Advanced.GaussianFit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d4e9b9b8-b391-49dc-bc1c-fe7272c9131d"
    }
   },
   "source": [
    "# Looking at the MUSE data for LLS sight lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e7b99675-783e-4430-ab76-1d7e33775cfa"
    }
   },
   "source": [
    "Use this notebook to look at / build up the techniques for dealing with the MUSE data Lorrie Straka has given us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "db9928c2-cc8d-43e6-8551-9255b93a9ff4"
    }
   },
   "source": [
    "[Read in the MUSE data](#Read-in-the-MUSE-data) / [Plot the MUSE data](#Plot-the-MUSE-data)\n",
    "\n",
    "[H-beta, \\[O III\\] fitting](#A.-H$\\beta$,-O-III-fitting) / [\\[O II\\] fitting](#B.-[O-II]-fitting)\n",
    "\n",
    "[Integrated fluxes, errors](#Integrated-fluxes-and-errors)\n",
    "\n",
    "[Results for SFR, Metallicity](#Making-use-of-these-results)\n",
    "\n",
    "[Mg II Absorption](#Mg-II-absorption)\n",
    "\n",
    "[Lehner talk plot](#Lehner-talk-plot)\n",
    "\n",
    "**[Summary of results](#Summary-of-results)**\n",
    "\n",
    "[Set-up pyMCZ files](#Set-up-pyMCZ-input-files:)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bffa2d4f-a88f-43a2-a698-5dc9162a56e3"
    }
   },
   "source": [
    "## Standard set-up:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "dbfecbcd-298e-4416-bbd5-7c8ddb27c997"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from astropy.io import fits\n",
    "from astropy.io import ascii\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy import constants as const\n",
    "\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "421128aa-23e4-4607-beed-db107b91ce5b"
    }
   },
   "source": [
    "## Read in the MUSE data\n",
    "**Be aware:** the MUSE output data are stored as *air* wavelengths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0290a160-895f-4722-be75-5f3ed9610ae7"
    }
   },
   "source": [
    "* Read and extract the spectrum (this process is specific to MUSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "4aea1b52-c4fc-411f-95de-f03ef4089e85"
    }
   },
   "outputs": [],
   "source": [
    "input_file = 'Data/PG1522+101_OBJ143.fits'\n",
    "pg1522=fits.open(input_file)\n",
    "\n",
    "# Create wavelength, flux, error vectors --> specific to MUSE.\n",
    "delLambda=pg1522[1].header['cdelt1']\n",
    "lambda0=pg1522[1].header['crval1']\n",
    "\n",
    "wave = lambda0+np.arange(pg1522[1].data.size)*delLambda\n",
    "flux = pg1522[1].data*1.e-20\n",
    "variance = pg1522[2].data*1.e-40\n",
    "\n",
    "err = np.sqrt(variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "576c84fc-9ee5-4395-affd-1367cd71cd42"
    }
   },
   "source": [
    "## Plot the MUSE data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbpresent": {
     "id": "ffe7e8b7-16b1-4353-b7ca-74d5ffa34dba"
    }
   },
   "outputs": [],
   "source": [
    "figXsize=8\n",
    "figYsize=4.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "41a87c21-9c13-47a9-a528-6704e1190c57"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x128696450>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 285,
       "width": 480
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(figXsize,figYsize))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(wave,flux,label='Flux',lw=1)\n",
    "ax.plot(wave,flux*0.,'k:',lw=1)\n",
    "ax.set_xlim([wave.min(),wave.max()])\n",
    "ax.set_ylim([-0.1e-17,1.5e-17])\n",
    "ax.plot(wave,np.sqrt(variance),label='Error',lw=1)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e976f135-132b-4877-8f10-644e295c5d5b"
    }
   },
   "source": [
    "## Fit the data\n",
    "\n",
    "We'll be following the fitting tutorial from the AAS python tutorials: [Modeling](https://github.com/spacetelescope/AAS2016/blob/master/notebooks/aas2016_Modeling.ipynb), [Modeling Exercise Solutions](https://github.com/spacetelescope/AAS2016/blob/master/notebooks/aas2016_Modeling_solutions.ipynb) . The full AAS tutorial is here: [Using Python for Astronomical Data Analysis](https://github.com/spacetelescope/AAS2016) .)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e695ce71-1187-401c-8e6d-dc16b32893ca"
    }
   },
   "source": [
    "#### Set up some quantities that are needed to analyze the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ca7602e6-4290-412d-8223-4e956a01835e"
    }
   },
   "source": [
    "\n",
    "We are going to assume an initial redshift guess: z=0.7292."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbpresent": {
     "id": "5be10676-bacb-4148-8199-eefc3a11254a"
    }
   },
   "outputs": [],
   "source": [
    "z_init=0.7292\n",
    "scaleFactor=(1+z_init)\n",
    "\n",
    "zwave = wave/scaleFactor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f46a22d7-4562-401c-99f3-0b311f432eb3"
    }
   },
   "source": [
    "We are interested in a subset of the following wavelengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbpresent": {
     "id": "f455e41b-c9fd-4400-81e6-7b4e25bf100c"
    }
   },
   "outputs": [],
   "source": [
    "#Wavelengths:\n",
    "Hgamma = 4340.471 \n",
    "Hbeta = 4861.333 ##Air\n",
    "Halpha = 6562.819\n",
    "OIIa = 3726.032 ##Air\n",
    "OIIb = 3728.815 ##Air\n",
    "OIIIa = 4958.911\n",
    "OIIIb = 5006.843\n",
    "\n",
    "##Old: vacuum wavelengths?\n",
    "#Hbeta = 4862.721 ##Vac?\n",
    "#OIIIb = 5008.239 ##Vac?\n",
    "#NaIa = 6549.86\n",
    "#NaIb = 6585.27\n",
    "#SIIa = 6718.29\n",
    "#SIIb = 6732.68"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "920d8088-e9b2-4703-a533-5d4ad317591b"
    }
   },
   "source": [
    "In a few cases, we're also interested in the transition probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "nbpresent": {
     "id": "da4bb5ca-4829-42b4-aae4-228e03af7901"
    }
   },
   "outputs": [],
   "source": [
    "#Transition Probabilities\n",
    "a3726=1.86e-05+1.59e-04\n",
    "a3728=1.98e-06+2.86e-05\n",
    "\n",
    "a4959=6.21e-03\n",
    "a5006=1.81e-02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ef39ca42-046d-481e-bd39-91030d001d10"
    }
   },
   "source": [
    "### Set up the fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "nbpresent": {
     "id": "4be811cf-3904-4835-a7ae-5e5e5d52041f"
    }
   },
   "outputs": [],
   "source": [
    "from astropy.modeling import models, fitting\n",
    "\n",
    "fitter = fitting.LevMarLSQFitter()\n",
    "linfitter = fitting.LinearLSQFitter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5631c1d1-8b1f-46ec-84fe-e59321266c1e"
    }
   },
   "source": [
    "### A. H$\\beta$, O III fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0bd37391-5fd7-4624-8ced-17041aceb280"
    }
   },
   "source": [
    "Our first model is going to fit H-beta, [OIII] 4959, 5007 only. We will separately work out a fit for the [O II] later. (I'm separating the two regions because of the 4000 Å break between them, which makes a single continuum fit more difficult to work out.)\n",
    "\n",
    "We need to define the spectral region to be fit, which will be 80 Å from either side of the lines to be fitted. (This choice is arbitrary, chosen in order to exclude areas of bad sky subtraction, spectral regions that are unimportant and disconnected from our particular lines.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "48ce100b-1714-45ed-8c61-8dfd52a0d66a"
    }
   },
   "source": [
    "#### Set up our variables for fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "0ed895ff-1108-4259-aee0-64ad94d906c8"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422 3682\n"
     ]
    }
   ],
   "source": [
    "#Define the indeces over the region of interest:\n",
    "fitWaves1 = ((zwave >= (Hbeta - 80.)) & (zwave <= (OIIIb+80.)))\n",
    "print(fitWaves1.sum(),zwave.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "53676965-ac31-41db-8ae3-9fbd7a389154"
    }
   },
   "source": [
    "Extract the actual wavelengths. Note the use of the values, which removes the units (important for when we pass it to the `set_xlim` command later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "9634dd25-5223-468b-96e5-9fd41180262f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8268.7060546875, 8794.9560546875]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waveLimits1=[wave[fitWaves1].min(),wave[fitWaves1].max()]\n",
    "waveLimits1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0031e93c-e13e-4904-93b3-6fafbc03a78a"
    }
   },
   "source": [
    "We want to fit the *continuum*, meaning we have to remove strong emission/absorption lines. We do this by calculating the local mean and rejecting points >2$\\sigma$ from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "8be57bfb-869a-469d-a9df-d7e8be9b6a2e"
    }
   },
   "outputs": [],
   "source": [
    "mean_flux = flux[fitWaves1].mean()\n",
    "#cont = np.where((np.abs(flux-mean_flux) > 2.*err) & (flux > 0), mean_flux, flux)\n",
    "cont = np.where(np.abs(flux-mean_flux) > 2.*err, mean_flux, flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8e55bdb6-a250-4cce-9ce9-3de737453d69"
    }
   },
   "source": [
    "#### Set-up and check the continuum fitting process:\n",
    "Our continuum model is going to make use of Chebychev polynomials. To do this, we first start by initializing a model continuum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "nbpresent": {
     "id": "9fa11c6b-c4a3-4d65-ac08-23dd2c0dfa9f"
    }
   },
   "outputs": [],
   "source": [
    "#cheb1 = models.Chebyshev1D(degree=3, c0=1, c1=1, c2=1, c3=1)\n",
    "cheb1 = models.Chebyshev1D(degree=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "1ddcf685-38cd-41e1-9880-25be5af6739d"
    }
   },
   "source": [
    "* *Fit the continuum:* Note that the end result is a function `cheb_cont1` that takes the a wavelength array as input and returns the continuum fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "824d71c2-bc1f-42b0-a1f5-513cc249d802"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cheb_cont1 = linfitter(cheb1, wave[fitWaves1], cont[fitWaves1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "466257df-2937-4d37-898a-b1aeb2e6bec7"
    }
   },
   "source": [
