{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running *species*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows what a typical workflow with [species](https://species.readthedocs.io/en/latest/species.html) looks like. We will compare the colors and magnitudes of a few directly imaged companions with those of field dwarfs from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiating *species*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the [species](https://species.readthedocs.io/en/latest/species.html) toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the [configuration file](https://species.readthedocs.io/en/latest/configuration.html) (*species_config.ini*) is read from the working folder. Since the file does not exist, it is created with default values for the path of the HDF5 database and the location where the external data is stored. The database and data folder are also created since this is the first time that we run [species](https://species.readthedocs.io/en/latest/species.html) with the current configuration file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initiating species v0.3.1... [DONE]\n",
      "Creating species_config.ini... [DONE]\n",
      "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
      "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
      "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
      "Creating species_database.hdf5... [DONE]\n",
      "Creating data folder... [DONE]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<species.core.setup.SpeciesInit at 0x13db6c0b8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.SpeciesInit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a photometric library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database) object is now created, which can be used for importing data from the chosen data folder into the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = species.Database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The photometric data and parallaxes from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html) are downloaded and added to the database with the [add_photometry](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_photometry) method of [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Database of Ultracool Parallaxes (307 kB)... [DONE]\n",
      "Adding Database of Ultracool Parallaxes... [DONE]\n"
     ]
    }
   ],
   "source": [
    "database.add_photometry('vlm-plx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding companion data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A library of distances and magnitudes of directly imaged companions are available in the [species](https://species.readthedocs.io/en/latest/species.html) package (see [species.data.companions](https://github.com/tomasstolker/species/blob/master/species/data/companions.py)), which can be added to the [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database) with the [add_companion](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_companion) method (alternatively, photometric data of individual objects can be manually imported with [add_object](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_object)). In this example, we add the distance and magnitudes of 51 Eri b, beta Pic b, and PZ Tel B to the database. This will automatically download the filter profiles and a flux calibrated spectrum of Vega. These are required for the flux calibration of the companion magnitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Vega spectrum (270 kB)... [DONE]\n",
      "Adding Vega spectrum... [DONE]\n",
      "Adding filter: MKO/NSFCam.J... [DONE]\n",
      "Adding filter: MKO/NSFCam.H... [DONE]\n",
      "Adding filter: MKO/NSFCam.K... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_B_H... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_H23_2... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_1... [DONE]\n",
      "Adding filter: Keck/NIRC2.Lp... [DONE]\n",
      "Adding filter: Keck/NIRC2.Ms... [DONE]\n",
      "Adding object: 51 Eri b\n",
      "   - Distance (pc) = 29.78 +/- 0.12\n",
      "   - MKO/NSFCam.J:\n",
      "      - Apparent magnitude = 19.04 +/- 0.40\n",
      "      - Flux (W m-2 um-1) = 7.52e-17 +/- 2.83e-17\n",
      "   - MKO/NSFCam.H:\n",
      "      - Apparent magnitude = 18.99 +/- 0.21\n",
      "      - Flux (W m-2 um-1) = 3.12e-17 +/- 6.07e-18\n",
      "   - MKO/NSFCam.K:\n",
      "      - Apparent magnitude = 18.67 +/- 0.19\n",
      "      - Flux (W m-2 um-1) = 1.42e-17 +/- 2.49e-18\n",
      "   - Paranal/SPHERE.IRDIS_B_H:\n",
      "      - Apparent magnitude = 19.45 +/- 0.29\n",
      "      - Flux (W m-2 um-1) = 2.06e-17 +/- 5.57e-18\n",
      "   - Paranal/SPHERE.IRDIS_D_H23_2:\n",
      "      - Apparent magnitude = 18.41 +/- 0.26\n",
      "      - Flux (W m-2 um-1) = 5.66e-17 +/- 1.37e-17\n",
      "   - Paranal/SPHERE.IRDIS_D_K12_1:\n",
      "      - Apparent magnitude = 17.55 +/- 0.14\n",
      "      - Flux (W m-2 um-1) = 4.64e-17 +/- 6.00e-18\n",
      "   - Keck/NIRC2.Lp:\n",
      "      - Apparent magnitude = 16.20 +/- 0.11\n",
      "      - Flux (W m-2 um-1) = 1.79e-17 +/- 1.81e-18\n",
      "   - Keck/NIRC2.Ms:\n",
      "      - Apparent magnitude = 16.10 +/- 0.50\n",
      "      - Flux (W m-2 um-1) = 8.47e-18 +/- 4.04e-18\n",
      "Adding filter: LCO/VisAO.Ys... [DONE]\n",
      "Adding filter: Paranal/NACO.J... [DONE]\n",
      "Adding filter: Gemini/NICI.ED286... [DONE]\n",
      "Adding filter: Paranal/NACO.H... [DONE]\n",
      "Adding filter: Paranal/NACO.Ks... [DONE]\n",
      "Adding filter: Paranal/NACO.NB374... [DONE]\n",
      "Adding filter: Paranal/NACO.Lp... [DONE]\n",
      "Adding filter: Paranal/NACO.NB405... [DONE]\n",
      "Adding filter: Paranal/NACO.Mp... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_K12_2... [DONE]\n",
      "Adding object: beta Pic b\n",
      "   - Distance (pc) = 19.75 +/- 0.13\n",
      "   - LCO/VisAO.Ys:\n",
      "      - Apparent magnitude = 15.53 +/- 0.34\n",
      "      - Flux (W m-2 um-1) = 4.27e-15 +/- 1.36e-15\n",
      "   - Paranal/NACO.J:\n",
      "      - Apparent magnitude = 14.11 +/- 0.21\n",
      "      - Flux (W m-2 um-1) = 6.87e-15 +/- 1.34e-15\n",
      "   - Gemini/NICI.ED286:\n",
      "      - Apparent magnitude = 13.18 +/- 0.15\n",
      "      - Flux (W m-2 um-1) = 6.99e-15 +/- 9.69e-16\n",
      "   - Paranal/NACO.H:\n",
      "      - Apparent magnitude = 13.32 +/- 0.14\n",
      "      - Flux (W m-2 um-1) = 5.47e-15 +/- 7.08e-16\n",
      "   - Paranal/NACO.Ks:\n",
      "      - Apparent magnitude = 12.64 +/- 0.11\n",
      "      - Flux (W m-2 um-1) = 4.04e-15 +/- 4.10e-16\n",
