{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "007c00f6",
   "metadata": {},
   "source": [
    "# How specutils and muler propagate uncertainty\n",
    "In this notebook we explore how `specutils` does uncertainty propagation, and how to combine two spectra with different---but overlapping---extents.  That is, if a spectrum has close to, but not exactly the same wavelengths, how does specutils combine them?  These two issues are important for combining spectra of the same objects taken from different exposures and different nights, when the target's rest-frame wavelength solution can change greater than one pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0602192",
   "metadata": {},
   "outputs": [],
   "source": [
    "from specutils import Spectrum1D\n",
    "import numpy as np\n",
    "from astropy.nddata import StdDevUncertainty\n",
    "import astropy.units as u\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736aff92",
   "metadata": {},
   "source": [
    "### Spectrum 1: $S/N=20$\n",
    "\n",
    "First we'll make a spectrum with signal-to-noise ratio equal to 20, and mean of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80724aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_points = 300\n",
    "fake_wavelength = np.linspace(500, 600, num=N_points)*u.nm\n",
    "mean_val, sigma = 1.0, 0.05\n",
    "snr = mean_val / sigma\n",
    "known_uncertainties = np.repeat(sigma, N_points) * u.Watt / u.cm**2\n",
    "fake_flux = np.random.normal(loc=mean_val, scale=known_uncertainties) * u.Watt / u.cm**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0619ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec1 = Spectrum1D(spectral_axis=fake_wavelength, \n",
    "                 flux=fake_flux, \n",
    "                 uncertainty=StdDevUncertainty(known_uncertainties))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34730415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "known_uncertainties.value[0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35ae34d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axhline(1.0, linestyle='dashed', color='k', zorder=10)\n",
    "plt.errorbar(spec1.wavelength.value, spec1.flux.value, yerr=spec1.uncertainty.array, \n",
    "             linestyle='none', marker='o', ecolor='k', alpha=0.5)\n",
    "plt.ylim(0, 1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3fdb55",
   "metadata": {},
   "source": [
    "### Spectrum 2: $S/N = 50$ and *conspicuously* offset in wavelength\n",
    "\n",
    "Now we'll make a spectrum with signal-to-noise ratio equal to 50, and mean of 0.5.  The wavelength axes are *offset* by 10 nanometers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea504ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_points2 = N_points\n",
    "fake_wavelength2 = np.linspace(510, 610, num=N_points2)*u.nm\n",
    "mean_val2, sigma2 = 0.5, 0.01\n",
    "snr = mean_val2 / sigma2\n",
    "known_uncertainties2 = np.repeat(sigma2, N_points2) * u.Watt / u.cm**2\n",
    "fake_flux2 = np.random.normal(loc=mean_val2, scale=known_uncertainties2) * u.Watt / u.cm**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21d88dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec2 = Spectrum1D(spectral_axis=fake_wavelength2, \n",
    "                 flux=fake_flux2, \n",
    "                 uncertainty=StdDevUncertainty(known_uncertainties2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e1f726",
   "metadata": {},
   "source": [
    "### Add Spectrum 1 and Spectrum 2\n",
    "#### What happens?\n",
    "\n",
    "We expect the uncertainties to add *in quadrature*:    \n",
    "\n",
    "$ \\sigma_{net} = \\sqrt{\\sigma_1^2 + \\sigma2^2}$  \n",
    "$ \\sigma_{net} = \\sqrt{0.05^2 + 0.01^2} $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa2687d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.050990195135927854"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hypot(0.05, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b404af50",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_net = spec1 + spec2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f653724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StdDevUncertainty([0.0509902, 0.0509902, 0.0509902, 0.0509902, 0.0509902,\n",
       "                   0.0509902, 0.0509902])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_net.uncertainty[0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0051d34",
   "metadata": {},
   "source": [
    "Woohoo! Specutils *automatically* propagates the error correctly!  You can turn this error propagation *off* (I'm not sure why you would want to) by calling the method with a kwarg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2dea820",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_net_no_error_propagation = spec1.add(spec2, propagate_uncertainties=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ff5b81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StdDevUncertainty([0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_net_no_error_propagation.uncertainty[0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e64b7fd",
   "metadata": {},
   "source": [
    "### Wait, but what about the offset?  How did it deal with the non-overlapping edges?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea0e138c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(spec1.wavelength.value, spec1.flux.value, yerr=spec1.uncertainty.array, \n",
    "             linestyle='none', marker='o', label='Spec1: $S/N=20$')\n",
    "\n",
    "plt.errorbar(spec2.wavelength.value, spec2.flux.value, yerr=spec2.uncertainty.array, \n",
    "             linestyle='none', marker='o', markersize=1, label='Spec2: $S/N=50$')\n",
    "\n",
    "plt.errorbar(spec_net.wavelength.value, spec_net.flux.value, yerr=spec_net.uncertainty.array, \n",
    "             linestyle='none', marker='o', \n",
    "             label='Spec net = Spec + Spec2 : $\\sigma_{net}=\\sqrt{\\sigma^2 + \\sigma_2^2}$')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(0, 2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe85a32",
   "metadata": {},
   "source": [
    "Whoa! Specutils pretends like the signals are *aligned to Spectrum 1*.  That is probably not the desired behavior for such an extreme offset as this one, but may be \"good enough\" for spectra that are either exactly aligned or within a pixel.  It depends on your science application.  PRV applications should not just round-to-the-nearest pixel, since they are trying to infer changes at sub-pixel levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd947871",
   "metadata": {},
   "source": [
    "What if you add the two spectra *the other way around*?  Math says addition should be communitative..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ad3179f",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_alt = spec2 + spec1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "771d56ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55psBeO9sfOCAozO0tzbqRER2wQCdqIRJrROHDskJAXUU7ZicELH250YozViUJgqKHTt2uNqMqynD76nbY3fq1Anbt2/Hxo0b3WqSvenUqROys7OxYsUK2wfoAJCXlwfAEayra5c3bNiA/Px8t+YtgPe25cOHD8c777yDTz/9FD/99JPuPHpC1Qb91VdfxcSJE9G2bVusXr0aNWt6v5FUhuJctWoViouL3UZyKSwsxObNm1GpUiV06tTJcDqJiCKBwywSlUDjeqUgxmAz2xgBPNErJbQJ8uOTTz7B6tWrdV/ffurUKcycORMAXE06FOfPn8dzzz3nNi07Oxv/+c9/EB8f7xaUPvbYYyhXrhwmTJigW8N67do1bNy40fX/I488grJly+L555/Hnj17POa3YhSXvLw87Nu3z2uTDLV58+bhq6++8rhJARzjfz/88MMAgPHjx7s1b1m0aBFiYmIwcOBA17Ti4mLs2rULlStXRvPmzd3W1a1bN1czl0Bq0HNzcyGlNPxjZGjJ559/HhMnTkR6ejoyMzN9BucA0KRJE2RkZCA3Nxfvvvuu22eTJk3CpUuXcN999yE2NtbvtomIIok16EQlUNemNfHykFv8vqwoRgCvDGkd8eYtWVlZePPNN5GYmIhu3bohOTkZgGOc888//xxXrlzBwIEDMWzYMLflunfvjlmzZiErKwtdu3ZFXl4e5s+fj+LiYsyYMcOtKUuzZs3wwQcf4IEHHkDLli3Rp08fpKam4vr16zh27Bg2btyIWrVqYd++fQCAFi1aYNq0aRgzZgzatWuHgQMHIiUlBfn5+cjOzkZcXBzWrl3rWr/SmRW40cTmm2++wahRowAANWvWxGuvveaW/qeffhpz587F7NmzXfN588EHH2DdunVo2LAhunfvjgYNGuDixYvIycnBV199haKiIowdOxbPPPOM23LKGO7q9vp79+7FxYsX0aVLF9dLixQxMTEYMmSIK8Bt2LAhatWq5TNtoTB37lw8++yzKFOmDG677TbXC5rUGjdu7HHcpk2bhi5duuCJJ55AZmYmmjdvjqysLKxduxapqal48cUXw7QHRETmMUAnKqHuvrUh6levjLcyDyBLp7lLx+QEPNErJeLBOQA8+eSTSElJwVdffYXvvvsOX375JX755RfUqFEDt99+O+655x7cc889HqNvJCcnY/r06Zg4cSKmT5+Oq1evIi0tDc8++6zHEHwAMGLECLRp0wavv/461q5di1WrViE2NhZ169bFsGHDcPfdd7vN/9BDD6FVq1Z47bXXsG7dOixZsgQ1a9ZE69atXUMcKnbt2oW5c+e6TTt8+DAOHz4MAGjUqJFHgB6IV155BZ999hnWrVuHDRs24PTp04iJiUH9+vVx33334aGHHkLnzp3dltm2bRuOHz+OCRMmuE3fvn07AO8148OHD3cF6JHqIHrkyBEAjjb0U6dO1Z2nR48eHgF6kyZNkJ2djWeffRYrV67EF198gaSkJDzxxBOYNGmSbudSIiK7EXqPS0sqIcT2tLS0NOXiRBRJe/fuBQCPJgahkHO6EBlvbHD9v2pCd1u3OfcnNzcXycnJGDlypOG3cJZGzzzzDF5++WUcPnzY9VSCiIiCY/T6nZ6ejh07duyQUqb7nFEH26ATlQLaYDyag3MybvHixWjTpg2DcyKiKMMmLkREJZRSy0NERNGFATpRKTEuwiO1EBERkTEM0IlKiQm9S87LWRo3bqw73CAREVFJwDboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGykY6AUQUJmtfvvF3z6cjlw4iIiLyiQE6UWmx/pUbfzNAJyIisi02cSEiIiIishEG6ERkC0VFRZg5cyZ69OiBhIQElCtXDrVr10br1q3x4IMPYtmyZZFOolf5+fmYNWsWBg8ejKZNm6JSpUqIj49Ht27d8P7776O4uNjyba5btw733HMPGjdujIoVK6Jq1apISUnBoEGD8MYbb+hu8/Tp0yhTpgyeeOIJ17TevXtDCAEhBHbs2KG7rYceeghCCCxevNjy/fClcePGrrRpfxITE70ud/z4cTzwwAOoW7cuKlSogMaNG2P8+PE4e/ZsGFNPRGQem7gQUcQVFRXhzjvvxMqVK1GtWjX069cP9evXR0FBAQ4dOoSPP/4Y+/btw4ABAyKdVF0LFizAI488gqSkJPTs2RMNGzbE6dOnsWjRIjz44INYsWIFFixYACFE0NsqKirCI488gpkzZ6JKlSro27cvGjVqhIsXL+LgwYNYuXIlsrOzMWHCBI9lly5diuLiYgwePNg1TR2Uf/rpp0hLS/NYLjs7GwDQvn37oNMfqPj4eIwfP95jepUqVXTnP3ToELp06YIzZ85g4MCBaNasGbZu3Yo333wTK1euxObNm1GjRo0Qp5qIKEhSylLzA2B7WlqaJLKDPXv2yD179oRvg5Oq3vixmXnz5kkAsk2bNvLcuXMen1+6dEmuWbMmAikzJjMzUy5btkwWFRW5Tc/Ly5MNGjSQAOT//vc/S7Y1ZcoUCUAOGDBA91jl5+fL1atX6y7bp08fWaNGDfnrr79KKaU8ePCgBCDbt28v69atK2+66SaPZX755RdZrlw5Wbt2bUvSH4hGjRrJRo0aBbRMRkaGBCDfeustt+kTJkyQAOTo0aMtTCERlUZGr99paWkSwHZpImZlExciirivv/4aADBq1CjEx8d7fF65cmX07NnT9X9ubi6EEBg1ahT27duHQYMGISEhAbGxsejWrRtWrVrldVtZWVkYNmwYEhMTUb58eTRo0ACjR4/GyZMndeffunUr7r77btSrVw8VKlRAUlISMjIy8Omnn7rmueOOO9C/f3/ExLgXqYmJiRgzZgwAR5OUYEkp8dZbbwEAZsyYoXusEhIS8Jvf/MZj+vnz57FmzRr0798fZcqUAXCjZvzWW2/F0KFDcfjwYdc0xa5du3D9+vWI1J4H6vDhw1i1ahUaN26MRx991O2zKVOmIDY2FvPmzcOlS5cilEIiImMYoBNRxClNDnJycgJa7siRI+jcuTPy8/MxevRoDB8+HNu3b0ffvn0xf/58j/lnz56Nrl27YsWKFejZsyfGjx+P9u3bY9asWWjfvj2OHTvmNv/MmTPRpUsXLFmyBF26dMGTTz6Jfv364cyZM5g2bZqhNJYrVw4AULasZ4vCUaNGQQiBOXPmGFrXhQsXkJ+f77Zeoz7//HNcu3YNQ4YMcU1TN10ZPnw4ALjdeGjniYSrV6/io48+wksvvYQ333wTa9euRVFRke68a9asAQBkZGR43CzFxcWha9euuHz5MrZs2RLydBMRBYNt0Iko4oYMGYJXX30V06dPR2FhIQYPHoz09HQ0atTI53IbNmzAU089hX/+85+uaY899hg6d+6MMWPGoG/fvqhatSoAR/A/evRoNG7cGOvXr0e9evVcy6xZswa9e/fGuHHjXB0h9+zZg7Fjx6Jq1arYuHEjWrZs6bbt48eP+92vX3/9FR9++CEAoE+fPsYOhg/x8fGoX78+jh8/jt69e+PRRx/Fb3/7W9SvX9/vsosXL0ZsbCx69+7tmqYOvlu1aoW6detiwYIF+Mc//qE7jy9Tp07FuXPnDO9L27ZtMWjQIL/znTp1Cvfee6/btOTkZMyePRs9evRwm75//34AQGpqqu66UlJSsGrVKuTk5KBXr16G00pEFHZm2sVE6w/YBp1sJKxt0H/c5t4G/cdt4dluAObPny8TExMlANdPQkKCHDRokFy2bJnbvEeOHJEAZHx8vLxw4YLHukaOHCkByDlz5rimjR8/XgKQn332me72Bw0aJMuUKeNa32OPPSYByH/961+m9+nJJ5+UAOTvfvc73c9Pnjwp9+7dq9uW3JvNmzfLJk2auB2nhg0bytGjR8sdO3boLnPlyhVZpUoVOWzYMNe04uJiGR8fLytVqiSvX78upZTy8ccflwBkVlaWa75WrVpJAPLkyZM+09WoUSO3NPn7GTlypN99nTx5sszMzJSnTp2Sly5dkrt375ajR4+WQghZqVIluWvXLrf5H3roIQlAzpw5U3d9zzzzjAQgX3rpJb/bJiLyJhxt0FmDTlQaHF7n/v+R9UB9e7UpvuuuuzB48GCsXbsWmzZtws6dO7Fp0yYsWbIES5YswX333Yc5c+a4jYSSlpaGuLg4j3XdfvvtmDt3Lnbu3ImRI0cCAL755hsAwPr167Ft2zaPZc6cOYOioiLk5OQgPT3d1Qyib9++pvbnrbfewuuvv45mzZph3rx5uvMkJSUhKSkpoPV26dIFOTk52LRpE7788kusX78eWVlZmDFjBv79739jxowZeOihh9yWWbVqFS5evOg2ektOTg7Onz+PTp06uZrfDB8+HG+//TY+/fRTdOjQAZcvX8bevXtRt25dv+nMzc0NaD+MmDRpktv/rVq1wvTp01GlShW8/vrrmDx5ckBDP0pHRY0lo+kQEYUSA3Si0uCm24E1z9/4P7mH11kjqVy5csjIyEBGRgYAx5CCCxcuxAMPPIAPP/wQgwcPdmsWUadOHd31KGNknz9/3jVNabutbg6j5+LFiwDgaq6hbgpj1Lvvvotx48ahRYsWyMzMREJCQsDr8CUmJgbdu3dH9+7dAQAFBQUYP3485s2bh6eeegoPPvigWxC6ePFilC9fHv369XNN02u60rVrVyQlJWHBggX45z//iZ07d6KoqMh2HUTHjBmD119/HRs2bHCbrnSaVee72oULF9zmIyKyKwboRKWBtrbcZrXn3pQpUwZ33XUXdu/ejRdeeAFr1qxxC9BPnz6tu9ypU6cAuAdi6uBNaZfuS7Vq1QAAJ06cQLNmzQyneerUqZgwYQJatWqFzMxM1K5d2/CyZiUkJODFF1/EvHnzcOHCBRQUFLg63hYVFWH58uW444473I6HEqCnp6e7psXExGDo0KF45513kJWVFVAH0VC1QdejHFPtaCw333wzAO+djQ8cOADAext1IiK7YIBORLanNGNRmigoduzYgcLCQo9mLsqQhu3atXNN69SpE7Zv346NGze61SR706lTJ2RnZ2PFihWGA/RXX30VEydORNu2bbF69WrUrFnT0HJWyMvLA+AI1tU19hs2bEB+fr5b8xbAe+fP4cOH45133sGnn36Kn376SXcePVOnTsXRo0cNp3fkyJGmA3SludJNN93kNl0ZinPVqlUoLi52G8mlsLAQmzdvRqVKldCpUydT2yUiChcOs0hEEffJJ59g9erVuq+nP3XqFGbOnAkAriYdivPnz+O5555zm5adnY3//Oc/iI+PdwtKH3vsMZQrVw4TJkzQrWG9du0aNm7c6Pr/kUceQdmyZfH8889jz549HvNrR3F5/vnnMXHiRKSnpyMzM9NQcJ6Xl4d9+/Z5bZKhNm/ePHz11VceNymAY/zvhx9+GAAwfvx4t+YtixYtQkxMDAYOHOiaVlxcjF27dqFy5cpo3ry527q6devmauYSSA16bm5uQB2g/A0t+cMPP6CgoMBj+tGjR/HYY48BAEaMGOH2WZMmTZCRkYHc3Fy8++67bp9NmjQJly5dwn333YfY2Fi/+0NEFEmsQSeiiMvKysKbb76JxMREdOvWDcnJyQAc45x//vnnuHLlCgYOHIhhw4a5Lde9e3fMmjULWVlZ6Nq1K/Ly8jB//nwUFxdjxowZbk1ZmjVrhg8++AAPPPAAWrZsiT59+iA1NRXXr1/HsWPHsHHjRtSqVQv79u0DALRo0QLTpk3DmDFj0K5dOwwcOBApKSnIz89HdnY24uLisHbtWgDA3Llz8eyzz6JMmTK47bbbXC8TUmvcuDFGjRrlNu3pp5/G3LlzMXv2bI/PtD744AOsW7cODRs2RPfu3dGgQQNcvHgROTk5+Oqrr1BUVISxY8fimWeecVtOGcNd3V5/7969uHjxIrp06eJ6aZEiJiYGQ4YMcQW4DRs2RK1atXymLRQWLFiAV155BT179kRycjLi4uJw6NAhfP755/jll1/wu9/9Dk899ZTHctOmTUOXLl3wxBNPIDMzE82bN0dWVhbWrl2L1NRUvPjii2HfFyKiQDFAJ6KIe/LJJ5GSkoKvvvoK3333Hb788kv88ssvqFGjBm6//Xbcc889uOeeezxG30hOTsb06dMxceJETJ8+HVevXkVaWhqeffZZ/Pa3v/XYzogRI9CmTRu8/vrrWLt2LVatWoXY2FjUrVsXw4YNw9133+02/0MPPYRWrVrhtddew7p167BkyRLUrFkTrVu3xoMPPuia78iRIwAc7b2nTp2qu489evTwG4T78sorr+Czzz7DunXrsGHDBpw+fRoxMTGoX78+7rvvPjz00EPo3Lmz2zLbtm3D8ePHMWHCBLfp27dvB+C9Znz48OGuAD1SHUR79uyJ/fv3Y+fOnfjmm29w6dIlVKtWDd26dcO9996Le++9V3c0liZNmiA7OxvPPvssVq5ciS+++AJJSUl44oknMGnSJMs77BIRhYLQe1xaUgkhtqelpaUpFyeiSNq7dy8AeDQxCJnJqpErJvtvUmFnubm5SE5OxsiRIw2/hbM0euaZZ/Dyyy/j8OHDrqcSREQUHKPX7/T0dOxwvKAi3eeMOtgGnYiohFq8eDHatGnD4JyIKMqwiQsRUQml1PIQEVF0YYBOVFr0mBjpFBAREZEBDNCJSoueT0c6BZZp3Lix7nCDREREJQHboBMRERER2QgDdCIiIiIiG2GATkRERERkIwzQiYiIiIhshAE6EREREZGNMEAnIiIiIrIRBuhERERERDbCAJ2IiIiIyEYYoBMRERER2UjQAboQooYQ4kEhxGIhxEEhxBUhxHkhxCYhxB+FEAFtQwhRXwjxgRDipBDiqhAiVwgxVQhRPdi0EhERERHZXVkL1jEcwHsA8gCsBXAMQB0AQwDMAtBXCDFcGngvtxCiCYCvAdQGsBTAPgAdAIwD0EcI0VVKmW9BmomIiIiIbMmKAD0HwAAAn0spi5WJQohnAGwFMBSOYH2hgXVNgyM4f0JK+bZqXf8CMAHAiwDGWJBmIiIiIiJbCrqJi5RyjZRyuTo4d04/BWC689/b/a1HCHETgAwAuQDe1Xw8CcAlAPcKIWKDTTMRERERkV2FupPodefvXw3Me4fz9yqdYL8QwGYAlQF0si55RERERCXTli1bMHHiRAgh+BPgT6RZ0cRFlxCiLID7nP+uNLDIzc7fOV4+PwBHDXsqgMzgUkdERERUsi1atAjt27eHgW6AZDMhC9ABvAKgFYAvpJRfGpg/3vn7vJfPlenV/K1ICLHdy0fNDKSDiIiIKOp9+eWX+Nvf/hbpZJAJIWniIoR4AsCTcIzCcq9Vq3X+5m0gERERkQ8HDhxAYmIiqlatGumkkAmW16ALIR4F8CaAPQB6SSkLDC6q1JDHe/m8qmY+r6SU6V7Sth1AmsH0EBEREUWlpUuXon///pFOBplkaQ26EGI8gHcAfA+gp3MkF6P2O3+nevk8xfnbWxt1IiIiIgKwfPlyDBgwINLJIJMsC9CFEH8B8AaAXXAE52cCXMVa5+8M7dtHhRBxALoCuAJgS5BJJSIiIiqxCgoKcOnSJTRs2NCydb788su49dZbUbVqVdSqVQv9+/fH999/b9n6Qyka025JgC6E+DscnUK3w9Gs5Wcf85YTQjRzvjXURUp5CMAqAI0BPKpZbAqAWAAfSikvWZFmotJm2q5prh8iIiq5Pv/8c/zud7+zdJ3r1q3D2LFj8fXXX2PNmjUoW7YsfvOb36CgwGhL5siJxrQH3QZdCDESwHMAigBsBPCEzviRuVLKOc6/6wHYC+AoHMG42lgAXwN4SwjRyzlfRwA94Wja8tdg00tUWr337Xuuv8e2HRvBlJAvubm5SE5OxsiRIzFnzpxIJ4eIotCyZcvwl7/8xdJ1fvml+4B88+bNQ3x8PDZv3mz7tu7RmHYratCTnb/LABgPx1s/tT+jjKzIWYveHsAcOALzJwE0AfAWgM5SynwL0ktENlRUVISZM2eiR48eSEhIQLly5VC7dm20bt0aDz74IJYtWxbpJEZMbm4uhBAYNWpUpJPiIZrzLT8/H7NmzcLgwYPRtGlTVKpUCfHx8ejWrRvef/99FBcX+19JGEXzsabwuXbtGr7//nukp+uOl2GZwsJCFBcXo3r16iHdTihEQ9qDrkGXUk4GMDmA+XNxY8hEvc9/BHB/sOkiohsOnj3o8X/T6k0jlBpPRUVFuPPOO7Fy5UpUq1YN/fr1Q/369VFQUIBDhw7h448/xr59+9jhyWaiPd8WLFiARx55BElJSejZsycaNmyI06dPY9GiRXjwwQexYsUKLFiwwBZvFYz2Y03hs27dOvTo0SPk39tx48ahbdu26Ny5c0i3EwrRkPZQvqiIiCJsS94WTP92Orafdn931+Blg5FeJx1j2oxBp6ROEUrdDZ988glWrlyJNm3aYP369YiPdx9t9fLly8jKyopQ6sibaM+31NRULFu2DP369UNMzI0Hyi+99BI6dOiAhQsXYtGiRRg6dKjpbcyZMwf3338/1q5di9tvv930eqL9WFP4LFu2LOQ3an/605+wadMmbNq0CWXKlAl4eavOCzOCTXu4hORFRUQUeYsOLMLo1aM9gnPF9tPbMXr1aCw+sDjMKfP09ddfAwBGjRrlEXgAQOXKldGzZ0/X/+omH/v27cOgQYOQkJCA2NhYdOvWDatWrfK6raysLAwbNgyJiYkoX748GjRogNGjR+PkyZO682/duhV333036tWrhwoVKiApKQkZGRn49NNPDe/fnDlzMHToUNx0002oVKkSqlatiq5du+Kjjz7yu+zkyZORnOxoSTh37lwIIVw/kW6jHu35dscdd6B///5uwTkAJCYmYsyYMQActZF2EO3HOlQuXryI5557Du3atUNcXJzb+aH+OXMm0IHlotfatWtxxx13+J3P7LGbMGECPvnkE6xZswY33XRTqHbDLzPpt0vajWANOlEJtCVvC6Z8MwXF0ncb2mJZjMnfTEZSlaSI1qTXqFEDAJCTE9hrDo4cOYLOnTujVatWGD16NPLy8jB//nz07dsXH3/8Me6++263+WfPno2HHnoIFSpUwIABA9CgQQMcOHAAs2bNwvLly7Flyxa3YclmzpyJRx55BGXKlMGAAQOQkpKCM2fOIDs7G9OmTcNdd91lKJ2PPPIIWrRoge7duyMpKQn5+fn44osvcO+992L//v14/vnnvS57++2349y5c3jzzTfRpk0bDBo0yPVZ27ZtAzpeVivJ+VauXDkAQNmy9rhMluRjDThuPObOnYvZs2cb7mtx5swZ9OjRA/v27UPr1q0xZswYXL16FQsWLMCpU6dQrlw5NGzYEDVr1kTt2rUNH7NotmvXLqSmpqJixYo+5zN77MaNG4f//ve/WLduHZo1axbq3bE0/XZJu2FSylLzA2B7WlqaJLKDPXv2yD179oRk3SNXjJSt5rQy/DNqxaiQpMOoHTt2yHLlykkhhBwxYoRcuHChzM3N9Tr/kSNHJAAJQD711FNun23btk2WLVtWVqtWTZ4/f941ff/+/bJcuXKySZMm8vjx427LZGZmypiYGDlo0CDXtB9++EGWLVtWVq9eXX7//fceafjxxx8N79/Bgwc9pl29elXecccdsmzZsm7pUfZt5MiRPqcF4o033pCTJk0y/LN48WJD6y2p+Xb9+nXZqlUrCUCuXLnS7/y+zJ49WwKQa9euDWo9JfVYK0aOHCkByNmzZxteplevXhKA/POf/yyLi4td048dOyYrVKggy5YtK3/66SfD67NaKM67RYsWyd///vdeP58yZYr84IMP/K7HzLEbO3asjIuLk5mZmTIvL8/1U1hY6Hd7WsGeF4Gm38q0S2n8+p2WliYBbJdmYlYzC0XrDwN0spNQBegHCg4EFJwrPwcKDlielkDMnz9fJiYmuoIKADIhIUEOGjRILlu2zG1eJfiIj4+XFy5c8FiXcrGfM2eOa9r48eMlAPnZZ5/pbn/QoEGyTJkyrvU99thjEoD817/+ZeFeulu4cKEEIOfOneuaFooAvVGjRm7H1d9PINspifn25JNPSgDyd7/7nel1KKwK0KUsmcdacfLkSbl371557tw5Q/OvWrVKApBdu3aVRUVFHp//5je/kQDk6tWrg06bWVaed9evX5d//vOfpRBCli1bVhYUFOjO16FDB3nmzBmf6TJ77Lyle9KkSX6PhVYw54WZ9FuZdinDE6Db49kdEVkm65S5jmJZp7IiOrLLXXfdhcGDB2Pt2rXYtGkTdu7ciU2bNmHJkiVYsmQJ7rvvPsyZM8dtZIK0tDTExcV5rOv222/H3LlzsXPnTowcORIA8M033wAA1q9fj23btnksc+bMGRQVFSEnJwfp6enYssXx0uK+ffsGvW/Hjh3Dq6++iszMTBw7dgxXrlxx+/zEiRNBb8OX3NzckK27pOXbW2+9hddffx3NmjXDvHnzAlq2cePGOHr0qO5n6vbhikDHui9px1otKSkJSUlJhudX+m9MmDDBow8BAFc7/UgOlWnleVdYWIj/+7//Q1paGn7/+9/j888/x4gRI9zmOXnyJMqXL49atWr5XJfZY+eIcwNn9XlhJv1m0x5JDNCJSpiL1y6GdTkrlStXDhkZGcjIyADgGFpu4cKFeOCBB/Dhhx9i8ODBbm2w69Spo7uexMREAMD58+dd0/LzHa9R+Oc//+kzDRcvOo7DuXPnAAD16tUztS+Kw4cPo0OHDjh79ixuu+02ZGRkID4+HmXKlEFubi7mzp2Lq1evBrWNSCsp+fbuu+9i3LhxaNGiBTIzM5GQkBDQ8uPHj3dtX7Fr1y4sXboUI0eOROPGjd0+M9OHoKQc62Bt3LgRMTEx6NOnj+7nx48fBwA0bWp9pcNTTz2F77//HitXrrR83d4o43UPGzYMNWrUwNKlSz0C9OXLl+POO+/0u65wHzurz4tI5n04MUAnKmGqlK8S1uVCqUyZMrjrrruwe/duvPDCC1izZo1b8HH69Gnd5U6dOgUAbqNdKH+fP38eVatW9bvtatWqAXDUbgfToehf//oX8vPzdTu/ffLJJ5g7d67pdRs1depUjwukL23btnU7zoGKxnybOnUqJkyYgFatWiEzM9NUp8Lx48d7TJszZw6WLl2KUaNGhWQ4uWg81sEqLi7G0aNHUbt2bcTGxnp8fvr0aWzbtg3JyckhGalj27Zt6NGjh9/5QnHelSlTBv369cOiRYtw9epVVKhQwfXZsmXL8Nprr/lcPhLHzsrzItJ5H04M0IlKmI6JHcO6XDgoj+i1jyl37NiBwsJCj0f4ytB47dq1c03r1KkTtm/fjo0bN6Jfv35+t9mpUydkZ2djxYoVQQUfBw86XhKlN5b2+vXrDa1DGau3qKjIVBqmTp3q9RGznpEjRwYVoCuiJd9effVVTJw4EW3btsXq1atRs2ZNQ8vZSbQcaysoTXiUt0Fqmzn84x//QHFxMUaPHu2advz4cTRo0ABLlizBe++9h40bNyIxMRGzZs1ya2Zx4sQJPP300/j8889RVFSEXr16Ydq0aahTpw6uX7+O2NhYXL9+HRs2bMDzzz+PFi1a4IcfftBNZ6jOu4EDB+LDDz/EmjVrXM2LLl++jB9//BHNmzf3uayZY2cnZtL/8ssvY9GiRdi/fz8qVKiATp064eWXX0arVq3CmvaAmWm4Hq0/YCdRshGO4nLDxx9/LFetWqXb4ScvL082bdpUApCffvqplNLYCBXx8fFuI1Ts3btXlitXTqakpMj9+/d7bOfq1atyw4YNrv/VI1T88MMPHvMbHaFi9OjREoBHJ76VK1fKMmXKeHRU0usQWlhYKIUQsnv37oa2GS4lId+ee+45CUCmp6fL/Pz8wA6AAVZ1Ei0Jx9qXQDuJtmvXTgKQH330kdv0BQsWyJiYGNmsWTN55coV1/Tly5dLALJ79+5y9erV8sCBA3LAgAGybdu2rnkOHz4sa9euLSdOnCj37Nkjd+7cKbt37+4auaaoqEhmZ2dLADIrK0vm5eV57awZShcvXpQVKlSQDz/8sGva4sWL5Z/+9CdDywd67EIhmPMi0PRnZGTIDz74QO7evVt+9913ctCgQbJOnTpBne/sJEpEpoxpMwajV4/2Ow46AMSIGIxuE9nakqysLLz55ptITExEt27dXC/mOXLkCD7//HNcuXIFAwcOxLBhw9yW6969O2bNmoWsrCx07drVNcZzcXExZsyY4faYvlmzZvjggw/wwAMPoGXLlujTpw9SU1Nx/fp1HDt2DBs3bkStWrWwb98+AECLFi0wbdo0jBkzBu3atcPAgQORkpKC/Px8ZGdnIy4uDmvXrvW7b2PHjsXs2bMxfPhwDB06FPXq1XO1X73rrrswf/58v+uoUqUKOnbsiI0bN+IPf/gDUlNTXeNOt27dOpBDbaloz7e5c+fi2WefRZkyZXDbbbfhrbfe8tjHxo0bGx6XO5Si/Vj78/TTTwc0Dvqzzz6LIUOG4P7778fKlSvRoEEDbNu2DV999RVSUlLwxRdfuI0F/u2336Jq1aqYP3++q/39sGHD8PTTT7vmGTNmDP74xz/ipZdeck37+9//jiFDhgAAYmJikJeXh7i4ONx6661unXHDKTY2Fr169cLy5csxffp0CCGwbNkyV2dffwI9dnYTaPq//PJLt+XnzZuH+Ph4bN68Gf379w938o0zE9VH6w9Yg042EsoadCmlXJizULae29pnzXnrua3lopxFIUuDUceOHZPvvPOOHDRokExNTZVxcXGyXLlyMjExUfbt21fOmzfPreZQXcu8Z88eOWDAAFmtWjVZqVIl2aVLF59jV3/33Xdy5MiRsmHDhrJ8+fKyevXqsmXLlvLhhx+WmZmZHvN//fXXcsiQIbJWrVqyXLlyMikpSf72t7+VCxYsMLx/mzdvlj179pTVqlWTVapUkV27dpWLFy+Wa9euNVSDLqWUBw4ckHfeeadMSEiQQoiAx4wOhWjPt0mTJvkd+q5Hjx5BHSOratCj/Vj7Y2Yc9CVLlsjOnTvLypUry0qVKsk2bdrIF198UXds62HDhsn/9//+n9u0Z5991pW/R48elQBkpUqVZGxsrOunYsWKMiEhwbXMc889J7t162Y4jaEyY8YMCUBu2bJFFhUVyWbNmslff/3V8PKBHLtQCPa8CCb9J0+elADkxo0bTW1byvDUoAspo2/oGbOEENvT0tLStm/Xf/U5UTjt3bsXAPy2GQzGlrwtmPHtDGSfzvb4rH2d9hjdZnRE3yBqVm5uLpKTkwMepo4ii/kWPjzW7lJTU/H444/j8ccfd00bMGAAbrrpJkydOhXLli3DvffeC734oHz58q63pypPwfSetoRTXl4e6tWrh4kTJ+LOO+/Ee++9F/CwoKXVXXfdhQMHDiA7O9vVvydQRq/f6enp2LFjxw4pZXqg22ATF6ISrFNSJ3RK6oSDZw9i8LLBrumLByyO6JjnREThcunSJRw6dMitQywA7Ny509V8pVy5crh06RISExNRpYr3Ea2+/fZbS8Z9D1ZSUhJuvfVWLF26FFJKezfVsJE//elP2LRpEzZt2mQ6OA8XzxHeiajE0QbjDM6JqLT47rvvALiPr52fn4/jx4+7pnXq1AnVq1fHvffei507d+LQoUNYvXo1Hn30UbcX3vz666/Yt28fTp48GdAQiqEwYMAA7NmzBx999JHXMcHphgkTJuCTTz7BmjVromIIRgboREREVGJ9++23SElJcasZ37lzJ8qVK4cWLVoAcLwIaMWKFTh//jx69uyJtm3b4qmnnkL9+vXdhvJ78cUX8d///hf169d362AaCQMHDgTgaGZhZNz60mzcuHH4+OOPsWbNmrAOCRoMtkEnipBwtEFXm7ZrmuvvsW3HhmWbREQUOk2aNMGECRPw2GOPRToptvXoo49i3rx5WLJkieuGDHCMjuWrOZMvbINORJZhUE5EVLK88sor6NatW6STYWvTpjkqp3r16uU2fdKkSZg8eXIEUmQMA3QiIiKiKDR8+PBIJ8H2orWlCNugExERERHZCAN0IiIiIiIbYYBORERERGQjDNCJiIiIiGyEAToRERERkY0wQCciIiIiMiBco8IwQCeKECEEALi9RpqIiIjsSwnQlWt4qDBAJ4qQChUqAAAuXboU4ZQQERGREco1W7mGhwoDdKIIiYuLAwCcOnUKhYWFKC4ujtoXKhAREZVUUkoUFxejsLAQp06dAnDjGh4qfJMoUYQkJCTg0qVLuHz5Mo4fPx7p5BAREZEBlStXRkJCQki3wQCdKEJiYmLQoEEDFBQUoLCwEFevXmUNOhERkQ0JIVChQgXExcUhISEBMTGhbYTCAJ0ogmJiYlCzZk3UrFkz0kkhIiIim2AbdCIiIiIiG2GATkRERERkIwzQiYiIiIhshAE6EREREZGNMEAnIiIiIrIRBuhERERERDbCAJ2IiIiIyEYYoBMRERER2QgDdCIiIiIiG2GATkRERERkIwzQiYiIiIhshAE6EREREZGNMEAnIiIiIrIRBuhERERERDbCAJ2IiIiIyEYYoBMRERER2QgDdCIiIiIiG2GATkRERERkI2UjnQAiIiIiO5q2a5rr77Ftx0YwJVTaMECnUo8FMBER6Xnv2/dcf/P6QOHEAJ1KvdJYAPOmhIjIt4NnD3r837R60wilhkobBuhUqpXWArg03pQQERmxJW8Lpn87HdtPb3ebPnjZYKTXSceYNmPQKalThFJHpQUDdCpRjNYMl+YCuLTelJBvfKpCBCw6sAhTvpmCYlms+/n209sxevVoTO48GYNTBoc5dVSaCCllpNMQNkKI7WlpaWnbt2/3PzNFpVvm3uL6e/fI3brz+CuAASBGxJS4AtjbTQmAEn9TQv4ZOXeo5IrUDZqR7YYrbVvytmD06tE+rw2KGBGDGb1n2KrMtONNth3TFE7p6enYsWPHDilleqDLsgadQiISJ6WRmuEteVv8BucAUCyLMfmbyUiqkmSrAtgs1gqFTzRekKLlqUo0HttoEalmb/62e/DsQbd5MhplhOy7Of3b6YaCc8BxjZjxrb0CdKvy0IrzbNquaSj4pQDz9893TSv4pQAJFRN47hrEAJ0sF84CFQisuUq0F8BqgTTnKY03JZESTe37o62pVzQdWzswWkZE6gbN13bD/d08ePag7tNFX7JPZ7vSHOmbRyvz0Oh55m2ft+RtcVuHQgnW0+qk2apcsSsG6GSZSFzsA6kZvqXmLUEVwHZjtBC1601JpC9ooWD0ImmHfbfyqUo49idaavntxFcZMW3XNBwvPI7s09nIu5Tn9lmob9D8XSta1miJj/Z+FNYnflmnskwv17R604jdPFp93TV6nnmriFt0YBEmfz3Z5zYeXvUwpnSZwqe1frANOlkiEu26t+RtwcOrHoaE/+9wjIjBXal34b/7/xvwdiZ2mIg/NP+DmSSGzMGzBzF42Y3juHjAYo9CVO8Ro1F667NaSWrzHGj7/kjveyBtbQUE/p3xb58X+VDuD/tOmOOvjFDnmTeh6Itj5FphlJXtwGd8OwPv7Hon4OUea/sYejXs5bc8DgUrr7tGzzNf89WLrYcTl04YTv+f0v+E+1vdb3j+QCkVB9tObcOtibcCCH+FCNugU0RFqgnF9G+nGwrOlW1vOL7B1HYuXrtoarlQCKS2RO8Ro1FKrVCg7P5IPRSM1EQ/vOph9KjfA2/3etsW+x7IUxUJ6fOpSij3h30nAmekjDhZeNLQuqwus41eK4yy8olflfJVTC239NBSj8A+HE3ErLzuGj3P+t/UH8sPL/c6XyDBOQD8a/u/UK1CtZCdu+prYPbpbADR9bSWNegUtFErRwXUdKR9nfaY3Wd2UNvU1g6F0sQOE3H+6nnX/3ZtkgDcqC25peYtQR2fx9o+htFtRhuaVx2UqwtEvZrUZzY+o/tIHQhdbWgom18EUhMNAKnVU5FzNsdjejhrgs2eO9pawVDXbEf7iBqBsOo7amXttJq3MjvQdAd6rTDKihrrUFxTgnkC4e/YBnvdVdafeTQTB84dMFzZZTUjT+jM8Jaf4Xq6oWANOkVMsB1rzNiStwXPffOcqWXNKLxWGLb2hb463RitLXn262eDTkcgtUneauq1ebzowCIsP7zc63pCVRsayrwLpCYagG5wDoS3JthsW9sn1j6BZzs/i05JncJSs23XvhOhYMV31OraaTVvZXYg6X5hywshCc4B80/81JpWb4r0OumWpjGYJxC+jq0V191gnrBaydcTOjM3rr4qDgD7doDXwwCdghJsx5pABVtDVDe2Lk5eMvZ4V6EuJIDQNknwVigHGggGq2NiR0PzaZs3qKkLQgB+Ow4B1j9SD2XzCzMXSV/CNYqO2SZbPxb+iNGrR+Pe5vdi3t55IW3SFokb/0gJ9DvqLWgJdRmhLbMDTbeZvjBGWdUMcUybMQE9ETOiWBZj0uZJGNh0IABjgaa/YxvsdXdRziJTy4eKkRtAhV5nZ0VibKKh+CBamsYxQKegmC0Y1xxbE3DHSytqiLrX745Pcz4NaB3aR39G78ADvfv3VihbHQj6075Oe7eCUm8//NVSKJT21+XLlA+ov0CwtaFmRjYINL/MXiR9sWLf/e2H2ba2gCN9c/fMDWh+M/sT7hv/cFHnTVqdNFOjb+jdxIejjFDKbF/nVlJsEtrXaY/6cfXdPstolBHStO08sxPTdk0L+glZp6ROmNR5kuVPIk5eOunKN18vYzI6qo7Z6+7e/L24c/GdOHrhqKnlQ+m17NfQulZrt++0mrfjpz4fYkRMQE/d7D6sMAN0CorZi/3WU1tdFyujhaoVNUR333w3alSqgXd3vRvUenzdgSv7ZeTuH/AfTKZUSwkqrYGIETEebc+1AUGgTzEkJK4WXQ0oHcHUhpppfuFv7H69oPfYhWMBp82IYMdW9tfswOjTEauYyUuzAciHP3yIJtWaWPa43BczI0QYDSa8lS/ebuJDcbOotfXUVr/nVt6lPN1mbKFuTrH55GZsPrkZQPAjdgxJGYK6VepixrczXB0L1RrENcCPhT+aTqv2XPB1vVBTfycOnT9kattLDy01tVw4KHno7cZV8Xjm42heoznGth3rcT4EGh/YvWkcO4lSUKzoWGNkWDYrtqN0kvnP3v/gla2vBLUuhV4HF1/Dl2n31UiwKyDC0oFHr0OT9rhP6TwFk7+ZHJb0mBneMtDhAzsmdcTRC0d9dlqtWbGmxzFYdnhZSGsslX0PdPhCI8NvhrODtcJIXqoD6PgK8abPUW8d86weClLvPPe1XjPHXen8CsBnh9z6cfWx9GDog69wlUVWCTaf9c6nzGOZpoZjVFPXhhsZ7lIt2vIgUEb3z1uHezNC2XGUnUQpYqzoWLPwwEIMTRnqc55ga4jUNcNWDpuo7eDir22fel+NNtkJR2Hcvk57jG4z2rUf3mr1J30zKeRpUZjJp0CHD9ySt8Xr59tPb8dDqx7ymB6OY3Dx2sWA2vgaadIDeA/yQs1IXqprEBcPWGx6W8qj622ntrmaWmibWATbVt1b3wu99RptDqanWBbjqXVP4cK1C17Lge2nt2PH6R0Br9uMaAsMzeaztt+RwqqnFephCwMVbXkQKKP7Z1VwDti3aRwDdApasB1rJn89GbO/n42/dfqbW020upAsI8qYTp+AQPd63bHj9A7sOL0D3/30nel16ck+nY1FOYvwwQ8f+G3bp97XcHf89EZbexCqodoCFehj3HC31Q+l/+z7j+GxlY2Oww5E7uLurymcNuANtvlQsSx2a2qhbT6gHMu6sXVdQbyR5hC+hglVr1fJIyvOpfPXzvudp6QHbWaZHbFD/X15pM0jbp9Z1USsWBbbuslJaWKnd52osYkLWWLRgUWY9HVwNYsCwu31v+pHfxM7TLSsWQq5Uz8GDnRc71DSNh/y14b4xS0vmnpTbLRRmnDkXcrD8cLj+OzwZ7YP0B5t+6irFl8tmNplq+0eudvnd2z6rul491tjfVdiRIzh0W7Ik3L8fsj/QbcduJn1GR2xw18zsWm7pmHJwSVeb9Io+oTybeFs4kIR17pm66DXISEx6etJSKqShJoVa7p9Vni1MOj1q7Wo0QJ78vdYus5otSVviysItkutPuDZfMhX58dFBxaFdBg3OymWxZj09STbB+Vq03ZNQ9vabd1qMR/PfBzrj6+3zX6sObpGt6Pwlrwt+MfWf+DAuQOG1xXoaDd0g7a5nRV9JoyM2GF05Ce7jB9O1gl3x3mjGKCTJawaRUBC4tGvHsW14mtu043WXBkVrcF5KDoIKUGwHZuIKCOAaKnblipt+e0S6IVDtO2r9mZrS94WrDu+LrKJ0hi3bpzb/4OXDUajqo1w7MKxqDre0dyJ8O6b70ZCxQTdp2bB8jVih9GRn8a2iZ7XxJMx2mGF7YQBOlnCyjZc2uCcbmhRowUqla1kyWNfRfbpbLyw5QUcLzxu2Tqt9MTaJzyGNVPXatmp1p+8U75nCRUTsP74+kgnxxA7jhftT2r1VOw/uz/SyTBFeQqmPCFT11Z3rdvVNZSiWXpDfgbyluZgR28prZpVb4Z9Z/dFOhke9IYVtpMYK1YihBgmhHhbCLFRCHFBCCGFEB+ZWE+uc1m9n1NWpJVCI5iXn5BxP+T/gNl9Zgc1yoWe+fvnB33xCxVvYw4rnR/tVutP3s3fPx/vffte1D7BigbegnMrxn4Pl4NnD3o8OWtUtZEl634t+zW3mnne4Ifebxr9JtJJ8CAgMLnzZNuOgQ5YV4P+NwBtAFwEcBxAsyDWdR7AVJ3p9uxmSwDs24arJIrG15mHSrQ+yicKNyubi4SaXpvzj/d9bMm6lRfiKC+64Q1+6FUpXwWPtHkE3/30nW0qgu6++W5DnYYjyaoAfQIcgflBAD0ArA1iXeeklJOtSBSFjxXjoZMxyiu1G8U1wtHC6HsET0QUaTO+ncEnv2HSMbEjmlZviunfTrdVgG53ljRxkVKulVIekKVpzEbyMKbNGMQIS75S5EfepTwG5xQSLWu0jHQSiELunV3vYOZ3MyOdjBJP6YS5JW+LbUbAsXPHUDU7RlMVhBAjhBDPCCHGCSF6ChHEW2oobDoldcKkzpMgICKdFCIyaXz6eKTXCXjIXqKok/9LfqSTUOJduHbBNYSlHdr6CwhbdwxVs2OAnghgHoAX4WiLvgbAASFEj0gmiowZkjIEKdVTIp0MIlupUbEG6sbWjXQyDKlZsSbGtBnDG20iClrO2Rxbdebvk9zH1h1D1ew2zOJsABsB/ACgEMBNAB4D8DCAFUKIzlLKb/2tRAjh7ZsQTOdVMuDg2YPIOZsT6WQQ2Ur+L/lYPGAxFuQssKyzW6gofRxSq6Vi/7noHK6PiOzDTp35m8Q3iXQSDLNVDbqUcoqUco2U8rSU8rKU8nsp5RgA/wJQCcDkyKaQ/LHqhUVEJc1r2a9FzbjaeZfysP/cftSpXCfSSSHyqkJMhUgnwXba12kf6STYWjR1DLZbDbo30wE8CaC7kZmllLoNKJ0162kWpos0rHxhEVFJYpfRCwJx+vLpqH4zJZVsV4uvRjoJtiIg0KJGC0tfZFfSRNOQ0NESoJ9x/o6NaCpIl3p83fgK8RFMyQ1VylXBxeu8WSAKVvky5XGt6BqDdCIbExCY0mUKZu7myDTeRMvoLYpoCdA7O38fjmgqSJd66CQ7jC0aI2LwRs83ULNiTd0XXhCRcVeLWEtJZHeTukzCLTVv8frm5dIumkZvUYQ9QBdClAPQBMB1KeUh1fSWAPKklAWa+RsBeMf570dhSygZon0d8/z98yOUEodoeH0vERGRlSZ/PRkN4hpEOhm21bNBz6iLCyzpJCqEGCSEmCOEmANgonNyZ2WaEOI11ez1AOwFkKlZzXAAJ4UQK4QQ04QQrwoh/gdgH4CmAL4A8BrIFrbkbcGolaNsV0MtIV2v79XePJB13rz9TZ//ExFFQplS/NoU1p571yGpQ6STEDCratDbAhipmXaT8wcAjgJ4ys861gK4GUA7OJq0xAI4B2ATHOOiz+ObSu1h0YFFmPLNFFu8dEDPwbMH0bR6U44oE0Lj1o1z+/+ORncgvU66bca6JaLSqUgWRToJZEPR1DlUYUkNupRyspRS+PhprJo3VzvNOX29lPL/SSmbSSmrSSnLSSlrSSl7Syk/ZHBuD1vyttg6OAcc4zhn/C8Diw8sjnRSSjwBgdvr3w4ApfLlNjUq1kCNijUitv3SdryJjGBTj+Bph2uc0nlKhFISvGjrHKqw1TjoZH9WvK538YDFIX+VeN6lPOw/G9mXrAxoMgAxomSfYhISG05swJa8LeiU1Al9kvuYWk/fxn0tTlnoTe4yGevuXod1d6/D4gHB3wy2q9UuoPmTYpNwb4t7g96unVhxHMldrUq1Ip2EsKtRsUbUl73lY8pHbNuLByzG7D6z8UibR1w/Q1KHRCw9qdVSkVo91dSyMSIm6jqHKqL7G0xhdfDsQcuaMIxpMybqC1B/Xuz2Imb0nlHiXxxRLIsx49sZAICHb3nY1Doebv0wbq9/e1TVCA9NGer624rama71ugY0f96lPHy458Ogt2sHypMYX8dxZsZM251LA5sMDGk51r5Oe8zMmBnUjctjbR8LKg0ta7QMavn2ddqHvEJG69ufvsWI5iPCfo0J9lgpYkQM3v3NuxG7YVXOw7Ftx7p+ALiC9VD6/c2/d/t/8YDFWDhwIRYOWOhxPPxdL2JETFQPGhEtwyySDVjVpjvrVBb+0PwPmNR5ku2bywSqVqVa6JTUCfXj6gMAOiV1QqekTjh49qDtOtRaKft0tqvtf6Bt0ZXHj2/3ehtb8rZgxrczbPuijbqxdZFeJ92Vv2q3178d64+vNz1eeJXyVVwXv8yjmThw7kDUjj0uIDAhfQI+O/QZcs7l+J1f/STGWwCgnEuLchZh0jeTrE6yy9g2YzHt22n+ZwSw/PByjGg+Ah/t/cjycmzxgMVB3/i1r9MerWu1NjRvjIjB2DZj8c6ud1zTFg9YjJ9/+RkPrXrI1PbVtZejV48OW1kvIbEnfw9m9J6Bf279p6HvoBX+e+d/Pcr6xQMW47ufvzN8rYt0UOlrqGQlUAeAvfl7gyrv9KRWS8VfO/0V/93/X9c09TmgPR/+nfFvr9eL9nXaY3Sb0VEbnAMM0CkAVr0lVFnPkJQhqFulrq0DskD98ZY/4g/N/+AxPVTt31rUaIE9+Xs8pi8esBgvZL0Q8BOP9nXao2m1pm4FpFFZp7LQtHpTjGkzxvDFWPv4sVNSJ5y8eDLs3wcBgUfbPuoWnGj5Cpi25G3BhhMbgrpYdUzs6Fr/1lNbozY4B4ApXaZgcMpgrD++3vAyypOY2X1m+5xv2eFlwSbPp0AqIoplMfbk78H4tPH41/Z/WZoO7XftkTaP4HjhcXx2+DND3w0BgQvXLhiqGFAHM+pOlkpgaYY20Ax3hUz26Wz8teJfsXDgQkzfNR3vfvtuSLfXta7jCVjT6k3dbjKbVm+KptWbGrrW6QWVj7R5BN/99J2pNxFXKFMhoPcYtK/THn/r9De/8ymBur8KlT+l/wlTd0w1fC34vw7/BwA+a+nVn3mrALPi5tYOGKCTYVXKV7F8Pd5OsGh9vbivm5gOiR2w9dRWS7e3J38PBAR61O+Bi9cv4tbEWwEg4EAZcLSpHpoy1NVcJVDKvndK6mToYqxXU6R0Qg6nxlUb46+d/opOSZ3cAvRH2jzi9hIuXwV+sH0z1J2YrGxKFgl/Sv8TBqcMNrUf6icxekJ5bJTzaN3xdQEtl306G5d/vWxpWpRgT00Jitontvd7bimP/nPO+q851gYz6uDLbK23XqA5JGUIIBHSpx9arkqDtmNCHqA/1f7GQHXqmmaFv6ep3oLKsW3H4uDZg9i8LPAA/ZkOz2DyN5MNXUvNtNX2tk9333w3Eiom4P5W9yO+QnzA1wK946fQ+0x73EpCcA4wQKcAWDVMkd56Anl0ZWe+bmLuaHiH5QE6cKN5wIzeM9wuiEYDZcXQlKHYkrcFSw8tNZUO9b77ezri7fGjFZ2QA6HclCjUtTO+LhJqwQaN2gtjtA4PqrxqXHkPgdn9UIIqb5+FghIgXP71csABOgDdp1jBUAd7Wv7OrdRqqQE1j/r5l5/RFJ7H2+y5qBdobsnbgunfTg/7jae6wkQ5twt+KbD8hXqBjBLibT5fy5ttOjgkdQgggElf+74pCrZZjTbt6lp4f9/XurF1MaXrlKCbooS6bXwkMEAnw8wUElq+CjIjj67+2OqPeP/7901vP9R83cSEchxWpXmAtpAzUjgqbaqDHd9eu3+BPn6MRM2xOjgHPINyI0F6MEGj3oXRqqZkit/f/HscPHfQZ/7vL9iPU5dP4cK1C6a38++Mf1uyH76WM7vOgU0G4sTFE35vFs0+PbKSkWDP17n1QtYLAT191Cs3gjkXtWmP5Hsz1JUG6nPZygDdTM2zcq3bdmqb66mnP2abDg5JGYLsU9nYfno7Tl466TGvVW21fQXIoWiKMm3XjX4i6o6sJQkDdApIoM0m1PwVZEYeXd150522DdD9XVibVm+KurF1dQtJK3hrHmCkcAzmcTbge9+NPn4MR83x3TffjUPnDhm+KBphNmj0VnNkVVMyRc1KNfHXTn/1e3Gc8e0Mn23wvWlVoxVuq3+bZfvhazmz62xeozle6PaC32Nw6PwhU+u3SqDBnt65ZEWzIrPnorZpTqTfm+GtUkQJJr848gWOXThmujml2ZpnM8FkME0HX7rtJQAIaVttI/tkZVMUdfPDkhicAwzQKUBKITH5a2Pt2hQCwvQjNG2HGzu+sdLohbV7/e6mOmAa5at5gK/CMZimJUb23cjjR6trjrWMdoAKlNmg8b6W9+meD1Y/aVHS5+/iaHY/7mxyp27HaLP7EYqnUMpyeh34FFvytmDlkZWm1m8Fs8Geen+salZk9lxsV9t9LH+rmqzFiBg0rdbUUJt6ha9KAyWgG9t2rM+OjqnVUgGh35Y/EqOEmG06qLBDW20rmqIcPHvQ4/+S0u5cjQE6BUwpJF7Y8gKOXjjqd351JzwztHfHwdTih0IgF9a7b747pAG6vwurXuEYzONso/tupIbD6ppjtVC+rMLqQNTqm1D1dnxdHO2wH0aeQgW7Tm/fxenfTjdVk9qyRkv8kP9DwMtp1zE+fbypMlK9P8F28FZY8fTDqiZrShmTVCXJ9OhQvhh5wminUUKCbS4S6bbawdR2e+vLMHjZYKTXSceYNmOielhFLQboZEqnpE74bPBnfsck1nbCs2rbdhlDPdBalFA/AfB3YdUrHM3WujWIa4BnOz9rWYFoNkD0N+JPqMcVDkUgatVNqNHgFIj8fhgNqkKxTrPBpIDA+PTxQXWAbFmjJf57pzU37VY1K7LiZs2KJmva8tVsEw8jfNUu+3ryEilma8OjtTmIv74M209vx+jVozG582RXR/VoV7Jf5Ugh5+/1v1YH567tpgyx9C2dRt5Ipn0jn/I65EAvBKF8i6qZC2swHe+sDHqVADFQ/874t9fvQfs67TGj94yQF9iB5KmRoFG5CQ3me2LmqUGk9iOQoCoU6zQbTPZJ7oNOSZ2COqfHp483tZweq56CmDkXtTdrwTZZ0ytf/ZX7wZ7v6lfba2nfqmkHvtJbkhjty1AsizH5m8nYkrclTCkLLdagU9C0PdID6ZkeDCvfLDi582QsP7zcb7s+9Us8zNaihOoJQCBDfamFojOfWYHUjgoI3HnTnbZ4WUUwHbi8CeZFXmZrESOxH2ba8lq9TrPBZJP4JgDMndPKuOuhuMm14ilIsE8qgi0fvJ27oTzf7RR8GxFt6TUrkL4M3kY0i0YM0ClokS4khqQOCSpAV8aLHZI6xG+