pPXF_sdss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is to test with the sdss spec and potentially try\\nsome new higher res spectral templates = Indo-US?'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This is to test with the sdss spec and potentially try\n",
    "some new higher res spectral templates = Indo-US?'''\n",
    "'''INdo-US is actuall about 1A resolution'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pyfits\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import heapq\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "import matplotlib.patches as patches\n",
    "from astropy.io import fits\n",
    "\n",
    "\n",
    "from astropy.cosmology import WMAP9 as cosmo\n",
    "\n",
    "import pyfits\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import heapq\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "import sys \n",
    "\n",
    "sys.path.append('/usr/local/lib/python2.7/site-packages/ppxf')\n",
    "sys.path\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import glob\n",
    "from time import clock\n",
    "from os import path\n",
    "\n",
    "from ppxf import ppxf\n",
    "import ppxf_util as util\n",
    "plt.clf()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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hZx2rzx+gscPL6HxPQvaVDB2DCQy9VCV5EjAGlgl3V43bJg/jdqWuNNnW5eOTbXWcPWNs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\nlFIqggYGpZRSETQwKKWUivD/AXgDecgmEyAmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9e0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sOpiXdW3aS51Z16ZmNsTMlpvZ22a2zcxmZ2l79lRnNrbn5VH1bDSz42b2d9nW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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c4ec9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wdisp 2891 lam_gal 2891\n"
     ]
    },
    {
     "data": {
      "image/png": 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zhyGycncNNz2wkskluTzy2YWU5GXGuyQRkVOmM4chsHZfLTfc/wbjCrN46CYF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\nmvnctdR5UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e685e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "velscale 69.1451441267\n"
     ]
    },
    {
     "data": {
      "image/png": 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6ivR15dYp1rlSPtf0Qf7s//OcVvtTp/b349PlU/BvYa26KD9X+vu7sikul+yS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbbb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input vel 18526.0\n",
      "Outliers: 62\n",
      "Outliers: 1\n",
      "Outliers: 61\n",
      "Outliers: 3\n",
      "Outliers: 61\n",
      "Outliers: 1\n",
      "Outliers: 61\n",
      "Outliers: 3\n",
      "Outliers: 61\n",
      "Outliers: 1\n",
      "Best Fit:       Vel     sigma        h3        h4\n",
      " comp. 0:     18522       190    -0.002     0.001\n",
      "chi2/DOF: 1.081\n",
      "Function evaluations: 41 Status: 1\n",
      "Nonzero Templates:  2  /  156\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "      21      21   0.091   0.056\n",
      "Elapsed time in PPXF: 45.69 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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cOXMEkPXr14uIyNmzZwWQ119/3bqJy5YtsyrQxWPGiPOeeyQ7r3g0bSrSu7f8\nGh8vc4KCRBo3FgkIOLdPfLzIZ5+JZGWJiEirVq2kWbNmYrPZ5OWXX/b55U2aNEkA2bJli7z44osS\nEBAgZ8+eFRHjQbz11lsFkCVLlngcd/bsWQkKCjonjC6XvNuhg2y32y170kGOXnutyBNPiAwfLj+E\nhEhaYKBVvgEkPfe8t9xyi9SoUUNcLpd1jT59+kh0dLTY7XZ54YUXfNpfq1YtAfL9/4YNGyaAKKXk\n5MmT0q9fP4mNjfXYx+VyFXiNvJiNhLJly0qzZs3kzJkzhR6zfPlyAeSZZ54Rp9MpjRs3Pme30ymy\naJHI0KEyv0oV+So6WmT6dFm8cKG89957csCtEnFn7969UrduXdm/f79fdvvLn3/+KYAEBwdLWFiY\niIhs3LhRAPnqq6+kU6dORa6ci8qTTz4pZcuW9XgeCqN+/fpy1113eW0fNWqU9b4BcuzYMf9OePCg\nfFqunGS5v2N2uyTVqiUfgKQ8+aTIv/8tct99ItWqWfscrFhRmoFcffXVftl8ww03iIhIbGysPPDA\nAyJHjogMGXLu3XY4RB56yKjk8/D1119bdcuUKVMEkNatW4uIyPXXXy9t2rQREZH09HQBZPTo0R7H\n97r9dnk+Nla2BgfnL5bun5YtjTrG7ZkfOnSoAFKtWrVzJ87MlKQ2bURAsoOD5fSKFdKsWTMJDg6W\nP/74o9D7UlqiMgJ4BIgGKpmfQo65HmhWgKjcCvyYKy6tgJW52wOA3UAtjIECG4EG/thZuXJlSzB+\n+eUXERE5dOiQADJhwgTrJiYkJEg1kFllylhfoMtuF9dddxkth/R0a9+xY8cKYFQmhw+LvPuuSO3a\n5774mjXF9dFHUjk0VIYMGSItW7a0HrS89OvXTyIjI8Xlcsn06dMFkA0bNojIuZfRbrfLnXfe6XHc\n+vXrBZAZM2aI/PGHyHXXWdfPiYuT/7vvPgkBj5Z83759pWZMjMgXX0hyhQrG/kqJvPKKTJk8WQBZ\nuXKltX/z5s3l5ptvlqZNm+bb6gsNDRVA+vfv77P85ptvloCAAAFk0aJFEh8fLz179vTaLyoqSgYP\nHuzzHHkZPHiwhIWFybx58wTw8rB88cYbbwggJ06cEBGRpUuXSoBSsumJJ0RiY32+wFscDmmf29Dw\n1QI3v6+5c+f6Zbe/DBs2TAIDA+W5554TQI4fP279rytXrpSRI0cKIJmZmcV6XXfuuusuqVOnTpGO\nufbaa6XSOW+1AAAgAElEQVRLly5e2x9//HGpUKGC/PLLLx7vYb7k5IiMGSPi9i5Kly4i//ufyKlT\nsnLlSm/vOTtbZOZMw1sByQZ5o2xZo8GQDzt27BBA3n//fRERueYf/5Ap9euLhIQY1wwIEPnnP32K\nick777wjgKSmpsq2bdsEkBdffFFERIYMGSKhoaHicrnk2LFjAsgHH3zgcXyvXr2kfv360vAf/5Bn\n2rcXef11kRtuEKlXT6RqVeNnr16ysHNnqZnPd37HHXcIICEhIR7b3xo1Sma5NXiP7NghtWvXlkqV\nKsnevXsL/ApKS1T2+vjs8eO4GgWIykdAH7e/t+eKVmtggdv2F4AX/LEzJibGavlNnz5dRES2bNli\ntfpERCQ7W3JGj5YM8wsICjJa9/m0UM3wy5dffnluY3a2yNSp1kMtIPtBFt91l/zr6aclICDAqtDc\nadSokRWCMe2cOnWq/Pzzz6KUkv79+8uzzz4rAQEBHi3mGTNmSB2Q4506Wdc7GxIij4Os+f13eeGF\nF8Rut0u2W0jODD3t379fBtx5p3xcsaIV1stu21biHA556qmnRMTwHsqXLy/Dhg2TQYMGSVhYmFer\nNTs72/Lu2rVr532jXC5pEBcnfTt0kMDcyh+Qf//73167NmjQwEs486NZs2aWyN13330SFBTkEdr0\nxeDBgyUiIuLchvXrJatJk3OVVu3aIi+9JPPvuEOGg2RERBgCbbPJ29dfL4AkJSV5nPM///mPAPLR\nRx/5Zbc/ZGZmSmhoqPTr109mzZolgKxbt85qyCQlJfkM5xY3LVu2lBtvvLFIx9xwww3Stm1br+19\n+/aVWrVqWRXrv/71L3nzzTd9Pgdy6JBIx47W9/I1yKdPPOGxS3p6uiilzoWSPAtlSni45JgNrI4d\njYZfLsePH7dCr6Yg7N29W2TqVEkKCjr3PHTrZoSgCmHYsGFSoUIFcblc4nK5ZNKkSXL06FERERk3\nbpwAcvDgQdm/f78AMmnSJI/je/fuLXXq1JE6depI7969872OGUJMTEz0KmvZsqX1Hrq/