    "* *Check the fitting process:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "0588ff5f-d7a6-48bc-a67c-5e534ee7a921"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x127ef2f10>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkX5HgYqIiIiIiPQ7mkell7iuS2FhYZvlmvBRRERERA4E7U34GAqFoIfJEWVUeklRURG5ubltbnl5eX3dNBERERGRPS4vL6/NuXB+fj5Adk/2Z6y1vdvCA5AxZuGUKVOmzpgxo81ryqiIiIiIyIGgvYzK9OnTyc/PX2KtPaa7++tXpV/GmC8DzwI/s9be04PtRwM3AGcDo4BS4CPgbmvt/A62CQDfBr4LTAIiwGrgEeBBa63blWM7jkNOTk53mywiIiIisl9o7wJ9IBAA6NL5dGv9rfTryp5uaIw5HFgMXA2MA4qAQcAlwMfGmG+0s40feB64D5gGNAJNwEnA/cDL0XVERERERGQv6heBijFmrDHmr8D0Hm7vA14AhgCvA6OsteOAwcD1QAB4wBgzudWmNwDnAyXAp6212dbakcCJQCFwHvCbnrRJRERERER6rk8DFWPMncaYQmAzcM1u7Ooy4GBgB3CxtbYIwFobtNbeBbwGpADXxR07A/hZ9OnV1tqZza9Zaz8Bfhx9+n/GGHUyERERERHZi/o6o5IdbUNR9Bbq4X4uit4/aq2tbef1x6P3Fxhjmn/ms4AsoAB4qZ1tXgGqgfTouiIiIiIispf0aaBirf2WtXZE8w2Y3d19RAOP06JPZ3awWvN+B+F1mAf4VPT+w/Y6zFtrQ0BzB/wTutsuERERERHpub7OqPSGEXgBCMDaDtYpwOskDzA+en/YLrYB2NBqGxERERER2Qv2hxGtBsc9Lm5vBWutNcZU4pWaZbbart1toiqi95mdrAPAqlWrmDZt2q5Wi1m4cGGX1xURERER6S+6c867atWqHh9nf8ioxAcRjZ2sF4zeB1pt151tRERERERkL9gfMirxHfDTgPY604M36hdAfavt0jrZd+ttOjR58mRlSURERERkv9edc95p06axaNGiHh1nf8ioVMQ9zmpvBWOMoSWDsrPVdu1uE9Xc92VnJ+uIiIiIiEgv2x8ClU20ZEcmdbBOLpAUfby21X1H20BLJ/o1PW6diIiIiIh02z4fqFhrw7QMI3x6B6udEr1faa0tjT5uHrK43W2MMQHg+OjTD3e3nSIiIiIi0nX7fKAS9XT0/tvGmOT4F6JlXz+MPn0y7qWX8YYsHmeMmd7OPr+JN9njemvt/HZeFxERERGRPWSfClSMMXcYY1YbY95t9dJDwFZgDPBfY8yg6PoZwP14E0IWAn9t3sBaWwzcG336sDHmhOg2xhhzEXBP9LXr99CPIyIiIiIiHdjXRv0aCRxCy2hcAFhr64wxFwNvAF8CzjfGFETXTwaqgEustdWt9ncDMA1vlvq5xpgd0fWb51i521r7/J76YUREREREpH37VEalM9bauXhBx0NAEV6QUhR9fpy1dlY72zQBZwM/B5YCGdGX3gcustZeu+dbLiIiIiIirfWrjIq19oxdvH4FcEUnr28EvtPNY4aBvOhNRERERET6gX4VqOzLXNelsLCwzfL09HTS09P7oEUiIiIiIntPTU0NNTU1CctCoRD0sIprvyn96mtFRUXk5ua2ueXlKVEjIiIiIvu/vLy8NufC+fn5ANk92Z+x1vZuCw9AxpiFU6ZMmTpjxow2rymjIiIiIiIHgvYyKtOnTyc/P3+JtfaY7u5PpV+9xHEccnJy+roZIiIiIiJ9or0L9IFAAMDtyf5U+iUiIiIiIv2OAhUREREREel3FKiIiIiIiEi/o0BFRERERET6HQUqIiIiIiLS7yhQERERERGRfkfDE/cSzUwvIiIiIgcyzUzfT2lmehERERE5kGlm+n5IM9OLiIiIyIFOM9P3U5qZXkREREQOZJqZXkRERERE9nsKVEREREREpN9RoCIiIiIiIv2OAhURERGRPmatpSkc6etmiPQrClRERERE+lBDMMK5f5nFcb9/h9kbSvu6OSL9hgIVERERkT70wdoSVu+soboxzNPzt/V1c0T6DQUqIiIiIn2opjEU9zjchy0R6V80j0ovcV2XwsLCNss14aOIiIh0pincMsVEY0j9VGTf1d6Ej6FQCHqYHFFGpZcUFRWRm5vb5paXl9fXTRMREZF+TIGK7C/y8vLanAvn5+cDZPdkf8qo9JLs7GwWLFjQZrmyKSIiItKZYEKg0qMJvEX6hWuvvZarrroqYdn06dPJz88v6sn+FKj0EsdxyMnJ6etmiIiIyD4mfljiRg1RLPuw9ro8BAIBgB5F4Cr9EhEREelD8aVfTcqoiMQoUBERERHpQ/HBifqoiLRQoCIiIiLShxJKvxSoiMQoUBERERHpQwmjfoVV+iXSTIGKiIiISB+KD1QiriUUUbAiAgpURERERPpUU6tyL5V/iXgUqIiIiIj0oaZW5V6aS0XEo3lUeonruhQWFrZZ3t540iIiIiLNmsLKqMj+oaamhpqamoRloVAIepgcUUallxQVFZGbm9vmlpeX19dNExERkX6sdUaldeAisq/Iy8trcy6cn58PkN2T/Smj0kuys7NZsGBBm+XKpoiIiEhnWk/yqNIv2Vdde+21XHXVVQnLpk+fTn5+flFP9tfngYoxZjRwA3A2MAooBT4C7rbWzu/C9uOATd045C3W2puj22YBc3ex/jvW2h/taqeO45CTk9ONZoiIiIio9Ev2H+11eQgEAgA9ir77NFAxxhwOfAAMAcJAATAUuAS4yBhzpbX28V3sJgSs6cLhJuGVupXFLZsIHLKL7VZ3Yd8iIiIiPaLO9CLt67NAxRjjA17AC1JeB75trS0yxiQBPwPuBB4wxiyw1q7qaD/W2gLg0F0c6zLgCeAd4B9xL02M3t9orb21xz+MiIiISA+1DVSUURGBvu1MfxlwMLADuNhaWwRgrQ1aa+8CXgNSgOt25yDGmEnAP/FKyr5hrY3/628OVNbtzjFEREREeqrNPCrqTC8C9G2gclH0/lFrbW07rzeXfF1gjOlRO40xBngMSAeusdbubLXKhOj9+p7sX0RERGR3qfRLpH19EqhEA4/Tok9ndrDa7Oj9ILz+JT3xLeBEYKa19ql2Xm/OqChQERERkb3OWqvSL5EO9FVGZQReAAKwtoN1CoCm6OPx3T1AdESvO/FGGbimg9Um4pWEfc4YM9sYU22MaTDG5BtjbjbGZHb3uCIiIiJdFYy0zZ4oUBHx9FVn+sFxj4vbW8Faa40xlXgTxPQkYPgFMAx42lq7ovWLxpgBeAETwH+AIF5/meHAEdHb5caYz1prd5lxWbVqFdOmTety4xYuXNjldUVERGT/FAy3DVRaZ1hE+pvunPOuWtXhmFi71FcZlfjAo7GT9YLR+0B3dm6MGURLFuX2DlZr7p/iAjcCw6y144CBwAXAdrxMzqvGmG4dX0RERKQr2gtKlFER8fRVRiUU9zgNaK8zPXijfgHUd3P/1+B1oJ9prV3WwTrFwNeAAmvtrOaF1loXeMUYsxZYhjf08UVAe31cYiZPnqwsiYiIiHSLAhXZF3XnnHfatGksWrSoR8fpq4xKRdzjrPZWiI7Y1Zx5aT1aV4ei87N8N/r0vo7Ws9butNY+FR+ktHp9NdD82gldPb6IiIhIV7Uemhg06pdIs74KVDbRklXpaESvXCAp+rijDvftmQ6MAqqAl3rSuDil0fuUTtcSERER6QFlVEQ61ieBirU2DMyPPj29g9VOid6vtNaWdrBOe74WvX/VWhvsaCVjzG3GmAeNMcd3sq/mGe83deP4IiIiIl3SbqCizvQiQN9O+Ph09P7bxpjk+BeiZV8/jD59sqs7jJZ9nRt9+touVj8SuDLuOK33dSZwVBf3JSIiItJt7Zd+KaMiAn0bqDwEbAXGAP+NjtSFMSYDuB9vQshC4K/NGxhj7jDGrDbGvNvBPk+gZejjD3dx/Aej95cbY24yxgyMO87peDPaAzxlrV3Z9R9LZP9VWNmgL1ARkV6k0i+RjvVZoGKtrQMuBiqBLwFFxphNeKNxfRevj8kl1trquM1GAofQMrRwaydG77dZa3fs4vivAH+KPr0ZKDPGbDTGlAIf4PVzmQf8oHs/mcj+6b+fbOXkO9/jzD+9T0NQX6IiIr2hvUClSZ3pRYC+zahgrZ0LTMPLrhThBSJF0efHdTQiVyeOjd7P73StluP/AjgLr9N9FTAaMHjZmGuA0621FR3uQOQAcsOL+QDsqGrkjeWdXgcQEZEuagq3U/rVzjKRA1FfzaMSY63dCHyni+teAVzRyeuXApd28/jvAe91ZxuRA03rmZPbm0lZRES6r73siUq/RDx9HqjsL1zXpbCwsM3y9PR00tPT+6BFIr1nXXFNwnO/r0+TsSIi+432+6joYpDsm2pqaqipSTxnCIVC0MMqLp1t9JKioiJyc3Pb3PLy8vq6aSK7bUVhdcLz+mC4j1oiIrJ/abf0SxkV2Ufl5eW1ORfOz88HyO7J/pRR6SXZ2dksWLCgzXJlU2R/sLJVoFLXpC9REZHeoFG/ZH9y7bXXctVVVyUsmz59Ovn5+UU92Z8ClV7iOA45OTl93QyRPWJFYVXC8wZlVEREekW7fVTUD1D2Ue11eQgEAgA9elOr9EtEOuW6tm1GRcMTi4j0ivZKv4JhF9e1fdAakf5FgYqIdGpLeX2bwER9VEREekd7pV+dLRc5kKj0S0Q61brsC9RHRUSkp9YX13D/BxtpCrvceP5h7WZUwOunkprk28utE+lfFKiISKd2VjW2WaaMiohI99399lr+/t46mqu6RmaldDgLvSZ9FFHpl4jsQqSdOmllVEREuqestom/vtsSpAAs3FxBMNJBoKK5VEQUqIhI58LtBCrKqIiIdE9FfbDNshWF1dR3MDiJhigWUelXr9HM9LK/CrVzta+jL1YREWlfe5+bDaEIq3ZUt7O2AhXZN2lm+n5KM9PL/iocaS+joi9QEZHuaOjgc3N7RUO7y1X6JfsizUzfT2lmetlftVf6VafSLxGRbmnoQoYkLckXuxCkzvSyL9LM9P2UZqaX/VW4vdIvdaYXEemW+IxKwGcItZOtzkwNxAKVJpV+yT5IM9OLyF7VXkYlGHEJajIyEZEui8+oHDducLvrZKYGYo9V+iWiQEVEdqG9zvTQcb21iIi0FR+ojB2SxvD05DbrZKS0BCq6GCSiQEVEdqG9eVRA/VRERLoj/uJOSsDHlNFZbdbJSG2pyO9oxnqRA4kCFRHpVHt11KCRv0REuiM+UElL8vG5I0e2WScjrvSrSRkVEQUqItK5sNv+l6UmfRQR6br40q/UgI/PHJZNSiDxNCy+9EvzqIgoUBGRXWhvHhWAOo38JSLSZfGBSkrAx4BkP2dNTpxaQhkVkUQKVESkU8qoiIjsvsTSL68vynmtyr8GJPlijxWoiGgelV7jui6FhYVtlrc3nrTIviQ+o5Lkd2Ij0dSpj4qISJcllH4ledeJz5qcTW5WKgWVDRw1KpNkf8v1Y82jIvuimpoaampqEpaFQiHoYXJEgUovKSoqIjc3t83ym266iZtvvnnvN0ikl4TiRv3KSAlQWtsEQH2TMioiIl0Vn1FJDXiZkyS/w3++cwLvrynm3CNH8t7q4tg6yqjIvigvL49bbrmlvZey21u4KwpUekl2djYLFixos1zZFNnXxc9Mn5nqjwUqyqiIiHRdYkal5fRr3NABXDF0PEBiRkWBiuyDrr32Wq666qqEZdOnTyc/P7+oJ/tToNJLHMchJyenr5sh0uviZ6aPnzW5QX1URES6rL2MSmvJ/vg+KroYJPue9ro8BAIBgB5F3upMLyKdSsyotAQqyqiIiHRd6+GJ25PYR0UZFREFKiLSqfiMSvzQmeqjIiLSdQkZlaQOApWASr9E4ilQEZFOxY/6pYyKiEjPJPZRUemXSFcoUBGRTsXPoxI/a7LmURER6bpul34poyKiQEVEOhefUclIbRl/QzPTi4h0XX3ChI/tByopcQFMo+ZREVGgIiKdC7ntd6ZXRkVEpGsiro1NlguJmZN4yqiIJFKgIiKdinTQR6VefVRERLqksVXZlzGm3fUSOtNr1C8RzaPSW1zXpbCwsM3y9saTFtmXhDoa9UuBiohIl3Sl7AvUmV72fTU1NdTU1CQsC4VC0MPkiDIqvaSoqIjc3Nw2t7y8vL5umshuiZ9HJb4zfZ2GJxYR6ZL4jEpKBx3pQaVfsu/Ly8trcy6cn58PkN2T/Smj0kuys7NZsGBBm+XKpsi+rqPhiWubwlhrOyxhEBERT1eGJgYFKrLvu/baa7nqqqsSlk2fPp38/PyinuyvzwMVY8xo4AbgbGAUUAp8BNxtrZ3fjf18C/jlLlb7obX23VbbBYBvA98FJgERYDXwCPCgtbZLnxSO45CTk9PV5orsM+InfBw0IImMFD/VjWHqgxE2lNQycbiCcRGRznS19Mvvc/A5hohribiWcMTF71Pxi+w72uvyEAgEAHoUefdpoGKMORz4ABgChIECYChwCXCRMeZKa+3jXdzdNOCQXayT8JszxviB54Hzo4uK8X6RJ0Vv5xtjLrTWqsZFDljx86gEfIaTJgzhrRXehZGP15cpUBER2YX4Wek7K/0CL6vSHNg0hRWoyIGtz979xhgf8AJekPI6MMpaOw4YDFwPBIAHjDGTu7jLidH7MdZa08HtpVbb3IAXpJQAn7bWZltrRwInAoXAecBvev5TiuzbrLWE4kq//I7DKROHxp5/tL60L5olIrJPaT3qV2dU/iXSoi/D9MuAg4EdwMXW2iIAa23QWnsX8BqQAlzXxf1NBBqB7V1Z2RiTAfws+vRqa+3M5testZ8AP44+/T9jjC4ZywEpElf2ZQz4HMPJE1oClbkbyxI624uISFtdLf0CjfwlEq8vA5WLovePWmtr23m9ueTrAmNMp+2MlnCNBTZYa21n68Y5C8jCKzd7qZ3XXwGq8crFzuriPkX2K/H9UwKO92c4YdgARmSkAFDTGGZ5YXWftE1EZF/R0J2MiuZSEYnpk0AlGnicFn06s4PVZkfvB+F1cu/MWLz+Nuu60YxPRe8/bK/DvLU2BDR35j+hG/sV2W/EByp+nze6lzGGkycOiS3/WOVfIiKdig9UUnaZUWk5NWtURkUOcH2VURmBF4AArO1gnQKgKfp4/C7219w/pcwY8ydjzAZjTJMxptwY87Yx5sJ2tjlsF8cH2NDF44vsl+LLunxOyzDEJx7UEqisVEZFRKRTDcGWMXnSdpFRie9sr4yKHOj6atSvwXGPi9tbwVprjTGVeBPEZO5if82BypXR+yqgCG+447OBs40xjwLfjsueNLeh3eNHVUTvd3V8Vq1axbRp03a1WszChQu7vK5IX4nvSB+IG3lm6MCk2OO6oAbFExHpTEOwJeDobB4VUGd62Td055x31apVPT5OX2VU4k/8GztZLxi9D3SyDsCE6P0WYDowyFo7Bq8Pyp14Qw5/E/h1O23ojeOL7JfiO9P74zIqqYGWaxzxnURFRKSthi7OTA/qTC8Sr68yKqG4x2lAe53pwRv1C6B+F/t7FpgHfGStjY36Za2tBn5ljAkBvwV+boy5y1objGtDWif77erxmTx5srIkst8JReLnUGm5rhE/ak2DAhURkU4llH51J6Oi0i/pp7pzzjtt2jQWLVrUo+P0VUalIu5xVnsrGGMMLVmPnZ3tzFo7x1r7VHyQ0sq/ovcZQPO8LM1taPf4Uc39aDo9vsj+Kr4zfXwflfgv2nqVfomIdKrHo36p9EsOcH0VqGyiJaPR0YheuUBzIXxnHd67In5YouYsSfM+OxtRrLkT/ZrdPL7IPim+M33zqF+QWGOtjIqISOcaQt3po6LSL5FmfRKoWGvDtAz9e3oHq50SvV9pre1w/FNjzERjzIPGmH9GszDtOTTu8eboffPwx+0e3xgTAI6PPv2wo+OL7M/am0cFIC2ppWq0ToGKiEin4i/oaGZ6ka7rywkfn47ef9sYkxz/QjTg+GH06ZO72E8V8C3gauCMDtZpnoF+nrW2KPr4Zbzhj8cZY6a3s8038SZ7XG+tnd/O6yL7vXCk7TwqoD4qIiLd0RBqKZHt1qhfIX2+yoGtLwOVh4CtwBjgv8aYQQDGmAzgfrwJIQuBvzZvYIy5wxiz2hjzbvMya20JXtAB8IQx5uzmzIoxJtUYcwPw9ejrN8VtVwzcG336sDHmhOg2xhhzEXBP9LXre+9HFtm3hNy40q+4PirJfofmp8GIm1AiJiIiibqVUYmfR0UZFTnA9dWoX1hr64wxFwNvAF8CzjfGFAAjgWS8TMkl0ZG7mo0EDqGln0mz7+P1NTkCeBuoMMZUADlx6/7GWvtmq+1uAKbhzVI/1xizI3rs5jlW7rbWPr/bP6zIPioxo9JyXcMYQ1qSn9om7yphfShChq8vr3uIiPRf8cO473p44riZ6TXqlxzg+vTMwlo7Fy9QeAhvgsaR0fuHgOOstbO6uJ8i4DjgWmABXrCRC5QATwGnWWtva2e7JrwJIX8OLMUbFQzgfeAia+21Pf3ZRPYH4Q4yKqAO9SIiXRUfqAxM7vwacWIfFX22yoGtzzIqzay1G4HvdHHdK4ArOnitEbg7euvO8cNAXvQmInHCHcxMD62HKNaXqcjeZK33t9nxGDLSnzRnnwEG7DJQUemXSLM+D1T2F67rUlhY2GZ5eno66enpfdAikd0Xn1Hxtc6oBDSXikhfqGoIcem/5lJRF+Tf3zqOQ0dk7Hoj6VPxgcquMiopAWVUZN9VU1NDTU1NwrJQKAQ9rOJSUXkvKSoqIjc3t80tL0+JGtl3JWZUEgMVjfwl0jeeXbCNFYXVFFY1cvlD8/q6ObILoYhLMJoZcUxiINKehIyK+qjIPiYvL6/NuXB+fj5Adk/2p4xKL8nOzmbBggVtliubIvuy+HlU/E7r0i/NpSLSFxZvq4w9Lq5pwlqrErB+rK5V2deu/q80M73sy6699lquuuqqhGXTp08nPz+/qINNOqVApZc4jkNOTk5fN0OkV4U6mJkeWnemV+mXyN4yKis14fn2igZGD07ro9bIrnSn7AvUmV72be11eQgEAgA9irpV+iUiHUoYnrhVH5UB6kwv0ifqWl0YmL+5vI9aIl1R19Ty+birjvSgzvQi8RSoiEiHIm7786gApMaVfilQEdl7ahpbByoVfdQS6YrujPgFrWemV6AiBzYFKiLSofiZ6dWZXqR/aBuoKKPSn9UllH51PtkjtO6jos9WObApUBGRDiWWfmkeFZH+oLohlPB8fXEt5XXBPmqN7EpCZ/oklX6JdIcCFRHpUHxn+jbzqMQHKiF1phfZW1pnVADmbVJWpb/anc70jSFdBJIDmwIVEelQfB+VNqVf8RM+NunLVGRvqWkMtVk2d2NZH7REuqL18MS7ooyKSAsNT9xLNDO97I/CnXSmT1NnepE+Ud1ORmXOBgUq/VX8PFNdClQ0j4rswzQzfT+lmellfxRf+hVoVfqVFtcptEGlXyJ7RcS1CaVEzX+Wa4pqKK1t6qNWSWdqu9uZPmHUL10Ekn2LZqbvpzQzveyP4jvT+9SZXqTPte7vMHlkemx44rkbyzjvKE083N+o9EsOJJqZvp/SzPSyP0os/WrVmT6g0i+RvS1+xK+MFD8nHTQkFqjM3qBApT/arXlUwi7WWowxnWwh0n9oZnoR2WvCEc2jItKfxI/4lZ4S4KQJQ2PP1U+lf6rr5qhfjmNIiusTGIwoqyIHLgUqItKhhIxKp6Vf6qMisjfEj/iVnuLnmDFZJEWvwG8qrdN8Kv1QXVP3OtMDpMR1qG9vOGqRA4UCFRHpUHxn+jalX+qjIrLXxY/4lZEaICXgY/LIjNiy/IKqvmiWdKK7nekBcrJSY48LKxt6vU0i+woFKiLSoUinGRX1URHZ21pnVACOys2MLcvfXrm3myS70N3O9ACjBrUEKtsrFKjIvqG4phFr7a5X7AYFKiLSoVCk48706qMisvcl9lHxTnqPHNUSqCzbroxKf5MQqCR1LVDJjcuoFChQkX3An95aw/G3vcsVD8/v1f0qUBGRDoXdjjvTJ/ud2BwOwYib0PFeRPaMxFG/AgAcPSortkyBSv/Tekjprhg1KC32eHtFfa+3SaS3PbNgGwAfrC2hqLqx1/arQEVEOtTZPCrGmMTyL01MJrLH1TQljvoFMGHYAFIDXoZzZ3Ujxb14kiC7x1rb7ZnpQaVfsu+JD8jjs4i7S/Oo9BLXdSksLGyzvL3xpEX2FQkZFaftOP6pSb7Yh1NDMBK7wisie0Z7fVT8PofDczJYsMWbTyW/oIqzMlL6pH2SqCnsxvr6Jfmc2Ahtu5KYUVGgIv2btZaGuIuVWwt3khJqeQ+HQiHoYXJEGZVeUlRURG5ubptbXl5eXzdNpMfCCX1U2n5caHZ6kb2ruiFx1K9m6qfSPyVO9ti1Eb8AcuMyKgWVDb3eQVmkNzWGXOLfotM/f37CuXB+fj5Adk/2rYxKL8nOzmbBggVtliubIvuyUCcz0wOxchPo3VSviLSvup2MCsCRcSN/rd5ZvVfbJB3ryYhfAIPSAqQl+agPRqhtClPVECIrLWlPNFFkt7WeS+2Z51/i2NEt57/Tp08nPz+/qCf7VqDSSxzHIScnp6+bIdKr4jvI+9sp/UoY+Ut9VET2uPhRvzLiApX4UaJKazXpY3/Rk4704PUBHDUolbVFtYBX/qVARfqr1hUVaelZ5OS0JFACgQBAj0bcUemXiHSos5npQXOpiOxt8RmV+D5hQwa2nMRWaHb6fqMns9I308hfsq9ofaGyNy9cKlARkQ7FZ1RaD08MibPTNwRV+iWypyXOo9ISqAyKu9peXq9Apb/oaekXaOQv2Xe0vlCpQEVE9oqEjEo7nekHJMX3UVFGRWRPa2/UL4CstCRM9FpCZX1I8xr1E4mlX13vTA+J5XwKVKQ/a91HpVGBiojsDQmjfrXTRyV+1KH4EygR6X3BsEtjyAtAfI5J6CPmcwxZcX+PlQ36e+wPejIrfbNRg9L4hu9/vJL0a8pLe9QPWWSvaGiVUVGgIiJ7Rfw8Ku2N+hV/Rbe6UaVfIntS62yKMYl/k4MGqJ9Kf1O7G6VfI7NSuND3EUc5mxhauaSXWybSe9qUfgV7L6OrQEVEOpSYUWn7cRHfmbdaV3APOB+sLeGlRdsJqcxor4jPksRnT5oNjuunUqZApV+oawzzj8A93BP4e7dG/QIYMiCJTOoA8NWX74nmifSK1hmV3uyjouGJRaRDIbfzzvTxpV/VKv06oCzaWkHZ41dwqNnGM40vcNnJE/u6Sfu9+CxJe0PVDlZGpd+pq9rJ53zzAPh3Sve2HTwgiUbjBSpJQQUq0n/tyT4qClR6ieu6FBYWtlmenp6uSR9lnxWJy6j42uujkhLfR0WlXweSG17I5wVnPmmmie++8pEClb2gor7lYkB8UNLeMmVU+odQyebY4/EDu3cxZ2CSj2S8YYnT3SoagpGEkRZF+ov6VoFJWWVNwjlxKBSCHlZxqfSrlxQVFZGbm9vmlpeX19dNE+mx+JnpA+2M+pXYR0UZlQNJU101aaYJgBSjk+K9ITGj0rb0S31U+qGqrbGHo1Iau7WpCTeSZLwLQEOopqyuqVebJtJbWpd+PfXc8wnnwvn5+QDZ7W68C8qo9JLs7GwWLFjQZrmyKbIv29XM9AmlXw3KqBxIUoJlEH1LpKITqL2hIm5+lEHtlH4NGaC5VPoTay0D6rZDNAmSk9y9QIXGytjDwaaG8rpgwiSQIv1F68705553AXfe97PY8+nTp5Ofn9+joev6PFAxxowGbgDOBkYBpcBHwN3W2vnd3NfRwE+AM4ERQBBYD7wA/MVaW9tq/Sxg7i52+4619ke7OrbjOOTk5HSnuSL9XkJn+nYyKhnKqBywBoTKIXpenKZAZa/YVelXwqSPyqj0uZKaJkba4tjzAZGa7u2goTL2cIipVjmf9FutAxXrCyScEwcCAYAejbrSp4GKMeZw4ANgCBAGCoChwCXARcaYK621j3dxXxcDj+F9dYaBwui+pkZvFxtjzrDWxvdImwgcsotdr+76TyT7KmstC7dUMDIrNWGSrQNdwoSPu5xHRRmVA8kQqmKPBzo6gdobdlX6FR+8KFDpe9sqGhhlSloWdHfkrviMCtWsr9X/qfRPDfvjhI/GGB9epmMI8Dowylo7DhgMXA8EgAeMMZO7sK9RwL/xgpSHgBHW2rHAQOArQBVwJPDXVps29/680VprOrh9cTd/VNkHPDprLTUPfZFH865jR5VmAG7WrXlUGkJYa9usI/unoaYlUMkKKEjdG+JLvwbvYtQvBSp9x3UtywuqWFdUkxioNFR0b0eNLX9jXumXl7l8Y1kBtz4zi/XF3czQiOwhbeZR2R8CFeAy4GBgB3CxtbYIwFobtNbeBbwGpADXdWFf3wTSgEXAVdbasui+rLX2ubh9fM0YE1+f1RyorNvdH0b2bVuWzeJM31Iud15n1trSvm5Ov2CtJRRX+hVoZx6VZL+PZL+3POzaXv1wkr2jqj6U0BepKyKuZQjVseeZfpX97Q3xgYqGJ+66vX0B5bZnPmDLfV/mvy++xCgT933S3UAlrvRroGmksqaGVTuq2fnMz/jtyvN46JkXe6fBIrup9Xd/6871u6MvA5WLovePtu47EtVc8nWBMWZX7Tw9ev+stba9b9xXo/cOcHzc8gnR+/W7aqzs33w1BQDkUMa2km5+meynInFlX44Bp53SL1D5177sreU7+NcdP+S6O/Ioqel6P5PK+mBCRiXJ7WYnYemR+D4qgwZ0PuqXOtN7Zq7eyZ2/+wU3PvAMrmt5bNZa7n5l7h4N5EL5L/B53zz+FLiPZBMXxHc7o1KZ8DRYVcw/Xnqfr/veASCwo+0APtKitLaJr94/h0semENZrfrR7UmtMyqNoX18Zvpo4HFa9OnMDlabHb0fBEzaxS5HRu+3dvB6/G8w/jJUc0ZFgcoBzFpLcv0OABxjqSve2Mct6h8S+6d0/FHRuvxL9h1vvPMWP/c9zU2hu/nTjCVd3q60NjFQ8UUUqOwNlbso/RqQ5CMpmuFsDLltJmE7EP3t0Sf5lf0X52y7h1+9kM+4/32bqxZewL/fXbJHjmetJR2vfPhgpyDxxd3IqAAUFGxnasETBIx3SpNjyhP+j7ubGd3f3fLqSuZtKmfuxnL+8q4KZ/akuqb9r4/KCLwABGBtB+sUQGwomfG72N+JQDrwdAevfynu8cq4xxPxRhn7nDFmtjGm2hjTYIzJN8bcbIzJ3MVxZT9QVhck27ak523Zhj5sTf+REKi00z+lWfykjxr5a98SKvbe65mmnoZlryRk0TpTWtvUKlBpUP+kPcxam5BRaa/0yxiTEMD0h34q1loKKvvu/ZFtvOBgjCnm6QXbmOKsZ6Bp5P05n+yR4zWGXNJNfcKyahsdUng3MyrJFeu4xNdybXeEKWNHlXeR4PZn3ufum3/IfW/nd7vNva0xFOGu52Zxy3/e6XLmak+8P15d2jLh4AuLCjpZU3ZXm9Kv/WBm+sFxj4vbW8Faa40xlXgTxHQaMFhr6zt6zRhzCnBX9Olsa+3y6PIBeAETwH/whjLeAQwHjojeLjfGfNZau8uMy6pVq5g2bdquVotZuHBhl9eVPauwsoGcuDrilOotWGsxpuOT8wPBruZQabY7c6lU1gepaggxdsiA7jdQdovrWkY7LR+/X/XN5OP1pZx+8LBdblta28SRcaN+pdJEMOKS7Nes2XtKdWM4FkgOTPbHMietDRqQxM5q7+S1oi7EqEHtrrbX3PTEOxy65h8sn/Bdbv/W5/b68bOMV1mebcpJp54M42U7hpjqzjbrsZqmUCyj0my5O46TfSuhobujfnl/Y641OMZymf8dUk2QUpvBUFPNSFPOzqpG/I7hoGV/5hL/+/ztw0aqTn2AzNS2pYF7y19eW8Dl+ZeTTJAnZr3BNdOP6nT9tUU1/Otf9+ImZ3Dd975NdkZKr7dpQLI+m3pb/Dlv+em/gOSWeQPrm0JMnTateaotVq1a1ePj9FVGJT7w6KxmoDkU7/ZfnDEm2RhzK15pWQZeQHR53CrN/VNc4EZgWHTUsYHABcB2vEzOq8aYPvuLd7t4hVN6rrCygVxTFnueHdnRL65E9rWEjvTtzKHSrKdzqeyoauAvd/6S2X++jDeWFe56A+lVJbVNjKZlRKJTfSt4d868rm1bk5hRSaWJxqDKTvakyvrOhyZuFj/pY1/PZF5S08T4Nf/iUv97HLbhwb0+oqK1lkF4gUqSiXCEsyn22mD2TKBS2xhmoGkVqNhoUUgPS78KGQLANOOVL70cOQWAkXgZlY/WlXCaz8ukTDef8O6qHs2r12XLtlfyu6dm8v7qnW1eW19cw7CFdzPSlDPY1LJm5eJd7u/Z9xdxR+gubqu9kT+9+PGeaDIDkvt82sD9m6/VZ5JxwOmd33lf/c/Fn82kAe11pgdv1C+ADjMm7THGfBHIAw6KLpoNXGat3Ry3WjHwNaDAWjureWG0M/4rxpi1wDLgULyO/091dszJkyf3apYkGHb59X1PklK2inMu/SmnThra5W0jrsXXyRXw/iAccbntsVexJWu46NLvceSovquyK6xs5MS4QGWcKWJLeT1DBib3WZv6g4hrCRDmIFNIhdNxN7H0hNKvrmdUZq4q5ifmabL8dfxu0TzOPeqLu9PcfV4o4vLorDUY4/C1kyaQlrRnP563ldcz2ngZlSKbRbapZMT6pymuPovhu7iiWVFdE7syDV6gUh8Kk9n9a0rSRfEXT9qblb7Z8FTLO0k/Z53Npbj64b3RtA7N3lDKKc5yAI53VjNvUzkXTMndI8eqrA8S8DkJJ6QNoQiZpuX04hjTUhwx2OyZoX1rm8IMjGZUqmwamaae0mEnQsWMNn1Odila+rXZjmSUKcUx3sWj1yIncqX/DbJNBTsq6ijbtobLot9hk5wCHls4ly9N/XJv/Uht/Pe/D3Nn/c3cseoKjr0hj4Fxv/OHnp/Brb7/xZ4PC27f5f7WL52FP8nFj0v22iepqDstYWCIrghFXGYs28GgAUl86uBhbS7yDtjDn6cHouZzXmstE254ndbVe+99MCtWojpt2jQWLVrUo+P0VUYl/rJCVnsrGK/upvnstW3Y3v42Y4wxbwIv4gUppcD3gdNaBSlYa3daa5+KD1Javb4aaH7thK4cP159MMyWsrrubhbz3uoiLi/+A7fav/HW/17DdS1rdtYQ2kVnuZmri/jT737K9X97dJfrdtVH60p5dv5WmsK9V3P4xvKdnL/pd9xcfxt/ePyFXttvT5SWlZJlWv6vxpqdu/V/t7+oagjxK/9/eSv5ek5lSYfrZaT2rDN9Y9XO2O/drerSn3ifW1tUw2vLCnu1o2CzZ+Zv4cz3LuT89z7LzX/+GysLW674VtYHuXfmet5b1Xu/p20VLYHK3eGvAHCp8zZPzFrZ2WYABCsT25Fqgr06HKW0VZkw4lfHJ3EnpBYw0SnkXN988pf17ahQy1atjXUoP9gpYPl6L6NRVtvEE3O3sKGko2uU3bNkWyV/ueM6brvt1wlzi1TUh8ii5bP8GKelQ/UQU71H/o5r4jIqvwh9jxuy/sBnv3Cpd4W5qRoi3ejH15xR8bcEd9U2leL0wyi3A0k2YarLdhDY+mHCZsO2vkHNHuovWNMY4qDq+QCc7C5izc6Wz6mCygaOKngKv3FpsN57NKNuU6ed/BtDEaY4LQPYXO5/m6fmdn98oyfnbCDnxQvZ+dh3mL+5nNJW2cTeOh+StprCLu0V//RWP5W+ClQ20ZJV6ehSbS4tI3R11OE+xhhzEt48KudE9/0nYKK19r4OhizuiuaOC90qmKxuDPGbP+Tx/p+/ycMzV/TowKu3l3Ko2QZAaslSbn/uQ1beezHX/e2JTjud/fPRx/klj3BRyd95a0X3TmqstTy3cDuPz9kcq4VeXlDFtke/wzGvnsPjH67u0c/SnjkbyxhvvPZl1/a8drE3NJZ5g8UVWC+9PsqUsrV4z5QF7EveW7GdL/m8WP0zaR3/CcZ3pu/O8MT+8paTBl99u13V+pXS2iZe+vt1THruM9z3Zu+fAG5YtZQJzg6Gm0r+0HAz/7n/Dqqigd8/XpvLxe+fyZb//Ji1RR1fCa6oC/L7x1/nzqffJRju/GNve1ktudG+WUsyz2a+ezCZpp7Q/Ed2eZITrkksLUmjqc3wlNK7EjMqHWeuzhzc0t8ue/OrVNX33QAX7sYPEp6HNnqDef7hsef59Btn8Og/bqO0F4aNvf+N+dzke5hbzf38/rnZseWV9UEGxWVOjnHiMirUUNZLJb7WWvJe+ICb7n2ExVsrYn1Ucscdyu0//R7Hjh8KKVneynGTOO5SNKNSljw6tmixO4nPTxnNzuj31dp1a5kSXgrAW5FjAfis+YR3V+2Zz9SNJXUcZLxS3UOdraze2fL73VJax0GON4Lm267Xf2EMO2KDKfx75irufGEOlfVBrLXsrGpk1Y5qDjdeoBK2DsNNJVUfPUj+9m78noBHZ8zkeGcNX/Z9wC3Pz2NHZWKvgo4uolXUBXltWWHss1a6r6OLVL01RHGfBCrW2jAwP/r09A5WOyV6v9Ja2+kMfMaYXGAG3iz3a4Cp1tpfWGs7fKcbY24zxjxojDm+o3Xwyr7AC6y67N1VRfwg+Ajf9L9NyTv3dPpBXNsUZta6kjbDSFZuXRkbgnBCeCMDlj7Khb6PObn0WbaVt1/n67qWI6M1uCNNOZtLu5cVeGnxdnJf+grT3vgCj87yTkznbijl8765THQKKVi3pM02PR2pY6ATYlA0JX+I2danHxJulZea3uiOpNAOJmAiVBdpiOLCxW/GMh5TkzseMaWnfVTSqlp+x0mNJZ2s2T+8trSQz5pPOMTZzqo5r/f+AXYsAaDGpgLwBfseKwq8j7BNS2Yy1FTzJd8s/vlex0Hjn55+k/9bfwXfXPkdHvuo86uSVUVbSTIRSmwml512KC+neaUil9kZPPNJ5+9/U+edBAWt10E1haY9cnVaWsRP9thp6VdDy9fV581HvLm8b/p/bSuv59AGr39CmfU62eZWL2ZjSS2TCl8hx5RzrfsIf3ltPv98O5/738nv8XDKRZu88jKfsSRvnxNbXlUfIjMuWz40rgP9YFNNeW3vBCqv5+/k5MXXc0vJT3j67VkMjFar+1IzWlZKjY5q0J1+KtGgpsBpmad6gXswnzksmx3WG5Mopb6AkxwvC3p35BKqbRqHOtvIX9mzi6S7sqGklgnRQCXbVLJ12zbWFdWwqbSO7ZUNsYsfH7lHAHCQ2cnG0jpmbyhj3HtXc/XSi7j31dnc+tRM3vnjpfz0ny/EzlvujVwAwPX2IT65//u8md/1kbqGm0rAew+klS6nsDLxPKm9suTimkbu+HMeo547j9/97QEN591D9R189vdWlr0vJ3xsHkr428aYhM4A0bKvH0afPtmFfV2PN9zxduDTzSN77cKRwJVxx0lgjDkTaB6q4rUu7C9mxfrNTHS8P+Sr/DN4+J2l7f6HFVU38vs/3sXAx6fzx/8kHsJX0vIjHO5s5jjHy2YMN5WU1Dbx4doSXl1ayM6qlqsG2yrqOczZAsAwKuluL5UFH8zgJN9KDnO2sGnOS1hrKS0pitWim6rEWtM1O2v45e1/4Od591Fc3f6YCNWNIWatK2nz84crW/Z1iNnW6VXiXWkIRvjHOyt4YeGWHgVOSbXeh+EOO4QtrjcQXKSs5ct+Q0ktd726iDnrD5wZ6zeU1HJU5Xux58PqOh6DPnHUr64HKpl1Lb/jzEh5v/+SSEvykxodMX2iKezVOQuq6kOMqPcCkNcjXqXpOLOTTdESxFHGC+QyTT1ucfsZyFnrSvjUpntIM02MNOVsWfpeu+s1C0ff41vtcMYMTmPyGV9lk5vNKFPKtvyPOt3WX+/9LWy33ghhqSbYq8NRdqS4ppFf3fcMv33k1QOu1Cyh9KuTQIWSlvfHeKeI5fM7mqpsz/p4XQmn+LzvsQfC5wFwnLOae2duiJ1YZ5p6Tl9xIxd/NJ0vzfo8N97/NLVN3f8cODK15bP5FGd57HvRK/1qv7xsiKnutcEGXli4laMcb6jvMaaY9Oh3pj9tNwKVcBBC9WB8LKhq2c8Cewhjh6TFApWznMUMNrVst0M5aPIxrLNemVikfPPu/VBxqupD3PXsB9x03xN8sHI7Y0xLtmbZotl8+Lfv8vI9P+L1pdsZgTey2ceR5kClkE3FtbyyeBsnOyvIMnXULnuF0Svu4+v+d7kvcA/ZppJqm8ai8VeTZy8jbB2+45vBvBf/3uWS89G+lt/rFGc92yoSuzbXNoUTPrODYZc/PvwMt4TuZoqzgV/W/YF7XtkzQ1bv7xo6+O5uCEWobgzx5Lytu3Uhqy8DlYfwJmgcA/zXGDMIwBiTAdyPNyFkIfDX5g2MMXcYY1YbY96NW+bQMsv9r6y1Xb189GD0/nJjzE3GmIFx+zwdeCz69Clr7a6LtuOEN8+NPc4ydRy18Fc8+rvL+d0jL8dOpINhl7sefoabQ3/mGGc9WRtejv1HVjeGyG5omcvjYLONqdGU9TBTxeOzN1H++OUc8vzZvPPHS/nFQzMIR1xWFlYz2XhlTMkmTE1lSwfxXVmzs4ap5a/Gnp9Y9w4rCqtpKm05mUyuT/zVPvfBAu4I3sGt1b/l/hlzaC3iWm76y304j13AbU8kXoG2ldtijw91tiWkj1tbuLmMv7yVz8w1xTw6ay2/uetP3Pyfd2I1p3+bMZdLZn2WcS9/ib+/tbTLPzNAUzjCwCavjKWQIWyxw72ftXoTrmux1nL/I49w7YKzeP/xW/dY3W9/8+aSLZzj88qbwvhx6oqgtv2sR0YPO9MPbdocezzMVHZrZvS+4DiGtGigMsnZzpbybo3x0akVO6o40nh/a2+702iyAbJNJQU7i6lqCMWuUgJkVyxs01E0FHGZ8eITfNbXMqDH2JL3O/2dBqq9v8FtdhhjBqdx/PihsdGJfNUdzZ3rZVFTgt5nS3Og0lulX68vK+CmB57mwzXFLC+o4rq77ub6+5+Nnaw8PjOfX++4hqs2/oznFm7bxd4S9WYfu74QP9N8e7PSx0QD2f9FvNKbsTve3K3yqvpgmJ1VjV367NtWXs+zC7ZRXhdk9erljDKlVNiBvBI4h7B1OMJsZs6iJUw2Wwlb7/TjM76FDDa1DDNV/Lr0Om554MluXQRoDEUYGtdh+xRnOR9HLypVNgRjwxO3Npiadkd3fG9FAb/705948H/d6PhbU8gA0xTbb3Nn+kB7GZX6Lg5R3DyHSmoWuaPGABCyPsyoYxk6IJli45V+fcHnlbp9EDmarx4/hh3RkjBfbe9k0lYUVnHHPX/i+8sv4cYdP6Jx5Rv4TMvnz0W+WVzpf4Of+l9g2/p8/Mal2GYRGDyGaptKpqmnqKiQAY07STHee+hsZxHn+rwRBic73mdNvjueq8+YxEXX/JE7/d8D4Cvh13i9CyNCWmsZTnygsoEP1rb9vor/frr/f4v5SdktpJog9TaZ4aaSKUtvZtba/l+G3N909NnfGIrwp6ff5qTXzqaypOfvxz4LVKy1dcDFQCXehIxFxphNeKNxfReoAi6x1sZ3FhgJHELL0MIAObTMTH9zNJDp7HZ89Piv4PVjAbgZKDPGbDTGlAIfAKOAecAPuvLzlDSU8MyaZ3ht/Vu4TbPZ5vfzgT0EC5zjW8DV/teYuuEfrNpRw3/nbuR3f/oj/1d+S+wP9zCzJZZVWL2jhslmS2zfSSZCWvRDcJipZPbKDXzRN5uDnQK+7n+XK7b8ihcXbWd1QRkTTcsHdqi6631UXpqzks873tUE1xrOdhbz+vw1mMqWdgwKlyRc7QoVLMVnLKkmSM7KB9halnjitqWsjk/Vvs4pvhUcsvERtsdd4fDV7og9Hm4q2bqt5Tiua3lrxU5mrimmoLKBWf++np/MOZXU/3yBU96+gN833Mqla37Ms/O9vjR1i19isKllqrOewz7+Kc/O63qlXlFVU2xo4trkEWzyjQPgnMgHvLKkgBWF1Zxe/Qp+43KGOy9WN2utZXNpXbev/j05ey2/f+INNnWzLG9vK17+HhmmnlXuaKoGRxOLRe1PJBbfmb47gVxOqOVEcziVFHcxUCmubuRv/8vno3V7N8PVEIqQYloyKuuLe6cjMMDy7RUcHi1/2BA4OBYwNxSto6CigVFxgcpRkZWsLU4M7B//aB3frb0faKlTP9tZxMwOhikNhl3SG71M4nY7nNxBqeQOSqUgGngMaChsd2j0hmCEP748j1PMsui23vq9UfpVVR8i/+nfcUvhVbz9zL08987H3Fn/O75VcDOv53ufFzWbFjDQNDLaKWHrtpZgamtZfYd/UxHXcvtT7/CvW64k74UP2l2nPeGIS95Lc/jdf9/t8qR13bWhpDZh2OGqhhB/fnUeD7yb3yY7XNmV0q/6cqgtgkAaHw/9KgCfcpbFTty762+vfcLDv/s27/3xEp647UpeWri5w3UbghH+/o97OO6Vs7jjoafwb/F+13Pcw/jaaUew1E7Ab1xuDzyEYyzz3UN5NuxVfv8z8kXejkxlkKnl4pK/MnNN10tBt5XXx/o7AkxwdrBilVf2VFkX7DCjMthUtxuozJrxGDfW3or/wzspruls9oQWmbUtpZIjTDkBE6HJBkhNS2tZqbsZleYRwlKy+MG5x/G7yBXcaL/HbV89Accx1Kd42f/mc4g1g8/kuHGDKYwGKmn1O3Z7EsVwxOW/D/+NO4N3kmHq8RnLVf4ZCetc6LSMR3RqdIS3AjuUUw8exibrnZoFi9bilrZk5c/yLWaESfw95NuDOCI3g3FDBzDk5MspsZlMdrYy//1Xd/lzVDeEGWJbAsCjnQ18vL7thdrmjL+1lspFLzLKlLLCHcuVyX+i1qbwOd883nm/JQO5rqiGO56fze9vv5Eb7nmA4ppG7n8nnxv/ej+z1yW+R5vCEd5fU8z7a4r73eS3e3qai44ClbK6IEPWv8A4p4hh0dK8nujLjArW2rnANLzsShFewFEUfX5cRyNytTIi7vEEvECms1vsk8Na+wvgLOAlvMBoNGCAD4FrgNOttV36VCmpL+HWubfyq49/zouj1/G50Tn86KAGjhk7jlNHHcRlI7N5ceQ2vv/m9Sya81UmJ/2V5QPreTVpBBsDfkb7N8fq0VfvqIpdZVjiTkg4zmCqGRr2Iv4CO4SddhCHO1tY8vZ/KN+8jCTT8oaJVHdtLPVg2CWy7BlSTIiPI4czxz2MZBOicdmLJNe2BD45ppQd0brPiGsZWLkm9tplzts88tbchP3urGpkpPE+PD7n+4QZi72fyXUtaQ2JQVSosKXU7ZUlBfifuoTIE1/lK39+nSuNl+k50VkVK6k72Clg7duPMHtDKWfalgDrLN9iNr5+T5evnhZUNpCD94EWSR9FyvHeB+RUZz0LX3+IVxZs4NPOEsArwWv+P7rvrYUs/stXuO2ev3a5f8364hqS3/gZv1l/CY+89EaXtukrybVeELHUnUDyqGigsrP9isqE4Ym7WvrVVEu2bfmgH2aqupxR+ecLb/Ldj88k//FrKeqg5HBPaAiGYxmVCaaQ9UW9N+DCzs1ryDANFNssTjz68NgXvCnfQEFlA7mm5Xd1nLOa+RtbvoRLapooe+9vTHB2sNEdwV+yfkmZTWecU8QDL77BE3O3JHSoDkdcPlpfwqho+UZVcg7Jfh9pSX4qAl6ANMKWUhJ3Fb4hGCHvlXn85fb/49LFl3KCs5pam8Kr7kkApJmWjEpFXZB1RTXd/rJ+f20xF/i8ORQmNq2gdtsyHGM5xNnO2q07vM+Nspb3YKjI+/xZsq2SZ+++hjfv+R4ftXMVNe+VeXxl5Y/5ke9FBi+6t8My1dZm5O/gnEVX86M1l/PYB12pJu6eZ+dv5qO/XMEf7rqZHVUNNIUj3PrgM1y54AJO++BSnl+YWGobqiri0cCdfMn5sONApSQ64MmwQxh+2OnU2hQmOQUsXeGduIcjLku3VXapL9naohqGfnIHP/S/zKX+mXzf9zJL3ul4lP7XlhbwneATjHOKOL74WY4OedntJf6juOr0g3gx2et/cIbPWz6Pwwmf/zd+N/lVPvX9vzF/2h+ptSkc66zlwzmzOzxOa5tK6xgXDVR2Wi8YMJvex1pLfV0NySZMxLYUQtfbZMLWIcM0UFXTNohJi372jTFFbTpkt8day7DGzbHnzWVRNaSSHj93R3cDlbiMyrSxg/jxr/7Ijb/5PeOHepPjRgaOjK1aaQeQM+VsBib7Kfd7Fw+G29JuDxbwZn4Btz/2auyC3LxN5Zzb6FVDLHAPBmBadOS0pa43+0N8duVTjvd/W2CHcOrEoWyy3umZr2IDKVUtVSLNXoqcTKP1vj/y7fjYd8lXT5jAU/ZsAE6veIFFWzv/nRXVNDI8LvDJNWUMo+02zd/VKwqrObbJO195xXc2t1x5If9zvQs8KYVzcV1LaW0Tz957Az9fdh6/Cf6Fmypu4GcPvsW4D37K78qvY8YLj8b2+9iHK/nz7/+Pgf/5PMlPXMAjH3SrCGeP+vsrH5N3yzX8+50lPd7H+uIaHpy1MaGrQbyOynDfW1XE6cbrp+Zj3yz9AsBau9Fa+x1r7WhrbYq1dmz0eZuieGvtFdZaE52YsXnZguiyrt7eb7XP96y1F1prh1trA9baIdbaT1lr/26t3e1alIjjUhUIsywlmXkDfJT7ZvHW4CbuHDKYXwwfyg25SVwwKoevjBvAHau/wFnPnMW/1n+f60em8NNh2dw6ZDD3ZmXyn4yBzBiQxidpyYxKXUah38dKO5K/h78AwNcb/0PjtsSJlZwORlKqbgyxakd17GR+zc4ajgt7276b9GneTfoUAGeEZpFjW4KdHFNGQTRQ2Vpez0HWCzxqbQopJsSoVQ8kdODfUdUYq1cdYmrYvsg7OS+tbSI7ujzWGbdiTezEZu6iRZzlW8zZvsX8y95CumlgvnswPw99j+vdH3BD6EoAvhV6kmseepeTneVErOFP0SFWJ0fWdDjgAMCqHdXc/I9HuPeNheRvLWFidPhMkzWK75x1FPc7lwBwVfBxKuY9GctmZZh6dm5ZTVV9iODH93Gh72P+r/4eHv2ga6OWLd1WxTTj9UMIFC3p0ja7a1cjP7Un4lpSm7wT4RKySBl1tPdCUfsnaz0p/Wq+ulZqvdKI4aaiy4FK+dpPSDEhTmcxzy/qfIz+ulZ1ybujvilManQO2lQTpLyg7RdvT9loR/p8dzznHzUy9gWfXreFLWV1sYxKg00i21RSOuNWbrvxJ8xeX8I/X/uYq3kOgAfSruKer5/Ee5FjAHgh6SbOeeM07nrsBVzX8t+5G7nlztuo+8/lnOZ4GbJQxphYO4IDvfr2XFPK9grvb6i6McRv7n+Kbyz8CtfzCKNMKUvdg/j9qPsZe5jXnyaVJhqCEUpqmrj7jzey5u8X8ct7n2DhlvIuByyLly3lUMc7SRxvdpLZ0JJxa9i+jI2ldRxsW65cByq9ctj/LVrHtYHn+L7/VWYuWJKwzxcWbuHkhT9jUvRv/FRnObM3JF5pfXzWGm56+BVW70wMPFdvKeQIZzODTS2VW3o/UFk+53980/821/EIry7Zzh1Pv8/PSn9LhqlnsrOVJQtartNtKavjlB2P8infMm4L/Jtx/g4yJMXRE6Thh3HKoTnMcQ8HwGx8D2st97z8MWUPXMCdf7w9IcPdnife+JAvOx8SsYZ3o++ncTULOwz0Fn/8Ruz3/Bnfwlg/lOCYU0kJ+Djqs5ez0h0bW39b5jS+dsI4brz4dA7LyeDiUw6N9c8aufklyrpYrrappDYWqDwZ/jQAJwQ/YdHWCppqvN9TCVlURwep2GEHU4HXub+pOjGwbQhGSHe9k/SOMi6tFdc0MdZt+RwaHb2oUGNTGRg30Ahpg6MH6WqgEh0LKDpaWFZaEqlJLbOrN6W1BCr/ixzLOUd5f8fB6PKRpqxNh/LOfLCmmIZnvssNG7/OU4/9Hde1fLh0LSc6qwhbh5+HvocbF/A1/1/Fa/4/L7BDOemgoWyOXnAZ1LiVnLD33mj+zAd4PeU8/sTlfBg5kkknfzG2fMjAZCoO/Tph6/AZZwEzl21ut83ri2v46/9WMGtdKdnRK/Z11uvyPMVp+/ncHKi8t3wrpzteVjg0YTqThg9ktX8yAIdHVrGhpJZFWyr4Ah8SMBEq7ECSTYiry/8QK4meWDOP0tomqhpC1L19J9fzCMc6aznJt5KtHz0VGzm1JxpDEV5YtJ3FuwjQdqW2KYwz7z5+YR6neOY/e/Rd2BSO8O/77+aL73yKvPsfaHeY544yKnOWr+Vos/vfk5oBp5cku2lcWF1Lmc9Hud+hyEmiPJBEhK6Xwlhcihu84OKT1OYRkYtYG5tOptlsziEXqMZhFh+HRzEkEiLFncHPI0PIjLgMcl02uYt5ZcMrZCRlkJGUQXpSOsZN418PPMhZ9TOZYSZTf/AXmTDpUE4y3oeIHXkUvgFDYc0/mOqsg7j3ZI4pY000ol69o6U/zG3hy7gj8BCXOu9w51tzufmyswDYWdXACNOSjp1S9Q7ri79DXVOEkdFyq3nuoZzqW8G48GYKqxrJzUrFX9IyWsnh0cEBXk77Mn+47joAnpi9gQ3/e50Jzg4eS7qTJBNhrjuZT1zvg2aMKWJLWR0Th8e6HbX8jq3l0cf/zZ31N7Fs53iedD/DMH8VG9yRTJp8NOkpAcZ99mpWvfEGk52t3Gn+BXjZGsdY3B3LeHnxoVzkeOnhYaaKpjkPUHn63bGJjTqyuaiMC6NfYhkNBQTDLkn+PXOtIOJafvvIawzb+CIDzvgpV5191K43iiqvC8auRtUFBuPPac6o5EOo0ZsPoPnmOD2aR6WhcBUDgAXuIZzteDXqpVVdG1BhSHS40YPMDl6q7eCKp7Usm/sOc954ggqTiXPM17nirKMYnt7xSOPbyut5+M05jBszhm+cMhFvTI8WoWB9bMI1gEjJGuDcLrW5M7VNYYbXrgY/rGQ83x07iJnJoyHiDe05c8N2vmNqabQBPnSP4hzfAn4WeB6AXzw/nnG1i0n3NfBu5BimX/gNDs5O5+Gsz/KV2g+jA2E0cMz2J/i/hwfxvS3XcqmzFaLnO2HrkDRycktjMkdDjReorKxs4KhRmdxy3xPcWHEDmaaeJe4Enk/9MoedcQl3nDieP73ufdmn0kRDKMLMNcVc5T7HaF8JnyuZx9sPPsKtSSdwyNnf5uKTJib83BtLapm5pgSfgYOGDSR5Y8skcePMTraY7NjzpNJVLC+o4gizObZseNNWqhpC1Be0fF5Eti8CPgN4f+uL336SW30rKLEZpBJkklPAU6tW8sVjvIBs0dYK0v/3U25y5vCrp/7IXT/9bmxfjTtahmN3Krr5ZWsttHr/xPOyQ/lgvH6MH816n280PUmur4yINfiMJbvgbWoav0J6SoDH313AtY43OEKqCTJqzs2Q+1Ds53StS8RGCO9YQtgxhAePZcSgIM8lH8qhLGGUO5/n8hdSv+KvZKeu4CJWc+PjcOWXzsE4LhE34m3vhgnbMNvL6wgX3cur6SnMtxNZ7BxMoVlPCSu5c869HDxiAGE3TMRGiLgRimvrKTMzuH3wIFzjfW24xlBrR1KQ9T43zJpP2I3wj2GjmObUEjQ+Ng2exzXvrsTFa7u1lrXjQrwZHk4j83l5xtcZlp6Ma92EW8RGYo8tlrKael4ePQgXh0K7jrdMDrCDiplfBFw+GpVDEB8wEH+0JMvgJ2AGUNX4W9579g4sFixErKVpfDnPkEOYEKGFXyZ5actntDEGg8EYg2McHByawi7OuCL+w0gcCyHK+TMjCRIgvO77PLEtCcc4mLoyTE42TuEMnNfyMcbBgPcaDsaAg4NjHHyOD39tCb7hQ/GZUvwfXoff+PE5PnzGh9/x84mzkz8OzsJn4Y1ICiXbH8FX4GPV4G084k+nxJZRvfYpVtcNxmd83j4dP37HT5KTRMAJkORLIsmXRE2Dy8vPP8Q1KZ+w3fqYGnyLWRsvpX71K1jjMjdyGGeefBLL5o9nSnQo4cXuRArtYHJMOSU2k2GmilTjBXblgWwy0wJUDxgLTd5ndXp0JLRHwufw88CzFNrBnPSp6Zw1eSRbyuu4ZkLipNZTjziUDatzOMTZTl3BCmBq4p9YzU42P/BdfhD6iB+HfsRn/d531vvu0XzeN4+v+t5nlnskQQIkE8TFiQUqZflvM8A0sdwdx7FHH4kxhqbcE2D7v5jmrOWDzRU0hCKcEM2O/ST0Qx5LuovTfC0XLKY661i0pYL0lABH4114mx05jJN9Kzm76V1mb/gxp00a1uZzobQuyJABSW2+X5o1BCP85oGn+VHxzbxpT2TwT//J2CEDOvwsacN1wfHeszurGmIZvsOcLSzaWsm7i1aTPTiLb50xuW0b4rZttnZnLWcGP2Cor5rv1N7Ps/PP49ITD0pYp6OBcE52F+P4LUvcCViWQQ+TIwpUesnIUAO/K2v5z3o6fAaX/W0uv/y/7/Dlq67g9Yd+wvFJiyhxAlT6De/4JjP68EMpqiulcOcqgv4m6pzu/x+6hCn1O5T6vZPkpcS/odex4KNft90oB/5nLRnuB6Q1fMjzS8dx4kiXDHcImzJmM3TAEP6cMZLRtprUyGbmuMlkuJZUt5aNJVuoDw1ldUEZZ5kCXGvYmvN53ti5jHN98xm76gE2lZ7I+KEDqCzbQbIJ02T9JJsw5zgLuO31jzlt2tGMjwYwM90pnOpbwRHOZhZsKmPIESMZXr8WfNBkAySbEJvcbA791FdxHO+P6uITx/Pbj67i1obbOCpa1/++OYGjj5wCa72Z5ReVtX+1cM7GMs6peQF8cJSziUPMvwF4IvAlfjnVG6v+kuPH8ZsVv+dH2/4vdiX7ZfdkLvR9zNCaVeR/+CKXm1LqbDIDTBPf4mUeff87/ORzU9ocLxxxmbWulGHpydQVroud6I4yxRRWNjBuaNz/13++4tWXX/k2+JPb7Ks7npyzgcs2/5rD/Vu4c1Y67qfvif3+mtU1hbn/nWVkDhzAt047OPZ6SU0Tw4x3NS+UOgyGTwaMd7X2tuzWhyLVOKxLNjSQxHWh79EQ/GzClb/2hIq8k8C1NpdjWEc2lTRW7MAbjK9jTeEIg6NDjKaaIOHyrcARbVd86fsctfRJjor+SdUsfobrV/2Wv1z3ffy+9v/OHnzlXX676XLmrDqMF1Of5EvTxiS8HmlM7AORVrkO17Wx31vzh3V3Z5R/acFmzna8jrtlGZNJCfgIZY6Hci+z8OiG1eDzrlI+FTmTY5011Ng0xjlFnFj7Dic4Xkbv3cGXcPshXunWFV+/gtvfHs/ilWt4Nvl3nO+bQ+rmIJN9WymwQ/iPOY9g8iBCGWO58qyWEdqTh46H7V6g8nZ5Pf9bUcTl5X8l06nnrcixbP/037jlU5NjP3NycioRa0gyEZoam9haW81XnRIi1uDicI5vAedEFnD/69soO/JBhgz03td1TWEe++ftXBv5N2F8LHfHca6pj32N5ZrS2NV5gLGhjXy0cjNfMC192yaYQjaW1BIoawkohlavpD4YJi3Jz7xN5ZxR9yb44L7w+ZzkrORs32LY+AHWno0xhldnLeDXzlwcYzm89C0KK79OTlYqETeCrVhFtWMIYfAF17GhYgvGiRCKhAi7YUJuyLtFQszesJNNJWUcO2EIQ+pWEVrwb0KTPkNNzom8M38hNtLIEUdMJj3VEHJDlNXVUzRkPr92BhMyhir7KC85O3nODGc1OYx1CqlmAQ8+di5ppo4kGvhgzFDq8WOMS7gpn8h/jidsDOHWJxtjR8OmJ7zbaHiZXKAYFn8LxsCzsS6dzzH/3ec6fmOOgFcZgjc/8xz+QLR0aecjvN1e98csmBvNVCSofo/lzcmqDPhf83dU0wK2tE6IBqA0EL2Y0Liawi5WdtYGmrO6lRTETmu8E9f6QPzfox+weBXefqCGhvpWF0j8UNu8j3AFtV1JEgdi/8QvgIbN7IxPaiQnQ6QGyro4dPCANIhUwKZ2SoX98Fhmc3ZiFfctXRU79PIh0f+rbffyUlfHnMiFt2keBrkCPv4CjIIXGQPUklR5OS+PcxlILgFrKecVLnOzGGKTKHaHchDJpFhLsrWs822gcfZNLBmxnburs7B2M37XsIWBvO0bzNLIVzGpI7l81HaKQuUMGpTKxqpKUnwppPhTSPYnM35YCuvsKA5hO07ZWlj6NLz+C7j0aRgyAfvPUzg7XArGy+A1l349FP4cZzmL+YxvER85PyGdBpKj/XiWLP8RBWN/wyGVH4If3rPH8u2DvWAid9IUqralkWvKWL9+FUmpmWSaeuptMs7Es3h709t8xreIGptKKk0cbjbz9qZCBmVmcVH0gupN4St4zfk1Jzkr+d3cBZw2KXohK9QID5xBY/k2VgbH89bI7/H7qy/DPHGhNwHoN18DxyEccbn5kZe5vuR6hjlVXGBn8eGGsq4HKvP+Be/eCle8BiOPYmdVE9nRc61JZjvXPvk+TzX9kLV2FM8NfI6vHNeS4WTncnj4XDj7JjjuO7HFq3ZUc3L05zvE2c5Tbz/E6nG/ZOKwgbHv0uYRH6eZNTyU9CduC1/Gs5Ez+LTPq9R5MXQi1XY9UNP2BKILFKj0kjTTRLEdxrWhqznbWciinK+xZeU/yMjIID09nRl2Cp+p/xiio4EUjfgGt3z661hruePW67jBfYCn3JNxL7id2jd+xuGBVTzsn87UM8/kncXzqHabGNaUzwB/BRWOj0qfodSXhGt6VtYSNoZyn49yH8BWZjfPadnwLmsa4OMhAbxpaVop/QFP/RewhmfHjiTZNTgD/8ztqT5eahpGqruQF17/MaceejBLqzfxcGY6NZFBhCOpTGMLxxf+H7/adBX/DVRQisPboSP5iU3mCGczz796LxmpP+NQvD+K28KXMtlsZcmgz3Jz3Eljst/HT6/+Pjc8nMnPK39PFrWEDjmP7Jwx1K1JJsvUUVy0AxjfpvlvfDCbW31LCVkfARMh2YTZboeSe9o3SQl4J9d+n8Pvrjifmx4LcPHmX7PWHc177jFc6PuYw8xmJtYXgA/uDV/AZ3yLOMZZT/XiF6BVoLKysJp/P/UUX6p4mPfsoexwR8emMB1jitlaXt8SqFRth3XRK8qrZ8ARX+rR/yt4pXXb3/4bX49+uExyNyQeK+reNxbyzcVfZY07mlkjXyQ9xc/SreUMHpjC6Gga3R2QDUkDYMqlsPwFsG70FvHuAWNdAgYCNPBD/0u8u/p7nHdUDp0x0Y75a93RlDhZZJtKwl3oU1VZH2IwcSU6JauBzyWuFKyD5c/jYng0/FmOdDZxrLOW8+pfZE3R1zk8x8tQWmu5/dlZhFe/yZTzr6Z07Tz8SS6n+Zbzmxfu5rSD72JYekvA2DpQOdhu5Mc338YhJ57LZ6cezMP33UVOpICS0Z/jc2d9mpMmDKG2KUxDMJKwn3i1TWE2vfMvvu4UsNUdRu6x5wOQlD0pFqgMDRfFApUvfOUKXqy9lNffn8ULkR/zRecjfMay3Q7l0BM+G9vvISPSueEb5/HRuhP58LEXOd2Xz3m+uYStwz0j/sAtV36x3YBq6JAhVNs0Mkw9lWU72L5qM7c5G6m0A1h24p/4xZmHJayfmuSngWQG0kgoWE/jTu/K4gabw3fcG/ia7z2+b57nVLOUTzaW8ZnDhxFyQ8zeuJOT7DvUBIKEMIw0awgZWEIyFSaNVFNPA1t4z0klBFSwkSXbnuC5jAHUEMBvwhRTxJol/8QOnEuek0XIGLawnJ/P/DWZaQ5LNm1j0shtPGuGs35gMe/XRXjYZFPC65z3Qj6WCBUNO/lwzAhCxtBkVvPnV07FJYxrXciB52meaC+fJ185r6O3ZcyHzYMOZqVBycfeLfo3v2pDYh8+MmEhzVnfKjaSGn1cTjktGfWWXhTN/199XrEtB6CgGwQfVDSnY6mgFCglCahmYcK82JvYtm4TOLAxK27kM1KAN2ge/2nxBy92ekxnguEONxfXncGCxTNIG5JC2gc/Iy11CKkDLKSOZKhtwLrrqbCpOG6AIUel8t2iH3BB5bNMpJRUF3xuKoNsE7nbXuD1FVdybvQEuiT3LAZG+xFNGzeURe4kzvQtha1zaRrszUW+1Q7nK8eO4b3g1Rxc+Bv+Ffk8F/tmcqSzmZoN82gYehBZpo4ym87UqSfy1rJj+YJvDlnrXqC68WyvNHrTh1CyihTgdF8+1TseZ/na4zly4/veD1q8EkYcwYz8HXxz+80Mc7wLhTmmnK07ivAGx+2C/GehqQp2LPUClepGTogG7BNMIRNrF5CRVM+xZi3/evFhvjj1RgLNF+42vAtN1bD544RAZWNBIV+NG8zlx+FHmH3vPB5KO5df/+RHZKUlxUq/rvU/S5ap4wvObJ6PnB4rr2sa92lqnVeAml1/ybdDgUovut/3Nf7xq59T2xTm8oyUhLRaOPtomv86a2wqIw72Om4ZYwgOPRyKYQrbuG1JkD+EtjAy3MjbY77ID6ZeyA+mXkFdU5j/3Hp5wogbN2fczHXXfI/KpkqWFRby/JvPEoqUUxEJ8ln/TFaY4YQPOR2/v5HqYDXVTTXsLN9GxAkSdNpPO3aZsdT4HGp8QNMW8MHstOgXrZ3H5lXzwA+rBkev7NDAwwyPPn6ISxiEN/XNPziFbJJdlxT7Ci989AG5Y6p4yGZTnLSR7dm5ZCSv4/fzbiTVn5pwm/LZVO5cdy3B+iDnH+9jU8kG3k7K5lCzg+KSeRTWjiPJl0SyL5lkXzLF1WHGbfwv+OHFyKmEcbjUP5N/Oxdx7UmJgxYk+R1+f8W5vLNqKmNTA+R+NBc2eh36kwkRsj5qJ1/MR6uDHOOsJ71hO+GIG7vCUFjZwKsP/IY/2MdwfJZpdi0P0HKiM8YU87/44W23xA3vvPhxOOJLhCMuf3h2JrUVxXzvq19oc1Ul4lpca4m4lifnbGBLQQFfP+tYnnl/EdfYp2meSOcws4VVO6rbBCob571OdlIlaU4TP5i1kambH+IKZwbnB2/jyYD3QemkRy+AfPEf3q01a8GNcO/b+Vw25/Mc6Wzm6dkfct5Rl3T83rGWlBIvUFlmD6LYZgFgals+w7aV13PvszPwpWZy3VfOJDM6V0tZbTBW+gWQVLGeW//zP8zWuZhII+640/jVsQZ/JMgaZxK3hL/JBFPAu8m/YKqzjre3VMQClbkby5mW/zum++bzy1eTY+WIAL/y/5c7X/gst36zJQiKBBOzdF/0zeaLzOaFOXO4Y8PVPGT/js9nofAF/vbIl/B9O48XH8kjO1LIEV+7nbMPH0lrD76bz1XuU2DgwaRvcMOpXofVIdljqF2ZwiBTGyt32m6HcdaEoWRnpJCblcrSZw/iaGcjEeBFexIXHJZJeWM5oUgodrV/2OAgf888gYzQGkLG8IY7jamnDmTOjg8IuSGCbjBh/XVN5fw1cyhZThUfFD1ETmg9tw0ZxHI7hpGpT3Pdh0962YToNtsqa3gvZzCYCNurfovrq+aTUSOpJgAD/sWDoSYejowiZFzCi8/hl/Hd6MYB5HbwJml9ZT4CfMStDE5cXPEkDIZ5NJ8MBWFny1Do2wdEx05x10AqFJMMhKB5lKZA3NVzALtnRvfa0wwmVtYTXyLkGD/11VUMoQa/BR+WMjsYa32MpZgqO5ASdzBHm83U2gGEc08kHIYRO2aRQSNL/cdw/FFHYYyP5fM+4nizinWR0Yw54UsMTkslGDbM/eg9pruzCdkAbw+6mNE5oxmz/B8MpYZ3RnyP8084DJ/jw2CibYqWNxkfxpg290889RTfCj7F0sgEBp5/F5NHZiauE7efprDLS/f+ikt97/FoZDpf/8ltLNhcSfLL32Oqbx0r3HEc4Wzm1fBJLDBH8BPnaf6e+gOOqP2Yr/hm8U++wg/+75bY7/H1/J1MeevLjHK8q9B/OegBfnrhp2Lf5c1ldsGIy99e/ZjD19+HwXKmbwnldgAZTh0WL2fzP3cqk7/+R0ZmpnqlarhY6w153/y4uXzNtW5suet65W3xpXjNpXkRN0LYhr2yu7hyvebXQm6IlTsqGb7+SQaYeuZmfIZjDhkTW6chFGTDyrmMMYXsJBOTewR+X4SdBevIdKooJp2wMaSZOoKGaADv4O7m6UJPucZS7fOBL+QF7MnJYOugvg4GNn+fNWeyoheDau6FNFiS5idxrCWPs+5C/j12ACnuQCIDn+U7b73HwKSBpPkHsGFoMittJiXhDyit28z/0lJZG84k17eJy788jfc3PM+5Q4ex4OHtHOlsJrN0EeXRvkQr3bFcdOxoXtkynS/UzuEc5vDh2hLvot1aLyP2buQYzvItZrLZyo7NS1oatelDGHEEi5cs4gJnC5V2AOU2nYOcndTvWA0ct+tfViTkBSgAEa9/V1FVQyzTlGQiXORr6ff2E/8LvLjoW3y1OavSPCpbKLFfU+M2L9hY7o6jjhROcFbzed88TmxcxZNzpvP9sw6jPhjhcLOJk31eH6UjnM1MMtvJNPVst0OZcszxpAzMAgp6dGVdgUovKbFZTL3gR6SnBBJGQWo2eNyRNBV4pUwL3YM5fsLw2GsDxhxNpMgw0RSwbX0+I5PLqbJp5ExoKWkZkOynyhmUsE8nYxRpgTTSAmnkHJzDOZO8cfN/d/8T/HDnS+S7g9g55pd85jDvZPOdlUUMfupzTHW28cuknzM4uJqvBN6kxCTR5HN5makc8blLCbr1vLVgHoc3vEeN41DoDKDS8eF3GqhzHOqMoakHZWqdaXIcbzwlXw3bcPA+dLZQWryl8w2jPvo4+mAUeIPHPcTbzz/UZr2kCZaH7CiCppi0lIHc2zSVrPR1XPXuN2MBTbIvOSHACdQF2JBRyx+zhjKQIH6bwlz3aI46ZCuLixt4wR1AcWQLz6x+lWED0wg4Ad7KL+IzyS+xmCSabDJZNDDN/YhNxk/AWgK2ho1Fm6hozPC+tDd/gGMMPixmw0z8FZt5dp3DhSt/ykGmkD+8NYzfXnp27OdYuq2S/z6Ux4jIDur8g7g88iI5pozrC+9iaM0K0k0Dn7iHcpxZwwRTwJsFZZx7ZMuJ8o6qBk6JDiWZbhpYsa2Ua8wSBplaTnRWxmb5Tc5q+0GfwBjw+fnC8Yfwykcnc7n/bQ7a/hI7q77IiMwO+oPU7CC5sYQqm8ZWO5ySaKDij/bPCkVc/vLIf7iz6jrqSeH2/9zPHVeejzGG8rpgrPQLYJLZxolrvx8r0duw9gXWBU/nYODdyOFgQmxkMAUMYKCvmlUbFlJy9AAiNsKrixdxZVI+W4yflMhqkpLrWeMPUGeTSDIhBm77GzPWZjI8I4mIjbAznM/7qamUMJB0p44wXmayiXxMw4M8lTmACtJIMY00MJsfzbiVrw56mYiJ8LePf8HcmkNjJwxhN8zmsipswXw2ZPupYCw1w9fz/Xe/Q9gNU1bXwGu5w/GbILXM520zkgq28uCbnyPshgm6IRrHu1gzGtcYYCkPvHxG+7/vYfB+7At7Oyz6Ref/p0MAMoGFFADzSQcqWL5tRvvrp/gBP9gCCOAFKbgQjPZP6+XPir3BWPBbSzKWgLX4LTQmj2TIgDT8jp+AEyDgC1DfCL6ilQynhlJ3EEnWErYB0lMHMKZhPUnWErBQYwcynGoCFgLWstMdxkGmiGJ3KANskBwqcazDy+Nu5rLTjuLhp1/kRw3/ZllkIhun/pYzDhvPiIwBZKWmEnACsUCk+bFjOv4df7yuhEWPX881znMErY8/HP4AkboKbtryTUpsI69FJvIt/3wabSX3jP0RNRtmcVv1UxTYIbz9mbu44mSvb9FNy/7Lz3d8n+3Wz8eBL3PxlDE8+OF67q24lcGmlpt9P+If37uRzNQAf6w+nKKqcn5/3rnkZKV22Lb2vDOsiuM2PMpBdhuvVo/i6KMO6nDdlYXVTAlXMNaGMcmHcFDWOMYeZbn7hWO5wK4m127EcS0mPIhf/fg3zNv0Q26dPJwn/7CakUQYFm5kSMrw2FXlcLCOQ9xqBljvCnFSbZBhaYn9DBpDEW586AV+vOP6lmHDw7AyMomjox2tAQaEMzly2ORYuePeMntDKc4nr3OiU8gt7tHceNI1sdfeW13EoJmf4xinlBIb4vWjb2HauMEUfnIhn/Wt4ZaUb+Abdwo3rLoQx1g2uiN4aFweN18+nQhhgm6QbRU1BHwuKUmWYCRIyA2xvLCMild/wWTfRipMMh8ecRMnTsykMdzIjAXLOLX8KRqNYS3ZJB32aVKTXRrDjTRFmmgMN9IYaaQp3OTdNy8LNxK2vT8JsGtcyn0+r59e03qKdq5veTELVpIJlAAlXMswoBoWfC9hH2ai4d9uLv7IJxBZxtUZwygJG44q/Adbxjbx2NYMMt1yZq57k1EjppK5/k0yHYe/N53Pp5yljDM7KdjeMucVm2cRPv5qkjd7UwTOco/EYDmInfjKOp5sOUHxSghHayUjXqlbdflOkk3L7zA2gIr1cZizhf++81++cuz1XiBeFu2HF2q5IGetJbnMCz5WuONYfewt/HHBh9zm3M8hznZ2zn2WyJk30hAM8924i+iDTC3n+bwM8iJ3ElPHDiIrLYmunc21pUCllwweMZbPHz2qw9cPHz2EVXY0U8xGFjKZa3KzWl4bO4JV88ZyhLOZn/m9TrJL3IkcMzax9KoxZSjEXfBLGpx4vOarPv70bNjpdfT+pKyOwsoGcrJS+SR/Fb8yG2iyAYYd9TlqV6VyUN1rHIT3Rv7QdzTfOvIbAETKPsXlH7zGQON9kRXYIXwv+kZssn4cE6becbg5+Wq+f9W3qAvV8eyC1Ryz/Nc4TiNPDfwSA+vXcJh/LXOZwPijplFaX0XR1iWkOVXUOQ7Vjp9gIIUmd+8NMRt0DEEMUEdTsA4MVNeWsrULU2IsHJRGy+jWW5i38s8wBOYzBCjm1QW/SVj/tZxMiA2EkBW9jxtXv/Iann06boNxo1sev+KVAPkOsjiMJNL0c2b8NyV2JbIxGCF9dCUOFp+Fj/HjI5s6ex9J6RHmM4JC62e4ySWZMDt2/Iy5rw3GRNMsFfUhBoxezZt4QazL3/gD9aSSTYl9n6vMICLWoaT+j6x8MzW2XawzKSaWsbHWYrFsPKiO94LDCbKEp17/OtmZqbi4eP1U464kNlRBzghqSCHN/oV/mEqeMiMotTP55KULKK9tJC29gC9mZGOBRn7Nqf/5CwNT/NQGg/jGNmBMLi4QYSMRk0KE0bFOvBEzB8aPARaRjtf3Y3pzGWPkZl55tuXX/OqY5gsGXtD2OPFZj+0w5+qWp0kwa0TzSUvrk6+NLIhd7W8O0N7h0VifsaVsXNPOZKQDwXtPWKhbwsb46rLk6EGxeFcNg9Q1xI1S5EDsP2Ef4Y+e9IdtEpm2kYgNkJQxkoFJyd7JvxOgqGAb420RAWuptuk41pBDZTTAh/edUzgivIYJFPFR5GjOMUtwrZ+d7jAmmwKeCX+ak8xqjjDb2eTmUH3CDZx28EjqGi1PP/0k33VfYhCNBLDgOtztfAdf8iBurbudAF4gEbbeV2OyCVNrUxhoGvn9IX/nNxd+OvazWGu5/i8Pclfla9TaFL6Wlse8Cm+783Oq+W25994ptwP5UuAOzml8k1EU8Q3/O4CXFXwy7PUP+qx/Jh9EjuLrx13ECTlDyLzkMF6ceTZTjjiM3x7VUdapa06ZNIzAt/7IjU+OJ+hL5ZrPHs//lu+gdHMGw0wVF/o+Arz5ONasWMJ5NTPABy+6n+KyY1pKToZPnEpjYYBRppTNBTvhuDHs2LyGwaaWYpvFuDOvjJ2U/+qrZ/S4vbnjDqF8/UCGmBq2bVkPpx7EJxvL+N/85XzuhCOZNq4lq7a9oj42ypab5V0Z9jmG2mFToeyJWJ/AWjOQicPTmTjcy9Q1BgaBhSFUU1EXZHiG9zdbVVMdm7gRwNa3HV3tL6/O42c7rmOkKWeJexBgvAkG3aP5tLOYQHSKgFpajfq1l+RkprIoOpeKP26+MoB5qzbxi+goTMNMFVs2ryVrwBEcYbxyDztkEhecOpX7VlzARLbxyRE3ccuXT8PvcwjgJ4UUDs/OoLXcASGerh/Haf6VrHaHYXLP5fyDvfLfkaaI9Cff53hnDf8In84Vn/p9l/vx5b2xhG/OO4uQY72LpCkZNIRqqXcc5qQezyN1Uzg/8D6TfJuoNw7LTQ7ph52KcYLUh+upD9dTVFPNjuoqkn21WCfYtk9XD1hjqfT5wOcCdZSSClSxdr33hb5kaJa3Yu2fefcNYJAfBo0CHuGUSC6D3Aip9g0eHzGMzIhLZsVC6j/4A0kDZ/O6TeN1dwzDm6o53vjIbNhAQzBCSsDpsAM+AAVxgU/Yew+HKgoSVnGMxbWGByKf54f+V5hcN5+Smibv/V/WNqOyo6qRceFN4IdN/nHceMHRNH3+SO6+fQs32Ac5t+kNPlz7faoK1vB55xPC1mGbbzTj3S1c4vMG/1jpHMJ5wwbi240qHgUqvWRX/wnHjhvM3c45DHRfpnrSBQmjPZ15yHD+aj7FEWzmgugss0uZxHdzEkf7ctOGxQKVepvMoMGtRpSIShnkXT0dQjVL33iIyFubGHbhHbhr/4djLLMjh3HGEeN5s6YKWqZCIRKtyQSYMmYoS9wJnOpbwTY7jJ225cvhpcipFJHFGc5SJh73ZQ4e5JWrjDxlMq/Pfolv+9+