hb9W+Wv0W1WDaWIeiM59ZgQaI6pqySHeACkUg6i0YmdJ5iqEbSrvvRzBBlZXrtOImNZBzOpQdDK1qAhTszZrZ8kF5yY0/kT7fKTxC8cKzaMEAnUoE5TXY3sZ69UZ7gQpnoe8rwDDyem6FFS+ZsLrtcTCCCRAj/ag3VLV72mWN3lCaFa79sCKtVqzTqptUf2+LBEL/ZMfKpyDBnItmy5VARlqK9PlOoReKF55FCwboVCKoa7Z9XRzVvF2gwl3o63VAUv5Xj/Wqx6qauFB0vAuG2QAx0k9zFKEIRPW+l6G+oQzXfkR6nVbfpPobiSbUrHwKEszNWqjLFbuc7xQ6oXjhWbRggE4ljpELoK8LVCQKfSNvsMxolBGyNtahaHtshWh+jG11IOrtexnqG8pw7Uek12l1MBnp2l2rn4KYORftWq5Q9LBTH6lwY4BOJZL64hjKwDbUtIFHKIf6CkXbYytEOtAxK1w3eqHeTmmppbQ6mLTLcbPyJtfMuWjXcoWig536SIWbkNLcK26jkRBie1paWtr27fZ6CyWF3i1zb3H9vXvk7gimJDrY6cUcROHk682S0RpMTts1zfV3JG8cWK6QGaNWjgq4+dnsPrNDmCLj0tPTsWPHjh1SyoDHEGYNOpUK0VoLGynR3LSEKBh2GLbTaiWxNp9KD7v1kQoXBuhUKtjlAhVNeFNDpRmDydBguUKBKq19GRigE5Eu3tRQacdg0nosV8iM0tiXgQE6ERGRDgaTRPZREpuf+RIT6QQQERERERlRWpqfsQadiIiIiKJGaWh+xgCdiIiIiKJGaWh+xiYuREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2UjbSCaDo8MbqHNffE3qnRjAlRESlRzSUvdGQRqJowwCd/Mo5XYg3Mw+4/u/XOgmpdeIimKLw4EWHiCJNXfbasRyKtuuDHct1O6aJIo8BegTZ/aTcfPBnvJl5AFuPFLhNz3hjAzokJ2BcrxR0bVozQqkLPbtfGImoZMs5Xejxv12C32i9PlhVrlt5/ea1hvQwQI+gSJ2URgqW+duO4elFu1Es9dex9UgB7n0/C68MaY27bm0QimRGlJ0vjBQ5dr+pJmtFKr/tHvxG6/XBynLdquu3Nk1/W7wbNapUYPlCDNAjJZIBoL+CZfPBn30WvopiCUxc9B3qVa9ky5oSM+x+YaTIYk1X6aLN73AE7HYPfqPx+mB1uW7F9dtbmj7KOgYA6JCcEPHjRpHFUVzCbPPBn3HXjG+Q8cYGt+kZb2zAXTO+weaDP4d0+3oFi9abmQf8Fr6KYgm8pbqIRbP5247h3vezPApMhXJh/HTbj2FOGdmBkXOHSg69/H4z84DrJxQCDX7V14s3Vue4fkIp2q4PVpbrgV6/veWJvzQBwIhZvNaUdqxBD6NI1owYrUHIOV3os9DQk3WkIOqbgERjrVBJZ5fmJFbVvoVrf+xy3KKF9nj5ym+1UJR5ZoJfpdwOR0fNYK4Pn3+X55oWru+lleW6meu33hM3o2mSAP688DucvXwNo3s08T0zlUgM0MMkkgFgIAXLpWu/mtrG5oM/2zJANxqsmL0wlhR2C+rsMjKEVTfV4dwfNsMxTpsvMTHAm18ZKwusbvZmNvjt//Ym7D5xPqRpU5h9wqvc9CjC9b20qlyfMH8Xluw8AX+rUl+/a8VVcPtMuaF7/rM9htMEAC+v2Ifqlcvbqi0/hQcD9DCJVAAY6I3BkLT6prZz8RdzgX2oGbkohOupgRIEbzmcj0431fCZpnAzcpysDOK9rctOfQACOXf+slD/pjrc+8POzcZ4y5c3VgfWHMPKp55mg19tcK4IxRNZs+V87s+X3P4Px/fSynJ98c4ThtdRLIExH21HoeZYZbyxAfWqVcKJc1cCShMA/CWET23tfF0q7Righ0Ekm40EemOwLcB0KqpUtN9XyV+wohRMu348Z2r9gTw10NbUZTmPsx0KQqNBnZWjFujVJtutc1wg544E8MqKvVj++G2uaeHcHzvd2Nidv3wJlFVPPUNRyWH1E1mz5fzcb466/W/V99JXpUEwtf3q8m/VD6cCXoc2OFeYCc4BQIbwqa1dr0vEAD0srCooAmXmxuBowWVT2/JWIIWCv5pco8FKsB29jFxQvaVFMX/bMdx9a0Ovy4ey6UkgQZ3Zmll1+jskJ3jdXo3Y8si/dM3v+sLVB8DMubP7xAX0f3sTJvZtBgBha9Jm5EZgxKwsvDrUXkPeRYLRpyKBsuKpZ6gqOax8ImvlOae9QTVT1vmqNDB7w6Ms56/sDje9Srtgrw/eOrrzyZs9MEAPg2ALCrPM3hg0SqgccKA+9ascpDeqHvJaOn9teY3WWo7/TfDBrvqCqldQGqmp+8vC3Zi+/jBeGNQKXZvWdFtPv9ZJIWu3bPQ4tW1QDacvXPWo+TFaA+bWvlfA6/aMBOeKcPQBCKa5wb3vZ6FRjdiwNGkLpMOZ3o2A3foehFogT0UCZeSpp6/jHcrvs1VPZFPrxKFDcoJlQatygyohAy7r/FUamL3hqVKxrOVPWazy4ud70bZBNUzonRpUvxZ/Nx988mYPDNDDwGxBkX30bFDbNRvg39o4AT+evRxQ4RRokKFt9+av/ZuR2l7AeK3lvywYikxdu6wtKH8qvGq4gD/y8yWMeD8Lrw5p7bYebQ2/0obxH8NaB1VoBtK2esexc14/99dEQ3sBtfJiF0zAYSQoDebmuFg68jQQZvcn2L4tkRjn2wwr0mXmqUigfD319BdQWR38BpK2QIzrlYJ738+ytInQXxbudpvmK0A0+uTvwi/XTaWncvkytgzOAWB9zk9Yn/MT1uw7E1DHYPX5U7daRUP7F+nx9okBeliYDabW5/zkdx5fne0WbD9uarst61VFgxopAXeY0gYZvi6qeu3evLV/M1rbG0itZbA6Jifgp8Kr+NuS73UvFOXLxASUFunsaOjPiXNXgm6uYGUtorqJhnIcjhVcxolzVyIaDPlipC19JPpUBLo/Zvu2KM2qvI3zrbAyQA82wPaXLu369bYX6ndMAPo3doE0JatXrRIE4He0ELNps+JGp2vTmnh5yC0hD2L1AsQ/zt2GNXvPeD0+yjJ/7JaM9zcdCXibHZMTsHDHCVsG52q+OgaPmJWFXs1rY9bIW13TjT7J1OKwwpHFAD0MgqkZ8fXaX7Od7fzp2rRm0O3mfdUWzd92zOc61O2yA6ntDbTW0qwYAbSuH++zFulaUXHA6zWaXcGMjxuKWkSlZjYrzG01zdRyG21LH4mLUaD7Y/YcVZpVac+XUI7zHUjgrw0ijeSZdv162wvHSFPaG7v5245h4sLdPgNKJaD6c59mAY0WYiZtU5bvcf0fzA3Y3bc2RP3qlUN+3isB4jeH83HhynVk7jtjaJmZGwMPzgFgSFo9j9r8aCMBfLX3DDYf/Nk1Rr5aoHFBSRxWOFowQA8Ts48Fldf+1q1W0S1o9VYjc3NiHPafMv+Gw47JCUitE4cvvw+85zoALMg+jlkbj+i2Wb45MQ7nL1/DqQtXfa5D3S47lG1GzYgRcNXORDpdZsbHDVUtYriDc8ARcBitEQx0lJNQNzfQE2itfTABp5GbWavaoQbawVgdXH9zON9nnm09UoD8i+7liXbUDWV74XgqUq9aJdffSuWCv2JCCai+2us/+ASAW+pVxe4TFwJO2/Zc9+Poq/LHiK5Na7oCQPXN3cA2dbH025Om1qmnWAY2zGEwnlPdwES7vy3ejVpVK1pShpWElxFGIyGljaKfEBNCbE9LS0vbvn17RLYfbM12cs1Y/KZ57ZAFhzECGNi2HhomVMauH88ZamJTmnRMTsATvVJs1bMfAP7zYEfDAdTbmQfweohfBR4uf+qd6taXIPeVfh7zvLE6B9+fPO/zsTjg+O6rH6W/sToHxwouG3o5iVVWTeju9QKoN1ZxuM5RAaBX89poWTfebzt17ag9f/7fd7pDy3kL/LXBnr90Gc2bDskJGBqm2lFl37ztezCU78hdM76xrAwKpPzwpvHEzy1JC9nXpP4tcH/X5EgnI+qkp6djx44dO6SU6YEuyxr0MAr2seCRny+ZfnTnjxKg/NlAO+jS6JZ6VTF/dOewdDQLlPL40Uhtsh3HqzfrDc2Nhr+mD74oLxv65nA+YsuXcT25AgAhHH0EQs1o58JwP61Q1/B6az6iMNrWVdu+2MyQdoFkydYjBdh2pADVKpfDucvmOg8Gsq0/zMoKybqV74iVHTWtGIKzV/Pafm+CKbrZ9WWEJVnJuVpHia5Na+L42cvYmlsQlou+EfGVyuGOZrXRtmG1SCfFtnafuICc04Vh6WgWKOXxo792vhPm77Jl+s3Snj7aJhnexvj1tT69R+lSOkZ2uHytyHxiDXgr8wCecI5GBDiaSISiFjYY3pqPKH+rGR0D/sCZwrA0GZNAyIPzUFI3n7Gyo6aZPi3aJyVr9zE4L+lKUuVOtGATlzDbfPBnS4eoovCZ1L8FLv7yqy2biCRVrYi8C7+4/tc2l5i/7VjUd34ySgCID0NNaSg0S4zD3+9sgeNnL9t2qDetGrHlUSuuAvYF0fcl3ML1VMRqIzo2dGs3bvV5/Q+DNenqJi3h7qtBkeGrCR55xyYuUcRunR7JuB9OXMDWXHteiNTBOeA5PvzERaUjOAeiu6Z036lCjHA2j4iWYiL/0rWAXjRlB9EYnAPugwYs3HHC8sD4LwaG1NM+KWFwbo1VE7ob7n8RbsrgERRelgToQohhAHoAaAugDYA4AP+RUo4wsa76AJ4D0AdADQB5AJYAmCKlDO7NPRFmx/bLZNzCHcejJmgCbgzhVqFsTNQGJKURs4r8CdXTMCmB5z/bg5Xju3t8ZqafABmXWicO43qlIP/iVbc+MJEWI+DW9I7Cx6oa9L/BEZhfBHAcQDMzKxFCNAHwNYDaAJYC2AegA4BxAPoIIbpKKfMtSXEElKT2v6VRNAZOEsAvvwY+JjsRlU77ThV69IcIdgQy8q2z5i3a2UfP2qLJmDJ4BMdAj4wYi9YzAUAqgKoAHgliPdPgCM6fkFIOklJOlFLeAeANADcDeDHolEYQe0ETEZHd/Wt1DibM3wXA+MviyLyMlnVcf8/fdiyod5lYpXX9eMz7Y8egRveh4FgSoEsp10opD8ggepwKIW4CkAEgF8C7mo8nAbgE4F4hRKzphEYYe0ETlQz1VSNqEJVEi3eecDVrYXAeWkoNtdGXW4XaLfWqYtlj3VhzHmFW1aBb4Q7n71VSSrdn8lLKQgCbAVQG0CncCbMKv+xEJUPPZrUgIp0IE6pVKhfpJFAU+cOsLLY5D4O/Lfkemw/+jOc/2xPxmyEBYGLf5pFNBAGwV4B+s/O3tzHslEGezb2X2AaU14cTkb5/3xvwSFQRcW/nxujfJinSyQjIq0Nvwbkr0Tm6DVFJprzcKtLtzoUAXh3KNud2Yac2F/HO3+e9fK5Mr+ZvRUIIbwOdm+q8aqVxvVIwYlZWxB9hEdnRhjC8ut4K98/ehqqV7FR8+jexlIyDT0Tm3Fwnjm3ObcRONej+KE+Uozq27dq0ZtTVvBGFi52GF/PlxLkr2JsX+Y5cgYjqgpOIQm7fqcKA38BMoWOnKiClhjzey+dVNfN55e2NTc6a9bTAk2atlNpxcAzvTkRERGQPmw/+zJcS2YSdatD3O397a2OuDMpqv/esB4ijuZRszRNZuBERUfThcND2YadIca3zd4YQIkY9kosQIg5AVwBXAGyJROKC9cbqG/cV+RevRjAlFEqpdaogo2UiypYR2H3igmv6qgndseL7PLyx+oCPpYmIKFQyWtTBqj2nI50MW2MFon2EPSeEEOUANAFwXUp5SJkupTwkhFgFx1jojwJ4W7XYFACxAGZIKS+FM71WeTOTgZmVkmvG4sjP9vsq5Jy+iJzTnnmdWicOqXXisGL3qYj31CciKo0YnPvHEVzsw5IAXQgxCMAg57+Jzt+dhRBznH//LKV8yvl3PQB7ARwF0FizqrEAvgbwlhCil3O+jgB6wtG05a9WpDfc2OnCWgLAmB434S82G5XilnpV3WrNR3RsiBpVKrjN8/c7W3AUHyIisp1GNSqz/bmNWNUGvS2Akc6f3zqn3aSaNszISpw16u0BzIEjMH8Sjtr2twB0llLmW5TesNh88Gf8duoGZLyxIdJJKVHqVqtku+AcgFtwDjhGJPnmcL7b2Pddm9ZE7xZ1tIsaMrJzo6DSR0RUksTzxVuWGnt7k0gngVQsCdCllJOllMLHT2PVvLnaaZp1/SilvF9KmSSlLC+lbCSlHCeljKrXmc3fdgwjZmVhP5szWO7EuSuRToJhW48U4N73s/Dpth9d0zo3qWFqXY1rxrr9v2pC96DSFi7JNSpHOglEumrHVUC9apUCWuaWelX9z0QBMfuCsgVjOkekHOzfJgkdfbx0cHC7egF/r6xi9g3HyTVjcfetDS1NCwXHTqO4lBibD/6MiQt3B9WMYdWE7rilnrcRJymaFEtg4qLvsPngzwDMt/Hr2rQmxvVKwbheKRjcrh7+tuR7K5MZEiM6NsTa/+sZ6WRQGEXqbbAPdksOeJk37m6LfwxrbXj+GOF4Dbr6PPQVqJExGS0TA37LdsfkBFffnnB7/I4UzB/teXMwomNDjOuV4vpexZiNlk2Kcb4JNFACwAuDWlmfIAoKu+uGwJuZB4JuY5xaJw4T+zbDH2ZlWZImu4qrWBa/v7UB3t90BMUluGF2sQTeyjyArk1rIrVOHDokJ2DrEeMPhVwXo95xmL/tGN5ecyBix6tetUpolhiHzH1nvH7eITkBDRMqY0Jvx6ipIzo2jJqXENmZgP1fOPTwPG8vcg4NAUdQctetDTBr0xHDy/RqXhtdm9Y03EcoRgCvDHG8Bl17kz1/27Ggmt11TE6ABAIqE4Jhh++RADCoXT00THA8YRvXKwX3vp9lqFyLEcATvVJc/4/rlYJdP57D+jC8ibhetUqum4LUOnEYp0qHUt4BjgqVl4fcgqcX7ba0rK5fvRKOn/V8itwxOQFP9EpB16Y1XU+Zvz95Hmv3nfG5ffX3muyFAbrFck4XBl3IjujoeMzUtWlN9GpeG2v2nol4YRoKAsD0Eeno2rQmbr+5Nt7KPICsMF2g6lWrhCd6NQ1rW/asIwXIOV3oKtTNXIw2H/zZ8gI/EDEC+McwR2HeeOLnHp+vmtBdt0arSe0q4UheiafN9lUTupf6Pi4fPdjRFVwowdKxgss4ee6KbnmiBDIAcNeMbwyV1+rgR2v+tmN4epH5ckQ5v78/cT5sAforQ2/Boh0nwlbeailBofq18kYDWr2AckLvVLydecBUgJ7RojZW7dGvbNDbtvaJizoo17r71oaoX72ypde2P3ZLRtemNd3Oe225q07T5oM/e92+r+81RR4DdIspzRiCoQ5m3h95q88TLFoptV5KwdC1aU3UiqsQlmBDXZgt3HEibBdF4MZb2sxejN7MjFzNuTYtSjC05XA+Ot3kaFfv7XFzsC+/6Jic4NqGsr3/bT8eVf0RtJQbcTNPFpQRgtQ1eEZqy0qS9o2qe9Rma4OlnNOFuoGMElQbOVbebjqB4G+YlXPq+NnLeHXlPnMrMUFAYP7ozh7HJxx8BYX+Alpfy5odv7tzk5qGAnSzNc3Kd9SqY608hVXX3Ptq5uNt+yM6NsQLg28JOj0UOgzQLWbFW7i067D6BI+0RjUq46XBt3gUdFbc3BihLswCqcm2gjpvA70YWfF0xiy9C6OvmiOtYF5+odQwqredc7owqt8t0DE5AS8MvgWzNxtrkqHVpHYV3N/V0eZanQ/BNrWIJi8NucVv+2Pt56l14gIOqn8qvOp1O8HcMKtr8sNZBgGOPjH1qlfyKIP/82DHkDar9HWzo/B2vfO3bLB9e44VXMbWIwW6N/1W1DSrg2qzlQxKU0cgsPJX2b4ag3P7Y4BuMSvewuVtHdoTLEYgKmvLZt7XXregDfUrhutVq4Rh6fXdpoWqnaA32rwN5GIUrhsYLSMXVX/MXtiEl1qrSB0LK6ibLJn9zntb7vK1IlPr89cm2W5ljbodsD/qmkYg8KBa6TuiFcwNs/qcumvGN2E/tuo+Merjo/yff/GqpX1GlPbmgZQjejdX/uYPpm+PItAbg0Bog+oOyQmm292boT0XyN4YoFvMirZcvtahPsE6JCd4rX2Nq1gWhSEOeM1Q1wBohfoVwyfOXUG/1kke043UZKfWicO8LUeDToO3vDVyMQr1DYyW8l2z4uJk5uIZV7Gsq4+CVriPhaJapbI4d8X8trWPyc1+570tZ/a4DE2rjx/PXtb9/terVgn/GNYaW48UhK0jni967YB9UQdFZoJqdd8RtWBuEpV1RfKpmLJf2qBR+V8doPdqVttrp3B/9NqbGxVoQBlMR1NFoDcGwQim3b0Zgda6U2QxQLeYmUBEzVcAC3ieYN5qX38qvBr2x6b+CPiuAQhHRxWlDbjetn3VZL9tQXMKf3nr72IU6hsYwP3tp1YX5oFcPNUdiPWE41ioKRfIS9d+xZTle0ytQ+8xeTCP5fWYPS4t61XFa3e18Vl72LVpTdMd8awSbKBiNqjWKzfM3gx1vunGexAi/STIW3kIwGN0Em+dwn8qvBqyToiBlkFWBbzhrGkOpt09lWwM0ENgXK8UU69zN/sIS++OP7VOXFibbvhj5MIa7M2NEf4uqt463wQbEBrJW38Xo1AX0Eq76FAxevHUdiD2tq5wUV8gzd6ojezcCFMGeo4zHMxjeT3BBvz+Op+dKbxqav2NEirjaMFlU8sqWtePx1/6NAsq761sUmS2TMhoeeNNwpF6EmRk+9ryyFuncKXTeyibhgTCioA33DXNZtvd+/PG6hzX36w9jz4M0EOga9OaeGXoLQG9rCjYmiG9O/5QDPFkRiA1AGZvbowyclHVK8iCCQqsejwZyhsYK9o3GmFVbZHVx6JHai20bVAN/Von+bxAmg3KtG+BVbPisbzCioDf14V8549nDa9XbWzPJkFVFtxSryqWPdbN3MIqVjYpsuLpR7ifBGkFsn1/AV44m4b4E6qAN9SsPobqjvQM0KMPA/QQUQKR5z/bg32nfL8Iw4pHWN5OvnCOABMjgJeH3OI2ikSgBWLXpjXRu0UdrNpzOhRJNH2MzQaEVj+eDHTUmeaJcdh3qtBvB8BwvqjCqounlSPw/LVfc91ta6dZ3SRF+czKdqhWBvxqOacL8f2JC4bmVWtdP971CnGzQfrEvs0DX0iHlflnxc2QVeec2RcPWX3O260Top1uGoyy6hhqX8Cl14+C7I0Begh1bVoTK8d39xkch+uOXnl0vXbfGXx34nzAyxsZ5UHpCKQO0M3sW+cmNUISoPtrA+5PoAHhq0NvcQUmVgl01JkV47vb9kUVwV48rRqBR/u98HWBtLpJisLKdqih6nhmtr10m/rxAMw90VO/8dMKVudfsDdDVjwJUr4bb2YesPx7GSg71tLa7abBn2CP4eaDP+t+FzLe2IAOyQkYx/bsUYMBehj4KgTDeUc7oXcqysYIUwG6r1EetMFDsAViKAoPK5pwBBoQWh2cq9frK9CpV60SOiQnuF6hbefHvcF+V4JtxqX3vfB3gQxVDbWV+RSKjmdm20vXjqvo+juQJ3qhunm0Mv+suBkKtPO0ejbtdyOcQ/ZFCzveNISKvxdwbT1SgHvfzzI9qg6FFwP0MBnXKwVbDue7/u+k6skfTqEc5UERbIFodftiK5twBBoch4qZYC6Qt8+FixUXT2/HQghAhmDoslAPjWZVPll9Y2Zl+23tPvpr+28lq/Mv2JuhQNPz54XfuaZpm8uEc8g+shejL+Aqlt5fVEX2wgA9TOxyFx/qUR6sYlX74lDUwtmpRjrQZiJ2+R6GgnbfP/pjx5A17Qn10GhW5pNV7XCtbn/vax9DfR5ZnX/BlgmBpMfXmyc5ZF/pFcgLuNQvqiL7YoBeyoR6lAerBFIb5O3zUAfMdumAFG1tLENJ+1bEUN5I2elGzR8rviOhan+vCPf3OBT5F0wFhtH0GBmONVq+l2QNK1/ARfbBAL0UClUbWqsZrQ1SF0zqYaXCUfDYITguybXigdI7FqG+kbLLjZovVn1HQll2ROp7bHX+WdHEz9f/gazHbk3aKDSsfAEX2QcD9FIomtoqGqkNimT6GBxHh1DfSNnhRi0coqnsCITd8s+q9LB8Kh2sfAEX2QcD9FIq2toqGq0N4gWJ9IT6e1GavnfRVnYYYbf8s1t6yN6s7MBN9sHcKcWira0iL1pE9hBtZQdRSRaKF6hR5DFAJ7ZVJCJToqH9