{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatially-Resolved Mass-Metallicity Relation\n",
    "\n",
    "We're going to construct the spatially-resolved mass-metallicity relation (MZR) for a MaNGA galaxy, where mass refers to stellar mass and metallicity refers to gas-phase oxygen abundance.\n",
    "\n",
    "### Roadmap\n",
    "1. Compute metallicity.\n",
    "2. Select spaxels that are\n",
    "  1. star-forming, \n",
    "  2. not flagged as \"bad data,\" and\n",
    "  3. above a signal-to-noise ratio threshold.\n",
    "3. Compute stellar mass surface density.\n",
    "4. Plot metallicity as a function of stellar mass surface density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "path_notebooks = os.path.abspath('.')\n",
    "path_data = join(path_notebooks, 'data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Maps for Galaxy\n",
    "\n",
    "Import the Marvin `Maps` class from `marvin.tools.maps` and initialize a `Maps` object for the galaxy 8077-6104."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: No release version set. Setting default to MPL-6\n"
     ]
    }
   ],
   "source": [
    "from marvin.tools.maps import Maps\n",
    "\n",
    "# REMOVE FROM NOTEBOOK\n",
    "filename = '/Users/andrews/hacks/galaxies-mzr/data/manga-8077-6104-MAPS-SPX-GAU-MILESHC.fits.gz'\n",
    "maps = Maps(filename=filename)\n",
    "\n",
    "# maps = Maps('8077-6104')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure Metallicity\n",
    "\n",
    "\n",
    "### Pettini & Pagel (2004) N2 metallicity calibration\n",
    "\n",
    "We are going to use the N2 metallicity calibration (their Equation 1) from Pettini & Pagel (2004):\n",
    "\n",
    "12 + log(O/H) = 8.90 + 0.57 $\\times$ log( $\\frac{F([NII])}{F(H\\alpha)}$ ).\n",
    "\n",
    "One of the benefits of this calibration is that the required lines are very close in wavelength, so the reddening correction is negligible.\n",
    "\n",
    "Get [NII] 6585 and Halpha flux maps from the Marvin `Maps` object.  Note: MaNGA (and Marvin) use the wavelengths of lines in vaccuum, whereas they are usually reported in air, hence the slight offsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nii = maps.emline_gflux_nii_6585\n",
    "ha = maps.emline_gflux_ha_6564"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the necessary line ratio.\n",
    "\n",
    "Marvin can do map arithmetic, which propagates the inverse variances and masks, so you can just do `+`, `-`, `*`, `/`, and `**` operations as normal.  (Note: taking the log of a Marvin `Map` will work for the values but the inverse variance propagation does not correctly propagate the inverse variance yet.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n2 = nii / ha\n",
    "logn2 = np.log10(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, calculate the metallicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "oh = 8.90 + 0.57 * logn2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Spaxels\n",
    "### Using the BPT Diagram to select star-forming spaxels\n",
    "\n",
    "Metallicity indicators only work for star-forming spaxels, so we need a way to select only these spaxels.\n",
    "\n",
    "The classic diagnostic diagram for classify the emission from galaxies (or galactic sub-regions) as star-forming or non-star-forming (i.e., from active galactic nuclei (AGN) or evolved stars) was originally proposed in Baldwin, Phillips, & Terlevich (1981) and is known as the **BPT diagram**.\n",
    "\n",
    "The BPT diagram uses ratios of emission lines to separate thermal and non-thermal emission.\n",
    "\n",
    "The classic BPT diagram uses [OIII]5007 / Hbeta vs. [NII]6583 / Halpha, but there are several versions of the BPT diagram that use different lines ratios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BPT Diagrams with Marvin\n",
    "\n",
    "Let's use Marvin's `maps.get_bpt()` method to make BPT diagrams for this galaxy.\n",
    "\n",
    "**red line**: maximal starbust (Kewley et al 2001) -- everything to the right is non-star-forming.  \n",
    "**dashed black line**: conservative star-forming cut (Kauffmann et al. 2003) -- everything to the left is star-forming.\n",
    "\n",
    "Line ratios that fall in between these two lines are designated \"Composite\" with contributions from both star-forming and non-star-forming emission.\n",
    "\n",
    "**blue line**: separates non-star-forming spaxels into Seyferts and LINERs.\n",
    "\n",
    "Seyferts are a type of AGNs.\n",
    "\n",
    "LINERs (Low Ionization Nuclear Emission Regions) are not always nuclear (LIER is a better acronym) and not always AGN (oftern hot evolved stars).\n",
    "\n",
    "Sometimes these diagnostic diagrams disagree with each other, hence the \"Ambiguous\" designation.\n",
    "\n",