      "   - Paranal/NACO.NB374:\n",
      "      - Apparent magnitude = 11.25 +/- 0.23\n",
      "      - Flux (W m-2 um-1) = 1.69e-15 +/- 3.61e-16\n",
      "   - Paranal/NACO.Lp:\n",
      "      - Apparent magnitude = 11.30 +/- 0.06\n",
      "      - Flux (W m-2 um-1) = 1.59e-15 +/- 8.79e-17\n",
      "   - Paranal/NACO.NB405:\n",
      "      - Apparent magnitude = 10.98 +/- 0.05\n",
      "      - Flux (W m-2 um-1) = 1.61e-15 +/- 7.42e-17\n",
      "   - Paranal/NACO.Mp:\n",
      "      - Apparent magnitude = 11.10 +/- 0.12\n",
      "      - Flux (W m-2 um-1) = 7.86e-16 +/- 8.70e-17\n",
      "   - Paranal/SPHERE.IRDIS_D_K12_1:\n",
      "      - Apparent magnitude = 12.57 +/- 0.00\n",
      "      - Flux (W m-2 um-1) = 4.56e-15 +/- 1.26e-17\n",
      "   - Paranal/SPHERE.IRDIS_D_K12_2:\n",
      "      - Apparent magnitude = 12.21 +/- 0.00\n",
      "      - Flux (W m-2 um-1) = 4.92e-15 +/- 9.05e-18\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_R_PRIM... [DONE]\n",
      "Adding filter: Paranal/SPHERE.ZIMPOL_I_PRIM... [DONE]\n",
      "Adding filter: Paranal/SPHERE.IRDIS_D_H23_3... [DONE]\n",
      "Adding filter: Gemini/NIRI.H2S1v2-1-G0220... [DONE]\n",
      "Adding object: PZ Tel B\n",
      "   - Distance (pc) = 47.13 +/- 0.13\n",
      "   - Paranal/SPHERE.ZIMPOL_R_PRIM:\n",
      "      - Apparent magnitude = 17.84 +/- 0.31\n",
      "      - Flux (W m-2 um-1) = 1.83e-15 +/- 5.29e-16\n",
      "   - Paranal/SPHERE.ZIMPOL_I_PRIM:\n",
      "      - Apparent magnitude = 15.16 +/- 0.12\n",
      "      - Flux (W m-2 um-1) = 1.09e-14 +/- 1.20e-15\n",
      "   - Paranal/SPHERE.IRDIS_D_H23_2:\n",
      "      - Apparent magnitude = 11.78 +/- 0.19\n",
      "      - Flux (W m-2 um-1) = 2.54e-14 +/- 4.47e-15\n",
      "   - Paranal/SPHERE.IRDIS_D_H23_3:\n",
      "      - Apparent magnitude = 11.65 +/- 0.19\n",
      "      - Flux (W m-2 um-1) = 2.43e-14 +/- 4.27e-15\n",
      "   - Paranal/SPHERE.IRDIS_D_K12_1:\n",
      "      - Apparent magnitude = 11.56 +/- 0.09\n",
      "      - Flux (W m-2 um-1) = 1.15e-14 +/- 9.58e-16\n",
      "   - Paranal/SPHERE.IRDIS_D_K12_2:\n",
      "      - Apparent magnitude = 11.29 +/- 0.10\n",
      "      - Flux (W m-2 um-1) = 1.14e-14 +/- 1.05e-15\n",
      "   - Paranal/NACO.J:\n",
      "      - Apparent magnitude = 12.47 +/- 0.20\n",
      "      - Flux (W m-2 um-1) = 3.11e-14 +/- 5.76e-15\n",
      "   - Paranal/NACO.H:\n",
      "      - Apparent magnitude = 11.93 +/- 0.14\n",
      "      - Flux (W m-2 um-1) = 1.97e-14 +/- 2.55e-15\n",
      "   - Paranal/NACO.Ks:\n",
      "      - Apparent magnitude = 11.53 +/- 0.07\n",
      "      - Flux (W m-2 um-1) = 1.12e-14 +/- 7.25e-16\n",
      "   - Paranal/NACO.Lp:\n",
      "      - Apparent magnitude = 11.04 +/- 0.22\n",
      "      - Flux (W m-2 um-1) = 2.02e-15 +/- 4.12e-16\n",
      "   - Paranal/NACO.NB405:\n",
      "      - Apparent magnitude = 10.94 +/- 0.07\n",
      "      - Flux (W m-2 um-1) = 1.67e-15 +/- 1.08e-16\n",
      "   - Paranal/NACO.Mp:\n",
      "      - Apparent magnitude = 10.93 +/- 0.03\n",
      "      - Flux (W m-2 um-1) = 9.19e-16 +/- 2.54e-17\n",
      "   - Gemini/NICI.ED286:\n",
      "      - Apparent magnitude = 11.68 +/- 0.14\n",
      "      - Flux (W m-2 um-1) = 2.78e-14 +/- 3.60e-15\n",
      "   - Gemini/NIRI.H2S1v2-1-G0220:\n",
      "      - Apparent magnitude = 11.39 +/- 0.14\n",
      "      - Flux (W m-2 um-1) = 1.06e-14 +/- 1.37e-15\n"
     ]
    }
   ],
   "source": [
    "database.add_companion('51 Eri b')\n",
    "database.add_companion('beta Pic b')\n",
    "database.add_companion('PZ Tel B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading color-magnitude data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colors and magnitudes from the [Database of Ultracool Parallaxes](http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html) are read from the database in the working folder by first creating an instance of [ReadColorMagnitude](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_color.ReadColorMagnitude)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormag = species.ReadColorMagnitude(library='vlm-plx',\n",
    "                                      filters_color=('MKO/NSFCam.J', 'MKO/NSFCam.H'),\n",
    "                                      filter_mag='MKO/NSFCam.J')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data of the field dwarfs are now read with the [get_color_magnitude](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_color.ReadColorMagnitude.get_color_magnitude) method. This returns a [ColorMagBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ColorMagBox) with the requested data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbox = colormag.get_color_magnitude(object_type='field')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a list with the color and magnitude filter names that we want to extract for the directly imaged companions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects = [('51 Eri b', 'MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.J'),\n",
    "           ('beta Pic b', 'Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J'),\n",
    "           ('PZ Tel B', 'Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a color-magnitude diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now combine the selected data in a color-magnitude diagram. The [ColorMagBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ColorMagBox) is provided in a list to the [plot_color_magnitude](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_color.plot_color_magnitude) function. The list with directly imaged objects is provided as argument of `objects`. We also add a reddening vector to show how the color changes for a size distribution of enstatite grains with a geometric mean radius of 0.1 $\\mu$m and an extinction in the J band of 1 mag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading optical constants (87 kB)... [DONE]\n",
      "Unpacking optical constants... [DONE]\n",
      "Adding optical constants of MgSiO3... [DONE]\n",
      "Adding optical constants of Fe... [DONE]\n",
      "Downloading log-normal dust cross sections (231 kB)... [DONE]\n",
      "Adding log-normal dust cross sections:\n",
      "   - Data shape (n_wavelength, n_radius, n_sigma): (67, 20, 20)\n",
      "   - Wavelength range: 0.4 - 10.0 um\n",
      "   - Mean geometric radius range: 0.001 - 10.0 um\n",
      "   - Geometric standard deviation range: 1.1 - 10.0\n",
      "Downloading power-law dust cross sections (231 kB)... [DONE]\n",
      "Adding power-law dust cross sections\n",
      "   - Data shape (n_wavelength, n_radius, n_exponent): (132, 50, 50)\n",
      "   - Wavelength range: 0.4 - 10.0 um\n",