PVFJF+oO3BQZDNAJAGunicgcu7XfJiqNomXwBzKOAToREZnGm3uiyCupHbhLMwboRERERFGuJHbgLs0YoBMRERGVAOzAXXIwQCciIiIqQTj4Q/RjgE5ERERUwrB/SHSLiXQCiIiIiIjoBgboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdlI2UgngCgqLH0cOJsLVG8MDHw70qkhIiKiEsyyGnQhRH0hxAdCiJNCiKtCiFwhxFQhRPUA1pErhJBefk5ZlVaigBzPBnZ+CORucPw+nh3pFBEZs/RxYE5/x28iqx3PBja8xjKRKAQsqUEXQjQB8DWA2gCWAtgHoAOAcQD6CCG6SinzDa7uPICpOtMvWpBUimaRqsU+vM79/yPrgfrtrd3G8WzHdm66Pfh1a9dl5bqtVFKfSthhv5Y+Dpz6Dsjb5fg/dwOQPjJ8+R/JY2CH42+UXc9NI45nA7N+A0ACa14AHvwqMvvg7xhG0/eBSMWqJi7T4AjOn5BSus4AIcS/AEwA8CKAMQbXdU5KOdmidFGkWVU4KrXYgHuw4W/9Vmz/ptuBNc/f+D+5h7Xrt/JC57au54EaqUB+juOzUF1E9S6Q/o6Lt/yMRur9B8ztl7fjdTwb+PxJAAJIvMXYd0x9bNXUN5bBfG/9BURmz9VA+Dpe4fpeeTsOvvZT+11Rn6vtHwDa/iH854GZfFn6OHBkAwDpnCD9V1xYkf/adXz8eyBnheMzpXwDvJ+PDToAF0/7vyEKJK28AQiMmZvSUpofQQfoQoibAGQAyAXwrubjSQAeBnCvEOJJKeWlYLdHUcSqi+XxbEfhq3ZkveO3r/Vrt39qt/EgB3A/0dvdB5w7ClRrdGMbevu3fa7/wkFbgBxeh4AudNr1lI91zH/T7Zp14UZw7m/dZgs1vZsLwHu+KNu5Wui+nlA8lTDKzL4rF5kqdYBlj8O1/+3vd5/vyHr/3wlv58nxbGBWrxvz5e3UP4eUtBzPBq5d8jy2iuQewdesa28AB7wDpN3r/rmZcxXwzIeljwMHvgQggLrtbhyTwjzv6Tf7tCvQ74C3m2q9vFTSpftdUZ2r2R84fiolAGXKAykZvisd1Od9MDf0/vJF74mc3g2guuJCnc7qjR3rDeRaoPdd0H5vG3S4EZwDACSw6z9A9mx4PR+XPeb4ra2s8JVWX0G9v2uMkWA0lAGlr+0b2a4yz4WTQNW61lS0BVoZFcgNf0mq+IE1Neh3OH+vklIWqz+QUhYKITbDEcB3ApBpYH0VhBAjADQEcAnAdwA2SCmLLEgrhYL2JFZ++wrC9JapUAWAAPq95h4EKye0WnIP/YuxOhiq3sj987ydjp8GHTyDCm0hpj3RH8z0PNG129/1H2MXO+082vab2gud1vFsYOd/3C+SOSscBd4AP4Wnsm5fFyRvNzJ6x0l7c7HrP0DBEffllHz3dmFXpytQ3i4yRi96Rgt07fHS+066/hfOvwUQWxvIfM77+l01kSoL/wg07u75/QVuBLt6tbDeJLVz5CfgvWYduFFTH5foSGOVOo7ARAn8XeeUalvLHgNqN1fdUGjTIryfq75upo9tcb+5zFmhCcZUFv4RqNX8RprVfH2v1IGusm5tHmlvftzKNs1N9fa5nnmpDhjd+MivKwWO3zs/9F3pANw4780+GdPmi/LdUwc82huyi6fdl6meDNRqdmNdejcqZ35wX8ZXRYFeIK73vf1hoZedUuWLsh69eZR91ZZ/ZTRhkbegHvA8fso1Rrkx06u88Hazoy17zTyhUfMVDPsq9/TOCwAoOOh9Xr0AXi+detcLvX1U77te2QF4Hre4RODXX9zn/fwpoEKcdTcYYWZFgH6z83eOl88PwBGgp8JYgJ4IYJ5m2hEhxP1SyvXmkkgho71gFBx0/622Zzmw82NHIK4uOLXzzvrNjcJEWyNcPRkQZYCvpjgKEDVtMDTgHf00L3sM2Pa+oyBUB1trnncEM3onunJB0QZq6qYved+6L6MUDh4FlMqu/7gXglXrO/ZNXXPk7ZGuBwlcOuOo7T+123GhUKiDtOUT3Au3g6vdV6O+yOjdKKkfx2ub/+gFI0rNrTZ4SWoHVKwKnD/hvs/eqGtUUzL0a+W2z/W8wF/6Cbjnv/oXE20Q7C2fPYINLwHWiZ1Aah+gwa3eA1PAEQxfu6x5wuF0Ntfxo/f93fXfG9/xNc8D9dL101K+iuNHqYXVq9nWWycA5EH/O+btnFL2ac0L7mlpcgfQ8683vkPaZmLqY6zNB73j4o1yvHJWABBAal/g+mX3p11q2iBQa+0LQM+/Of7Wu/nRK9t2/ddHmr18V9r+ASj61fNc1UuL8p3UnkPK+rUBr/a7rtS2q2+2Br7tPH9V+aYcS+VccmvGAkfZqf0O3Pak48mA+mbh86fc5zmx3f1/bf77Ktu8BeLlNOV/1frAiR2a7Tr/V8p19fqVfdWWfwBu3GSrSc8bGG35p1j4R6BidXgEo+rafb3jpK5EUj9tefAr/bLt0Bog4Sb9CgptEyT19chbueerEsXfvEoAf+kn/adcgKOsV9MeD+0+Ks001fYsd/x4HDedtKrPK70bDJsTUvq4kzeyAiH+DeAhAA9JKWfpfP4igGcAPCOlfNnPuiYB2AjgBwCFAG4C8BgczWR+AdBZSvmt9zW41rPdy0fN0tLSKm/f7u1jCtiG1/QLKEVSO8dvbxcgb6o3vlG7oX7MP+CdGzUagPvFuHoj97T0ehYoyPV9AYxLchQm/jzovLdUpyWpnaMmrUKc//1zzau5OfG1fe2+NrsT2PeZ/3SqazeUZjnpIx217tv1avO86PWs4+KrBHeH13rfr/h6wC8X3I9D9WSg8W2eeaheVnvRVG4k9GrvtevQHrv2DziaCOjRHktv0/Xy2d93pFKC47dS86msR6+ZirpPgFbCTUDB4Rv/K9/fA6uAomvu6zfKtT86gWaz/sC+5R6L+FS9sSOovHD8xjRv+6R96qR8H8+f8DwPvOWPL97ypUnPG0Gtsl0lWNVu1yvhaB7h7fsEOPK9WiP9c1+pVY5LArZr1qGcM+oavaWP+yinhOPJmK/jo9Rsu56q6JxvWsq5lj7ScSNwSHV++zqXqjcGRFnHOa9X7vo6X7yVCb7yv9mdwL7P4fr+NuvvLAfljf3QliPaNChl2dLHHUHa2Vz9bQE3zhntMdHOo/5++brGeEuTr2PcpKfx/FB4u8boGfDOjRsAiBvB8ZENwLlc38sq++7v2q9WL81ReaHOs3NH3cs0f+Wsr7LTY3vpnjeFCuW7ECbp6enYsWPHDilleqDLWlGD7o9w/vYbFUgpp2gmfQ9gjBDiIoAnAUwGMNjS1FFwtDUwWsrj8kADdHVNjrr9t/bx6k97HYE8AOzVBBvJPW6ciN5qZ/wF58oFRSmQ1JR9av+A//3Tfq4ER762v+pv7v/rBeft/+gIPK5fBspVdn/MrPeY2he3AtLZNMHfssp+1UwBfjnv/tnQWY5C/39/1N+OXq2HUoOkftTrreZQe+z2e3uyAM9jqVCeOKj7F2jz2VseKfuhFzgrtUz127s/0fB1gen2J/cgZc9yxzF4ar//i6HyZEn+6h58HFkPSAndmu3D6wIP0JV1p/YFCk/p75P6nNGrydWrgfb25KdGqmOZuETHd/z8iRuBobcbv0NrgUPrPPtD+ON2g2TgJvZKAdBykP65r9Qq662nXjtHsKWu0VM3Czi1G7h4SvW9k57fXyW/4+s5zntXbevzju+lEera2p5/cw8ItU8D1ZTvwJAZN25C1d9NX2XaUGcdnvZpzqbXPedVAjKl3Gv/R6DtPZ7f2xb9Hd9xdfl+c1/3gFZdY1+ruX6Arn46W72x5zFRUz+xUOedNvhX38QZqQhSnNPUNPsq2xSBVEJtfO3Gk74ftzme6HkL6tWVMOqnndonyL5on2zUa+eZXn/Hp1o9z7LGW9Ce1MZ7gG62OWUEWBGgK1fleC+fV9XMZ8Z0OAL07kZm9nan4qxZTwsiHaRVv73jQnhkveNEv34Z+PUa8OM3js9zVgDN+kH3kaH2xL928caPQnmkOHKZ43/txUAJ5PXWq242oTRx8FbT4e3ufeisGwWxt8eZZly/6P6/UuOmvsj8cs73OlL7Anf+y/G3EkjrtUnVNhNSuBVuArj7I8efSl7+9x7H43D1st6Ok14tOKAfHGkvnnqU4EHbFlmPkiZfBby3Y3n+uHunozn9PZtOabej8LUf6ovAwLcdAba3C6hyvNLuBX7ceuM7qm5qpL0R1l6YlO+p2w2VuJEO9ffWW7MTb8Hw+ROegX+DWx1Bkd4+qdOi1/zNg3C/mT6e7fgOJvfw/yhaubnySKNU3ZwYITxvkE7sBBKa3giCG9zqpykLbuTlxdMw/KRKL9jTPnnRfn/V5dKM2923FUggCDj2+cFM9xskbXCT2tdRGaK9+VPfhHqrmVY/5QS8NBs64rlM/fbu38/4et6bSwGq80PcaD6k3HgD7t9F9Q2m4rYnHcdCfeOkfL/KVXYv484fd1T6qJsLKU3J1PmmdxOnlPUScOuvktrH86ZXOc8DzVMXoV9OKdfNZv18NJl0atH/xpPUWb1uHJ9Tu2+cH6d261dUGH1CrUdbxrUc6nnD1Pb3jqeMShmg3Ly3/YP7PletD9Ro4r3Zm01ZEaDvd/5O9fJ5ivN3AI0KPZxx/vZy5aSIUgppxYbXbgTogKN2TAniY2s7/vd28dXW2Kpr0o1cDAD9GiptjfKu/7ifwNrgVLmgbJ/rHujr1fKpLwbagtzbHb62sFEuuHq1MNpCTq8ZiLbzjfqir72xUC/vLRhSt0lWu/sjZztBH490lQJdWxOt1Ky2/YOqrbrz8f3JHfrBrvbYxSUB5ePcp2svQMp2AM9jqdSSKc2Ssj9wpGXA2+4X8Kr1gdha7vuo7Lty0ddeBLRtn331V/A2UodeMH9kveN4KueQklfqJkxK3qlvmNV5qjcKkXIuKdONdjjzFvgr3ytvI6roUZ4Aqc8xbXnii3aIRXVw9OM278GBUhsLuB8r7Q0ScKOmGHDMp96GNhBUlzHqY6P+bmi/N3o1et7KOfXTCWU73s5DJRBUakmVmy69JnlH1ut/95Tt6QWf2ptQ7ed65dSG1+AWnGubdWm3pxeIa7+3yrHQfu+11yS1BrfeaPLi7emsckwU6goedf6przH12/tvjqJ9uqJ8F5Wnd+rjr601Vp5+Ken7cZvnjYZC/R33lh6vHW1VlOOu1yEWcJwfgPt1W71tve+E9hxQU39v1PmjDOygvolW39hrGS3bbMyKAF2JMjKEEDHqkVyEEHEAugK4AmBLENvo7Px92OdcZA9utX1Cv8D0RgkwtO3/tLVMRpttaJdVtlG/vfuFVR20KNQXHL1H0epl9draKgWDuu2tcoev1JZqt++xb8IzMNQrbLRBuPri6SsQ08sXvcBKHRho27hrb0qUbWu/B+paP+3FNO1e/x3mAMex0Aan2sJevR3tsVQ+c7sQSs8L1YXjwF1z3Y+73rHydlz1RkkwesHwlpfa7Xtbh146vc1r9MJlJPDXrstb8ze3YPUez3PMbA2X+nuu/U4q7ZSVzrva8kDh7QbJ202Nt3LN382PXoCppVcWqL/bgI+bIJ151XbM8wx0AM/vnnod3gJjo/ust37tUwuj29Nbt69rjN41SbsebzcE6m16exKm/o5oKyC0N3HapyvqJwPadLYc6miupfyvfvqlvVZpK5eUp6uA5rxY6WX9uBFYa8s9V9p0niB7q0DQblv7nVBPB/Tn0eazt+umnigNytWC7iQKAEKIL+EYqcXbi4pmSCnHOKeVA9AEwHUp5SHVvC0B5EkpCzTrbgTgKwBNAfxVSvlSEOncnpaWlsZOomEQyGNqb8urL0x6w4ipt6EuUNyGwfOyrBHadr/h7Fxi5vhpbwyC2ba2ba/eMJNGtm12P7yNQuNrvF5v+66XBu0+6nVSCya/g/3uWJWXkaYXOGhrVENxjgWzXiNlTzj5Ooe0aR3wtu8nlGrevmOh/u55q8AI9XfdSFnkLy16ZSPgWT76zbNexpcN9lqqtw9m1+9RNtvg/LC5YDqJWhWgNwHwNRxvE10KYC+AjgB6wtG0pYuUMt85b2MARwAclVI2Vq1jMoCJcNTIH4FjFJcmAPoBqAjgCwCDpZTXgkgnA/RoEkzBZEWh5q8gLcmOZ98YAiyQlztZKdyBgrYjcTD5bbcgz65CdZyCPXetDopCKZrSWhLoPRENtHyK9htwfucMi3iADgBCiAYAngPQB0ANOMZnWAJgirpW3EeA3gPAGADt4BgLPRbAOQC74BgXfZ4MMrEM0Clg0V6QUmCszG9exIwJ1XHiuUtEEWaLAD0aMEAnIiIionAIJkCPCUWCiIiIiIjIHAboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjVgWoAsh6gshPhBCnBRCXBVC5AohpgohqkdiPURERERE0aisFSsRQjQB8DWA2gCWAtgHoAOAcQD6CCG6Sinzw7UeIiIiIqJoZVUN+jQ4guonpJSDpJQTpZR3AHgDwM0AXgzzeoiIiIiIolLQAboQ4iYAGQByAbyr+XgSgEsA7hVCxIZjPURERERE0cyKGvQ7nL9XSSmL1R9IKQsBbAZQGUCnMK2HiIiIiChqWRGg3+z8nePl8wPO36lhWg8RERERUdSyopNovPP3eS+fK9OrhWk9EEJs9/JRm7179yI9Pd3fKoiIiIiITNu7dy8ANDazrCWjuPghnL+lDdZTdOXKlfM7duzIDTItZjRz/t4XgW2Tecy36MW8i17Mu+jEfItezLvQaAzggpkFrQjQlZrteC+fV9XMF+r1QEppuypypVbfjmkj75hv0Yt5F72Yd9GJ+Ra9mHf2Y0Ub9P3O397ahqc4f3trW271eoiIiIiIopYVAfpa5+8MIYTb+oQQcQC6ArgCYEuY1kNEREREFLWCDtCllIcArIKjnc2jmo+nAIgF8KGU8hIACCHKCSGaOd8aano9REREREQlkVWdRMcC+BrAW0KIXgD2AugIoCccTVL+qpq3nvPzo/Ds2RrIeoiIiIiIShwrmrgotd/tAcyBI6B+EkATAG8B6CylzA/neoiIiIiIopWQMtjRD4mIiIiIyCqW1KATEREREZE1GKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboGkKIXCGE9PJzyssyXYQQXwghCoQQl4UQ3wkhxgshyvjYzkghxFYhxEUhxHkhxDohxJ0+5q8khJgihNgvhPhFCHFGCPGpEKK5Ffsd7QLJNyFEihDiL0KINUKIH4UQ14QQp4UQS4UQPf1sh/lmMTPnnGb591XzN/UxH/POYibLS+HMi3XOMvOKEOKI89imelmGeWehQPNNCFFBCPGoMw9+dubDXiHEW0KIRj62w3wLESHEbUKIhUKIPCHEVefvVUKI3+nMyxglCvFFRRpCiFwA1QBM1fn4opTyNc38AwEsBPALgPkACgD0B3AzgP9JKYfrbOM1ON6SehzA/wCUB/B7AAkAHpdSvqOZvwKATABdAWQDWAOgAYDhAK4BuENKmWVmf0uKQPJNCPFfAHcD2ANgExx5djOAAQDKABgnpXxLZxvMtxAI9JzTLNsfwDIAFwFUAZAipTyoMx/zLgRMlJcVASwAcCeA/QC+AlAIoC6A2wA8IaX8TLMM885iAZaXZQGsg+N47oMjz64CuBVAdwDnAXSRUu7RbIP5FiJCiL8BeB7AzwA+A5AHoCaAdgDWSin/rJqXMUq0klLyR/UDIBdArsF5qwI4A0dh1V41vSKArwFIAL/XLNPFOf0ggOqq6Y0B5MNxEjXWLPO0c5kFAGJU0wc6p/+gnl4afwLMt1EA2ulM7wFHYXIVQBLzzX55p1muFoBTAP4LRwAhATTVmY95Z5O8A/Cu8/i9pHf8AJRj3tkr3+AIsiQcgXmM5rMpzs8+YL6FLe+U/FgNIE7n83KqvxmjRPFPxBNgt58AC64HnF++uTqf3eH8bL1m+ofO6ffrLPOc87MpqmkCwFHn9GSdZTY4P+sZ6WMXLfnmZz2rnMdzKPPN3nkHYDEcAXoN+A7QmXc2yDsATQAUAdgK59NbA8sw7yKfb39xHrMJOp+lOT9bznwLS77FADgM4BKAWgbmZ4wSxT9sg66vghBihBDiGSHEOCFETy9tte5w/l6p89kGAJcBdHE+/jGyzArNPIDjotYQQI6U8ojBZUoro/nmy3Xn718105lvoRVQ3gkhRgEYBGCMlDLfz7qZd6FlNO/+HxwBxlwAVZ3LPC2EeFh47zvAvAsdo/n2g/N3XyGENmZQ2iR/pZnOfAuNLgCSAXwB4KwQop9w9KcaJ4TorDM/Y5QoVjbSCbCpRADzNNOOCCHul1KuV0272fk7R7sCKeWvQogjAFoCuAnAXiFELIB6cLTxy9PZ7gHnb3VHKa/b8LFMaWU033Q5Ozv1gqPQ2qCaznwLPcN558ynNwF8JKVc4mulzLuwMJp3tzp/xwM4BMeTD4UUQrwHRxv0IoB5FwZG8+1zAIsADAGwWwjxFRxNAdMBdAPwNgBXm2TmW0gp59BpADsA3KL+UAixAcAwKeVPzkmMUaIYa9A9zYYjSEsEEAvHCTADjvZXK4QQbVTzxjt/n/eyLmV6NZPzm12mNAok3zw4axD+A6ACgMlSyrOqj5lvoWU475w1eHPh6BT6hIF1M+9CK5Dzrrbz93NwdCS7BUCcc/lDAMYC+LtqfuZd6BjON+lopzAMwGQ4grEnADwFoCccFRkfKzdVTsy30FHOoTEAKgH4DRznUCsAX8LRaXeBan7GKFGMAbqGlHKKlHKNlPK0lPKylPJ7KeUYAP+C44SYHMDqhLLaQJMRhm2UKMHkm/Ox7jw4eqDPB+B11BB/yQhgXuabU4B5NwGOzrwPaW6igk5GAPMy75wCzDul+UQegMHOeS9KKdfAEQAWA/iTEKJ8oMkIYF7mHQLLN+fIO/PhCMofBZAER1D2OwCNAGxwjhQScDICmJf55qCcQwKOmvJM5zn0A4DBcIy60sNLcxc9jFFsjAG6cdOdv7urpil3hvHQV1Uzn7/59e5EA90GudPLNxdncP4RHD3jPwUwwlljpMZ8iwy3vBNCpAB4EcBsKeUXBtfBvIsMvfNOuaFaKaW8op5ZSvktgCNw1AYq4yYz78JPL98mwlE+/lVKOUNKeUpKeUFKuQKOG6tycDQ5UzDfQkc5hw47zxkX5zn1pfPfDs7fjFGiGAN04844f8eqpu13/vZoW+UcOzYZjs6GhwFASnkJwAkAVYQQSTrbSHH+Vrfl8roNH8vQDXr5BsCVR5/AMb7rxwDukVJqO4cy3yJHm3ct4WiCdL/QvFwFjlp1ADjgnDYIYN5FkK/y8pyXZZTgoxLAvIsQvXxTOoKu1c7sDBILADQSQtRwTmO+hU5A5xAYo0Q1BujGKY+MDqumrXH+7qMzf3cAlQF8LaW8anCZvpp5AEfbzGMAUoUQyQaXoRv08g3Ox+j/g6Nm6EMA92raUWox38JPm3e5AN738qO8/XCB8/9c1XqYd+Gnd95lOn+30s7s7AOiXMhzVR8x78JLL9+UET5qaWd25ptSQ3pN9RHzLTQ2wBFQp3hpCqacW7nO34xRolmkx3m00w8cNXQJOtMbwdETWQJ4RjW9KoCfwJcARFu+VYBjZAIJYJaRY8d8s0fe+VjPOvBFRbbOOzjeRngIjrbmvTXLvOCcfx3zznb5Ng03XlRUQbPMy87PtjLfwpZ/HzmPxwua6b2d59Y5ANWc0xijRPFPxBNgpx84Osb8Ase4ndMAvApHLesV55fscwDlNcsMguOO9iIcwd4/4HgdsvJl9XghB4DXnZ//COANON6u97Nz2mM681cAsNn5+TYAr8DRJOM6HC8s6BjpYxdN+QbHCAbSWXBNcS6v/bmd+Wa/vPOxnnXwEqAz7+yTd3AMy3fZWWYugKND9nrn/GcApDLv7JVvcAy796PzsyMA3oOjM2mWc9plAJ2Zb2HLv9q4cSO1wXkOLXCeU9cBDNfMPwiMUaLyJ+IJsNMPHO1YP3F+ec85v1w/wfFK3fv0vsjO5brC+eIAZyG3G47RJsr42NZI5xf5EoBCOC5Sd/qYvxIcweQBOO6Gf3KeXC0ifdwi/RNovuFGMOfrZzLzzX5552M9Sp7qBujMO/vkHYAWcIwKcgaOZhE/wjHEX33mnT3zDY7mLa8B2AtHcH8NjrdHzgbQjPkW9jxMgOMm6YgzL/IBLAXQycv8jFGi8Ec4DywREREREdkAO4kSEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdkIA3QiIiIiIhthgE5EREREZCMM0ImIiIiIbIQBOhERERGRjTBAJyIiIiKyEQboREREREQ2wgCdiIiIiMhGGKATEREREdnI/weP7TuYTfT7VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(spec1.wavelength.value, spec1.flux.value, yerr=spec1.uncertainty.array, \n",