    "Try using `maps.get_bpt?` to read the documentation on how to use this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZMijKA1nU58Dt27exWkUKFCgY3ebddim5i8RqhaAgDfnzi3z9teG9J3BJ1bKn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\nYIiIiIjP/QMZ+q8P6B0bMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 612x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "masks_bpt, __, __ = maps.get_bpt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The BPT masks are dictionaries of dictionaries of a boolean (True/False) arrays.  We are interested in the spaxels that are classified as star-forming in all three BPT diagrams are designated as ``True``, which is designated with the `global` key.  Print this mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks_bpt['sf']['global']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Masks\n",
    "MaNGA (and SDSS generally) use bitmasks to communicate data quality.\n",
    "\n",
    "Marvin has built-in methods to convert from the bitmasks integer values to individual bits or labels and to create new masks by specifying a set of labels.\n",
    "\n",
    "Show the mask schema with `n2.pixmask.schema`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bit</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NOCOV</td>\n",
       "      <td>No coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LOWCOV</td>\n",
       "      <td>Low coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>DEADFIBER</td>\n",
       "      <td>Major contributing fiber is dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>FORESTAR</td>\n",
       "      <td>Foreground star</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NOVALUE</td>\n",
       "      <td>Spaxel was not fit because it did not meet sel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>UNRELIABLE</td>\n",
       "      <td>Value is deemed unreliable; see TRM for defini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>MATHERROR</td>\n",
       "      <td>Mathematical error in computing value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>FITFAILED</td>\n",
       "      <td>Attempted fit for property failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>NEARBOUND</td>\n",
       "      <td>Fitted value is too near an imposed boundary; ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NOCORRECTION</td>\n",
       "      <td>Appropriate correction not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>MULTICOMP</td>\n",
       "      <td>Multi-component velocity features present</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>DONOTUSE</td>\n",
       "      <td>Do not use this spaxel for science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bit         label                                        description\n",
       "0     0         NOCOV                         No coverage in this spaxel\n",
       "1     1        LOWCOV                        Low coverage in this spaxel\n",
       "2     2     DEADFIBER                   Major contributing fiber is dead\n",
       "3     3      FORESTAR                                    Foreground star\n",
       "4     4       NOVALUE  Spaxel was not fit because it did not meet sel...\n",
       "5     5    UNRELIABLE  Value is deemed unreliable; see TRM for defini...\n",
       "6     6     MATHERROR              Mathematical error in computing value\n",
       "7     7     FITFAILED                  Attempted fit for property failed\n",
       "8     8     NEARBOUND  Fitted value is too near an imposed boundary; ...\n",
       "9     9  NOCORRECTION               Appropriate correction not available\n",
       "10   10     MULTICOMP          Multi-component velocity features present\n",
       "11   30      DONOTUSE                 Do not use this spaxel for science"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2.pixmask.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select non-star-forming spaxels (from the BPT mask) and set their mask value to the DAP's DONOTUSE value with the `n2.pixmask.labels_to_value()` method.  Note that we are selecting spaxels that we want from the BPT mask (i.e., `True` is a spaxel to keep), whereas we are using the pixmask to select spaxels that we want to exclude (i.e., `True` is a spaxel to ignore)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select spaxels classified by the DAP as bad data according to the masks for spaxels with no IFU coverage, with unreliable measurements, or otherwise unfit for science.  Use the `n2.pixmask.get_mask` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select spaxels with signal-to-noise ratios (SNRs) > 3 on both [NII] 6585 and Halpha.\n",
    "\n",
    "`ha.ivar` = inverse variance = $\\frac{1}{\\sigma^2}$, where $\\sigma$ is the error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_snr = 3.\n",
    "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n",
    "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do a [bitwise (binary) OR](https://www.tutorialspoint.com/python/bitwise_operators_example.htm) to create a master mask of spaxels to ignore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Metallicity Map\n",