kurKYDJPERb5KbvOfxK/PuA2A3/zreX5f8G0AXoycwpSfPMu4IWmsKS7n/X9cyfmBD2kwDi+5x3L4137DgBSX+nA9DaEGGsLerTHSmPC8+dYUaWLT1vUMpZhSk4aTOZSIDRKMBGkMNxG2+96s8hFjvNHHTYS6UGIfiUZ/ex3Wm39Gr264Fif6eCeVZa16wKY0Z64ACliDiT5voKD5ZLtpJYVdrSr1QVnzaHXBNezobM625CTAxcdOyoFykoBGKqu8kpzKJENC59RIMdXNP77fEBu2SgBDsi8pdqLffLU/4AQSrv4nvN7eMl8Av/Gz8OP3OJ382NX/t3yf5oJzziPJ17JNkpNEwBdgRUEdWe/8ksMo4JfBq/mF7zkmmGLuHfZ7fnH5Vwg4Af7wj4e4ueoW/LSEVavc0Ux2Cvhr5CKuuvFfsf5hADfn3c3NNV45zhPho6hiAD/0v0LYOtzuXMXd37qeeQ/8iKv9azkxUspJvmoWupP42J3Mxf41TLfvkGnqKbUZzJ54J7d86uzYF/xRP5zG0x9/l8qlr2KNj9RDzuKnpxzG+ysLqHj3AVJNkEo7MNaRf6cdxHY7jGPNWsLFa6kPnh47yfpkUzlnl/0XfPCYPZfbLzuD8//unfS/WpjB95PGcpizhRfc03nqms+Smfp5PtlUxitPXM4XfF6p53I7nqYR0zi8ZDOvZF7KH6In4ZNHZjD50s/02jvk+PGDOe5Xv479Ho4bP4QF7iFM980ny7R8rmRVruAzAe+Ep/igCxk0oGUkwwnZmWyyI5lsttKwYzVwPG50bqVV7hiOHJ3VK209anQWy93xnO7Lxy1YSGPoLF599I/83vyTX6+/jqk33BD7OQorGzg2mtUIDGnpGJw5YRqhUl8saGhKSmxbKGUwNMBgU0NZXKASrEz80PI1JA5l/fH6Ug5d/DtG+spZ5E7ko5MfZtSwLF5dsZQqJ4fKdc8xDK8UqN4MILndz+g9a0RmCjui39cDGncmlCQ3rvsgYd4Tu30Rm4aM4nOmGNcaBuYcwpGjMkn78b3UNIb5zajMzk+OozJSAqyzXkBdYIcyOO59c/rBw7g+7TIy6v/FxhHTuzXYyKSc4VREsplkCyASYcm4rzFp7aMMMTXcVXEuITuGDF8F3w94F4H+GT6NK66+M2Ewlw/WlvDNf88jixI+SvkJTcaww6Tx9AmP8/mpw6kJ1VAbrKU2VMuibTswSx9goK+SCidAo2NZFjiIYSMHx9apCdYQcnt+LlHvM9T7vN/B+vgLXtv+A8PgBYYCXjbiBXKBZTz0xPGkugGyB49jTNYwBqcMZlDyIO8+JXq/5X0G+3wMciMkRzMq1HgBaPOopQBr7SgWu965Xo4ppaCygeGBRqiLvvfjApXVO6uZHO3r2jjIGyUsJeAjaerXqFvwOCc6qzj3iVf4oXmWgC/Cs+HTyc0dz/jCRxkWrXxoGH5Mm4F8ukuByl6SmRrgGz/4NYu3/pBrj0gsp0lN8hE+4isElz8Rm7CxLOuoNiMnmYHZUOk93mEHM6KDlPqgjHSqbBqZpp4/Be4n2YT44YxjOCe0CHww1zeN68YMYvXOCRStzoqNP5404tDYPo4ZM4g/+E/jVLuCzZknUFHR8oE9wz2Bm3/2YzaX1fHdiS3B0ojMFDbmnA/Fb/IF32w2RsdQD8dNTHXumZ/i/UeP5gzfUnbYIZyb6fXlOTR7CH8Z8nW+W/Y+PhOhKnQI0yec0qUPyXi3/OUf3FTxK+a7B9N4xn9jwwPe+cIcfrL0PKxxuWPSY/z4whNpjDQSjARpijTF7uMfByNBGiONsXr8sBumIRwEIrHn89Zv58iK16nHz8YRZzFmSApF1XXYwnmkmXo2+kZD6kCG1q8hbKAJH9UmmWTTSDWp2EASjhvEsU24xkeDdfCZcLScR3aHtQ7YaD28taTaJiwOJmUQkQikB0vxW4sPi2t9+LAkEaEkaTw21MjYSCFh/FRlHUlu1kC2bNnKIZFNlLlZpE84mUFpKazaWsaUipkk4VLoDiP18PPZvuITTiWfgPWyXT4LASxvpl3IF888jXDEMPe1x5lu5jM3cgSHnfd/5GQNwG/8sX4GPsdHWU2YZx7/F9f5n8Vv4b4J9/GTL5zuBR++QGyIUZ/TuydDt7zbyPcbvVrK7XYokbN+y8WHTmp33UBTGZGGVI70BRkWTOXUQBE+XIbmnMjQVG+40UGTzoD5v8fETUY7OTpfSnnGYQlBCoAzZEJzwoFCZyT1B03nrQ2FvDvgPH7wnasYN3QAr6WMhzCcFK2JXuOOZmPyYRB5iUxTT7VN5c60n3PLV89M+AzJzkjhx+ceDecenXDMsadO4l9NLxJ2LeUfP8xNeKMBbnBzKLRDONZZy8iSD3n0dx8RPO5qfnLBqbz6zkxu8y2i0QaoOPLbHJGbweABSbG5N24PX8o3fG+zZPTlfCdaBnnGIcP5zqAr+FzVJ/iNix15NDd+52LmbDibX48bvFtXHXcl/vcweWQGM5zJTGd+wjqX+GeSYRrY6I7g6KMSh4OdOHwgq2wOk9mKv3wtwbBLZvVary+iHcNl2e2M+NUDh43M4BF7EKeTz9DqVXy8vpRz3Q/BB2MbVlAXjMQ6QBeXlTPY1NJk/WQObRnA46hxI1kxd2xsKF03OSvhGG7qkGigUk1p3HwtkdrEQCWpqWV4/YhrmfHMA9zum02dTea5Mb/ltnO8oW2/NG0Mr+fvoHxtesuIif5uDCnbi1ICPqqTssGFEZRRXNNETlYqRdWNHFQ9H/xQbVPJMA1k16xg05ZJBEyEre4wxmZ7mZgJw9oO7b8rY065mJfmrOCjjM9z5/iWC5sBn8Mvf/A9Ptn0FX57cPsXVztycPZA1tlcJuFdOJhvJ/Pr4PUMM5WssV62b7k7Lrb+Tju4zXlTc//GAoaxyR3BQc5OloWP5itTT2jzc346N8iz733Ed/0t/dxuGXUHN5331YT1aoMN3PC3f/DThjuo9hlKTRKvHHYjx08cQGVTJe999DqTnNWsNUPA10jQF6LAGUDYCWHiAsWucn1B6nxBNtasYGNno/iP8YLF9C1PMvSlWdQNqOK65CEQHsihbjlDIy75wVwiA9Ior3IY4ZaxsbKRY5zCln3ElX6tLqzkW8Ybmi828TNw0UmTefmTk70+vr7bGGnKabQBlh/yIyYOK4RCbzLMJutn0IRp3f55W1OgshfFp55bO+/EI3n7/9u78/i4qrrx459zZ8tkMtn3pWmb7jstpYWWAoVCQXaQsomAWFFE8akgIAqIgqh1x8f1EfRxRxRBkcefIJtsbQVS6Er3Jk2afV9m5vz+uHcmM8lkmck67ff9et3XZO7cbZKTe+/3nnO+550lfMD2BgD+gsV9lnGk54OVzrFSZ1KQFj1QyfG6OKrTSVNtobR8S7veYIWVA7yz9DRshmJBcRrvBKayxraFWu2lsKCniYHbaeO86+7gobev4Ibl0/nur/4EzVCvUyhevJapOSlMjXIyO+Gk09j5lyJmGIdDnZ5Vek8TtVPKsrij8FZaKn7CocmXRdykfOySs/j9j07nUttL6CmnxxykANiyp0G9maL477VtnDrdTBHs2/on3MrHS/55rF12MjnJ2YNvbAj+u2031+/8PW7VxcMlH+FzZyzl609t5tNH/oiNAF9f+EuWzp5C5m/O4wRjN5sD03nGfxL3OH7F1sBkZqkd2K3MbX/wXMPttR/gk7Y/8VnHH6ymTRBQsD+Qw+vnPc2Fiwrwaz/f+P63+XT793k+sID8a39IUYaLx37wADeoJ/Gj+Km6mGtv+gw/+/WvWN/2Y3MU4fO/yYLidPbWtHDg8btZaSsneMr8vu8ibrE/iUaF5v07MJviSx+gOMMsZ8EmXsFmXGA26TLHAVB0dAd47pcPcYHtNX7rP5Orb7o91HZeKYWBOW4Av7wUo6Oej3TdTpXOZI7azzcdP0RhPnE/GMjl95kf46ZLzuH/3qngojc/hFd18N3ib+JMSuVTuz5Co/ZwKJDHEmM37/knU33JE5w6I4/7HvoqXzZ+zK3dn+aFgHkzesHCQoqc7dxZfi6d2sEtpT9k6b4f8THjbd4NlDJLHbDGM9D4tMH3V/yZzBQXZz57FkWqivuNe7l33Tq+/JV7uKf7Df7on8nSK7/KpKxkXn2/lo5HL+EM29t80fZh7lv9Bb609/d86vB6wByn5HH/Kq60/4uuuqOcXnghr75fyyda7meW0cZr3g9w5bx+xmLJgcwby/jlo9k4upu48tQ15HnSR6TcDqS8JTV0ZXjMdzafOmlyv8u6nTaOWrVys9SBnhuegp7/ryXTi3nrjTKWqp383ncaV9hfCH1mK1rUZ5ve/DL8e82xRNq9U/jCdeez48hpPJjXkw6z7IzreOHvL3CazezoecA+mdUXXMP9f6xEuzNIW3QR95w+B49raJc4u83g42ebqbG/3HAh/m0/x6Y07+vCUG1ysOnrdzc52HrifOYd+F+ww+OBVVx5+mLzgUu+NzSg5MuB+bwcmM+dM2dE7OuuD53PA4+8RZHvACevNLMCnTN3kL5gI8xmKNryT4KqXwDwvH8hZ9jeZplhpnp+JTCPNdMiz5GTMj381Up+kNW+j/LDjcywxtI6mlwWtW9mPDwuOzXe2dD+JPPVHn7y4jZ+apgD5eaoBo42d4YClY5ac/8VOouijJ7AYNGkdJ4KTGeRYQYqnc7I1glGehHUwQK1h3ue/APzPn4TGR5nn6ZeKf4GOn1+XHYb+2tbOan9JbDBj7ic29atjbhGpSc7QgNJAvgcsd/sj5QuTyE0m2OfVTaaTb9f2V0T6pf4S/8abrH/hYXqfTbvfxecZqa+sihjjw3VJ89byt6THue8dHefFPC5qUlcsHDgTJDRTMn28H+6GHgDnzb4e10e72qz2V7QNl0aqjGo0hl9tpEa1vzuqcApfNp4gle9Z3NRlPuX9GQnh70Lob0nUEnK6dtHKsXp5mNX3cQLP3idG+zP8laghIumX8yZs82m1DWvpnBf9adp0u2kqjbqdArLOh+mG4OV9jd5KOkRGg2DGncmTzhPY3nHX2mw2aizGWxSJaSWTWNvbSVG5wEabEbMDy6bdTfNjXshCZ7BA2ieCaYXpwJ4jNPSi7FpTdI7V/ELp4OcvByy/X6ybJ1kb/8NOe4cdhx4j0a7n2pfFlNKeu7jpmR7+O2C23h7634WWv9jvwicy00fOJVDe3sGwH5XT2bh5LgyEkeQQGWCWFCcxiPqTD7AG+wMFLF4Vt9/Dk96Lj5tYFcBjpDFsn46KwcDlWn0RMkftL2AR3VySGczY7Y5yvDMfC/PM5U1bGG3LurzdGHplCyWTjGfsFyy9hy+/turqEyaxufXzu33e6ydX8AP/7KKDfwmNC8poycAUkrx5Y9eyu7qszk3P7Kt66KSdA598Ht8c28lN58WZWyMIUjLK6Vzp4Mc1Uhl9VGglD01rSzqfgts8IJjJXdNjZJ2OU4F6W4qdBZlqpKu2oPAUpp3vIBT+flPYBpLZ09h6ZRM/jcwhxOM3bwfKKTGYdYwzTP2AeZo4224+FGdeWP9uH8Vn7E/bt4oBYqYpKqZoY7yRGUzacvMWi9vWzt5AT/al86s7MnkpyXhmHIlU3Y+gaE0KdMuZ0HuXLJzVzN35yMU6UN8a5+XK+bP4TdPPs293W/h9PnZFJjBicZOFvs6OcEfmfHoHV8eZ5QuH3Qgy3Cvd5VyonqBcp+fKSnzIpqO/N/WSl597s/c21RNg/ZQ2TkXUCQXT6Wj+mkAnjZWk7XyBr6/eh42Q5GsCtn9yjwusL1G1r4ttBevJM/vpyngZX9gKqvtu/ib7zQ+P7eUJIeN/XnnsODQ8ohjyk91MbMwh/ffLqDMqOS/9n2cOcZ+fNrgG74reMDx81Cn2CoyKMjwMC3Xy+5AEUW2Wmg0n/7bfWaVeId2hZ7aLZ+ayVfn38/zu17mlAtvwjAUxTOXUH8ohQzVwjt6Kk8Zq7mSf7HG2My/th/l3fLNXGQcpFEnk7/wbAayqCSdWXeazaB61zyMliWLFuMrN+jEQc30Kwa8AXU7bLRbgcp8a0yj3bqI6WE3PKum53Bf8e388/ArVM6+muk7DnOCsZujOpVJpWV9tjk1P4O9uoBpqgJyZmAzFHMKI88V16yYwR/sv+AnT9/DmWoTvulnc+GiYi5Y+K24HnCEO2XRHF5/dzan2N7jfV2I35MX0TdwOgf59lNv8IjtJQJasW3ydVxrnTtn5aeGApXQ9soizzdlOSl85o4v0dLpozgjmfGSO+NEmo4kk0I7P/RdaKZltez2LuVDva4vTrtBo2cKdJipTp8pr+RqK1Dx5/R/TYiHKloMu2GesZfA/tdwOc0Hbtk0UtPSyRQrg2GgwfzfrNDZEZ32s1Nc/CcwnRt4FoC9LZEd2s9eeQq/23UG62zPc1/z/Xz5sXQe+vhVfZp6ZdJMXWsXBWlu9te1UWoNnteUvajP4LGZHid7dU+5186RqWGKh0orCg3curW+nSWlsH33Hi41KmnRSfxWn80t/IV5xl6mB8ynn3t0IZdnDy+4mpI9srVILruNBs9U6ITtehKbK/pm5WvFzV6dT5mqpMbo+wAyWKMC8B3fpfzat5rLlvefRcsxeTlY99q12kt+TvSHmnML03hzzX088g8Xu7zL+WpYYJ86eRG+KoNUZdZOPOlfQbd1u73bN40Sn48S4LB3Cm/WXcKczjbKVCXT0Rhz7ubuc8/iD5sOsuwvZ1BsHKXRMKi3GTzsuo7LP3ge9R311HXUUd9RT31HPdsPbMPRvpN6m0G9zTbkfjh+pWj11/JeO5Ac9uD79QfNVwXPTSpCaUh7/zqerC4gNzmXPE8eubNy+V/PJ/jd239npq+O1qUfpyQzmb1HS2jQHtJVK/8JTOfSEWgSKoHKCAkEAlRUVPSZ7/V68XoHP2EppfjQtTdyz68bac+cyVfm901lmpPqpoY08qmnUmeSlxo9UMlOcbHNStfZqR104CDN+od52T+PU63qV5fdxpGCs+is+jMvciIfLerbSS5ozdx8Trr7e3ictn4HzAPzaVjLjEsJ7PothtI06WQyMyMv1C67LZQitrfzF5Zw/sKSqJ8NRWmOl4M6h2mqgrYj7wMnWmNvmLU7gdSSEW1eUZCWxBGdSRmV+BsP09Lpo7BhC9jh33oeH56aRYrLTvXcG3n8vQbeKLgKw+cLjkXGO4EpXNL1JfxhfS6OkMWLgQWcYXubfwZOYIWxlflqHx0H3waWmpk4uurBDnV4yfCYJ+IrzzyZL+z4GDYd4IozTgagZNJUqnaYzfuu2nINt7xxGZfYXsFp8/O4fxX1OoUTjZ1MVZW9vxq1Kj3iJD8Une5c6IB8VceRpo5QoPLc9ioCv/sQ99rMpiabAzMI9lj43AWL2XH0KbxJdv5rZm7EDfmsfC+/d53EBb7XOMW/mf/eUwRO83u/UPgR9jYu5MSLrgutMzPfy9uHGiOOKS81iZXTcvgxa7hD/4o5xn4CWnG//VOc84HrOPT006FApVJnUZDmpjjDzT+1eeFxtprNDmz+DjCgjZ5ARSnFXVecAZwR2t/yshxe/8ds1tre5FU9jxNXrqH+31+j1Kjmh29vIfvA38GA/xdYzNkLBi/rYxWgBF26ciF37bob7Urn85efMuCybqeNdm3+jedbzWx26yKuDAtUbIbigfVXoPUH6fIHeOjeUzjB2M07gTLmFfc9D5wzN5/7X/ocSY17uOjMVf3u+4PLprC37Me8c6iB26wnmcMNUsAMrD6X80n2Vv+RtOUfoqu9hZZ3k6jUWUw3DjNdHabzwCZcTh+bAjM4c8XJoXVn5Uee671J9qjnuvRkZ0wPAEbDlcumcu/me7B3NTNp7lk0vfsNUlU7fq1wTz8t6jo6awYcNgOVB9/ey92qim5tI71kZAOV0ikzqN6VTq5q4HZ7T8aRbNXIvuZOGtu7SXbacLaYN9mHdTanpEdeD7foaaGf97RGnseWTc2i4pLv8Nc/fZQP2F5nQcXvOdJ0Cc6OOjDgsM6iSNWSpZqobTEDlQO1bcy3AhVHdt8AOyPZyRbd8/fXzvGrUXFmTw0N3PpsvXnt764xO9G/rwuZOWM2h/eY3/EcK1NZlbOEtOSRqRUbSc2la/j9e2/y18DyqJ9neZx8z/EJilu3cuMVl/X5PDXsGhbAoIpMzh6gBnPWtGnsfTePKUYVB3UOJZn9Z627/rQ51J34M1Jc9oh+x7NL8tj1WjGzrUD+cX/PeewImaEb+R26hMrWAF/lKgCe+uRKPmc9lJmWm8JuXcQkjpIRCJARCDBJe1hTGtl3TWvNd750K7fpF+nUdhzKR+Pia3lp2g1U/PlWipz7+LX7HLrTvDR3VpOd56aypZq2tgO02oZ2vtQKGrpqaaithdpeHxbBUwA1H+OPv03Da88iNS+P2YEG3uz0o977X3KScmjtbAUZmX58VVVVUVTUNzvLvffey3333TekbZw6I5cVX3wQpaJfcLNTXNToNPJVPS3O3Ih/jHA5XhcvWFWgzwZORKNCnfS3e07kysyep3i3XHUR33/jBFZOyxn0wjnUm9YzTjqBV3fMYYXt3QGbqI2GSZlmyttpVKAa9gHQ2N5NgRWo4U4f0f0VpLl5w8oo5WytYFdVMzOtk1ONd1aoicK9V57OnpqlXJLl4Vt/3UzXJhsGmm8m3cqKyfm8uDOybfQDvg9RpTN4q/haMg/9N/ONfbjq3kNrTVO7jwxr0MNWW3qow+bMfC/33PMgAa1DTV7OW1jIgy99hs92fI9ZxkF+5Pw2YCZj2Drz03i3/x6AKVEClU5Xdsw3fgFvgRWo1HOkqYPZBeZJd9OuCu6wvYlPG/zMfy4/9fWMT5LrdbGktG+VPZj/B/aZawhs/R7LjG08FTBvCuu0lzsvPZlZ+edELD8jSjv53NQkcrwurrjlQTa+cjW+7c/Q5srhxg9dj8tu47VADsut0d0rdSaz05PISXFxRJkBfWZ3FS2dPuyB9p5AZYDgYU5BKv+T/WHaalzUzb2By+YX88rL8zjf9hq2vc9zbjBhRsoqLuunKeh4mpnv5et3DZLC2BJeo1JqmDdxVa7J5iBnvSilcNltTFn7STY+20FV0Vl8taTv3z3JYeOhW69Haz1o+ZuS7Rnxp7h2m8HXPnk1VU2XUpjupr61i8fS/x9PvV3B31rWMVkdYZEyU5puDUzm/OL00LqzCiL/nsunZo1qv5PhyEpx8c3bPwHAK7tr2ba1lGVqO+V6KidGqdEHSCmaSeCQolRVkd2yHcOl2REoZEbRyNVSA1ywqIif/ONiPsejoeZbYGaq+tZ/DvOf33+FI7ZCpvuqwA6VZJHf68HdzReeQfkzk8lQLdy09qQ++7hkcSn3vvJBPlD7OguNPbyxt46UQCMYhGpTM1VzqM/RkaNHyVZNdGgH6Xl9HzCYTb/CghNX/w/+RlteTjb12qzVra+uAKZjNJqdog/qXC5bXMRru2dzme3lUNOdzvRpA2xx/CydXsQd73ys38+n5aaw8aO30tLpi3qP4rAZeJw2Wq1BCfNTk1hQFP1BKcCJkzN4IzCTKUYVh3QuMwep9QxPHBA0vziNVwJTmW0cYFughHf15LBPFdv1JJarbfyrvqcWpjAtiflhD27KclP4jS5iNW+F5tl8fTuoHKpvZ7pvB9jMccmWqp1kaHAGijito5nZXW1s8Z7NvddeHVpny4F6mn5yESfb3+YL3s+wLukZGlr2U2OzmdNJN1LZcpTde9/Ab2+lxmYbUoLJxs5GGjsbwQPvkQLe7XzrPbM56f6W/QAyMv14ysvLY9OmTX3mD6U2JdxA2RGKM9zs0bnMYx8daX1HXQ9KczvYln8R/6iq5uXi9Xhq3uai7n8T0ArHtNN7bTOZDefMir6hOK2Yls0DztWs8L/Lfp0X6uMwForS3ZRbT8KT2swarsb2bmZhZrYxkqPfEMcrL80VyrCS3FHFtspmVlkZg8idHVpOKRVqWnfJ8ll8futdKJuT+z9yJb9542CfQOWmi89h5YxrWJfu5uH7/w68QGn3HiobO+j0Bciw+v90uSK/T++OhLneJB7acCuPvXw29S/+mKsDf6XUqOYH+jI+dsEKHt3zLATMPj29tTpi78djSyuCo5Cn6tjR2JN2ur3KvLHbr/N4yHdNxDoZUU704ZbPm8l/yqexxNjF+YaZDaVOp7IkSmA9M7/v/1vwBmZ6nhncQM8TcJ8/wGF6OnhW6CxWp7kxDEVrciF0mk8l9xxtwY2V8lElDXjzaRiKr33yGvbVXswl1t/8j44TOD/wGh+zPU2JcZQanUr+4g8M+L0TQbLTThuRzWr82bP7Wdr04VNn0r7sh4Om2xyJ2pF42QwVakqU4XFyy9nzyc/K4MCTuUw2qkJjBBxyTSc7bJyM6b0Cz8WTRvZ8M9KCv+PZBV7+FJjCMmM7LwXmc/3UzKjLl+Zlc0hnM8k4ylqrdnS7nsTCKP93w5HpcVK45la2PftPZhsH6dI27ATIpJn3t23hx65f0qiTeV4tAqAlqaBPTf9Vy0r5UfMvaevq5ublfWtAALxTluCrMZipDvLz9w5wovUAaJcu4jTeIZMmDlmBSkeVWSNxQOcyKatvbYnLbqMurEal3Ri/Zn3FGckc0tlkqBa6a/fi859KSushsJvHf8PMXL698E62v/NxZlmJLZx5I3sfMFJOmTZwEFyU4cZmqAEfpKa6HaFAZc2cvAHvsyZlJvMDzyo+2PkiW2wLOCsz9r9jUbqbLSmruLz9BX7ou4Ded/nf8V3KYduLPN59Ymhe7/GGUpMcVM78MI/t6MJNF1fYX8DlayYQ0BHH/5+DDSy2gs1NgZksNXaCr4sjjR2cZA326MiIfIhenO7mHzoblwZ3bR1z7Htwqy60MlA6ACfeztajPipfupg1tp3cl3w76z9xI9Vt1RxpO0JVaxXVbdVUtVmv1vshjHsjI9OPJ8MwKCyMvbNYLGYXpPL0/Dv5z55XOOvcywdc9sGb1/FuxVq+VJDKI38r5MiWxygPTOXUhaN/MrIZiiUX3Mx9f+qke/IKvhylecdoyU5x9jwJ91XT3NFNU3s3qVYKTodnZG8cXHYbzc7cUIaVN3fs52pVQ6e2k1k8M+o6ZTkpEU+sZxf0vchPyfaE2q93ZMyGephjHGD7kSbS3I5QUzZ/UvQbinBup42bV8+hdcU3+NPmW/n13h2cdcoyCtLc+Fzp0E4o6UKV7skCV+GP/YmgO9M8IebSQHVj2MBR9Wb/hQM6l1PKsiLa8XucAzdtWj41i0f1XJawi5ONdwGoxRs1wIkWqOSl9j/gmt1m0OIuCmV1brDnhmqjAt5i6IRiVcPu6p5AxWcfPPC224yIxBl66hmw+78pMcyA9Lf6LK46OXomrUTisht0EPl38BQP3gyod0CdCKZbGYgmU8VMw2xy5M+bH7GM22ljem4Ku6rNgZnOmBVblqPxkpXiom7RLXzj7VRcK27ut19SeHOUYLC2W5VyYdbIZ7i6+uQy7nz1Fh5s/jwvBhawyHifbNXEYsMc7yFNtbHKMJMp+FL6tmawGYpPnL2gz/xwc0sL2PlGCXOM/VTtfIMsK6XqTm12HM5STdRagQoNPeew0qzoN68NYYFKkx67B3S9FWe42adzmM8+bI0HqGzsoNi6R6x3FeJ22vjcpSt5MPAIa7beQbt2sWTOjEG2Oj6KM5IpzUpmf21b1M8zh9CEMjvFRaX14OzsuQM/1FdK8aHr1vPlV0/hrEUz4mp6q5TiphvW8523