      "   - Maximum grain radius range: 0.01 - 100.0 um\n",
      "   - Power-law exponent range: -10.0 - 10.0\n",
      "Plotting color-magnitude diagram: color_mag.png... [DONE]\n"
     ]
    }
   ],
   "source": [
    "species.plot_color_magnitude(boxes=[colorbox, ],\n",
    "                             objects=objects,\n",
    "                             reddening=[(('MKO/NSFCam.J', 'MKO/NSFCam.H'), ('MKO/NSFCam.J', 1.), 'MgSiO3', 0.1, (-0.8, 10.))],\n",
    "                             label_x='J - H',\n",
    "                             label_y='M$_\\mathregular{J}$',\n",
    "                             xlim=(-1.2, 1.5),\n",
    "                             ylim=(21., 8.),\n",
    "                             offset=(-0.08, -0.08),\n",
    "                             companion_labels=True,\n",
    "                             field_range=('late M', 'late T'),\n",
    "                             legend=None,\n",
    "                             output='color_mag.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the result. The plot is stored in the working folder. The discrete colorbar shows the spectral types of the field dwarfs and the directly imaged objects are labeled individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jiYJtmDOkJ0bBGgxpztBxg8jITu7UmjBrbDdLY9gfepWCNU4uQ2/E4XRw5hUnQ4t+ggNG9GfOOTNTI5TBOLkMht7E7i17+OS1T6mtrGfUocOZecpUXB5XqsXq0/SGOFiDwQAMHjOQc757WqrFMOwHvcpEYDAYDOmEUbAGg8HQRfQqBdsTnVyxWCzVIhgMhgPAOLnSmEgkgsNhTOAGQ0/GZHKlKVu2bOGYY45hxYoVqRbFYDB0EWYL1c2oKiKCx+Ohvr6eRYsWMXXq1FSLZehhbFmxjU8XrCISijBx9jgOPXYSdrs91WIZWmAUbDcjYkWLP/PMM0yePBmPxwNAOBzmjTfewOPxcOKJJ6ZSREOa89aj7/POfxcmjle+v5YV767h4pu/iM1mPpSmE2nx1xCR2SKySUQuazE+TUQWichCEXkh3j6mTXqCk6umpoZ//etf5Ofn86Mf/YitW7eyYcMGHnvsMb7+9a/zwAMPUFFRAUBPt48b2mfvjlJKdpbt1zUVe6t454mFrcY3LN3MmkUbOks0QwfoiJMr5QpWRM4DrgGqWoy7gOeAG1V1LrAM+Ht7axUVFaGqaa1gRYRt27YxdOhQNmzYwNtvv42IEAqFmDNnDg8//DA5OTlUVVUldrsm0qB3sWN9EX+66p/8+Xv38aer/slffvAvijZ1rJnhpuWfQxvvuxuXbulEKQ37orCwkKKionbPSQcTwRJVfUZE3mkxfgYQVdUF8eP7gK0iUqCqJd0qYSeSlZXFz3/+cyKRCHa7naOPPpqBAwfyv//9j5tuuomlS5fy0ksvUVxczPz585k7dy4+ny/VYhs6ifoaPw8VPkGgLpgY27OthH/f+l9+dN93cXvd7V7v9radGtvenCE1pHwHq6o725iaDaxrct4OoB6Y0R1ydRUNu1GHw4GIMHz4cO69915WrFjB4YcfzoMPPshRRx3Fn/70J1566SWuuOIKPv74Y958800ee+wxamtrU/wEhoPhs3dWE6gLUlddT/meSmor61BV/DUBVry3ts3rotEo4VCYSUeMw5PlaX2CwLQTD+1CyQ0HQjrsYNtiIC3MBkAlMKD7Rek8WjohRIQf/OAHzJs3D4fDwejRo7Hb7QSDQXJycjjrrLNwu9089thjzJ07l4ceeoiCggLOP/984zXugZTtrmDbmp34awOJMXeGi+ETh1BT3vrN018X4KV/vMEHT39MdXkNBcP6c8z5R7Dqg3XUV/sBsDvtnP6NExky1jRBTDdSvoPdB8msTZJkDGh0cjV8past9qmnnuKyyy5j8+bNiR3tYYcdRn19PXPmzGH79u38+Mc/pn///kyZMoWxY8fi9XoZPXo03/3udwkEAuzZs4ctWyybW4MzzDjF0p/iLXubKVeAYH2IPdtKGTGptcPkocIneP6e1yjaVExNeR1bVmzj0dufYsbJU/nqTV/kguvm8+MHvsecs03Zwu6isLAwoWPS3snVDnuB3BZjufHxpDQ4uRq+0lXBnnbaaYwdO5YjjzyS733vexQVFREMBvnvf/9LMBjk6KOPJhQKceSRR1JSUkJRURGXX3459913H48++ihnnXUWL7zwAn/961+BxtCvhu9G0aYve7bvxetr/RFfYzFGTGn+z7p9XREr31vbSiFHIzFe+NtrjJwyjKnHTSEzO6NLZTY0p7CwMKFj9uXkSmcFuwSY2HAgIsOBDGBpyiTqJLKysrj55ptZv349Pp+PqVOn8rOf/YyTTjqJ8vJy7rnnHs4++2ymTZuG0+nk/vvvp6amhv/7v//jmWeeIT8/nzfffJPjjjsOgIULF/Kvf/2Ljz/+GGhUtIb0IxqJMXzCEPoPy8eT4caT4aZgWD+GjB/U6vNa6c4y6qrrk65TXxPg85Xbu0Fiw8GQzgr2FcAhIsfHjy8Hnu7JEQQt6devH3feeScrV66krq6OY489llWrVnHNNdfwhS98Aa/Xy9SpU/nVr35FcXExTz31FEcffTSrV69GRDj33HO59957ee6555g4cSJ79uzh97//PRs2mHjIdGXS7HHY7Db6D8ln1KHDGXXocPoNyWPc4aNbFc8eMLIAuz35v6g7w4U7w0QNpDspV7AiMjMeojUNuEFEngZQ1SDwBeAOEfkAmAV8J0VidilDhgzhnnvuYfHixWzatIkjjjiCu+66C7/fj6ricDg488wzueGGG5g/fz7f/OY3mTt3LmCZRc444wyOOeYY5s+fz9lnn82iRYtS/ESGtjjl0nlk928edpeZk8GZ3zq51bnDxg9mylETW407nHZyB+Tw7hOL+O3lf+Xft/6Xz1du6zKZDQdOr6qm1cCtt96atvbXjrB69WpuvvlmFi9ezM0338zll1+eCOsCKC0tRUTo168ft912G5dddhkjR44E4D//+Q/PPPMM//vf/4DG2geG9MFfF+DTt1exZ1sJ/YfmM/2kw9q0o4YCIf76w/tZ/MpyopEomTmZ9BuaZ9WzyGiMmRWb8PVbLmD8jDHd9Rh9nsLCQm677bamQ62qafUqBduTyhV2hCVLlnDTTTexZcsWbrvtNi666CJEJBHqpaq88sor7Nixg9mzZzN9+nSOP/54rrrqKqZPn86gQYPIyclJ8VMYOoPaSiuCwJPp5pX73qK0qLzVOcMmDOHbv7skBdL1XfZVrtAo2B7AggULuOmmm6ipqeH2229n/vz5zXale/fu5fnnnycajeL1ernkkku45ppriEaj/O53v8Plsmx1DbtZv99vBa6Hw+Tl5aXqsQwHQCgQ4hcX3p18UuAXz93QvQL1cUw92F7ACSecwMKFC/n1r3/NLbfcwpw5c3jrrbcS8wMGDOCKK67g29/+Nueccw4ABQUFzJw5E5fLlYi1FRFqamq47rrr+Oijj4hEIil5HsOB43Q7ycyxzAnRSJS920vYumYne7eXkpWbmWLpDC1J50wuQxNEhLPPPpszzzyTJ554gu985zuMGDGCX/7ylxx55JGICIFAgPfff59du3aRk5PD/Pnzm63x+eef8/zzzzN69GhOPvlk/H4/Tz/9NKeeeipZWVkpejJDU6pKq1n5/lpC/hATZo1l2ITmn8hEhCPPmsnL/3yDDZ9sIRQMW+OAvzbA7i17GDxmYAokNySjV+1ge0K5woPFZrNx0UUXsWbNGi666CIuuOACvvCFL7By5Uo8Hg/z589n9OjRjBs3LvHxv8Fmu3r1asrLy/nGN77BkiVLuPvuu9m8eTMPPfQQL7/8ciofy4BV1/X3V/6d1x5YwILHF/KP6x7iub++2uq8eV8+mpqKOsKhuHIVwelx4q8N8I/rHupusfssPaJcYWfSE8oVdhZOp5NvfetbbNy4kXnz5nHyySdz8cUXs2nTJk477TROO+20xLkNdvY9e/YwaNAg8vLyePPNNxk6dCg//vGPyc3NpbKystm5hu7FXxfgmT+/TDTSvDTloueX8PhvnuWd/y6keKuVxBgJR6jcW0VmdgYZ2V4yc7yJGNoNSzcndrWGrqUj5Qp7lYLti3g8Hq655ho2bdrEpEmTmDNnDt/+9reb/eEbHGK7d+9mzpw52Gw2VJWZM2cSCAQoKysjGAw2Oxdgx44dPPDAA/j9/u59qD7IxqVbCAcsxaiqxKIxKkuq2bJiG28+/C5vPfo+f/3h/bz+73cIB+O280RESePfLBaLYbOZsLx0wSjYXoLP50uk3+bm5jJ16lSuu+46SktLE+ecc845iU62tbW1FBUV8cgjj1BVVcUhhxwCNC/uHQwG+f73v89nn33WvQ/TR1FVSnaWsXHpFla+v5YNn2ymtrKOumo/sZj1yeL9pz6itKi8lW22gdGHjsThNK6VdMEo2F5Gv379+M1vfsPKlSvx+/1MnDiRwsJCqqurOfzww5kyZQoAt912Gy6Xi7KyMq644gpmzZoFWPbaBiX72muvcdZZZzFnzhxjOuhixs8cQ9nuCkqLyqmtqicYCKExJRKKUl9dz64mHQ9WfbCOb/76q2Rke5utkZmTwZW//Xp3i25oh16lYPuCk6ujDBkyhL/+9a8sWbKEzz//nHHjxvG73/2OUCiEquJ0OjnxxBO5/vrrGTRoEH/729945ZVXgEan2L333ssNN1hxlS3b1hiF2/l4szxEI1Fi0cbftdiEWDRGbWU99TWNpprJR07gzjdv4cwrTuTw46dw9rdP4ffv/5zRh45Iheh9EuPkMjBmzBj+/e9/s2DBAhYtWsS4ceP4xz/+QTgcbpZG+5WvfIXZs2cDEAqFuOOOO5g5cyYzZsxAVRPFvauqrBropjRi51JVUk1mdgb5g/Jwe124PS7sDhs2uy2hcP21ATSmqMb48PklZOZkMP2kqUyYNZbsfj4q97SsT2/oSnpKTy5DN3DIIYfw1FNPsWTJEn72s5/x29/+lttuu42vfOUr2Gw28vPzE8pywYIF/OY3v+Gee+4BrHYlDoeD+vp67r77bsaMGcMll1ySKDoMpubB/qCqLH55GcvfXkXIH2LcjDHMOXsGTo8Tt9eF0+0EwGa3EfSHsMUragXqAmxctoX1n2xGVYmEI/QbnE/+4FxcHhfL3lzBiV89lhMumpvKxzM0waTK9lHeeecdbrrpJqqqqrj99ts599xzmynIjRs3UldXx7Rp0xJjoVCI6667jgsvvJCioiKqq6v5xje+kXCcNbyWjKJtn2f+9DLL3lzRbCx/UC4TZo/jg6c+YsvK7Yldq8Zi5A7IwZPloXx3RSKCIBwMWztaVTJzMnB7XfQfmk/+4Dx+dN93yemf3e3P1RcxqbKGpMybN48PPviA3/zmNxQWFjJnzhzefPNNwLK3Dh48mMmTJwONivOZZ57B4/FwzDHHsHHjRtxuN0uWLOGvf/0rNTU1zXa0huSU7ipn2VsrWo2XF1eS09/HWd8+hclHjsed4cLr8zBiyjBmnXo4s06Z2hieBQT9lhMMhZA/RDQSY8+2UqrLaqzW3oa0IC0UrIjMFpFNInJZi3ERketFJNSRdYyTa/8QEc466yyWLVvGtddey1VXXcVJJ53E4sWLefjhhzn//PMTH/0DgQAPPPAAHo+Hxx9/HFXli1/8Inl5efzqV7/i/vvv5+GHHyYajTa7R0//hNTZ7Fy/K3mnOWDnht0cc96RFD79Yx7Zcg93LbiNmx67hu/cfRmRJgkIsZg2c4RpTGlYtGJPlWnf3U30CCeXiJwHXEPrDrIA/wZ8gLMjaxkn14Fhs9n48pe/zJo1a/jqV7/KhRdeyMsvv8z27dv585//zPbt27nllluYMmUK3/ve9/jggw+orKzE4XBw33338ac//YlTTjmFV199lSeeeIKnnnoqsfbu3bvZs2dPCp8uvcju5+vwXMGwfvQbbKU7Hz7vkEQCgaoiNkGx+kJFozHqquoJ1geJRWNMPGJc1z2AIUFPyeRaoqpfBWqSzN0I/Kub5emzOBwOvvnNb