    "             linestyle='none', marker='o', label='Spec1: $S/N=20$')\n",
    "\n",
    "plt.errorbar(spec2.wavelength.value, spec2.flux.value, yerr=spec2.uncertainty.array, \n",
    "             linestyle='none', marker='o', markersize=1, label='Spec2: $S/N=50$')\n",
    "\n",
    "plt.errorbar(spec_alt.wavelength.value, spec_alt.flux.value, yerr=spec_alt.uncertainty.array, \n",
    "             linestyle='none', marker='o', label='Spec alt = Spec2 + Spec : $\\sigma_{net}=\\sqrt{\\sigma^2 + \\sigma_2^2}$')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(0, 2.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594e52d4",
   "metadata": {},
   "source": [
    "Weird, so the wavelengths of the result are taken from the bounds of the *first* argument.  This means \"addition is not commutative\" in specutils.  Let's see why:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ce2c6de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[500,~500.33445,~500.6689,~\\dots,~599.3311,~599.66555,~600] \\; \\mathrm{nm}$"
      ],
      "text/plain": [
       "<SpectralAxis [500.        , 500.33444816, 500.66889632, 501.00334448, 501.33779264,\n",
       "   501.6722408 , 502.00668896, 502.34113712, 502.67558528, 503.01003344,\n",
       "   503.34448161, 503.67892977, 504.01337793, 504.34782609, 504.68227425,\n",
       "   505.01672241, 505.35117057, 505.68561873, 506.02006689, 506.35451505,\n",
       "   506.68896321, 507.02341137, 507.35785953, 507.69230769, 508.02675585,\n",
       "   508.36120401, 508.69565217, 509.03010033, 509.36454849, 509.69899666,\n",
       "   510.03344482, 510.36789298, 510.70234114, 511.0367893 , 511.37123746,\n",
       "   511.70568562, 512.04013378, 512.37458194, 512.7090301 , 513.04347826,\n",
       "   513.37792642, 513.71237458, 514.04682274, 514.3812709 , 514.71571906,\n",
       "   515.05016722, 515.38461538, 515.71906355, 516.05351171, 516.38795987,\n",
       "   516.72240803, 517.05685619, 517.39130435, 517.72575251, 518.06020067,\n",
       "   518.39464883, 518.72909699, 519.06354515, 519.39799331, 519.73244147,\n",
       "   520.06688963, 520.40133779, 520.73578595, 521.07023411, 521.40468227,\n",
       "   521.73913043, 522.0735786 , 522.40802676, 522.74247492, 523.07692308,\n",
       "   523.41137124, 523.7458194 , 524.08026756, 524.41471572, 524.74916388,\n",
       "   525.08361204, 525.4180602 , 525.75250836, 526.08695652, 526.42140468,\n",
       "   526.75585284, 527.090301  , 527.42474916, 527.75919732, 528.09364548,\n",
       "   528.42809365, 528.76254181, 529.09698997, 529.43143813, 529.76588629,\n",
       "   530.10033445, 530.43478261, 530.76923077, 531.10367893, 531.43812709,\n",
       "   531.77257525, 532.10702341, 532.44147157, 532.77591973, 533.11036789,\n",
       "   533.44481605, 533.77926421, 534.11371237, 534.44816054, 534.7826087 ,\n",
       "   535.11705686, 535.45150502, 535.78595318, 536.12040134, 536.4548495 ,\n",
       "   536.78929766, 537.12374582, 537.45819398, 537.79264214, 538.1270903 ,\n",
       "   538.46153846, 538.79598662, 539.13043478, 539.46488294, 539.7993311 ,\n",
       "   540.13377926, 540.46822742, 540.80267559, 541.13712375, 541.47157191,\n",
       "   541.80602007, 542.14046823, 542.47491639, 542.80936455, 543.14381271,\n",
       "   543.47826087, 543.81270903, 544.14715719, 544.48160535, 544.81605351,\n",
       "   545.15050167, 545.48494983, 545.81939799, 546.15384615, 546.48829431,\n",
       "   546.82274247, 547.15719064, 547.4916388 , 547.82608696, 548.16053512,\n",
       "   548.49498328, 548.82943144, 549.1638796 , 549.49832776, 549.83277592,\n",
       "   550.16722408, 550.50167224, 550.8361204 , 551.17056856, 551.50501672,\n",
       "   551.83946488, 552.17391304, 552.5083612 , 552.84280936, 553.17725753,\n",
       "   553.51170569, 553.84615385, 554.18060201, 554.51505017, 554.84949833,\n",
       "   555.18394649, 555.51839465, 555.85284281, 556.18729097, 556.52173913,\n",
       "   556.85618729, 557.19063545, 557.52508361, 557.85953177, 558.19397993,\n",
       "   558.52842809, 558.86287625, 559.19732441, 559.53177258, 559.86622074,\n",
       "   560.2006689 , 560.53511706, 560.86956522, 561.20401338, 561.53846154,\n",
       "   561.8729097 , 562.20735786, 562.54180602, 562.87625418, 563.21070234,\n",
       "   563.5451505 , 563.87959866, 564.21404682, 564.54849498, 564.88294314,\n",
       "   565.2173913 , 565.55183946, 565.88628763, 566.22073579, 566.55518395,\n",
       "   566.88963211, 567.22408027, 567.55852843, 567.89297659, 568.22742475,\n",
       "   568.56187291, 568.89632107, 569.23076923, 569.56521739, 569.89966555,\n",
       "   570.23411371, 570.56856187, 570.90301003, 571.23745819, 571.57190635,\n",
       "   571.90635452, 572.24080268, 572.57525084, 572.909699  , 573.24414716,\n",
       "   573.57859532, 573.91304348, 574.24749164, 574.5819398 , 574.91638796,\n",
       "   575.25083612, 575.58528428, 575.91973244, 576.2541806 , 576.58862876,\n",
       "   576.92307692, 577.25752508, 577.59197324, 577.9264214 , 578.26086957,\n",
       "   578.59531773, 578.92976589, 579.26421405, 579.59866221, 579.93311037,\n",
       "   580.26755853, 580.60200669, 580.93645485, 581.27090301, 581.60535117,\n",
       "   581.93979933, 582.27424749, 582.60869565, 582.94314381, 583.27759197,\n",
       "   583.61204013, 583.94648829, 584.28093645, 584.61538462, 584.94983278,\n",
       "   585.28428094, 585.6187291 , 585.95317726, 586.28762542, 586.62207358,\n",
       "   586.95652174, 587.2909699 , 587.62541806, 587.95986622, 588.29431438,\n",
       "   588.62876254, 588.9632107 , 589.29765886, 589.63210702, 589.96655518,\n",
       "   590.30100334, 590.63545151, 590.96989967, 591.30434783, 591.63879599,\n",
       "   591.97324415, 592.30769231, 592.64214047, 592.97658863, 593.31103679,\n",
       "   593.64548495, 593.97993311, 594.31438127, 594.64882943, 594.98327759,\n",
       "   595.31772575, 595.65217391, 595.98662207, 596.32107023, 596.65551839,\n",
       "   596.98996656, 597.32441472, 597.65886288, 597.99331104, 598.3277592 ,\n",
       "   598.66220736, 598.99665552, 599.33110368, 599.66555184, 600.        ] nm>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_net.spectral_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47ace1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[510,~510.33445,~510.6689,~\\dots,~609.3311,~609.66555,~610] \\; \\mathrm{nm}$"
      ],
      "text/plain": [
       "<SpectralAxis [510.        , 510.33444816, 510.66889632, 511.00334448, 511.33779264,\n",
       "   511.6722408 , 512.00668896, 512.34113712, 512.67558528, 513.01003344,\n",
       "   513.34448161, 513.67892977, 514.01337793, 514.34782609, 514.68227425,\n",
       "   515.01672241, 515.35117057, 515.68561873, 516.02006689, 516.35451505,\n",
       "   516.68896321, 517.02341137, 517.35785953, 517.69230769, 518.02675585,\n",
       "   518.36120401, 518.69565217, 519.03010033, 519.36454849, 519.69899666,\n",
       "   520.03344482, 520.36789298, 520.70234114, 521.0367893 , 521.37123746,\n",
       "   521.70568562, 522.04013378, 522.37458194, 522.7090301 , 523.04347826,\n",
       "   523.37792642, 523.71237458, 524.04682274, 524.3812709 , 524.71571906,\n",
       "   525.05016722, 525.38461538, 525.71906355, 526.05351171, 526.38795987,\n",
       "   526.72240803, 527.05685619, 527.39130435, 527.72575251, 528.06020067,\n",
       "   528.39464883, 528.72909699, 529.06354515, 529.39799331, 529.73244147,\n",
       "   530.06688963, 530.40133779, 530.73578595, 531.07023411, 531.40468227,\n",
       "   531.73913043, 532.0735786 , 532.40802676, 532.74247492, 533.07692308,\n",
       "   533.41137124, 533.7458194 , 534.08026756, 534.41471572, 534.74916388,\n",
       "   535.08361204, 535.4180602 , 535.75250836, 536.08695652, 536.42140468,\n",
       "   536.75585284, 537.090301  , 537.42474916, 537.75919732, 538.09364548,\n",
       "   538.42809365, 538.76254181, 539.09698997, 539.43143813, 539.76588629,\n",
       "   540.10033445, 540.43478261, 540.76923077, 541.10367893, 541.43812709,\n",
       "   541.77257525, 542.10702341, 542.44147157, 542.77591973, 543.11036789,\n",
       "   543.44481605, 543.77926421, 544.11371237, 544.44816054, 544.7826087 ,\n",
       "   545.11705686, 545.45150502, 545.78595318, 546.12040134, 546.4548495 ,\n",
       "   546.78929766, 547.12374582, 547.45819398, 547.79264214, 548.1270903 ,\n",
       "   548.46153846, 548.79598662, 549.13043478, 549.46488294, 549.7993311 ,\n",
       "   550.13377926, 550.46822742, 550.80267559, 551.13712375, 551.47157191,\n",
       "   551.80602007, 552.14046823, 552.47491639, 552.80936455, 553.14381271,\n",
       "   553.47826087, 553.81270903, 554.14715719, 554.48160535, 554.81605351,\n",
       "   555.15050167, 555.48494983, 555.81939799, 556.15384615, 556.48829431,\n",
       "   556.82274247, 557.15719064, 557.4916388 , 557.82608696, 558.16053512,\n",
       "   558.49498328, 558.82943144, 559.1638796 , 559.49832776, 559.83277592,\n",
       "   560.16722408, 560.50167224, 560.8361204 , 561.17056856, 561.50501672,\n",
       "   561.83946488, 562.17391304, 562.5083612 , 562.84280936, 563.17725753,\n",
       "   563.51170569, 563.84615385, 564.18060201, 564.51505017, 564.84949833,\n",
       "   565.18394649, 565.51839465, 565.85284281, 566.18729097, 566.52173913,\n",
       "   566.85618729, 567.19063545, 567.52508361, 567.85953177, 568.19397993,\n",
       "   568.52842809, 568.86287625, 569.19732441, 569.53177258, 569.86622074,\n",
       "   570.2006689 , 570.53511706, 570.86956522, 571.20401338, 571.53846154,\n",
       "   571.8729097 , 572.20735786, 572.54180602, 572.87625418, 573.21070234,\n",
       "   573.5451505 , 573.87959866, 574.21404682, 574.54849498, 574.88294314,\n",
       "   575.2173913 , 575.55183946, 575.88628763, 576.22073579, 576.55518395,\n",
       "   576.88963211, 577.22408027, 577.55852843, 577.89297659, 578.22742475,\n",