zmVOkosnn9ke+mxSZjLlrQt5tTPyPFkQJYvrvdeeTXXzaTz2jQ0AeGmjpcsXUWO96/33uVDV0KzdbAtY4x75uzja0ESmaqFL20jJjGzqlp3i4ogyH0SeZryNW3VRqTNJtfnx+Buhu52Ddd1MtsYoUmlTKUwppDCl/3vcgA5Q11FnBi+tZhBT1VZFVWsVla2VHLAdoIMOGZn+eHD7FWcCZw66nNNucIL1NO/mtSfys+RnyE9L5ooY0wPG64JFxZy/8Gtj/lRUKUV72JPwww3tNLZ14sXsCO0chaxJ3Z4CaIYCVceR998CZXZMnJ4/tH3NKegbEIQ3Z3HkTod6q2NkQwf+ABQEM6p5hl7r4XHZufaUMjil5+liwBpPIGhLYDrnWk9KD3bH/pQ0JyOVWu0lSzXTUlcJzEJrjbv5ABhmjcqXLprHJT94heYOHwuGkKff47JTnbEYmv4cSt/dbk8PjSYdsf8UF2luB43tPXnu++vLFeRLnRRqd9uZ3NM3zJY5CWrMzDn/V93CFCtQ8dtivwGZP3cuu3cWMs2ooFvbqJ117ZiPWD0aDEPRbfT8PvYHcplSmDvAGomrLCeFV3QRa6xBRLu0jbTSvjfC9104l68/u4M1c/KYlT9+zX9idcflp9J9yYqo/1dBU3M8vKkLWc1b5Kt6AlrRVHDKqDRvsxmK//ro9Xzt6alsb7RTfOTTZKsmTlA9I3VnKjMgtGVO6m8zA1pYks4LganMMfYzo3snWTYzUNkXyMenDdJUGw3NreY5rOUgGHBA57E0M3pgVkd4oDLweWc0FWe4eUmb/4futgr217YxyWqa6fOavyvDUNx9+Sr+teAvaA3nzJq4/7enlGX1G6gMxfpVU7nnz1tZOT2bFWWDXzPnFaUx7/IVce8PzBr8/zpnDtXNHTwUFqjkpyWR6raz9XBTxPKF6X3Li1KKvNQkuh2p4IdU2mjuiAxUOveb1+vywBQ6gw+N/F101pktO6rJID8tMrA2DEWbuwC6zEAF4N+BOaw2duLxA91tHKrr5lQrUHFlTx70+xrKINudTbY7m7lZfR9WvZP+Dlusc2es4urYIhKLx2XnU2tmc8VJpYMvPILGq+mGTjWfhhWpGg7Xt9PZUm917HfjTR6Fi0eq+TSvQNVS5DPbAe/QxUwf4rgCU7I9Ef2N3A5bRC1AcmY+Aa3IoomjjS3UtXaSaT35M2IIVKJKjqyR+U9gGgGt6NY2pk6KvbzkepOosprC+RrME2VNSxcFAXOwySO2AspyPPzqpmV86szpfO+qE4Z2mGWn4Nc95anbFb0mSSlFjjcyABjsiZgj7CSs03pSMHpzSvBpg1zVwN4jdbitEasDQ6hR6W3ltJxQuuS/BpZx2WknDrJG4vDbev6nduoSZo7QeBoTjcdl56i7p+/GTl3C7OK+zVJWTMvmz7es4JYzJmab/4EMFKSA2RzliMtsdtyhHXzOtoEbLr9k1I6nIM3N5685h0+ddwJHtVkzf4Kxu89ySdnxBSoFaUm87zRrvhcYe0I11TWkhfqbdDZVc7S5kyLrHFZlL+i303mDlfWrU9vpCIxfx3RvkoNah1lzUKir2Ly/nhIrEYCR1VOGDUOxelYeZ87OG9emloNZ3itT52WLzfO0UrBu6eAJSS5YWMhbX1zDI1cvHvbgg7HK6fVASmvNlCjZ1Yr6GRcPIOAyz6mpqo2msIdwXb4A6fVmreLbuozOYN2Dvwt/k1lej+p0cqK0KvB7zfuW4ACgL/vno4PXtu4OamqOkKI6aNJusnOGn2J4OKRGRRxz7JmlcNQMVN5paMfX1gBAE56IDCAjxZlZAofNTFczrMGR9lDC+UNs22q3GczK9/KOla2qNCs54qKRk5ZCLankqEba6o5Q53SFLqiutOE9BbOnRN7w79d5PKivx284uPP82FNE56clcURnMIf90GR20D9Q10qp1QemI2USSikWFKezIKwT8mAWlpXw3uZS5qt9APjd/bdbzogxc01a7iQ6tztwqW7smT0XvYKMFI6QSTE1NFfvw23lqNWO2AOVHK+L6kW38P23nDTMu5GLB+jMmWg6VFJoXIMdupiVwxiLYaLzZ80A8/rP1sBkTj2G/o5DNeW0a/n+Pyo5lHcGt193RWhU99GUneLiLczf9UzrHBt0VKeRl5ke13aVUvjyT4AKWKjeDz0AUp4c6rpSyVFN7Hv7RT59qIFPWOewTm//D3CCNSqtJOFNGt/bq66UYmiGYnWUv+2u4Fbq8WmDlLyxfWA5ElaEpf4tznBz/0VzmVuYypQcz5AfCI5XINZ7v80dPk4u61sjN1DSoYAzDdrMpl/hgcr2I03M02bg/lagLJQCGV9naODGFp0USuoTzp5RGpHB65XAPLqN58w33W10HTX7vRzWORTH0U9nJEmgIo453pxiurbbyFZNHKmtJ9Bmdihr0p6Y0+0ORXpmLm3aRapqZ6U1oFZz6vQB0zj3Njs/NRSo9M5ilOt1UaUzyFGN+BsraPakkaS66dAOUlKG17TEmRJZI1NYUMiGj30RjSbZGfvpIT81ifesjHPONvOObn9tGydYF3mV1X8SiIEsnZzBk4FZzLfGnunqp0YFzMxfb+6rH/K2L1hUzFdf+DCpgSbWntQTnBWnuzmssylWNeiGAyTbzBoVHeeI03ddfiptF54c1+91IjvaYSPY4mBnoCQh+58MVUrRnFCgstdRxrp+xrI6ll27ag4dJ/90TNNmZ6c4qbFqVAzrCXBwkL/DOpviAZ5GD7rtqQtpP+xksmGeo7q1jfy8POoOmOfWHzu/RWNTMt2G+X9rDHAO+9RlZ/Hck4vYridx93kDJ5UYbSq9NDSWSsX+nRguzcFADiVZidMcMSg7xcW31i3kL29VcNOpU0lx2blxZXzXkvGw7sQSfrfJTFpw44opOOx9g6ZoTb+CVJL5N0tVrVR19HRYf+tAHRcZZpKHtwNllFhNtfB3o/xmn5xOHKTb+/6vpmQX499lDqy7I1BMNRl0KavmpbsdZY0fdlDnjOt4TyBNv8QxqCjDQ6U2n7i31+xHt5s3rY2jFKgsK8viucAigFAGFSPGDCpLJvd08p/fK/lAXmpSaMRd3VxFV5M55kctqWSmDG8shhRvKh2653eikrNwO21x30xnpzipxgwiUrqP0unzc6CmKTROSXJe9LSng8lKcfFGoOd3urO5//4dt66eHkoffP+Fg3fsLs3y8NnPP8z1dz7CktKeAKgow80hK4NckarBrawsZs74T9rHWpACRGT9qkmOnl3pWFFakMP+gFmL2ZE9b0I3lxlNYz22T4rLTr2KTITycsB8qHBYZ/XJmBSLJVNyeTPQk/jkHT2VNXPy2a57alfTVBvZqomAVnhy+z+HXbpkEp4bnmDNrT8Y8pP+0ZKUMxkwk4EEa7QP6FwmjfPT8XhdckIxP7/hpIjalURxx9qZnL+ggKuXTeLiE4pibvplJKcD4KWdpo6eGpXDu98lTbVRpdM5QmZPjYq/06xVwQxUXI6+t/opHjdV1mj1LwfMpCDdhhks6e423K1mzeUhnUPJOAcqx95VUxz3ijLMJ+GlVBOoP4it2+xw2UQyJQOMsh2vxZMyeHLGBk7b/WG8qp127SSzKLaMTpecUMSeo620d/n40PLIqvm81CTe0+kAONuP0N1ithet114yPcPrkG3m/vdSQB0Adu/wki3YbQatrlzwm2OpVDd10ly1H4fyU6kzKcqOf7yFgykLoMt8kpqV0/+AXflpSfzr9tM50tjBgiFmnPO47PT+VRakuakID1Sspl/GMAKVY1Ew61e3tqGzEq9fRizOmZvPfc+up7h9FyevOne8D+e4oZSiKykrlJ2vTbv4lf9MVtnKeVfN5IwBBuUbzPKpmXx3yVd5YdcrBLyFTJpzEtedPJlfdN/LF7ZfxKv7W/ib8y6cyk8lmRRlpw94nMumxn+OG0l52dmhxCZLjJ2AOYbK6gQNVBJZVoqL71+9OPR+Sq8seW6HbcCHqEayeR1LVa00h9Wo6MObAbM2BRRdoUClGyNUo+LEFWXMvbLcFPYGCii01fFiwEwK0qXMc3lzcxN5gWowoNqeP+4DgUqgIo45ReluXguOKt5yiGAO80btYd4o/cPdesnpfO2hdTzgeJRtehIzC2JLg+ywGdx5bvRaGLOWwtyep6uGmhazdqJOe8n0DO/7ZCQ7aNApFCgzUEnyDv9plc+TD02QRx1VTR34a3vGH+gvredQfPW6s/jyDz8ESnHLmoH7z+SlJg2a7WswTrtBk6sA/OZTyWQr65dyjnwq1kTWZTefHL+vC1k+vWCQpRNbmtvB1+7cQGunb9xHlj/e+JJzwGwdywGdS9HJ67i3dikrliwaVk2lUopPX7wSWBkx/4bT58Lpc9lX08r/fPslbrY/xb5APpOGcQ4bSyWZbg7qHLJUM6dYad0PqzxyvYmfbTDRpSU7yPI4Q6mvC9OTBqyddVrjv3lpp6nNXKexrZuC1vfAbvZPAXoCFV8nht+qUdGO0KDQ4VZNz+Huotv408FNvGAFKp1W06+6hsZQ8oVOT3GfdceaBCojJBAIUFFR0We+1+uNedBHMTz5aUlUYt5wp3YeoYVksEMjHlJHqYNjdoqLM669ky/8PoX2vHk8OIJpoO02g1ZnDgQghwYONFSD3ey4WTLsGhVnKFNNk04mNWX4uf+VtwCazBqVHU0dOJvMTGj7A3mcPIynefOL0yi+65v4ArpPZq/R4vMWQUOw6Zd54jdcEqiEu3ndxXznd1s4lLqIB06Lr2lfInHYDAlSxoHy5IYClYM6h4tPKGRB8ZxR3+/kbA+uM+/iB/9U7Ew7la+V9t8/biIpzkhmp85hEXtYqMyHRW2ekjHPeiWim5ztCQtUBr7uepKTaddO3KqLjnazhchbhxpYFOyfooOBivXg0t+FzW8NVNpPjYrNUDx88xX8+vXlPP6ncgA6rWa8jU2NoebaKj32jHrNzc00NzdHzOvu7oY4u5tIoDJCqqqqKCrqO+jUvffey3333Tf2B3Qcc9gMmpMKwAeFqoYKq79KC56o2S9GyurZBay+9yujsm1fci60QJ6qJy1gXq3rdOqQBrsaSHqygz1WGk6zhmb4N2COzOJQFrS/VbeS2VUBdjik8obVlhwGH8V+pBkZk6HBzJwTrFGxJ0mgEm7N3AJW3jP4SPNCDIc9LQ+sZ4GHdA5Lx7AJ0w1nzKV26U9JdTsGTeE8URRnuHnOGkslmIJW545+YCeGZkq2h837zf6zhQNk/AJIddtpJhk3XXS1NgDwzr6jrLeyYO5xzIBO6NY96YmNQCcY/fdRCQofGiFYo9Lc0sJ0K1BxWn2dYrFx40buv//+aB/FledYApURkpeXx6ZNm/rMl9qU8eFL6XkS3moNvNXp8CbujZQ3PxSoZFqDPTbgJdU9vH/hjLAalfp+RnuPVWZWHq3ahVe18/auvXzQ6sjZllwyKgPDjSZP7hT8exQF1IYu9g5XYjT9GEvHcqYvMTF0ONJDmb4O6txRSYwykEQboDXZaWdn7lreqdnKtkApb2Wdx40fOGu8D0tY5hel8fhms8P6zPyB7xO9SQ6adDK5qoGANdxC3Z7NuJSP9wMFzJpSTOWOo6GmXzpYoxIMVKI0/Qpy2HquyR3Kai7dWkuy6qRTO0hNi70GccOGDaxfvz5i3tq1aykvL6+KeWNIoDJiDMOgsLBwvA9DWIzMSaFA5YiyBiB0Jl5axiBbWiFUQq6qD42h0uHMHHbgZXamtwIVnULWCDRpKcnycFDnMksdpPbQLibbzHOTP2PysLc91kpy0qjQ2ZQYZtrHDu0gyZVYNyxCHAtauxV1eMmmiYM6J3EfOo2h+9ev46VdZ7I038u6nGN3fKNEtG5pCXuOtuDXetBBK1OTzBoVAN3RgNYaR9VbALyly1g+NYvndxztafrl6yRJmU2/fMo54APC8BrC4Mj2LmtogQY8pCfHfr2L1uXB4XAABGLeGBKoiGOUJ2cKvvcNCqgllwYAAq7EHZytZ3T6ZnJVAwD+pNg67EfjsttoM8wArh4v00YiUMlI5pDOZhYHKdDVTLJqVFw5iZe6tjTLw36dSwlmoNKGi2SpPRBizJ06I5sD7+aSrZroSI8tq+Lxypvk4Lz5x3aCi0SV5LBx/0VDG1Q51W3WqADojiYO1rUzw7cTbLDDNoOVBeY1PDzrl8tKkec3Bg407GFBTIc2r/+eTjNQqdfemAdQHg2J0dhSiBhNzc9gv87DpjTzDXOEVZLSx/WYhiM3zUMNaRhKs8DqQOd3j0w++aaC5VTrdN52LaVggEGnhqo4w81Bq230ImM3HtVJk04mJzfxLpilWckc0D3NattxhcZoEUKMnQsXFvF02X3c672fu649f7wPR4gxk5pkp8mqUbF1NrGnpoUyZXbY6sqaHXp4FhxHRfk7SbLS6Q8WqITXqLRbNSqp3eaDuUY8456aGKRGRRyjpuWmsFsXUUYlKdZAfSqRAxVvElU6nVzVQI5qol6nkD1j+Yhs+9MfuoJnt57KjdNyRqSjaHqyg2qbeXO/wtgKwH6dS2lW4nVCz/W6OGT0BFjt2iX9MYQYBzZD8cUPXzDehyHEmEtNctBs1ajYupuobOxglqo132eUhAZgDWDgx8BGgGTrvidgG7iVhD2sj0q7lfUr02cGKvXaS+kEyHAoNSrimFSWYwYq4Wye4TeVGi95qS6qdc/x/zFwGpcvH5nmD9kpLq5ZPpnJ2SMTSCil6LByr89VVmpinTesMVTGi1KKjpSe9IzS9EsIIcRY8iY5aMK8Pju6W6iqayaXBgJa4cmaFApUoCdFsZc2ALRt4FYS4Q8n2wJmUJJMOwAN2kP6BKhRkUBFHJPcTht1yZMj5tlTEjlQMWtUgg5MXkd+2vCbaY0aq+O8YWXKOqDzKEnQEZF1xpTQz9L0SwghxFhKchi0KvP6maxbqa8+gKE0R0kjLzOFpLD0w8HmX15lBhsB22BNv3pqVNqIrD1pwEuG1KgIMXr8WT0jvXdqBymexM16kuVxUqXN7GUv+uezZtUp43xEA0vKmRLxvsFVFPHUJ5Ek5fYkAZCmX0IIIcaSUoouu5lFK5VWmqv2AVCpsyhMc0c8PAt2qE8N1qjYB+tM37dGJahFpUyI67YEKuKYlVw4m4A2nxY04hnzvPsjyW4zqJ1+GX/1n8STebewomxkOtKPlpzsHBp0T1Oy7rTJ43cww1SYl8NRbWaMM5t+Sdc+IYQQY8fvtAIV1UZ3vTn+SoXOoiA9KbLplzXoY4rVfEvbB255Ed5HpVVHBipdzvRhH/dIkCvuCAkEAlRUVPSZHy2ftBgbkwuyOayzKVFHadQeUpMSN1ABuP+689h+ZCVfyUnBmOADJ5ZkJnNQ55CuWgGwZ00d5yOKX2lmMvt1HjmqUZp+CSGEGHN+Zyp0mn1PCqyO9JU6ixVp7ohApVPbQYFXmTUqDBKoOCP6qETeI/ld6XEda3NzM83NzRHzuru7Ic7KEalRGSFVVVUUFRX1mTZu3Djeh3bcmp6bwi6rQ30TyQldowJm1pu5hWkToip2MCWZPSmKO7Wd9PxJg6wxcZVmmYEKQLt2StMvIYQQYyvJHCslVbVRoOoAqDWySE2yYzNUKOAI9lEJ1qgYjsFqVHrCgN41KgF3fP16N27c2OdeuLy8HCBvsHWjHmNcRyH6yMvLY9OmTX3mS23K+CnLTeE3uojVvEWj9pDmkhvMsVKSkcwLOgeAQzqHkuzUcT6i+BWlu/lDIB9s0EwyHilHQgghxpBhDa/gpY1Cq0al01OIUmbrCpfDoMsfCGX9sitzEHg1WKAS1jqjtVcfFeXOjOtYN2zYwPr16yPmrV27lvLy8qp4tjfugYpSqgS4GzgLKAZqgJeBb2qt3xztbSmlHMCNwEeB6YAf2A48CvxUax0Yyr4Nw6CwsDCWwxWjLDXJwfaA+ST/qE5n8gTIXnG88Ljs1DkKQFupiRM04xeYT5zsS67jZ1taqJ99jfRREUIIMaaMFPPBX4Gqw481boq3557T7bDR3OEL1agEqcGaftl7alRa/JGtTuzerLiONVqXB4fDATCk++nexvWKq5SaC7wAZAE+4DCQDVwJXKaU+ojW+pejtS2llB34IxAcRaoa8xd5sjVdoJS6RGvtG9YXFeNm5UUf5eFn2mHueazLSdysX4lITz+Hzdtf4B9J53BfQWLXLN526Soazz054ZsPCiGESDxzZpSx690iphuHma0OAODIKAl9HmwS3tXrtt5wDr1Gpdkfua4zzkBlpI1bHxWllA14AjOw+BtQrLWeDGQCdwIO4MdKqdmjuK27MYOUo8BqrXWe1roAWA5UAOcD9wzvm4rxdNmyMu6499t87qpzxvtQjjufveIsWq99httv+ywue+I3l5IgRQghxHg4fUYuzwUWh977tIEnu2dQ62CSl2DWryDD6R5wuxF9VMI607dpF94JMqTDeHamvwaYAVQC67TWVQBa6y6t9cPA00AScMdobEsplQp8xnp7s9b6+eBnWuvXgU9Zb/9LKZXYj4OPc8E2nGJsOe0Gq2bkkOmRJndCCCFEvDI8Tg7nnhZ6X0UGBek9gURw0MdgH5Ug+yCBSviAj90BTZcyx12pJ2VCDPYI4xuoXGa9Pqa1bonyebCZ1kVKqcGOM55tnQmkYzYR+3OUdf4CNAFea1khhBBCCCHGXPH8VdRrMziptMZQCQo2/erdR8XuGGxk+p7ba59f02kFKo06hbTkidGKYFwCFStYONV6+3w/i/3bes3A7OQ+0tsKhqYvRuswr7XuBoId8Jf1t38hhBBCCCFG0+q5RTwfWARApc6kIK1voBLeR6VDO3A5Bu6KHt5HxRfQdGDVqGipUcnHDBoAdvazzGGg0/p5yihsa84g6wC8P4T9CyGEEEIIMWrKcjy8lnEBrdrFm46llIRl03RHqVHpxIHLPvBtvlIqIlhps8ZSacBD+gSpURmvrF/hyZmroy2gtdZKqQbMAWLSRmFbmQOtY6m3XgfaPwDbtm1jyZIlgy0Wsnnz5iEvK4QQQgghjl9KKW69/jr+Z8tqrpidF5GkJthHpTOsM30HTlyOwesjHDYDX8APWB3qFTRo76CBSiz3vNu2bRvysr2NV6ASfuPfMcByXdbrQL+teLcVXG+4+xdCCCGEEGJUlWQmc+tZM/rMdzuDTb96blc7tYOkIWTctNsUdJs/t2qnGajgId09MZp+jVeg0h32czIQrQM8mJm6ANpGYVvB9QYaiW4o+wdg9uzZUksihBBCCCHGVLB2JbLp19BrVILatdlHpdVIjRgMMppY7nmXLFnCli1bhrx8uPHqo1If9nN6tAWUmVM2WOtxZBS2FVwv6jqWYN+XgfYvhBBCCCHEuIjWmd7sozKEGpWwPiqdmLUo3c70kT3AYRivQGUvPTUa/WX0KgKC9U4DdXiPd1vB134zitHTiX7HAMsIIYQQQggxLuLtTA+RNSqvB2bRopPY7x50rPUxMy6BitbaR0/q31X9LLbCen1Pa10zCtsKpiyOuo5SygGcZL19sb/9CyGEEEIIMV5CAz7qnj4qHdo5xEClp0blp/4PsLDzJ1QlTZxkt+M54OPvrNcblVIRI9JYTbVusd7+ZpS29SRmyuLJSqm1Ubb5YczBHndrrd+M8rkQQgghhBDjqqczfWSNSrBJ2EDstshQwI+NpCEEOGNlPI/kZ8ABYBLwa6VUBoBSKhX4EeYgjhXAd4MrKKUeUkptV0r9c7jb0lpXA49Yb3+ulFpmraOUUpcB37Y+u3OkvrAQQgghhBAjKZjdq08flRg704e2N4QAZ6yMV9YvtNatSql1wDPApcAFSqnDQAHgAhqBK7XWTWGrFQAz6cnGNZxtAdwNLMEcpf41pVSltXxwjJVvaq3/OFLfWQghhBBCiJGU5Oyvj8rgAUd406+goTQZGyvjeiRa69cwA4WfAVWYgUWV9X6p1vql0dyW1roTOAv4LPA2kGp99C/gMq31hri+mBBCCCGEEGMg2FQrvI9K5xD7qIRn/QptT2pUemit9wA3DXHZ64HrR2JbYev4gI3WJIQQQgghRMJwR6lR6Rhi1q/efVSgp3P+RDDugcqxIhAIUFFR0We+1+vF6/WOwxEJIYQQQohjXbAGpLPXgI9DqRlxjnAflebmZpqbmyPmdXd3Q5ytuCZOyJTgqqqqKCoq6jNt3CgVNUIIIYQQYnQMZxwV+wj3Udm4cWOfe+Hy8nKAvHi2JzUqIyQvL49Nmzb1mS+1KUIIIYQQYrSExlGhp49KN46ozbp6sxsjW6OyYcMG1q9fHzFv7dq1lJeXV8WzPQlURohhGBQWFo73YQghhBBCiOOIN8kMULp1z22933D1t3gEp31kO9NH6/LgcDgAAvFsT5p+CSGEEEIIkaByUlw47UbEOCo+29AClWg1KpKeWAghhBBCCDFshqEozUyOCFT0UAOVaH1UJlB6YglUhBBCCCGESGClWckRfVQCQwxUHNH6qEiNihBCCCGEEGIkTMr00E1PTchQa1QcI9xHZaRJoCKEEEIIIUQCK81KjhiZXtvj76MigYoQQgghhBBiRPRu+qVtSUNazxGlj8pEGpl+4hyJEEIIIYQQImalWR66wpp+KcfQApVoY6247BOnRkXGURkhgUCAioqKPvOj5ZMWQgghhBBipBSluyNqVLqUc0jrOaIEKsOpUWlubqa5uTliXnd3N8RZOSI1KiOkqqqKoqKiPtPGjRvH+9CEEEIIIcQxzGk36A6rf6hqHdp6DmNkO9Nv3Lixz71weXk5QF4825MalRGSl5fHpk2b+syX2hQhhBBCCDHa/Njwa4VNaQ636CGtE63pV9Iwmn5t2LCB9evXR8xbu3Yt5eXlVfFsTwKVEWIYBoWFheN9GEIIIYQQ4jjVjR0b3dR09K0piSZaZ3rXMJp+Revy4HA4AALxbE+afgkhhBBCCJHgzl9QQBMe/Foxf+rQHp5H7aMinemFEEIIIYQQI+VLF83jq9+/jaTuJj530dIhrWMf4RqVkSaBihBCCCGEEAku0+Pk4TtuI6DBFqWTfDSOKAM+uuwSqAghhBBCCCFGkFKKKJUk/XLYIxd22Q2UimEDo2zihExCCCGEEEKIMWPvVaMynNTEo0ECFSGEEEIIIY5DvbN+DWewx9EgTb9GiIxML4QQQgghEknvrF/DrVGRkeknKBmZXgghhBBCJJLeAz4ONzWxjEw/QcnI9EIIIYQQIpE4emUHG25qYhmZfoKSkemFEEIIIUQiGekaFRmZXgghhBBCCDFsvTvTT6TBHkECFSGEEEIIIY5LI92ZfqRJoCKEEEIIIcRxyN67RmUCjUoPEqgIIYQQQghxXJIaFSGEEEIIIcSE4+gzMv3ECg0m1tEIIYQQQgghxkTvpl/Dzfo10iRQEUIIIYQQ4jjUp4/KBKtRkXFURkggEKCioqLP/Gj5pIUQQgghhBhvzhEeR6W5uZnm5uaIed3d3RBn5cjECpsSWFVVFUVFRX2mjRs3jvehCSGEEEII0UefAR+H2Zl+48aNfe6Fy8vLAfLiOr5hHY0IycvLY9OmTX3mS22KEEIIIYSYiOxGrz4qw2z6tWHDBtavXx8xb+3atZSXl1fFs71xDVSUUiXA3cBZQDFQA7wMfFNr/WYc21sIfBo4A8gHuoDdwBPAd7TWLb2WTwdeG2Sz/09r/cnB9m0YBoWFhbEeshBCCCGEEOOid3pi1zCbfkXr8uBwOAAC8Wxv3AIVpdRc4AUgC/ABh4Fs4ErgMqXUR7TWv