cemge4nHIGiloQeiRyL4M/tqYhnBjDmMlsi+Ds5Zr5K9E9nSxpdapdhoCqxTZIZjb967ffs8j59/LTgjxuhj+YVN+kx0CO/5hrBXabcLnPJbInq6o5IyjbA2Lo0T2YTiJT8vASSL7MuKofBwWR4nsw3ASn+aNk0T2ecdvzflHQUsi+3I4bGO6dG5LZF8Ob8lV8lcie7rYUhdVOw2BakV2iNp4RYv9j0I3xW57D3+4FN9Axz8K4/Y+RXOOtSK7a8uK6JzL2rbvbGey524lsqcrKrljKXv9sZTI3o+ReLQcjCSyLyeWysv+WEpk78dIPJo/RhLZ5x/DteYhBS2J7Mvh8M13bV/rtHReS2RfDm/J1e89dnj+tWS3nVr2bxJx3wAOc+252vOiqv43GYFqRfbJK8o8MBfRXTsS2Zf3wHBjrPNtjCWyb7EQL5aPhUT25cdYebyNsUT2LRbixXKx4IdlvS5muTFeav5KZF8eZ/HaH4rPS+Ut18V15vrWs97Jvrx8IZdyt9w3SGTPLPBGMCeRPRDE3MlGexLZVcDJhTW0EtnF9zXwnGuUyC6+kwtraCWyi+9r4Dk/LEtkF9/nxneJ7MvjrET2dDGVyJ4O27nVzlB/uW+QyB4o2BYYLpE9EPTQ5Jk6XiK7CvhU7sxxnER28X2OvJ3qs0R28X0qd+Y4TiK7+D5H3o71mR+WJbKL72O5U7q/RPblcVYie7qYSmRPh23pWpjbPvcNEtkDBdsCwyWyB4KeO9loTyK7Cji5sIZWIrv4vgaec40S2cV3cmENrUR28X0NPOeHZYns4vvc+C6RfXmclcieLqYS2dNhO7faGeova+8fv0PvZA+UbLMPl8geCHlo8kwdL5FdBXwqd+Y4TiK7+D5H3k71WSK7+D6VO3McJ5FdfJ8jb8f6LJFdPB/LmVr6U+jBtykhzo7165VzrzUv/XYef6AQa8R/yNex65xTf4ns43k8NL4S2dNhOzQGS+nH2luiHgVKpKsfLpE9kAKlklgiuwp4Ke6VsCuRXXwvwbtSNiWyi++luFfCrkR28b0E73LblMgunufmXCx7FHoksi+Hw2sS2clftLFyomseiezLyZOuOOe4R+5KZA8UbAsMl8geCHqOBPPZkMiuAu7jxVKvSWQX35fKbd+6JLKL7z5eLPWaRHbxfanctuuSyC6eWz7M6ZhCz5pE9suv1TfZ58TRLl/JX4nsqsFdPKnxHrkrkT1QsC0wXCJ7IOilElIiux4UpbhXwq5EdvG9BO9K2ZTILr6X4l4JuxLZxfcSvMttc00i+xdvPmg+eM2yRcrc/Clpj0LPmkT2T39x2fzVN9nTPXf1TfZ02JasgyVss/ZKZA8UbAsMl8geCHqJhINNiewq4KW4V8KuRHbxvQTvStmUyC6+l+JeCbsS2cX3ErzLbXNNIntubGUvbQ2h0LMmkR1rXjqvEE/8t/R13v/omeaSE2ebp0/n+bsAEtnT1qOl89Wuj7VXInugYFtguET2QNBtIuQ8lsiuAp6Tb6VtSWQX30tzMKd9iezie06+lbYlkV18L83BHPYlsovnOXiWwgaFHonsy+LwWkT2FDnRNadE9mXlSVesU99j7ZXIHijYFhgukT0Q9NTJ1Ta/RHYV8DZuLPG6RHbxfYm8bluTRHbxvY0bS7wukV18XyKv3TVJZBfPXU7M5ZxCj0T2ZXFYInuaeEpkT4PrXOplTD9ZeyWyBwq2BYZLZA8EPWYijZlLIrsK+Bi+zL2vRHbxfe4cHuO/RHbxfQxf5t5XIrv4PncOD/FfIrt4PoQnNfah0CORfVkclsieJp4S2dPgWmNtTO0Ta69E9kDBtsBwieyBoKdOrrb5JbKrgLdxY4nXJbKL70vkdduaJLKL723cWOJ1iezi+xJ57a5JIrt47nJiLucUeiSyL4vDEtnTxLOUyI4/OI2YlhBk51LL5uYna2+JmAZKpKsfLpE9kAKlklUie5oHY6l4ym53PCWyd+Mj/iwLH4nsy4qn8rM7nhLZu/ERf5aBj0T2ZcRxjflIoUci+7I4LJE9TTxLiewPPHpGIvuv08S0VN1n7ZXIHijYFhgukT0Q9FJJJ5F9WUW0FI/mYlciu/g+F67G8FMiu/geg0dzmUMiu/g+F66G+CmRXTwP4U/JsRR6IMpO8eOVc681L/32YNLYKfZCxlB4xppD5pnDWK51Dr7OyUeJ7Kr1sfjK2iuRPVCwLTBcInsg6LGSaOw8EtlVwMdyZs79JbKL73Pm71jfJbKL72M5M+f+EtnF9znzd6jvEtnF86Fcqa0fhR6J7MvisET2NPGUyJ4G19rqYg5/WHslsgcKtgWGS2QPBD1HgvlsSGRXAffxYqnXJLKL70vltm9dEtnFdx8vlnpNIrv4vlRu23VJZBfPLR/mdEyhRyL7sjgskT1NPCWyp8F1TjUzlq+svRLZAwXbAsMlsgeCHiuJxs4jkV0FfCxn5txfIrv4Pmf+jvVdIrv4PpYzc+4vkV18nzN/h/oukV08H8qV2vpR6JHIviwOS2RPE0+J7Glwra0u5vCHtVcie6BgW2C4RPZA0HMkmM+GRHYVcB8vlnpNIrv4vlRu+9YlkV189/FiqdcksovvS+W2XZdEdvHc8mFOxxR6JLIvi8MS2dPEUyJ7GlznVDNj+craK5E9ULAtMFwieyDosZJo7DwS2VXAx3Jmzv0lsovvc+bvWN8lsovvYzkz5/4S2cX3OfN3qO8S2cXzoVyprR+FHonsy+KwRPY08ZTIngbX2upiDn8eePRMgzyVyB4o2BYYLpE9EPQcCeazIZFdBdzHi6Vek8guvi+V2751SWQX3328WOo1iezi+1K5bdclkV08t3yY0/GaRPYbPnV4XtTCmucUoym+SmRPU5MksqfBdQrH5z5GInugUFtwuET2QPBLJa9EdhXwUtwrYVciu/hegnelbEpkF99Lca+EXYns4nsJ3uW2KZFdPM/NuVj21iSyc60S2ZWvU/OnlMj+/C8333q+4Z+X/wOiqbGZ2ziJ7IFCbcHhEtkDwS+VrBLZ9fAvxb0SdiWyi+8leFfKpkR28b0U90rYlcguvpfgXW6bEtnF89yci2WPwvMaXhfDtUpkV75OzZ9SIvtUfzWuXq5LZA8UagsOl8geCH6pwiSRvd6CWIoTS7YrkV18XzK/3bVJZBffXU4s+Vwiu/i+ZH5zbRLZxXNyYW7t/Y8enH+FikT2ZXFYr4tJE0+J7GlwnVvdjOGvRPZAobbgcInsgeDHSKApc0hkVwGfwpu5jpHILr7PlbtT/JbILr5P4c1cx0hkF9/nyt0xfktkF8/H8KWmvhR6JLIvi8MS2dPEUyJ7Glxrqom5fGHtxf4hl03aCZRIVz9cInsgBUjE3K1EdhXw3JwraU8iu/hekn+5bUtkF99zc66kPYns4ntJ/uWyLZFdPM/Ftdh2KPRIZF8WhyWyp4mnRPY0uMaua3OYj7VXInugYFtguET2QNBLJahEdhXwUtwrYVciu/hegnelbEpkF99Lca+EXYns4nsJ3uW2KZFdPM/NuVj2KPRIZF8WhyWyp4mnRPY0uMaqZ3Oah7VXInugYFtguET2QNBLJapEdhXwUtwrYVciu/hegnelbEpkF99Lca+EXYns4nsJ3uW2KZFdPM/NuVj2KPRIZF8WhyWyp4mnRPY0uMaqZ3Oah7VXInugYFtguET2QNBLJapEdhXwUtwrYVciu/hegnelbEpkF99Lca+EXYns4nsJ3uW2efNdmz8eeenxo+bV117L/n7V3OuVveXkNYUeiezLiSny8+4HD5tb7j5QLfp13LhKZI+L55qfJU//bLNvuPqjeid7oGSbfbhE9kDISyW+RHYV8FLcK2FXIrv4XoJ3pWxKZBffS3GvhF2J7OJ7Cd7ltkmhUiK7+J6be6H2yF2J7OJuKJfWMF4iu/IkJs9feKkMnoES6eqHS2QPpEDMJBozl0T2MgVnTIzUN16MJLLHw1K8rB9Liez1x0h5FC9GEtnjYSle1oslhUqJ7PXGSPnjjw25K5Hdj494I1wsBySyiw+WD3M9DpRIVz9cInsgBUoljkR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9shdiezibiiX1jBeIrvyZAk8D5RIVz9cInsgBUolkUR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9shdiezibiiX1jBeIrvyZAk8D5RIVz9cInsgBUolkUR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9shdiezibiiX1jBeIrvyZAk8D5RIVz9cInsgBUolkUR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9shdiezibiiX1jBeIrvyZAk8D5RIVz9cInsgBUolkUR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9shdiezibiiX1jBeIrvyZAk8D5RIVz9cInsgBUolkUR2FfBS3CthVyK7+F6Cd6VsSmQX30txr4Rdieziewne5bZJoVIiu/iem3uh9sjdNYjsN9910GCdD//woAnFTePXmesS2dcZ96Xle6BEuvrhEtkDKVAqoSSyq4CX4l4JuxLZxfcSvCtlUyK7+F6KeyXsSmQX30vwLrdNCpUS2cX33NwLtUfurkFkD8VK45XfEtnFgSXUgUCJdPXDJbIHUqBUEklkVwEvxb0SdiWyi+8leFfKpkR28b0U90rYlcguvpfgXW6bL/zyTPPBa842dz9w1Lz62mv6luyvxfvcHJxqTyK7uDqVO2scJ5Fd+bIE3gdKpKsfLpE9kAKlkkgiuwp4Ke6VsCuRXXwvwbtSNiWyi++luFfCrkR28b0E70rZ/MX/O5DILoF9Vj9kkciuGl2qXs7RrkR25csceev6HCiRrn64RPZACriEzHUukV0FPBfXarAjkV18r4GHuXyQyC6+5+JaDXYksovvNfAwlw8S2cX3XFyLZUciuzgbi0trmEciu/JlCTwPlEhXP1wieyAFSiWRRHYV8FLcK2FXIrv4XoJ3pWxKZBffS3GvhF2J7OJ7Cd6VsimRXXwvxb2pdiWyi7NTubPGcRLZlS9L4H2gRLr64RLZAylQKokksquAl+JeCbsS2cX3ErwrZVMiu/heinsl7EpkF99L8K6UTYns4nsp7oXYxR891R8+FXdDOLSWsRLZlSdL4HqgRLr64RLZAylQKokksquAl+JeCbsS2cX3ErwrZVMiu/heinsl7EpkF99L8K6UTYns4nsp7oXYlcgu3obwZ01jJbIrV5bA90CJdPXDJbIHUqBUEklkVwEvxb0SdiWyi+8leFfKpkR28b0U90rYlcguvpfgXSmbEtnF91LcC7F71cnD5tNfOJz0B1tfOfda89JvDyaNDfFZY5VrJTggkV28K8G72DYDJdLVD5fIHkiB2IQeOp9EdhXwoVxZQj+J7OL7Eng8dA0S2cX3oVxZQj+J7OL7Eng8dA0S2cX3oVxZSj+J7OL8Urg8ZB0S2cX3ITypvU+gRLr64RLZAylQKkEksquAl+JeCbsS2cX3ErwrZVMiu/heinsl7EpkF99L8K6UTYns4nsp7pWyK5FdnC/FvRJ2JbKL7yV4F9tmoES6+uES2QMpEJvQQ+eTyK4CPpQrS+gnkV18XwKPh65BIrv4PpQrS+gnkV18XwKPh65BIrv4PpQrS+knkV2cXwqXh6xDIrv4PoQntfcJlEhXP1wieyAFSiWIRHYV8FLcK2FXIrv4XoJ3pWxKZBffS3GvhF2J7OJ7Cd6VsimRXXwvxb1SdiWyi/OluFfCrkR28b0E72LbDJRIVz9cInsgBWITeuh8EtlVwIdyZQn9JLKL70vg8dA1SGQX34dyZQn9JLKL70vg8dA1SGQX34dyZSn9JLKL80vh8pB1SGQX34fwpPY+gRLp6odLZA+kQKkEkciuAl6KeyXsSmQX30vwrpRNiezieynulbArkV18L8G7UjYlsovvpbhXyq5EdnG+FPdK2JXILr6X4F1sm4ES6eqHS2QPpEBsQg+dTyK7CvhQriyhn0R28X0JPB66Bons4vtQriyhn0R28X0JPB66Bons4vtQriyln0R2cX4pXB6yDons4vsQntTeJ1AiXf1wiew9FPjN737fvOnYxc0jP3zc27NUgkhkVwEvxb0SdiWyi+8leFfKpkR28b0U90rYlcguvpfgXSmbEtnF91LcK2VXIrs4X4p7JexKZBffS/Autk2v8KmLgxFYnMh+0y33Nsdv/EIrALiHPr5/Tz/3YnPsvccbCOv8J5FdhTJ20dJ84zklkX08ZuLZfDGTyD7f2CnvxsdOIvt4zMSz+WImkX2+sVPeTYudRPZpuIlv88RNIvs846Z8240btVC10xBYjMiOb5q/4Y1vPv+fT2SHsM77rshOIR338a11iey7SaaiIzxKc0AiuzhYmoM57UtkF99z8q20LYns4ntpDua0L5FdfM/JtxpsSWQX52vgYS4fJLKL77m4ltLONGlZo4jAYkR2Lij1N9kpyFPIT0nurrn1uhgV8C5+LO2eRHbxfWmc7lqPRHbxvYsfS7snkV18Xxqnu9YjkV187+LHEu9JZBfnl8jrtjVJZBff27gxp+vUVtVOQ0Aiu8Gt73UxBwdHzbsvuWHn/ew///WZpsR/FNlL2JbNMjFfM+4U2deMgda+nryjyK6Yryfma441RfY1Y6C1ryfXKbIr5uuJ+dpjTZF97Tho/evIeYrsivc64r3UOBuJVIcTEJDIbkDrEtnvffBH5wV291UzELtL/Ee3S9iWzTIxXzPu4rs4tyb+i+/iu/guDqyJA2taq+q7cntNfMda+W9t69Z615nr4vs64760fCeP1U5DQCK7wa1LZPe9rx1DS/3aBxIZPzkrZV92hX1ODvCb7DltypY4XooD/CZ7KfuyK+7n5AC/yZ7TpmyJ46U4wG+yl7Ivu+J+bg7wm+y57cqeuF6CA/wmewnbsinOx+KAkUh1OAEBiewGtC6RHd9gxx9Xxeti8NoY/otF5LHzSGRXER3LmTn3l8guvs+Zv2N9l8guvo/lzJz7S2QX3+fM37G+S2QX38dyZu79JbKL83Pn8Bj/JbKL72P4Umtfap1qpyEgkd3g1iWyQ2DHP4jtVmgvlRgS2VXAS3GvhF2J7OJ7Cd6VsimRXXwvxb0SdiWyi+8leFfKpkR28b0U90rZlcguzpfiXgm7EtnF9xK8i23TSKQ6nIDAYkR2iOB4pYv9j8I4xXF77w/+8KIGojr+/eZ3v2/edOzinbHHb/zCzj07F+5Zof18R/1PCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIARWh8BiRPbVRU4LFgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiB4ghIZC8eAjkgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJzRUAi+1wjF+C3+175gKk0VAhUjwBeC/VHxy6+8Hqo6h2Wg0IgAAHxPQA8DZ0FAnhlH1/pNwuH5aQQiIiA+B8RTE1VPQL6zFp9iORgRAS0h48IpqYSAgURkMieCXzfe9/te+GtG3y/vH0PvL2PY/bhe+bb3hF/cHB0/v3x9r42LC6aOk+BAD4Ikp9swT33HzYUyAXfPfYdwnfOA1s2t7RhIYpqUyIA/pLnbH1CIHnaxXf24Tz4myF4hth/Nr/sffHdoqTjXAgM5b9vT+Lz0fIbeWD3Q7jnyy3fPLomBGIgMGYPD27amuyz7+aLfR4wR1j/xX8fgrqWGgG3BoOHlqe0z/25rdG8x9bd09jPpOzD1s0f2Ozqz3FqhUAIAm5NBt99+wxy2ZcLtO8+L3zPA7fOcz7t4YmiWiEwbwQksmeKHwtu1yYErnCz4m6qXTdRjDkXC7X7MOB1zGU3KBhrz925dS4EYiAAPrqcdOflZqVt887+fXzv2pR03eP8aoVAKAJD6upQvqO2Yz7+Qx7Zmu3asufiO1FTmxMBy8E2u217Erc/9kvgPPrjn8vpIc8Wd06dC4EQBIbu4cFN7Gd8ogrtg9fohznxz53b5T+eB/aLA+I/kVSbEoEhPJvymRU+t82N627+DHm2pMRBc68DgSE8m7qHd+fmXgjX3X/ufse9r3MhIATmgYBE9kxxcjfRPrMorMfee7x55vmfn9+gU0T39XWvuQUc97FZwXX3nu/cbuDduXUuBKYg0LaJ5lzICfD9sSeeOS8ggpdD/1kOc7PSli/uhoWbpDH2hvqlfutFwHLSh0II38FtwYO5IgAAEUJJREFUK9q4uWXvi+8+9HUtNQJ9/If9tj1Jn2/u/snlP85tfvTNp/tCYCwCLgd945ED4KKtx75+7jXuYdr2JK5t8d9FUOcpEHB55tqI/Zm1LX/cZwvO9ZnVjYbOQxFweebOF3MPT667NnCuPbwPFV0TAvNDQCJ7pphxk4yf0OM/d4Ngiyr7tomGPpfdzZA9dx8c9hw27DckfXPrmhCYggA4SL6jBe/4z3K87wMmx9jW8ptzWVuW025uQdjHNf0TAjERAL8tB8FR/iNHUW+n8N3WbMwJ/uIZQhtomV/iO1FXmxOBLv7DD1uzXT73+Ym8sXsmdy7mQd88ui8EpiLAGs4ab/mIOS2nwdcxP/RhPcc43z9b03Ff/PehpGuxEQDPyHe03GPAjuUkc6ONv65f3APZut2VP+49u79359a5EJiKAHhm+W75aTlO/tp86LOJuSxv3dyyzxM3t/SZtQ9d3RcCdSIgkb1QXFCcuQlH8bZF1BbzIe65G3rMbR8OOLfFnef3PvijCz4MsaM+QmAqAvZDJDco3JDzHLwc8s/lO+ZG/iBv8I/zMQe4YeE42h1iS32EwBQEWMPBafKRvOP5UL6Tv2j5j3O89d0fOv+hgM8S3Gd/8Z1oqc2NgOU/bIPrrMc8t3uSNv/AZXz4xAdf5g/6Yi78h3mHzNM2v64LgakIgHusu+Cm5SFrL/ckbTaYJ66Aafuz1tvnhfhvEdJxDgRYi8Ft8HbqZ1ZwF3x388U9Z25hbazz+syaI9KyAQRYm8E91mDuQXhua3IbaugDvls+czznw1j0Yx/t4dvQ1HUhMC8EJLIXipfdpHDzgkLs/meLsM9V3Lc/AUUfbmLcubiJYdHH/SEPCZ9dXRMCYxGgKMLNi8vPIXz08R35Y0V2+IV+dsNCoYY5MNZ39RcCYxFAbQXnQ/jOZ4P7HGAu0Sd3gy6+Exm1pRAg/2G/b0/S5yNziHlg5+O1vjl0XwjERMDu4cF1336Ge5A+uxRdMI/9x+vgu/0n/ls0dJwLAfAOHOW+xMf5ofUY83A/3pc/9j6O9U8I5EAAXAPnuf/w8X0oH5EXfB6wrttcoQ1cs/nFHMmxXtkQAkIgLgIS2ePiOXg2u0F3B9li696z5yjGrsBu7/MYDwFbqHGOYv/zX/76/HVb6DlGrRCIjQA36O683HD0bVba+O7LJfQl57lhwTXYgB/6JwRSI9DGtaF8t7y1vnK8rdvo+0fHLj6/Obfj2nyw8+lYCKRAoIt7uMf6PNS2fX7gGOOxh7HfqBw6l/oJgVAEfPsOzonaTEGF1/paN19Y5337FfG/D03dT4GArcF2/qGfWe0Y7FPcL8fwvps/yA19ZiU6anMh4NZk2mVtxv2h/9znhZtL9r728ENRVT8hUDcCEtkzxQebBiuKoDi3fcj0bVi4qUZxxz93E9K1DNeWPffZ6ppL94TAEATAq8/d9M0LXa0IeOHi6we+DQs4aj+k9vHd5oc7n2sbffGf/gmBWAiAc5/90p3nf60Uc3bVVZef6G831TynaO7z0fId922+iO8+xHQtJQJj+E++2v2Pj/83fvbrF1x279sa7vL9wiAdCIGICIzZw/v2K7Zm4/mAc+QN/vF5gTqOf75nxPkbr/9P/Ldo6DgFAmP28OQveM9/dk+Ca6jnqNX8Z/OB19i6+YO5+Lzw2eI4tUJgKgJj9jC++uzuUcDZrnzAPfslSctxd09j6/3U9WmcEBAC+RGQyJ4JcxZg/rqRFRBdF3ybCHdDgnPOxdYWbDunLd647p7TN/tAsON1LATGIsBNCLmJto1f7Ate8h+ObY708Z1z0B7685+7YWFf24d91QqBqQi4HLV8tnOSf/a+W4Nxj1y2LXOIc/CezRXx3aKt41wIDOU//AG/KZrg3OW/y2/wnNxHf9iy9Rv32vY/udYvO8tGgBz11Vx35eCjrcm4D75aznfli2uLNsl5tDzG3OK/GwGdhyLQV4Pt/L7PrKjxNgfAUfIYrc0FOxeO3fzpe16443UuBKYg0FWT7XzMDfCS/1izwV384zk5b3OBYzDedx9j7ZdsaM/WfM6hVggIgXoRkMheb2zkmRAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAIVI6ARPbKAyT3hIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkKgXgQkstcbG3kmBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAKVIyCRvfIAyT0hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEKgXAYns9cZGngkBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQOUISGSvPEByTwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE6kVAInu9sZFnQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASFQOQIS2SsPkNwTAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIF6EZDIXm9s5JkQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACFSOgET2ygMk94SAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCoF4EJLLXGxt5JgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBAClSMgkb3yAMk9ISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBCoFwGJ7PXGRp4JASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEDlCEhkrzxAck8ICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOpFQCJ7vbGRZ0JACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhUDkCEtkrD5DcEwJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBehGQyF5vbOSZEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAhUjoBE9soDJPeEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQqBeBCSy1xsbeSYEhIAQEAJCQAgIASGQAYFHfvh484Y3vnnvv5tuubfV+m9+9/vmTccubv7gDy9qnn7uxfP9eM03F68dv/EL5/tjHK/ZljbbfMJ43z/2f/clNzQHB0c7XeCfzx7G4F/bfevX//emv/L6yz5tfu04ohMhIASEgBAQAkJACAgBIbBQBCSyLzSwWpYQEAJCQAgIASEgBITAMAQgNluxHKMomENIx7H7j2P+6NjFDYVxXx/feIraFLndcTjn/BTwcY3jfPYgcsMX/GfH2HHWHo4hkNtr9APz+8R63scY37p4X60QEAJCQAgIASEgBISAEFgbAhLZ1xZxrVcICAEhIASEgBAQAkJgBwGIxq7Ijg74RjjEZp/gDFEb/0GQbhOc28RoiuU+gZuO+Xyi8O+K7Lx+74M/Ou+r+61ynz2uzZ0L9iWyMwpqhYAQEAJCQAgIASEgBITAMAQksg/DSb2EgBAQAkJACAgBISAEFoqAT9DmUn33IGofe+/x898Y9wnYdqxPgO8aY8e6wr/PF/THdf4gwCf6++xRmJfITsTVCgEhIASEgBAQAkJACAiB6QhIZJ+OnUYKASEgBISAEBACQkAILACBNvEaS/MJ1O43vfmtdhcKzNslsvN95ral6I2x9vr/38696zYSA0EA/P+/PkzQAE00b5w4kUoJ16NZPkpZe7G5vue7n0hPeD735/M6wz1X+u/zpZ7xda58byRAgAABAgQIECDwbQJC9m/7xZ2XAAECBAgQIEDgh8CExvdT42m4A+o71J6+V+j8qt9zZq1zfO1pAvAzHJ+57vew36F/1ktQP+P9SplzbSH7qeGaAAECBAgQIECAwC4gZN+NdBAgQIAAAQIECHywwCvQniPf37XAOuF1nkIP1V+E7PeT6rNm1j/H858G2fPsZz65J39nvxmF7JEwEiBAgAABAgQIEPidgJD9d066CBAgQIAAAQIEPlTgDtJzzDy1fj71/QqgpyfvRc/9fxGyn4F59jd7Oj8J4lM/70nf7PcM4lOf8XXG9LzOle+NBAgQIECAAAECBL5NQMj+bb+48xIgQIAAAQIECPwQaCF7gurz1SypJbw+J2lzvMLoFnqfc811m2/qE45nT6+e9CX0b+sloM9c5/pC9lPDNQECBAgQIECAAIFdQMi+G+kgQIAAAQIECBD4YIEJq89XreT6DtP/F2q3AH76W4id0DvrnGPWfO0pwfn8HO3p+fxM516z3tTOT4L2+4l2Ifup5JoAAQIECBAgQIDALiBk3410ECBAgAABAgQIECBAgAABAgQIECBAgACBKiBkryyKBAgQIECAAAECBAgQIECAAAECBAgQIEBgFxCy70Y6CBAgQIAAAQIECBAgQIAAAQIECBAgQIBAFRCyVxZFAgQIECBAgAABAgQIECBAgAABAgQIECCwCwjZdyMdBAgQIECAAAECBAgQIECAAAECBAgQIECgCgjZK4siAQIECBAgQIAAAQIECBAgQIAAAQIECBDYBYTsu5EOAgQIECBAgAABAgQIECBAgAABAgQIECBQBYTslUWRAAECBAgQIECAAAECBAgQIECAAAECBAjsAkL23UgHAQIECBAgQIAAAQIECBAgQIAAAQIECBCoAkL2yqJIgAABAgQIECBAgAABAgQIECBAgAABAgR2ASH7bqSDAAECBAgQIECAAAECBAgQIECAAAECBAhUASF7ZVEkQIAAAQIECBAgQIAAAQIECBAgQIAAAQK7gJB9N9JBgAABAgQIECBAgAABAgQIECBAgAABAgSqgJC9sigSIECAAAECBAgQIECAAAECBAgQIECAAIFdQMi+G+kgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVQMheWRQJECBAgAABAgQIECBAgAABAgQIECBAgMAuIGTfjXQQIECAAAECBAgQIECAAAECBAgQIECAAIEqIGSvLIoECBAgQIAAAQIECBAgQIAAAQIECBAgQGAXELLvRjoIECBAgAABAgQIECBAgAABAgQIECBAgEAVELJXFkUCBAgQIECAAAECBAgQIECAAAECBAgQILALCNl3Ix0ECBAgQIAAAQIECBAgQIAAAQIECBAgQKAKCNkriyIBAgQIECBAgAABAgQIECBAgAABAgQIENgFhOy7kQ4CBAgQIECAAAECBAgQIECAAAECBAgQIFAFhOyVRZEAAQIECBAgQIAAAQIECBAgQIAAAQIECOwCQvbdSAcBAgQIECBAgAABAgQIECBAgAABAgQIEKgCQvbKokiAAAECBAgQIECAAAECBAgQIECAAAECBHYBIftupIMAAQIECBAgQIAAAQIECBAgQIAAAQIECFQBIXtlUSRAgAABAgQIECBAgAABAgQIECBAgAABAruAkH030kGAAAECBAgQIECAAAECBAgQIECAAAECBKqAkL2yKBIgQIAAAQIECBAgQIAAAQIECBAgQIAAgV1AyL4b6SBAgAABAgQIECBAgAABAgQIECBAgAABAlVAyF5ZFAkQIECAAAECBAgQIECAAAECBAgQIECAwC4gZN+NdBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSogZK8sigQIECBAgAABAgQIECBAgAABAgQIECBAYBcQsu9GOggQIECAAAECBAgQIECAAAECBAgQIECAQBUQslcWRQIECBAgQIAAAQIECBAgQIAAAQIECBAgsAsI2XcjHQQIECBAgAABAgQIECBAgAABAgQIECBAoAoI2SuLIgECBAgQIECAAAECBAgQIECAAAECBAgQ2AWE7LuRDgIECBAgQIAAAQIECBAgQIAAAQIECBAgUAX+AYV6jkrYKtH/AAAAAElFTkSuQmCC)"
   ],
   "metadata": {
    "id": "g7K1oIih6A2J"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Knowing and fixing the header\n",
    "\n",
    "---\n",
    "\n",
    "With normalized data, stored in `data_normalized`, we transform them into `Pandas==1.1.5` DataFrame and now we are going to understand what `DATEBARRT`, `WHITEFLUXSYS` and `STATUSSYS` means.\n",
    "\n",
    "[Reference: Part II.4 The “ready to use” CoRoT data from The CoRoT Legacy Book](http://idoc-corot.ias.u-psud.fr/sitools/common/html/doc/cII_4_data.pdf)"
   ],
   "metadata": {
    "id": "VC9mWxsbNI7I"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import pandas as pd\r\n",
    "\r\n",
    "data = pd.DataFrame(data_aux)\r\n",
    "data.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      DATEBARTT   WHITEFLUXSYS  STATUSSYS\n",
       "0  54236.757582  112626.773438          0\n",
       "1  54236.763508  112605.609375          0\n",
       "2  54236.769435  112771.500000          8\n",
       "3  54236.775361  113113.601562          0\n",
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      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 30
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "9GhTgMv4Meqb",
    "executionInfo": {
     "elapsed": 211,
     "status": "ok",