    "\n",
    "Plot the map of metallicity using the `plot()` method from your Marvin `Map` metallicity object.  Also, mask undesirable spaxels and label the colorbar.\n",
    "\n",
    "Note: solar metallicity is about 8.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7MjFeO0YboAHVUhjWmApuzKIl09CnC33//gOl6Q1Czlv59rFPaYwUUuZ2TEy9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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = oh.plot(mask=mask, cblabel='12+log(O/H)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Stellar Mass Surface Density\n",
    "\n",
    "1. Read in spaxel stellar mass measurements from the Firefly spectral fitting catalog (Goddard et al. 2017).\n",
    "  - The [Firefly stellar population fitting results file](https://data.sdss.org/sas/dr14/manga/spectro/firefly/v1_0_3/manga_firefly-v2_1_2-STELLARPOP.fits) is large (1.8 GB), so we have extracted only the measurements needed for our purposes [DOWNLOAD CSV FILE](https://sdss-marvin.readthedocs.io/en/latest/tutorials/exercises/data/manga-8077-6104_mstar.csv).\n",
    "  - [Summary of the Firefly Value Added Catalog](http://www.sdss.org/dr14/manga/manga-data/manga-firefly-value-added-catalog/)\n",
    "  - [Datamodel of the Firefly Value Added Catalog](https://data.sdss.org/datamodel/files/MANGA_FIREFLY/FIREFLY_VER/manga_firefly-STELLARPOP.html)\n",
    "2. Convert spaxel angular size to a physical scale in pc.\n",
    "3. Divide stellar mass by area to get stellar surface mass density.\n",
    "\n",
    "### Read in stellar masses\n",
    "\n",
    "Use [pandas](http://pandas.pydata.org/pandas-docs/stable/) to read in the csv file with stellar masses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mstar = pd.read_csv(join(path_data, 'manga-{}_mstar.csv'.format(maps.plateifu)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot stellar mass map using `ax.imshow()`.  MaNGA maps are oriented such that you want to specify `origin='lower'`.  Also include a labelled colorbar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EuboAOoENWY51HGeCk4jye4wB/cQJXVpU9UZGUAevkKSqj4vI/cQ1AWoGihyr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\nddwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(mstar, origin='lower')\n",
    "ax.set_xlabel('spaxel')\n",
    "ax.set_ylabel('spaxel')\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('log(Mstar) [M$_\\odot$]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate physical size of a spaxel\n",
    "\n",
    "MaNGA's maps (and data cubes) have a spaxel size of 0.5 arcsec. Let's convert that into a physical scale for our galaxy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "spaxel_size = 0.5  # [arcsec]\n",
    "\n",
    "# or programmatically:\n",
    "# spaxel_size = float(maps.getCube().header['CD2_2']) * 3600"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the redshift of the galaxy from the `maps.nsa` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "redshift = maps.nsa['z']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the **small angle approximation** to estimate the physical scale:\n",
    "\n",
    "$\\theta = \\mathrm{tan}^{-1}(\\frac{d}{D}) \\approx \\frac{206,265 \\, \\mathrm{arcsec}}{1 \\, \\mathrm{radian}} \\frac{d}{D}$,\n",
    "\n",
    "where  \n",
    "$\\theta$ is the angular size of the object (in our case spaxel) in arcsec,  \n",
    "$d$ is the diameter of the object (spaxel), and  \n",
    "$D$ is the angular diameter distance.\n",
    "\n",
    "\n",
    "The distance (via the **Hubble Law** --- which is fairly accurate for low redshift objects) is\n",
    "\n",
    "$D \\approx \\frac{cz}{H_0}$,\n",
    "\n",
    "where  \n",
    "$c$ is the speed of light in km/s,  \n",
    "$z$ is the redshift, and  \n",
    "$H_0$ is the Hubble constant in km/s/Mpc.\n",
    "\n",
    "Calculate $D$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 299792  # speed of light [km/s]\n",
    "H0 = 70  # [km s^-1 Mpc^-1]\n",
    "D = c * redshift / H0  # approx. distance to galaxy [Mpc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rearrange the small angle formula to solve for the scale ($\\frac{d}{\\theta}$) in pc / arcsec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = 1 / 206265 * D * 1e6  # 1 radian = 206265 arcsec [pc / arcsec]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now convert the spaxel size from arcsec to parsecs and calculate the area of a spaxel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "spaxel_area = (scale * spaxel_size)**2  # [pc^2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we simply divide the stellar mass by the area to get the stellar mass surface density $\\Sigma_\\star$ in units of $\\frac{M_\\odot}{pc^2}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_star = np.log10(10**mstar / spaxel_area)  # [Msun / pc^2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot metallicity as a function of $\\Sigma_\\star$!  