4xhe+uAXwBOa3sV1vYWW9M6pdTpWuu6sNWmATMH2fT2oR6DEEIIIYQQicLWq0bFKQM+glLKhlnLkQX8DSjWWk8GMoE7AQfwY6XU7CFurxj4H8wg5WdAvta6FEgBPgg0AvOB7/ZadZr1+kWttepnungYX1UIIYQQQoiE0DtwGW/jFTZdA8wAKoF1WusqAK11l9b6YeBpIAm4Y4jb+zCQDGwB1muta63taa3142HbuUopFd4+Kxio7BrOlxFCCCGEECLRSaBiusx6fax3vxFLsMnXRUqpoRzjKuv1D1rraG3gnrJeDeCksPll1uvuIexDCCGEEEKIY5ahjvNAxQo8TrXePt/PYv+2XjOA6UPYbIH1eqCfz/1hPzvDfg7WqEigIoQQQgghjmu9s4CNt/GoUcnHDEAAdvazzGGg0/p5yhC2uRzwAr/r5/NLw35+L+znaZiZxs5TSv1bKdWklGpXSpUrpe5TSqUNYd9CCCGEEEIkvInW9Gs8sn5lhv1cHW0BrbVWSjVgDg4zaLCgtW7r7zOl1ArgYevtv7XWW635HsygCeBXmKmMK4FcYJ41XaeUOltrPWiNy7Zt21iyZMlgi4Vs3rx5yMsKIYQQQggx2nJTXUNaLpZ73m3btsV7OONSoxIeeHQMsFyX9eqIZydKKZdS6gHM5mWpmEHRdWGLBPunBIAvAjlW5rEU4CLgEGZtzlNKqbiOQQghhBBCiInsK5fMI8lhcNGiQuYWTqzGREprPbY7VOok4HXrrbefzvQopaqBHOAyrfUTMe7jYmAjMNWa9W/gGq31vrBl8oHTgcNa65eibGMW8A5moHSV1vq3A+xv8+LFixdLLYkQQgghhEg0Xb7AqI2hsmTJErZs2bJFaz30ahjLsI9IKbVPKaWHOgHnhK2e3s82FT01L0diOJZJSqm/A3/CDFJqgI8Dp4YHKQBa6yNa699GC1Ksz7cDwc+WDfUYhBBCCCGESCQTbaDHoJHoo9JJT8f3odgOdGPWVEzHbGLVWxE92bn663AfQSl1MmYa4ixr+98Bvqy1bozh2HqrsV6ThrENIYQQQgghRIyGHahorWfGuo5S6jbgFMzxT6KlKF5hvb6nta6J8nnv7RUBf8XMJrYDuDzYaX6Adb6C2Vn/x1rrN/pZbJb1unewYxBCCCGEEEKMnPGq5wmmEb5RKRWRXsBq9nWL9fY3Q9zenZhByiFg9WBBimU+8JGwfUVQSp0BLLDePj3E4xBCCCGEEEKMgPEKVH6GOTjjJODXSqkMAKVUKvAjzAEhK4Dvhq+klHpIKbVdKfXPsHkGPSPd36W1rhjiMfzUer1OKXWvUiolbJurgF9Yb3+rtX6vz9pCCCGEEEKIUTMugYrWuhVYBzRgDsZYpZTai5lC+KNAI3Cl1rqp16oFwEx6UgsDFNIzMv19ViAz0HSSdQx/Ab4RXA+oVUrtUUrVAC8AxcAbwCdG9MsLIYQQQgghBjUeAz4CoLV+TSm1BLgbMxNYAVAF/AN4WGu9a4ibyg/7uazfpXokhx3D7UqpZ4BbMfvFlABNwIvAH4CfaK1jSRQghBBCCCGEGAHjFqgAaK33ADfFsPz1wPW95m0C1DCO4TnguXjXF0IIIYQQQoy8cQ1UjiWBQICKir7dY7xeL16vdxyOSAghhBBCiLHT3NxMc3NzxLzu7m6Is7vJxBzdJQFVVVVRVFTUZ9q4ceN4H1qEJUuWsGRJzAODiuOAlA0xECkfYiBSPsRApHwcPzZu3NjnXri8vBzMIUFiJjUqIyQvL49Nmzb1mS+1KUIIIYQQ4niwYcMG1q9fHzFv7dq1lJeXV8WzPQlURohhGBQWFo73YQghhBBCCDEuonV5cDgcAIF4tidNv4QQQgghhBATjgQqQgghhBBCiAlHAhUhhBBCCCHEhCOByjhrbm7mvvvu65PK7Vgwlt9N9pVYxvp7Hat/MykfibWvsd6flA/Z10TY11g7Vn+Px+q+BqW1lmmYE7B58eLFOh6HDx/WgD58+HBc68dq8eLFOt5jjdVYfjfZ1/Adq2VjrPd3rO7rWC0fUhZHhpQP2ddApHwc3/tavHixBjbrOO6xpUZFCCGEEEIIMeFIoCKEEEIIIYSYcGQclRESCASoqKjoMz9aPmkhhBBCCCGONc3NzX36tnR3d0OclSMSqIyMyVu3bqWoqKjPBwUFBRQUFPS7ovXHY+3atcEBcUbVtm3bAFiyZMmo72ssv5vsa/iO1bIx1vs7Vvd1rJYPKYsjQ8qH7GsgUj6On31VVlZSWVkZ7aOZ8RyL0mZncDEMSqm9QBpwKMrHfgYejdMA8oCqQZZLRGP53WRfiWWsv9ex+jeT8pFY+xrr/Un5kH1NhH2NtWP195go+zIAW695JUCT1ro01gORQEUIIYQQQggx4UhneiGEEEIIIcSEI4GKEEIIIYQQYsKRQEUIIYQQQggx4UigIoQQQgghhJhwJFARQgghhBBCTDgSqExQSqkVSqknlFJHlFJdSqkapdQzSqnz+ll+nlLqMaXUAaVUp1KqWSm1RSn1JaVUxgD7WaaU+rNS6pBSqk0ptUsp9S2lVOEA62QppR5SSm2z1qlUSv1FKXXWSHx3MbBYy0aU9Q2l1L+UUlopddsAy0nZSECxlA+lVLpSavsg0/f72Y+UjwQUz/nDWudJa9kupVSV9Xc7dYB1pHwkoKGWj7BryFCmfVH2I+VDDI3WWqYJNgEfxRx/RQOdwH6g23qvgQd6Lb8W6LA+81nL14UtfwCYHGU/V4ftpxXYB3RZ76uABVHWKQTet5bxW9tusN4HgLvG+/d3LE+xlo1+tnFX2PK39bOMlI0EnOI4d5wY9ll/05+lfBwbUzznD+Bm6++jgTZgT6/rzflSPo6NKZbyAfwC2D7IFLwP2SzlQ6a4y+V4H4BMvf4gUBb2D/sFwG7NdwFfCjthnGbNdwNHrXm/BnLCtnU6UGl99sQA+3kASLLmZwK/t+bvAFy91nvO+uwNYIo1zwA+bG0vAKwe79/jsTjFWjb62caJYduIGqhI2UjMKZ7yAVwZXD7O/Uj5SJApzvJxEmYwooEvAm5rvhd4zJp/CHBK+UjsaSSuL722lwNUAC3AHCkfMsU7jfsByNTrD2KeIDTwaj+f/8P6/IfW+6vpqTWxR1n+g9bnHVgDfFrz/8ea/1SUdZKBI9bnHw6bf6o1rx0ojrLe96zPnx/v3+OxOMVaNqJ87rEuAG3AXvoPVKRsJOAUT/kA7rHmXRnDfqR8JOAUZ/n4P2ved6Ms78J8+q2Bs6R8JPY03OtLr2UV8Ddr+Zt6fSblQ6aYJumjMvEUWa/v9vN5ufXqsV4XWK8vaK19UZbfar06rQmllAFcbM3v0/5ca90GPGG9vSTso8ut1z9rrQ9F2dcvrddVSqnMfo5fxC/WstHbt4AZwGcxq/T7kLKR0OIpH2XW6+6h7EDKR0KLqXwopXKAszCb2Xy598Ja607gOszmQvutdaR8JK7hXl/CfQw4F3hWa/3T4EwpHyIeEqhMPO9br8uUUvbwD5RSNmC19fY96/WPwFWYN6HRLLVe91gXFjCDmwzMC9BL/az37+BxhM07zXp9vp91tmDW3Bhh+xUjJ9ayEf75xZg3FH/XWv9ggH1I2Uhc8ZSPadbrkAIVpHwksljLxyrMJ+Nvaq2ro21Qa/2s1vqnWutd1iwpH4kr7utLr2WzgQcxm2Pd2utjKR8iZhKoTDyPArXAPOBxpdQCpZRbKTUH+B2wEKgBfgSgtX5Ta/1brfUWME8oypSjlLoe+La13fvC9jHHej1kPcGIJnjSyldKua2fZ1uvO6OtYNXoHLDeThnCdxWxeZQYykaQUqoA+Im17g2D7EPKRuJ6lNjLxzRr3nlKqX8rpZqUUu1KqXKl1H1KqbRe+5DykbgeJbbyEbxR3GZdT76vlNqnzKySlUqpXyml5vfah5SPxPUocVxfongIMxj5VlgAGyTlQ8TMPvgiYixprY8qpU7DrMq8yJrCHQTO0VrX9bOJjwL/Hb5J4BNa6/8NmxesGo36lMxSH/ZzqvVExRnDer1vcMQwxVM2lFIKs9NrNnCZ1vrIILuRspGgYi0fSikPkG999ivMJ6CVQC7mzco84Dql1Nla62CNi5SPBBXH+SPPek0H3gYKMDtGVwLFmP0jL1dK3aC1/rW1rJSPBDUC9x4opWYBN2Jm8vpGlEWkfIiYSY3KBGPdWF4LzLVmdWM+Kei23pcA9yqlkvvZRD3mE4ng0woFbFRKXR22TPAfuWOAQ+kK+9lB5D//UNZzDLCMiEOcZeMzwBrg51rrJxiclI0EFUf5CPZPCWBmdMrRWk8GUjBvUg5hPp18SikV/JtJ+UhQcZSP4Phbl2A21bkASLXKSC7wG8wbyJ8rpYJPsaV8JKgRuPcAMzmHAfxIa10T5XMpHyJmEqhMPF8C7sQ8OdwEJGutSzGzYVyPmZd8HfDdaCtrrX+ntZ6mtfYAS4C/Y6YwfkwpNdlaLHjiGeiEkxT2c1vYOkNdr79qXRG/mMqGUmoBZlvhvcCnh7gPKRuJK9ZzRzVm/7bTtdYPaK2bALTWAa31XzAD3G5gFnCZtY6Uj8QVa/kI/1tdrLV+WmszxZL1VP1GzAxNTswUsSDlI5EN697DClavxHzw8b1+9iHlQ8RMApUJxGqK8Rnr7Qat9c+Cmby01j6t9WP0XBCuVwOMOG+tswUzw8ZhzGZ+V1kfBatI0wdYPbjtTmv5RsxmZENdb7AmRiIGcZSNHMxxdRzAdVrr5iHuSspGAorn3KG1PmL1b4vaqVVrvZ2eDq/B/gpSPhJQnNeWFuv9Tq315t7b1Fp3YI57AWb/BZDykZBG6N7jo4ANM9PXvn52JeVDxEwClYllBj2p//7czzLPYP4D24D5SqlvWNO0aAtbmb6CWTRKrddgh7QSpVRS37WAng5pu7QpOEotwPRoK1htSUustzv62a6IT6xlowyzCt8AXlJK6fCJniwq37Lm7bPeS9lITLGWj5lD3G6w+UawLEj5SEzxlI9Ka36/fRIwmwdCz5NuKR+JaVjnDytLWDBRy6MD7EfKh4iZBCoTS/jfQw+wTHC5JuAWYAPmgEj9CT5paLReN2G253QSmQIw3Arr9cWwecGAZ1U/65yIOQhYE/DWAMcjYhdr2XBgnrD7m9qt5aqt98FMK1I2ElOs5aNDKfUVpdRPlVInDbDdWdbrXutVykdiirl8AP+xfi6LvjgAk63XYMYlKR+JKZ7yEe50zMQcHZgDPfZHyoeImQQqE8sOzCcW0NMmvLfVmDehzZj5zF+x5l8cbWGrX8rJ1tuXAbTWrcBfrXkfj7JOFj3NxH4T9tHvrNfLrVzpvQVzpv9Ba+3v5/hFfGItG69rrWf1NwFvWOs8ZM07E6RsJLB4zh3zgY9gPuzoQyl1Bj0Dyj4NUj4SWDzl4ynAB+QopS7vvbBSKgW4wnr7V5DykcDiKR/hLrBe/6W1bqEfUj5EXPQoDnsvU+wTZmphjflk4EbAYc1XmCeDSuvzL1nzL7Tea8zMPZ6wba3EHGVWYz5lMMI+OwGzk5oGPg/YrflTgVet+U/1OjYFvGl99hJQZM1PAu635rcAk8b793gsTrGWjUG29S9r2duifCZlIwGnYZ477gVSwra1CjMdqQZ+I+Uj8ad4zh/AI9a8auAcQFnzczCbCGnr7yrXlgSf4ikfYevutD67awj7kfIhU2xlc7wPQKZefxCzre9fw24gujHbZ7aFzXsy+M9trfNI2GcdmM00msPm7QKmRNnXJzDTTmpr+f2YGTs0sBUoiLJOGWa7ZI35tG0/Zs50jflE5vLx/h0eq1M8ZWOAbf2LfgIVKRuJOcV57vh62GedwB7MfinBea8DGVI+En+Ks3y4MWvTgp/XWmUkeKO5B5gs5SPxp3ivL5hjowQ/Xz3EfUn5kGnI07gfgEz9/GHgg9ZJo9o6YdQBz2Fm3lD9LP8scNRavh7z6cQdgHeA/ZxmXYiqrRPSNswnFGkDrJMLfMu6SHVgZhX7HbB4vH9vx8MUa9noZxv/YoBARcpG4k5xnDtWA38KW74WeAH4JOCS8nFsTXGUDwMzPe3L1k1lG2ZN/VeIEsRK+UjsKY7ycbZ1LQkM9LeV8iFTvFOwGlcIIYQQQgghJgzpTC+EEEIIIYSYcCRQEUIIIYQQQkw4EqgIIYQQQgghJhwJVIQQQgghhBATjgQqQgghhBBCiAlHAhUhhBBCCCHEhCOBihBCCCGEEGLCkUBFCCGEEEIIMeFIoCKEEEIIIYSYcCRQEUIIIYQQQkw4EqgIIYQQQgghJhwJVIQQQgghhBATjgQqQgghhBBCiAlHAhUhhBBCCCHEhCOBihBCCCGEEGLCkUBFCCGEEEIIMeFIoCKEEEIIIYSYcCRQEUIIIYQQQkw4/x/uVTIhVDlYUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 460.8x259.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 236,
       "width": 405
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(figXsize*0.8,figYsize*0.8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(wave,flux)\n",
    "ax.set_xlim(waveLimits1)\n",
    "\n",
    "ax.plot(wave,cont,linewidth=1)\n",
    "ax.plot(wave[fitWaves1],cheb_cont1(wave[fitWaves1]),lw=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cc5155a9-086c-40d8-bd19-89d17bbb35b9"
    }
   },
   "source": [
    "#### Set-up the line fitting process:\n",
    "Our lines are modeled as single Gaussian functions. We need to initialize the H-beta, [O III] emission lines. \n",
    "\n",
    "The initialization requires intial guesses to the amplitudes, wavelengths, and sigmas. For the central wavelengths we adopt the emission line rest wavelengths modified through our initial redshifts through the `scaleFactor`. For the emission lines we'll just adopt the maximum flux over our wavelength range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "2956234b-077f-4f9f-98ed-57162c5cbd4a"
    }
   },
   "outputs": [],
   "source": [
    "amplitudeInitial = flux[fitWaves1].max()*0.5\n",
    "h_beta = models.Gaussian1D(amplitude=amplitudeInitial, mean=Hbeta*scaleFactor, \n",
    "                           stddev=5,name='h_beta')\n",
    "o3a = models.Gaussian1D(amplitude=amplitudeInitial, mean=OIIIa*scaleFactor, \n",
    "                        stddev=3,name='o3a')\n",
    "o3b = models.Gaussian1D(amplitude=amplitudeInitial, mean=OIIIb*scaleFactor, \n",
    "                        stddev=3,name='o3b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b8df347e-2222-4617-a6bf-803007aa8b72"
    }
   },
   "source": [
    "#### Define the combined continuum and line fit:\n",
    "Now let's combine all of the models in this region: H$\\beta$ and [O III] lines as well as  the continuum. \n",
    "\n",
    "As a check, I'm going to print the parameters of the models so I have them as a reference for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "d1f69195-e335-4b5e-8aa8-c7aa02c0f8ea"
    }
   },
   "outputs": [],
   "source": [
    "combinedFit1 = h_beta + o3a + o3b + cheb_cont1\n",
    "#combinedFit1.param_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b967b077-9f93-42c0-9fd5-bb543a29896e"
    }
   },
   "source": [
    "#### Apply physical constraints to fit parameters\n",
    "\n",
    "We constrain some of the fit parameters to physical values. In particular:\n",
    "\n",
    "1) The ratio of O III lines is as expected from atomic physics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "nbpresent": {
     "id": "ec9bed0a-7526-4a31-a57a-8acfebbca5a5"
    }
   },
   "outputs": [],
   "source": [
    "def tie_OIIIratio(model):\n",
    "    return model.amplitude_2*a4959/a5006\n",
    "\n",
    "combinedFit1.amplitude_1.tied = tie_OIIIratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9bfa5f5c-733c-4fbc-bb8e-f8d18c78959e"
    }
   },
   "source": [
    "2) The redshifts of the lines should all be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "8fc69faf-2c73-4dd8-970c-abd0f7a508d3"
    }
   },
   "outputs": [],
   "source": [
    "def tie_waveA(model):\n",
    "    return model.mean_0 * OIIIa/Hbeta\n",
    "def tie_waveB(model):\n",
    "    return model.mean_0 * OIIIb/Hbeta\n",
    "\n",
    "combinedFit1.mean_1.tied = tie_waveA\n",
    "combinedFit1.mean_2.tied = tie_waveB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0bda9f30-002a-4503-8e5b-0855bf0137a4"
    }
   },
   "source": [
    "3) The dispersion of the O III lines should be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "574d7674-fa2d-4348-920d-2f824a90a86a"
    }
   },
   "outputs": [],
   "source": [
    "def tie_sigma(model):\n",
    "    return model.stddev_2\n",
    "#tie_sigma(combinedFit1)\n",
    "\n",
    "combinedFit1.stddev_1.tied = tie_sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d7786d6f-0654-4a59-b9f4-b9bd244c8764"
    }
   },
   "source": [
    "#### Define the fitter for the total model and do the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "1a7046ca-26f6-4aa6-9272-791d8b74ac77"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The relative error between two consecutive iterates is at most 0.000000'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model = fitter(combinedFit1, \n",
    "                      wave[fitWaves1], flux[fitWaves1],\n",
    "                      weights=1./variance[fitWaves1],\n",
    "                      maxiter=500)\n",
    "fitted_lines = fitted_model(wave)\n",
    "fitter.fit_info['message']\n",
    "#fitted_model.tied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "26c23482-91b4-444d-b003-9f2ef23266f6"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Gaussian1D\n",
      "Name: o3a\n",
      "Inputs: ('x',)\n",
      "Outputs: ('y',)\n",
      "Model set size: 1\n",
      "Parameters:\n",
      "          amplitude               mean             stddev      \n",
      "    ---------------------- ----------------- ------------------\n",
      "    2.3229371948915855e-18 8575.251132929749 2.2464792759342593 Model: Gaussian1D\n",
      "Name: o3b\n",
      "Inputs: ('x',)\n",
      "Outputs: ('y',)\n",
      "Model set size: 1\n",
      "Parameters:\n",