     "timestamp": 1627494051872,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Quick data analysis \n",
    "\n",
    "We will do an data analysis in our light curves, aiming to prepare them for the filtering techniques and future Machine Learning model.\n",
    "\n",
    "We can see by the `shape` method that, in this sample, there are 23951 rows and 3 columns."
   ],
   "metadata": {
    "id": "-B1-M0-hIfkt"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(row, columns) = data.shape\r\n",
    "print(row, columns)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "23951 3\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tHElOS_IIiww",
    "executionInfo": {
     "elapsed": 204,
     "status": "ok",
     "timestamp": 1627494053160,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "25ec51e0-f46d-4e52-b687-28eefafbfe64"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The `isnull().values.any()` checks if there's any Not a Number (NaN) value on our data and as it returned _False_ there is not missing values.\n",
    "\n",
    "Note. No Machine Learning model can work with NaN values."
   ],
   "metadata": {
    "id": "unjTu8nIJpX2"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data.isnull().values.any()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 32
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Y3vKVtQImJ7",
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1627494053378,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "bf1d319d-b51a-4b65-a8c9-3ec20b67901f"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The `describe()` method returns a DataFrame with the statistical summary of the data. Thus, we are aware of parameters such as Average, Standard Deviation, Minimum Value and Maximum Value of the values. "
   ],
   "metadata": {
    "id": "piETiJzKKrFO"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data.describe().T"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                count           mean  ...            75%            max\n",
       "DATEBARTT     23951.0   54307.807634  ...   54343.325513   54378.820951\n",
       "WHITEFLUXSYS  23951.0  112501.031250  ...  112724.828125  113609.890625\n",
       "STATUSSYS     23951.0     101.434429  ...     256.000000    1024.000000\n",
       "\n",
       "[3 rows x 8 columns]"
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       "      <th>count</th>\n",
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       "      <th>std</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DATEBARTT</th>\n",
       "      <td>23951.0</td>\n",
       "      <td>54307.807634</td>\n",
       "      <td>41.011608</td>\n",
       "      <td>54236.757582</td>\n",
       "      <td>54272.313222</td>\n",
       "      <td>54307.818667</td>\n",
       "      <td>54343.325513</td>\n",
       "      <td>54378.820951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITEFLUXSYS</th>\n",
       "      <td>23951.0</td>\n",
       "      <td>112501.031250</td>\n",
       "      <td>292.866730</td>\n",
       "      <td>111110.515625</td>\n",
       "      <td>112276.492188</td>\n",
       "      <td>112529.890625</td>\n",
       "      <td>112724.828125</td>\n",
       "      <td>113609.890625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUSSYS</th>\n",
       "      <td>23951.0</td>\n",
       "      <td>101.434429</td>\n",
       "      <td>204.139657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 33
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "qLfkyVfeIp66",
    "executionInfo": {
     "elapsed": 436,
     "status": "ok",
     "timestamp": 1627494053812,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
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    "outputId": "bf1d2a3b-8339-43ae-9f9e-28a2ebd471ed"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "### STATUSSYS \n",
    "\n",
    "It represents the Flag of the status in a `int` type. As this information is not useful in this research, we will just discart it."