Remember to apply the mask.  Also set the axis range to be `[0, 4, 8, 8.8]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 8.0, 8.8]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y7c6qJ35pnkYu2nv/pTr1Zzk5L9GSN4bpqTF62VWeJ+MQQiCFJ9OKogmFVFJ4\n5o3l4/0CJSCNBJ8fByXQbiTJOjL4pyPFahZRW/egYEoGH7snxLhjLcKkax2RkmghWVQ1eVWDEHSi\nhGllmC4MNZAKmPmKxkDT+ODD5/lqn9wyjmAcVLV/7DVOMu61CFlHxgTj34nBOI93kGWSbiQoaksn\ndigp+HA3RyWS71zqoaUMAWkfjImW4lTqWiBY78b0kojGBDVRT+g6d3dcsD+taLznzz8/4u64AAd5\nFVxxG72YtU7YNZ5M4Gcn+NK4UCnsHEl0IuERFjA31zrP9Xy2nA8X3hC8bvK0j1P2vNznsTuFlU78\nVHLRs9KglXyqTIwXKTP9LMxrw/7ejM92pqdB8SxSDFLNvXHBcV4zzCLy0mKtZdiJEUIyKSqmVRBT\nE0JQGs9+XjNMNceLmqJxfHqYEyvBtHHUJkyusQp/J0rhnccpj1ICpUFJgbFhgiqVpbHmtC2n8SBc\nWK1D+Pt5657ORnhOXEhyeb2zCbkCyA0MYtBRcEMVzTKGAIja4qxnbdkZbS+vSaKCXx6tkkk4XNSM\nl43tG+sZZRHXV1LGi4b7k4qiLlk0juuDjG4qmRQ1q1mCcZatvRm3jxZMS0OkwCOY1Db0R+5opAq6\nRP0kBICL2vLxfk4/0YA/bUpzYyWkHNc2/Ga7syrUbSxqnAo73jZT6NVxoT/1VyWxcNH4sp3C08hF\nP2uv1RclM/20nNznbjd5SFju8iDo+P/W22v8+P4E7zzr/ZhICsqlqJsSQX8okqHmIZKO2nkQnqJx\nTIqS40WDQKKkpW4euF36Hc1KJ+TJz2tD4zxdp2lsSNv0HhamwZoH7hlDCOSeTP6eB+6cr8vJezx6\nrRMDUZgQU9DCn2Yl4aGswShHL4HxImgkaSXYnZWspop5ZUl0COxrLSlrC96zaBxZLJgsDHoBAk+n\nVBTGUwwcd6YwLg15Heo8kijEEEKhl2NaGm5uaL5/bcjeMkU30WEXsj2tOMobkkhyc7XDojb81d1j\ndmc13Ujx/etDrvQTBqkmiiSXlkkRLa+GC/3Jv0qJhS/jVXTVetzkfNYfe8I3MeB2cp8npSGJghJo\nbRyz0nCpnzCrDIM0WrZGFJSNxWPoxiEgmSoZcvllEIwbJRpjHceLGggO/BA7EDjlaWxY2fcSRTdS\nTMqaorH0lvcwVoJUSRrrqEzI8nnSqt8RGnI/ryE4meRPjNPjjjvCF7UXw6LyNP6BAUo0LAzUFu6N\nS5SEVMP1UcJ6P+OD3ZxhppmUnrIJDWykFHx+XLDeTZhVgs4wpHbeGxfcnxR87+og3BMV+hHXjUFp\nSYZEaclAg3WSXqr5u99eZyVNodONAAAgAElEQVSL+HB/zs3V7DRIHCtBPw3pxrOy4cf3pgyzoGRq\nneen96ckb45Y7cTEkXrl3+nXnQv9yV/EzIIvFH1Zz63jl7NL2c8rfrYzZVIYhpmmm2giJZ4r4HaR\nXG4n9/lwYYhkCFZGKmTGWOe5fZQjEBzPGy73Y7wSNFLROBikEaojELMCgSCLQoVr0cAbo5hxEdxi\n3QRqI5HCEgkHErqRZqMfs9KJuH1csNFLmJYN692ESCnyuiEvLZX9cufPk5N4vxxBcO+cTOqPPtVi\n+eck7XReQ+MfHLNAaR4YqQZOs4h+uj0FGVb/H+7lrPdChXo30VSNYS93mGWdxjvrXQ7mNRu9mONC\ncpg3HC6CYmgcCfpZRF5bjA9SF5UPO6aVzll3jz8tAvx4P2e+lJZunKM2BW+tZqx1E24fLeguhQI/\nOchZfXOVREuOvmGLl583LrQhuIiZBY/uUtLoQf7zea5o9vOK/29rP0gGEKpBnQ9VnlcG6RdjB4fz\n00n+O2ckIeBiudzmteFgXvH5scUriXKh8Xuz9M38ze6Mylj6qeIwF3xykLOUC2KQ6tCw3XpGWShO\nq62jn0SsdSPeWulyMD9m0YQAaS9SdGJNXjZoKZBSUhnP9VF6qoZpCZPyqBvx8Z7DRGCdobQPVuyP\nmoXnjRGcZNWfyM89CckypXQZm1A8iE08znh4D8cLwyd7c670E5yDadGQ145UheMdLUgihcSzk5fk\npUMJTy9RTBaGTiSprKUbRyzi0P9gthSwU1Ly5krGtVHGn356xHvXBssiwIaf3J9yb1IySBT9JCKN\nFLcO5hTGMKtseG5jTSeWjItgQivTZgu9ai50QdnlfkJlHJWxD2UWnAicvQjmteHW4Zz3t6fcOvzy\nIi14fNFXrGQQMztHfnjnOIi0aUUnVqRa0VjHZBkIPinmujJI2ZmWmGUvWWMdH+3OHvq9Lkoxz4lB\nGmURSgjSSPH5UdDEr4wlrw2704rVTsrVQUqsJf00ZrUTIaUg1YKbqx0GcZCpSLXiW+sd3rvW563V\nzmlgdK0TYb1nWpvTArA4kiRRkDU4ntfEQrA9LYmloGxClhCANQZjHwRzH0Uu/6gzxxVf/cVShApk\nLR9cIzpz/OR8JR4OTD9u53AW64PBEMtOaJWxCAGHi4b7x3Puj0s8sDFI0FJwuDDcOy4oqlAoF2IB\ngrVuxHgRgr9KilB30VgipbjUi7nUS1hUQSX2s2UvgVsHC3amJYNE4r1gZ1aRaoUHdqc1mZb0YsXO\nskq8l0gqYykb+0K/0y3PzoXeEZx3BsvzrIxf1S7l7nFo5hF07UM+fKole9OKv/+97unrbh3OvxBX\nedQH+7Jcbl/lfjprkBKtKBCs9xIWteXqesrubsW1Ycq8NmxPSgZZRBIJ7hyVKCkQQnL3eEEnVtxY\nyYikYK0Tc3Otw6KyTIqG9V4oiCotzKsG7z3OyyAd4SVZHFQ6PztahHTOWPHZcUFjHFJ5SrfMMiKs\nyh9duWsgSwQSwaJxSB8qfyv3IPj7KAJIlsZIeM+iDk5/KcP1GvfA3XRSf3DW2Ngzf5+9vl7+vHIh\nzbUyIRU0iyWLOtROaO8xzpMXlmllwIeCsjiSeATOe0ZZzNw4nPc0xpJFmkQqJIJ31rqsdGJmlUHF\ncKWfMqstH+xOuTpM+XxcUBtPJ5FczWJ284I3Rim3DoJmVCdWDDPNwbzmF64M0Ery7uU+pqhpeXVc\naENw3jxPMPrRoq+yeTn5z7VzfLZfECtFEkkyrRkXFavdh90+j5vkH/XBvgxj9jRG9uxYQ1P0jBUt\nmNeW964OuD8pOZ7XdFN16i+fLAydRPGLl3v82WcT9mYV10YJax2NlKFZ/GoWU1Qll/sJkQpN4jta\n4GxQ41QiNCForGUlSfn4YMG4aujFEfvzmqo25Mt56dH0UM3DMQG5lHuwDjwOqRQKUI3FNPDoHuuk\nQKyxkMYSjz2VpEh0CGxX7ovndOOgn+R82EmI5eDOuodOxqXg1EdUNhZrIdIK1Xi8h4N5zZV+Ah6+\n/8aIyaIJGVHOLRcZUDcGJEHwrxshhMRLOFrUHJcNWaRZ68Vszyq0CoquqZYM0hi73IkpCfcmhsu9\nhN+4uYLzoc/BMNP8wuUBf+edNQB6iWbcGoJXyoU2BOfty36elfGju5RuX5y7b31em5DTbhypVjjn\n2J9XSAmb3Ye31I+b5B/1wb6MVpRPY2S/yiAlWrAwlkVuOSpCcxgFdFOFkgqBZ62jybRCSkUWKTqR\n4mhRc30l5aOdnMo6VrohRfTzcUFeGLwIFblHi5q/bhx53SB9qISdLiwNDzJ1JF+UjIh4sBo3PqRs\nnqz+uwoSFQq49ppw5slqHsL8XBNcPgJPsYz+OggTtw3XX+Y7nbqDnPckCiobjI8jZBEVTTAMjXtg\nEDoahllEHCms8+zOazItUCLsWmIX1E8HWYS1IeaEDLGDatmp5zuX+tybFNyeL/jJ9pRRljBIJLPa\nUjuPSz0f7xoqB9+/NqAfCwrjsM6xMJakCZLeWSTpZ5pfvDJYZn0F0ULd9hq4UFxoQ3De6aPPuzJ+\nkj7OebE7q7g2zJZSwDWLJlTIXuolXBumD732cj/h/e0ZeVXinENKyZU1wVtnfLAvo2jsaYzsV+2u\nurEGF1aq3VixO63oxpLVLGY/LxFScK2XIZRgJYsZZjHjoqGTaJyDQSfiaiz5eN/xk0lF3TjeWu1w\nXDRszwqsD5N21TiKOvQAPruCf1K0qCFM7HqpMur9A6VQCIJyQjwo+JJAvPTbGAvaLQvaXJCXsG6p\nLSQF0njK8FIyHSb9ooHGhLTQ1U6EsUF/yfhQsKUV9HSobzAO0liRxRohBTuTEmQwX0opvKlRUnC8\naHh3o8ustGRR6Pw2tZ6iMqx3JXuzip1pzTCNyYua7dmC+5PwnZFCEjeOhfekkWKYRoDHLsypHIoW\nkk6ieHuty9Gi5tPDOb1YMcii06b1LReHC20IztuX/bKatH9ditqy1gl59Bu9lMoEDRwlxRdcQ4Ew\nO/llDv3jhHDOu2jsaYzsV+2urPe8sWwEH2SlBYd5ye60YphFXB+lTAtDVyvWejGSpYRyN2aUaQZp\n8EUvGkfRNPSS5Sp50ZBpzayoOZiXiKW+TvMMeaDJsjJZEzLHqC2RgrKxmKXi6EmGTxaFdFglBSUO\nK6ATKxCQSYkyYcdXG48l7AhSHXr/ZrGkNiF1s/HhOsMkpnKOvXGFXo6lm2pmhSWVHusF14YpFsGs\nrDFLMT0lCOJyShJpCSIYS2cdpQ3JGMMsQuD5/GhOYaExjoVx4ATzylLWjhsrHVY6MdOyZl5ZPt6f\ncXOty7uXusyrhqLxXBkm/OobQ3amJf00Ylo25JVh3gTxwrZ47GJxoe/GefuyX5WcwrOSxYqBiyhN\nzVonJlKht671fCHbYndWMcoiLvcf7BTiLP7S1o1fVUfwPDUHT2tkzxqkSkr+9MPd0zqJxnq890ED\nx1gaGzRrrgxSEi25NylQUpBFmqKxaCG4MkxDkdKy49iNUcakaHh/OzSSb6xnXjY47zHeI5xglAUB\ntrz58rKwk+Ivu/yPsWHCts5iXdgVJCJk7rB8nSas1Bvj0So0h49jySiLsT5kDRWNY15bEh1cS0UT\n1EqV9OS1RatgHGIdVEOLelkbEYMQEuegH8Uo0TBeNBhj+JudGQYQxmKFJ9OawbKjWNU41nsRZeMZ\nJHBQOmIJbwwzjhY129MaJyQrGdyd1BjrEXi6sQChMN6xPQ0xGCUN4zLISITWogkrnYSbax1mlTnd\n0Z902auMZVaZlypg2PLVXKwZ7xFexor9ZcspPA+nDVz6MdOiYVIapIBfu7HyhQn5aYLFzxJ7ed44\nzbMa2f284k/uTJjmJZLQaGY/D4YviWRwYWhFP1FoKVjUQYnzrWFKL1OMS8NKP+VXrg+5fTQP+jdp\nxHo3JtWSLFIczCt2pyWVCbLOWSTpJQqtFJVpHjuuEyIg0kHnRxIKqzTLWIIItcFahMm/4UEqqCS8\nV2MgltBPNIUN/Q9i5ZmYYEgk0I1UEM6Lgzz0vAytN1e6IV1WS8HBrCJSniQK7p9F7dEaKhtkrJsG\nlIbaBP99syw4W+nAShrTOE+/H7HRibg7qegkEdcjzfa0YGdasjOtcISCscOFwztPN5IkUQjoJjrE\nNxbGoiRcG2TUNri4dicl/Sxio5dwuZ9w+/Dno/L9deBCz4DflBX7eXP2c9BScn3lyavypwkWP0vs\n5evEaZ7FyP7V3TGHeUNZGbwLyqDToqYylt+8uYZzPhiAxqGWDWys82xPKtaWPRIGWXBB4GFrNz9V\nK+3FCuuDLz7WEiGC+FxtYb2XcbRoQtN4npyjH8ngxz9xBxkHaQSIIEmxWNYEIMCZYCDiZWGBlIJE\nhSY7SazQNtR3TAqD8KAjRSpCGmeqJAd56KbmgW4a+hsPsoiQYwS2AU8o8hIChBdMnAEfKu2Gmcba\noKjqrcP4kMdvXM5KFnGzE2osVjvh3hjjmFdhx1VbR20tdhm4xgVFUqVcCHQ7x7SwIe1WK5QS3Bh0\n8MC8cXz/eoeba50gGngBC0JbHs+Fn1G/CSv2l8HTfg6P20W5Rwp2niX28rJqDj45mOOFQMtQuWoJ\n4nGuNuzNK7anIUtqlEbcPSrItKCXRLy1mnFjpYP3ng/35wxTzccHc0bZg+rVH97PybTgzZUuu7OC\nWWXpJmCM596kYK0bs9GN2R3XFGfGdJIdFMuwGygN9CKQWpIqgXUCRNilpjq8VklJZYKLyfplUFkF\nKW3jYJDEdJOgx2M8ZFqipESpsNo/XhqlbqQoG8ui9qSxxy4D/4kMrSvtUoJaSSisJ4uDEJ0UUC4z\nkRQCrYJhUgKc88yqhg+2Z9xczbg87FDUhu1pjRaC48rSOItAkGpFqhy1C1Xs1juuDjLy2lA1HucF\nhbFcG2Z8e6PLmytZ6E639qCmJSQuTMnLEJhXAnqp5r2rgxf67LR8fdoZ9ueMx+2iHi3YeZaVWhYr\nJmXDrDTL9FVJP9X00+gLr/061MYTJxK9rKnVKKSH49KG7mOx4sd3J+waRycJiprjwvCDGyOA0x7H\nH+zMMM6FAC6w0onYzxU7s4rVTNGJIgQS5y2xFAgpWMkinPEIXTOSIRWzWrpUhgkopVjtRJSNo2oc\nOpJkWpIoSdEY5sKGinMZDFlBcA+d9BJY1I5EQuMto0wzK80yphEmxto48sqglCR2FovAeHhzJWV/\nXlIWjrIKKaCNDzsR74MRODE2sQIlJIV1OOtCJbQMvRYiDZ0sCMtVjQ3ifEnEejfizlHBuKhCDwJr\nmRZhR9VLo9DDuRNzf1pQ1o557cALLg/iUD+RRGSxJK/Ml7hsBQiBONnmPbY+u+VV0xqCn0Me3T08\nWrDzLLGXfqL58d0JvSTk6C8ay35enxYDvSiuj1LuTGs89sGY3NJ94UNM4Mog5bioSZRCSsnlbtAN\nkiK0QFzraD47mjOrGqaFJdUq+M+N5WhRk6mUfiqZ14Z55djoxfSS0EYxjkITdesFynm6CXQiRXfp\nVlIypJq6RLA2iMkXDXt5jfNBbM3rIOnQNP6hit8T+QipJP0sCq0zRcjpH6YxB3mNwZJKRd0YrAMh\nQnB8L1/6/X0IOBfWEwsY9jSTIlgqrcI4hRAksaQyNVIJlA9xBrM0FHVt2LWOeulm2p5VXO4nLIyl\nbjzjsiFeChgKEWQqCuPoGkc30qx3FJV1xEqy1ou5McoomlC5/KSm8yFxQT+0G62MbZVGLyDt3XgN\neZbYy6wyvL3eZVYZysaSxZpL/fSFZ35890ofHRfcO8iZLrNNrgxSNi91URJmpaGfat5cyXDAG6OM\n/Ty4Uhzw9nrok/vHt464N6mIhKCsLXEsmBaGlTTCeEfVCFLt8V6xaAzvXe0zKw15HYrVokgSKxny\n9J1DSMF//ktX6SWhUnZ7UvHR4Zz7Rwu8h0GmqRqHUQ5lJVY6Oir0CDiVlhYQa0VHhVTQtV4c3C+R\nIo6CfLQVYVJPtKayjqZxNC7ITDtCkZgLC3KKZZonBIG+snFY70mUJIskxodJ3PiwU7AuKJdqGVxW\nZWM5mJW8vysZpgopQ61GpjW9VFM2llhLLvViahMytd7e6IZkgcYjZND/2ugmrHQiVrvxY5+di6ge\n3PJ4zsUQbG5uRsAfAjcJrtPf29ra+uDM8b8F/EvCPnEH+CdbW1vleYzlPDnpqrV3OH8uKedXKQX9\ntDGHILL38HY+VuKFi+zdXO0gtGagxKk/eVYZ3hh1QuqhEFjnKBsXeg8AQgjeWe8uf5+wKo4kFJVh\n6kJwc+hDL96317t89/KAj/ZnWO9Z7YTV/lo3YaWb8MnhnChWaBl89saDVpKNfsJvfmuNTw8X7OcV\no47C7Hsq4xlmiizSYYIV0I08s9pirKeRQXdILFtjGm+pnOKdtQ6l8Syqhm6seWPUoajDBJ0LT2U9\nzjuqpXxEY0KgWimBVkGwOtUhMG1siCgrCXKpZb3WSZBSUBiP9Z6itpR1cPekscJ7jySI7RVFg/Kw\n2omJlhN01YSqXy1CEDmNFL9+Y8Qgi0JAOXJMy4ZJYdi83Ge9m9BPL5YuV8uzc16zzu8Aemtr67c2\nNzf/HvD7wD8E2NzcFMC/Bv6zra2tjzc3N/8L4C1g65zGci6cpFWur3SfS/7iIklBfxU/ujflaFFR\nNY4kkqx2En7p2osN+HVjzS/d6JAKf2oY+4lmZ1pSmVBQ9ze7OXuziptrKUoI5o3lcF5RmtCPuJ9o\nZrWjE2usczgPWitibZiXhu9dG/C9awPGRc2//+yIWWmZVg1aSC71U6ZF2F14Z+mlCVp6fuFSn71Z\nRTdRKJFwuAiTqpehACyJwqp8XDY0ztNYS16FVTiAsEEJNHZBJHCjnyAQNDahm2i2pyXDLOLz49AO\nUosHstSnMtU+SEykWpFpyWonYlKFFOIsVnQILrJYa4apZlo0jFIFUuAzx14umC6Ca3CQaIQIghgh\nG0iQRhKx7PA2r2ompSFSiqujjDdXMt67NiDRinuTgs8OCwZpxHcu9bgxyr5UDfibUrDZcn6G4ENA\nb25uSmDAw3It3wEOgX+6ubn5PeD/3tra+kYZAXiQVplGilqIZ0qrPHv+Reu+9ijHRc1f3x9TL/3B\nArgbFdxYSb/q1Geml+iHsk4gVODuzioaa9FKkCnJ9qSml1jeGIXUTylACUHRBMVRIWC1lzBINFLC\nrgjiaXnVIIH3t6dBBjlVIeiqlg3cI8XCOI7yGiVCt7OVTJ8aaqclv/rGkJ/t5/zg2pB70xqBx1nP\nShJxZ1LgzlQVwwNxOOMcw1SjpGQl0+zmFdZ6upEi14pUS4pQoAyEQrXGBM0hPLg6NJ43kWW9n3Jt\nGPPmSsZGP+HDnZxIhWcoSxSxFkRSspNXdCPJpV5EJAXOQz/VCCDSglhJvn9tQN043t+d0hgYpnrZ\nmCe0nPztdzfYmZZIAe+sdVnvJmxPS0ZZ/JV9sNv0728O53VHcoJb6ANgHfjdM8fWgd8C/ivgY+D/\n2tzc/Iutra0/OnsBIYKOz0VFzWpGaWjePRiEFY73nllpnmrcJ+eH1RnPfP4zjVXJ577mRwcFQiq6\nmUYsFSuM83x0UPC7Pzj/cY6A65dCX+L784arqz0SLait57PDOZf6CVpK3lzrsDerWOnGHM0bhJTM\nGof3oSn9KEn54CAnL0Ou/A++tcrR3JBXDaNexlDArKnoZYo4Dn3DpJBMak+UxQzj4Dv/fLxgvZeE\nAHCkOZjVlMZihF/KMzck1lLaB6t6SXDL9LMYK6DbTejbIAcdoxl2Qy2BPMo5yhtqY4PWUBSqjA3L\n+gYH8wZu7edsXu1TWJj//+3dfZRkd13n8fetW09d/TjT3fOYSYZJJj+SMCCEJ5MA2UiiYtwQWTkr\nB10WSaLoeoyedUlcRaNRV8UYOYgEBBMgoC4LsuAS0QFJAGWBRYVkviFPJiaTycz0c1V3Pd7941fd\nU9OZ6eru1K2unvt5nTNnpu+v6tZ3bnfXt+7v4furRbjdQ8wu1NkykGW2VKUvGzKzUOV52