/KHP/yBvLw8tm7dyjXXXMOaNWsYNmwYc+bMYeDAgfzlL3/hO9/5Ds888wxPP/00p512GgsXLuS0007j1FNPZcGCBSxbtgywzA3PPfdcip8sfRh92AgKhvdvNW532Jh12uFtXjfjpMOYdOR4AGy2JqYYsZxhqhAJR8npn43T1aH9iKEbSLmCVdWdBzJn6DreffddQqEQJ554IhMnTmTp0qX88Ic/5IILLkjEw44fP56LLrqIp556itdff53//e9/zJgxg/z8fNauXcuoUaMA2LBhA6FQhyw8fQIR4eu3XsCoQ4YnxnILsrnohvMY0ETxRqPRZuYVu8POTY9fwwXXncOIyUPJzs/C7XWRne+zmidmuvHlZ5KR7WXbWvNvky6YMC1DK/74xz82O24aHdAyLGv69OlMnz6djIwMgsEgX/7ylznqqKPIz8+nrq6OyspKqqurCYfDOJ1mZwWQNyCHb/76Yir2VhHyhxgwon/i97p9XRGvP7iAbWt24sl0M/OUwzn568fhcDpwuZ1ceN25XHjdufz3zuf48PklVO6pJBKO4s3ykD/YiqEt21XByMnDCAVClO2qILu/z7T2ThG9SsG2NDj39N5c6UKyyICWivb0008nFosxd+7cREPGdevWsXz5cmbMmIHT6TSxsi3IG9C8nc/eHaU8ePNjiWiBQF2Qhc8uprqshgt/fG6zcweNKiA7P4vs/NZ1fAeO7M+CxxfywTMfE/KHsDtsTD3uEM656lRjPugEkvTiapO0iYONd5Z9UFUfbDE+CvhcVZP+Z5o42PRDVVm6dCmvvvoqI0eO5Ctf+UoiVtbQNs/f8xpLXl3eekLg2nu/Q121nyWvLKeqtJoBI/qz9PXPqC6vxe6w43RZv9/xM8Yw5eiJPPeXV1otM/v06cy/6rRW44YDZ19xsOZVb+g0moZ07dq1i1NPPZXZs2cjImb32gFKdpQmn1BY9OJSPnrhk3hIFix/ayWlu8pxOB0EagNk9/Nx8iXHMfX4Q3jhnteSLrP87ZWcfvkJidqxhq7HKFhDp9GgQL1eb6t2NUa57puC4f3ZunpHq3FFWfbGioRybWgNowoZvgxGTBpK2e4KXrnvbVa9v46Nyz7H5XEyZNygxM4WIBKK4K8NGAXbjaQ8ikBEZsbNA9OAG0Tk6SZzNwGPx39+R0SuTImQBkM3MOecmTjdrfc8ow8dQbA+mDiuLKmmwbJXX11PbUUde7eXUldVh78ugNfnwV8bYNem4mbr5PTPxpfEZmvoOlKuYFV1qarOU9VcVZ2kquc3mfulqs5RVYmfc297a5lMLkNPZsDw/lz2i68kQrg8WR7mfuEIvvCDM5qdFwlZTjAFYtEYW1dvp66qnroqPyXby8gflIvNJvhrA42KWeCkrx2bKEVpOHg6ksmVNk6uA8U4uQy9Eav1S6MyfPCWx9n86VZiwJaicip2lROLRLEHwkg0hsYUATKyM8gpyCZ/cC7luysYdchwRkwexlHzZzFh5tiUPU9vxRR7MRh6IC13ml+85myGTRjCLhHqBuUiLif2eD2CaMxyMDrcTsQm1JTXYHfYGXXoCH7y0A84+tzZfLZgNY/e/hSLX1me6Ea7d0cpaz/eSNnuim5/vr6C2cEaDD2E2rogN9/xDMFAGIfAjqc/JlhWY6XLxhSvx0lDA8QRk4Zy8jdP5K2XlrFh0Xrsdhv9+2czcGAOwycOwevzsnHpFmthgUOOnsSXrj3b9PPaT0yYlsHQS6ioqsfpdeGMV8vKmTyUmrgjy2m3kedyUF/jx+l2cN61Z/H3B9+l9KMNqFpFYWq3l7C3tIadm4rJzstsrBmrsHrhOvIH5XLqpfNS9HS9k15lIjBOLkNvpl9eJk6HPXGcO3kYtvhxRoab/sP6MWLyMM793hl8umonlXuqrBbgQDQSIxaJUVtVR9HWvWzf3LrCWcsi4Ib26RHlCjsTU67Q0JvJ8LqYM300ELe5ZnoYfPJUMofkU9DfR3Z/Hyd/7TjO+vYprNuwCxw2S8FGW5gBFfw1fkpa2F79tYHuepRegenJZTD0Ms466VB2fLyRD59fQqA2QP7gPL767VM4/cKjm53ny3Bjy8lE3U6oa4yhRQQcNgShtLiSgni9WYCxh4/qpqfoOxgFazD0IN7574fsXryR0YNyE2MLHnmPAQNzmXzkOD5a9jkbPt+LJzeT2I4y7IPziW0uJpGZIII7N4todX2zVuzuDBenXHJ8Nz9N78coWIOhhxAJR1j0wieJ4xpgl81GEPjNP98k95XlZGW4sTtskOUhx2mnMieDsNsJkSg2gcwsD3aHnZjHycQZYxg7aQgDRxYw5+yZ5A3MTdWj9Vp6lYJtMDibMoWG3khdVT2BuJ00AGy12YhFY8TKaqgoraYqGMaX72P0SKtw99BZYxnsD+EeN4hVr3+Gw9HochkwcQg3/uu7+HzeVDxKr6AjZQtNHKzB0EOIRqLcedlfqK/2UyRCaThKaO1ONBAiMqIAzc5A7ML4w0aSnZeZuO7r5x9J1d4qXvvvQuprAhx65HguuGweWZmeFD5N78DEwRoMvQS7w84x5x3J6/9+B380RmhrCeoPofG+XFFAo8reHWXNFGym18Vhx03hmOOmtLt+JBpj+84ybHYbI4fmmwponYBRsAZDD+KY849k2ZsrWPHRRiJl1cRyMogNzIUMNzGXE1s0StgfJBaJYXPYKMj3MXpE6yaLLVm7cTdPvrycmjrLBJGXk8FXzp3NqGH9uviJeje9Kg7WYOjtfPTiUkqLypk8vB+Sn0VkSD9idjuEI9jCEdTpIGSzgcDA/tlc+qU5+9yJVlbX8/DTixPKFayssQeeWEQwGO7qR+rVpIWCFZHZIrJJRC5rMjZKRO4XkbdF5CMReUxECtpbx2RyGXo7DdlWe0SIDM5PjMdi4FQlRyDX5+W7lxzPj648mQH9fftcc+nK7USi0Vbj/kCIFWvbD6Tvy/SITC4ROQ+4BqhqMXUZUKmqJwJHAVGg3XqwJpPL0NsJ1oeoAcpFEJ8XcdgQrBIv0ZjidDsYOmYA+Tkd7yJbX992W/XaJoW+Dc3pSCZXyhUssERVv4oV1teUZcBfAdQKdXgcOKWbZTMY0opx00dTHf/Ib7cJNp8XW5YHW6YbTz8f+SMLyMvL2q/wqzEj2/5gOHZUux8aDfsg5QpWVXe2Mf68qm5uMuQB2ugKZzD0DY6/8Ci8WVZ4lashxNJlx5OXid3rQsTGCUdPxGHv+L/25HGDGD9qQKvxaVOGMWJIfpIrDB2lJ0URnE18R2sw9FVy+mfz/Zu/yO/+8hqB2gA+mxBx2InEYrhdTi69YA6HTRzCB0s2E43GmDJ+EAX92rfD2mzCNy48io+Wb2X1hl3YRJg2ZRgzp47spqfqvaRNokG88eGDqvpgkrmjgd8AJ6pquMVcItGgJSajy9Bbef3dNby1cD2K9f9rt9n48jkzianyvxeXEW1SZ+DkuZM49fjGGNjde6vY+PlevG4nh04agtd0md0v2sngapVokPYKVkRGAg8DF6hqqyKWJpPL0FcpKathzcZinA4bh00eiiD86i+vJiICYjGlpi5ALKpcdclxTD90BM+8+imLlm1JrOFxObnkS3MYZ2ytB0SP7sklIv2xlOtlqrpHREaIiDvVchkM6UBBPx/HzxnP0bPG4sv0sGbj7oRy9QdCbNpawq7iKopLqvnDvxZwz0PvNlOuAIFQmP88u5hINJbsFs149NFHmTdvXodke/bZZ6msrOzQuT/72c8YMWIEhYWFXHbZZTz88MMAnH/++fu4ElatWsWMGTPYunVrh+7V3aStghWRLOAp4BZgb/z4O8DglApmMKQpDWYBVWXn7kqiTZSmqvLBks3UJCmqXVMX4I331/DKgtV8+Mlm6v3Jw7YuvvjiDsuyPwr2iiuuSCjY++67j1tuuQWAp556ap/XHnrooUydOrXDcnU3KXdyichM4C5gGnCDiMxX1fOBnwLHAQtaXNJuLKzB0FeZMn4wz7++guraAJFI8x2pL8tDRWUdVdV+fFmNRV5iMWXHrnJeeGMlWZnWh8PX3lvLNy86OmkEQWVlJXfccQdLly7liiuu4LTTTuPZZ5/lvffeA+Dss89m2LBhfPrpp/zhD3/g9NNPx2638+STT9KvXz+GDRvGVVdd1eYz1NTUkJGRwXvvvccll1zC1q1b2bNnD7feeitjxoxh8eLF/Oc//8Hlam43fuSRR6isrCQQCPCXv/zlgH+HnU3KFayqLgXmJRm/Cbhpf9Yy5QoNfZkcn5ezTjqMx55d0mw8LyeDzAwX4UiUmprmO9jyyjoCwQgZ3kaF5Q+E+N+Ly/jRlSe3ukdNTQ033HADdXV1zJ07l1NOOYWf/exnrFixgkgkwrx58/jwww+ZNm0aV199NaNGjWLx4sXcddddZGVlceKJJyZVsNu3b6ewsJCysjL+/e9/M2vWLEaNGgXAr3/9a8477zxOO+00XnzxRZL5jY477jiOO+44Lr74Yj7++GOOPPLIA/kV7hcdKVeYcgXbmRgnl6Gvc8zssYwclscv/vgKoVCErEx3Qnnm+DwMG5TbLDurti7I4AE52GzN6xXsKa1mT2k1Axs6z8YZNmwYAJmZmVRUVFBSUkJ1dTV33nknAAMHDmwlU2ZmJj//+c/Jz89v01baYCJIxvr16/n2t78NWDvkZAwfPhyAkSNHsmnTpm5TsFdeeWW76bJpa4M1GAwHxvDB+Xzn4mMZVJDdbGc6btQAfvq905h12Ajq6oOUVdThdjlaKdcGhNbjO3daeUF1dXXk5uZSUFDAgAEDuOGGG7jhhhu45JJLALDb7agq69at48Ybb+TCCy/khhtuOKAN0KRJk9i2bRsAL774ItXV1W3KtW3bNsaOHbvf9+gqetUO1mAwWMw4bARDB+eydMV26v0hxo8ewCETh7BwyWY+WbmdzAw3mRluSstr2bGrguGDc3F7nNhtNmw2YVBBdqJQTDgc5dPVO3jokUcIR5Sf3HATmzet54477sBms3HzzTdz7bXXkpOTk1Buxx57LHfccQcTJkzgwgsvpLCwkHnz5rFjxw7+97//ccEFFyRkffDBB9mxYwePPfYYX/nKVwD44IMP2LZtG4899hg33HADN998M8uWLSMWizXbxa5atYoVK1bw0ksv8eyzz5KTk8OcOXO68TfdPmkTB3ugmDhYg6FjRKIxfvXnV5qZCGIxZdPWEur9IdwuByJW+NfNPzyDUcP7EwiG+fsj77NrT2XiGrvNxte/eCRTxpuAnh4dB7u/mHKFBoNFJBJl09YStmwvJRazNlG1dYFW1bH8wTDRaAyX007//CwGFmSTl5PBL++4G4D3F29qplzBCgd75tVPm4WB9UU6Uq6wV5kIzA7WYIBV63fx1MvLqfNbyjQ3O4OvfmE2wwbl4nU78Tcpol1RWQdAhtdFQb+sxPiLzz1B9R03s25TcdJ7VNX42bWniuFD8rrwSdIb4+QyGPoYFZV1PPrM4oRyBatjwQNPLCIajTF3VnMHUChsZX71y8vkyQdu4b1X/01tdQWqUFntx+Gwt3kvp8Ooj31hfkMGQy/ik5XbmxV6acAfCLFy/S5OOW4ypx0/hawMK6mgX24mQwbm4Mvy8Pmm1cw+fCSP3HMtwUA14WAN0w8ZnvQ+QwbkMGhATpc+S2/AKFiDoRfRVporQF19CBHhpLmT+NkPz+TnPzqH2649u1k5wz//+U+sX7+Or150AbNnTefxh/7I2KGZzdbJ9Xm56NzZXfYMvYleFUXQgMnkMvRG7r33Xu69t/1McX8gTFlFXdK5gf19OJ2tP/KHw1GqawNs3bKuWZbU8uXLOeGEE7HZ7eTlD8But+F02PF4nEkiZA+cK6+8kiuvvLITV+wekmRypW+5wgPFhGkZDI3EYsq/Hl/Ixq17m43POmwkF54zs91rRQRVZffu3dx+++08+p/HmDT1OGYcfQ5ZvjxsInzprBnMMoW4E+wrTKtXRREYDH2dhu4ESz7bxqr1u7DbbUybMozphya3pTZQUma1xPvBD37Ivx96CJvNyXHzb2TC+NGJTK+YKv994RO27SyjuKSabJ+XuTPHtNvTq69jFKzB0MtwOOwcNXMMR80c0+FrnnplOaPHHcKSFdv52lV38d/7f4HaMigtr01kdIXCEbbuKKe6JkBOttVUcdW6XVx4zkxmHjaiS56lp2MUrMHQx6muDbBleylf+sbPW83V1AYSCrasvK5VcoGivLJgFdOmDMO+H40W+wpp8RsRkdkisklELmsyNkhE/iEib4nIhyLyvohMa28dk8llMBwASfwwdpulGprO1PutKISGurENVNcGKCmv7UoJ05KOZHKlXMGKyHnANUBVi6lDgQxVPUlVjwbeBv7T3lpFRUWoqlGwBsN+kO3zMnxw84ys6UeeSl5uBr4mytThsDNkYE6rnaogzap29RUKCwspKkrabzVBOpgIlqjqM/Gmh01ZDqxtcrwYuK7bpDIYeikbP9/LqvW7EBEOnzKU0cP7c/4Z07nvsYWJDLDDjziVIQNyOOeUqXy+owynw46ivPz2qlbrTZkwmOwmXRIMjaRcwarqzjbGyxp+FhEvcAnwz+6Sy2DojTz9ynI+Wv554vjDpZs54agJnHHCofz4O6ewfPUOKqrqGTYol0MnDcVhtzG2SZRAIBDm/cWbCEesFNsJowdwwVkzuv05egppEwfbTtvu64HrgfeAr6lqfYv5VokGDZiEA4Ohka07y7jnoXeTzv3oypObdS9YuWcPb27ZREl9PcOzczhj/HhG5VpmhHp/iF17qqisqiMSVQYNyGbUsH7d8gzpQDutYtI30aAtBRufE6AQOA2Yq6rRJnMm0cBg6ACvLFjFgkUbks6dMe8QTjh6IgAf7dzBv5Ytbebgctps/HjuMYzJy6feH+L+/37I9l3lifmxIwq47II5uN1OakMhQtEo+V5vVz5OWtArEg1UVUXk58ANwAnAmykWyWDocdjtbVfGaqiapao8s3YNLbdd4ViM59ev4+o5R/PCmyuaKVeAzdtLePqtFZT3j/DZnmJiqgzx+bjo0MOYUjCgsx+lx5DyKIK2EJGz47tTAOK71iCQ2fZVBoOhLaYfMixpny27zcbhk61dWFUwQJnfT00wSFF1NbtqqqkPWwVktlRUEInG+GxNcs/5o+s/Y3nxbmLxT8W7amr488cfUVxb00VPlP6krYIFZgGXNRyIyJcBAT5OlUAGQ0+moJ+PL5x2eCLGFcBht3PB2TPI9lkf5zOcLoprathSUUFpfT0ldfVsLCtnT20teR4PGlOi0RiKUmYLsslRzRZHDbtt9VRq60pe4ViMd9voJNsXSLmJQERmAncB04AbRGS+qp4PPAncKiLvYb0R2IBzVTV5iXVI2EKMc8tgSM5RM8dwyMQhrNmwG7tNmDJhMJkZjbGuG8pKqQ4G8UesrgdOmx2Hzcae2lpmDB6C02lnzMj+vLRrE2W2QOK6oD2Kx+NMes/S+vqk4z2ddpxdCVKuYFV1KTAvyfgq4IJWF7SDcXIZDPsmIBF2ef1UBgIESoQjhw3DZbcTicX4/aIPqQ+HESAcjRGORvE6nQzPziHDaSnQsdMHUVmyBqKNa7oddgJ263xnC1vvyNzc7nu4bqQjLWNSrmANBkP38VlxMX/75GMi8UaIH+7YzoKtW7ju6GNYsaeY4tpaRASPw4nbrsRUsduEfK8XR9y0UBSqZcyI/lRW+wmFIrjdDnKzMyiqraY6GKRfRgbhugj+vX4yXS4Omd0/lY+cUtLZBmswGDqRmCqPrPgsoVwb2F5VxRubN7Fq715yPY0ZWSISt9cK9eEwM4dYOzWX3Y7DYXWhHTIol355WdjtNoZl53D8qFHYdkcoW1yGbVeYrFLh7w+8x/uLN3Xno6YNRsEaDH2E7VWVVAT8zcZC0Si7aqq5f/kylu3ehQJDfD6kSbCBTeDciZPIdlu22iOHDUu6fobTyXkjJ5JTJvgGeSkeEGZZRhlLXSU88PYSSvtgQZhepWBNNS2DoW1c9uYWwUAkwsayUkrq6qkKBqgMBNhUXobbYWdS/wKGZWczPCeb2UOHcuGhhyWuG5OXz/mTp2BrooXddjvfmjGLDZv2st1Rx3ZHHWGs0oZ+ibLeWcnzn6zungftJjpSTSttMrkOFJPJZTB0nJ+/u4DtVVbhum2VlVQGrEiAYdnZ9MvIoLS+jnK/nwn5/UCEHLeb784+gnH5rVNhy/1+Vu4pxmm3M33QYLxOJy8vWMUdyz4g2ipVAaYPG8Jfvnxu1z5gN9MrMrkMBkPncMWMWfzhow8p9/upDQXBFiLDZcfljKIao39GJrkeL189bCoFmVlMLihIOLdaku/1cvyo0c3GRo7pT3RZ8k2bejuzVWLPwChYg6EXEtMYa6vXs75mIwATfOOYkj2JIT4fvzrpFD7ZvZVfLHqB2mAUu0QpCZZSEa5kiGcQHoeLOcNHJMKy2kJV2VpZid0m9Hdl8NmanVTU1NPPl0lZTfPOtgP6+xjTv+8UhGnAKFiDoRfy8u7XEsoV4PO6rWyp/Zz5Q8/CYbNRZd/C6IHKmh2Nu8pILEJpqJxzh8/cp3JdW1LCg58uo8zvJ1gVonZVNUMzfGQ4nWTahdoMB1lZHmwi+LLceFxOzhw/ocueN10xTi6DoZexo76omXJtYGPtZrbX7wBgQ80mRhUIowZIs4iBzAw/Xz70kHbXrwoE+MvijyjzWxEJFWsqqfeH+LyiglhMGR7NZEi9l3y3hwH9fEweOIAfHnkUkwt6V/fZjji5etUO1ji5DAYSSjTpXN1ORmRYLbxFhEOGC2MHKjV+8Loh22Pf5+510c4dBKNWGle4Nky4LgJAJBajMhAgP8PLkGgGw4N5/OCrJ3TSU6UfHcnk6lU7WIPBAF572+1bPPG5Cb5xjWMuoSBHyPIIo7NGATbK6usJR6NJ16gKNNYgaBmEFInF2pzri/SqHazBYIBJvgl8ULKIsIabjTvEweRsq6j2sf2PZrd/D2WhRGcmcpzZhKuH8aMVr1AXDpPpdHLK2HGcNX4C0sSOMKFfP97YshkAZ5YDZ4aDcL21i810WbvfKDFq+se46a03AJg5ZChnjBuPdx+7496GUbAGQy8jw5HB/KFn8eru16mLWpWsMuxeTh98CpmOjMQ5Xx91EZtqt1AaLCPPmcv2EuHJjY19RuvCYZ5dtxanzcZp48Ynxg8fNJgJ/fqxoawMESHvkFxKl5fjszvJdLmIEWNnvyCVsWqkzlLML2/cwNqSEn567HHNEhQASstrWfLZVqpqAowcms+Mw0bgdvUO1dSrEg0aMOUKDQdKJBZhbfV6Pq/bisPmYEr2JEZljky1WAdEVKMU+XehCsMyhmCXtjsaAPz49ddapdIC5Hk8/PbU05uNbS4v47XNm9hVU0OWy8XUfgORiihv7NzCpmAlxYFast1uBmf5qAuHqfD7ianyxcmTuWLm7ISdd93mYh568mMiTcwRA/r5uOqS49O+FXiScoXp2ZNLRGYDjwG3t9GT6zbgUlUdlWTOZHL1EVSVpRXLWV75GdHwDmxiJ9cznvG+iUzJnoTb7t73Iu0Q1ShP73yO7fXNGx3P6Tebuf2POqi1052YKle+8Fyb838/ez4Om43aUIi/LVnM+rJSwKqAP2PwEAZnZfHAp8sJRiPEVKnwW3bacCyKM14wBmBAZiazhw7lp8cch8tu5zf3vE55VV2r+zV0uk130j6TS0TOw6r7WtXG/EDgCiCcbN7Qd1hYuohVFe8y0bGcT4J2AjEbu+vXsrlmJEvKx/LlEV8kx5lzwOuvqVrH5trPsYsdmzT6fxeXLWVqzqH4nL7OeIy0xCbCsOxsdlZXt5ob6vMlsrkeXfFZQrmCVSzmkRWf4XHYqQ5aHQ3CMWs3ahcb9eEwDrGBWPdQlJ3V1Xy4YzuTM/slVa4AazYW9wgFuy/SIYpgiap+FWircc/NwN+6UR5DE1RDqP85YlU3EKu8Fq37Fxot3feF+3uf4EJi1b8gVnk1sZo/oJHm5e1CsRDLKj/lSPcSNvmVQMx66dpQNLKDmvBe3i/58IDvv7R8Of/d8SQ76neyrX47JcFSGj7dxYixtX77gT9cD2H+xEmtO3apcvjAwazau4eqQIBlu5tt0NhbV0coGqWiSWSBQ2yEolFC0QjRWIxwLEo0FiMSi1FaV09NMMjG8jKczrb3dy5n++aMnkLKd7CqurOtOREZBwzBah9zRbcJZWik7j40/FniUEOLIbwe9f0UiayF2B6wDQHXDEQOzEOsgddR/1ONA5G1aM0G8F2LOKxwoqpwNdnsIUNqKQ43L+AcIwaxMjbXbjmg+6+r3sA7Je8TVSvESFWpCVvv9wVu615u28GZH/aHUCxMMBogy5HVzHvf1djFRr7Xy8q9exCEUbm5BCNRXt60gZc3gcNmo8zvb1YztjZk7VqbSikieOwOAtFI4thus+F1OFBgd20N2W43/fIyGTk0n21FzTvUAkw/dERXPmq3kQ472Pb4OXBrR09uyORq+DKOroNDI9ubKdcEsRKo+j+0/t9o4FW0/n60+hdorLJD68Y0Rihm/WOqhtHAq0nOikKTcZ/Dh9cWRbDqkzbFhg00uk8nTlssr/wsfo+sZuO1kVpiGiPD7mVM5qgDWjsZtZFaPipbzGvFb7K84rPE7yIcC/NG8dv8bdM/uXfLA9z/+UOsr97Qafdtjw93bOfP8eysIb5sBmRmsrx4d6KjLFgxrnvraglGIs2ujcRi+FxuPI7G/VqDQvU4HPhcLjKdroTZJRCJcMQQq6bsl8+ZRb+85r/3GYeO4OiZY7rqUQ+awsLChI7psZlcIjILCKvqShGZ15FrjJOrk4m28eEiuguwgaPJLiO2B/zPQOY32lwupjE+LP2Yz6pWEogG6Ofqx/H5Exipye1wGtmR2Bl57G4GZx0NoU8Y5gqyPdi4i3LYHGDLZKLvwHLdG3arbrub/u5+lIXKUVVUFY/dzblDz7bu0Qns9hfz1M5nCcYaFdeyik/58ogv8u7eD1hX06hQK8NVvLT7NTIcGQzPSF7kujNQVZ5bt7bZWHUwSDgao7iuljGu/MT44CwfVcEABfZMdtfWUBMKEohEsAlkulzkeNzUBINkudxkOp1kOJ3sqq3BH7aUssNmY0xeHqPy8gDon5/Fj799Chu27KGqxs/IofkMGnDgdvTuoLCwMLF5a+HkakXaKljgNuB7qRaiT2NL0ktJY6A1YGudV66hZUgTBauxSgh/ah04D+ed0hUsr2jcEZeFyni++AO+2S9Chj3JS9He/P7HDDidjSVrmKVvURV1UB114rQ5sUsGg7yTOLbg6AN5SgZ7B1FTY9l8s53ZZDoyCUQDZNi9fHvsN3HZOi9c6O297zZTrmAp0nf2vM+G2tZtVRRlWcWnXapgq4KBRF2BBhoysvzh5r5ln9vNsSNHsbeuls8rKxia5SOsVoqsPxzBZbdz5vgJ/OCIOSzcsZ3n1q9jgtuNPxwmporX6eS0seOaxcLabMKkcYO67PlSSVoqWBHxAeOAB+M2qFxgkIi8A/xXVY3TqxsQ5wTUPhKi21rM2MCWn+wKIG5aqH8CQu+AeEByCOrjrCwfDJLX7IoIdtYHBzI9o6zZ+M6gsrQml/KSf5PvymdW/gyGZwxl4oDriAXnMT73A3b4q6jQwfTPOo6RmRMP2F55ZP5sPq/dlsh8soudTEcmJw2c16nKtS5SR3FgT9K5DbWb0CRFqsGyP3clGU4Xbrs9UV8AIMtlPXfLDrEARw0bzssbNzAmr/E14HP5qQ2F8LlcXHPUXLLdbs4cP4HKQICFO7bhdTqxiTAuL5/qYJC7Fy1kTF4eJ44eQ7a77dTenk5aKlhVrQEmNhzHTQQPquq8FInUZ5GsH0D9f9Dwp0AMHKPBMQqirb3q4pqB1j+O+p+H8ErrfADJoDo2nEgkCo5MkOZK67PgBGbkZaGh90EDbA35eK4im5jND/ipDFextW4b5w49izFZo7G5Z+Fyz2Jsduc84wBPAReN+BKLyz+hOLAHn8PH9LzDm+XrdwY2sSNIUkWa4cggGA0S0UiruQHurq1C5bLbOXbkSN7c0ugk9Dqd9MvwkuFo7ricUlDA9MGDeWL1SsAK09paWZEwAVQE/Dz46TK+f8Qc7DYbp8fTY8vq68nzeHhjy+bE068pKWHh9u389Njjyfd6u/QZU8UBKVgROVlV3+wMAURkJnAXMA24QUTmq+r5TeZvAc6ncQd7vaouTrZWgy3EZHJ1HmLzQda3QQOgYcTmQ2PlaM1dEGsSrmUbjDqmQP2/ILqFhHIFUD857MZJNuFYJdgHNLtHf3cBknEWeOeD1vPh9leJ2fY2OydGjIWlHzEmq3kF/c5igKeAs4ec0SVrN+C1exiVOZLP67Y2G49pjPGZY3HbXXxc/kmzOafNyez8mYnjUCxMOBrCaXd26u76S1MOJRJTPti+jUgsRobTyQ+OmENBZiaLi3YSicWYPmgwc0eMxCbC5IICPti+nZ3VVQnlaslrY0VxMQs+34LTbufhzz5Fsey8a0tLyPN4GexrjCeuCAR4eeMGvjb18E57lu4iSSZXKw50B/t7EVmZZFyBPcDfVLV1QcpkF6guBea1M/9zrGiCfWKcXF2HiMf6uA+ILR+yb4XQcsu5ZR8CzmnWTlf9oKEWVysuW5jpGVUsDjR3CNjFnlAgIi4UJ3uCe0nG3mAJkVik0xxOqeCUgSfw1M7nKA2WURuppTRo5fNHNcpQ7xAOzzmMXYHd1EbqGOodzFH9jqSfO5+acA2vFr/JJ+XLqI3U4bV7ODRnCmcOPo0BnoPf4TpsNk4bO46y+nqW7d5FxG6nzO/nhNFjOGJoo/1XVVlbUsKgzCwrnC3Y+LcWgcG+bBDh7c+3UOavT+xWA5EI4WiMvXV1ZLvdZLoa3xzWlpQctPypoCPlCg/0lboJeK2NuVzgTuC8A1zb0AMQcYH7yGZjioJGsV5WwWYzaJCjM7bhds/ks4CL2miEIZ5BHNX/SAZ7BzVZV8hyZFIbaR1ZkGH3JkKxNLIdgu+gsVLEPgzcJyH27m1JUhOuZU31WmoitQzxDGaCb9w+lb/P6eMrwy/gPzueYGvdNqIaxYaNnf4iQrEQFeFKLh311WYZaVGN8sSOp1lVtSYR0uWP+llW8SnVkRq+OfrSRBGXSCxCjFiHd7fBiOWY8kci3LnwfSoCAZx2O6FolPe2bWVndRU/PeY4RISy+nr+9PEiimqsqIv6UAiHTfA4HDjtdvpnZJDhtO5bVFONw9Zov3XYbIhYJQyrgoFmCrahAldv5EAV7N2q+n5bkyLSO12ChvZxzYDg+3Ebazi+k43FvwSxZTPLvYNZmeWI7wakhamggem5h/N+aeusrGl5hyMiaHgVWnsPYDllNLIeQovAdz1iH9xVT9eMHfU7eWbnCwnH2GesZFnlp3xp2Hl42qiJsK