      "          amplitude              mean             stddev      \n",
      "    --------------------- ----------------- ------------------\n",
      "    6.770557685593833e-18 8658.138068650835 2.2464792759342593\n"
     ]
    }
   ],
   "source": [
    "print(fitted_model['o3a'],fitted_model['o3b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "nbpresent": {
     "id": "9905f0f2-84c5-4e77-adbb-4ad5c8ee3595"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b321dd0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 285,
       "width": 494
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(figXsize,figYsize))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(wave,flux,lw=1.,drawstyle='steps-mid',label='Data')\n",
    "ax.set_xlim(waveLimits1)\n",
    "\n",
    "#ax.plot(wave,cont)\n",
    "ax.plot(wave[fitWaves1],fitted_model(wave[fitWaves1]),\n",
    "        lw=2,label='Fit')\n",
    "ax.plot(wave[fitWaves1],\n",
    "        flux[fitWaves1]-fitted_model(wave[fitWaves1]),\n",
    "        c='c',drawstyle='steps-mid',label='Residuals')\n",
    "ax.plot(waveLimits1,[0.,0],':k')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d3d37dea-6c22-4e58-a228-7fcca0cb8165"
    }
   },
   "source": [
    "$\\rightarrow$ *Note:* This approach does not account for potential stellar absorption under the H$\\beta$ line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "f0796831-5168-4856-948d-5c75a84f64a6"
    }
   },
   "outputs": [],
   "source": [
    "##fitted_model[0], fitted_model[1], fitted_model[2]\n",
    "#fitted_model.tied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "58b09a5d-528a-4464-b299-b54821776dc0"
    }
   },
   "source": [
    "```\n",
    "```\n",
    "* Calculate the redshift based on the H$\\beta$ line. We've tied all the fitted central wavelengths together so that they have the same redshift, so it doesn't matter which we use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "a0ef8527-b6af-4e03-bbb0-d6433f5aab46"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.729260947197832\n",
      "10.566452837305963 km / s\n"
     ]
    }
   ],
   "source": [
    "z_out = (fitted_model.mean_0 / Hbeta)-1.\n",
    "print(z_out)\n",
    "print((z_out-z_init)/(1+z_init)*const.c.to('km/s'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "52a311f9-834c-4e5b-ac88-bc7270f9c3ff"
    }
   },
   "source": [
    "* Calculate the Hbeta, O III fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "f2a5ee6b-1d1e-4289-a9ad-0102f6c56beb"
    }
   },
   "outputs": [],
   "source": [
    "fluxHbeta = np.sqrt(2.*np.pi)*fitted_model.amplitude_0*fitted_model.stddev_0\n",
    "fluxOIIIa = np.sqrt(2.*np.pi)*fitted_model.amplitude_1*fitted_model.stddev_1\n",
    "fluxOIIIb = np.sqrt(2.*np.pi)*fitted_model.amplitude_2*fitted_model.stddev_2\n",
    "fluxOIII = fluxOIIIa + fluxOIIIb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "d58f7f5f-1fbb-4f88-855b-f6bcbe8ffd3b"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.4993942344740975e-17,\n",
       " 1.3080664858008536e-17,\n",
       " 3.8125609328495095e-17,\n",
       " 5.120627418650363e-17,\n",
       " 2.0487473916766095)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxHbeta,fluxOIIIa,fluxOIIIb,fluxOIII,fluxOIII/fluxHbeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "126b58dd-1b95-4ee3-97b7-c9b2c9e34fd8"
    }
   },
   "outputs": [],
   "source": [
    "##Diagnostics:\n",
    "#print(fitted_model.submodel_names)\n",
    "#print(fitted_model['o3b'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "41548cec-909f-48b1-9750-492188ac8815"
    }
   },
   "source": [
    "### B. [O II] fitting\n",
    "In this case, we now assume a redshift:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "eb7e554b-6127-4c83-8bab-bac1fcf02892"
    }
   },
   "outputs": [],
   "source": [
    "scaleFactorOut = (1.+z_out)\n",
    "zwave = wave/(1.+z_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "515971a9-cd55-42df-8e24-8ead204acc3c"
    }
   },
   "outputs": [],
   "source": [
    "#Define the indeces for the fit region\n",
    "fitWaves2 = ((zwave >= (OIIa - 80.)) & (zwave <= (OIIb+80.)))\n",
    "\n",
    "waveLimits2=[wave[fitWaves2].min(),wave[fitWaves2].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "7dc0bf1f-4ac7-46ba-89d0-175fd44c2b3d"
    }
   },
   "outputs": [],
   "source": [
    "mean_flux2 = flux[fitWaves2].mean()\n",
    "#cont2 = np.where((np.abs(flux-mean_flux) > 2.*err) & (flux > 0), mean_flux, flux)\n",
    "cont2 = np.where(np.abs(flux-mean_flux2) > 2.*err, mean_flux2, flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "171e2ee8-e52f-4a42-9f6e-2b762092c5e3"
    }
   },
   "source": [
    "#### Set-up and check the continuum fitting process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "nbpresent": {
     "id": "84f25d51-1a61-47f7-8629-44f648d94594"
    }
   },
   "outputs": [],
   "source": [
    "cheb2 = models.Chebyshev1D(degree=5)\n",
    "cheb_cont2 = linfitter(cheb2, wave[fitWaves2], cont[fitWaves2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "61fcc491-a44a-4ae0-a0e5-945651af1100"
    }
   },
   "source": [
    "#### Set-up the line fitting process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "d4290cfa-bbd2-4faf-9445-74fdf63130c3"
    }
   },
   "outputs": [],
   "source": [
    "amplitudeInitial = flux[fitWaves2].max()*0.5\n",
    "\n",
    "o2a = models.Gaussian1D(amplitude=amplitudeInitial, mean=OIIa*(1.+z_out), \n",
    "                        stddev=3,name='o2a')\n",
    "o2b = models.Gaussian1D(amplitude=amplitudeInitial, mean=OIIb*(1.+z_out), \n",
    "                        stddev=3,name='o2b')\n",
    "o2a.stddev.min = 1.0\n",
    "o2b.stddev.min = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "44c52da2-1458-4156-af56-7d9f35b31419"
    }
   },
   "source": [
    "#### Define the combined continuum and line fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "4e00e1bd-1ad3-4dab-8a2f-c148cbfedc7f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('amplitude_0',\n",
       " 'mean_0',\n",
       " 'stddev_0',\n",
       " 'amplitude_1',\n",
       " 'mean_1',\n",
       " 'stddev_1',\n",
       " 'c0_2',\n",
       " 'c1_2',\n",
       " 'c2_2',\n",
       " 'c3_2',\n",
       " 'c4_2',\n",
       " 'c5_2')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinedFit2 = o2a + o2b + cheb_cont2\n",
    "combinedFit2.param_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "436efe8f-5841-4f1e-ba87-df88f408183f"
    }
   },
   "source": [
    "#### Apply physical constraints to fit parameters\n",
    "\n",
    "1) The dispersions should be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "c62d1de2-a5d8-4c27-bc4d-7918b3656b2b"
    }
   },
   "outputs": [],
   "source": [
    "def tie_sigma2(model):\n",
    "    return model.stddev_0\n",
    "\n",
    "combinedFit2.stddev_1.tied = tie_sigma2\n",
    "combinedFit2.stddev_1.tied = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6ce39195-2c44-4c61-ac4e-055774ce761d"
    }
   },
   "source": [
    "2) The redshift is fixed, thus the means of the Gaussians are fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "ab12b606-4454-44b2-b89e-2a7e8d75f874"
    }
   },
   "outputs": [],
   "source": [
    "combinedFit2.mean_0.fixed=True\n",
    "combinedFit2.mean_1.fixed=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7d302305-646d-4922-8c5e-6fa0fb7f9e4f"
    }
   },
   "source": [
    "#### Define the fitter for the total model and do the fit\n",
    "Whereas I used an inverse variance weighting above, this causes problems for [O II] for unknown reasons. You'll see this line commented out below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "2d3ddd5a-76ce-4a37-b6bb-ba0aff06cd5e"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Both actual and predicted relative reductions in the sum of squares\\n  are at most 0.000000'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model2 = fitter(combinedFit2, \n",
    "                      wave[fitWaves2], flux[fitWaves2],\n",
    "                      #weights=1./variance[fitWaves2],\n",
    "                      maxiter=500)\n",
    "fitted_lines2 = fitted_model2(wave)\n",
    "fitter.fit_info['message']\n",
    "#fitted_model.tied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e4717ade-411b-4052-8815-be00c67cd298"
    }
   },
   "source": [
    "#### Plot the resulting fits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "nbpresent": {
     "id": "e6a4e336-2196-4443-9978-8efccb108ad3"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12bc9e190>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 285,
       "width": 494
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(figXsize,figYsize))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(wave,flux,lw=1.5,drawstyle='steps-mid',label='Data')\n",
    "ax.set_xlim(waveLimits2)\n",
    "\n",
    "ax.plot(wave[fitWaves2],fitted_model2(wave[fitWaves2]),\n",
    "        lw=2,label='Fit')\n",
    "ax.plot(wave[fitWaves2],\n",
    "        flux[fitWaves2]-fitted_model2(wave[fitWaves2]),\n",
    "        c='c',drawstyle='steps-mid',label='Residuals')\n",
    "ax.plot(waveLimits2,[0.,0],':k')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bae0cbe8-4127-45ad-8e65-136e84e74f84"
    }
   },
   "source": [
    "*Looks pretty good!*\n",
    "```\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "nbpresent": {
     "id": "42799d08-0ead-4eb0-9e71-b8116e1578fb"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('o2a', 'o2b', 'None_0')\n",
      "Model: Gaussian1D\n",
      "Name: o2a\n",
      "Inputs: ('x',)\n",
      "Outputs: ('y',)\n",
      "Model set size: 1\n",
      "Parameters:\n",
      "          amplitude              mean             stddev     \n",
      "    --------------------- ----------------- -----------------\n",
      "    7.719784422012131e-18 6443.281625609433 2.146604365338434\n",
      "Model: Gaussian1D\n",
      "Name: o2b\n",
      "Inputs: ('x',)\n",
      "Outputs: ('y',)\n",
      "Model set size: 1\n",
      "Parameters:\n",
      "          amplitude              mean             stddev      \n",
      "    --------------------- ----------------- ------------------\n",
      "    1.140748803904346e-17 6448.094158825484 2.0221827124757783\n",
      "[ 7.71978442e-18  6.44328163e+03  2.14660437e+00  1.14074880e-17\n",
      "  6.44809416e+03  2.02218271e+00  1.54417964e-18  3.05281517e-19\n",
      "  1.64990782e-19 -9.01190083e-20  5.02414950e-21 -4.54442539e-20]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Gaussian1D(amplitude=0., mean=6443.28162561, stddev=2.14660437, name='o2a')>,\n",
       " <Gaussian1D(amplitude=0., mean=6448.09415883, stddev=2.02218271, name='o2b')>,\n",
       " <Chebyshev1D(5, c0=0., c1=0., c2=0., c3=-0., c4=0., c5=-0.)>)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(fitted_model2.submodel_names)\n",
    "print(fitted_model2['o2a'])\n",
    "print(fitted_model2['o2b'])\n",
    "print(fitted_model2.parameters)\n",
    "fitted_model2[0], fitted_model2[1], fitted_model2[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if I want to adjust a parameter based on prior information or to apply to another situation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Gaussian1D(amplitude=0., mean=6443.28162561, stddev=35., name='o2a')>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model2['o2a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_model2['o2a'].stddev=35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Gaussian1D(amplitude=0., mean=6443.28162561, stddev=35., name='o2a')>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model2['o2a']"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  },
  "nbpresent": {
   "slides": {
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