   ],
   "metadata": {
    "id": "v43WeCEzOVku"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data.drop('STATUSSYS', axis=1, inplace=True)\r\n",
    "data.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      DATEBARTT   WHITEFLUXSYS\n",
       "0  54236.757582  112626.773438\n",
       "1  54236.763508  112605.609375\n",
       "2  54236.769435  112771.500000\n",
       "3  54236.775361  113113.601562\n",
       "4  54236.781288  112621.789062"
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     "height": 204
    },
    "id": "ep9IEdwSYUvU",
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1627494053813,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
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  },
  {
   "cell_type": "markdown",
   "source": [
    "### ​​WHITEFLUXSYS\n",
    "\n",
    "It represents the amount of white light flux captured by CoRoT, after the correction of the SYSTEMATIC.\n",
    "\n",
    "SYSTEMATIC is a data corrections procedure applied to Faint stars data. It is formed by the correction of residual systematics skews in the whole set of light curves of the run added to BARFILL.\n",
    "\n",
    "The BARFILL is composed of correction of the jumps and replacement of the invalid and missing data using the Inpainting method added to BAR.\n",
    "\n",
    "The BAR os the process where it occurs the correction from aliasing, offsets, backgrounds and of the jitter of the satellite and correction of the change of the temperature set point and of the loss of long-term efficiency.\n"
   ],
   "metadata": {
    "id": "2CVDm-Q-OTC9"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### DATEBARTT\n",
    "\n",
    "It represents the date of the measurement in the solar barycentric reference frame. It is usually measured in Julian Days.\n",
    "\n",
    "\n",
    "The method of counting days sequentially, starting at an arbitrary date in the past, determined at noon on January 1, 4713 BC by the Julian calendar, or November 24, 4714 BC, by the Gregorian calendar, became known as the Julian Date or Julian Day (JD), proposed by Joseph Justus Scaliger and based on the Julian calendar.\n",
    "\n",
    "Julian Days are counted continuously, without separating them into days, weeks, months or even years. Each day starts at noon and lasts until the next noon. Thus, there is a great advantage in the night period (where Astronomical observations are made) as there are no time interruptions in this period, unlike the Gregorian Caledary in which the day starts at midnight and ends at midnight following. In addition, with the adoption of Julian Day in astronomical activities, it becomes much easier to measure the period between two events since it is only necessary to subtract one DJ from the other. \n",
    "\n"
   ],
   "metadata": {
    "id": "o9KMEp_-OGRR"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "With that in mind, it is useful to turn `DATEBARTT` into convetional date form (day, month and year).\n",
    "\n",
    "So, we are going to define a function that will transform Julian date to standard time and them apply to `data`.\n",
    "\n",
    "Note. We are using `julian==0.14`."
   ],
   "metadata": {
    "id": "x_dRnrl0OruJ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import julian\r\n",
    "from datetime import datetime\r\n",
    "\r\n",
    "def julian_to_stdtime(old_date):\r\n",
    "  \"\"\"\r\n",
    "  This is the function used to convert Julian \r\n",
    "  date to Gregorian date\r\n",
    "\r\n",
    "  :param numpy.float64 old_date: Represents Julian date of float type\r\n",
    "  \"\"\"\r\n",
    "  aux_1 = julian.from_jd(old_date, fmt='mjd')\r\n",
    "\r\n",
    "  try:\r\n",
    "    aux_2 = datetime.strptime(str(aux_1), '%Y-%m-%d %H:%M:%S.%f')\r\n",
    "  except:\r\n",
    "    aux_2 = datetime.strptime(str(aux_1), '%Y-%m-%d %H:%M:%S')\r\n",
    "\r\n",
    "  new_date = str(aux_2)\r\n",
    "\r\n",
    "  return new_date"
   ],
   "outputs": [],
   "metadata": {
    "id": "dTTJW_hZOwdC"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data.DATEBARTT = data.DATEBARTT.apply(lambda x: julian_to_stdtime(x))\r\n",
    "\r\n",
    "data.rename(columns={'DATEBARTT': 'DATE'}, inplace = True)\r\n",
    "data.rename(columns={'WHITEFLUXSYS': 'WHITEFLUX'}, inplace = True)\r\n",
    "\r\n",
    "data.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                         DATE      WHITEFLUX\n",
       "0  2007-05-16 18:10:55.071642  112626.773438\n",
       "1  2007-05-16 18:19:27.113766  112605.609375\n",
       "2  2007-05-16 18:27:59.155929  112771.500000\n",
       "3  2007-05-16 18:36:31.198092  113113.601562\n",
       "4  2007-05-16 18:45:03.240256  112621.789062"
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      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 36
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "OmuqLfGvPnSi",
    "executionInfo": {
     "elapsed": 358,
     "status": "ok",
     "timestamp": 1627494054167,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "60d1143a-afb2-44d0-ce10-0ff251a41096"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, we can see our x-axis in Year/Month/Day format."
   ],
   "metadata": {
    "id": "FtNoyS9VP2gl"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig = px.line(data, x='DATE', y='WHITEFLUX', title='Light Curve in standard time format')\r\n",
    "fig.show()"
   ],
   "outputs": [],
   "metadata": {
    "id": "QCAvhMa6P6Nn"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    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)"
   ],
   "metadata": {
    "id": "89P2BEKZZ5Z2"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Algorithms\n",
    "\n",
    "---\n",
    "\n",
    "Here, the `fits_to_csv` is implemented for each light curve and it is the sumary of all the work we did until Section Sampling Light Curve but expanded to all files into our database.\n",
    "\n",
    "Note. `fits_to_csv` use `julian_to_stdtime` function (Knowing and fixing the header - DATEBARTT)"
   ],
   "metadata": {
    "id": "W6ULdBD_T0CZ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Libs already imported\r\n",
    "from astropy.io import fits\r\n",
    "import numpy as np\r\n",
    "import plotly.express as px\r\n",
    "import pandas as pd\r\n",
    "import julian\r\n",
    "from datetime import datetime\r\n",
    "\r\n",
    "# Libs used for files/folder manipulation\r\n",
    "import os\r\n",
    "import shutil\r\n",
    "\r\n",
    "# Lib for mesure the execution time\r\n",
    "import time"
   ],
   "outputs": [],
   "metadata": {
    "id": "yy57shhgXJsE"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "def fits_to_csv(path):\r\n",
    "  '''\r\n",
    "  Normalize one .fits files and convert it\r\n",
    "  into a .csv file\r\n",
    "\r\n",
    "  :param str path: Path to .fits data-set folder\r\n",
    "  '''\r\n",
    "  image_file = fits.open(path)\r\n",
    "  scidata = image_file[3].data\r\n",
    "  data_aux = np.array(scidata).byteswap().newbyteorder()\r\n",
    "  data = pd.DataFrame(data_aux)\r\n",
    "  data.drop('STATUSSYS', axis=1, inplace=True)\r\n",
    "  data.rename(columns={'DATEBARTT': 'DATE'}, inplace = True)\r\n",
    "  data.rename(columns={'WHITEFLUXSYS': 'WHITEFLUX'}, inplace = True)\r\n",
    "\r\n",
    "  # Creating folder with .csv files\r\n",
    "  CSV_DIR = 'csv_files'\r\n",
    "  if not os.path.isdir(CSV_DIR):\r\n",
    "    os.mkdir(CSV_DIR)\r\n",
    "\r\n",
    "  # Renaming .csv file\r\n",
    "  name = path[path.rfind('/')+1:path.rfind('.')] + '.csv'\r\n",
    "  name = name.split('_')[3] + \"_\" + name.split('_')[4] + \".csv\"\r\n",
    "\r\n",
    "  data.to_csv(name, index=False)\r\n",
    "\r\n",
    "  # Move to .csv folder\r\n",
    "  shutil.move(name, CSV_DIR) "
   ],
   "outputs": [],
   "metadata": {
    "id": "dRxIyfa_T1a5"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Applying *fits_to_csv()* to `./database`."
