QuickTutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sky coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several coordinate systems to express the position of an object in the sky. See more at: https://en.wikipedia.org/wiki/Celestial_coordinate_system\n",
    "\n",
    "* [Equatorial/celestial](https://en.wikipedia.org/wiki/Equatorial_coordinate_system): This one is the coordinate system that we are going to use more often. The position of the objects are given but a couple of numbers: right ascension (RA -- longitude, $\\alpha$), and declination (Dec -- latitude, $\\delta$). The equatorial plane is located at the celestial equator (i.e., the projection of the Earth's equator) and the zero longitude mark is aligned with the vernal equinox. It is usually centered in Earth's center and since Earth's vernal equinox changes position (due to nutation, etc.) we usually use an epoch to fix the equinox. The most common is J2000 (i.e., the equinox in the year 2000).\n",
    "\n",
    "* [Galactic](https://en.wikipedia.org/wiki/Galactic_coordinate_system): This coordinate system is also widely used in astronomy, especially in the case of Cosmic Microwave Background experiments. The position of any point in the sky is also given by two points: Galactic longitude ($l$) and galactic latitude ($b$). The equatorial plane corresponds to the galactic plane. The zero longitude corresponds to the position of the galactic center.\n",
    "\n",
    "* There are other coordinate systems but we are going to use mostly these two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical coding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to use the most typical operations that we use starting from reading and writing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E.g., write/read a table with data\n",
    "min_x = 0 #Let's assume this is right ascension\n",
    "max_x = 360\n",
    "nsamples = 10000\n",
    "min_y = -90 #Let's assume this is declination\n",
    "max_y = 90\n",
    "rnd_x = min_x+(max_x-min_x)*np.random.random(size=nsamples)\n",
    "rnd_y = np.degrees(np.arcsin(np.sin(np.radians(min_y))+(np.sin(np.radians(max_y))-np.sin(np.radians(min_y)))*np.random.random(size=nsamples)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a couple of arrays that we can save in many, many ways (numpy arrays, ASCII files...) Data scientists like to use `pandas`. It is a convenient and powerful tool to read and write in multiple formats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading/Writing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'x': rnd_x, 'y': rnd_y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.006866</td>\n",
       "      <td>-48.385056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>327.660621</td>\n",
       "      <td>60.503543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>194.414382</td>\n",
       "      <td>-38.063870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>296.958962</td>\n",
       "      <td>-77.316550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>205.959139</td>\n",
       "      <td>45.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>9995</th>\n",
       "      <td>124.237228</td>\n",
       "      <td>67.258401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>328.468683</td>\n",
       "      <td>-7.615533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>343.710482</td>\n",
       "      <td>67.181625</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>313.075176</td>\n",
       "      <td>23.498947</td>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>192.689871</td>\n",
       "      <td>-19.782020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               x          y\n",
       "0      11.006866 -48.385056\n",
       "1     327.660621  60.503543\n",
       "2     194.414382 -38.063870\n",
       "3     296.958962 -77.316550\n",
       "4     205.959139  45.554700\n",
       "...          ...        ...\n",
       "9995  124.237228  67.258401\n",
       "9996  328.468683  -7.615533\n",
       "9997  343.710482  67.181625\n",
       "9998  313.075176  23.498947\n",
       "9999  192.689871 -19.782020\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Astronomers however like the [`FITS`](https://docs.astropy.org/en/stable/io/fits/) format and we use a library called `astropy` to read/write them. In particular, since this is a data table we are going to use `astropy.table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab = astropy.table.Table.from_pandas(df) # We can create it from a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab_from_dict = astropy.table.Table({'x': rnd_x, 'y':rnd_y}) # From a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_from_dict == tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab_from_arrays = astropy.table.Table([rnd_x, rnd_y], names=('x', 'y')) # Or from arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_from_arrays == tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab.write('test_table.fits', overwrite=True) # So now we write the table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many formats available using `astropy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m   \u001b[0mtab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserialize_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mType:\u001b[0m        TableWrite\n",
       "\u001b[0;31mString form:\u001b[0m <astropy.table.connect.TableWrite object at 0x1178974e0>\n",
       "\u001b[0;31mFile:\u001b[0m        //anaconda3/lib/python3.7/site-packages/astropy/table/connect.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "Write this Table object out in the specified format.\n",
       "\n",
       "This function provides the Table interface to the astropy unified I/O\n",
       "layer.  This allows easily writing a file in many supported data formats\n",
       "using syntax such as::\n",
       "\n",
       "  >>> from astropy.table import Table\n",
       "  >>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))\n",
       "  >>> dat.write('table.dat', format='ascii')\n",
       "\n",
       "Get help on the available writers for ``Table`` using the``help()`` method::\n",
       "\n",
       "  >>> Table.write.help()  # Get help writing Table and list supported formats\n",
       "  >>> Table.write.help('fits')  # Get detailed help on Table FITS writer\n",
       "  >>> Table.write.list_formats()  # Print list of available formats\n",
       "\n",
       "The ``serialize_method`` argument is explained in the section on\n",
       "`Table serialization methods\n",
       "<http://docs.astropy.org/en/latest/io/unified.html#table-serialization-methods>`_.\n",
       "\n",
       "See also: http://docs.astropy.org/en/stable/io/unified.html\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "*args : tuple, optional\n",
       "    Positional arguments passed through to data writer. If supplied the\n",
       "    first argument is the output filename.\n",
       "format : str\n",
       "    File format specifier.\n",
       "serialize_method : str, dict, optional\n",
       "    Serialization method specifier for columns.\n",
       "**kwargs : dict, optional\n",
       "    Keyword arguments passed through to data writer.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "\n",