       "   578.56187291, 578.89632107, 579.23076923, 579.56521739, 579.89966555,\n",
       "   580.23411371, 580.56856187, 580.90301003, 581.23745819, 581.57190635,\n",
       "   581.90635452, 582.24080268, 582.57525084, 582.909699  , 583.24414716,\n",
       "   583.57859532, 583.91304348, 584.24749164, 584.5819398 , 584.91638796,\n",
       "   585.25083612, 585.58528428, 585.91973244, 586.2541806 , 586.58862876,\n",
       "   586.92307692, 587.25752508, 587.59197324, 587.9264214 , 588.26086957,\n",
       "   588.59531773, 588.92976589, 589.26421405, 589.59866221, 589.93311037,\n",
       "   590.26755853, 590.60200669, 590.93645485, 591.27090301, 591.60535117,\n",
       "   591.93979933, 592.27424749, 592.60869565, 592.94314381, 593.27759197,\n",
       "   593.61204013, 593.94648829, 594.28093645, 594.61538462, 594.94983278,\n",
       "   595.28428094, 595.6187291 , 595.95317726, 596.28762542, 596.62207358,\n",
       "   596.95652174, 597.2909699 , 597.62541806, 597.95986622, 598.29431438,\n",
       "   598.62876254, 598.9632107 , 599.29765886, 599.63210702, 599.96655518,\n",
       "   600.30100334, 600.63545151, 600.96989967, 601.30434783, 601.63879599,\n",
       "   601.97324415, 602.30769231, 602.64214047, 602.97658863, 603.31103679,\n",
       "   603.64548495, 603.97993311, 604.31438127, 604.64882943, 604.98327759,\n",
       "   605.31772575, 605.65217391, 605.98662207, 606.32107023, 606.65551839,\n",
       "   606.98996656, 607.32441472, 607.65886288, 607.99331104, 608.3277592 ,\n",
       "   608.66220736, 608.99665552, 609.33110368, 609.66555184, 610.        ] nm>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_alt.spectral_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "516c333d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0,~1,~2,~\\dots,~297,~298,~299] \\; \\mathrm{pix}$"
      ],
      "text/plain": [
       "<SpectralAxis [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "    12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "    24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "    36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "    48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "    60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "    72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "    84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "    96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
       "   108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
       "   120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
       "   132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143.,\n",
       "   144., 145., 146., 147., 148., 149., 150., 151., 152., 153., 154., 155.,\n",
       "   156., 157., 158., 159., 160., 161., 162., 163., 164., 165., 166., 167.,\n",
       "   168., 169., 170., 171., 172., 173., 174., 175., 176., 177., 178., 179.,\n",
       "   180., 181., 182., 183., 184., 185., 186., 187., 188., 189., 190., 191.,\n",
       "   192., 193., 194., 195., 196., 197., 198., 199., 200., 201., 202., 203.,\n",
       "   204., 205., 206., 207., 208., 209., 210., 211., 212., 213., 214., 215.,\n",
       "   216., 217., 218., 219., 220., 221., 222., 223., 224., 225., 226., 227.,\n",
       "   228., 229., 230., 231., 232., 233., 234., 235., 236., 237., 238., 239.,\n",
       "   240., 241., 242., 243., 244., 245., 246., 247., 248., 249., 250., 251.,\n",
       "   252., 253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
       "   264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274., 275.,\n",
       "   276., 277., 278., 279., 280., 281., 282., 283., 284., 285., 286., 287.,\n",
       "   288., 289., 290., 291., 292., 293., 294., 295., 296., 297., 298., 299.] pix>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec1.add(spec2, compare_wcs=None).spectral_axis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e23d1b5",
   "metadata": {},
   "source": [
    "Pixels instead of nanometers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b782d7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[510,~510.33445,~510.6689,~\\dots,~609.3311,~609.66555,~610] \\; \\mathrm{nm}$"
      ],
      "text/plain": [
       "<SpectralAxis [510.        , 510.33444816, 510.66889632, 511.00334448, 511.33779264,\n",
       "   511.6722408 , 512.00668896, 512.34113712, 512.67558528, 513.01003344,\n",
       "   513.34448161, 513.67892977, 514.01337793, 514.34782609, 514.68227425,\n",
       "   515.01672241, 515.35117057, 515.68561873, 516.02006689, 516.35451505,\n",
       "   516.68896321, 517.02341137, 517.35785953, 517.69230769, 518.02675585,\n",
       "   518.36120401, 518.69565217, 519.03010033, 519.36454849, 519.69899666,\n",
       "   520.03344482, 520.36789298, 520.70234114, 521.0367893 , 521.37123746,\n",
       "   521.70568562, 522.04013378, 522.37458194, 522.7090301 , 523.04347826,\n",
       "   523.37792642, 523.71237458, 524.04682274, 524.3812709 , 524.71571906,\n",
       "   525.05016722, 525.38461538, 525.71906355, 526.05351171, 526.38795987,\n",
       "   526.72240803, 527.05685619, 527.39130435, 527.72575251, 528.06020067,\n",
       "   528.39464883, 528.72909699, 529.06354515, 529.39799331, 529.73244147,\n",
       "   530.06688963, 530.40133779, 530.73578595, 531.07023411, 531.40468227,\n",
       "   531.73913043, 532.0735786 , 532.40802676, 532.74247492, 533.07692308,\n",
       "   533.41137124, 533.7458194 , 534.08026756, 534.41471572, 534.74916388,\n",
       "   535.08361204, 535.4180602 , 535.75250836, 536.08695652, 536.42140468,\n",
       "   536.75585284, 537.090301  , 537.42474916, 537.75919732, 538.09364548,\n",
       "   538.42809365, 538.76254181, 539.09698997, 539.43143813, 539.76588629,\n",
       "   540.10033445, 540.43478261, 540.76923077, 541.10367893, 541.43812709,\n",
       "   541.77257525, 542.10702341, 542.44147157, 542.77591973, 543.11036789,\n",
       "   543.44481605, 543.77926421, 544.11371237, 544.44816054, 544.7826087 ,\n",
       "   545.11705686, 545.45150502, 545.78595318, 546.12040134, 546.4548495 ,\n",
       "   546.78929766, 547.12374582, 547.45819398, 547.79264214, 548.1270903 ,\n",
       "   548.46153846, 548.79598662, 549.13043478, 549.46488294, 549.7993311 ,\n",
       "   550.13377926, 550.46822742, 550.80267559, 551.13712375, 551.47157191,\n",
       "   551.80602007, 552.14046823, 552.47491639, 552.80936455, 553.14381271,\n",
       "   553.47826087, 553.81270903, 554.14715719, 554.48160535, 554.81605351,\n",
       "   555.15050167, 555.48494983, 555.81939799, 556.15384615, 556.48829431,\n",
       "   556.82274247, 557.15719064, 557.4916388 , 557.82608696, 558.16053512,\n",
       "   558.49498328, 558.82943144, 559.1638796 , 559.49832776, 559.83277592,\n",
       "   560.16722408, 560.50167224, 560.8361204 , 561.17056856, 561.50501672,\n",
       "   561.83946488, 562.17391304, 562.5083612 , 562.84280936, 563.17725753,\n",
       "   563.51170569, 563.84615385, 564.18060201, 564.51505017, 564.84949833,\n",
       "   565.18394649, 565.51839465, 565.85284281, 566.18729097, 566.52173913,\n",
       "   566.85618729, 567.19063545, 567.52508361, 567.85953177, 568.19397993,\n",
       "   568.52842809, 568.86287625, 569.19732441, 569.53177258, 569.86622074,\n",
       "   570.2006689 , 570.53511706, 570.86956522, 571.20401338, 571.53846154,\n",
       "   571.8729097 , 572.20735786, 572.54180602, 572.87625418, 573.21070234,\n",
       "   573.5451505 , 573.87959866, 574.21404682, 574.54849498, 574.88294314,\n",
       "   575.2173913 , 575.55183946, 575.88628763, 576.22073579, 576.55518395,\n",
       "   576.88963211, 577.22408027, 577.55852843, 577.89297659, 578.22742475,\n",
       "   578.56187291, 578.89632107, 579.23076923, 579.56521739, 579.89966555,\n",
       "   580.23411371, 580.56856187, 580.90301003, 581.23745819, 581.57190635,\n",
       "   581.90635452, 582.24080268, 582.57525084, 582.909699  , 583.24414716,\n",
       "   583.57859532, 583.91304348, 584.24749164, 584.5819398 , 584.91638796,\n",
       "   585.25083612, 585.58528428, 585.91973244, 586.2541806 , 586.58862876,\n",
       "   586.92307692, 587.25752508, 587.59197324, 587.9264214 , 588.26086957,\n",
       "   588.59531773, 588.92976589, 589.26421405, 589.59866221, 589.93311037,\n",
       "   590.26755853, 590.60200669, 590.93645485, 591.27090301, 591.60535117,\n",
       "   591.93979933, 592.27424749, 592.60869565, 592.94314381, 593.27759197,\n",
       "   593.61204013, 593.94648829, 594.28093645, 594.61538462, 594.94983278,\n",
       "   595.28428094, 595.6187291 , 595.95317726, 596.28762542, 596.62207358,\n",
       "   596.95652174, 597.2909699 , 597.62541806, 597.95986622, 598.29431438,\n",
       "   598.62876254, 598.9632107 , 599.29765886, 599.63210702, 599.96655518,\n",
       "   600.30100334, 600.63545151, 600.96989967, 601.30434783, 601.63879599,\n",
       "   601.97324415, 602.30769231, 602.64214047, 602.97658863, 603.31103679,\n",
       "   603.64548495, 603.97993311, 604.31438127, 604.64882943, 604.98327759,\n",
       "   605.31772575, 605.65217391, 605.98662207, 606.32107023, 606.65551839,\n",
       "   606.98996656, 607.32441472, 607.65886288, 607.99331104, 608.3277592 ,\n",
       "   608.66220736, 608.99665552, 609.33110368, 609.66555184, 610.        ] nm>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec2.add(spec1, compare_wcs='first_found').spectral_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d901adbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[500,~500.33445,~500.6689,~\\dots,~599.3311,~599.66555,~600] \\; \\mathrm{nm}$"
      ],
      "text/plain": [
       "<SpectralAxis [500.        , 500.33444816, 500.66889632, 501.00334448, 501.33779264,\n",
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       "   503.34448161, 503.67892977, 504.01337793, 504.34782609, 504.68227425,\n",
       "   505.01672241, 505.35117057, 505.68561873, 506.02006689, 506.35451505,\n",
       "   506.68896321, 507.02341137, 507.35785953, 507.69230769, 508.02675585,\n",
       "   508.36120401, 508.69565217, 509.03010033, 509.36454849, 509.69899666,\n",
       "   510.03344482, 510.36789298, 510.70234114, 511.0367893 , 511.37123746,\n",
       "   511.70568562, 512.04013378, 512.37458194, 512.7090301 , 513.04347826,\n",