   ],
   "metadata": {
    "id": "d77IMLapszi7"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "i = 0\r\n",
    "my_dir = DATA_DIR\r\n",
    "t_o = time.time()\r\n",
    "\r\n",
    "for root_dir_path, sub_dirs, files in os.walk(my_dir):\r\n",
    "  for i in range(0, len(files)):\r\n",
    "    fits_to_csv( my_dir + os.path.abspath(files[i])[os.path.abspath(files[i]).rfind('/'):] )\r\n",
    "\r\n",
    "t_f = time.time()\r\n",
    "\r\n",
    "print(\"It takes:\", round(t_f-t_o, 2), \"seconds to apply\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "It takes: 39.21 seconds to apply\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p0_u4NNWr0-g",
    "executionInfo": {
     "elapsed": 39419,
     "status": "ok",
     "timestamp": 1627494136655,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "50f452ed-bc82-447e-add5-aea3681059c6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Updating folder structure\n",
    "\n",
    "Now that we created `csv_files` folder, our `./database` folder will be..\n",
    "\n",
    "```Json  \n",
    "./database\n",
    "   │\n",
    "   └── exoplanets_confirmed\n",
    "         │ \n",
    "         └── dataset_exoplanets_confirmed\n",
    "         │     │ \n",
    "         │     ├── ..._.fits\n",
    "         │     ├── ..._.fits\n",
    "         │     ├── ..._.fits\n",
    "         │     ⋮       ⋮   \n",
    "         │     └── ..._n.fits  \n",
    "         │\n",
    "         └── csv_files\n",
    "               ├── ..._.csv\n",
    "               ├── ..._.csv\n",
    "               ⋮       ⋮\n",
    "               └── ..._k.csv\n",
    "               \n",
    "```"
   ],
   "metadata": {
    "id": "4DFXDGtaY7Rr"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Zipping *csv_files* folder"
   ],
   "metadata": {
    "id": "aHeSujbTskYN"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!zip -r /content/csv_files.zip /content/csv_files"
   ],
   "outputs": [],
   "metadata": {
    "id": "x4q767YLqDO-"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Downloading zipped folder"
   ],
   "metadata": {
    "id": "7yMwoQ4ltNpW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from google.colab import files\r\n",
    "files.download(\"csv_files.zip\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  "
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": "download(\"download_8378339a-e477-4852-839d-6cbe202ce3ed\", \"csv_files.zip\", 7057713)"
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "V8CU6EkutL2H",
    "executionInfo": {
     "elapsed": 220,
     "status": "ok",
     "timestamp": 1627494152083,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "7580002c-84d7-4f3e-e238-551a0d99fb70"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Preprocessing data\n",
    "\n",
    "---\n",
    "\n"
   ],
   "metadata": {
    "id": "XwpF0AqjN3zD"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Extracting informations about dataset"
   ],
   "metadata": {
    "id": "hOBUW8K7OhWf"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import os\r\n",
    "import pandas as pd"
   ],
   "outputs": [],
   "metadata": {
    "id": "o-L6Vg6_UizU"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "DATA_DIR = '/content/drive/MyDrive/01 - Iniciação Científica/02 - Datasets/exoplanets_confirmed/csv_files'\r\n",
    "\r\n",
    "sample_rates = []\r\n",
    "sample_size = []\r\n",
    "\r\n",
    "for root_dir_path, sub_dirs, files in os.walk(DATA_DIR):\r\n",
    "    for j in range(0, len(files)):\r\n",
    "        if files[j].endswith('.csv'):            \r\n",
    "            path = root_dir_path + \"/\" + files[j]\r\n",
    "\r\n",
    "            data = pd.read_csv(path)\r\n",
    "            data.DATE = pd.to_datetime(data.DATE)\r\n",
    "            \r\n",
    "            sample_rates.append(data.DATE.diff().min())    \r\n",
    "            sample_size.append(len(data.DATE))"
   ],
   "outputs": [],
   "metadata": {
    "id": "CW0R8rZ4OkxS"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from statistics import median\r\n",
    "\r\n",
    "print(\"Minimum size:\", min(sample_size))\r\n",
    "print(\"Median size:\", median(sample_size))\r\n",
    "print(\"Maximum size:\", max(sample_size))\r\n",
    "print()\r\n",
    "print(sorted(sample_size))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Minimum size: 584\n",
      "Median size: 15050\n",
      "Maximum size: 25631\n",
      "\n",
      "[584, 584, 584, 584, 4097, 4097, 4097, 5356, 9228, 9734, 12930, 12930, 12930, 13062, 14101, 14101, 14102, 14725, 15050, 15051, 18840, 19345, 19346, 21548, 22168, 22169, 22169, 23947, 23951, 23952, 24403, 24403, 24404, 24404, 24404, 24456, 25631]\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3XwwHIuLO1iV",
    "executionInfo": {
     "elapsed": 231,
     "status": "ok",
     "timestamp": 1627496035665,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "d4748f68-3f26-4746-a6a8-74fc04e0afbc"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(\"Minimum size:\", min(sample_rates))\r\n",
    "print(\"Median size:\", median(sample_rates))\r\n",
    "print(\"Maximum size:\", max(sample_rates))\r\n",
    "\r\n",
    "# sample_rates"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Minimum size: 0 days 00:08:31.935304\n",
      "Median size: 0 days 00:08:31.958318\n",
      "Maximum size: 0 days 00:08:32.036530\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FB5xXaL5O2wg",
    "executionInfo": {
     "elapsed": 211,
     "status": "ok",
     "timestamp": 1627494512142,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "fed3792e-a62b-4420-eaf0-1d47182992d3"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "It's notable that the sampling time doesn't vary much, so let's default all to median sample time and resampling the entire dataset"
   ],
   "metadata": {
    "id": "NoMqzXngKFch"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "sample_time = median(sample_rates)\r\n",
    "sample_size = median(sample_size)\r\n",
    "\r\n",
    "print(f'The new sample time is {sample_time}, which will contain {sample_size} points')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The new sample time is 0 days 00:08:31.958318, which will contain 15050 points\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SwYwStGTO6VM",
    "executionInfo": {
     "elapsed": 249,
     "status": "ok",
     "timestamp": 1627494525382,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "86b673e8-a091-4f3a-ca99-2e061c98f5be"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Resampling dataset"
   ],
   "metadata": {
    "id": "3aar78VVO9BN"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(\"All curves will have:\" , sample_size, \"points\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "All curves will have: 15050 points\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PNuXmyOPO-VH",
    "executionInfo": {
     "elapsed": 219,
     "status": "ok",
     "timestamp": 1627494606391,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "ce8d0490-37e5-44f7-e8d3-a07dab18e753"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import scipy.signal as scs\r\n",
    "\r\n",
    "DF = pd.DataFrame()\r\n",
    "\r\n",
    "DATA_DIR = '/content/drive/MyDrive/01 - Iniciação Científica/02 - Datasets/exoplanets_confirmed/csv_files'\r\n",
    "count = 0\r\n",
    "\r\n",
    "for root_dir_path, sub_dirs, files in os.walk(DATA_DIR):\r\n",