73feeac0\nqFFDpFEj43+67TbZtWOHlClTxvDICqBUROW8T16wqHwPtHP7exHQAmOo8qdu2wcA4wq4xmBgDbCm\nUqVKcuTIEQFk3LhxIiKensu6dUZYy/ROevUSKUTFs7OzJSQkRIYMGeJd6HSKTJ8up2vUOHdOm01W\ngiS0by8ybJjIY4+JDB4srnvvldl2u6yuU0dk8GBxDh8uDwYEyIhOnaRBpUpydYMGcvr0adm7d6/Y\nbDYjPHTmjMhPP8m2evXOhefKlhV58UX5K7cFN3XqVOnVq5dcddVVHqaZ3s20adPk+uuvl+uvv94I\n+0RFiYAcDwyUvpUrS05OjiQmJlr3zKzEdu7c6XE+874C58Ix27aJjBolcsMN4nJz6XNAEpWSWSB/\nPvmkUXm40a5dO+nQoUOB911E5MyZM+JwOGT48OEiIrJv3z4JDAyUBx98sMDjOnfuLC1atBDJzBR5\n9VUjnAkiMTFGCzhXMGfOnCmAtG3WTMY7HIbtQUHSDuSLL77wOKcZ2vvXv/5VqN3+sm/fPgGjj8ps\nvMyaNUuefvppKVOmjLhcLvn2228FkFWrVhXbdfMSExMj999/v8jJkyIffihy993y9oh68vbLV4k0\nbGj0Naxe7XFMjx49pHHjxl7n6ty5s7Rs2VJEjD4r85lRSnn22ezYIVKzpvG9REZKcq73/PHHH3ud\ns06dOj49XhGRsLAw6R4UJEfM9yMsTOSDD0Sys+X222+X8PBw2bNnj7Rv106GxcWJNGtmPad/BgWJ\nLFzo933q3r37ub6MPCxZskQAWbBggeXFmA1bk759+0rt2rWlevXqct999+V7HbOBsXHjRq+yatWq\nWfc0JSXF2l6zZk0jHLlrl3EPQGT0aBk6dKjY7fYCw7al5anc6+vjx3ElLirun6uuuspqUY8YMUJE\nRObOnSvlQJLuu+9c3DQuTuSnn/K9yXnp3Llzvg+TiMjECRPkTpAz114rLrd+jqJ8coKDRZo0Eene\nXdZERspau11cuRWdgGSB4Z4fPCgiIllZWVbfRLNmzbxCEU6nU8LCwuSf//ynxMfHyz333GMUHD4s\ncuON5yr/u++WX+fPF0B+/vlnS4w8PDMR2bp1qwDS9KqrZBBITsuWXv9DBkhGhQrizDvQwWYT6dTJ\niA+fOCE9evQo8H6arFq1SgD5+uuvrW1PPPGE2Gw22bZtW77H1a1bV15v29bo9zJtGDrUqDTdMCty\nQO67916RQYNEQNKUkifyeFI9e/YUQB599NFC7faXNWvWCCDz5s2T48ePCxgDGO68806pW7euiJzr\nHP7222+L7bruZGVlSRWQ31q1EjHDpCCDPoqXQR/Fe36PnTpZjbB+/fpJrVq1vM7XtGlT6dq1q4iI\nPPPMM9KuXTvLa7aeqfXrRSIjjXNec41IUpJs2rTJEtW89OrVS2rWrOm1/eTJk5bnHA1yvHXrc427\n2FiZYrfLyyBfh4XJXvf/IzZWPu/YUSqHhxfpXjVq1Ei6devms+zo0aMCyJgxY2Tt2rXW9+rOgAED\npGbNmhIZGSkPPfRQvtdZunSpgBFCdicnJ0fsdrvExMQInAt3p6SkCCBvvvmmseN33xn/p90uh+fN\nE4fDUeBzW9yi4u+Q4mvcPtdh9LHc5uex+ZEIuI9hrJq7Lb/thRIYGIjdbicsLMwY2nj2LBFz57IT\niJoyxRiG+MQTsGUL3Hyz34a2a9eOLVu2cNxtdqw7f+3YwY/BwQT+8QcqLY0BMTGMu/ZaeO89+O9/\n4cMPOTByJH2A1Y89BhMmwKuvsrxGDVYBWeXKYcvIgA0b4LvvaH70KM2cTmO4YMOGjI+O5t4OHeCT\nTyB3xr/D4eCqq65i27ZtPtcVDwgIoF27dixbtozDhw8THR1tFFSpAgsWkPnSSwgQM3MmTfv350Gg\nXvXqVm6sRYsWmaIOImQtWcIkYPnevXwM2FauhPLlYeBAmDWLHz7/nGDgz0WL+GTsWK4CXomMhO7d\nISAAFi409o2K4pUNG+iSkAB79hiveT6YqWhatGhhbXvxxRcJDg6mXbt2DB482DPjcWYm8tVXTNqx\ng5d+/x127oS6dWHpUvjgA8idH2RizlUB6D9gAHz4IXTtSqgIXb7/HnEb62/OhyjOiWXm8NuIiAjC\nwsIICwtj79697N+/35ozUqVKFcC/IbNF5uBBzjzyCLuBtitWwMmTcN11xrDda66BNq1h9Wp49lnj\n3i1cCM2awfz5hISE5Duk2Jzr9fbbb/N///d/DBs2jIiICH788Ufj3evUCY4ehc6dYfFiyF2YCs6l\naHGncePG7N271yvlvPmdXHvttRwGfn3+eZg7F2rWRCUmcq/TySjgrrQ0agBnq1aFMWNgxw52tW1L\nyvHjRZrP4f695KVy5cpUrlyZP//80+f69OA5o76wyY/gPY/m2LFjOJ1OGjRoABhZCwBreoA1ebVb\nN3j8cXA6qfLkkzzUpw+ffvppyTxDPsh/sLQbIjLM/W+lVBgw4wKv/S0wVCk1A2gJnBCRw0qpY0C8\nUqomhpj0Bvr6c8IyiYnw5puMVIqm330HU6fSLvdBzGnRgoBx46BlyyIbet111yEi/PHHH9x6661e\n5X/99Rf16tUz5riUK0dK48ZMTkpi6OOPW/us++YbZgDP3HuvMfYciOjXj02bNnHtXXdBairs2gWJ\niWTk5HBLr150ee45hr/5Js+HhPBgI+/FNuvXr89vv/3GyZMnfa4r3r59e77/3lipwKycAAgIIOj1\n13ln9246fPUVLY4f51NAWrZEde7MF/Xq8eukScxZu5YeV12F/Y8/aJyYSGOAnBx+BcKfe46rX30V\ncle32/Kf/wAQHx+PzWZjN7CnUyf43/+MVBWzZsEXX8Cvv9J8/36agzFxKzQUGjSAmBiIjISgIGNu\ngMNBzA8/8EbZssRNm2YIk1JUttnYcu+9/LZ8OZunTOGXOXOo16sXautWWLUKdfYsbYHMMmUIGjXK\nmOEcGOjze61QoQKVK1fG4XDQsWNH4xoffURWfDw3nznDgbFjqf7EE0DBojJv3jzi4uKsFDD+4i4q\ncG5S7v79+2natCkAkZGRgP+T+wolIQF++w2++QZmz6ZC7oz9pDZtqPL++2AK+I7c3HL/aGFse/55\nuP9+3qm7AZY8TNettZiSp5IXEY4dO2Zl2jWx2WzcfPPN7Pz+e2TBAlRKipEna84c67vJm0zSHXPC\n6ubNmz1SybiLCsDhpCR46CG47TY+GDSIhM8/Z9Rjj7H6yBEWHjrEa4sWGd8xnoth+bpmdnY2IkJg\nrn1paWmcOHHCmrzri8aNG7N27VruvvtuwFtU3HN/FTT5MT9RMbN+NGjQgIULF1oLea1duxaAZs2a\nndt59GhYtgw2bODN+HjGZ2YyadIkXn755XyvW2ycj3sDOIDthewzHTgMZAMHgQeBh4GHc8sVMB5j\npNdmoIXbsbcCO3LLXvLXruY+wkoHo6Kkb0CAuHJy8nX/CiM9PV3sdrsV289LXFyc9O3b1/r7scce\nk/Lly3t0dv/3v/8V8H8uQMOGDeXmm2+WhIQEAWT8+PFe+7zwwgtW+Ob777/3KjfDR4BMmTLFq3zX\nrl0SANIPZEMhwxzTQ0PlTZDln38u4N0J+eCDD0pUVJSIGCGVli1byuzZs73/sQMH5KeePWUuiCu3\ng7w4P6fj42UIyI8zZ/p1n//zn//ItGnTPLalvv66CMipChVETpywhqUDPkdJhYWFSbly5WRhEeLz\nIueeidTUVBExOlvNmLl7x3ZERITvPr3CyMkR2bRJZMIEkb59RapX97xfAQGyr1UraYIxzN2dQdsH\nyaDtg7zON+iXzlZY7H0Qp9sIpdOnTwsgb731lpcp34wZIwnuYbQ8Q8M//fRTAXzG/s2O77zvwCef\nfCJg9P/BuZC3iDGa8brrrsv31kybNk0A2b59u8/y3r17S1xcnBzMDTdv2LBBAJlZwHP1yiuviM1m\ns85tTmswGTRokFSpUkUAefXVV/M9T1ZWlvX/uFwu2bx5s4iIfPfddwLGYBE4NyKuT58+Ur16de8T\nbd9uhTQ/qFrV58AKkeIPf/nlqSilvst9qcAY8lsfmFmIWPUppFyAR/Mp+wH4wR/bPIiLg7vu4stv\nv+VgRgbPLV7MyHfeYfF336Fs/kb6vAkODqZ58+ZWenB3MjIy2L9/Pw8++KC1LT4+ntOnT3PkyBHL\nQ9i3bx/ly5f32SryRatWrZg5c6YV3qlbt67XPvXr17d+dw/lmDRt2pSQkBBOnTp1LvzlRu3atenW\nowf/mzePzFtv5euRI41wx5YtkJnJ3IULWXvsGK8vXcqE+fN54fnnSe7WDZvN5jWrfseOHdSpUwcw\nQnPuKWQ8qFaNA7feyuBvvuHAunVUczhgxw7mjB/PopkzGfLAA/yjTh2yMzJ4a9Qo2rVpQ8f27Y3q\nyOXy+Jl+6hTvf/wxTW+5hVuGDIE2bfjul1/4sE8fHnG7NwXx7LPPem2r+MILbHz9dRqfPAljxnBw\nwADA8GyOHj3qse/JkydJS0sjICCArl27MnfuXGvGdmEkJycTEBBAaGgoYHgqs2fPBjxTplSpUsWv\n0MWTTz5JxYoVee7hhynzv/8ZM61zZ+ZbhIYaM7Svuw769eOr6dPZsGKFf7PpbTaoHgdlyuIM2MNj\nOTmk3XILHRMTmfDZZ1bevLyeCgcO0O2//yUA2B8XR9w330CelnpBnkq1atUICwvzSteSkJCAUoq4\nuDgiIiKse3Ts2DHWrVvHv/71r3z/FXdvwHxuTXbt2sVXX32FiNC1a1eWLl1qrb1TUCqb1q1b43K5\nWLp0KeDbUyloKWETh8NBxYoVSU5O5ptvvuGOO+5g6dKlHp4KYHkqBw8e9O1B1akD06ZBjx48kpjI\nD4mJHD9+3CPEWBJLUPhb074DvJv7eQO4XkSGF7s1F0pEBLzzDj+3acM4mw3q1PHK+3W+tGvXjlWr\nVnmlYDFTM9SrV8/aZoaizHxacC4eq5Ty63qtW7fmxIkT1hok7uc3MUXFPaW/O3a73QoX+BIVgCdy\nwzt169Y1wlD33Qdvvw1jx7K6Z09Gp6WRU68eKcePY7fbqVSpElWrVi1QVArDTJmTkpICVargateO\np1etYgJw+6+/kvnEE4wS4VURsl99Ff79byP761tvGW7922/DO+9Q7qOP+LFdO144dMjouwkPt9Kb\n+JvHyic2G0u7dgVAxozhcG6W3ObNm5OWlubxDJghmLFjx1K7dm2efvppvy+TnJxMeHi4ETYFj+/Q\nvZKIjo4uNPzldDp5//33WfLaa6THxMBTTxmCEhsLffvChAms/ewzbm/fnuxvvoHhw6FaNRISEggN\nDaVChQp+201kJD898QRpQNjixXywfTtLv/7ayvvlnj+PlSuhbVsCEhLYUq4cD0ZFWSFTd1JTU3E4\nHJTzUaaU8rm2SkJCAjExMTgcDo97tHDhQkSEmwvoN/V4BvMwduxY7HY7n332GVu2bCEmJobnnnuO\nevXqcfXVV+d7zlatWlnXB999KmZ/S0HhLzDuYXJysrVG0ty5c0lMTEQpZdUFpqh49Jnm5bbb4NVX\nsYkwS4TNYzyXwXrxxRcLtON88EtURGQZ8BdGhuKKQP5pai8BzC8EvPN+nS+tW7cmKyvL68H+9ttv\nsdlstG/f3toWHx8PeIrKvn37CozH+roewJdffkn58uU9siebmA9X9erV82353HTTTQQGBlK1alWf\n5e3bt2fChAkMGjTIqywuLg6n08nhw4dJSUkhPDwcpRQ1atTwEJUTJ05w5MiRIouK2TpdsmQJ+/bt\nY+DAgezatYt77rmHUaNGMXDgQG666aYCz3XHHXewceNGK4fXvn37CA8Pt5J2ni+xvXvzE0b+qbBP\nPgHODRhw91ZMUWncuDEPPfQQ27ZtY8eOHX5dw71TGzxFpaieSkpyMsNEWKgU4Tk5bC5fHr79Fg4c\nMPq1hgzhtVmz+Obbbz3SpickJPid88udU82b0w5IANoBQ8eNI3jyZILI9VSSkoyGwHXXwcGDRjr4\noUNZsmaNzwy87mse+aJx48Zs2rSJ9PR0a9uBAwcs293v0YIFC6hUqZJnH0Me8uu3SEtLY/LkyfTp\n04f777+fzz//nOuvv56ZM2eyefNmn6JnUrFiRRo0aGA1bHx5KmaG74I8FdO+vXv3Mn/+fMCoZxIT\nE4mKirI8DbOjvkBRAXjtNVwPPEAw0PrNNyG3oZqVlcXkyZMLtON88EtUlFJ3A6uAXhjr1K9USl2S\nqe/B+ELOnDlDRkZGsXkqZmege9p4EeHrr7+mffv2VocqGBWC3W6/IFGpU6cOFStW5Pjx49SpU8fn\ny1a+fHmqVavmM/RlMmzYMDZs2EBYWJjPcqUUQ4YM8dmyN+3dt2+fx32Mi4vzEBUzmeZ5eSrApEmT\nCAsLY8KECXTr1o158+bRtGlTxo8fX6hnd/vttwNYyTWLep/zo2XLlryS+3vdn38minMdoe6d9aao\nVK1alR49egBGgkh/yCsqZpJLu93uUUmYrXApYKRczttv8z4QIMJvbdvS9PRpjrVqZYSsMITwp59+\nArDCM6b95yMqISEh/Am82KEDW6KjCcnOpsGHH5IBtLjzTsNDeuklyM42BkssXcrN99yDy+WyBo+4\nkzeZZF7uvvtuzpw541EJuttu3iOXy8WCBQu46aabfK4Bb5Kfp/Lpp5+Snp5uefD9+/fnp59+olev\nXtYCcwVhNgYBLwFyt8cfT2XlypWcPXuW/v37s3fvXhYtWkRsbKy1im1aWhqnTp0iPT29YFGx2bB9\n8gk/1aqFIycHevaEPn1YOnVqiWQL9zf89RJwjYjcJyL3YiSWfKWQY0oN9wfGbGFfKFWrViU6Otpa\nRhRg69atbN++nbvu8tRXu91OrVq1LFE5ceIEaWlpRQrJ2Gw2y532FfoymTBhAqNGjcq33OFwePS9\nFIW8omLexxo1apCYmGi1uhYsWIBSyq+laAGPTMXHjx9nzpw59OvXjzJlyjB27Fj69OnD7NmzKVu2\nrF82NmvWzFpcrLhEpWrVqhyKiWFN1ao4srN5tUwZ67zuonLw4EGUUsTExBAXF0ezZs2YO3euX9c4\nduyYh6iY4dHq1at7VEBVqlQhMzPTCnd48cUXxOSGNba/8AJl3nuPHLBEBGDGjBnk5OQQERFRLKLS\nsmVLBgwYwFtffMGS4cPpCRyqXBkX4Dh82BhlddttMH++0bcTGEiTJk2Ii4vzeX8KE5W2bdvSpk0b\nxowZg9PpREQ8bK9SpQpJSUmsWbOGpKQkunXrVqD9ISEhOBwOD09l9erVvP7663To0MEafVdUzHfA\nZrNZI8dM3L9TfzwVMBYNfOONNwDj2TYjFmFhYaSlpXlkIC8Qm42Dw4fzHOAqUwZmzKDzoEEkXEBf\nc76X8nc/EXHvoUwpwrEXHTPUs3LlykIfVn9RStGyZUsPT2XWrFkopazWsjvx8fGWqJjucFErO7PV\n46uT3qRbt25+V+ZFpXr16oC3qLRo0QKXy2XFjr/77jtatWrl4a0VhLvof/nll2RmZloDHWrWrMmX\nX37ps48oP/r27cuqVavYsGED+/fvLxZRUUrRqlUrzK7egVlZROe2VPN6KtHR0TgcDsDwnFasWOFX\nx3pycjLVQkNh40b48UeCjh4lNjbWq/FhDvbw2a+yeLEx/wd4EpB776VZs2ZERUVZoROAadOm0bRp\nU/r06cPvv/9OVlYWZ8+e5dixY+clKpUrV2bq1KnExsbSrHlz5gFdqlQhxGZDtm835qHMmwduQ/CV\nUvTs2ZOff/6Z06dPk5GRwdixY0lNTfXrPX322WfZt28fs2fPJjU1lTNnznh4KllZWUybNg2bzUaX\nLl0KPJdSivDwcKulvnbtWjp37kylSpUKXKiuMMx3MTg42MvLtrlV4IV5KuZgh169elGtWjXLSzZX\nps0rKr7C43m5uUsX3gbeHzyYzB49OAVULYF1V/wVhp+UUguUUvcrpe4H5nM+o7MuEjfddBP16tXj\n+eef5+zZs8XiqYARAtu5c6fVFzBr1izatWvns5UQHx/Prl27EBErVFTUys7sZDdHe1xsypYtS5Uq\nVdi/f79H31SXLl0IDw9nypQpHDp0iDVr1ljLBPtDUFAQ5cqVIyUlhUmTJtG0adPzbhkCDBw4kLJl\nyzJixAjOnDlzYZ30brRs2ZLvDh7kl6AgyrpcxOQuPpa3T8W9Uu7ZsyeANcAiP1w5Odx77BjvTJ0K\nTZoYlW/NmiyqVo0X83i+5vPlJSo7dsBdd4HTydoOHXgPY16LzWbj1ltvZcGCBTidTv766y/WrFlD\n//796dChAxkZGaxevdpafO58RMWdJk2aoJRi8+bNhFaujKpTB/IJt95+++1kZmayYMECnn/+eR5/\n/HHuvfdev8LUt912G3Xq1OG1117jkUceAc41fEzh/eKLL2jdurXnYIF8MPteXS4X3bt3JywsjCVL\nlljnPB/MsHXe/hQomqdiioo558V8v87bU8H4nm+44QaeGjuWpjt2EApsLWD58POlsDXqr1JKtRWR\nZzESQDbK/fwBfFzs1hQTdrud0aNHWy9NcXgqYFQyYLjJW7duZcuWLfTq1cvnvvHx8WRkZHDo0KHz\nHpHUsWNHZs+ebcXqS4O4uDj27t3r4amYywLPmzfPWpmwe/fuRTpveHg4ixYtYv369R7Dsc+HSpUq\nWfZA0cU7P8zw40u5o70cH31EtXLlvDwV90r56quv5qqrriq4X+XkSZzdu/O2CPacHGPU3XXXAVDn\njz/o9Mwz4LY8rs9Z9ampxoi348ehRw/mtGqF3W63OnFvvfVW0tLSWLp0KS+99BI2m40+ffpw/fXX\nA0a/ysiRIwkKCvIYZHI+lCtXzgrReg0nzkPbtm0JDw/ntddeY9y4cTRq1Ij58+eTkJBQ6Htqs9l4\n8cUX2b59O4sXL6Zbt25cl3vfzEo1LS2t0NCXiempbN26lcOHDzNixIgLbpDYbDbatGnjczRdUTyV\nvn37Mn78eOsZNOsA04MPDQ3lxIkTRRIVgB9++IE33niDffv20bBRI+rfcYdfxxWJgiaxYOTnauhj\ne0Pgu+KcMFMcH/dFrFwul1x33XX55hM6H06cOCFKKRk5cqT06NFDypUrJ4fdsqK68/PPPwsYa6M8\n9dRTRV6v4lLhnnvukejoaK9JbWbeqrJly0rNmjWL/L81bdpUAAkKCrIm/10I7nm8Nm3adMHnEzEm\n85mp/Pfk5hEbFxYmffr0ERHjGQsODvZYAE5EZODAgRITE+P7pE6nkdodY1GlJe5ZeffsMSYpmpME\nhw0TOXPGWrTLWqzryBFjfR8wfp46JQ888IDHNdPS0sRut0vZsmW9vrtGjRpZ3+moUaN8mulz8mMB\n2/v37y+AX4lCBw4cKIDUq1dP0tPTpXv37gXa4o6ZWj7v87Z9+3br+zcnCxbGnXfeKfXr15eJEyda\nkyiLgx07dshvv/3mtf3ll1+2bPz111+LfN4//vjDSoc/YMAAqVGjhjz77LMSFBRU5PcvKSnJysTN\nRc79FSUiXmve5m6rcQFaVuIopXj33XeJjIwscGx5UahQoQL169dn4sSJzJs3j1dffdUz/Ykb7sOK\n9+3bV6Q5KpcSNWrUsFpD7i3JZs2acfXVV3PmzBm6d+9e5P/NPNedd97pM99TUWnatKkVLiyu8Fe5\ncuVolJseZ2e/fgA8cPIkWbkjvo4fP05GRoZX+Cg2NpakpCTfE8uefx5++ons0FCuBc66z6WoWdNI\nZTNhgpGq5oMP4NprqbBnD2XKlDG+h3XrDK9m40Zjctv330P58hw9etSjTys0NJQbb7wRpRSzZ8/m\n+eeft8o6dOjA4cOHqVu3rs/Jn+eDmXeqME8FYMCAAYSGhjJt2jSCg4OZPHky7du3p127doUeq5Qi\nIhNY/rYAACAASURBVCLC63kz38O4uDi/33fTU1m+fDmVK1cucBRlUYiPj/dIJ2NSlPCXL1q1amV1\n/ruHv6Kjo4v8/kVFRREVFVVkG/yhMFHxHRg1KHxoTilzzTXXkJSUVODoqaLSsmVLDh8+TP369a1h\nh76oVq0aZcqU4bHHHuP7778vtpDMxcbdbve+KaUU9913H1D00Jf7uR544IELM9CNd999l5deeqlo\nE/kKwQx5lr3hBrjtNsq6XNy+dSvgOZzYndjYWFwul3eesC++gHffBbudVc89xy58VMJKwZAhRn6u\nq66CzZtRzZqx3elk2KefGnnjduyAxo2N9c9zr51XVMCY47Rr1y7uyBPi6Nq1KwEBAUycOPG8Kjdf\nmB3J/vRldOzYkePHj1vzfswRaR06dDjv64eEhBAZGckdd9zhdwUbERFBSkoKv//+O23bti3xRl9R\nwl+FERYWxokTJzh48KDfoa+LRWGiskYp5TUrTin1T2BtyZhUvBT3g2LGcMeNG+c1ZNCdgIAApk+f\nzpAhQ7j99tutjsXLjfxEBWDo0KFMnz6dG2+8scjnbdGiBS1atDASORYTLVu25PXXXy+284Ex6CMo\nKMiYg/Pvf+MCeqWmwp49lqj48lQAEhPdkmvv2AEPP2z8/sEHbM9tWedbCV97LaxfD48+CiEhVHc6\nqZ6WBhUrGhloly4Ft5bm0aNHvVqelSpV8lnhdO7cmZSUlAuqxPPStGlTHA6H9b8XRnG/l0op1q1b\nZw2/9Yfw8HBycnLYvXt3iY2gdOdCPRV3wsLCEBF27tx5yYlKYbN5ngDmKqX6cU5EWmAs8+s9jvZv\nwIABA2jdurVf3k/Pnj2t0UCXKwWJStmyZendu/d5nffZZ5/lmWeeueRDgrfffjuHDx82QnRRUWxs\n1IimmzbheuopEjp3BvwQlcxMuOceSE+HPn3goYdIfvttoJCWffnyMG4cvP8+z9x0E6d372bi9u1e\nebPAt