       "The available built-in formats are:\n",
       "\n",
       "=========================== ==== ===== ============= ==========\n",
       "           Format           Read Write Auto-identify Deprecated\n",
       "=========================== ==== ===== ============= ==========\n",
       "                      ascii  Yes   Yes            No           \n",
       "               ascii.aastex  Yes   Yes            No           \n",
       "                ascii.basic  Yes   Yes            No           \n",
       "     ascii.commented_header  Yes   Yes            No           \n",
       "                  ascii.csv  Yes   Yes           Yes           \n",
       "                 ascii.ecsv  Yes   Yes           Yes           \n",
       "           ascii.fast_basic  Yes   Yes            No           \n",
       "ascii.fast_commented_header  Yes   Yes            No           \n",
       "             ascii.fast_csv  Yes   Yes            No           \n",
       "       ascii.fast_no_header  Yes   Yes            No           \n",
       "             ascii.fast_rdb  Yes   Yes            No           \n",
       "             ascii.fast_tab  Yes   Yes            No           \n",
       "          ascii.fixed_width  Yes   Yes            No           \n",
       "ascii.fixed_width_no_header  Yes   Yes            No           \n",
       " ascii.fixed_width_two_line  Yes   Yes            No           \n",
       "                 ascii.html  Yes   Yes           Yes           \n",
       "                 ascii.ipac  Yes   Yes            No           \n",
       "                ascii.latex  Yes   Yes           Yes           \n",
       "            ascii.no_header  Yes   Yes            No           \n",
       "                  ascii.rdb  Yes   Yes           Yes           \n",
       "                  ascii.rst  Yes   Yes            No           \n",
       "                  ascii.tab  Yes   Yes            No           \n",
       "                       asdf  Yes   Yes           Yes           \n",
       "                       fits  Yes   Yes           Yes           \n",
       "                       hdf5  Yes   Yes           Yes           \n",
       "                   jsviewer   No   Yes            No           \n",
       "                 pandas.csv  Yes   Yes            No           \n",
       "                pandas.html  Yes   Yes            No           \n",
       "                pandas.json  Yes   Yes            No           \n",
       "                    votable  Yes   Yes           Yes           \n",
       "                     aastex  Yes   Yes            No        Yes\n",
       "                        csv  Yes   Yes            No        Yes\n",
       "                       html  Yes   Yes            No        Yes\n",
       "                       ipac  Yes   Yes            No        Yes\n",
       "                      latex  Yes   Yes            No        Yes\n",
       "                        rdb  Yes   Yes            No        Yes\n",
       "=========================== ==== ===== ============= ==========\n",
       "\n",
       "Deprecated format names like ``aastex`` will be removed in a future version. Use the full \n",
       "name (e.g. ``ascii.aastex``) instead.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?tab.write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how we can read a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab_read = astropy.table.Table.read('test_table.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the format of the table is not `FITS` we have to specify the format, e.g.:\n",
    "\n",
    "`tab_read = astropy.table.Table.read('test_table.txt', format='ascii')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data visualization is very important since it allows us to understand them and think of useful ways in how to manipulate the data to obtain more information or to try to compress the information somehow. Let's try to represent our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qWe5Hlf4oRdvtOkVVwV6W4OC0UDgo4aj6hHXhHCnbQMb8oMarsXZ0LhliU0ogpWS4vxENo810zGPt8lI30FcObUCKCSNGUhYrewepyduOYlAWSxXBqCxMdd//V2XKqkoJfzPOPlnbURQpy7AKXlhHNCFTk9iharaNqiNS7ikh3Iki6NVYYplIobHwZSs8Mkq4TjYcysY6WgtM1ZlSiGVzAdvw+Z9SmilFg/1/7NnXpHcRQbfKo68cKgIOeFWiuzs7cXmqUI/yDvwelisTDgpHfDZ6hvFPpS8qvFIegWq/DFKKbefhsZYQW5mVqNpnoVolPdIXbMt2qqdy8dvJWFFjVCacUveUolBttqM0vVxKMKb4KGWwRMYOp4aqcE2Eg2pvrl5M9VQ4pzyHKmFCbAMzwpAOijauUPC6wonbT9GgU+grhzbAGTXLqk+klBKILPRo0FOTUuGE/5UFoyYJ4xXRQeHt/6tkPzF+SrF5XdGkVs9WOadHZZZxuWjNg8vsODSWPfbsa4UJzbSqohiyrDhGkRJsV+k6DlF/kaeqKjFP3S0736sqfhEg3mxgRQKRy0Qeuirv3xOXp5LHj0R1lNUbrUVg2R2HzuYeVDQmLB8iA6QbxZBlfeVQGVhgtPOyD2VFKAXATMVCuEzwKSHtG7S4be6Pg1rcK2uH8XHGjoRbtLgftZe6VgWfVL3+PAv3FP74OyqXZeW0xPY51IjhNKyvXSGgxl7xSbtCPYq1d7ImFS3kY1tMR3Velmq7ytof/t55+Fz22LOvFZRRO31L8SMq66geNe+jctheZJx0oyD6yqEiuNeAgrWqxYUDqBhHCXyVAdOOIG1YIWPZtmdGSxfNVF9TdSMd2H3HvkaCEfvdi6yLMuHL+PO9yasz2WM7X2tRklVDeincldKJ8OD2q7ZdZqjwPa63E+WQCqOV4YH/ea0hMoKUAaMEeISvuqb6kVL2VWjM5ZbjDKRoDnU7t/rKoQJMzcxlj+18Ldtx6KxkkCoLjhFjKeXgg8rHMlS1GFHYlKWlVtmhygytTsXkMEw0yRTd2g3VIVRd6FOb4LBPvsOZFVxZeylBqazIVLioLJadEkBlhkrZvSptMh0ULmWL5Uq5IY/iGEQwV58vhOBS5aLrVc7dUlClb3wvUordGkXYVmqOdwp95VAR8MUukZBBaNeKceDwQiQwIgbncpELi4t7KVyrrn0gw0euvOqPeqYKHbG+lKXneKmMJ1xDYnqXKR32JFM4OkzNzGU7Dp2tdMRzKrTA45YyIPBa1XJRO+q64suoLuYLHo8yfvQy0Rhx/6I1o6pzh59J8RvzcVnoqB3B3Y5Cx99RqKkqpJRDf4c0wNDgQL4rkXc78w7L+vyC7Rp9p2V3s++65N2a/qzvWPbdq7y7NtpJ/MLr79rlqdm8Ln+mPr9ge09ckDsv952cMDMr3aGqdkZ7eeyL2vHrZRStcDcr7xJ2upW9nrJsd7A/67vAzcx2jb6T76Dec/xde3z3G3Z5ajanh9endi1j3d96/oT90a7XbdfoeOG62gVs1hjbvScu2ODAgG3edLPcbYt9x/dWKxrML2QtbfBvpgPiwq+ZjIBfNxntnlc7txkHHPPdx87nY4F4eL9SeKkxUnNrfmHB9hx/t4X/fWe2Wcyf3GevQ80HM8tfYevzD+ddRLMq76FX44995Z3Z+B5pfw3skkCkNa6nT693SPv/VNnJqzPZtmdGC2fueB18Do9yyaN2uW3/jfH9qp5DZOHhfV/Q5r0GDt249o6bWlfBsFWVzKnI2vOwHH6i0JcKd/B4s2WPtGW8lUWKYcLI6kulYDKNUmOHePKYOQ3Yklf4tBMvr+Jt4X/06viMJ5WsEeHo1ycuT7V4Emrthp9V17lfPgdwrqX4Fj9V1iRToMYkyqjitZluwPphpWpQRZj6wO08PJbtODTWohywTMolRyibwFgOf1dhzDI3eufhc4ufsRbFgwIvVUcZRPF3bC9aAMb7LER8oXPn4XMFGioFqv571goqQD55ldtMCYQoVh3RLBKEDqnUWewrLrQ7TXYcGiuEP1T9yENVhCjyQ4RXlKDA4+ffOL5lYUpULOo+/6/yG5/hEGRk2ET8EUGVOcrjEhlOVRRRO7CilYOZnTSzQ2b2iiNqZjeb2TNmdmzxe22qjl55Dv4dWTSsBDj22E6MmNuOsliYcfya70RV97ls6r6KqfPvlODolFHxWZXhpU5uRcsTJ48SNhHd+L8LOv+NghaFhsquqdJG1euMO1uNTBu8jgvtWaYFMYMrlZ2HmwaOojkrF1Y4XH9kCLS7Jya6r04FSI1BVE4JZMXPSEuVPBIp99ScYlx5LnfjqbcLKeWwUtYc/nqWZZ/MsuyBxf+/bWbPZll2j5k9u/h/yYBPZ8SYaXRyo5kV4u9qjQHjnBF4rHPviQt57NJjmZenZltOLPX6HrhjjQ0O1JL184mp6r7H4Tmmjf31OqqsxaTawm+MTeOpq14mFW/3k3A9Js33cQ0HQa0BHDj9Xn5t/6mL9msP3J4/h3FjbBMhdVqpX1c8xKf3Oo3vX7/a6vML9m9ePG2/8vMfspHhofDkTl9jeOTe2wq4IE2isRkZHrLf+IU77eG719mB0+/lp/YyPg9uXFs4odXj7vtOTtj0bL0Fr7IYO/MBrpPgegwCn4KMp88quiheVuuAGLvn9Sh8/oXX37WLV6btG0/92J45fK4wdsy3vjbo6y0sAxSumzfdXDiBlU8FLqPfkkGkNZbrYw3PYR1dGzWz2xd/325mo6k6un2HNG4k46MclGXKFoPHKNHd5tTKyMJiy9yvoYUWZdlUiTeWWTDKbY28IIUrP+t1snWnspUib8R3JZftH1BWXIo2ZZbe9oNnsq999+Vsx6Hyl/qkPCrsRxTaUXg4H01cnsq2HyzuokWLmT2ZMsu7DKJxUL/9v+MVHaTHwHNKZf34/bL1uuJ8PSc9Hv6d2lOE46NCcP49dnGyxWuLPCv1/g817uo5DqMhr6XOruoEbIWHld4ws5fN7CUze3Tx2kUqMyGee9TM9pvZ/g0bNnRMHFcIOw+fyxeZneE5DooDyEzLZ6V4LF/F1T2WWeXduchQVd1OrqMs1hpNQCW0I4ZEOmx7ZrQlnTOa7NFE2XHobMumNb/H+Djg8c5layTRPR5btSDt/ILhHLUuEi1cq/a9Dl9wVYuhihdSdIz6Ht2rul6Ci8u4L6isbhZ2WdY0ojCsomiINIgOalQL38gj0ZzxccY1BqaH3+MF8dQ6U2SgRMAGodOF5VAvDAKHla4cPrz4fZuZHTSzz1ZRDvjpds0BF7rY6kWL57kjb2djFydbvISISZGRJi5PFRg9itkqUIIkulf2PP+O6ubramIzOP2cocsUWJmwYhrx5N1xqHn2D1uGSigrxZRSzDsPnysYC1gPK9BGxlfxHRPtLiaWrf9ENKtC51TbasxZMKv6fPG7Ci+nhJkyRlI4RHyrFDQK1ohOvtbkr6LFNrAdtf4T8VA7ShsBFRMbKksBKeVwzdccsiw7u/j9jpn9mZl92szertVqt5uZLX6/s5Q4jAwP2QN3rLF9Jyda8vtxr8An199k/+/L5+xXfv5Dtv/UxUIMc9fouO0aHc+feeCONXbozKQ9uHGtTc/W7d+8eNpm662xWRWLTMVQ/f/0bL2wBqBik2rNg+OjDByrxpx830cQ7QvYe+KC7T1xwR6+uxG3xVg19qssDuv3PN6OOfxf2rwhp93w0ECO04HT7xXuMW187cbfkqb2aOB4NmL5t9qXH95oj9x7ax679rUMX4fw6/tPXTSzWo4PriNhnal9Fb7W5JBa78IyTGeuF9+sxzTHNnBNxtd9orExW1y32HKnjQwP5XH2aF8F0o/xQ1ryM3gd+UaV5Tfp+fdDd91i9fkFe3z3G/JtcftPXbS7bnu/7T91sbBGwWsVOGd9vQP3wDCOuBepCuAa4IMb1y7ylEm6YVtLBpHWWI6PmX3AzG6E3z80sy+Y2R+Y2W8vXv9tM/v9VD3deg5odeA1/na3vxETP1uwEnccOpvHqtn68OeqWrKRteTfuMahnmHXO9XvKnRBCymVlsvWGeOs1gSUhRVZilE8Wv3mNtiK5LpV2iLHxMtCgCoMFPUpep55IuUVoMebqlfhgmGeKDtr+8E4NMihlceefS3bfvBMadhR4ZGiKfahyl4CFQoqWx+JaOTeb4Q3rytw+Eets5UB9hnDY9EYdutR2EoNK5nZJmuEkg6a2WEz+53F67dYI0vp2OL3zal6epXKGglkvo8CpakcxvK0wLL8d15QxLaQKbBtTqkcv3SlRaFgzJ0nfco95t9RGcazymRvTMqx3G1X9IgUJUNVgYuTPaXQeBIrmvMCoaIF462EjBL0POYqAcLpxdAI342Fx70gTtEakrcbKUulOFBoIvB+HwXRmCrgRetIgKs+cz3YL9W+uo4hs5RsQLwwmSWFU1Xgua3WNn9ilUOvPr3cIa2ElLJ2UVD7xPAB5JMk/Zksa7VG+T6WiZgfrXBnEPQmePEbF/xwMTzaqKesaMZBffNvvJbqLwriKvXhMyxAUSCyMEN8+X6Zt8bCP1KEXA55pMwLU0YDjpeDl/ONbqpNxl0pAUVf5nsu6/F53iyYGrcqAkzRWrWvkiuisVB94jbV/hW/x3sPIt523nUDSPGlQ7uL1P7M86PvZOOXrlR6T3Y70FcOFYGtE1yMRgWAVh4LY9y8FE28FNNj2QhHtDB3Hj6XZ4ywcEXGZYHDApG/o8VUZfVXcffL+hilGpZNelR40YLhjkNj2danj+YL5ZzFkhKeqh9VLGTGgZWC4g9+1v9XOaIFxz6Vhpxl5dlc2E8VKuHNYOr5KoJb8Sl7X9xOFLpjwy3yTBkH9IKiMqmxxrZTCRT4n63/KtmH3qcqx8O3A33lUALIBJ6qiBa+TxJ+MbnyKDAHO7JW/DsVv+fyDo6Pt7Hj0Nls29OjLSl2qj28xsJRXU/RiXHC36otJTAZX/XqRMSRFTHWEQlbPzfKrWxsX/WN61U0YEWiJiyPbSo2rdrAeqZm5rLtB89Imvh/FEKoKNg7UkJG8STizHQr42kVomPaMA2isAkrOuV98VwtU/xR+aprJXw/5XUyb6BSR6URtRHRsVfQVw4J4MHnBSC2TrgcD55KV43aTQ22EqD+39NpfRJWORyP61Uhpbl6eiE7uoYCSL0Tg1NBVQxZCSacxP6qTnXwmqJ9lhVz0/l6mSVYxVLMssZk/8f//pWkRZeK+Zet27jg/M0/PdBidHAbjg+v72BoEfnFxywKg/BYKE8imj9lYTqu3/cZKaUa4cRzE+9HfOH4qgMby8KoamwV3ykFyLzAG2cjxRBFIbBMN9BXDiUQuao8EdkKS8Vdy1z3yHLkV36q+yjwonq4H/7Ni5543/FOWZVRuAzxa82cig+zU4IeTxVFeuNEUjiobBQVA/bD+sompAKkA7ahBIkL5nbCSFwH0oSf4bF3Om0/eEZuBGso2bPh5kxVJysHpRQYf8VfkSL08rgmxv0uW0NRfKTCt4gfJwCoBJEoS4gVVmpOMD8gXiqMhmWxbiVLyni1CvSVQwKQkZRAZ6ZkIZkS/lU8B2YWtxCZkRCYMRWuviGLLb0yz8BPKsVJg4zKL52PBCzWqaw/pAECKj8O6+HzPPmjycw4ugDiSRspyKh/atEfaY1KKDXBU9dTBgb2m0MrLNxRCG97ejR826GX4XajsizMldKIlAhfZ08Rx14tDJcJ4yh0h+WUF6SUIo81h5cRxwgnDufhPTZg+Hh+b1fBUr7s55oL9l58uvUccJFZEVtZJuo6llcMyWWyrJWJ8VyfKsJBeTbI2AqXKI7pZXHCe7ye61XtMx4O3EcVc8Zy7CEot533TSjlXUYvb5M9CQ8VYQhLKTG0qDGNEevGCZ4SsvjNSQ/R2EfPqzKId4qfVEptVKdbrvg+EKRBVUHudal7/HwU+vHfaiE6tTiujI5I0SB/It+lzlFCLwUNHjRIObyEYS8VniqTP+1AXzkkAAcgEsZlAlXVlxpAZgBluWRZ8TiFSBhEkyiaDKw4lNBGmLw6k7/zIOqv15k6QjzCHydcSqjjb/SKVN/LQnrc7yh853XN1ecLClJZtCxAkC6RQmW8nF6c7wYAACAASURBVA68wVHRLfLeojKq7Yg+KuQU4Y7WM69lRPWn1llSgq7q3FLP+YfnnPJAI3p6f7mPqg5+VnkgWA9CysuuMobtQF85lECZsMf1hUiI8jNRnf574vJUtu3p0dzi5JgnCgm8p6yJlAWhcOHd3Q6RUFWWEQPGpLmv0XOOt+86jwQK18UWaiTI1G+8VqZE/P7E5ansN//0QI5nhGskrCPLL/qtFAkLk5SCR8WkLO0Uz3CbauE3pcBdAEex8EgBKPwVIP6puvj/c0ca+0K2PTOa/flLpwvrMOwVKJrjuCi6YztVEzqisYkW86so0Hahrxy6BBZ87YR78BpO2OeOvJ1tf/WtfMMcM5kKr2Bd6pkId7ZsUuGKaEJXCXHgfTXZovYwkyYlsNg6w1CGejZlaaWUPE5yv88WJ9ZfxYqLFIMrn9RzvJ7gz6Ri6k7bqlZ6xF+tWWON1OnI2vVnuE8phcRlUuUwjKeEaIqH5urz2filK9lv/umBbOziZIvlruiM9zhbLmojFfrCa8oLUkZWqp7U9SrQVw4VoMpE8e/IauHnmOk4x9ktGSWgyqzoKpMIlUzZIrJfjyzcSMgjU7MFWzXFtswS9P7w2gIeV8KxdOxLFNJJjR/TX7XvSo3TMNsxGiYuT8lUWC/Pyh1xqbIYqeiqhFcUnmG+x02Xfh09hamZ4rEeXL9aW0gpcYUnr+1gPVW8ehfwmPEX7c7Hj58CHHmNyhgq8065LUX7FPiYdKog+sqhBNBCYstKWd3RGevIZJx1g4LS2/GFPGb2BiOeDTMgOMzE9znbwZ8pUzb+O6IJT2x8nq9HZVNjoCY21s24OH08tz8SPFVDOkhTpD0KY+YPt6YjWqVo5BBt/kstSlfxXv0b12iUIohoFglM9KK8bqSNCkOysYJWMqfIqvEpuxbNh4hmGLbldTWkA4cRU+sx0bpCpPjxd+ThlRlW7tGUlYsgpRyu+ZHdKwVm6/O298QFM7PC0b/3r1+dvxKxPr+Qv9LPy5lZfmzvJz5yoz259027PDVr+09dtC9++qP5sd2rbhjOyz/+gzfs+0fethffmLDP3LnW9p64YLtGxwtHHh89N1l4VabD0OCAfXL9TbblnnX5fwc/RvjFNy7YlzZvKBxfrI5ENrPCcdZ8NPDUbJMm/upC9WpGPiL58tSs7T1xoeV4ZNU21sGvpHTcvM09x5tHT5tZPg5mZvfdfmNOA36VKb7KEdtWr470e4fPvlc4rvz+9avtwOn38vaxz4MDjaPD/fWc3A8+FpvbWnXDcAtOfuz7lnvWha+MTB3/XTzqu2afXH9TC38j8FHV969fnfMy0qk+v2DffuGU7Rodt/r8QkvfH7rrFlt1w3DOn/6M0+PBjWvz18KODA8VXk9q1nw1b2sfmngq8LkS0cNxcF5YdcOwffHTH83H9Gdvu9H2n7qYl/Vjzrfcs842b7o5xx+P1PZrzqdOW58nyDd8dD3OITxu3OmMfIS0x2+z4rHpPYdIa1xPn155Dny4mS9ANiz8sdw6RdfRrQ73EMYvXSlYnsqSce+BLSZ21dWzvjg6fulKYXMZW21oxZSFlFR7bpHwngsPGUSLv06Ppw6caXkbnNeL+LLFGlnz6nwhH4vHnn0tG790peDVoefDi7SRy1+0ts8VLEDuJ9KbPTwey5T1GIVzFG4YmmFLV9Xd7MtYHv5KWdZO5ygWj7iVeWdszfM4qP473dkLiLw+vJ7K3sJQlIPPi1RYEmmAfVft84ueIs8L21brNtGx8YhnWXJIO2D9sFI58KTYuuNI9pXvvJRtf/WtAqOg0HEm8OOzJy5P5ZPWs5CYmSIGx2sqnIAT7s9feitfGIx2M/MzSjgiw+NJm3idF+k4ZZHrdzooJeLMrzYCRVkerAwiejheqq/Yn0hZMQ2x31FYyuvzGLwSlM5bKnSihCxCdCTHXN2PiG9V0hH9FP9xOd6rgNDOuhEaJcw/6pmoHv8f8RwbR44n01+NIRoS6r6HLfnY7mjRGBWJt5nKOlJrVFwH1+V8pPrdKfSVQwlgDBSZzK1mByVgeS3BD3jzhTtXFp5Kx8I5YiBmHmzDJ10qE4gZl9tjIeZ1q3bZo1KTV01q74eitVKUZQuJ3C+vD63oyKOJFAyPR+RJqPvenvJQ8IOb5FwA87oQK0dXpFEmEy/8ch1OzygjRtHXF/ixn6oeNQ7sCWMZVo5M+xSdow12kYDmtTHFN8ibPO+Rjrg+qJIRyhSQ2omOfI44lM1lxgfHpdP1hizrK4ckoDWrhAMLSmY4tqrR+vYBRGZmxlULaWoS82IalmVQ/UCho5514fDYzkaIRrWrhJg/qwSI7zTms4eUclITkPGMBDzSFLPBFF34f7Q3IwLGjfHDejEkhvRDnmCaYJ1+fn8UPlIhw6JCOlcQfNEz/s0hHa4nCgmyB6VwdMXDnh2GTRgvDJ8x/7Hhhs+oMVA8FAllFMA8NvwflQeOEyZJYJuIk8sejzykhLwbFkhHTxlOJSaUQV85lAAOCk9EtNxSVoj/Vgyr0viUJeITECci18XKrKxPqLRU3JfrxxeKKOWink1ZMI6nMzJbx0jPopLVZxKxhaqyyaJNWEqoq7WWFDA+6j7SyccLcUz1Afux8/C5FpqpNhQOmAmnaBQpaMX/iqYcauE0baQVeryc4up1qXCJKyzmRfcW0WtiZYB1KB7i9nFsUJDzWgIqUFdeHJpSyiIasyrp3v7MjkNjhcw4Njg7gb5yqAg8kSevzuQ7Yx2UQEp5F16ves6/GwPfeAf1N793JNu646i0whzY1cZ2GCcvjwwdCThk6qhfDKmJgHjtOHQ22/q9o/kajuOEkwOVaLSLGxWp163oyhOGJ69bYtueGS1YxmVHJDs9VT+jMY4EP44PCwhMLGBrPop7O96++OzCNuUtII4YokPF5vyP/WKh6denZubyvTusbFJ0RaXCvKA8AFZObO2r+nGM8D/SWXn3yDN8H89CYz6NhLbiGwQVMXB+ZUXZ7eJ0Xzm0AUz87a++VSm+yM8ry5BjnDzRXehH1iIzBWdNRW9DiwR+JCRUe5Hl48+kFr29/e0Hz+Qbxh7b+VpOWwwb+DNeTikHVghVPCKkAceUWRgpAeLtoOemLD7ELVKo6BmqmLMLX7bSeewwhImCXL3LWBkS2Gd/D8f2g2cL1jHu08Hx58VwHJttzzQSJTBpgucE8yjiGCUf8LiovkRjx3jib17PUOPFxhha7uoZXlPCfkSGmTJQOGxdpljahRWpHMxsvZk9Z2ZHzOywmf2jxevfMLMzZvbK4ueXyurqpXJQzKWEdHStqeWLkxtjpSic1ETxZ7B+PmpbMZzKbmFhykKbLSYUbMyUHBpQwocFkYq3e/+wTu4bWr941j8KNDVOkUDmbDQ1hkh3pdxwF7CXjSa64h0eS0yDZhz8iApWClgPx+yb2SzFDXxIZ6QdW/X+Yb7BLDzkb8QHrWr0YNAA4DAXKkU2MBhSwp6vY99TRoOXTY2j0xmt9qmZRjgJ3+HNz7FBpuYglvf1Pl5LUcYa97EbWKnK4XYz+/nF3zea2Wtm9rFF5fBb7dTV67OVUAAg+CDy5FMLeZxXzoI5ZVkqBcXKRD2jhAcLBGW5KZecJy1OZl6/8Dq4Ld+TocIwjIeyQufqzfBTpOAUHVRbGPuOLHu02lRdEU8oXCJl5ePigmP7wbMtvMPCVwkFHDdWXKi88J0SPI5qLUJ5vM7PqFhZmTcWpFuz8XAM2ahgJcU8yONXpiB4rBB/NQ7cR8aBPQqsh8N+PDfLlJUqrxbllVeJ8yuVsFAFVqRyaEHE7D+a2eevpXJoTsxzcpMRCn2cJOglOKhNW4rpU7jwt5okPEFZ4DIDspLACaIySbgNbIe9Gw4RzdWLMVkuW7Yoy9e9TbUIx0IztTAZWWMoWCOc1LipcUkpMhSK7HHh+T0MOHaupHktiT1MzhTD78j7imiK/WQa8odpo7KLGAfmC6axorUaB1QMfp3HNjKa0ItObeLEel0mRJ4PewJTM3P5WhfTCucQ0sfPgeIIxE98tpKZbTSzN83spkXlcNLMXjWzJ8xsbfDMo2a238z2b9iwoSPCOCBjuKCP8o4jActlIsEStc/AFpqa4ByeYuGuBCSW5z5wXUwDLKss74nLUy2b6SK3lwUn7xdhSwn7zBYql995eKzw/gX2cJSgYa8B60V6qX4r4cj9iWL+bOlzObSqsfy2Z0az7QfPtvAY7+wu87ai8VECnpUshkhTwtv7pcKiSD//rYyEMpzV/EC+3v7qW3L9Q+GCihZpp5SpWgxn3lRrCJ6Syu37ehwmpLiHELXTqWLIshWuHMxslZm9ZGa/svj/g2Y2aGYDZvZ7ZvZEWR29eNmPM8WOQ2O5ldOuQOf7VQaNJxMKKs7oaUdR4e/IUlWTjmP6Kuyg7qHAKCrbVuuoVZg387d54U8ttLPC4NAEZuoo5RKt3ZRZtk43Fl5IW1wraW5+PFt4dzPTAic+KgvcJ8K4bj94tiWjTSl/XuNiRcnjiH1KeU1KuSmaIh2VAmEFzkKvivJRwh7LOV9hcgnPFR5PVNQuE5gmPkbMLz5nue+OC86Bskw171ukSLqFFasczOx9ZrbDzL4W3N9oZj8qq6dXL/uZmpnLtu44mv3LHT/Otu44KokfWVqqzijGz+XUJFHWXwQsKHlSulsaWXsKF8Q3Ujh+LeVOq9g0L0xjqI6zlBz3iLbYDtaZEi4sDJQwVHRxQa9CDWhBYr9RWKRestSsfyzb9szRgoLAMXBhrI7FRlx4HCMFwKcGq3oiujPtlFXNfI3PRXMkNV7YB/zPShF5gP8jfsrwQvwmr85kX/vuy9mOQ2db+EMZLs+PvlPYK+TX/cQEDCexguDxwjUNJR+iFyq1AytSOZhZzcyeNLN/Rddvh99fNbPvltXV61TWxkRuXVyrMiApwcPuJjIon2uk6owmCgo23m8QeQxK8CvrUikcntRK0WAfmR5qgrq35lY3Zr4ohcf/0eNgqzSFB3tKKcWowj48Ruo/0sljx5ztg22wt4VCBBeYozFKCU9uC4WTWmOL+sLX+JvHuSqu0bVUWR4/VtYK5yzLCu90ZvyRh9SGUy/DhgvSFMtyJlfEZ4g7L3RzHxjvTmClKoctZpYtri3kaatm9idmdmjx+n9CZRF9luJNcNHkK7PgIwHD1qQrGVcMW3ccbZm8/K0Wv1GAesqhuh8JTjw0zvFTQkIJWPXbQVlVrES8Xn/pEaYG8gTH/ihrzScwex3ed1zow3CXmnzK5a+y2MjAZZ0mvjYTnZukFAN6RUoopASqj3O0CKuUIz6L4SOnHyobfga9taitMrz5PvMP8qwaSxx3/I33fSzYUPD7SpFhn1Xf/FkG7jN7THid5wDLD6yTcWwXOlIOZva1Cp//Pnp+OT+9fBNcNOiRpcMKBHPW2a1G5kVB5TFRngg8YdFCZiXjzMM7NtWhZV6/SjV1AcYZE3xcAQJPBhbY2KfIQvIjtxHHaK2F2/O6xi9dkTF6V8JMKxYgTi/1hjm1ToH0V3yhDAXHffzSldKXtCDfKNqWCQWVjsx1qjAU4+98hyeUcoqxEloNejZPJ+ajw5HPkUaq/z42+BziES2Os7DHulN7jpAmTfzPFfZ7eB2sMKMxw2u8KRXLqvbdMMRrVXbzl0GnymHMzP6pmf1vic9r0fPL+enFmoOyMtWgpEIu0eJplhXjyiwY1cRMhYF4Ivp1NQHQYuZF4Ln6fMvRICioVRxfKSwOtbESUgISXW8UYv6MWsBV4+bgdUTrE5NXZ3IrkXHB8cDXYEbhCSU4okmu+AT7mOof9zGiH5dHJe99Vh6SElqqLv9dFPrnZCgUedozb3CxnkOoGEJUyg6VtFIqmLChvCpWLDweiqf5ec/AcyMD6YGGFI8PKwO1XhAJdlUXz+My3qkCnSqH34/utVNmOT699Bx8EJCZ2eJVzzmgcGKho5RBNMCsQFRIBstGi8yRF8SLXV4nvvaRJw1bcqoPzLw8KVFhsVXr4Gs+Ki6P9aQ8M4TJqzPZtmdGs61PH81DV4wr9iXl4SjBqrJT1HixsC2z/vl5VsrK4nVjxj3DprXe+jIaZXxw+2z8RGPm5TG5AJU1tsV0ZIXt35xEoTaV8inIjp+3nYfxwOth2qr/CIg7K+XoqBvmF6ZXChesB9cqeuEpMKzINYdefnp58J5PLt7ElmWtkwUht5SACX2CTs3M5RkobOmXWW5YN17Hyc2CMWJ8tKB4oiINlEJx4M04LPi5bjzUrhlCax4Ixzg0BUHrxkIeA2UNK3p6WReWVbKXeNwVrfw6Ji94OVRCXpZDGGoxVI1xhCcv3iv8Iw+0zPhg3omsZC7L3iYKaxbsqk3v98Tlqewf//tXCmFaVuocjsX+K++U+Rr7V1VZ4K5k5HG1HwXDv+pFWhgOS9HTn4vWqLqBrpRDsNbw98zsk2XPLtenV54DWilKMKprft0tNNx8tePQGLwZ7lyL5eXKI2JY/41Kx68pSxb7oZhtaqb19ZKRAEThi24tvp8Bn/VJwoIXFe5TB95qsSx5wuBL35kWPA5qkVGlBfKZPqwMlUVd5q35sz72WK8LgyoWZUqpRcKV6RDdR2PEy0frICh8I0B+iPgrwpF5RXlXWE+WFdN4Vaqth+aYxspY4rU39jB4rS6ip5fHujCczH3FucEK2UOnSsHh76mZZtKGypzqBrpVDt9ZPPfom4ufo4sZRfvM7J+UPb8cn14en8HfbKErYY5hIw4vsBWFm2A4NMALjchYqHRwckVWaGQJcXyfhRNa9ZhR5Til3kymhINfx8PbImsNy3s4oMy6xjHY+vTRljRPV6xROiIu4itPxOtmmiKOrPj9edVXVsBIVw5PMV2V0MByjF/jcLizLdeVMOX1shRg26zYldem1lsiHlVeKxpZTBt1BhUulmObrMC97NYdR7OvfOelgtHDxgnLAjaElOJkA4Drww97RUyvxom5Z6QB1g10qxx2mNkq+L/KzL5nZjeY2Y/Lnl+Oz1Kksjqgth+/dKVwciJaAR6KwoyYKJ7NO2jxvsp0QCWDMU52XaMYPDMcWy7IpBwWYIs7snCVpT01M7eYonq2IPgUY3OduBCZsp4LbS0ej8AeWur8GSyjhFtTyI61hLp8LDysxIuOuCdBWew4vo89+1r21IEzLR4iZsjgWHH2kDrbJxKSapy8fBVADxR5g/lW/fbn+T4aQsxrrDQVv3J5PqOK54XyONFYcCtdjRsLfHXN29h+8IzsP/9XBz6qupHOZXOiCnSrHI6Y2TD8/xkzO7L4+0DZ88vxWQrlgAT3ibntmaPZ9oNnWoS4sjJdsKmy7mmo1zgqBvSJ76maGIeNQmDehlo3cRxURlCkZPg+WzpRSGHn4bGWLCHOTpmamctfqajGgGkbCR5XZugpKIuR60GvDgGFJo5ZpIDVGEQWnhIY3o4aK1e0zEdMUxZ8yqPh+qsoX8YV9xao9acomwrLs7XNuPs1xhfbU0YY0i2y5KMFdaehSttWdXKfkKbjl64UUsW9rFpYVvOHQY1XFU8vBd0qh//VzF6G9NX9iymuHzCzf1v2/HJ8eq0ceOL773YmO2c8MZOm8sTn6vOLlmrxSAm3cBFS7myUzYJhKQ5jcV8Uw7I3kWJqld6rFFaUmYR4OD3dGotox1ktOH7bnvaspbOFckpY80t9UHgpwRGtC/BvpIEDZtYoYe00ioQBjjkrsGiMOXxTRTEowaoMHPbEnOc9NMvhWSXA0WhgfJWyY3ojbjw+qn0W8KrfyovBNTkHXAh3PBgn5qXIuywbryrjFkHX2Upm9ikz+0dm9hUze6DKM8v5WQrPoUxoIqiBjRa3FZMhs2dZMRzjAsmVBLvuVSa9yhRRn1R8nyGyGKOyTK8yBo+Ukm/c81i/CmWwsEBB69Z5g55jIQ44JuhZsOJF3FIKg39z+M5fNM/ZRYrmjhvzDdJW0YSVEraD9apxwDY5FBkJWRSMDc97NBu/dCXb9szRbNvToy0bRlEBN3m+dU3BcVCehsIF1+uQ3riXqYqCdPyUQeJ1enYivmfBjRLmF5zXzIdRJpc/q/iqE0gphwGrBjeY2aUsy/6VmZ2q1Wp3VnzuuoT6/IIdOP2ePbhxrZmZDQ0O2PRs3V54/V2bnq3nZRAe3LjWhgYH8vIP3XWLjQwP2ZZ71uXXX3j9XavPL9iWe9bZA3essQOn37Pp2brV5xfsiT0n7fLUbF7fo5/dZJ+774M2NDhQqGPP8fOFth+66xYbGhywocEBq88v5G37taHBgRy3+vyC7Ts5YfX5hRyXXaPjOc4Pblxr+05OtPRV9dfx2rzp5rxur5OfGRkeaqHx7mPnbdfouO0aHc/x9meiuvadnLBVI8P2jb/1MRt535AdPvte4b4/4/hcnprNrz101y1mZnbg9Hs5Xcxq+XPeZ2wfx/P+9avtyb1v2uXpxhjNL2S298QFq88v2PRs3R7f/UaBbjgWyBPe9uZNN+djMTI8ZF9+eKOtef+IzS9kBV5hmpuZTc/W7VvPv27PHH47b9fxRl7AZ/x7tr6Q02vP8Xdt97EGP80vFPuBNJ2erduu0Xfs8R807g0O1PK+mjV438uamW25Z51tuWedrbphOG/nM3eutY9/eLWtef+IPfrZu+zLWzbaoTOTOZ4Pblxre09csL0nLtjmTTfblnvW2cjwkD1y720F3vZ+Hjl3aRHvrEAfp7fz9SfX32SvvTNZ4LGR4SH70uYNNjI8ZHtPXLDZ+nwLfzJMz9Zt38mJQhs+9kODAzYyPGRf/PRH7Tt/+ZaZmX1p8wY7cPq95hjWsgJ/7zs5YZs33WxmZofOXGyZA/iN/fNnUd44X/UcIq3hH2uEkp6yxd3QZvZhM9tT9txyfpbKc0CLBj0B1tx8QmaWtbrfaMG6RcTb8DlrRFn9Ktbq99D6QjyU+47xefYmsK0obMXhCr+u2lXgfYhc66hO/B2lALo19tizr8kTMnF80FqOLG7MKOJc/yYPtMbgozF0iOLpPL6MF1rOGKKI2sT2eB8Ors2k9g14/70e9KSc/xWN1QY0rJs9dFUG5yF7TmrtwPuJc0rVh+Plc1uFldRc9p30mEXFnhf+RnmCWWy+QRPrZlkTeRDdeg1Z1mVYyRoH4tUMFp/N7NWy55bz06sjux1w0QgHHicvu7gcD25eP1dgUmcgdzNVWEQxGMdYmVk9lrvz8FiB2fAZnBD4OwoR4QIsT/ypmbk87KUEOf5X9MUySkCkXGYWqHwPF2e5PS6rFJ8q48C4qldfqj75PVT8mHuvQhuRwsQ+YdlIoSjaY3nnc7XAqYwWr6dp6JwrpAPj9W3PjCbTnz0Ugzgh7VWYiOmg1ttUZhzWxwId6cB05HaRx3CNiEN2vEDt9bPxx2nWzruNdbHW/TII3SiGLOs+rDS7WEnjnO1a7QM9dl6uKdTnF3LX2sHdTjOzJ/e+WQivbLlnnZlZwY17+O5b7OG71+VhArxuZrb/1EW77/YP2JN738zdx49/5KbcReRQ1cjwUO6y7js5YRevTNsTe04W8JhfyFrCHq+cvmSfumONffzDqwv4YYjD2/zER27MQxD7Tk7YA3esyd1cr9Pxdzww9DI9V7enf/y2Tc/VZVgGwwAYIpmerRdCaPX5BXty75t2//rVhfHYfey8mbW6zDxeWHfkhnPICkMuGO5hXDFU4rDqhuFCWMHDGx569HHaf+pi7vrjOH354Y15mG1woGYP3LGmJSSA/SuGSt4p8AWGsTy0gfTCvjo4b3nYzMM5jq/j5mG5b//wlN2/frWtumE4D8V4KMxDpw/ffYsNDzXot/fEBbt//er8+sc/fJOtGhku0BT5/EubN9grpy8VQlfOGzg2/t/DmNgfHD8PV+4/dbHAcz6OPgee3PtmHnbEufHEnpO25/h5+8RHbmwJlSFf+f9DZyYLIR7v457j7xbCVR6y9RDiyPBQoX/5vJqt257j79qe4+dtcGDAHr57XSGs7PVj6HLJINIa/jGz3zKz/9PMTpjZ3zezF8zsH5Y9t5yfbt8EpxaE0LJ2S4GtPA8PKW8Cr7EbnmVFi8zdWne/0cXF++xeoxXn1psKZaGlonL6vT7erdmgTZwvjhYfW1mphWq125VpnFoYVXXjOGJ/fI+F6qOyulS9qgx7XjiuysJVxyQoj8GfxXHPskZ2je/jYC9UpVbyeUpRP3H8Wp9vHkKIvMceBuLC6dP8LIdukFYTl6daeAPpjYu3UV84RMr8izjzOHt/Jy5PLb6Up/kqT+WhYLq6qsujCZE3x7RxWvjRK8rjnavP5/swFC3aBetBttLnzewPzOxfmtnnqzyznJ9ulQOuI0THNjAzo7BRMU9mSGQEnOBY1kNQfM586o1fTTzGWnbkogDza14X99OZkVNKy4QotsUKJAUcS1XCRPUlqh/jzH5fbQJDeqfSFstyyFGx8mY37pu/vyF6rScLCeeF7QcbMW2P22MK7/aD+q1yXifuocGPAv38WOF5LIe08zOQxi9daUlvVunOaozdIPI9PGo8nC58zDnOR8Un0ZpEWer3zsPnWtKKVT24GdEBeRB5BWnI441lUakoxbP91bdKTxqoCl0rh5X+6dWaw+TVGbkZiyESgkoBKKsMPQaexI4Hpz0qRsBrkQXJ/XTGQwHJiibFbKp9xrkMePEvmqzYj7K2UBlwnxS9kBaKbrz7F+vgMcHx4/owrh4pWb+Owgjfg8A0mLw6k6fzch2sCFBZKEGpFCE/r2iNfDR2cbJl4RfHOfXWO6wf9x4gTZlXuB612ZPH3IENN1ZSXoYNi0gm4HPOg/xOEbVGhbgw/phswLJEvYNlqVJZU97CpJldij7Rc9fi06uD93wRiLMb8DdPJjWJeKKqyZVlRUZNCXac8Co0gWXwGS6LjIehC4Uz48qCjy1vJRyYfo4vZ/woixMFIeeM88uImPaRIo0WGZHW9fRe8gAAIABJREFUbp06HyglpsIbavzVWDLwgu/4pSs5bmxVYp9QOKClr/jDcVdK2MukssSYxxA/F2wqc42zbrA+fmkV8xm/XErNScWzkQJmWill6vg2vLTi0TLKC+dvFOCqffR8VPsRbTCM+PzoOzmPcP2dQFeeg5n9MzP7B2Z2o5ndZGb/g62QA/f80wvPwZlm+8EzLbsy2cJRFrfXg9/+m+8juJXCyoDr5bUGnzC4q5YZTiuHsYKg4EwJ7jOmOGLbPMmUYFI4eFn+zwIKJyWfmR+FEBTt8TdPdFZ4Xh5P3YyUGAs2pRTUmGNZVHRZVnw7nAsJjC0zDkg/DpVlWVGxKuEf0ZtpgkKejQkWdKrvXB+HrdDbwz7h83xmmTKoeI7yPEC6Y3lWfjj+iAuOl/K0mb8UTbwPkVEWjS/LnjIPvyp0qxxerHLtWn565Tkg87Dw4GsuNFUaJk8EtZDt93ccGsu+8p2XCvFoFlpZVoxxoyuqTi5NLYDhojVbKarPDQtqLF8Pwbo41JFSDpHw9BAKu/pq0VKl/HH/lDJXExb7qRb7/RhnPggPn52amcvXEiIlyMKDF8XRO/DFSOYDFsaYrIB952/sW6S4lIGjLHkWWl6nWg9T7Ska4ifyYh3UPgHmNRwbfHsbG3I8r1jpIy+q5A3uv+JDx5n5rjl/x1pwcl5QdGPjMeWNtgPdKocfmtl/Y2aDZjaw+PuHZc91+zGzL5jZqJkdN7PfTpXt5T4HJdSRwVAou1XHwoUHkgUcusxTM839Al5eTRIl3CLrJHWdFQp7A9i2u8Gp3O/njjQOBNy640gYA4+sncbkam6gYgHIwsFPR2XrHYGtLMSB6cDhEJzwrBCxz6hQI+HbLNsawkAFhKEGthhT48/7DhSgYlRCB4Wt1x9twmS83KPDMAnylFKKShBjmBMX9dE6xzFFLxbrZyMHvXHkA/xG3lJhSfcUon0azr+sqP1ZP8EZeccP7mRceL2M61AhO6Xw24VulcNGM/uPZnbezMbN7M/NbGPZc918FhXR62a2ycyGzeygmX0sKt+LbCWcSDyJ+dsZb8eh4i5nZnLVlrLM+LcSeu0yAk5IfJYnqLLoEJSiQw9o8mrjPPx/+Cf7c8GNgpcVkKqLhUu0fjF5dSbbfvBM/gwqCu471sPCRNGdv/mjQjBqkqoyOB7+H990pjKdlEWfGpvUdVa2qJDwGqfJqrpYgeM4sXeNz6AyRa9brTvg2CkjDecXKhpWOmothAH5TimPMgvd+6SyqDjhAU9p5bKsCB0fTCVWMqNbuO6ylczsITPbAf+/bmZfj8r3Sjn4f7zH2UToFqLX4NDwLmJrIrIA8Hc0IaNrilkcdxXy8PL8cveUQmN6sYXOAsWVBp9+qvrNfWI6+T23pPDVq/yKTqwfM8LKXrHIgp49xmgMlGKPcOKsHfZKUPihYEtZ8Qwu+LG/isfxNabuEftzKKxYqWC/lPUepfT6752Hz2XbX32r4HUr/lOKmj1zpeSwLT8xIBLw3Aa/e0PxZlSPpxlH/Oz448F8SiG5smH6Y/9TaxrtQqfZSo9G99op08nHzH7NzB6H/79uZv+a27bG8eH7N2zY0BWBIiZwhonCD5Frr+7h4GI5xdQ86ZQFqdY1mNlRuKk+swvLWUJct2JkphsKZn9hihKu7PKXjQmGFXjRUdEN6+eMp2hC4bPbnh7Ntr/6VsvGQqYfC0pWKGoRWyly5Zk4Lrz+wnzEQsb3R3BGl/92ocmCXPE4ts1niHHdaAi4wmHjA70A9jLLQiWK93GxHmk+eXUm2/q9o9nWHUdCpcNRAg7vqbCW4k/3HDAzy9uMkgEUfzgNcX2N2+R6ug0vdaocTpjZryQ+v2pmh6Pnu/mY2X8llMNjUflerTkww2RZMZMGBbRDyu1MeQA8sMgoKt6OljALAyVMUoIosjpQMfC7ohUjYj2RYGZ8sK9lcXMlLCM6lllZaI1FEwrxwxchRcIhZUXz2KEAUF6aGitsB8tjnbyG5c9wSMbbxU1rUbhP0USFkRg/xJPx5rnD3rj/Zm9KjTeWR0XkZ4qhMuNFe7V2h0oAyyDvqjAQPt8MNZ/NExnwoEZuD+cansHmR5mrNTrmcaRrp5BSDrXG/Vao1Wr/t7xRhPeyLPtKhXJtQa1We8jMvpFl2S8u/v+6mVmWZf+HKv/AAw9k+/fv76gtP4/Fzy/BM3vuX7/aDpx+r3Dssp9ro8788TJVzzzhY5XxPCJu7/71q23/qYs2NVu34aFBe+TeWwt4rrphOC+LZ/rw2UJ+ZtHmTTcXztFRZ7f4+S9RH51uZo3zgPzcG7zu+OGx3YjX5alZW3XDcKENrH+2Pm/DQ4Mt58swHVVfq1zDOl54/d3COT04Nn5+UUQPpxe3x3zkZfeeuCB5zs/4wfHzM4uQpp/4yI320qn3zCyzT92xxkbeN1QYT8TRecKscRbRxSvTtuYDI3Z5araAF48vAvYDz2DCviC9uC6kbePI8RP28Q+vtofvvsX2nrhgZo3jtX/3Px+1/+W//Cv2yulLNr+Q2WfuXFuYg84TgwMDNjhQy48+v3/9anvxjQl75N5bW8ZbjRfe4zmNY4ljwfzCtNp97LzNLyzYw3evy2nt/I1tYNven/mFzB6662Z76dRFe+Te2/J2WuvPcpp537s5srtWq72UZdkD8makNa7lx8yGrOG53GnNBemPR+V7fbZSlhWtdSwbWX1oYVR19SJLhi1fjg1jjjjnsrNVpSxeXgjkUAKHABy4LFo2HCfFTWu8uBh5HGy1eX9THpiiaTv/8XrqXpRtxePE2UkqfNbwTMZaxsjLexomeooq9u9WKm+aYs+RvQjOBsqy1mwZX/tQp84qq1nRK/Iy3arnUOlcvXE0RNMbGCusjXj70e5x5WngcxHe/KzTQJ2b5qDCYNiPaB6zrPDx2fr0UXmCLXp8WG+EV7tg19uCdANn+yUze80aWUu/kyrby7OVmHlYeKksmixrvme43QHDutUxAEq4MePyJGHhwAzsE18teGI8mBURhxVYqPs9jvHz5GBcWHDg8ymXnumDY4ntKsHGNC1b5IvGlRUBpi8qg8HL8Rn+CE36j+WL6QpnpBeGv5AXWRDxePh/TFnG/pRlznGoUxkOWGdqUZ3Hz1NleXGd10owsUKNJfK2A4Z8uLzzuX8rPsL3OKg+4PxzvJi2vIk0UjgeCuTMsF7Adakc2vn0cs3BAScXWju4e7rALLQZSllNqd/KUo9AlcVJgfFTNTFw4uPEQ4vRJwZmoKQsKRYkUe4405sFCgo4rpfHCfuGY6QysFRZxt8FbUr5KFDCkvvNY8F7W3Bc/PdzR97O0x9ZQCLefigfCrUIB8TFn0cPjYVT5LU16X0u92Bw8R7rVy9bin7juD0/+o58WRMLXpUw4mUVD/p1TlDwbz9EsLERsrlAjDRJnYjK9PW9EryZzhM28BmmifeVkwuqZK5Vgb5yKAEeEEV8FrouRJEJcIDZC/FnedEUf0deicJNKREUsigk1aSfqzcWXfndtt5PZ0bGOVVf9OIbtppx8qcW273eVLgOFZrXr4Qh00eFt3YcOptb/n6dx6iMV1hJMr6sxNB7Y+PDx4BPYOXxZw+LaYh9QTq6UYMhHg71sAfAIUcPf2Fqt8qm4jFL0Za/UwrKeQd5M6ojGhfEwa30sYuT2c7DY9nW7x0tHE9TNv/wN9OUaeLKFFOuUYawoaG87GuSrZQXMPvnZrYG/q81s98te245P92GlRST8kCyQImsd3wGy3qIBg/2Y8WTSpeLFJZiDBbEyiJp4tV6RLc604fpoPrA7j7SEoUyThYUkFE6YyRokdZRjroKjSgF4oKFJ70Ko/lvts59jJVyUX1BWnkdHkLaefhctvXpoy2x5pRyZrwUzv7tv5VFWgzZNPf5IK64JsJKOVLkfi/am6DojBDVh94Lvh6UaRK9C4JDcZ7J5UqPccXxRV5G3uOjV5j+bDCevnCpsN6jDBXOBkvRqiqklEOVJe6/mWXZRVjAnlhcD/iJAH7blL+RzF8e72918pee40vKN2+6uVBu97HzNj1bt8tTs7b3xIX8jV5Dg43Mis/cebOZ1ezbL5zKs1/wbVOIx/RsPX/esxXwBfX4xjTOTvEy+JYszqDxt3nhC9ydHo/ce6s9+tlN9pk7i1lPniGxedPNduD0ezmO/oa6wYFajufI8FCeIePZKFvuWZe/bY5f+P7gxrX5W9ZSmSH4HwHbNrP8DWl7T1yw+YXW7B2n8Quvv2sXr0zbnuPnbc/xdwu08Deh4Rv4UjC/0Hjhvb+FjHkL35bH4+P4fucv37L6/IL93PobbWEhsyd/+GaOz67RcfvW8yesPr+Q09ZBZavwm+2wjLe3/9TFwjOOD+LtPO7j2oCa7T910T65/iYzs5ymZs3xVDgNDQ7YfR+6qSWLyN9sxzhHfWG6Dw7U7LM/u85+Y8udLW/EGxocsAfuWGMP332LfFvg/etX27dfOGXPvzZuu0bH7eDpSzZbX1h8m926nH/NGmP45N437eKVafv+kXfs8d1v5PPd365Yn1+w4aEB++8euqOQQYb4Iy0vz8za1//Dj+yvfmRV/pa5z933QfuNX7jTVt0wnPPLA3eskW8AXDKItIZ/zOxVM/sZ+H+DLdH+hk4/vTp4z916tojYAsBnMBTiFozHEpW1GlmnHB/30FCU8+zl2DKpEi8vc7n9Gh86p9xadbwwusFufTk9mm+xambrRO6xor/qo4cDeNEQ8eR6/bnxS1eybc+M5gcMFkMm+k1oXI+Xj47zQPqpsBni6/zx2M7XsvFLV1o82KdeOZ17KKm1GPQ0Ut4lhy1U+CXyVHCfSsqrYuCwUpY1T8KNnsE+cTuY0aZ4yem5/dW3wve1oPeGPMB8n2W4N2GssL/BF8+jtUOmM/KUH8PNcgBx4SSCKLzVDliXYaV/Yma7zezvmdmXF3//xB3Z7d9qF62KeSoX2geT3VeMKaoUP5UZNTVT3NmrhIAK3SiBqyYrhnSeO/J2tv3g2UI/XfCz68z0UhO2+XwjjIY7dl3hKYGNUIzFtx6/gb857RhpmgJ04V0oo1LjEJgKW2F5FS5BPKOsOA5RztXn8wVm5iPMhsKwBMenPZGA01EjuvszKkNPjQ/ihHWlFI3TUYX+/FDFiKeQTkx/fkbNFRe+TuMo08i/o5Cs33eeQdya83ysZa7gs6zYca4WQ3pjuRIqm/+dQlfKofG8fcEarwj9ppn9YpVnlvPTyzUHZaFzeY6do3D2/w5jFyelgEdwYckWOHoZyIQYn8/PGXq6+c7bSIBhvS4UPVVSvZN2rj5fSPmrsj6AgEIPPQd+XaaXRaZXx10oJY1rIDh2nB2SGk8W1MpKwzHkSYleIfIT4q4mN17nTLcoG40TBRreTVOIoMLg35EH6vVjlpr3K7W2gYqR61dWN9JVjT8+o4wobE8lAfD88XLjl67khxxmWfNNeqzYWKFFitE9ETTIsB5unxUYzocUT/BpxY57KuuuXeiFcrjDzP6Lxd/vN7Mbqzy3XJ9eeA7tKAcWEFMzjbS0pw68VXh2/NKV7Nf/+IXs9IVLYT2Yw4xCKhLyWZYVFIMLLVQMEdPyN04EbJ+FdHORtJhdgcIzmkjcJ5Uh5ZMNLS5WDCzsWUjh2Hl7qaOWGT/Hg70lbB+VMx5PrrKfWOGk+Eopg1ToA40TFjoqYYDHXNEA7/OitBrHwnguGhaIjxKKyJvKe+BnWDniGEW0UMaTK4jigrF+nzePFeONeOA9hSc+5x6FK/Po0EjGh9t87NlGuBG9j26g27DS3zezfWb2+uL/e8zs2bLnlvPTC+WQciFVObw3V5/PnjpwJvvad19qyS7w9+uyAMUwU/RaQZ5o+CyHelScsgrzsEDgMATjzYJ48upMtnXHkVywR94EChS2hrwdDJFg9pErBszcidJa8Xe0ToNjzWsmfICaEpQe3mHBiW1w2KAsVBM9H+GtaIxjppSS95cNG8bVy7PBwmOE4RRFZx4rFqLeT7XmxoZJtAbIyk/F5dETUrzCY8B0x3AR04rpp8aIaYteklIyPPewzu0Hz+R7Ssq84iqQUg5Vlrr/RzN72BrvjrYsy46Z2W3JJ64zwOwMPM8GM5C8HGZh4PcXPvEh+9//9l/NswvMzOYXMltzw4g9uHFtS/bSA3essUfuvdW23LPOVo00zhbCs1y8Xs4cQZzNmmflYNbUvpMTeVYRZ/iobA88j6kBWSGzBjNs/LN5081Wn1+wF9+YMLOa/dz6m1qyRBzq8wt24PR7+dk8ZpbX6fV7vfMLmf3g2Hk7fLZR7sGNa+3QmUn70uYNtmpkWGZ2eRv7Tk4U+je/0HpuGNPTz+fx7CHPdnHwrBLOfrnv9huTZz35t/cLz1HyTBP/xvFU48N4q/OCvD4zS47DoTMX836ZNTKRZutNXvezt6Zn6/btF07ZrtHxfBwwIwnxOXD6PYk3jpW3xeU8u8vPa0J++OKnP2rf+cu3CtliOEbqjCzMAvKsqf2nLuZnG3lGFPOKwt9p8uIbjSy9R+69zbbcs87q8wv2/SNv57RCmngWmfcdxxuz/hxvx+P+9atz/HYfO2+7RsfzLCmcm8NDg4vZd1kpz3QNkdbwjy2+EtTMDix+D5nZq2XPLeen15vg0FXlIy0iy1hZL7i7GC0ntH68fLSzNbLIHDx+6gvKkevOi6Gp/rNrnor1T83Myfh+1EZknaHrrE7TrGJts9fWzln+DGpdgb0IpK2Kd0feAtfJvKM8Ef6OPJuIrt7eN//iSKGc8hwcMHEghXfKS8PfKQub5wfWrWiAvInfqv9RNh3W3ViDK766U/XNvS88BwmtfGzLARNLfK2PaccZV4gj05q9+24Wpa3LsNLvm9n/bGZHzezzZvZnZvZ7Zc8t56cXC9JqYNAFxPKqDiUIXNCpLBV8VzAyZDFGWcxuUsIky1o3Mjkw80fKRvWHhYL6z0qK476R4OW2lSLjNsvCLP6yGlQQ0aI54xelBLIg4ji+ipFznJnbiBZ4ETfGgZ9VQojb4dRer9vTrJVwZjxc4bPA9Pv4bNW1OvWfx08ZSV7Ow1AqYYHpoXiQDTSfO5htpjbRcV9wDcrrwjRWRa8sK6bs4r3UBr0otIvlOoVulcOANdYd/h8z+w+Lv2tlzy3np1vPAa0QZErPrFGCCcGZmy0Sv8cCxJmcBx2ZBCef3+dslZTFwBM2En6KUZWl5FY4v/ClWWYs2/b0aE4v5b0g7fAeX1fCN8peQbw5nTPyetTEZQMB7/nb51J8wPUqheq/U96MakNZz84HPFZTM42c+69+92WZMhwpk0iJIt8VlXHrabDcF26H6coGEStf7x+mcqoECgfcd8H14Bhh5hwfYKkEMoPjjgoCX1+L8xM9YFe4nEarxhHv8RhEa5SdQEo5lK45ZFm2YI33Rv+DLMt+LcuyP16s9CcCPB7OOzqnZ+t29Nx7ZtaMSdfnF+zy1GzLTs36fGOHpsciH9/9Rkss2eOOT+w5adOz9Xx3Na5p7Dn+rh18cyKPjyL4Ts79py7m8Ui1C9VjqnzNY5u441rRYs/xd0WsPjOzxg7kxqPF+41d1bfZo49sss/d90Hbcs+6PCbMO5Zx/YLj+B7j5fscs8dd5Y73vpMTLef443sHzBo7ntUazsjwUCHmi+shZmb33X6jjbyvWY/TFOvwekeGhwrxf/ztdT6x56RdnpqV9MffTo89x5s8Up9fsF2j4zkf+TVs56/ddYt94eO328j7hlr64uPioNZUsE/eL9y93uD3AXv47lvy9zjgegKu1Xn7+K4EH6/716+2PcfP2+M/aMyXA6ffy+nibT9wxxobHGji5e29+MaE7Rp9p3CKwIHT79mXNm8o9BFPEfA59OTeN+3i1Wn73H232cN3r8t5y99fgrvWeb7g+1UOnZnM5+ANiycm4JqJt+XjNDI8ZF9+eGP+/hJcY3QckGbIEw9ubLzX4r7bP2C/+5+P5nRK7RzvGiKtYQ1p8A0zO29m75rZBTMbN7N/Gj1zrT693ATHWr9oQY8VNiH5dQ4lsLWAbagcbbfEJ6/OZNueOdqSJcJWXuRWetiAX0qOFo1Ku8S+81ky7PlwyiXTEXFRqadlz6qNgliGPS7G0T0ftoYxt11Z+TjeaJVz3r/fj/ZQIO84LmzlR2nKiIPfb+7GPVfwKvE34vnckbez7a++FXoITWtc0xnxYV7mMeBr/CzOJf+t3p3sdXloh+PpygtFuiLtkZbsOTiMXZzM9z1U4edoDvIaCXsG6A1heBr7wOEzjg4wPdzjUfO/E7AOXxP6VTN7xszuhGubzGyHmX01eu5afHp1fAbmOUdhGGZUv45lHFhAqjINhdA8ujcV74wEJudvMzPxf6UYMKymFvuwbLS7lBUP9zuiLcfTVVjOf2NuOOPuLre3waEjtc6DysMXJTFZAGmAQlXRAOnn44DhBqYNjh3G9pWw4JBnpBwnLk/lR3wjvyqjRNEax5sPj0OhxrwYhVKRz1Q5vq7GTYWJePwihaBo5enjPl9S813h6XPKEyf4KG7/xpNqtz0zKtd7mA98zxIq15T86BY6VQ4HzGyduH6rLWYurZTPUigHxRhcvqw+nnz8rLfJRyVw3VUUBLfLAl9NJiVsWIDjAqHH9XF3qWrbgT0sF778tjRlxaMgQuUWWbNZlhX2Q7BVxvFfHge2CJUQiwQz9s+Fga9TYBYKfvPkxx3YCNFuYKXg/br33zeoOc155zMuwjqwguI1LjYUeMywXCqzjwUiehS4FoEL1Q2hXDyKXvGf8pKQv9D73HForMUgU3zJ19iT4/415vXZbPvBM3kZ3m/BdGN88VovFQJCp8rhR53cuxafXiiHLNNpc50MjD8Xue0qXTWaRDxZy9LWlDWmlA5OkkhpoKJgq7gMH7dg+bwcFm4pPFV5ha9/q4wibCOVQcVKTR3QlhKK3CZPbFbWvBlNpQ0rxckLtJHiRHq7MONMGr/O9MD2/PBB9CjV2CMObOkjYB2oDDgUywYJe7ZuaChPFduYvDqTbX/1rcI44JHc/kY+xROKZ9RYqzk+NdNYpN769NEWpacSF3hRfGqm+VZBPlZf8WSn0KlyeLmTe9fi0wvlEAmtKEUVIWKsSAhFQhnLsBDx65Fw8/tRfDQqr/rTtKrOFoS7W0to4UaekYd4EBfPslATg+mREubK8lK4qEmrFJpSmup4BWURR/Vge5Fg4fuRBavq9PJo+fv/KOSFz7Egx3IqTMkK3OtBnNR84dAdekgosBlcsCvaOZ6Iv3tfnkHkoTg/rqX5fyz76ndfztdlUDmWzUWWDThmyjPxozLYYIgMMmynMd/GZNtI8269ipRySGUr/VytVrskPpNm9oluFsFrtdof1Gq1o7Va7dVarfZntVptzeL1jbVabapWq72y+PmjbtqpCs1z/5tZOLjT2P/vPnbeLk/NFjJFLk/NFjKOPAPBdzdytoPvLsYMEcw48Hb2nriwmKlRaynDWRR4Xj9mnfjO7F2j78hMGM+O8Cwd7Pf0bN2Ojk2aZyYNDTayU8zMXnzjgu0aHS9kQTE8dNcttuYDI/mzn/jIjfbPt4/axSvTLVlBjLvjoLKx/B5mHuF1pAnSCXcrYzuc7eHj89mfvVVmI6ksK26P//M7N5CfcAetypqLMog8O8ffGVCfb7x/4K51q+zFN5qZQ9Oz9UL2m+985mw6zyLCLLj6/IINDw3aw3e39tffa3B5atZ2jY7nGXS4K59p7dlEnq0zv5CFWTa4Q1vthB4ZHipkxY0MD9mXNm+wQ2cm7RMfudH2n7poe46ft/kFsy9u/qiNDA/ZvpMT9tmfXWf/7G9/3D7/sdsXs+xulTvXEa+9Jy7YFGQBTs/W7fEfvGEXr0zbk3vftE985MacJxEeufdW+9x9t+Xj7dmJyLdOJ6fb/EKWZ8I9cu9t+XtjeFe8yxx8V0nPIdIaS/kxs79hZkOLv/+Fmf2Lxd8brYOQVa82waUsTcyhR2vlsZ2vZX/+8pv5Eb4c5+cwQGSxsbUSWZkKL2VtIq5sHaHLrupgS4fxZAuH73MoBS1HxD8VToro5Ral2qCl6sGQCtOQs7+wvSgrKtoPEf33+tyi9fWAxnHm+ijmKsA4N2PhY7mlj9k/uHbACQq45qHCYMp69edwfJgf1H8Ht+qjfUTseeJ1NQ+QZx3victT2danj+YHPXL7KoynxsHDT3jqrc9pr1fxJOMX9QX7y++cUPLJvQpc9+gUbCW/Q9rM/o6Z/dvsGimHLCtfcIyOkciyxsmr3/yLH2dbdxyVAgoVhdcRncaoBpnjr1g2il/yhFWCUQnSiC7qvlqQVhuzlBJEAawmNWeNqV3m2LcoFu73MT2XF3exTaRZRHcez5SCQKX4h98/lo1fupILYRQwZRAp4GinMypEbAvXHZhWqQQDj++nhLTiNUU/xN+PGh+/dKWlzzg2SiFHyp3x9v5z+ymjiPvvY6h4QtHQ8ec61Lhi/f5OCw4LYruK5t3ASlcOT5nZ382ayuGKNTKldpnZLySee9TM9pvZ/g0bNnRFoCwrnnGCi7D4n8EZ8KkDZ8JMGPztzIDCKrKyvTxab5EAdPw5BU6lRUZ4qb5Fi48YO8aJ4bnjLPCVUOF1C84wQeGplBnSPyoTKXtFG1QIOLH5GIrG+J0L21NrPt5H/GYlqiAaN8WX3CdcnHYrHQUp0i2Ka3N7inf8vvMDrtU4/SIF4da9H1mP9GGaKd5Vyh3bVh5JikeYJ1ARsFLl/uPzkWJT/WODBOcE481tliWnVIFrohzMbKeZ/Uh8fhnK/I41zmqqLf7/GTO7ZfH3p8zstJndVNZWr47sRtfbFUXq8DZnbs94wPqUtc73eFIqKyMlGLk9FgCuVKJnUtaTP69wwMVlFgSRcESaRamL3Ocqi21KEUVCFfHHCYnP4Zu3XOjhcQWovLhdpltqfKNxxXt1bcChAAAgAElEQVQqHMGKVPWJy+08fC73bLEPc3U8jrqo8BifsrHwl07hgvjUTPP1nBySRZy5PBtDTKNIuWNZ9hi4n8wP/ryXY0UQHcCpvFnMvuL7yPc+Z9UZaqlzraK51QmsSM/BzP5bM3vBzN6fKPP/mdkDZXX1QjkoZkxNYGdkj5tyWQ4lKRdYhYQi7yHCywGZuhnTPptMyVQuuwqzKC8FJwBPMG6H6YJWraItXleCk59BfJRQVXggLixU0XNUazplliEKAOQN7Dtb+VUFfeTFsvXq1/xbnXSLFn+K/yK6YzvOZ0wvVMJIG+wXlsX3giBfYpuYjYT088/2g2eyr3735cLrX3H/S8Snrtwa7xM/1zLm/AyPFXrFPD+wHnzey3PdyMtVstg6hRWnHKzx2tEfm9mtdP1WMxtc/L3JzM6Y2c1l9XW7IJ1KqcuyViGKDB9NLDVBlevr3xg+SglEF/xKoPCCL1ozWFdZnNW/U8qMd4Yq2kV0idpUz7PwUAqLJxD2nZWc8lCUQPPNV9F4qP74N1qN6FViW8qKR7qzlarWcxSdmFaIk9qtHxkOql62ghEv3tjIdbLnwO2pOYbX8VkfH99xjCmffs89GQ5jpuYqzmk1v1L4c92+vpRScEph4hxHBdgrZcCwEpXD8cWQ0SuLnz9avP6rZnbYzA6a2ctm9req1NcL5aCsMR8UtPYwAwTfY6AGMBVyYHCm9k1HLMSwXJW2/DkWmgoPZRE1mbRpQSlLL0VX1W6qXEQXhY8S0KxEfdwU/ihocJEY240Ug/K2kD9QsKuFXKWwEW+ug0MdEb3xN4bH/Jra/6DqUPzA9PTQIu/s5rFwWqYMMKShl0eaKiHa2H0+VqCNZ2lFyrbMMIu8QaSfMhjUvOAwdeR9+n0epx2HxrKtTx9t2einaNiN4lhxyqHXn27DSpFgRVceB9GZ1VMUlYBjIRJdw3vPHXk7G790JX8nL1oiOClTCgYZlI8eiGKfHOLAeDRn1ESTW/1XfWfmRmGC95DG6oVJLGhTC5bqOoc38BoLChYoKWHQOE+pGEpK4YWfqZm5PIMHQyb+WsiyxXdsQx0ZEXkdSE8VwvD7yjvjFG+Po6MyUyE/HnveBY3HYTuw14plkNZKCCujKqKh4nEfGzfc2LNT4SKkuzLGlIJCJcwbTr0+dfxMpwqirxwqQBnTuLBFpkidZx/VGVkmeI9jkOihsGDBZ52xeJco4sBuu8ITJ1vZQiULfadLdBwET2K0tvGd2uiduWBkocyWZYSfAmUZekhBxbu9XyycuS0/z0iFE/w/CzQ8dZV3JKNljAoT15dUUoESSGoslMWueEPRxf833/VxLo/r89jwOUQo6DDxA+dVNEYqhOr4My+zYuBwZGqceOw9jOV0VzzCykAppMhj8D7gHFUehzI4OoW+cqgAKU2Mk5gHVDEa3sM6Xein8qsjYcKLiUrIunBDq4onT3TUNPfFf7P1h2VZKXiZhjt8Vr6NrDHR9FvwePI3PYcxORFRsfCE4dCMoquiMb+MBSeqe2EpoRIpKmwL+4r4+bPYBq83cCYNWunIm2odJVo8LYuNY/hI8QyePIpjhcoLacfzhwUejxm2w6FC5H3FB8hTzAcpD5HDaGouqLCp01SFsBgnN4CUh6eMOKZ7L6CvHEoALQG2sLAMC0UEFkbMqD5Bth88IxmiTEHwb3Zt2f1mZkY8uC7GV008tbchYnp8g56y/lL7BPw7+q3owhajTzx/J7Cy5lRf/VmsB4Uing0VCZUoJOPffkKqOg7bLWgWDqyEsV5UDH4CK3sAKYGGfVYCJ5V5489y2JXx88XhqA11HXnPFc6OQ2elMaB4RdUTtRs9h+uLEb8yLzQNmvJ3PHMkIKoz6lMvFERfOSTAGdjdec4E8jLoNajBUrFApUA8dMCCWQlkbkMxolJEfI2FBCownBwRA/MibiSsXZDwuwQUrVKClX8zHogb1se4qAmHfVd1Mi0wRId7HiKaqXHDNRxcA+LQQ7TojN5IWvGcbfFusX5+nn+n1s0UDSPaYx28yY1pFykk7Lcff457ICI6RLSJgBUsQ8oo5P7jGHEUIcINn1M4R/0sU7ZVoa8cSgAZleP0WdYYiK07jhb2NKg6Itcc23DLOiWQlJutrF9sVzGkYmquM2WdqD5g3Ry2wCMi/FhktQjKQjFqk3HkcI9b9RyjTilYbpdp5kqe4+ZZlhUElKJrSoBEFrhShEwDTCxIWYwswLn+qF0eF4WDL4xj35lPo3o5YwzXGlghTl6dyV9WxLF5bpvbVNfKFITTd/vBsy3vtlBl+T+HdiM+iJ5jhRvxAvczlTzQDvSVQwXwgUI30q83GGiskUn09Gg4iTAGjgOPoQ1Mh8P6OayjJl0kPLlenGysNBhnrIsFRzQBeRGYBS2eIaQUQ9XJwCELta6ASjdl5XI/3BCIvJFoQR2P9FDKKLI+VZgnJbiYNmydRoDHl/C4KDxUewp8TaHMe/Rrqm4eI+8XJ3rwplJWeGpsI0VVtW+8vpACxS/cnsID/zczkorracrwUG3zWHYKfeVQAjgYPNgoSDz8FA08xhvVRFSTFYWfErj8jP9GfJsbkZpvIcMccGW9cxsYFsNJqxge640s1WjxMlpcZ1op+vmEwv+8rsNKVk00r58XWZUQU31QbaMBECk6rqtsYisho+r3/xOXp7Jf/+MXCunV2JeUtcmCUeGbOl+Jyyqaq2dc4UZhmMhQUcZFKu0a+8Y8nQrRMq7cZhTS5fnA+Pv8coWLSlKFzRR0qxiyrK8cKgEzDH8wayTL9GISPu9lUu2hIvG1CPYqMN6NbSJDoceQZcUTJNGTUJMOcUWl5umkbMk43spiYlqqtiKLHnPWOSvH6+DMGrXBTeEVjUNk5Ud9SGXU+DU1sSP+SW2gZLqhAGSDA8uMX7pSUPDYlwj3yavNQxNdAavspFTYJqq/TMgpujL9WbCr8Cp7Doq/lbLk8SobQzXmTEtWLilvGTPRMAW8Ct16sSjdVw4VoTlIYwVXN8tarXVOQ1N1KebD+8iI0aIYts91opLA/+g1+Pf2V99qeWUnMzmHw3D9JZqc3J+IFjxZGP9osZezNtDr4o18ygtQ5+lEeCplj/goy5kFku/JYMHKmS9Ih7LsG+wfWpecrYa8qxIrsJ/sYaFB4f9TaxdMRyV0eZ0soj23wVlVLFyj42OiucP3UZCzwFfjgWOh+oB4K6UajanaVFhGo6jOTqGvHEogsgSUAHVICX0sw0rGyyihV8VaSE00F7SNlMbmPoOJy1PZ1u8dzbY+fTQXlkpg8m8WwA5KcETCSE1M7HNKGOG3mnAqDMV4+uGILLyq4MnX1TiyAfHYs69lYxcnQ8tb8ZMSTkwP3Azn5dXaFt5Xhgv2B+me8rwi/KvQjgWgakPxltrbge2p8SujNdLE62bPWNWP98sUUWoe47OYZMA0U8+lQl3deBB95ZCAFHGVQFDP4292eaMjv5kROTdalY/ioSwsJq/OtOwzmJopbq5iRo8WadmzUX1s9LM1hRLpqIQH05cnLl5Pbd5LLWpzSqtf4zHg75Si4va5PVfIqUmbagP3OqT4QwncKHuG23ZBue2Zo+HmRn6myvoVX/NQiQN7FOoobMWnVcF5WCWW4N4S5ukIfzX2bGAgX0ZhU6aT4+m/U4ZLmRzqVDFkWV85lIKaCOim+0DzgCvLn1+4wxZ6BF6WBSnjpawhtmxZgGNZVYd/Yx4+P4PKhjNLGPdISEcL10qYMd2i8B32QV3nEI9b92wxM12qWHTR9bJnVDmVvID4I49F4R21Mzcq77RX2XepvrYjiJpebOv+H2Vo+DNZlhXCgVVxQQWGG+/wXqSoUnUpbz0V3mRFHeFfxQBR86SX0FcObQAzRdFyG2uxctwixwVjtoIia0y1nWVx7JvLchyeJyD+V9lFLDAa2VhjhT6jQlAx26/9u5cLjKuYm4Wfg4p943NMhyoTg+uIdiKjl8NC1yc8LpJXCUWlcCkD1YYrX98EljI0EGeFpyrvn3ZwbadPbnAg3pj0oMbc54zvcI/4KRLq3qeydQO+Fil5DospIyh16kBZGFi1ydA0VFvfhd4t9JVDBUBrRoVH1HWlBHiRMOXap3Apu8/tc5vcNtbZ9HJawxTMzGzNMh6Yk8595Ti2ol1K2EVrFRENo3UB1ScVRkM8eZ9DJDxSoYCqseAUjd34QC82wgfXk8o8rZ2HxwrvPU8pOX+mnU1Xih9wr0SZQlVlFK/jPXWf+budsUK6oHLjOlML/8hbbOhEa5ERr+EnlezSDqSUw4D1werzC7ZrdNy+9fwJ+/6Rt23P8Xdb7puZ7Ts5kf82MxsabJDvobtusVU3DBfKv/D6uzY0OGAP3XVLoSzex7rUPbzvv/G+13n/+tVWn1+w3cfO294TF+zBjWttZHgob3v3sfM5DvX5Bdt3csIuT8/akXOXbHq2XmiP+4j/GeehwQH73H0ftKHBARsaHLAHN67Ny03P1m3viQt2//rVtu/kRKGd6dm67T52voAX0wD74dceuuuWvKz3F+my98QFm1/ICnUNDQ7YyPBQ3j8cl/r8gj2x56Tdd/sHzMxs74kLtmt03HYfO28jw0OF9hWOu0bH87p4fPednLAHN65teY5herZuj+9+w6Zn6wXaYT1mZlvuWWcP370uH0Mei/r8gh06M2kP3LHGHrhjTcs4ehnnm8/cebMNDNTyvj24ca3tPXFB8uT0bN12jY7bt184ZfevX10YA64f6e79d5z3n7pof+VDN5lZk69w/JDOQ4MDhT74N9Ia63B64H0cz+nZemGssN5orLC8j8sg0CwF9fmFwhz0NhTdov4xLvi5f/1q23viQmEO9BwirXE9fXp1fAa6pKylleXEFhdb2liGLb6UxlfhFq4DrX9//0Pk8ai4LqZ+osusNq+p34qG7KmobJrI6omsMX+ejzaILC/8z14Htzk1M5f9/l/8uGDNqnGPxih13EJViw6t0SzLWhZjPeNKeTJoZft35Dk4vThJAesqyzZj+qu5wF5j5MUxLygPG1ObozCRg1qrQ/6JcOU2eY6l6ld4KFowjTFknepf1AbO6aX0HK65YO/Fp1fZSjxZmIGiuDkuCPMuTa/Py5ZlP2G97Eo64BoHKopUXVGICXHkNZUqkFIe0b3UpEFa4VoHZ544HSJli8eJRALCBbMKCao6uW9lYRgExCMq6zzGO9A9dKeULG8M5Da4v6n9C1UEU+p62VxBoaZCkIiLb8yL1rNYMfrvVFhG4RoZe7i5NEWHTmgWhT+jMWC6RIq8E+grhxJwoqeYIRKo+EkJfx/Q6AgCFkqocFhootWv2lHgC5W+SUoJwbLJrWhSZeFcweTVmZbFSuwjvgCHcXUPBzcicmzYF9eVIFICERVRmYDByakEINPPcX3qwJmQx1JjwW/CU7irMUEFgha6MnCqQJnS5LaVQFfpo5GCipI5IsWYomfVvuD1MrpUXVdScoSzyrBfEe/hCbcpZdIO9JVDCTSFSeuJq8rCyLJWF77M+sK68H+kBMqURlS/Yi48HsEFLioZhbti/ChUoH5jecZ34vJU9rXvvpx983tHCtlRXod7YCr0xAcbsnCPlI0aVxRSrFxUeSyD7xDHVFNegPRvPJpC1ZkSCrzjuao1696XWlhnQ6Cq4K/KAz6ObCwpvku1m+pjJMAjXJlXIsWQwiE1FyM82dtDvCP6M02Rj/yap5d3oyBWnHIws2+Y2Rkze2Xx80tw7+tmdtzMRs3sF6vU16tsJcUs0YRUccNO21Xtl1k8ZfVlWdEi5PUFv4/x5NSkZ+uc21RMzlYrXvc8dJ40jCN+7zw8lsffES8UdKlYO/dn69NHW179yXs5EJAGLqxd6OMx30y/FI9E93gclDCPjAGng9rdjjRhryzKRioTXGUZNgyp+H1VUO1GfIPlcby4fJTJVKZssE9q/qiwbkox8hirdaSpmeZ7LrqBlaocfktc/5iZHTSznzGzO83sdTMbLKuvl/scENxqw5CDX0/F5ssmV5ZVO5SvqsUUTcBoV7GqL5Uax8qwaswaccVneGdo6lmeVDxB/KiQVEhI1e/PsjBXG/2QxvjNCrfdmHfqXipcofhICaLU2LuCw+eiV4F2GjpU99kAKHs2qouf9bmasqTx7CPF9zzGOId4UyQCywe/pjw2vF/FIIjKen+qhLVSkFIOKy2V9ZfN7LtZls1kWfaGNTyIT18LRDw9zFPX5heyQqrflnvWmVlreienGfp/T9/0VM4n9pzMUxYVeEom1tNIn3zHvn/kHfvW8yfs8tRsoV6EkeEh+9LmDYVUUP9gu5iOi/1RuHkaHabb4X9Mwbs8NZvXh/VPz9btyb1v5qmFnt4XpfoiXt4vLPfxD6+2v3bXLbblnnV5+qlKSWRajgwP2aOf3VRIQR4aHMjrwfHFNFysC/uhaOPAY8TpyipFWKXHqv7sOzlhl6dmczwxZRJpxTA0OGCDA836R4aH7MsPbyykeUa4qLoiiPrqvOxpplUA55JKBR8cqNnmTTe30M3L+ZxYdcNwXsZTeHeNvpOX97EaGhywL23eYGZWGGue755CzePi6abIN35/z/F3JU/4vDGzwpgqWHXDcD4/lgQirbGUH2t4DifN7FUze8LM1i5e/9dm9neh3P9lZr8W1PGome03s/0bNmzoWHMipKxhjk9HFgzWg/fZG1DhD/zNC624NtE4O2mscAKnskzYNc2PBd/Zeiw4h5jY60itczDgwhn3AS1axEstkqZcb8cRzzDCvjCO7awPpSxz/O332KPisqlEBPyduh9ZiMpCTXlkqp/o+fA7k1Pea6rO6BrzI49fCniNKbUw723xLn/1bmf0FNELwHAkjjV72cqy93mUOpqDvX+VeRbRFNvpxnuwaxFWMrOdZvYj8fllM/ugmQ2a2YCZ/Z6ZPbH4zB8K5fCrZW31as0hEkZq8SylSPBbLTIzKEHIYQ12a6sunuI1FAJlwomfjcI1ii64cBbVj+1EJ7qmjj5GHPlaWWgpJcwx3KL67dccNxeobDzwelQk+PEajnmVox9S9UUhwKhdF1YYcon2FkQ5+SnlzO07H6rFWgUq7MX0jpQzjns0H9XYqbp87JWxEdG0jBZVQ4EMeIJtp3BNlEPVj5ltNLMfLf7+upl9He7tMLOHyuro5TukGSKmjIQix6vLsgmUFeHXGQe29sssilQ/27U2IsGCE0+lMSoBonBVioEXVKvirSZZpMyR/nP15qbCKFbMuE3NtL4TnK3OqH3Gj63XKkov6r8yWCI+835wzD41hkrZocDn/qas6Khehv+/vauPtay66r/FDMM0QmEGRkU+OgwhtS02QGfMUIFU22hBEyRBM/zT/tHYVGusNSTS0DSY2D+qqY2tRoJKPy1FsURaMTi1LQUEylQYGISB6TCVr0CBYSxmhpn33vaPc/ZjvXXX2h/nnnvPfbz1S27uPfvss/ba++y919fe+2o+dllHS+hHpCxgbXxY/PI+ZrWP7Ifau+cCJwVZT81y7oKZEw4ATma/P4omzgAAb8PSgPReTDggndKEIrTgb2rQyOC1VS4vPzeJch745i+LttYJ5Sa3XJk54SOFoOYukpONNTi0NP53l6l2l/xqwjSlMcoDzXLvLpbB6xknWI1PGbBMTax8MpXtkdMqUxPtkbnRpY+872tBVV62FFQRudNteZtq9GW+3LLOUou2tK/JdrAEjWxji5a0ArR9GLKNUoKfjy9pqXALpytmUTh8GcBDbczhFiEsrkazSmk3gItL6PVlOeQgB0DNsjaeJ/Wsdp+D7xxN1UOjyc/W13jQXFtyqRzvrNz9YmmcqQlLtgtH1GQ1GjkXFx84vC6pttH4LNHIuNb9mdseVf/HOG7Ik65C6d9O/Z+HNsFY9bfqbE3+UijG8qy+zY8b0f6wRvIUx4N2pLjknX+0GJEWX7FgtZPWrrL+2vOyja2VXbIdZR7LXSe/tXbh90r7aAozJxz6/vR9ZLeVLs0/ayDKe/x5OXFZSOWxBIPcBCbpaX+vaNX9yNx8uHXn04tHcss6aB1W5uG0ajsy9+VzGhrf/D4foNoCgBwfso7aff6bu4JS51vxCTjyxxcTWG4XzhO3Vqx9GJrg0XjnadKlFNteE1ZcOByZG/3rVKu9Ih1OMyUopGXKedUEjVbHnCuOa/ayX+QUPG13ttXGWpvk3nOuD5YG8XNw4VCI3MTBB7Z8LhWg45PD/lcOJg9s4/RK4hXyWpuIuWDTBJhFs/GnP2NOMFZ7WRNormwJ6RayBJ9WX1mu1DhTsOojJwzNXRTL1dqMT2ypfBp/crLUJlXpesitg4/PRCGlWQ7aRGRpxqlytDpY/dtyv0U+XjvCfHQFWGrlmta+pf/FIOtjKYo10MaD1sYacvdL4cKhANpg06BpeCkJLrWdz33rsXDrzqeLJqicYMgNfItvTYBYNLUAY2m947fWXjVWBOfZ4tsSGlY9SiE1VX58gTYBH5kbje1oE3Z0y2h1tfzumjCx4jG5fsxpaM/JPFr7aQJYey9cG7bcVVadNF4lHZnH+q1p5bnFAak2y/FcOn6196gJttSY6goXDhloLydl4mtB1pSWwVfDlLiU+LP827pfAq7lplZipAYcb6dcEC0VU5FpuUGYW6FSoiXz53K+ZVmPKBhu3fn0yB4RjX9rIMe0+C5SfYyXz5+zAqR9QE5YvC68D8t0rR1StErfk1a/XB+10i2aKXq5eqXKt/qtVa58Xo5TuWjChcOULYdS4SAnl5R/UgYdSzpx/JaajoaUj5tfp9wqucleo10SeLPqqNHS7qXiJ9qkmTO14yCTsQzujrEGvSVYSpASxCXtbfGTy5OjleKVKxJSseDfOfeoVm5JO2t9XxM4Ml3SzI0hbayneIj3LatP1lMrx6qHRS8+K88U6wMuHAogO5imOfCJpERz4MscLYEjO6IVpNNw8NXm8C15Ro62ASdFI+av2UzD6VnBOZm/ZKDz/F02WlnXMY0PbM6LDMr2CWvyyk1aGu+SnjaBau/e4sHiVwsG8++c9WPVXRtDMY88wj0EfUWQVpecQEzdt5ZbWwpByR4cjY61VDY17nh7c4E87iqlCBcOBbC0EU1LLX0p1mTPy+ArRKwJUeM1fvMgc20H4nXs2uG0Dp4bqLl88V7KVVczIWlp8t1oq3v6RKmQk7zyicWaXKQgkC6IlBtR481K0/J0ycfrFRHbX/4rnqV0lPYxLU0T0tp9q92jIqUJBEk31/65Omp9VlsE0RUuHDLILVuztAOJVMe07sWOJicDC1wzlCt4rNUZKWjaYS1Sk3BXejWrk3I8yTQ5EeU0uNTkMm49U32Jv1Mrr/buuLJh8WgpPzl+c+nab22CtAQ4n/TkCruoxFh8lATNtQk9N6Yl36k4C//NrbuS+1adcryMY+26cEigdlLNLW/LDY54zTu8jAfkOqu2pDHHt4U+JnON5jjPyo/lkpHllU5w2oRrrcqyJhOpFdbWUaOtodYCjNc1S6Fr2i0lqK3fMZ+WlqLFn9n/ysHwl9t3txsNR/dWaOM4pvNDLLXJOFf/HN8h6P1HCnjZfrk5J9WePM84482FQwaWRNfylVgYWt6UtLfoWSiJDUgNI5d3UiiZnPh1avcov5aDJrfkVis3946sAH5qxVeu7ilFIaUQ5NJTh0PW0EwhpRhZv0vGSrzmAltbcBBP+bUUBau9+WIDKdw1vkuu5T1NwdNiW1rfswSbLNeq3zhw4VCAXKe27tUEFUvo5549+Gr+T3z4JFu6kmcS6CpoSzq8NmisgVe7SixqmyXPpfhLWZK8rBL3TqmATPEjy6yZWHLPpN6xpqlLQXbbQ8+OrCLj+S2hkhsPUejwtrLcipbSUALJn/YPiJYywvPHfDmh0JVPCRcOGdR0jtpBGZ8Zhx+JkvJ4B8vFVPpAyaRRqlWW0JU0UhpqiXtF2wMybvuklAjOVy64mBKk2m/r+ZTFU9L2XSapknhebAvNIpY8Wy5Gq07yfXLXUsp677pkmfejeGJqztLk7yfSSG0ulM+OAxcOBZCdROtwWkeq0SC7aCKptFSgq2Sp57gWRY2mpQnVHN+5SUdaR7UCiqdZSzf7gGa9yYlEW9lS089ySNUr9T5ybSoneS2P5VvPjQ2rT2h0apaY5+5Z7ZGiG9OiNSIthxqXUQy81/DcFS4cCsE7YzQ7c8cDlNItnUCtNM00jv8zoQW6SszNLi4xfr9Wy84JWmu1iaTBJ1Zr0ktNNrk/ESp5V7myJK3U5CAFRM1klys/x3dKaFiKA48BcE1cixNomr+2AifFo5YmhUqt8pWiycvVxq7VPw6+emRJ8FtbeWWVx2mk3v247i8JFw4VkJ3CmqhSL8Z6WTmfsKU5884mB6RVnsa3Fswr0SRT9Rmng9YKXD7IUhNniqeSDXslfKTqoPGTohvfpbaPQebTJsUuSktKAKXo8ncgLR65Wkrrn5p7rUZpkn25z3haSXtbPMU6W8eu73/loPrfDlp5qXFaMn5r4MJhQtA6bUoDyU24coKQz3NroRRcy+PHMFs88HI1rYnft/Yh1KD2WSvAXirQIo2SfBa/JZOxNmFaz3JrVVNKJF05aUl6peveU3sGeL20MuPzkq/421puyr9lusWDZnlYsYMcurzv3P2cBc0Faq4c+fy4ClgOLhwyGLfRUwLBOg7D0nxzyzhrtCTZKflkkBtUKa1J8jppAZHT2krp8HwlsQrr2dL8mvtFmwTkJFyy1DklbHJ9KKaVaN1WkF7r8zyfxsM4Fqb8LcdSzqef4qGP8T9OHosvrgxOQjCEkBYOR2GFY25+AXf/8EXMzS8U55d5V686CuefeSJWrzpq8VqDlc7vX3DWSYv5NN7Wrllt8qXR27JxHe5/8gDm5hcWn52bX8Bde17E/EIYqVekw+u0etVRap0vOOskzM0v4M7HX8Cdj7+gtk0Orxw8nN3oy/UAABAqSURBVGx/3gacN3mPp2s0JOYXFkbem4X4fCyvFGvXrMaWjeuw40cvY8vGdUvaUuM73t+6af1iP5B5+bXGf0kfivnOPe143LdvPw4dnjPrfd++/Yu8A1hS9paN6xbzyf6yddP6Eb5K2lqDpCO/t25aj/v27V/SRlq9NR5qx3+Ovy55OF+Sj/dtPR33P3lgkdepwpIay+nTx/EZJYhaivXvV5aGZ90rLbMkT+nKkqgtxsBZTG/q9WxRIEymx9UVNZp4dAmUuMlSpnWN2c/TSl0RllVY8lyqrBKLzcpb2845V0cqYG7xzcdCn1ZCqtyc9i3L7vKu+tLQu9Di74Tv37CsxD4AdyvZKG30+IIsd4zslPJ6HJN6XLNVo2cdU1yyC1am89VduSW0MU0G1VP8plwyJbB4KKVT0yba++6D31webYLLTZQxzVolllM4+KdLHSy6UuCkVvBYNGrvN+Xaf6Nb8y7j+Co9GZmDL5iwxlJXRVPDzAkHADcCeKD97APwQJu+EcBBdu/aEnqTjjnIDpsbMNqA7DJR1A4KjZ+ae12EGNd2ajTI2jLG4VErL9KpEU48zUqvXd7bB3hdrFM7x1lGLdElVlNabpxYtWWgtZaAdS+1wEKWy9svt5lN0ivZwKfxW7KqUVMAumDmhMMSBoBPA/hEeE047Kql0ec+B+06Coca95P1u8YdYC1rK+Uh9XxKwOXyyPwpwddFiyspsza/FfArtRo1Wqn6jjtoa8HL1SaxWj5yG9dSy6g1vjTacULOacIl+18kf9r7yWneKT54nhphm+K5hFaJgjlOH0sJh0ED0kREAH4bwA1D8gGMBqbk9aqjqDiYlgqY8uCeLF/SOP/ME7F2zericmXwbcvGdbhn70uLweKIQ4fnzCBcLpApy7vz8RdGnpX0eJvK52WweVxYddKCoWvXrC4KklqBVau+qTInFVTk5cZAOLA0gFyKQ4fncP1d+3Do8Jzah2O/zPUR7T6nnatLhHxPqXIjf7zu8hkrIC3LtQLhueC6RVMrF0ByTuDjo4TPXmFJjWl8AFwEJrnQWA7/B+B+ALcDuDDx7AcB7ACw4/TTT+8sOTlS5l1XDczaFcnp9GGmp8zlUs0vp5FYprjmctHKs/J2sagsrX2a2notclYHz9eVfgjlp8amUOorz9HXrO3cRrAS2iVWfKpfWvlq7uXKy9Hqajn0CQxhORDRt4hol/K5lGW7AkuthmcBnB5COBfAHwH4KhG9UaMfQrguhLA5hLB5w4YNvfDMJfChw3NLlsfJZYWaFixx6PAcvnTP/yxqYJpWoy0XtJDSOi1tRmq4luZnLf3jddHuy2WTfFlrii+uOdUsc0zlLaEz9eWADJZWy9F1aWV8LvZbvnS2C/iS6dwyTAtyDMVvi3auP/J8nG6ON97XUtayVk7Ju9D4tfq7bIuchWlZHlOBJTUm/QGwGsBzAE5N5PkugM05Wn3vkI7S3DqDpyYoZC3D7OLbL1k9kno+l57z45as2slpaCV5+/SpSjqlq6T6Rk2dxrEc+Kcv5AKkVjofQ7k+pNHrU6OPn5pd/TxfydjI0eS0Uv9bMk0LGDMac3gPgEdDCE/FBCLaQESr2t+bAJwFYO+0GZObx7T7ctObpbHGmIG2WUn7neLJKiNq7Lfvft60YHIxhhQf3NrI0c5pq5IPeW3FflI+av5sCrEec/ML+Ls7nyii2QcOHZ7D7bt/vKQ8bcMTv8ch2yeFaLWNu7Er4tDhObOtcv2Kj6F4HTX+krp3tVi0/iTv5/qUtDZy9bxv336TluQ5Wtya1c3bauqWgsCQpW/DaCD6IgAPEtFOADcB+FAI4aWpcwY9WDlOUKiPF52avLduWo9VR+mCo9RtVVs2p52D3EUbaWpCU+bhk0qKfsmEGIXXW372jZ3bo2bSnZtfwF17XsBDT+3HXXuWCj25UMB6nrtaSnai9+les9qqpF/JMRSvAdutNi6sAHp87xecddJiwD4lqORz1g7mufmFJfWsadeUO6vEbRbzTgyWSbGcPtM8eG+awc7asmpdTn3UpWT38jhmMg+yluYtpduVny57QaTbQQvmp57nz4xz1HqXOnQJ5Hal2Qdq6jXOYgjruqvLsourueseqAjMqFtpWaJG2xxHqtcEwyzeatw3XXmMGo7UIKWV1eVcHRlkLQ1AlqJkqa5WRsq9Z/EllyTLYH4K0h2RWoI7rosyVb6WXmtF5WjKfF1QU69xFkPwBQbxWlq5NXUZNPisYHgOXgeoWXVSY3aWuAZKVr2UuG9ySPlb79n7Eu7Z+9LI4M8dRlgaIyjdj1ADyZ8UnKVuH/5MrbDtUp9Ue3ahMw5q6lyj7EzC5ZRzw5VC6y+cX+liGqcuJc+VKhhd4MJhTGgdwJqQaztLSRyjdFlnVy0TSPO9ds3qkeBaCf2aGEENrzXgA71kCaXMK2nVnPA6NMblo9YiLM3b1cpMoYsVnkovUbzid9e6lPbDSYIat9PyxubNm8OOHTumUpYWjOZp8aWlXA9d3CtDm5mT4GEW6hUh32HU/Pp6h/G5VN+YFsblY1bqUQL5LlPvVKtTKn3Sdc+V0QcPRPSDEMJm7d5sv9kZQ0pr5L9Tgya1QqGmzJJna9GX6V2KLkJyUrA0v1ze2jJmYYniuNp5X9r9pK0oqeGnxpJVp1T6pFFiZU0SLhwqUGMa16BLpy15ti8erPzTxKR80dNEzRLFSWPomMU03qcVQK4V+kML86HgbqUpo09XRcpUnoT7Smpg03QrxElkuQ/UIV1ps+TGA+r4mTXep4lJ1t3dSjOCnIXQlZYmGLpoZaUB5EkEDVOYRvBtGhhaMMya5dXHgojXO4asu1sOU0afE0SK1ustgLzcNcdZCOIupzaUvC4n3vuGWw4rBH2v1Z9GOZOkuRzK7gPTtrYsHpYDNG15ufA+CQxV95Xb4g7HlLGSJ7gazIIgdbhwcDgcMwgXDMPD34DD4XA4RuDCweFwOBwjcOHgcDgcjhG4cHA4HA7HCFw4OBwOh2MELhwcDofDMQIXDg6Hw+EYwevi+Awi+jGAH41B4iQAL/TEziThfPaL5cInsHx4dT77xaT5fFMIYYN243UhHMYFEe2wzheZJTif/WK58AksH16dz34xJJ/uVnI4HA7HCFw4OBwOh2MELhwaXDc0A4VwPvvFcuETWD68Op/9YjA+PebgcDgcjhG45eBwOByOEbhwcDgcDscIVrRwIKL3EtFuItpDRFcNzQ8HEe0jooeI6AEi2tGmrSei7UT0ePu9biDeriei54loF0tTeaMGn23b+EEiOm9gPq8hoqfbdn2AiC5h9z7W8rmbiH5tinyeRkTfIaJHiOhhIvpImz5TbZrgcxbbdC0RfZ+Idra8/kmbfgYR3du26Y1EtKZNP6a93tPe3zgwn18goidYm57Tpk/v3YcQVuQHwCoAPwSwCcAaADsBvHVovhh/+wCcJNL+DMBV7e+rAHxqIN4uAnAegF053gBcAuDfABCArQDuHZjPawBcqeR9a9sHjgFwRts3Vk2Jz5MBnNf+Pg7AYy0/M9WmCT5nsU0JwLHt76MB3Nu21T8C2NamXwvgd9vfvwfg2vb3NgA3DsznFwBcruSf2rtfyZbDLwLYE0LYG0I4DOBrAC4dmKccLgXwxfb3FwH85hBMhBC+B+AlkWzxdimAL4UG9wA4gYhOHpBPC5cC+FoI4dUQwhMA9qDpIxNHCOHZEMJ/tb9/AuARAKdgxto0waeFIds0hBBeaS+Pbj8BwK8AuKlNl20a2/omAO8mIhqQTwtTe/crWTicAuBJdv0U0h192ggA/p2IfkBEH2zTfiaE8CzQDFQAPz0Yd6OweJvFdv791iS/nrnmZoLP1p1xLhoNcmbbVPAJzGCbEtEqInoAwPMAtqOxXF4OIcwp/Czy2t4/AODEIfgMIcQ2/WTbpp8homMkny0m1qYrWThoWsEsrev9pRDCeQAuBvBhIrpoaIY6Ytba+W8AnAngHADPAvh0mz44n0R0LIB/BvCHIYT/TWVV0qbGq8LnTLZpCGE+hHAOgFPRWCxvSfAzGK+STyI6G8DHAPw8gC0A1gP442nzuZKFw1MATmPXpwJ4ZiBeRhBCeKb9fh7AzWg693PRhGy/nx+OwxFYvM1UO4cQnmsH4wKAv8Vrbo5B+SSio9FMuP8QQvh6mzxzbarxOattGhFCeBnAd9H46E8gotUKP4u8tvePR7lLsm8+39u68EII4VUAn8cAbbqShcN9AM5qVy+sQROEumVgngAARPRTRHRc/A3gVwHsQsPf+9ts7wfwL8NwqMLi7RYA72tXWWwFcCC6SoaA8M9ehqZdgYbPbe2qlTMAnAXg+1PiiQD8PYBHQgh/wW7NVJtafM5om24gohPa328A8B40MZLvALi8zSbbNLb15QC+HdoI8AB8PsqUAkITF+FtOp13P6lI93L4oIn8P4bGF3n10PwwvjahWeWxE8DDkTc0PtD/APB4+71+IP5uQOM+OIJGk/mAxRsaM/iv2zZ+CMDmgfn8csvHg2gG2sks/9Utn7sBXDxFPi9A4xp4EMAD7eeSWWvTBJ+z2KZvB3B/y9MuAJ9o0zehEVB7APwTgGPa9LXt9Z72/qaB+fx226a7AHwFr61omtq79+MzHA6HwzGClexWcjgcDocBFw4Oh8PhGIELB4fD4XCMwIWDw+FwOEbgwsHhcDgcI3Dh4HAwENF8ewrmLiL6RlyDzu5/lIgOEdHxxvPvIqIDRHSrcf8LRHS5dq+AtwuJ6L+JnTLrcEwKLhwcjqU4GEI4J4RwNpodsh8W969As4HysgSNO0IIlyTud0II4Q40+wocjonDhYPDYeNusEPNiOhMAMcC+DgaIZFFu5P1r1qN/1/BDkskoncQ0e3t4Yq3sV2xW9oD1+4moj93S8ExBFw4OBwKiGgVgHdj6ZEqV6DZdX0HgDcTUcmpuJcBeDOAXwDwOwDe2dI/GsDn0JzZ/w4A1wP4ZPvM5wF8KIRwPoD58WvjcNTDhYPDsRRvaI9PfhHNaZjb2b1taP6fYAHA1wH8VgG9iwDcEJqD6Z5BcywC0AiMswFsb8v7OIBT2xjHcSGE/2zzfXXsGjkcHbA6n8XhWFE4GEI4pw04fxNNzOGzRPR2NAfHbW//A2YNgL1ozrnJQTujhgA83FoHryUO9NevDoeEWw4Oh4IQwgEAfwDgytYFdAWAa0IIG9vPzwE4hYjelCH1PTQnk65qYwq/3KbvBrCBiM4HGjcTEb0thLAfwE/aEzeBxlpxOKYOFw4Oh4EQwv1oTsbd1n5uFlluRn7yvhnNqaoPoflTnNtb2ofRHA39KSLaieaE03e2z3wAwHVEdDcaC+PA2JVxOCrhp7I6HD2CiN4F4MoQwm+MQePY0P6vMBFdheYI7I+01xsBfLNdautwTAxuOTgc/eIwgLOtTXCF+PW4EQ/AhQD+FGg2wQH4BoAXxmfT4UjDLQeHw+FwjMAtB4fD4XCMwIWDw+FwOEbgwsHhcDgcI3Dh4HA4HI4RuHBwOBwOxwj+H3V0zC7JEIPUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Typical plots: plt.plot, plt.hist, plt.hist2d, plt.scatter\n",
    "plt.scatter(rnd_x, rnd_y, s=0.01)\n",
    "plt.xlabel('RA [deg]')\n",
    "plt.ylabel('Dec [deg]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(rnd_x, rnd_y);\n",
    "plt.xlabel('RA [deg]')\n",
    "plt.ylabel('Dec [deg]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x117bf3978>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rnd_x, histtype='step', label='RA')\n",
    "plt.hist(rnd_y, histtype='step', label='Dec')\n",
    "plt.xlabel('X [deg]')\n",
    "plt.ylabel('Number of datapoints')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple tools to visualize data! Check for example `seaborn`. And there are even interactive tools like `bokeh`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sky map representation (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import healpy as hp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`healpy`](https://healpy.readthedocs.io/en/latest/) is a library that allows to decompose the sky (tesellate) in equal area cells. It is pretty widely used in the cosmology community and there are some nice mathematical properties about that particular way of breaking the sky into pieces but for the purposes of this notebook we are going to just use it as a tool to make 2D histograms of the sky"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.967782589078496\n"
     ]
    }
   ],
   "source": [
    "nside = 64 # This parameter (which should be a power of 2) controls the resolution of the map, the bigger the number, the smaller the pixels\n",
    "print(hp.nside2resol(nside, arcmin=True)) # This is the pixel resolution in arcminutes\n",
    "pixel_numbers = hp.ang2pix(nside, rnd_x, rnd_y, lonlat=True) # This function tells us the number of pixel that corresponds to each pair x, y. We use the lonlat=True option to pass them as RA, Dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42998  3265 39690 ...  1978 14942 33033]\n"
     ]
    }
   ],
   "source": [
    "print(pixel_numbers) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a bunch of pixel numbers (one per pair RA, Dec) so we have to count them to see how many pairs are in each cell/pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hp_map = np.bincount(pixel_numbers, minlength=hp.nside2npix(nside))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x388.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.mollview(hp_map, title='Sky plot', unit='Galaxies/pixel')\n",
    "#Our first sky plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}