    "    for j in range(0, len(files)):\r\n",
    "        if files[j].endswith('.csv'):           \r\n",
    "            path = root_dir_path + \"/\" + files[j]\r\n",
    "            data = pd.read_csv(path)\r\n",
    "\r\n",
    "            # If sample size is less than 584, the lightcurve will be discarded\r\n",
    "            if (data.DATE.size != 584):\r\n",
    "              flux = data.WHITEFLUX\r\n",
    "              time = data.DATE\r\n",
    "\r\n",
    "              flux_resampled, time_resampled = scs.resample(flux, sample_size, time)\r\n",
    "\r\n",
    "              # Creating a new pd.DataFrame\r\n",
    "              concat_dict = {\r\n",
    "                  \"DATE\": pd.Series(time_resampled), \r\n",
    "                  \"WHITEFLUX\": pd.Series(flux_resampled)\r\n",
    "              }\r\n",
    "              data_resampled = pd.concat(concat_dict, axis=1)\r\n",
    "\r\n",
    "              # Creating folder with lightcurves resampled\r\n",
    "              RESAMPLED_DIR = 'resampled_files'\r\n",
    "              if not os.path.isdir(RESAMPLED_DIR):\r\n",
    "                os.mkdir(RESAMPLED_DIR)\r\n",
    "\r\n",
    "              # Renaming lightcurve\r\n",
    "              file_name = 'RESAMPLED_' + files[j]\r\n",
    "\r\n",
    "              # Saving lightcurves resampled \r\n",
    "              FILE_DIR = file_name\r\n",
    "\r\n",
    "              data_resampled.to_csv(file_name, index=False)\r\n",
    "              shutil.move(FILE_DIR, RESAMPLED_DIR)\r\n",
    "\r\n",
    "              count += 1\r\n",
    "              print('Resampled and saved: ' + files[j])             \r\n",
    "print(\"\\nTotal of files resampled:\", count)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Resampled and salved: EN2_STAR_CHR_0102708694_20071023T223035_20080303T093502.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0102764809_20071023T223035_20080303T093502.csv\n",
      "Resampled and salved: EN2_STAR_MON_0102725122_20071023T223035_20080303T093534.csv\n",
      "Resampled and salved: EN2_STAR_MON_0102671819_20071023T223035_20080303T093502.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0101086161_20070516T060226_20071005T074409.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0101206560_20070516T060226_20071005T074409.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0101368192_20070516T060050_20071015T062306.csv\n",
      "Resampled and salved: EN2_STAR_MON_0221686194_20081011T143035_20081112T081512.csv\n",
      "Resampled and salved: EN2_STAR_MON_0105118236_20100708T204534_20100924T063628.csv\n",
      "Resampled and salved: EN2_STAR_MON_0100725706_20070516T060226_20071005T074409.csv\n",
      "Resampled and salved: EN2_STAR_MON_0105228856_20100408T223049_20100705T044435.csv\n",
      "Resampled and salved: EN2_STAR_MON_0310247220_20090403T220030_20090702T022725.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0652180991_20110708T151253_20110930T044950.csv\n",
      "Resampled and salved: EN2_STAR_MON_0311519570_20090403T220030_20090702T022757.csv\n",
      "Resampled and salved: EN2_STAR_MON_0630831435_20110708T151253_20110930T044950.csv\n",
      "Resampled and salved: EN2_STAR_MON_0652180928_20110708T151253_20110930T044950.csv\n",
      "Resampled and salved: EN2_STAR_MON_0110839339_20081116T190224_20090311T103233.csv\n",
      "Resampled and salved: EN2_STAR_MON_0110864907_20081116T190224_20090311T103233.csv\n",
      "Resampled and salved: EN2_STAR_MON_0300001097_20081116T190224_20090308T103056.csv\n",
      "Resampled and salved: EN2_STAR_IMAG_0102671819_20120112T183055_20120329T093058.csv\n",
      "Resampled and salved: EN2_STAR_IMAG_0102708694_20120112T183055_20120329T093058.csv\n",
      "Resampled and salved: EN2_STAR_IMAG_0102725122_20120112T183055_20120329T093058.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0102890318_20070206T133547_20070402T070302.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0102912369_20070203T130553_20070402T070126.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0105819653_20080415T231048_20080907T224903.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0105833549_20080415T231048_20080907T224903.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0105891283_20080415T231048_20080907T224903.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0106017681_20080415T231048_20080907T224903.csv\n",
      "Resampled and salved: EN2_STAR_MON_0105793995_20080415T231048_20080907T224903.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0315198039_20100305T001525_20100329T065610.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0315211361_20100305T001525_20100329T065610.csv\n",
      "Resampled and salved: EN2_STAR_CHR_0315239728_20100305T001525_20100329T065610.csv\n",
      "Resampled and salved: EN2_STAR_MON_0105209106_20080415T231048_20080907T230359.csv\n",
      "Total of files resampled: 33\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zEZIDSn0PPdS",
    "executionInfo": {
     "elapsed": 11966,
     "status": "ok",
     "timestamp": 1627495073223,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "0590bba4-603f-4e15-bd75-ff6da9a23b16"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Updating folder structure\n",
    "\n",
    "Now that we created `resampled_files` folder, our `./database` folder will be..\n",
    "\n",
    "```Json\n",
    "./database\n",
    "   │\n",
    "   └── exoplanets_confirmed\n",
    "         │ \n",
    "         └── dataset_exoplanets_confirmed\n",
    "         │     │ \n",
    "         │     ├── ..._.fits\n",
    "         │     ├── ..._.fits\n",
    "         │     ├── ..._.fits\n",
    "         │     ⋮       ⋮   \n",
    "         │     └── ..._n.fits  \n",
    "         │\n",
    "         └── csv_files\n",
    "         │     ├── ..._.csv\n",
    "         │     ├── ..._.csv\n",
    "         │     ⋮       ⋮\n",
    "         │     └── ..._k.csv\n",
    "         │\n",
    "         └── resampled_files\n",
    "               ├── ..._.csv\n",
    "               ├── ..._.csv\n",
    "               ⋮       ⋮\n",
    "               └── ..._v.csv\n",
    "```"
   ],
   "metadata": {
    "id": "Pg5uq24sRRcz"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Zipping *resampled_files* folder"
   ],
   "metadata": {
    "id": "ZaNOS27SR5ug"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!zip -r /content/resampled_files.zip /content/resampled_files"
   ],
   "outputs": [],
   "metadata": {
    "id": "D4NSWuqsR5uo"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Downloading zipped folder"
   ],
   "metadata": {
    "id": "fOc0b3oXR5uo"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "files.download(\"resampled_files.zip\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  "
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": "download(\"download_a4821afb-0249-4982-a985-3dc4c612de7a\", \"resampled_files.zip\", 8493236)"
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "0P4B56xyR5uo",
    "executionInfo": {
     "elapsed": 233,
     "status": "ok",
     "timestamp": 1627495337132,
     "user": {
      "displayName": "Guilherme Samuel",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi5zFr3jUWJ1NdrUB6KSy3C4B0uR2RLqUiKRzi0S2M=s64",
      "userId": "17398030156076443435"
     },
     "user_tz": 180
    },
    "outputId": "d09783a1-eb4e-4006-fb60-60e7244dc92f"
   }
  }
 ]
}