wcples8\nPlGkXI9IpfzCukKzC+zs0QI/+oq93PvdowwO5jg6W2Gu7Etx+J3bsuw/aws7yzUOT89TKtfZNZjn\n4vPGGcit7q1j8fu31u97L9osca5HXIngRuAeM7vJObcHOOicO9AcBzgOPGRmDwA45z4HvBQ4KRFE\nEbEXc3su6pUqx+bLjG8dYGbGzz5frKq4mrgXn3/ywq/VP38tnkthvIePzpIHBnInpovOzVd4+Ohs\nV+JcHEf59uEZHj9eoi8bsqWQZUtfhlKpypMLVc7aWqC2UGVrJsV5o318ba5MpVKjHkVUag3K9Yi9\nW9KkGhFbciFHi3WePl6iHsFEqczcfJVao8H0fIVMGFJIp5mr1SiEIY8dneOBxzPs3zZIFEU8eazI\naCHDNx89TrHcIAgiCpmQyUqD0XyaieICtfqJjWUyoe/jD5rdO1NzFcbyGYJGxJ6hPE9Oz7Ojv4+J\n+SoTM/M0ChHFsh+byWVS1KMGQe3EVNFGA2YqEfcfnqG0UGdiNksYBmzrz9EXwp6hHPlMiokwYHAg\nSx/wvK15Dh2e42hxgflynRQRZw/n+KHnbyPX8OtMxvMhZw0MLl33cq1GpVpf+n6MZ0M/8gzU5isd\nLRvdjcKNnbBZ4hwfH2z/oGXiSgSTnOgOmsCvzVl8x3sEGHDOnWdmDwGvAv40pjhis9j/uVD1u6et\ntf9zs/SfFjIpFioNalGdEL/QqxZBIRt/GeHFcZRGBEdnymTSAY8dL3Fsruz7xjMpSg34nmbhvflm\nwbctBV90bqJY4Ui5zlAuzY7hPPVGQK3RoLhQY7pcYzgfEgYppheqFBdqBEHA7mHfd18ohyyU/RjC\nE1Pz7N82SKXeIJ9J8fjkAhERQRBRqvjtF2cXqr77KkgxkGtQrtOcCuoHi6sNaDQaNAKYLVeZmq8R\nBr6+Tz6TYudwH09OLxDOlf0is8GAag2erJUIM77KaV+YIqo3mK35WUCLdxsTxSoXbBvgZeds4cmp\neSp1cNsGeNG+saX1I1fsP/3EhN3DeR6bKBEEfoeyasOvGdi79cz89CvPFtdv823AS5xz9+I/6d8M\nXOOcu97MKsBPAnc75/4v8ISZfTamOGKz2P+ZDoOlbSLXMtB74vmpdT2/Ww7sHiZMBVRqvjplpRYR\npgIO7B6O/bUXx1FmyzWymRTz1YjRQrZZ8C3ieLHKln6/uCmKIp6aXiATpnjVuVvZNpijDgzmQs4a\nyZMNQ/qyfl3BbKUGUUStDqVKjSjyUznrUXNbxsAPoDbw1T5nFmpL26Ru7c9yvFQhiALmynVmy1Xm\nynVKlTrFaoNcOkU6DMmEqaW9BLb25xjJZ5ht7qkcELBva4Eg8DX8j5f8HYkb6ycTBn4Fc953z2RS\nIbXmzKGAgFLNJ4A08PRsmcmFGo2ozjefmGLv1gJXPX87Vz1/GxftHDqp+8bPEuvnop1D7Bs9+eds\n72iB8YEc9Ya/HvUGjA/4wnGSDLG865jZHPDGFdoPAi+P47W7qT+bZvdIwd82r/P5vTQwfCqvPGcr\nk8UKk8Uq9YZPAlv6M7zynK2xv/biPPRys7Io+G0hs/UM44MZJko19o2eSKa1RoN9owWy6RTbB/uo\n1iNmFqpU6hGZMMVwX5rDk35V787BHJUIUlVIhX7a5+yC3wM6lQp8v34mZHahQrFSo9qI2DfWz2PH\nS2RDv2VouV5nIJcmm04xV65SqdYZGsjRKNdJBX41sS8OFzKQCRnpy7JtKM+uYV9aezidopANGW8W\nFezPZ3jBrmEWKnUqtQbpMGCyVOG7R0uUq5BL15fGHYLAD0AP5lOUKxEz8zW+c3iGi3YOrfnDRH82\nzUU7BzdsKrNsPH2nz0DL1yec33eqPQtWZ3wgxw9euIP7n55ZKg994Y6hrpQRXpqHngmpRRE7h3JM\nFCtE+Fo3544V6MuESzON+rIhs81SE1BnayHL9HyVaq1BXzr00zgzIVsKGWqNyA86p9Pk0/hP3g38\nPsf9GYrNTdvd9kH2jRZoNKKl1yAFlUbEUD5NNvSx9WVCXxI7HTLan2WqWCUIfeXXF+wc9mWvG74M\ndbXeoFpvsLU/4/cHSIe8dM8WwH/vHjte4uFjRWYWagzkMvzbpN8HOMJvhrO4tWW94QvXlWsRe0by\nzC3U1j3rbDN8KJH46Dt/hjnV+oTvHpllWz5c9ye88YEcrzlvvMORtrc4jjKYS0PkS1NXGw0KmTTV\nWoPBXPpZW3D6ct05ZhaqfqP0Qpbzxnz55VK1zq6RPAd2D/K1f53gkaNFv8F7lGYwm+bFe4Z5eq7C\n4ZkyWwsZv3CuVme+Fi2Vhtg7WmA4lyXV3Pyn2iz/vGukwFRpgYF8hsv2bcGOzDFVrvPqc7dy4Y4h\n5qt1DtpR5mt1P721P0MqSDGUzzxrs56Ldg4tFWZ75HiRp2cW+M6RGcqVBv3ZBtOluq85FPjZVNm0\n/wRfqtY7vtBPkkGJ4AxzqvUJ2UzYc+sTVqN1HvrO4Txff3ySkb4MI31p8pmQp6bL7Bjq45HjxaW7\nnx1DvvxFOuW3Q7x03yiz5dpJXR6lSp1//rdpao2AXJiiWo+Yq9Q4e2uBsf4s/3J4jt0jfSxUGgzl\n076rJhPy0NE59o4WuPS8MR44PM2xuTKFMKA/nyUbwFh/mnza741w3rYBtg1k2TvaTzZMkQrgZeeM\nMFmqkU0HFLJphvIZUsGzNxdqNV+pc+5YgQYRk/M1302WWmC6XGMwl2bHcI6zhn3ZjePFCheqsqes\nw+Z6Z5C25it1UgE8MTVPuVonlwnZm0tv2k+KrV0W4wM5Zsu1pf9XJhXwwJFZ9o0Wlu5+np5ZeNag\n+/JurCOzZS7aOYwdLVJvNMilAxr1gAeemeXc0QLP29LHUD7Nlj6/CX21UWd6oepLVc+WueisLVzp\nxvnqI8eXqn9m0inOGt5X/IQAABqeSURBVCnwgxdtX3q9xS66xcVUF5+9Zen1V9sX35cNGWhufNCX\nTpFNBcxlQ7LpFPvGCuwd7YfIl+MoLSs3LrJaSgRnoEeOlxjIhfRlQqqNBt89Mse2vs39rZ6v1BnO\np5e2PAR4fKJIFEVrXp09X6mTSsHLzx7hHx+bIhUF9OegUg94dKLE1Rdu57HJeQazAVHk6/6XKjV2\n7hpivuIHiC/dN8poIcv9R2aZW6gxkAt53qgvzlfI+m640/W7r+XObPtgjrkF3/1E1KDW8DWW+rMh\nR2YqPD1boT8dsn97P7uHtWpX1kc/NWeapQI1wYm/g6BnysCvt9DeqfZFOFasMLbs0/5qqlv2ZUOO\nHa0wV66xayTHbLlKIwrYWkjRl81xZKZCEMEzc2UGchnymRR7thQIU74rCfydysVnb+H5OwaXxmQW\n14N0smZUfzZNRMRANkUu7Qf9wxQ8eswn+xfsHKRUiXj0+Lw2fJF1i39VkHRXBPtGC4QpKFX9vrL7\nt/WfKJC/gRYHsmv1Bv1Zv13jI8eKFCu1ts+bLFa47+EJv/Apiphv7otQb0Q8MVniu0fneGKyxPRC\ntW11y8FcmqNzFabLNUYLGXYP9TFWyHLe+CBD+Qwz5So7h/MMZNPUGg0KmZDxgdwpC6wdmS3TiCKe\nmS3z0LEizzS/PjJbfs7Xa1EQBKQCv4HO9oEcxXJzvUMqRT0KyGdTjPVneXqmc68pyaJEcIbpy/pp\njHu2FNg/PtD8JJvqidK/63nTLFb8/PgHnymSTftVwA8dKxIEAW7bAA8eLTJfrdOXTjFfrfPIsZKf\nZbSC2XKNF+0aIh+mmC3XyYQpxgYyPDVdZnwgz3njAwzkM4wN5ihkU0T4Ym2n+pQ/UaxwZKZMveE3\niKk34MhMmYli50owZMKAkUKGTDqk0vDlpbf1Z0iHUKlHpFKwZ4sfJBdZDyWCM8z2Qf/JtVzzpS/K\ntToLPTKIuJ43zccm/Jz6B4/M8OTkPNOlKrUIiCJSAYwPZOnLplmoNehrzjJq94Y4X/HX41XnjTFS\nyFKs1pmZ99VJy1X/3HLVb/7yPbtHOHes/1mrcRcVKzWCALLpFEEQNP+m7V3OWuwe7iOKYCAbsnMo\nx1DWl9veNpjnrOE8I30ZDs9W2iZAkdPRT84ZZjV7Fm+U1jdNgGw6oFKvr/imef/hWf71eIkwnSKT\ngnoUcHhqgXTA0nTPPS31maIoWtUYQaXeoC8TMj6QpT8TUq41qNUbPDWzwGA+y2DOD7Q/cry0Ys2d\n/myacrVCpX6iTk8U0dFB210jedJhwL9NlZiarzE+lKHc8NtGNqIGCxW/l7JKQsh6KRGcgdrtWbxR\n1vOm+eT0vN8oPhcyWaySDn1XyVNTC+wdLTCUz5z0+NXsgLW4UO3oXIXBXMhQLkOl3mB0IMPTM2Wm\n5isM5vpODLivMNC+td/v7jVb9qUrcpmQ7UPZ0+7atR6LC+VeuGuYbJjigSOz7BmpcrxY4XixylAh\nzZXnjDH8HFaQS7IpEUjXrOdNM5dOsdBcGzFSyDA9X6VSb9CfC3nJni1LO5itpYJrfzbNjqE8//Tk\nNJVaxGA+5LzxAY4XK+zd0seR2Qqlap18OuXLS6ww0L60mnkge1IMneyKW36XV8iG7BruO2kq7WIJ\nc5H1UCKQrlnPm+a54/08/EyRegOiBgz3ZRjOZzh3Wz/jA7mlHczWsgNWsVLj6ZkFdg33kQoiAlIc\nm6sQBH5nsbNHC0vdTeVafWnK6Kl0axeu1ru8xdlXa02AIqejRCBds543zRfvHmGiWKFWi0gFAY0o\nIp0OePHukRPnXGPpjMUyHLuG8zwxOU82HZFJBcxXayzUIrYN5te0x0S3C7ZpC0jpNP3kSFet9U1z\nfCDH952/raPVTxfLWwdBwJ4tfRwrVlio1