56A4vKPmZb3XZKgmWENYxTrH9BVaUkWEqGPYMVlas4tmBu4rrNtZ+zy1+cUK4NRDVKabCMVVWrmZp7GO/sfY/1NRuJapRc2wAGyCSGZAzgsIEDcbVwVi3bvYtn161lV00NPpeLgsxMyv3+Vo7CLRUVrCstZXJBAfcvX5ZQrgAZLiu2NcfjoX9GZrPrRubkUVTT6Jxz2u1kOp2U+wOU1VsRC/neDDwOB8eM2HcxHVVlXWkpVcEAY/PyKcjM3Oc16UC7ClZEfqSqd7Ucb6lcReQqVb0n/vMJQPvRt4ZeiTgPA/dxqFZCtAjUAdQBbrBlQzwrC62zkggyL0m6zuz8mQRiQT6tXEE4FsYpTg7PPYwj82dZl9c/SYNyTaD14H/eshd3MarK68Vvt6q3uiewl6UVy5IWhtlYs5mXdluJE3XReiKxMGENgYAzvuuNaQx/NNAqaqAyVElEk5fiiMTCVIQqebboBXb5d1v32h1jw+7d2ChmmHcYeZ4MvnfEkYnW22tK9vK3JYsTH99rQiHWlpZgF7E+4rdgV001AzIzm9UgAOvTxtBsH3tqawErdrYhA+zy6dP51fvvEY2nG0diMUJRK23WHhNK6uopq6/nS1MO4dh9KNjS+nr++NEidtdayl2A40aO4mtTD+/WTLcDYV872ItFZDG0TlFuwRXAPQCqugBY0Amy7TfGyZUGeM8FjVg7ylgFxMrBlmuFdTVkWqkfDbwc/3g/HNwnIvZ++KMBXDYndrFzXMFc5vSbTXW4Bp8jK1EpS2NVENud9NYaWbfPF2pnUBYqpzJcmXRuU+2WpAp2SfnSxM+CWFlNChEN42zybyhiRQ2UhyqIxCL0d/ejwNO/zY/8LpsLu9gTyrW0Wtmwq6GGglIdrsHmqueuFY9y2rixjPWN4vVNFVQHg+ypq6U+HMZlt+Oy2akLhxmYldWs0A3AwKwstlSUs62ykmA0gttupyAzkwyniyyXm0p7gGAkik2EDKeTqCof7dzJVw6byqMrPkOBknjNglG5ueR5vISiUTKcTvbW1RFTxd5EUW6pqKDC72d0Xh75Xi/3LfskoVzBcvS8u20rI3JyWrUN7046w8k1DXgn/nN7r93U1zzEOLlSjUZ2orV3WztUsVm71the63uDco1VW3G1kgGR9WhkPRur3udD/6GUhwO4bS4OyzmUYwqOwmVz0d/dwq4qHsBOqx0sgHTPx8b2UnLbmisPNebbZzoyqYvUYRc7UY2iKIJgFzs+u4+11esTJpJsp48TC45ndOZIKkKV+KONCQHWvYQVVSvZ7d9DttNHUXmLcCdvMdGsGuqBT8uDrK9Zz4LtLsqqrGsBgpEoAbXKDIajMdyORgU7IicHp83Gn5ctpT4cJhSN4g9HqAoGGZ2bRyQWw2G3MSAzi6pAgKjGCEejvLllM7886WR+edLJLCkq4onVq8j1eBIdDRq+VwQCbK+qYnReHpUBP39Z/DFbKysJRiJUBgIMzMqivL6eXK+3VaHuRTt3pFzBHmxPru8Aq4BHgCOB0Um+xgBJw6YMvQeNVaOBN1H/82g4eYCI+v9nKdcGxGkp0mhx41h0N6BgsxTn9qDyYlk95X4rRTQYC/FJxTIW7H0v6T1E3IhrdvI59zH7/2AHQJ4rl4Ge5PbjSW2k6/Zr8kaRac/A5/ThEhdumxunzYnD5uTI/Nk47U5Km7RxqQ7X8OLuVzhpwDxOHXgy/d39cdqc+Jw+fI4svHYPwWgIf9TPnsBeakKNClhsEdyZFQAoMcrCpWyv30FJfTV1kfpmZgcRwe2wM3XgQARw2IQjhw7lC5Mm8+/PPiUaizE022c5qrDSiBt2lV6Hg7UlJeysrmZ3TS0bysrYUV3Fmr17GZCZxVkTJjK5oKDNdjENNQweWL6MrZWVVAeDbCgrpbi2lmW7drGlooJN5WVEm/T7AisyId1pdwerqvcC94rIWVihUjuxHFzNEpdF5FddJ6Ih1Wh4JVr7DxpL8r6E2seA+wTEMQKxD0I1BJH1rS+2DYHo5xCrAeLOL9sgy2wALKuNf/jR2maXrapaw9z+R7Vq4KfRPShOy+Yaq7YUtbgR19HgPrUzH7tdTh90Ck/tfI7aSKPcY7PGMD0veTznEfkzea7oJWu3KkKBuz/ZDh+jMkdyaM4UJvjGUeTfxbNFL7a6NqJRNtRu4tTBJ3Lq4BMBeHfvB3xSsQwABw4yHBnUR+pxZdSgNW4EweMO4Wzw5Nv9RLEhgN0eQ1FCsTA2mw1bfNfdLyODy6fPxOd2s6m8jH9/upyPi4pYsacYp81mef7V2vGKWLvQU8eO5cFPPyXWonB/Wb2fXTWNv5ujh49gU3nrqlmjcnMZ7PNR7vezuqQEVaWouopYfDmbCKGYtWsuqa9jUFZjDO0hBcnf5NKJDkURqOpLwEsiMgO4SUSygT81JBuo6gtdKKMhhaiG0boHSShXjUB0K4RXQHgpassD1xGo92Ksj+5NdhWx2njGl4K4rRgd+zBLuWoIYuVUhrygLmu324SoRqkJ1zRTsBrZhNb8EQhZO+N4EWiyfog4J3fVryAp/d39+OboS9hcu4XaSB1DvIObhZu1ZGzWGM4ecgYflS2mJFhKjjObmQOmN1PIdZH6Nq9vGbZW5G9el3WAu4AKqURya4nUKaFAJnmuLETqUKK4nEpMrWgOp11xOZRoDMIawedw0j8jkxE5OfjcbkLRKH9bshh/pLFAS20oQKm/1jIfiBCOQnFtLf/4ZAm14TAOmw233Z4IzXLYbOyoruKZtWuoDgYZl5/P0cOGs2jnjoQ9cUBmJlfMsGKgG2oeBCIRQtEYUY0RicZAwG23I2I50RoU7MDMzGZ9wtKV/QrTUtVlwNdEZBjwJxH5LXBFPFkg5RgnVxcQ2dB8dxktsnaPALEqsOWhocWIbTC4ZqGhj6w5jcZrGETju0yXZfKL7AUkXqkrSoGjPxURBeygQUsRY2Uw5Tibe7S1/gmgSahSQ+JD8F3oZgULVhWvidkdr+A1wTeOCb5xqGpS7/cQb9shZkO9zX0LWY7m9mab2Mh15SAiTDukP+XVbsJ+H5XOCsK2cqqiIQIhYev2AuqCNsIRKy7VYYfx+f1w2O2cOHoMLrudD7ZvSyhXgHxfjPLSIIgQjCrRqFjvmSLUhcOgEI5aRbW9Tsh0Osn3ZvDWls1sLLfMHe9v38aw7GxOGzuOPXW1TBs0hKOGD0/YVQf7fHgdDopra6kPh5q3/7bbGZaZRUFmBjMGD2Fcfj7HjBiZ6OuVKrokk0tEjgauA+YDJUDaBKQZJ1cXo1HQ5IVHNLQI8f0Eonssk4BWAVHL8WRrsrOzD4nvai0n1czMGjaHcohit+yzjlEAzMid1qzHlsbqkxSdic+F17TpgdXwCtT/EkR3gG0A4jkFcc9t4+zuoa3Qov7ufkzJnsSa6nXNxgvc/RPtths4PPcwNtZuThxHNcYu/24c4qAqUoU9A+wZexlr78fqKiszrnhvDoGQA5dDcXqjBEMOYjGh1F/PlTNnc84E6x7VwcYkEUXJ7VeMoyKDcARisbjsQjxxQLHZbKhaJgOP3cHEfv1ZV1bK4CYf5yv8flbuKWbZ7l0MyMzisz17qA0FOW3ceLZXVfLnjz9i1d69VPjrE/Vm7TYbNhHcdgdl9fV8Z9Zszp1kvZGGolEWbt9OcW0NQ7OzmTl4SNLCNF1Jp2VyifWKOB/4ETAHWANcCTyi2iov0pBCNFoM/qfR8EoQF+KaA97zrFTXA8ExASQrvouN0SxgxNakbqf6EVsWkn0DGl6PBl6D0GKwtXz/FWs3a58AGmKQw8MX7U4W1Si7QzVkOXOZnjeV6bktbJniwHq5JnFstPFslu34nkaZY7vR+odAg4jnxP36NXQXpw06mcHeQaytXk84FmZM5mhm5U/HaWu+WxuZOYKTB57AwtJF+KMBasLVOGxOBribl3hcX7OBYRlDKA2Us7bGjT3u17Y5whS4XQzxDCLD6eacCY3VyCb268+LxO3p9nrszhDZmU6CITvBkI1YDEIRS7k6bQ4cdhv+cJhgJEIkFqPMX0+G05nofBCORtlRXWXVl62txetw4nO5+N+a1RTX1vL8+nV8XllBMGK1mYHGN6FMlwtByPV6EjvW0vp6frvw/WYlFl/IXM+P584l15NeRWP2lWjgBS4HrgbGAm8BZ6rqqy3Om6Wqn7RewdCdaKwGrfmdVa8VQANo8B2I7kJ8PzqgNUWckHkpWntvPKrHAwRAckByG89zTmny80Sw+dDI6jYWzY2XMbT+AYe64UtuAcnElps8+UDEBa7ZaGhR6zl38jqwGniFZBGEGngV3PMQ2VcQTfdjExvTcqcyLXfqPs89PPcwDsmeTGmojAV730vEwjYlrBGCsSADvQW47SHC0QiK1R5miHcwbnvr+NrJBQVMHTiQFXv2gFq7woK8EHtKMxGH4A9bu1cAh93qv2VFHtiwiyBiI9/jTSjJcr9VyjAai+GI2thSUUGG08lQn4//rV5FRcBPXSiEqhJTq9aYqOK02SjIyCTf68XrdGKP29wfX7WiVf3aPXW1PLlmTcKmmy7s6xW2E6vS1YfANFU9paVyjXNnp0tm2H+C7zcq16ZENqCRza3HO4g4pyI5v0S8X4KMC8ExERwjG//LJBs85zS/xj4Ecc9rvZjzMPCcnPw+riOTjifwXgiOSS1kmwmes5KfHy1KPq5VraIWeioOm4NBnoHkOXOTzntsbuxit7Kucp147B68dg+ZjsyECWbaoMGtzBZXzT6Srxw6lYl5Q8l3FjBliJe5E9wMyLVa9rgcVnhVLGaFbLkdDjwOJ8NzcumfkUFVEzNDWX19IsSqIaqhPhxmW1UVMZSaFsoVrLfFUMyKqfU6ndhFmDF4MOFo1FL8SWjZkDEd2JeJIAPYDRwHHNuG7UiAgZ0s1wHR551csXZeYNHd4Bh7wEuLLQc8pyCcgkbPg+C7ECsD+whwH4fYWqdYSsZXLGUcWgJErIpbriNBQ2isBJp2jnVMAe8X9iFDBuK7xir0EtsL9mGIvZ2yF7aBye22kmlFIfQipuYeyprqda1qzY7NGkMoFiKiESYPE6rqFX8I8l1W4fN+Xi8XHNK6PbbDZuOkMWM4acwYqsJTeWbnC5SFyhmS76CqDqoq+rOjBDaUl+FxOPA6nPTLyGBAvEaAVfjFQW0oTCgWBQSX3YbT3rinqw+HGJqdbdUCwvoSmnzmUKU6GCSmMS49fCa5Hi+RWKxNe3t3J812xMklqq0/QiUmRRao6gn7ulFHz2vn+tnAY8Dtqvpgk/FioKnF/wNV/VmLa4cARWCcXOp/EQ0kj5gT348THVq75N7RvVb4luSAY0KHcsQ1stlyitmHIHHnVqfKFFqC1t3Xalw85yDeszv9fqlmZdVq3i9ZiD9qtdAe5BnI2UNOJxQL80n5MkqCpWTZfBAYQiTsZnCWjyOGDsXt2LcrRlXZ4d9JXaSewZ5B5Los+/tP33ydndXVOO22RIqtqhKORbnluBMoqa/jzoUf4I+EE0VeGsj3ehiRk8uKPcWU+/3NYmntInicTkbn5nH1nKM5YXRjxtZfF3/M8uLW5pCjh4/g8ukz9v8XdxC06Mk1VFWb7XL29Zu9uoP3abvT3T4QkfOAC4CqJNOvquplB7p2n8N9LATfbp5NBeAY22XKVVWh/lE09AGJvYd9CGT9wKob2w7iGHtQu+p9Ia7Z1m458JK12xYf4j4RPF1bWDtVDPcO46LhF+CP+hMNHBs4Y/DBJWGICCMyhrcanzdqNP9b02hrrwoEKKqpJsvl4o6F7zMqN5fpgwezvaqKfG8GlQE/qpDjcXPi6DFM7l/AnQs/oC4UJhi10oZdNhtep5Oh2dkUZGZSFfRz+3vvsLeujiE+H3OHj2BndRUl9Y1xw4OzfHxpypRW8qWafWVyJenZnPS8rQchwxJVfSbercBwEIgtB7KuRf1PQmQd4LCUjPdLXXfT0LtWq5emRHehdfcjvuu67r4dRNxzwXU0qB/Ek5aOrYNlt7+YN/a8TUnQqnY12DOI0wYlt3N3NqeOHUdJfT3vb9tKXTjMtqpKfC43Q+NVubZWVpLpdCR6fDVEAuR7vZw/+RD6Z2QwqX8Bt7+3gHe3bcMuNhw2G/0zvAzIyMQuwosbGjvtbiovZ3N5Od+eOYuIKsW1tQz1ZTN98GActvT727ZrIuhO4gr2wRYmgo+BcsCHFRp2o6qWtrjOmAiSoBoG7J2uUDSy3YqFtY9CbFnEqu+w4l6TINm/Oqgi2G0F5Bsa8Uf8/OvzhwjGgs3GsxyZfHP0pd3W/aEy4OefSz9h2e5dZLpa18P91oyZVAWDFNfWMiw7m6OGDW9Vm+DNLZt5YvVKwtEYdpuN0bm5lNbXUxNqHQk6KjeXnx03r6sep8McrIkg1azGMlPUYEUqvCIiR2gb7wotA377rLOLeHhVJ6KxcrT2702cRk7ro7YG2rnI3/ZcW5doCPzPoaEPrbKGjsmI9zzEMeLABO/lrKle10q5gpVau6FmE1NyJiW5qnOpD4d5cs1q3vp8C2X1fjwOB4N9Wc26xYaiMU4d276Z6uQxYzl+5Ci2V1Ul4mh/8MpLSc/dUZXMotg9dMS51UD67amboKqXq2p1XKEWAjOAI9o6v6ioCFVNfPVV5doVaN19LTzyYTTwvJWEkAxbnmWL3V/q/oUG34yn4ypE1qC1d6PRsn1e2hepiSQJy+vAXGfyj0+W8NHOnXjiHWgDkQhbKysT9QUAxuW3b49vwGm3MzY/n8E+Hx6Hg2x38u4QLTsodCeFhYUJHVNU1EYoYJy0VrBNUdU6LHPBvvtLGDqEaoRYdC+xwHto/X/QwGtorPU/ZSyyE4KLILwOwqshss2qGwBWbIytZdM9O+K9aL/NExrdHW8P3nLCD8G2a7irRq0ddh9MKhzkaTtMrb25zmJXTQ2rS6xU3DyPJ1F6UNXKuAKYO3xEs06yHUVEOHlMcifoqWO7zjnamaStiUBETgSqGzLERMQF5AHpF03cw1BVCLxiZTqFVwJR1FYA9gIIvAJZVzcPm6p/1KofoGGsVNeAFajvGA8aRnw3QugDiGyxKmW5jkEcw5LfvD2ibf9pNboraZyjBt+1ag1oFeAG93Hx1ODuzUtPFeN9YxlYMYA9geadeEdkDGNExgH8DfaTkrrGiBW7zcbY/HxK6+uoDgbJcrn4+tTDOW7kqANe/4xx44mp8sbmTdSFw+S43Zw+bnxKC23vD2mrYIERWMkNV8RNBD8ANgMfp1asXkDwdTTwXDzTKWgVcYlstnamYkfDG9HsmxDXbKtFS+DN+I61oXZrxPqKlSHOSYgtAzwHFgaksdp47YSlEKuzZLIPblW+MFlCgYaWoPX/afpgaPANSxFndGHkRBphFztfGnYeS8o/YVPtFgRhom88s/NndouDcHhODjaRRAyrw2ZjUJaPQVk+Th077qAVoYhw9oSJnD5uPHWhEFkuVyJltieQcklFZGY8gmAacIOIPB2fehvrA+j7IrIQOAU4R7WN7m9YTi4RMbbXdlBVNPCmdRCrBo3F7Z1hoM5KtY1+DtWFaM2voe4hiJXSIsfGOl/D4D7wcCDVGFr7ezS00HKWSbwVTGSzJVcCNyRJu9XAW8nXDb7Xp8wFHrubYwvm8o3RX+ey0V/jqP5Hdlv0QL7Xm7QrbKbTyUmjx3TafRw2q3ttOinXwsLCditpQRqFaR0oJkxr/1ANopU/tA7C66yQKw3SWKWqYdfjtHaSsbr4x29oTGZ0WMrQfRK23N8euCyhT9G6v7UYjEKs2KpvYPOBYzzi/SLiaL0TilVel7z2AgcfImboODFVXt+8ife3baM2FGRKwQDmT5x0QHbXnkZPD9MydDIibtTW30pRteVCpBKrDGGzs6yx6O54zn5DmcCG4i7xDgSug6yrmqwYi9jBPhRcJyEZ57UfbmYfDpE1SdbwNS+laOhSbCKcPm48p/eADgPdTfrstw1djmoE9T8Nke0QWQ3Rynid1YZPMRL/stNYfsMeL47SQtE5JiLe0w5OIHvbNYLEMWifsbziPSsua4txz5mImL2DIfUYBXsQaLQEDa9LGtqUlvj/ZxXCtmWAfSTYHFiNCD1Yu1Q7jQor/t2WHS9N6It/ZVkf27NvwioXfBA4p1kVr1piy7Oqbu0DcYxDsq6xKnFJJthHIhmXp20xbUPfo1fZYBvo6gwu1QDU3Y+GV9CwyxP3CeD9UtqmdmqsHq26nsbOsE0nwxDeDFqO9TwOLLNB2FJ2tnyQgUAQEMj+FTZn68IfByZXOdQ/gYY/AxRxHgreCxF7+ncMNfRtkmR0tbLB9ioF211OLq17oLG5X1NZvF9O292TRnaiNb9IPin5YM+H0DqrkWFsuzVuGxhXujHAC85JiPcCxHPAlSnblk/DgFqdCwyGHoJxcnUyqn40tCT5XOj9tFWw2AsAN9YutDniGAmZV0D4U6u0Xyhi7VzFBTow7qmPQcaliHvfH90PhM6unWAwpAPGBru/qJ+GjqitSGNbrIgb8ZyUZMaOuk+Jp8BuhFi9FV3QsJMUu3Vsy0e0svsENhh6AWYHu79InpV7HytpPeWYkAKB9gPPfEQyrUaIsQpwjAH3mUjwFasLLUC0FGK7wT7M2sU2xdb1ue0GQ2+iV+1guyOTS0QQ7/m0+tWJF9K8DYmIIJ6TseXcji3vr9h8P0K0slG5Qlyp2q0YVW2yU7cNthoWGgwGwGRydSka2QzBBWi01LJhuk/qkZ5vrf07Gl7eYjBgJRnY+oEtF3FOg4wvI7bcVIhoMKQtxsnVRTT0k0rPoKz9IFlAvnjAMRoyv404DzMOKIPhAOlVJgLDAeCcbRVWicbbaEc2WT+TaZSrwXCQmB1sX8c51foea9IGWQPgnIJ5eRgMB0ev2sGacoUHQGQNiA0cEyxHln0YOCZbURKR1fu+3mDoo/QYJ5eIzAYeA25v0VU2B7gLmITVWXY3cLaqRpqcY8oVHgCqAQh9jPpfg+hGkFxL0TZBPGcg3i+kRD6DoSeQ9k4uETkPuABI1ibyUeBvqvqSWA2enqaX7boPFI2WQehdq+ygfQi4j0Naxq22ee1etOYu0Mp43OsuYK8VF9s0VVVMyT+D4WBIuYIFlqjqM/GuBglEZCZQoKovAahqDPhC94uXfmhkK1r7+8aW2eFPIfgu+H6E2Nv/yAKA/wlLuYKVpRXbA4QgWgwN7bHFC67ZnS+8wdCHSPluUFV3tjF1ErBVRP4sIh+KyAsicmh3ypauqP/JRuWaGKwD/7P7vlbDaHhV44A4wD4KcEOsHCKfQ3hDvJPr67TTocdgMOyDlCvYdhgFnA+8papHA/8FFohIm30oGpxcDV+90dmlGrJqBiSbC3fEKdVQVLsJtkxLyYrDKktoGwjRPWj9f9Ha+w5WZIOhV1FYWJjQMftycqWzgnUD21X1WQBVfQQIAee0dUFRUZHV1C/+1RsVrFUIu43YVPHs82oRB+Kc3npCy4Fs0CKIbbPMBtHt4H+cWHjtQUlsMPQmCgsLEzqmqChJ26MmpLOCrQD2tBgrArq+2XsaI2JHXEckn3Md1bFFMi5I0knAFW8c628xHoL6R/ZXTIPBQHor2E+Blsn9BcCu1qf2MbxfAsfEZkPiPAy853bocrHlIdm3IJnfRjznIplXWWu20aGV6E5UW9eR7amMGjWKefPmMW/ePBYvXgzA3r17+eIXv8jPfvazVudHIhEuu+wypk6dSmFhIYWFhZx++ulJ1/7rX//KW281byf+6KOPMm/evE5/DkP6kw5RBG3xHHCXiMxT1XdE5ASsWNgXUyxXyhFbBuK7Fo3EP8rbhiCO/dvYizjANaNxwD4IrbuHVrVuxWdFFJD6eOnO4rLLLmtlPlq2bBknnXQSu3a1fv92OBxcdtllRCKRxHVvvvlm0rWvuuqqVm2DLr74Yv75z392iuyGnkXKd7AiMjMeojUNuEFEngZQ1Rose+sdIrIQKATOUG276nNfy+QSx0jEdcR+K9eka9kHxnfAGQ0j8VYyI8AxGemAfben8P777/Pb3/6WO+64g0DAisY4/fTTycjI2MeVFg8++CDjxo2joqKCY445hksuuYSvf/3rXH755Zx33nk8+OCDra6prKzkjjvu4IILLuC1117rzMcxpIiOZHKlfAerqkuBeW3MLQbmdHQtk8l1cEjmt9BYFUR3ATYQAclBMr6catE6lTvvvJOZM2fyyCOP8Otf/7pl47o2Wbp0KVdffTVr1qxh3rx55OXlccUVV7B582Z+8YtfsGrVKj755JOk19bU1HDDDTdQV1fH3LlzOe20g2x5bkg5hYWFXHnlle0q2ZTvYA3pg9iykeyfIZlXIJ5TkYyLkZxfIPae2cng3nvvTTo+c+ZMAGbPns1HH7VuXtkWM2fO5A9/+AMPPPAAAwc2OgnHjh0LwKGHth2mPWyY9SkjMzOTioqKDt/T0LMxCtbQDBEX4p6DZHwJcR+HiDvVIh0wyRTs4sWLE/bTbdu2MXLkyP1ed+jQoSxatGi/rtm508qnqaurIzc3d7/vaeiZpNxEYDB0J/n5+dx9990sWbKEtWvX8stf/hKAjz76iBdeeIGysjJeeeUVzjjjjMQ10WiUhx9+mFWrVnH77bcD8PbbbzN37lxeeOEFAE444QTy8/MTxxdccAGZmZmAFUXgcrm47bbbWLlyJXfccUd3PrIhhaRFNa2DoWk1rQZuvfXWPuPo6ipUQxB4HQ0vA0CcM8BzSo/a0c6aNatNm6jBcLAUFha2tN+3qqbVqxSscXJ1DqqK1t7VOiXXMQ7Juq5VGFK6YhSsoavZV7lCY4M1tCayOnm9g8gmCK/ofnkMhh6KUbCG1kS2tD0XbWfOYDA0wyhYQ2vaa8/dpKi3RrajgdfQ4AdorL7r5eoAxxxzDNdccw27d+/e98kGQxfTqxRsX8vk6jJcs0EyW49LJjiPsGy0dQ+hNb9E/U+j9Q+j1T9Fw+u6X9YWLFy4EIBDDjmE4uJio2gNXUaP6cl1MBgnV9egke1o/YMQXm91P5BsyLgE8ZwO4U/QuvtAI1aRbg2AuME+Esm9y6pzkCJEBFVl9+7d3H777Tz22GNceumlXH/99QwePDhlchl6J/tychkFa2iTf/zlKv553xPWQUPkgGQCCrE6IEirIjD2oVaBmBSxdOlSmr6mly9fzoknnojT6WTEiBEpk+tAuPLKK7nyyitTLYahHdK+6aGhc7E6HmwD8R5UERiN7uVbF8f41sVfbD1pGwihxaBJ+lS6j8eWk7pA+oYQsoYd7OOPP843vvENrr/+egYN6pkpv4aei1GwvQgNLoz367IcTmofiWR+C7EX7P9i4VW0WaJQXG3UjrVDtAzVYEoTEq655hoeeughvF4vq1evNorVkDKMk6uXoJHNaP3DCeUKQHQbWvdXDsgM1J6CdExsFk0QvwDsw+Jtv+37f79OYu7cuYhIQrEa5WroKnpEuUIAEZkNPAbcrqoPxscuA64GKpucehjwLVV9Otk6fdoGG3yfpDvO6G6IbgbHuP1bzzkNeByrDVpTBHEdZSntwDOgtYDdUrjiQlwzU+rk+uCDD1J2b0PfoiPlClOuYEXkPOACoKVBLwR8X1U/iJ/nBDYAfbZasQbfRYPvQ6wacYwH79mIPe4Zb6vdC0Cser/vJbZMyPoWWvevJi3C7Yj3Asu2az8Xje2CSJOGiPbR4L1wv+9lMPRWUq5ggSWq+ky8q0ECVf1Pi/NOAz5Q1bpukyyNUP/TaKDxvUXDn1gprb4bEfsAcEyI201bYgfH2AO6pzinQs5vILwSNAzOwxCbFSEg4kZ8V6ORz60C3bYBiHP8Ad3HYOitpNwGq6o7O3jqxcDDXSlLuqKxWjTwdpIJPwTjvaFcxybpFItVONuWc8D3FvEgrtmI++iEcm027xiNuOca5WowJCHlCrYjiEgWMBt4q73zGpxcDV+9xtkV3QWEk05pZCvQ0AjxesRzlvVR3XGIFUHg/UK3iWkw9AUKCwsTOqZHOLk6wPnAs6oabe+kXuvksvUDhGROLLH1b/JzFnjnI975rc5TjUHgFTRk2XBxjEe85yKOMV0ouMHQ+2ho3Q6tEg1a0SN2sFjmgUdSLUSqEHs/xHl4shlwn9CxRfyPo4HnIVYBRCGyDq25G422blPdWzBZUIZUk/YKVkQGAgNV9dNUy5JSMi9DXEeSiDG19UMyv9kh26fGqtFgsvClMATe7FQx0wmjYA2ppieYCC4CWkYU9DlEvJB5OXgvspIJbP063lkgWgwkt65odKdlMvA/hYaWWvdyTQfv+UirZAKDwbA/pHwHKyIz4yFa04AbRKRlEsFX6KCC7QuZXGLLQOz9969ti70/lg03CbYCy1QQ+gjLkRZGQ4vRmrusugZxVKOo/0VildcTq/gusZq70cjmg3kUg6FHY8oVGhJo3f1o6OMWo3bwnAmBF5JeIxmXIe6jrOvrH0WD77U4w4lk/xSxt/8iMxh6K6Ynl8Ei42uI+yQQj3VsH45kfQ9pq6ALWGm2NNhwFyY5oXNtuPsIEjEYehw9wQZr6AREXJBxIXi/CISRuKJVbafViz2euNCuDbeoLeNDh9HwatT/nFWcRnyIex54zkTEvP8bejbmFdzHELEnlCsAzulgG9D6RFs/q3UMgL2Atl4qYj+4alUa2YTW/gWi2+IDNWjgBfA/eVDrGgzpQK9SsH3BydXZiDgQ3zWIcybWy8GGOKchWT+ydr2A2PKQBmXbDDu4Tzo4AQJvALFWwxp8L20aKRoMyTBOLsN+oRoBSFpuUDUM/mfR0AdWdS37CMR7HuKcclD3jFXdArE9SefEdxPi6FltXgx9C9MyxtBh2qvjKuKEjAua2HA7p2OB2AejSRWsE5qkARsMPZFeZSIwdAdhwNV5y3lOIdnLUNzHIbaMzruPwZACzA62j6PhDRBZB5IBriOTliQE0NAy1P88xHaDZFk1EDrB0y+OcZD1/UQUAU2iCAyGnk6vssE2cOuttxpH1z5QjUHdfWh4aZNRJ5L1HcR5aPNzw2vQ2j/RspqXuE9FMpJ0nT1gmaKIpK6fl8GwPxQWFnLbbbc1HWplg+1VCrYvOrk0VmP144pus0Kr3Md1KHRKg4vQ+gdbT4gPybmjmT02VvOH5q1hEriR3N+mtIOswZBKjJOrF6PRMrTmTtDKxsHge5D1PcQ5uf2Lm+1cmy5aA5EN0DQ6oA0vPwSt8ocHGQtrMPRWjJOrJxN4oblyBSCM+p/o3PvYBicfFw/Y8jv3XgZDL8Io2B6Mhtckn4juQmPl7V/snJF8XHxWA8WmQ57TSe7pPymRjNBZaKyuWRUvg6En06sUbJ/L5Gqa8toMG7APu6hrDtJKyTqRzEtbxcOKcwKS9QOr1xdi1aL1XgCecw5Q8NZoZAux6jvQqmvRyqvRun+hsdpOW99g6Gx6TCaXiMwGHgNuV9UHm4x/E/gBUI1Vyv