       "   513.37792642, 513.71237458, 514.04682274, 514.3812709 , 514.71571906,\n",
       "   515.05016722, 515.38461538, 515.71906355, 516.05351171, 516.38795987,\n",
       "   516.72240803, 517.05685619, 517.39130435, 517.72575251, 518.06020067,\n",
       "   518.39464883, 518.72909699, 519.06354515, 519.39799331, 519.73244147,\n",
       "   520.06688963, 520.40133779, 520.73578595, 521.07023411, 521.40468227,\n",
       "   521.73913043, 522.0735786 , 522.40802676, 522.74247492, 523.07692308,\n",
       "   523.41137124, 523.7458194 , 524.08026756, 524.41471572, 524.74916388,\n",
       "   525.08361204, 525.4180602 , 525.75250836, 526.08695652, 526.42140468,\n",
       "   526.75585284, 527.090301  , 527.42474916, 527.75919732, 528.09364548,\n",
       "   528.42809365, 528.76254181, 529.09698997, 529.43143813, 529.76588629,\n",
       "   530.10033445, 530.43478261, 530.76923077, 531.10367893, 531.43812709,\n",
       "   531.77257525, 532.10702341, 532.44147157, 532.77591973, 533.11036789,\n",
       "   533.44481605, 533.77926421, 534.11371237, 534.44816054, 534.7826087 ,\n",
       "   535.11705686, 535.45150502, 535.78595318, 536.12040134, 536.4548495 ,\n",
       "   536.78929766, 537.12374582, 537.45819398, 537.79264214, 538.1270903 ,\n",
       "   538.46153846, 538.79598662, 539.13043478, 539.46488294, 539.7993311 ,\n",
       "   540.13377926, 540.46822742, 540.80267559, 541.13712375, 541.47157191,\n",
       "   541.80602007, 542.14046823, 542.47491639, 542.80936455, 543.14381271,\n",
       "   543.47826087, 543.81270903, 544.14715719, 544.48160535, 544.81605351,\n",
       "   545.15050167, 545.48494983, 545.81939799, 546.15384615, 546.48829431,\n",
       "   546.82274247, 547.15719064, 547.4916388 , 547.82608696, 548.16053512,\n",
       "   548.49498328, 548.82943144, 549.1638796 , 549.49832776, 549.83277592,\n",
       "   550.16722408, 550.50167224, 550.8361204 , 551.17056856, 551.50501672,\n",
       "   551.83946488, 552.17391304, 552.5083612 , 552.84280936, 553.17725753,\n",
       "   553.51170569, 553.84615385, 554.18060201, 554.51505017, 554.84949833,\n",
       "   555.18394649, 555.51839465, 555.85284281, 556.18729097, 556.52173913,\n",
       "   556.85618729, 557.19063545, 557.52508361, 557.85953177, 558.19397993,\n",
       "   558.52842809, 558.86287625, 559.19732441, 559.53177258, 559.86622074,\n",
       "   560.2006689 , 560.53511706, 560.86956522, 561.20401338, 561.53846154,\n",
       "   561.8729097 , 562.20735786, 562.54180602, 562.87625418, 563.21070234,\n",
       "   563.5451505 , 563.87959866, 564.21404682, 564.54849498, 564.88294314,\n",
       "   565.2173913 , 565.55183946, 565.88628763, 566.22073579, 566.55518395,\n",
       "   566.88963211, 567.22408027, 567.55852843, 567.89297659, 568.22742475,\n",
       "   568.56187291, 568.89632107, 569.23076923, 569.56521739, 569.89966555,\n",
       "   570.23411371, 570.56856187, 570.90301003, 571.23745819, 571.57190635,\n",
       "   571.90635452, 572.24080268, 572.57525084, 572.909699  , 573.24414716,\n",
       "   573.57859532, 573.91304348, 574.24749164, 574.5819398 , 574.91638796,\n",
       "   575.25083612, 575.58528428, 575.91973244, 576.2541806 , 576.58862876,\n",
       "   576.92307692, 577.25752508, 577.59197324, 577.9264214 , 578.26086957,\n",
       "   578.59531773, 578.92976589, 579.26421405, 579.59866221, 579.93311037,\n",
       "   580.26755853, 580.60200669, 580.93645485, 581.27090301, 581.60535117,\n",
       "   581.93979933, 582.27424749, 582.60869565, 582.94314381, 583.27759197,\n",
       "   583.61204013, 583.94648829, 584.28093645, 584.61538462, 584.94983278,\n",
       "   585.28428094, 585.6187291 , 585.95317726, 586.28762542, 586.62207358,\n",
       "   586.95652174, 587.2909699 , 587.62541806, 587.95986622, 588.29431438,\n",
       "   588.62876254, 588.9632107 , 589.29765886, 589.63210702, 589.96655518,\n",
       "   590.30100334, 590.63545151, 590.96989967, 591.30434783, 591.63879599,\n",
       "   591.97324415, 592.30769231, 592.64214047, 592.97658863, 593.31103679,\n",
       "   593.64548495, 593.97993311, 594.31438127, 594.64882943, 594.98327759,\n",
       "   595.31772575, 595.65217391, 595.98662207, 596.32107023, 596.65551839,\n",
       "   596.98996656, 597.32441472, 597.65886288, 597.99331104, 598.3277592 ,\n",
       "   598.66220736, 598.99665552, 599.33110368, 599.66555184, 600.        ] nm>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec1.add(spec2, compare_wcs='first_found').spectral_axis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50075211",
   "metadata": {},
   "source": [
    "A ha!  The `compare_wcs` kwarg controls what-to-do with the mis-matched spectral axes.  Basically the sum of the two spectra just takes the wavelength labels from the first spectrum and uses those, when `compare_wcs='first_found'`---the default---is provided.  It doesn't actually interpolate or anything fancy..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2cf00a",
   "metadata": {},
   "source": [
    "## Resampling one spectrum to another's wavelength axis\n",
    "And *then* adding them together."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fbbf3e",
   "metadata": {},
   "source": [
    "Can we resample mis-aligned spectra and still get reasonable error propagation?  How would that work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c9371822",
   "metadata": {},
   "outputs": [],
   "source": [
    "from specutils.manipulation import FluxConservingResampler, LinearInterpolatedResampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe68e083",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampler = FluxConservingResampler(extrapolation_treatment='nan_fill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d885a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture \n",
    "#This method throws a warning for an unknown reason...\n",
    "resampled_spec2 = resampler(spec2, spec1.spectral_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "01fbe0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(resampled_spec2.wavelength == spec1.wavelength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c69ba9fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InverseVariance([   nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "                    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "                    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "                    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "                    nan,    nan, 10000., 10000., 10000., 10000., 10000.,\n",
       "                 10000., 10000., 10000., 10000., 10000., 10000., 10000.,\n",
       "                 10000., 10000., 10000., 10000., 10000., 10000., 10000.,\n",
       "                 10000.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_spec2.uncertainty[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360741b4",
   "metadata": {},
   "source": [
    "Hmmm... this process causes a new type of uncertainty \"inverse variance\" instead of std deviation... I'm not sure why!  Hmm...  We'll manually convert?  Variance is just standard-deviation squared.  Inverse is just one-over-that..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1cd878cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sigma = np.sqrt(1/resampled_spec2.uncertainty.array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62f70e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled_spec2.uncertainty = StdDevUncertainty(new_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ec3376b",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_final = spec1.add(resampled_spec2, propagate_uncertainties=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2f61c6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StdDevUncertainty([      nan,       nan,       nan,       nan,       nan,\n",
       "                         nan,       nan,       nan,       nan,       nan,\n",
       "                         nan,       nan,       nan,       nan,       nan,\n",
       "                         nan,       nan,       nan,       nan,       nan,\n",
       "                         nan,       nan,       nan,       nan,       nan,\n",
       "                         nan,       nan,       nan,       nan,       nan,\n",
       "                   0.0509902, 0.0509902, 0.0509902, 0.0509902, 0.0509902,\n",
       "                   0.0509902, 0.0509902, 0.0509902, 0.0509902, 0.0509902,\n",
       "                   0.0509902, 0.0509902, 0.0509902, 0.0509902, 0.0509902,\n",
       "                   0.0509902, 0.0509902, 0.0509902, 0.0509902, 0.0509902])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_final.uncertainty[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878a86bc",
   "metadata": {},
   "source": [
    "Voila!  It worked!  I am not sure why specutils does not go the whole way and do this step manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "41218239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.5)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(spec1.wavelength.value, spec1.flux.value, yerr=spec1.uncertainty.array, \n",
    "             linestyle='none', marker='o', label='Spec1: $S/N=20$')\n",
    "\n",
    "plt.errorbar(spec2.wavelength.value, spec2.flux.value, yerr=spec2.uncertainty.array, \n",
    "             linestyle='none', marker='o', markersize=1, label='Spec2: $S/N=50$')\n",
    "plt.errorbar(resampled_spec2.wavelength.value, resampled_spec2.flux.value, yerr=new_sigma, \n",
    "             linestyle='none', marker='o', markersize=1, label='Resampled Spec2: $S/N=50$')\n",
    "\n",
    "plt.errorbar(spec_final.wavelength.value, spec_final.flux.value, yerr=spec_final.uncertainty.array, \n",
    "             linestyle='none', marker='o', label='Spec final = ResampledSpec2 + Spec1 : $\\sigma_{net}=?$')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(0, 2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee85f883",
   "metadata": {},
   "source": [
    "Okay!  Now we understand how specutils combines spectra, both for error propagation and for comparing the spectral axes of the two spectra.  "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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