6dSEGa+seIiJCSE5cuXW2Hf0sBfQTNxv/e+wlXFTXF7KmA8Y5eVqIjIEaCNUqoj8I/czfNF\nZHGJW3aJYrfbizWcdqnjPryyuIZmm1zqggKGje59Ppt69aL2pk1UmDePiiLY7XavfjUvUXniCWOt\nnFq1YOJEUIrk5GTKlCnjc+ipFwEBnKlfn1mbNjHRR2VkzvkoiqiUBO7r3lwOuI9mLCilS3FRnJ6K\ne6PgshIVExFZAiwpYVs0lyDBwcFERkZy9OjRYhuafTlTrl49ngI+Bbr+9BONo6K80oGYa7QkJiYa\nnsbEicY6MTNmQG5/j5mixV9hrVKlCikpKT4XeDLnzZS2qFxumJ5KixYtLthz8IeS8FTAv4mPF5NL\ndla85tKhRo0alCtXrtg6dS9noqKimAQkN29OSFYWn546BadOeexjs9mIjo4mfNUqo/8DYNIkYzXF\nXHwtZlUQpsdo9uO4Y4pKSY3muVIxPZWL0Z8CRcv9VRjuonKpeSpaVDSFEh8ff8k9uKWF2X/ySGAg\nSTYbTU6ehJtuMiYhmojwtFI8uWSJsfbLyy9D7pBkk7zJJAvDTDZpTujNyMhg2LBhHDt2THsq50mN\nGjUYOHAgA3PT3JQ07qJS0CAff7iURcWv8Jfm783o0aOt1DR/d2rUqMFHH33EU089xVqXi9UVKlBp\n5Upj+O/ddxtr+ixcyGPm4ljPPgsjR3qdJzk5uUj5zUxRMVP8L1u2jHHjxhEfH2/1y2hRKRp2u71E\nUr/nhxn+CgwM9AiFnQ+XsqhoT0VTKLGxsTRs2LC0zbhkGDx4MJs3b+bGQYNInDHDGP6bmmr0nbz+\nOqxYwZnAwP9v79zDrKiuRP9bTT+kgeaNQDcCTUhAJkQjUWMSg+aOgo+QSRjQm5h8uWYUjROYa8Yr\n3x2VMbkxl1yDGv0mQUMyc/MhJFEnBEG5XsONE2IEhWgMYgAf0AYIzwaBfp11/6iqtrq66pyqOlXn\n1fv3ff31qTr1WGc/1tp77b3X5vNnnAFLl3aHn3cTtacyZswY6urqunsqO3bsAODZZ5/tXg8TxZ1m\nKDxOTyUJN7KzFqu6ujpSOSoEqfZURGQWWNs8AI+o6rc93/8j4PgFqrG2KR6pqodF5E3gONAFdKpq\neU0tMVQ0EydOZPlyO/zd7Nnw8svw059Cezt87GM8vG0bK5cs4V9aW3stxuzo6ODo0aORlEFVVRXN\nzc3dRsXZw2bjxo00NTUxePDgVAabv/lNWLECrrwSrruO7MuhDVlxeidJ5JOzS+aQIUPy7vUkTWpG\nRUT6AQ8Bfw3sBTaLyBpV/aNzjap+B/iOff3VwD+oqtvPcomq9tyazWAoRaZPt/5sRtg7FLa0tPQy\nKk649agtTLdR2bFjByLCsWPH2LBhQ2qur+ees6LY//CH8KMfwbUvQelPBC9NkuypgOUCKzXXF6Tr\n/jof2Kmqu1W1HVgFZAu3ey3waIryGAwFw3dVvY0TSy2qu8oxKqrKa6+91r0H+5/+9KfUjMrBgzBz\nJtxzj7V2s7Mjldf0CZLsqYA1zbwUwz+laVQasbawdthrn+uFiNQDs4DHXKcVeEZEXhSRG4JeIiI3\niMgWEdnit/e1wVAM3EZFVXtMdHA2N3PCmoelubmZ1tZW3njjDfbt28cll1zSvYFbmkZlxAhrd2Cw\nggMY4uH0VJIyKo8++ij33XdfIs9KklJxxl0N/Mbj+vq4qp4DzAa+KiIX+92oqstVdYaqzjADlYZS\nwW1Uli1bRlNTU/cakzVr1nDuuedG3hzLmQG2bp21P94HPvABLr30UiC9NSqHDsHw4eCsr2trT+U1\nfYKk3V+TJ0+OHJqmEKRpVFoAd61pss/5cQ0e15eqttj/DwBPYLnTDIayoH///gwdOpSdO3eydOlS\nTp06xfLlyzl48CCbNm2KFdnZa1SmTJnSHZAzjZ5KV8Zyebl7Ku2mpxKbpN1fpUqas782A5NFZCKW\nMbkG+M/ei0RkMPBJ4AuucwOAKlU9bn++DLq3CzcYyoLGxkZWrlxJW1sbkyZN4uGHH2b8+PFkMplI\n2y87OOtafvWrX1FdXU1zczPDhw9n0KBBTJ06NWnxu8dPRowAJ7yZ6anEJ+meSqmSWk9FVTuBW4Cn\nge3AT1X1VRFZICILXJf+DbBBVd91nTsT+A8R+T3wAlYQy6fSktVgSIPGxkba2tq48MILefDBB9m/\nfz+33347Y8eOjRXAcMCAAYwePZrTp0/zvve9j5qaGkaMGME777zD/PnzE5e/wzYqw4dboctGjDA9\nlXwwPZUEUNV1wDrPue97jn8M/NhzbjfwoTRlMxjSxvF3L168mMsuu4xJkyaxa9cuFixYEDtCc3Nz\nM/v27eseoAcYOHBgIvJ66XD1VMBygZmeSnz8eiqtna3c33I/CxsX0lCd3OZyxaRUBuoNhorjs5/9\nLF/60pe46qqrqKqq4qabbgKI5fpycMZVCrH9gteojB1reir54NdTWdayjDWH1nBfS+nN4oqLif1l\nMKTElVdeyZVXXtl9fMsttzBu3Lju9SVxKKZRaWyEt41RiY23p7L95HaePvw0GTI8dfgp5o2cx5T6\n8t+ryfRUDIYCUVdXx7x58/IKqzFp0iSgQEal0/rvbKMzdiy0d0BGU391ReJdp7L6wGra1fIntms7\nqw+sLppsSWJ6KgZDGTF37lza29u54IILUn9XRwcMGQLVtpZobAQUOsy4Siy87q/5o+az4cgG2rSN\nWqll/qjkJ1sUA9NTMRjKiPr6er7yla8UZCvmjo73XF9gFkDmi9f9NbV+KrOGzaKKKmYNm1URri8w\nPRWDwRCAr1F5x4RqiYvfQP2ixkUIwsLGhcUSK3FMT8VgMPjS2WGtUXEwq+rzw29KcUN1A3eMv6Ni\nphODMSoGgyEAb09l5EgQMe6vuCQdULJUMUbFYDD44jUqVVVQW2d6KnFx3F8mTIvBYOhzdGWgq6un\nUQGoqzVjKnExPRWDwdBn6XTF/XJTU5vhRFs7rZ2thReqzOkrsb+MUTEYDL3wrqZ3aK8+QWd7VUWF\nFSkUJkqxwWDos/gZle0nt9PR7zjaWc2TLb/itZOvFUe4MmXq1Klcc801XHTRRcUWJVWMUTEYDL3w\nMyqrD6xG6k5TVX+S08drKyasSFq0drbyjbe+0e0qrK+v59FHH2XMmDFFlixdjFExGAy96PAZU5k/\naj41w44x4OztDBh1vGLCiqRFJUYgDoMxKgaDoReOUXGCSYIVVmRYtXWiksKKpIE3AnFfchUao2Iw\nGHrR3pGhqrqLk/Sc5dVU18Tw6uEsalxUJMnKg0qNQBwGY1QMBkMvjrWdhOqOXq6baqlmwhkTKiqs\nSBrMHzWfWqkFqKgIxGFI1aiIyCwR2SEiO0Xkdp/vZ4rIMRHZZv/dGfZeg8GQDttPbqetPYNUd/Y5\n101SVGoE4jCkZlREpB/wEDAbOBu4VkTO9rn0OVU9x/67O+K9BoMhYVYfWE2msxqp7uxzrpskWdS4\niE8P/3QoV6F3plg5k2ZP5Xxgp6ruVtV2YBUwpwD3GgyGPJg/aj71E9+ktrGlz7lukiRKBOJKmimW\nplFpBPa4jvfa57xcJCIvi8h6EZkW8V5E5AYR2SIiW/7yl78kIbfB0KeZWj+VEYPq6df/dJ9z3RSD\nSpspVuyB+peAs1R1OvA94N+jPkBVl6vqDFWdMXLkyMQFNBj6ImaWV+GotJliaRqVFmCc67jJPteN\nqraq6gn78zqgRkRGhLnXYDCkh5nlVTgqbaZYmkZlMzBZRCaKSC1wDbDGfYGIjBZ7s20ROd+W51CY\new0Gg6FccQ/MV9pMsdT2qFfVThG5BXga6AesUNVXRWSB/f33gbnATSLSCZwCrlFVBXzvTUtWg8Fg\nKCTLWpax9tBaFOXO8XdW1F71qRkV6HZprfOc+77r84PAg2HvNRgMhnLHOzA/b+Q8ptRP4Y7xdxRb\ntEQo9kC9wWAw9CkqbWDeS0UZlS7tqpgFRIa+TSUthjP0pNIG5r1UlFHZ37E/7wVEpjIbSoFyXgxn\n6lB2Km1g3ktFGZXWzta8FxCVc2U2FIeklajb577+0Hpu3XVrWSloU4dyEyWES7lRUUZFUSC7nzKb\nAqi0la2GwuAo0aV7liZiXHr43Gln47GNBVPQTv3o1M5Y95s6FI4oIVzKjYoyKoIA/n5Kp7Is3bM0\nsBVV6QNohuRxK9GnjzzNLw79IpYBcDd23D53h0IpaMdA7m3bG+t+U4cMFWVUBlcPDvRTLmtZxi8O\n/YKnjwS3oip9AK0UKFd/e5DcbiWaIYOisQyA22Xk+NzdFEJBuw3k4c7DnOw6GfkZpg4ZKsqojKoZ\n5eundCqLomTIAP6V1D2AdumQS/nZX35Wdsqv1ClXf3uQ3H69iqgGwM9ltKhxETMHz6RO6oDCKGi3\ngVSUAx0HIj8j30Hocm10GN6jooxKP+nn66d0VxaHoErqDKABZan8Sply9bdnk9tRooJQZVenqAbA\nz2XUUN3AvZPuLegsIbeBFIRRNaNiPSefQehybXQY3qOijEoQ7spSRRWCBFbShuoG5o6cy7NHny07\n5ZeNUmgBlqu/PZfcixoXMWf4HC4fenksA5DNZVTIWULuXsaw6mHU96uP9Zy4g9DlPustCUqhnuZL\nnzAq7spy+dDLmTN8TtZKWq7KLxthWoBpF+hS8LfH+Y255HaU6G3jbotsAFo7W/n5X37OpUMu9TVI\nhZ4l5BixprqmgrzPTTFnvZUKldBT6xNGBd6rLLeNuy1nJU1b+eWjvOPcG9btlHaBLoVFX3F+Y1i5\nHQMAhM4jRx6gJNYtOL+hWlINC+hLMWe9lQLl6h720meMSpQWX9rKLx/lHefeMD2vMAU6iZ5MMRd9\n5eNeiSJ32Dxyy/Ps0Wf525F/W7R1C6XgdinWrLdSoVI8JH3GqEQlLeWXT2skruIP0/MKU6CT6MnE\nceckpfDyca+ElTtK/paSEikVt0sxZr2VCqXgHk4CY1QCSEv55aNI4ir+MD2vXAU6rjF00qTldEts\nw5CUwiuEeyVK/paKEknL7RKnMVCoWW+l0DPzUgru4SQwRiVBwii/fBRJPoo/V88rV4EOqyy9ldVJ\nk1t23RLLMOSj8LyyFMK9EiV/S0WJpNVjyid8Tdpu0lLpmXmphJhgxqgkRFjll48iyUfxh+l5ZSvQ\nYZWlu7K60+TttrdjGQb3b2rTNhbvXhxaOfkpjrTdK1HztxSUSBo9pnzD10TxFITtdTjXbWndUrID\n4pUQE6zPG5VU/PU5WntBiiSMLEko/qD3ZCvQYZSl17Auf2d5r0WnUVvC3jVGe9r35FROrZ2t3Lrr\nVl/FUQj3ShRDUQpKJI0eU1Lha8IQttfhXPfNt79ZMmNZSeJXr4vh5kvVqIjILBHZISI7ReR2n+8/\nLyIvi8grIrJJRD7k+u5N+/w2EdmSloxp+OtztfaCFEkYWfJV/GHf40cuZek1rECvMYyoLWH3inUg\nlHJa1rKMjcc20qZt3bL4LVhMq4dQCoYiKkmnRxLha8IQ1kPgvm5f+z5qpAaI1zMrxfEY8Hc35qrr\nrZ2t1DbWjk9SjtSMioj0Ax4CZgNnA9eKyNmey94APqmqHwS+ASz3fH+Jqp6jqjPSkDHJAcowCj2f\nsPvee4OOrz/z+qzKIZ/fnEtZeg3rjWNv7E6Ts+rOit0SXtS4iKbaplBbGzi/z022BYve31KqCiMM\n+cietCFMInxNGMJ6CNzXddLJ6JrRsctjscdj/PLZz9245K0lOafQL2tZRnVD9Ygk5Uuzp3I+sFNV\nd6tqO7AKmOO+QFU3qeoR+/B5oKDLeJMeoMzV2gsqjK2drSzevTirLN57g45/uP+HWZVDmN8cVzn5\nGVYnTR6c9GDslnBDdQP3NN/TbbBqqKG1q9V3RplfnLdZw2YxtnZs1t8UZmuEQpCPYSiWsguSOd/w\nNWHI5iEI2k6gVmr5p/H/FKs8+jXKCt0Q8ctnP3fjc8eeyzqF3q8BlgRpGpVGYI/reK99LojrgfWu\nYwWeEZEXReSGpIVr7WzleNfxXooqn4KRrbWXrYewrGUZe9v3Bu4H4713/eH1WY+z9T7CuOnyUU5e\nw+qkSeMZjXm1hN0Ga3TdaH597Ne+M8p6/D5qmTl4JosaF+X8TWG2RigEcdO+mKuxg2TOJ3yNQy6F\nnc1D4LedgHPdeYPOi1Ue/RplhTTmQfns527MkOnWKQ7ue/waYElQEgP1InIJllH5b67TH1fVc7Dc\nZ18VkYsD7r1BRLaIyJYDB8OH6l7WsoxfH/s1Z9ae2UNRJVEw/CpCUA/BHZYfrOiw3rD73nt/8M4P\nsh5n63HlCu+fb1C/NMcTFjUu4uLBF7O/fX/gjDL375s9fDb3TrqXlvaWrAo37NYIaZOPYchnllw+\nhJE5nzIRRmH7eQj8ynEu13AYvI2y8xvOL6gxD9Ijfu7GOqnjE4M/0eN+9z1+higJ0jQqLcA413GT\nfa4HIjIdeASYo6qHnPOq2mL/PwA8geVO64WqLlfVGao6o6uhK5RgPQbt2vZx7sBzuxWVt2DE6dr6\nVQS/HoLX7ZUhw7haK8nc9/uNVWQ7zuW3zhbev1hB/cLMXGmobmBQv0E5Z5R5lUwul59fiy2JnmtU\n8nHHhpkll4abJs2oAGGNrJ/R8ivHuVzDYfD2eH7X+ruCziTLFdHa625cMn5J4BR6v3VbSZCmUdkM\nTBaRiSJSC1wDrHFfICJnAY8D16nq667zA0RkkPMZuAz4Q64XHus8xhO/fYJMJsO2bdtQVVSVbdu2\ndZ/LZDLcv/F+Tned5uSOk7Rl2vjTy3/qPj7ddZr7Nt7Xff29e+5l1aZVfHfPd3s9x+8dj//2cdYf\nXM+JHSdYd3Bdtzxtr7dx2ZDLOL3jtPX/9dPcu+deXn/ldTSjnNxxkhqt4VOHPsUzh5/hxI4TrD+0\nnu3vbu9175i3x2Q9Pv36aY62H+XmdTdztP1oL3kH9RvEtHemdb/HLef0P0+nWqs5ueMkmUyGkztO\n9vg+TBrE+e9NZ1Xlu3u/2yvt/eSr1mqm/3l697MGVg3k6iNXM7BqINu2bWPeyHnUUON7rfeZZODU\njlOMqhnFUy881Svf3b81KI1z/Q9KL7ccfnJme4ZTRk7tOEUmk+HdHe/2yje/NA7KN1XlxIkTqGqP\nOqZY553rcqVtnHTwq6feepnr3mzlOJ/yqqosHLuQj+z7CF8b87W88ixOmfHTI95y//XGr/ORfR9h\n4diFDOo3iOuOX9d9z8yGmTyw8QH2nNzDzetu5ssjv0zHwY6j+Sr7noXEFjaNP+AK4HVgF/Df7XML\ngAX250eAI8A2+2+Lfb4Z+L3996pzb66/2sZaHTpuqK5atUqbm5t169atunXrVm1ubu4+t2rVKm2a\n0KST75mstY21OvmeyTp6/Ogex00Tmnyvc867n+d9x9BxQ3XityZqbWOtTvzWxB7yrPjJCh161lBd\n8ZMVPZ7d/K1mrW2s1bkPzO1x/9SVU/XGJ2/sdW+Y48898LnuZ/rJ637PhG9N0Pqm+u575z4wt/u8\n3+/IlQZ+8gTdG5TOj216TKc/Ot037R35zvvOeb6/0U++BU8uyHqt88zPPPAZHTxucGC+u39rUBrn\n+h9ULt1yeJ/pTc+gNK9vqvctf35pnE2Ov3vm73Ta3dN069at6ua2F2/TaXdP65bnuc3P5UzbKL8h\nV7kIk4betPSW4zD3Rsm7oDyLku9R3hu2bvnd85kHPtOr7gCnE9X7aRqVQv8NmDpAH9/0uHZ1denW\nrVs1k8loJpPRrVu3dp9z/t+1+y49e+XZetfuu3yPu7q69MYnb9RzN5+rU1ZO0XM3n6s3Pnljr+d4\n3/HYpsf0gi0X6JSVU/SCLRf0kMf93/3sczafozN/NlOPtB3pcf+FL16ofzzxR1/5s/3PJoMjr/ua\nD2/+sE5dObX7tx9pO6JfWPOFUL8jKJ3v3H1nr/QM+u+XFot3LdYPb/mwb9ofaTuiNz15k7797tt6\n05M36ZG2I1nfkclk9Gj70azXOs880nYka747z4qSPmHSy0+ObOkZ9IygvPf7TdnkcMvqxrnekWfJ\nG0typm3U35CtnoZJQ3da+uVTmHuj5F1QnkXJ93zKTNh73GXjnM3ndKeJ1MmuJPWw2D2DiqBxeqO2\nvNxr2MaX1s5W7m+5n4WNC2mobuh1DJZP9/od19OmbdRJHSs+sCLUlMi737qbXx76JVcPv5o7x9/p\ne022Z4e5PxtL3lzC2sNrURRBuHrY1dw14S5fOdccWoMgZMgkJkfUdHNfX0UVinLx4It5vvX5UM/w\ny7uwxMn3f37rn7v3QAGypnESRE1Pv3yLW5aTkCef9+eTtw751qdKwa0XHAThlU+/crBtb9vIpN5T\nErO/ksLZUzvMgKR3cM9vsC/sCnXv+8KsTs727HxXN4dd3Z9rUWFcOaIM3rp3PnSvmn++9XkubLgw\n1PqGpPenyZY3YRdXJknUwXC/fEsyFEucwfm4A/pJRAtPK3pCuS2U9ZvtVSu1dB7uDD9tNgQVZVT6\nST8g2UVgYQqk931hK0LQs/Odlhtlp0L3okKvcoyy6jzbQrNsCte986HXwPWX/jnTPq39aZy8uf7M\n63v81qDFlWlGGI4a8NGbb2GjLeQrTzYlW8gw/3HrY77vKXXcesEd4SJzKnMqyfdUlFGB5BeB5SqQ\naYY9yYewrbM4LVi/ypRtoVnQM707H3qnRl83+rqc6ZPW/jRO3jyy/5Hg6d2uxZVpEpSeuVrK3igB\nflNq47S2g+TJpmQLFea/UItAS2nr3yh56Bfhoqp/VX2S8lScUUlr3nxQxpXS7n1uohisKO4Bv8rk\ndy7MM71p90LrC5EVT6H3p/FbXJkrjdPahjmJKAFJBRcNo2QLEeY/qfqYK88KVe/DlJ0oeegX4aJ6\nWPWoJGWuOKPiVRRXDb8qEb9nUMal3a3PVyHFGV/Khl9l8jsX5pl+aRdV8RRjf5qoMqaxDXMuJR4m\nSkCSvewwStavTCQ9LpFUfcyVZ0GLmZMeY8klRxI9JjOmkgOvolh7eG3eFTpbxqXdrQ8TujpbQU7a\n7+tXmeJWZL+0i+MSDBr/iHJvlP1pwsqYbV+XfIkTJcCr/H6y/yeJtbbjloGw5TOswk6iPoZR1H7v\nSbquhZEjiR5T5lTmZN7Cuqg4owLvKYorhl6RSIXOlXFpdevDFKpsBTkNv69fZYpTkZMcPA4a/4hy\nbz770wQRZl+XuORS4t6wLYL0Un6nMqcS62VHSas4OzBGUdj51Edv6CQnjppfRGz3e9Koa2EMRiEn\nQISlIo2KoyjWHl6bSEssV8alNeCeq1DlKshp+X39Km1cl1AS8ZggvYHTuAoq19TjfF0luZS4+/vL\nh17OnOFzeim/KNO2wxA2raLuwBg1b/OZhrx0z9IeEcOdOGp+EbHd70mjroUxGIWaABGFijQqDklZ\ncb+MC6MUnGv8WjlJyJ+rIKfVinEqE9D9u6JU5GK16uIQt8GQa+pxEq6SXErc+f62cbcFKr8w07bD\nEiat4uzAGJS3uTaui4J7UoN7cSBY66b8ImK7SaOuhTUYhZgAEYWKNipJWnFvxoVRCs41fq2cJOTP\nVZCLPd4TRLFadYUk29TjpIxqLiXu9703ncJM206SoB0Y/bZh8JPZHT0618Z1YfGb1JAhw4CqAb0M\nTFB5TbquRXEPl9r21RVtVCA5K+7OuDBKwX1NrlZOXPnDFORijvcEUcxWXaHINvW4mNPQi51OQTsw\nQu9tGPxkdvY9cm+VG3WjOi9+vco6qWPxWYt9V6Bni1CRVF1L2j1cSCreqKRhxcMoBb+CGkeB5JI/\nV0FO4vdH2XQsDGkptlJzA3jlcdLxqmFXFbVXVcx08tuBce7IuTx79Nmca1zcG7T12Co34kZ1XoIm\nNcweNtt3BXq2CBVpjg+WS1iYijcqSePdhjhIKQTF2UlagRSi6xt207EopKHYSs0N4JXHScd1YbRO\n5QAACttJREFUR9YVtbdQ7HSKuoka9N6gzb1VbpyN6twETWpwy+pegZ42QelRLmFhjFGJiHcb4iCl\nEBRnp9humagEtZry7W0UW7EVGm86XjnsysSUVLm0YB28eR+2geK+ztkq1yl/7l5FnPLoN6nBLat7\nBXou8s0Pv/QopbAwuTBGJQLebYgvHnxxVqVQjFZO0mRrRZaau6mU8abj2kNrYxlVP4VVLi3YIMI2\nULzXLRm/pEf5y6c8JtnIyTc//NKjVMNB+WGMSgTcGdtBBw39GrIWwjitnFIjWyuyr/U28iGt8CHl\n1ILNRliD4L4uzPYVhSap/PCmR6nNbsyGMSoRKKeMTYpizxaqFNIKH1JOLdhshDUISRuOpF2HSeWH\n93eWUz2sLrYA5YSTsb889MuSz9gkWdS4CEFY2Liw2KKUNfmmo5/Cmj9qPhuObKBN2/pMQydJlrUs\nY+0hazfEJHaFTDM/yqUeptpTEZFZIrJDRHaKyO0+34uIPGB//7KIfDjsvcWiL44jlIJboRLINx39\nesrl1IItNQoVGy8pyqUepmZURKQf8BAwGzgbuFZEzvZcNhuYbP/dAPxLhHuLQrlkrKHyCFJYfbGh\nkwSFjI3Xl0jT/XU+sFNVdwOIyCpgDvBH1zVzgH9TVQWeF5EhIjIGmBDiXoOhz+HnAnHHYjOEJy1X\nVV/PjzSNSiOwx3W8F7ggxDWNIe8FQERuwOrlALSJyB/ykLkQjAAOFluIEBg5kyVROe8kf/9/AH0q\nPWsba8dXN1SP6GztPDi1ZepbCcjlpRzS8wNJPqzsB+pVdTmwHEBEtqjqjCKLlJVykBGMnElj5EwW\nI2dyiMiWJJ+XplFpAca5jpvsc2GuqQlxr8FgMBhKjDRnf20GJovIRBGpBa4B1niuWQN80Z4FdiFw\nTFX/HPJeg8FgMJQYqfVUVLVTRG4Bngb6AStU9VURWWB//31gHXAFsBM4CXw5270hXrs8+V+SOOUg\nIxg5k8bImSxGzuRIVEaxJl4ZDAaDwZA/JkyLwWAwGBLDGBWDwWAwJEbJGxUReVNEXhGRbd6pbyJy\nq4ioiIxwnVtsh3bZISKXu86fZz9npx0aRooho4j8tYi8aF//oohc6rp2oy33NvtvVFIyxpBzgoic\ncsnyfde1qaVlDDk/75Jxm4hkROQc+7uCp6eILBGRFtc7r3BdX/CyGVXOUiufWeQsqfKZRc6ilM+g\nOiQify8ir4nIqyKy1HU+ubKpqiX9B7wJjPA5Pw5rIP8t53uskC6/B+qAicAuoJ/93QvAhYAA64HZ\nRZLxXGCs/fmvgBbX9RuBGSWSlhOAPwQ8J7W0jCqn5/sPAruKmZ7AEuDrPtcWpWzGkLOkymcWOUuq\nfAbJWazyGSDjJcAzQJ19PCqNslnyPZUsLANuA9wzDeYAq1S1TVXfwJpVdr5YoV8aVPV5tVLq34DP\nFENGVd2qqu/Yh68C/UWkrgCyZMMvLX0pYlpCbjmvBVYVSJaolFrZ9KVEy2doSi09PRS7fN4EfFtV\n2wBU9YB9PtGyWQ5GRYFn7K74DQAiMgerBfV7z7XZwr7s9TlfDBndfA54yclkm3+1u6x3JN1tjyHn\nRFuW/ycin7DPpZ2WceR0mA886jlX0PS0+Xuxom6vEJGh9rlilc2ocropevnMIWfJlM8ccjoUsnz6\nyfh+4BMi8js7zT5in0+2bKbR9Uq4G9fodNWwumgXA78DBnu7ecCDwBdc9/4QmAvMAJ5xnf8EsLYY\nMrrumYbVzZzk85xBwAbgi0VMyzpguP35PLvQNaSdlnmk5wXAKwHPKWR6nom1tqoK+B9Ya6yKVjaj\nylmC5TMoPUutfOZKz4KWzwAZ/wB8D8uVdT7whv050bJZ8j0VVW2x/x8AngA+ieX3+72IvIkVwuUl\nERlNcNiXFvuz93wxZEREmuzrvqiqu3yecxxYiZXxiRFFTrW6wofs61/EUjDvJ+W0jCqn67Zr8LQC\ni5Ce56vqflXtUtUM8LDrnUUpmzHkLKXyGShniZXPrOlpU9Dy6ScjVk/jcbV4AchgBbxMtmwmacGT\n/gMGAINcnzcBszzXvMl7retp9Bxw2k3wgNMVRZJxiC3jZz3XVLuuqQF+DiwoYlqOdKVds12YhqWZ\nlnHktI+rbPmai52ewBjXNf+A5asuStmMKWdJlc8scpZU+QySsxjlM4uMC4C77fPvx+rdSdJls9Sj\nFJ8JPGG7GquBlar6VNDFaoWB+SnWviudwFdVtcv++mbgx0B/rMRZXwwZgVuA9wF3iogTv/wy4F3g\naRGpwepGP4PV4kmKqHJeDNwtIh1YLZoFqnrY/i6ttIwjpyPrHrX337GpowjpKSL/254yqljG70Yo\nWtmMLCclVj6zyFlS5TOLnI6shSyfQTLWAivE2h6kHfiSWpYj0bJpwrQYDAaDITFKfkzFYDAYDOWD\nMSoGg8FgSAxjVAwGg8GQGMaoGAwGgyExjFExGAwGQ2IYo2IwGAyGxDBGxWAwGAyJYYyKwVDhiMj3\nROQlVwBBgyE1jFExGCoYERmAFVTwRuCqIotj6AMYo2KoGERkmYgsch0/LSKPuI7vFZH/mvA7TyT8\nvCEicrPreIIdViPXfd07IbrPq+q7wBisDaEeEJH+dqj1dnHtmGowJIUxKoZK4jfARQAiUoUVgXWa\n6/uLsILrlTJDsOItxWGXqp7jPiEiw4F64DjQqaqn7Gve8XuAwZAvxqgYKolNwEftz9Ow9o84LiJD\nxdq9cCpWyPx/tzcvetW9yZKIfFtEvuo6XiIiXxeRL4jIC3YL/wci0s/74qBr7B7EdhF52H7fBhHp\nb393h1h7gv+HiDwqIl8Hvg1Msp/zHfvx/fzuD8k/Af8LaxfHaTmuNRjyxhgVQ8Wg1ja4nSJyFlav\n5LdYm3t9FGvDoVdUtR34L6p6nn3ua3ZrHmA1MM/1yHn2/fOBj9kt/C7g8+73isjUHNdMBh5S1WnA\nUeBz9qD554APAbNtWQBux+5xqOo/Bt0fJj1EZIKdDquB7RijYigApR763mCIyiYsRXoR8F2s7U8v\nAo5hucfAMiR/Y38eh6W0D6nqVhEZJSJjsfbrOIKl9M8DNtuhxPsDzt7eDp/Kcc0bquqMdbwITMBy\nzf1CVU8Dp0Xkl1l+k9/9Yfgm1v4ZKiLGqBgKgjEqhkrDGVf5IJb7aw9wK9AK/EhEZgL/Cfioqp4U\nkY3AGa77f4a1leporBa+AP+qqouzvDPXNe493ruwjE4UIt9v7+3xWeDjIvIQ1m98JeJ7DYbIGPeX\nodLYhDV19rBa27sexhr8/qj93WDgiG1QpmDtaudmNdbWr3OxDMz/BeaKyCgAERkmIuM994S5xstv\ngKtF5AwRGch7032PY+1Zni//E/i0qk5Q1QlYPS7TUzGkjjEqhkrjFSzX0vOec8dU9SDwFFBtu4O+\n7bkOVX0VS6m3qOqfVfWPWIPdG0TkZeD/YE3Rdd+T8xovqroZWAO8jLWbniPjIeA3IvIH10B9JETk\nUqBeVZ9xvW8/MFBEhsV5psEQFrPzo8FQJERkoKqeEJF64NfADar6UsxnTQDWqupfhbz+TWCGbWgN\nhsQwPRWDoXgstxcrvgQ8Fteg2HQBg72LH704ix+BGqy93Q2GRDE9FYPBYDAkhumpGAwGgyExjFEx\nGAwGQ2IYo2IwGAyGxDBGxWAwGAyJYYyKwWAwGBLDGBWDwWAwJIYxKgaDwWBIjP8PpJxfiRrvWI4A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf18f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=ppxf_example_kinematics_sdss(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wdisp 2891 lam_gal 2891\n",
      "velscale 69.1451441267\n"
     ]
    }
   ],
   "source": [
    "#def ppxf_miles(vr, plot, bs, conv, dg):\n",
    "#y=ppxf_miles(1,'no',0,'no',-1)\n",
    "#y=ppxf_miles(1,'no',0,'no',1)\n",
    "#y=ppxf_miles(1,'no',0,'no',3)\n",
    "y=ppxf_miles(1,'no',0,'no',4, 0)\n",
    "\n",
    "def gauss_1(x,*p):\n",
    "    A, mu, sigma, slope, height=p\n",
    "    return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))+slope*x+height\n",
    "\n",
    "lams=y[0]\n",
    "gals=y[1]\n",
    "err=y[2]\n",
    "print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18570.3821989\n",
      "error on MHU 18416.8261719 16.3781241206\n",
      "133.17382812\n",
      "z_stars from sol 0.0638305701505\n"
     ]
    }
   ],
   "source": [
    "\n",
    "redshift_J1354 = 0.0633581002031\n",
    "\n",
    "redshift_J1354_e = 5.63444982043e-05\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "c=2.99792458e5\n",
    "\n",
    "print(c*np.log(1 + 0.0639029))\n",
    "red_vel=c*np.log(1 + redshift_J1354)\n",
    "\n",
    "v_err_1=redshift_J1354_e/redshift_J1354*red_vel\n",
    "\n",
    "print('error on MHU',red_vel, v_err_1)\n",
    "\n",
    "\n",
    "print(( 18550    -red_vel))\n",
    "#18453 = sdss\n",
    "#18240 = PA 1\n",
    "#18404 = PA 2\n",
    "#18162 = PA 3\n",
    "\n",
    "'''So the redshift should be given by:\n",
    "z_stars=exp(all.VEL_STARS/ckm)-1.d0'''\n",
    "z_stars=np.exp( 18550     /c)-1\n",
    "print('z_stars from sol', z_stars)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from astropy.io import fits\n",
    "from scipy import ndimage\n",
    "import numpy as np\n",
    "from time import clock\n",
    "import glob\n",
    "from os import path\n",
    "\n",
    "from ppxf import ppxf\n",
    "import ppxf_util as util\n",
    "\n",
    "\n",
    "def ppxf_example_kinematics_sdss(vr):\n",
    "\n",
    "    file_dir = '/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    file = '../spec-1777-53857-0076.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "    z = 0.0637454081958   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    flux = t['flux'][mask]\n",
    "    var = 1/np.sqrt(t['ivar'][mask])\n",
    "    var_scaled=var/np.median(flux)\n",
    "    fig=plt.figure()\n",
    "    ax1=fig.add_subplot(2,1,1)\n",
    "    ax1.plot(var, label='Noise')\n",
    "    plt.legend()\n",
    "    ax2=fig.add_subplot(2,1,2)\n",
    "    ax2.plot(flux, label='flux')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    galaxy = flux/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    \n",