9aVf24xbV91sml/pfRwHOtxq1U1+92bI3wesuBJZnxMK3szhZz3zJEy9Dq24Bg8wnHWGy+67tdHoOho6S9k0tEzgMuAKpajB8K/AM4VFXXicjlwDMiMlTT4V0hHfCcisR2o6HFjWP2UZDxtQ4vIc6J4JzY+bLtD6F3aaZcNQCxGoiWEXOvwOaamjLRDIaDIeUKFliiqs/Euxo0ZTJQqarr4seLgMHAIGB3N8qXtog4IPOb4DkLItvA3s8K3O8haLQYgm+j/pcgVmmFmWklxEoaT6q5A/Vdj7impUhKg+HASbmCVdWdbUwtBOwicrSqfgicA2wF9naXbD0FsQ9K+1ApjRaDhsA+DBEbGtmK1twNBEHrQMshUgIoNLMBO9C6+8H5G6svmcHQg0hbJ1fclvFF4CkRWQ98AzhN2yl73+DkavjqM86uNEajey3nVfWtaM0v0eob0dBnVmpsg1nA1g/rvT4E2sRUINlg81rnhVd1v/AGQxIKCwsTOqZHOLkA4iaCBxucXCIyCXgPOFlVV4jIt4Cvxo+jTa7rs06u/UE1BkStqlj7fW0Ywp/GkwpGd6hVuHWdotW3QKzlhw67ZWdtKosGIbzeSnSw5VnxtbYBEK910LQ/mMGQLqS9k6sdvgF8oqor4sf3AXcBJwOvpUyqHobG6sH/ZNwRFkEdExHvFztcZ1WjxWjtHyzl2jDmOATJ+u6+lXVkbRLlChAFrQVp0hZc3OAYCdEd4JgMzbrmOsB5WIfkNRjSibQ1EWDVxAs3HMQjByJA98cR9WC07q9oaCHWr1Ihsg6t/T3aRGG2f/2DzZQrAJHV8U4E+yBW2facfVjrMcm2EiCaKVdBMr6M2LI6IK3BkF6k8w72TeBRERmsqrtF5EzAA3yUYrl6DBrZDJFNSSbqIfgBeNtPFNBoGUQ/Tz4XXoJ491FS0DEGEFpW4QLAfSYSK0JDH2G1jBHENRMyL4PIZgivtHa1riMQe/JUXY2VQ+AdNLoTsQ8A97yD7hFmMHQmKVewIjIT66P/NOAGEZmvquer6ksichvwsojUYinXC1R1R1trNdhCTLnCONHiA5trPKntqQ602Bb7IHAdHd9BN8E2GPEcYzVf9J5jyWIrQOwF1rxzsvXVDhrZidb+DtQfP14NwYXg+78eFapm6LkkKVfYirRxch0oxsnVNhr5HK25I+mceM5BvGfvc41YdSFEW4cdi+c0xHv+vmVQhdD71k5VA1atWfepB/2RX2v/goZXtp6wj8GW/ZODWttg6Cg92cllOEjEMRp1TG5dy1WywH3sPq9XjYBzLkT+AzgbbaP2YeA+rWMyiFg1at3H7af0+5AtnKw+LRDdgmrQ1Kg1pAVGwfZyJOu74H8mvoMMWjtI7/mILafd6zS8xnJwaRUgVjKAcwbimguumQcU7tWpiNcK6WqFE6tshcGQeoyC7eWIuCHjIiTjog5fo7EatPbvJBIBxGl9RT6HzG+lXrkC4j4aDbSO1hPXke12xzUYupN0DtPab/pcucIkaKwe9T9PrPpXxGp+hwY/YL/t7KEltKpsBUAYmhaWSSWecxDnzOZjjkOs1uIGQzfQY8oVHgzGydWIashyakWb9YBEXHORzEs6vo7/eTTwUtI58ZyBeL9wMGJ2KhothugusA1AHEliaw2GLmRfTq5etYPt84QWtVKuABpaaCmijuKYdGBzKUDsgxDXDKNcDWmJUbC9iWRJBYm5zR1eRpwTEOf0JOOHI870UrAGQzpjvAG9CWknMsDm27+1Mr+FhD5CQ0sBtbKsXKbYisGwP/SqHWyfd3K5jyFpiJKtn+UA2g9E7Ih7LjbfD7H5/g9xH4PIvsOfNLwB9T+LBl5H26tFYDD0cIyTqw+ioeVo/WPx+FXAPhzJvKLLc/RVFeruQ8OfNBl1IJlXIq7Du/TeBkOqMJlcfQxxTQfnVKvsn7jbLJTS6YSXtFCuABG0/sF4N4LObe9tMPQEepWJwGAhYkcco7pPuQKEliYf13qIrEs+ZzD0coyCNXQDsu9TDIZeSFooWBGZLSKbROSyFuOni8hCEflIRN4VkXZ7lfR5J1cqcc1KPi6Z4EhxW3CDoQvoEU4uETkPuACYCPy5SU+uccByYIaqbhSRa4HLgalqNZhquN44udIAVYX6++OtaRpwIllXIs6pKZPLYOhKeoKTa4mqPhNvetiUi4DFqroxfvxPrMLcR2G19DakESICmd8E9/EQXguSAa7ZiM10+DH0XVKuYFV1ZxtTQ4HSJufViIgfq/OBUbBpijjGgekoYDAAaWKDbYOdWEoWABHJBrxAXptXGAwGQxqRzgr2P8BMEZkTP74Gq6tsoK0LGpxcDV/G2WUwGDqbwsLChI5JeydXA3Eb7IMNTq742AnATVgND58HLgUKVfV/Tc4xTi6DwZASeoKTq01UdQGwAEBEvMCNwHspFcpgMBg6SNqaCERktIjc02ToOuA/qronVTIZDAbD/pByBSsiM+PmgWnADSLydHyqDjhMRD4RkUVAPnB1SoQ0GAyGAyDlClZVl6rqPFXNVdVJqnp+fHyvqh6rqrNU9ShVvUZVQ+2tZTK5DAZDd9EjMrkOlnRxcmmsHAKvoZENID7EfSzimp0SWQwGQ/fQo51cPQWNlaPVvwatbhyLrIfoXsR7VgolMxgMqSTlJoJeQeDtZsq1AQ28isbqUyCQwWBIB4yC7QS0zWaDIYhu71ZZDAZD+tCrFGyqnFxiy217sr05g8HQYzFOrm5Cw2vR2j+0nnBMxOa7ttvlMRgM3cO+nFy9agebKsQ5Gcm4GCSrYQQchyCZ30qpXAaDIbWYKIJOQtzHgesoiO4Cmw+x5adaJIPBkGKMgu1ERJzgGJlqMQwGQ5rQq0wEJpPLYDB0F8bJ1YfR6B7QMNitNx2DwdD5mEyuPoZGi9G6ByC61Rqw9YOMryLOQ1Mql8HQF+lVJoK+jmoUrf1To3IFiJWhtX9Ho6VtXmcwGLoGs4PtTYRXQaws2QSEPgDvF7pbIsNBoBqAwOtoeDkgiHMWeE5GxJVq0QwdJOU7WBFxisjVIvKOiLwrIotE5KQm89PiYwtF5AUR6dfWWn3eyaUVbc/FqrpPDsNBoxpFa/6ABl6yQv+iRWjgObT2nn1fbOgWeoSTS0RGYbWFmaaqVSJyCvAMMBEoATYCl6nqAhG5DZiiqhc0ud44ueJoZAdac3vSOcn4GuI+tpslSh80+CEaWghajzgmg+fU9lOcU4yGlqJ19yadk6yrEefkbpbIkIye4OSqAW5R1SoAVX1DRALA0UAIiMZ7cwHcB2wVkQJVLUmNuOmLOIaDazYaWtJ8wj4UXEemRqg0QOufRINvNB5Hd0H4U/DdiNiy2r4wlUS2tDP3ORgF2yNIuYlAVctU9eGGY7FiilxYu9fZwLom5+4A6oEZ3S1njyHjG4j3y2AfZYVoec5Asn7UZ+12GqtEg2+1noiVQfCdbpenw9jy2pnL7TYxDAdHOuxgW3I8sA2re+zFQEvjYSUwoJtl6jGI2MFzIuI5MdWipAeRbUAs6ZRGNpO2EcKuIyHwAmig+bj4wDUzNTIZ9puU72CbIiIe4FdYNteG/4pkRuKk/xcNTq6Grz7r7Iqjke2o/xnrK9Kz6tJqZCvqfxb1v4hGiw98IVtOm1PpbIMVmw/J+iHYBjcO2ocjWf+HiDt1ghkoLCxM6Ji0d3I1EDcN/Bt4QVX/Fx/7JTBdVc9scl4NcIGqvho/Nk6uJKj/RTTwQrMx8ZyJeM9NkUQdp6XNFATxXoB4TmrzmvaIVf8KottajAriux5xjDlgObsL6w1GEPvAVItiaEFPKld4F7BYVf8nIm4RGQEswYomAEBEhgMZwNIUydgj0OiuVsoVQAMvo5GdKZCo42hkUwvlCqCo/0mrseQBIFnfBcfEJgPZSMY3eoRyBRD7IKNceyhpYYMVkZ9gyfKgiGQBI4ALgV8DfxSR41X1XeBy4GkTQbAPQsvbngt/Co5h3SbKftOm7DEIfQoHYFsWWx7iu9bKZtN6sA9BJC1e+oZeTspfZSIyAbgjfviDJlO3qWpQRL4A3CMiUaACuKx7JeyBiL29yW4T48BoT76Dk13s/Q/qeoNhf0m5iUBVN6iqJPkqjM8vV9WjVPUYVT1HVZPlggImkyuBcwbJlZGkvwfaNauNCRu4pnerKAZDe/SITK6DxTi5kqOBt1H/EzQGYQji/RLiOTmVYnUI9b+ABl5sMmJDMi5G3MekTCaDIRk9IZPL0AmoKmgdiAcRhxUH6zwcwssBBecMxN5mGYe0QrznWDvZ8KeAHVyzTAseQ4/EKNhegIY+Qf3PQ2wPiAdcc8F7vqVQ7em/Y02G2AeDffC+TzQY0hijYHs4Gl6L1t1HwhSgATT4FqIhyPxaSmVLhkaLIFYD9hGILSPV4hgMXUrKnVydSZ90cgXfIlmym4Y+RGO13S9PG2iskljNnWj1z9Ha36NVN6CB11ItlsFwwBgnVx8gVnWLZRpIgvhuQhwj9ms9jZZA4Dk0vApwIa454D37oIvFxGp+C5FNrWXMvApxHX5QaxsMqaInZXIZDgCxt5U04AZ7wX6tpbFatOa3VrlD9YNWocHX0Nq/H5SMGi1OqlwBCL1/UGsbDOmMUbA9Hc9pJDOli+ckRLz7t1ZoIWiSzgeR1Whk6wGJB1g21zbQduYMhp6OUbA9HHGMRHzXgGMy4AbbQKserGf+/i8WbadOQXtz+8IxwopuSII0rRFgMPQyepWC7ZNOLkAc47D5rsaW9ydsOT9HPCdiFSfbT2ztmBTam9uXfOJGPEmqeNny4QArZBkMqcY4uQz7hcbK0eqfW/bXpthHYsu+8eDXD6+G4Huo1iCO8eA+CbFlH/S6BkOqMJlchg4jtnzIutpKsY1sBuyIcxpkfKVz1nceAs5D0r7cjMHQWRgFa2iGOEYhvuvRWB2Is8/28jIYOgOjYHs4Gt4A4RUgDnAdgdg7x0QitsxOWcdg6Muk3MklIk4RuVpE3hGRd0VkkYic1GReROR6EQnta63OdnKlu7NM6x5Ba+9Cg2+ggVesLKnA26kWKyWk+9/KYNGb/k49wsklIqOABcA0Va0SkVOAZ4CJqlokIg9hdZn9maq2Mt91pZNLREj176ctNLwGrf1jkhkbkvMrpL22z72QdP5bGRrpbX+nnpDJVQPcompFuKvqG0AAODo+fyPwrxTJ1ul02jt4+LNmhxvWr4//FLNMBp1AV+w2etIOpic9f0+StStI1+dP+Q62JfHuslXAfFV9Jz42Cvi8jR3sMGAHwNKlSxk0aFCnyTJ06FCKioo6bb3OXFP9z6HBhYnjN954g1NOOQXAKqztPuKg75HOz98d6/aUNbtq3Z6yZlet25E1i4uLmTkz0SVkuKo2y8hJRwU7D/gzcLiqxuJjo2hbwU4HlnWjiAaDwZCMGararGtnOpgIEoiIB/gVcFmDcjUYDIaeStqEacVNA/cCv1fVpftx6SpgRvznEsAoZoPB0F3YgIY88lUtJ9PGRCAidwNbVPUvIuIGBqrq9vjcKNowERgMBkO6khYmAhH5CdZu+kERyQLGApenViqDwWA4OFKuYEVkAnAH8AOskK0aYHWT+ZuAx+M/vyMiV6ZAxtkisklELuvuexvaR0Q8IvKgiHwkIp+IyKmplsmQnL74f5RyG6yqboC263+o6i+BX3afRM0RkfOAC7BCxwzpRyGWqWtO/M36IxGZrKrJ++gYUkJf/T9K+Q62B7BEVb+KtbM2pBEiYgOuIJ6IEn+zXg6kXztdQ5/8PzIKdh+0DBw2pBVjgH7AuiZja4BZqRHH0BZ99f/IKFhDT2Zg/HvTj52VwIDuF8VgaI1RsIbeQMtYQxPOZ0gL+rSCFZFXRKS2ja/XUy2fYZ/sjX/PbTKW22TcYEgpKY8iSCWqekaqZTAcFJuBcmAijUp1CvByyiQyGJrQp3ewhp5NvF7FP4knpYjIeGAa8GgKxTIYEqRNqmy6IiIzgbuw/nGLgTWqen5KhTIkiBcI+jswCesT2Y2qasw7aUZf/T8yCtZgMBi6CGMiMBgMhi7CKFiDwWDoIoyCNRgMhi7CKFiDwWDoIoyCNRgMhi7CKFiDwWDoIoyCNRgMhi7CKFiDwWDoIoyCNRgMhi7CKFiDwWDoIoyCNRgMhi7CKFhDr0FEpsU7DxeLSGX8Z+8BrnWbiKwTEY2vc1R83BU/rozf57nOfQpDb8IUezH0OkTkQWCUqs47yHUuAx5Q1VYdEkTkHWCrql52MPcw9G7MDtZgMBi6CKNgDQaDoYswCtZgMBi6iD7dk8tg6Ahxe2tLpgFbu1UQQ4/DKFhDn0JEBgGPNxm6Q1Vfbe+aZM6yNpSuwdAMo2ANfQpVLQbmpVoOQ9/A2GANBoOhizAK1mAwGLoIo2ANvZXowVwsIrcBN8R/bpXJheXkOt1kchnaw2RyGXo8IuIEBqhqUfz4SSCoqhenVjJDX8fsYA29gaHAfwHitQdmAe+mVCKDARNFYOgdVAFBEfkY6zX9AvBgSiUyGDAmAoPBYOgyjInAYDAYugijYA0Gg6GLMArWYDAYugijYA0Gg6GLMArWYDAYugijYA0Gg6GL+H/1cGoPdUorWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('color_mag.png')"
   ]
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}