QjDkMvOGWa2XOv5N1hVC5VO0k+S9LxOVz9dKm+d9mU4\n9oz0LfWxjw/kulJiW6SXaHRJEudUay06PcArspkoEUjibJZtQkW6RT/5kkjqYxc5QXcEIiIJp0Qg\nIpJwSgQiIgmnRCAiknBKBCIiCadEICKScEoEIiIJp0QgIpJwSgQiIgmnRCAiknBKBCIiCadEICKS\ncEoEIiIJp0QgIpJwsdThdc5lgDuBvUAduM7MDrW03wi8DTjaPHSDmVkcsYiIyMriKsj+OiBtZpc4\n564EbgXe0NJ+MfATZvaNmF5fRERWKa5E8CCQds6lgCGguqz9YuAm59wO4LNm9tvLTxAEMDJSiCm8\nzgnDVM/HuRliBMXZaYqzszZLnOsRVyKYw3cLHQLGgKuXtX8ceA8wA3zSOXe1mX2m9QFRBFNTpZjC\n65yRkULPx7kZYgTF2WmKs7M2S5zj44Nrfk5cg8U3AveY2fnAi4A7nXN5AOdcAPyhmR0zswrwWeDF\nMcUhIiJtxHVHMMmJ7qAJIAOEza+HgG875y4AisAVwAdjikNERNqI647gNuAlzrl7gYPAzcA1zrnr\nzWy6+fUXgHuB75jZX8cUh4iItBHLHYGZzQFvXKH9w8CH43htERFZGy0oExFJOCUCEZGEUyIQEUk4\nJQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSbi2tYaaZaN/CLgcGAWe\nAf4O+LyZRbFGJyIisVvxjsA5dwXwt8BrgH8G7ga+AXw/8LfOudfGHqGIiMSq3R3BfuAqM6svO/4X\nzrkQuB6fKEREZJNaMRGY2ftWaKsD7+14RCIi0lUrJgLn3KPA8nGAAIjMbF9sUYmISNe06xp6fvPv\nALgHuCrecEREpNvadQ2VF//tnKu3fi0iImcGrSMQEUm4dmME57d8WXDO7cd3E2FmD8YZmIiIdEe7\nMYLWWUPzwB3Nf0fAFbFEJCIiXdUuEdwFfMbMjnYjGBER6b52iWAK+C3n3DjwdXxS+Fb8YYmISLe0\nmzX0SeCTAM65lwHXOuduAZ40s5/uQnwiIhKzVc0acs6dA/QBHzKzfw/cEmtUIiLSNe1mDQ0AH8NX\nHX0MOM85dxT4sfhDExGRbmg3RvA7wF+a2V2LB5xzbwN+D7ghzsBERKQ72nUNvag1CQCY2QeAF8YX\nkoiIdFO7RFA9zfFapwMREZGN0S4RTDjnXtp6oPn1RHwhiYhIN7UbI/ivwF85574IPAw8D3gt8MMx\nxyUiIl2y4h2BmT0KvBz4eyALfA14RfO4iIicAdpNH329mX0K+MRp2q9tLjpbfjwD3AnsBerAdWZ2\n6BSPuwOYMLN3rCN2ERHpgHZdQwXn3P8B/ga/ef0RYAR4JX4D+7tO87zXAWkzu8Q5dyVwK/CG1gc4\n524ADuDvNkREZIO06xq6G/8GPge8FXgXfv3AJHCtmX34NE99EEg751LAEMtmHznnLgFewcnVTUVE\nZAO0uyPAzErA+5t/VmsO3y10CBgDrl5scM7tBN4JXAu88XQnCAIYGSms4SU3Rhimej7OzRAjKM5O\nU5ydtVniXI+2iQDAOfcksA04in9jX8B3E73dzD5/iqfcCNxjZjc55/YAB51zB8xsAfjR5jn+GtiB\n7346ZGZ/1nqCKIKpqdI6/1vdMzJS6Pk4N0OMoDg7TXF21maJc3x8cM3PWe1WlV8CXmBmu4ALgE8B\nPwj8xmkePwlMN/89AWSAEMDM/sjMLjazy/ElLO5engRERKR7VpsIzjIzAzCzh4GzzewhTr/C+Dbg\nJc65e4GDwM3ANc65659rwCIi0lmr6hoCDjvnfgf4CnAJ8HRzNlDlVA82szlW6P9vedyfrfL1RUQk\nJqu9I/gJ4CngB4DHgbfgB4RVjlpEZJNb7R1BFb8wDHx/f8PMvhpPSCIi0k2rvSO4A9iHX1i2F/hA\nXAGJiEh3rfaOYL+Zvbr57085574SV0AiItJdq70jyDvnCgDOuT6aU0FFRGTzW+0dwe3APznnvg1c\nCPxabBGJiEhXrSoRmNlHm8Xn9gGPmtnxeMMSEZFuaVeG+mNAdIrjmNmbYotKRES6pt0dwZ90JQoR\nEdkwKyYCM1vaK8A594tm9q74QxIRkW5a7awhgB+KLQoREdkwq0oEzbGCC51zdzvn7o45JhER6aLV\nTh99H+DQjmIiImec1U4f/aJzbrp1zEBERM4MaxkjuCG2KEREZMO0W0cwDrwDmMdvNrN4/J1m9usx\nxyYiIl3Q7o7gLsDwexF8yTl3TvP4a2KNSkREuqbdGEHOzO4AcM59C/gr59zlQBB3YCIi0h3t7gjS\nzrkDAGb2FeC3gU8Dw3EHJiIi3dEuEfwc8G7n3HYAM/tz/CY156z4LBER2TTalZj4FnD5smMf0aIy\nEZEzR7tZQ18AcqdpvqTz4YiISLe1Gyx+B/B+4FqgFn84IiLSbe26hv7ROfdh4IVm9skuxSQiIl3U\ntsSEmf1eNwIREZGNsZYSEyIicgZSIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhAR\nSbjVbl6/Js65DHAnsBeoA9eZ2aGW9jfgy1dEwEfN7PY44hARkfbiuiN4HZA2s0uAW4BbFxuccyHw\nO8Brge8F3u6cG4spDhERaSOuRPAgflObFDAEVBcbzKwOXGBm08AoEAKVmOIQEZE2YukaAubw3UKH\ngDHg6tZGM6s5534EeA/wWaC4/ARBACMjhZjC65wwTPV8nJshRlCcnaY4O2uzxLkeQRRFHT+pc+4P\ngLKZ3eSc2wMcBA6Y2cKyx6WAPwO+YGYfam1rNKLo+PG5jsfWaSMjBaamShsdxoo2Q4ygODtNcXbW\nZolzfHzwG8BL1/KcuO4IJjnRHTQBZPBdQDjnhoD/DVxlZmXnXBFoxBSHiIi0EdcYwW3AS5xz9+Lv\nBm4GrnHOXW9mM8BHgS855+7Dzxz6SExxiIhIG7HcEZjZHPDGFdrvAO6I47VFRGRttKBMRCThlAhE\nRBJOiUBEJOGUCEREEk6JQEQk4ZQIREQSTolARCThlAhERBJOiUBEJOGUCEREEk6JQEQk4ZQIREQS\nTolARCThlAhERBJOiUBEJOGUCEREEk6JQEQk4ZQIREQSTolARCThlAhERBJOiUBEJOGUCEREEk6J\nQEQk4ZQIREQSTolARCThlAhERBJOiUBEJOGUCEREEk6JQEQk4ZQIREQSTolARCThlAhERBJOiUBE\nJOHScZzUOZcB7gT2AnXgOjM71NL+Y8DPAzXgX4C3m1kjjlhERGRlcd0RvA5Im9klwC3ArYsNzrk+\n4DeBf2dmlwLDwNUxxSEiIm3ElQgeBNLOuRQwBFRb2srAJWZWan6dBhZiikNERNqIpWsImMN3Cx0C\nxmj5xN/sAjoC4Jz7L8AA8PnlJwgCGBkpxBRe54Rhqufj3AwxguLsNMXZWZslzvWIKxHcCNxjZjc5\n5/YAB51zB8xsAaB5p/C7wPnAG8wsWn6CKIKpqdLywz1nZKTQ83FuhhhBcXaa4uyszRLn+Pjgmp8T\nVyKY5ER30ASQAcKW9vfhu4her0FiEZGNFVciuA34oHPuXiAL3Axc45wbAL4O/CRwL/5OAeB2M/tk\nTLGIiMgKYkkEZjYHvHGFh2j9gohIj9AbsohIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwS\ngYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGI\nSMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjC\nKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCpeM4qXMuA9wJ7AXqwHVmdmjZYwrA54Gf\nXN4mIiLdE9cdweuAtJldAtwC3Nra6Jx7KfAl4NyYXl9ERFYprkTwIJB2zqWAIaC6rD0HXAvoTkBE\nZIPF0jUEzOG7hQ4BY8DVrY1m9mUA59xpTxAEMDJSiCm8zgnDVM/HuRliBMXZaYqzszZLnOsRVyK4\nEbjHzG5yzu0BDjrnDpjZwmpPEEUwNVWKKbzOGRkp9HycmyFGUJydpjg7a7PEOT4+uObnxJUIJjnR\nHTQBZIAwptcSEZHnIK4xgtuAlzjn7gUOAjcD1zjnro/p9UREZJ1iuSMwszngjat43OVxvL6IiKye\nFpSJiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikR\niIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiI\nJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCSc\nEoGISMKl4zipcy4D3AnsBerAdWZ2qKX9h4FfBWrAB83s/XHEISIi7cV1R/A6IG1mlwC3ALcuNjST\nxG3AVcBrgOudc9tjikNERNqIKxE8CKSdcylgCKi2tF0APGRmk2ZWAe4DXh1THCIi0kYsXUPAHL5b\n6BAwBlzd0jYETLd8PQsMLz9BKhUcGx8f/NeY4uuo8fHBjQ6hrc0QIyjOTlOcnbVJ4jxnrU+IKxHc\nCNxjZjc55/YAB51zB8xsAZgBWq/mIDB1inOMxxSbiIi0iCsRTHKiO2gCyABh8+sHgP3Oua34O4dX\nA78fUxwiItJGEEVRx0/qnBsAPgjsBLLA7c2mATO7o2XWUAo/a+g9HQ9CRERWJZZEsFrNweQ/Bl4E\nlIG3mdlDLe3XATfgp5n+ppl9pkfjvB24DD/eAXCNmU0/60Rd4px7BfA/zOzyZcd7ZtruCjHeCLwN\nONo8dIOZWZfDW5zd9kH8WFcO//P36Zb2nriWq4izV65nCLwfcEAE/JSZfbulvVeuZ7s4e+J6tsSz\nDfgGcOVzmaIfV9fQar0eyJvZ9zrnXgm8C7gGwDm3A/g54KVAHrjPOfd5Myv3UpxNFwPfb2bHNiC2\nkzjnfgn4caC47PjitN2XNdu+7Jz7tJkd6ZUYmy4GfsLMvtHdqJ7lzcBxM/vxZjfmt4BPQ29dy5Xi\nbOqV6/nDAGZ2qXPucvyU8sXf9V66nqeNs6lXrufidXsfMH+K42u6nhu9svgy4HMAZvYP+Df9RS8H\nvmxm5ean64eAF3Y/RGCFOJt3C/uBO5xzX3bOvXVjQlzyMPAjpzjeS9N2Txcj+F+0m5xz9znnbupi\nTMv9JfArzX8H+E9Wi3rpWq4UJ/TI9TSzTwHXN788h5MniPTM9WwTJ/TI9Wz6feBPgKeWHV/z9dzo\nRLB8KmndOZc+Tdspp5l2yUpx9gPvxn8y+wHg7c65jUpYmNknOHndxqKeuZ4rxAjwceCngCuAy5xz\nV5/mcbEyszkzm3XODQL/E/jvLc29dC1XihN65HoCmFnNOXcn/vfloy1NPXM9YcU4oUeup3PuLcBR\nM7vnFM1rvp4bnQiWTyVNmVntNG2nm2baDSvFWQJuN7OSmc0CB/FjCb2ml67nKTnnAuAPzexY85PM\nZ4EXb2A8e4AvAB82s7tbmnrqWp4uzl67ngBm9p+A84H3O+f6m4d76nrCqePssev5VuBK59wXge8B\n7mp2p8M6rudGjxF8Gd8n9xfNvvd/aWn7GnCrcy6PHwS7APj2s0/RFSvFeT7w5865F+MT62X4Oku9\nZjNM2x0Cvu2cuwDft3kFfiC065plT/4G+Fkz+7tlzT1zLdvE2UvX88eBs8zst/EfnhrNP9Bb13Ol\nOHvmeprZUldPMxn8lJk93Ty05uu50Yngk/is9hV8/+Z/ds79Ar5/69POuT8C7sW/wf5yc0FaL8b5\nYeAf8N0dd5nZdzYozmdxzr2JE9N2fwG4hxPTdp/c2Oi8ZTHejP90Wwb+zsz+eoPCuhnYAvyKc26x\nD/79QH+PXct2cfbK9fxfwIecc1/Cryv6eeBa51yv/Wy2i7NXruezPJff9Q2dPioiIhtvo8cIRERk\ngykRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwm30gjKRVWnWVnm+mb1jjc8bBX4L+Bh+\nIdCPmdnHW9r/Gfimmb3lNM/PA282sw+s4rW2A79iZj97ithvAf4Q+OZ64lh2vg8A/wF4ZWvpYZH1\n0h2BnOl+E1jc+OgQ8B8XG5xzB/BFA1eyA19/vq1mmd9Z59xrTtF8t5n9wXOIo/V13oYvNy3SEboj\nkE2lWWv9Q8A+/Panf4Cvv38XsAt4Ani1me1yzg0BLzOzn27Wlv8nfwo33Cxt/mZ8dcmzm+c+v3nu\nGv5D0puAXwYudM79KvACYKT5Ou8xs/c2P+2/tfn4dwJ3A78O/P0K/40V4xDpNt0RyGZzA7787iXA\na/Gf+H8JeNTMLgV+DdjefOwrgeW7R30C+JFmJcmXA19pabsSX+zwtfg39WH8xiT3A58BPm5mVwFX\nAb/Q8rxJM7usWfTtfnzhwXZWigPwmcI598fOuXc553at4pwi66JEIJvNBcCXAJplv+8HLqf5Rtrs\nM1/cRnAMWL4r0934bplX4wsatvpTfLnezwE/y8mbvBwBXu+c+wi+5n+mpW0p2ZhZHag2NyxayUpx\n4JwbA27CJ7Z3A7/vnMu2OafIuigRyGbzAPAqgOZmLAfwg6/f2zx2Lj4BADyD78pZYmaP4Pvjfw74\nyLJzXwPca2bfh9/567/hSxCngF8Evmpmb262BS3PWyxTvFizvmZmDVbQJg7wCeJ24Fr8Hc4ngItW\nOqfIeikRyGZzBzDqnLsP+CK+P/73gL3N0sG/BiyWK/8HTr1J0J8De8zswWXHvw7c4pw7iN+F6t34\nZJIFLgV+xjn39/jSxDXnXO4U5z4AfHWV/5fTxQHwXeAKM3ufmf0jcAl+/EOk41SGWjY959wl+Drs\nf+Oc2w98zszObbb9CfA+M/t/XYrld4FPm9l9Lcfewvqmvv40ftwiAD7S3OJzse2L+M1INH1UnjPN\nGpIzwSPAx5xz78T33f9MS9uv4gd8r4s7iOZWgUOtSaDFm5xzz7RMIW3LzN4LvPcUr/MB/PaEIh2h\nOwIRkYTTGIGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJNz/B3EUAKyr\nj+ATAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n",
    "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n",
    "ax.set_ylabel('12+log(O/H)')\n",
    "ax.axis([0, 4, 8.0, 8.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MaNGA Spatially-Resolved Mass-Metallicity Relation\n",
    "\n",
    "We have constructed the spatially-resolved MZR for one galaxy, but we are interested in understanding the evolution of galaxies in general, so we want to repeat this exercise for many galaxies.  In [Barrera-Ballesteros et al. (2016)](https://arxiv.org/pdf/1609.01740.pdf), Jorge Barrera-Ballesteros (who gave a talk at Pitt in November 2017) did just this, and here is the analogous figure for 653 disk galaxies.\n",
    "\n",
    "<img src=\"images/barrera-ballesteros_local_mzr.png\" style=\"width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best fit line from Barrera-Ballesteros et al. (2016) is given in the next cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting formula\n",
    "aa = 8.55\n",
    "bb = 0.014\n",
    "cc = 3.14\n",
    "xx = np.linspace(1, 3, 1000)\n",
    "yy = aa + bb * (xx - cc) * np.exp(-(xx - cc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remake the spatially-resolved MZR plot for our galaxy showing the he best fit line from Barrera-Ballesteros et al. (2016)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 8.0, 8.8]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y7c6qJ35pnkYu2nv/pTr1Zzk5L9GSN4bpqTF62VWeJ+MQQiCFJ9OKogmFVFJ4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\nSgQiIj6nRCAi4nP/Hwi+bVNOaq/7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n",
    "ax.plot(xx, yy)\n",
    "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n",
    "ax.set_ylabel('12+log(O/H)')\n",
    "ax.axis([0, 4, 8.0, 8.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spaxels in our galaxy are typically above the best fit relation.  Part of the offset may be due to systematic differences in the metallicity calibrator used, but the overal trend of flat metallicity as stellar mass surface densities decreases seems to be in tension with their best fit.  It would be worth investigating this effect for more galaxies to understand if individual galaxies typically obey the best fit relation or whether they typically exhibit a flat trend in this space.\n",
    "\n",
    "\n",
    "Ultimately, Barrera-Ballesteros et al. (2016) concluded that the spatially-resolved MZR is a scaled version of the global MZR (e.g., see Tremonti et al. 2004)."
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