    "    plt.plot(lam_gal, galaxy, label='Air')\n",
    "    plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    noise = np.full_like(galaxy, 0.0166)       # Assume constant noise per pixel here\n",
    "    noise = var_scaled\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    plt.plot(lam_gal, fwhm_gal)\n",
    "    plt.xlabel('Lambdas')\n",
    "    plt.ylabel('Interp FWHM')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    #lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    #fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    "    vazdekis = glob.glob(file_dir + '/miles_models/Mun1.30Z*.fits')\n",
    "    fwhm_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.\n",
    "\n",
    "    # Extract the wavelength range and logarithmically rebin one spectrum\n",
    "    # to the same velocity scale of the SDSS galaxy spectrum, to determine\n",
    "    # the size needed for the array which will contain the template spectra.\n",
    "    #\n",
    "    hdu = fits.open(vazdekis[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(vazdekis)))\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(vazdekis):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    fig=plt.figure()\n",
    "    ax1=fig.add_subplot(211)\n",
    "    ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "    ax1.set_xlim([5850,5950])\n",
    "    ax1.set_ylim([0.5,2.0])\n",
    "    ax2=fig.add_subplot(212)\n",
    "    ax2.plot(lam_temp, ssp, label='Template')\n",
    "    ax2.set_xlim([5850,5950])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    vel = c*np.log(1 + z)   # Initial estimate of the galaxy velocity in km/s\n",
    "    print('input vel', vel)\n",
    "    start = [vel, 100.]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    t = clock()\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, plot=True, moments=4,\n",
    "              degree=-1, vsyst=dv, clean=True, lam=lam_gal, velscale_ratio=vr)#was degree=12\n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    #print, 'Best-fitting redshift z:', (z + 1)*(1 + sol[0]/c) - 1\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    plt.xlim([5400,5600])\n",
    "    plt.ylim([0,2])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "def ppxf_miles(vr, plot, bs, conv, dg, regularization):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    file = '../spec-1777-53857-0076.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "    z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    flux = t['flux'][mask]\n",
    "    var = 1/np.sqrt(t['ivar'][mask])\n",
    "    var_scaled=var/np.median(flux)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(var, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(flux, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    galaxy = flux/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    noise = np.full_like(galaxy, 0.0166)       # Assume constant noise per pixel here\n",
    "    noise = var_scaled\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    #lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    #fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    "    \n",
    "    file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(ssp)\n",
    "        plt.title('Template')\n",
    "        plt.show()\n",
    "    \n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = h2['CDELT1']\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        if conv=='yes':\n",
    "            ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    vel = c*np.log(1 + z)   # Initial estimate of the galaxy velocity in km/s\n",
    "    print('input vel', vel)\n",
    "    start = [vel, 100.]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    t = clock()\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, bias=bs, plot=True, moments=4,\n",
    "              degree=dg, vsyst=dv, velscale_ratio=vr, regul=regularization)#was degree=12\n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    #print, 'Best-fitting redshift z:', (z + 1)*(1 + sol[0]/c) - 1\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    plt.xlim([1800,2200])\n",
    "    plt.ylim([0,2])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wdisp 2891 lam_gal 2891\n",
      "velscale 69.1451441267\n",
      "input vel 0.0\n",
      "Best Fit:       Vel     sigma        h3        h4\n",
      " comp. 0:       154       211    -0.049     0.194\n",
      "chi2/DOF: 2.481\n",
      "Function evaluations: 33 Status: 1\n",
      "Nonzero Templates:  16  /  1273\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "      35      44    0.14    0.16\n",
      "Elapsed time in PPXF: 59.15 s\n",
      "Best-fitting redshift z: 0.0639029279271\n"
     ]
    },
    {
     "data": {
      "image/png": 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MSlfS3vvp06fx8/Mz/ecAtWrVAsomKqdPn6ZNmzZ89NFHTo8zKlWtWrVKXfjLS1Ts7/Nq\ns1Ti4+PN55jfIrFYLISHh18yS0VEihwryczMNC3wjIwM3NzcgNKN/V0sRoNZlBgUh305LYn7KzMz\ns1T39sADD9CuXTsef/zxi89kCTHuxfAyNGjQgKCgIHbuvPhhZUMAP/zww3LNY3FoUcHqvlm6dGmR\nx5RGVEJCQvDz8wPA29sbX19fAgICSjWmYohKXFwc586dK7T3YVTSZs2alcpSse+pXqz7K3+Ftu+J\nG5ZKXl4eAwYMYPXq1RedN2fExsaavdTLiWPHjpmfjTJgT0RExCWzVN555x18fX0L7fHaC35mZqbZ\nizfKXH5EpFSNnDOMhr4sg+clFZXk5GRCQkKAwu+tMNLT0/nss88A1y4Ka+TrwIEDxMfHk5Njnd80\nYcIE85g9e/YwaNCgYjt9RvmaPXv2Je3UaVEBpkyZwl133VXkeMnFikpiYiIhISHmbHpvb28AwsLC\nyjSmYgzYF2aFJCUl4ePjQ926dUslKvZRa0ahPX78eKHP5uzZs+b95K/Q9uJpPL+kpCTmz59P165d\nLzpv+UlISKBevXrMmJF/hZ+L5+zZs7Rt25a1a9c6TRcR1q1bV+LrGaLyzTffMHv27ALp1atXv2SW\nyksvvQRcKEP5sW9wMjIyzPf/GH/z88MPP9C8efNyaVxLYqkcPXqUt956q9DAAaOc1qhRo1BRERGS\nkpKoV68eUPizKIw9e/YAEBIS4iBIu3btonv37uXWaBv5ys3NZeHCheZ++w7Ik08+ybx580yRKwzj\nnNTU1FIH7ZQGLSpYC3ZmZmaRglFWS8XHxweA8PDwUvVQjYJcElGpWrUqkZGRJCcnX3Rhtxei1NRU\nTp48Se3atfn++++dHm9YKRaLpUhLxciHfX7KWhHXrVvH+fPn+eWXX8p0HYDt27fz559/smnTJqfp\nP//8M506dXKI6isKQ1R69uxJWFhYgfRLaakYlkdhZdd+vCszM7NQMTHYtWsXAAcPHiQ3N7dMc6cM\nUSnKUvnkk08YP348f//9t9N0Q1Rq167NmTNnnOYnIyOD7Oxs6tevD1y8pWLc86233srJkyfNZ7p8\n+XJWrlzJvn37EBEGDBhw0eXx5Zdf5pZbbgGsohIeHg7AihUrAAgNDTXFZuPGjSxduhRPT08+/PBD\nZs2aVejvGh8fT/369WnevDnTp09nwYIFLh0PMtCiwoXKlpCQUGgPxqh4ZXF/gXWs4+jRoxedR6MS\nGL3bokQlKCiImjWtS6nZi0ReXl6xgQb5ReXo0aNkZ2cX6m4zRKVx48YFepvO3F/2Ddjy5cuLzEtx\nrF+/HqBEc3fs+fTTT83ee2xsLPfff7/5SoLCeroxMTFA0T3cmJgYBg0axLlz5zh27Bh+fn5UrVrV\n6bHVq1fn1KlTLnfd2f/ehblLCrNUAHJychg1ahTPPvusuc8YQ9u7dy/BwcGFdjjs2bJli9NOhFFm\n8pedmJgY83uMlb0Lm7ho3FedOnXIzs4u8D1ffvklX331FYBpqZRUVESEJUuWsG3bNjw9Penc2bq4\nh9G5M/J48uRJ0wr/6aefCr2eMxYvXsyGNWvI3biRG3fs4H0vLyZ5eNB0xQpuAFo3a2aWu8WLF+Pu\n7s706dM5ePAgI0aMYNq0aQUvmpeH38GD3O3hwetduxK7bRt33303zz77LDNmzGDgwIEsWbLkovJZ\nUi5aVJRSQbaZ8JUGQyi+/fZbwsPDzQbEICsry/RtXoyoBAcHm+4vw1KJjo7m2LFjzJw5kxtuuAER\n4fTp03Tu3LnQd60Yx8CFwnz69GmnAmGISmRkJOAoElOmTKFevXrk5OQwd+5c3nrrrQINjf3xaWlp\nZmEuzGVniFzz5s2dur+CgoJQSpkV3V5Ufv31V6fXLBHZ2ez75RdaA01jYzkzaRJMnMgfnTuzsFYt\nTo0dy9S2bTm3eDH89Rds3gwrVsC8eRx+5x1+nzIFcnNZvHgxn3/+OZMmTQIKFxVDxIsKfR08eDDz\n5s1j27ZtHDt2jFq1ajm8m96eiIgI8vLyXD4YvmPHDvNzYWU3/5iKvajEx8ezePFihw6A0ZCuXr2a\nlJSUQl2GBsnJyVx//fX897//dZpm/9fg3nvv5b777iMvL48//vgDKJmogKNgiAjjxo3jueeeAzAX\nm8zfOdi9e7dTd+SqVavo06cPH374IY0aNTIjOI3yYHSqTp48adaRAhZobi7s3w+rVsGXX8J//wvT\npsH775Pz8MNM2baNhJwc3Dp25MlDh7jn6FHGZGfzekYG64DF69bxZWIief/5D35r1/JoaCgjzp9n\nzwMP8FCtWqy2Hw/OyoLp06F+fT7bupU3du+mz9SpnALWA/U3bODDiRP59ttvue+++1yyQkeJlkhV\nSq0G+tiO3wqcUkqtF5Fx5Z6jCsCobL/88gu5ubls2rSJevXq0a9fP4YOHUqXLl0KHFsUWVlZpKam\nEhISYlYWe1HJyspi1qxZbNiwgUOHDrFt2zbWrl1L//792bZtm2n+Gpw9e9Y0t+0r/Ny5c2nRogVt\n2rQx9yUlJVGzZk1TVGJjY3nmmWcQEf755x/i4uJYuHAhgwcPBqw95vvvv988//jx4/j7++Ph4UFq\namqJRaVRo0acO3eOzMxM816PHTtGzZo1yc7OvjAP4uRJ3IGcIq5ZyBfBF1/ATz/Bzp2QmIhDP2uc\ntShea9uYPJnHAe68s8ClXrf9zYuMJKRRI9wpfnKp4c4qSlSOHDkCWK1SQ1QKw36uyuTJk7n11lvL\nZZwpP0ZoKhTv/lJKkZGR4RBuu3fvXk6ePOlQ7g4dOgTAhg3WhcaN8YbCOHLkCDk5OQ5zqAycjamI\nCHv37uXcuXPs2rWLlJQUAgIC+O2338jLy8NicewLG6LSsKF1+ZsTJ05Qu3ZtwDqobi8yYWFhVKlS\npYCl0rt3b7p168ann37qsP9///sfYLXymzRpYnoAnImK0UE4ceIEZGbCd9/B99/DypVQyJiRO9DJ\n9jmjVi0WnzqFb4sWpJw9S8bu3dxksdAoO5vbAJ5+mheMEx99lEbAx0DasWPk/OtfuIeFwdKlYOsY\nxilFclQUTaOiYOtWOmZk0PGff3gYmB4QwMTkZJesml5SS6WKiKQC/YAvRKQDcEu556aMOAub3LNn\nD2+99VaRimxUqm3btgHW3l16ejoLFy7k119/dejJpaWlkZeXx8qVKwu9ptEwOXN/GT0dw2WzefNm\ntmzZgru7OwkJCUycOLHA9Qoz1YcPH07btm0d9uW3VGJjY1mwYAFz5sxh7969gONs//xurbi4OGrU\nqEFgYCCpqanmdxcmAPHx8VSrVs1sJO0b5SNHjhAdHU0bLy9uW7UKoqK4qU8fzgM7lGLQhg3w0Ufw\n2mtw993QqBH4+EDNmtCzJ3/37s2ZMWOgZ0+oVQuefRbWrIHERMRiIR5IiY5mpcXC1iZN4IknmFy9\nOmOBn1u14itgb2QkNG0KbdvCzTeT0asX84GDgOXkSQasWcMuoK8tz4W5t0piqRiNyvnz50ssKrt2\n7eKdd94x3XGlJS8vjylTphRoJOwb/OIsleDg4AKWimFNJicnk5ycTG5urum+NcpGcaJiCLIzS8CZ\npZKYmEhKSgrnz583gzAeeughEhMTnbqW8ouKfbDJlp9+og3QDutSHUFBQQQHB3P69Gk2bNhgTlA9\ncuRIAQvDGCw3Jk02a9bMQVREpICoRALDtm2DGjXg3nth/nyroNSsCTfdBIMHw4MPwiOPwOjRrOvT\nhx5AGPDNhAkMyc5mc48e/N2rFw8D/Ro2ZMGUKQwBUvr1Y5XFwvb69eGhh+Df/ya1QQMCAPfvv7da\nQHFx0Lgx57/+migRvh81Cn77DU6eZMOIEWyxPYdX0tI4DGS5IDy6pC9zcFdKVQcGAM+Xey7KCWc+\n248//pipU6fSu3dvWrRw7rUzKpvRoOzYscOsACkpKQVEZeLEiTz33HMsX76cHj16FLieUdnso7/s\nLRXA9KVv3ryZv//+m5YtWxIdHc2XX35JYGAgDRs2ZNCgQQ7Xs1gsRS6dISIkJycTFBSEv78/YWFh\n7Ny5k8OHD5OXl2dWmt27dwPQPCIC2bTJWhBr1ACliIuLIzIykoSEhBK5v+Lj46levTpBQUGAVVQi\nIyPh1Cl67N/PoydOEJ2YCLbr5Hh4oLKzaSZCs9hYa+XKT1wcxMXh8Gu5u0PfvtaKet11vP3ZZ4x/\n8UUS//yTcV26ULt2bRa/+y6vzZrFGWBXaCg/A/V9fdlvF/7627JlDLC5C9aMHk3ktGk0yMnhO2At\n8KldKLA9JbFUDNLS0oiPjy9SVBo1agRgLty5du1aU4RLw9atWxkzZgyHDh1iypQp5v7Y2FgiIyOJ\ni4srdkwlNDS0wJiKvYvyyJEjBAUFmZN57fdnZGQ4vDfInqJExdmYiv3CrTNnziQoKIgJEyawfPly\nhg4dytdff03Pnj1N12JqaipeXl6m+ytr82YOzZ1LnT//ZMihQwyx+77sfv34ICOD1QsXMuabb3h9\n0SLqt2hhRofZs3nzZk6ePMnkyZPx8vKic+fOBAYG4u/vT2xsLGfOnDE7pLkHD9JwyxYOAl5Gx+ra\na9lQrx4vrF/PsgMH8PT05Ny5c2YHE2Da4MH8FRbGmcREtm3bRl5eHiEhIWZ7ERkZic811/AN0O+e\ne7jru++YPnYsrf7v/wDwOn+ehv7+vHHLLdx1xx3QrBl06sSJI0fIA3NJIPz98Xv8cVp99hm9gflN\nmhC+e7dV9MqZkloqrwDLgRgR+UMpVRe4tNM0S4Ax7mGP0Su3D8/LT/4e3N9//+0gKvbjAGlpaSxY\nsAAoPHrJXlT8fXzoAQw/ehReeIF6a9bQlAsPftOmTezfsoV7IiN5rWpV3k9IoMXLL+N1zz2c6dUL\nHn+c4IkT+RCY7+3NEmAdsArr8s3TPD1hyRLmf/014eHhpKSkWBv4kyd5JDwcFi0iwiZExvPJ3raN\nJW5u/B0fz5urVll7UU2bwsSJqCNHiLSzVAxRKcz3Hx8fT0REhDnJM2fDBrjjDqRGDSZmZhJ96hRp\nFgvLoqNh0yZmTpmCH/BE27a8Hxho7XE98QRjq1WjFZAWFwcHD7LvzTd5BXjPy4ucmTPJOXqUrLlz\noV8/qFGDtRs20KRJE6pVq0b9+vU5cOAAGRkZpqVkjCUcOHDA7E2uX7+eZcsuLCO32N2dRjk5zOvc\nmSR3dzoBnx84ALffDjarFaxibVgqhf3mqfHx3Ag8DVwzfjzLRRiybBnMnAlOGvOqVatSp04dh/yU\nJezTWMzzq6++chCF2NhYU8CKc3+FhoY6WCru7u4O43yHDx82XV9Gjx2sz6eo8OLCROXcuXPmuKB9\ng26MaYa7uRGQmcmTTzxBQEAAixYtolq1avTq1csaHHDgAMyZQ9t163je3Z3gV15hDzB44kTqLliA\nOnSIs0qxy92drUAq4HHsGL0SE5mYmclm4JZ+/Qi7+WamAc2OHQO7cUqjDHXs2JG+ffsSHByMUoqa\nNWsSGxvL0V276Ax8Dry/ZAlN167FA5gDZG/aBJs3M2jdOn49epRp06bx8MMP4+fnZ7oNV65cybx5\n8+jfvz+1atUyx46Cg4PNsZ/IyEiCg4MBzN/CsJzAOgNf6tRhnr+/tYPWuTMoZXYgTVHBGkzj4eHB\nocaN8d6xg1t8fVmRz9NRLohIsRtwQ0n2VfQWFhYmIiKjR4+WyZMnS15engQFBQkgLVu2lPwMHTpU\nHnvsMfH09BSsLwYzt48//lgA6dKli6xZs8bcP2rUKLFYLALI559/XuCaIiLz588XT5BjL74oZ4KD\nRaDAlgKyy99f9oLkOkm/2C3dy0tmgrwCcrhhQxGlzLQckJ9AHgf5DCTbtj/LzU3+slgkr0oVh2ul\n+vnJptBQmVGjhozp2VMA8fLykry8vAL3WqdOHRkyZIj8vXKlzLK7Rp67u3wPsuHxx6VLhw7SrVs3\nERF59913BZAHH3xQfH19RURk69at5vONiYkREZFPP/3U3LdixQq5/fbbpW3btiIikpubK1WqVJFR\no0aJiMizzz4rHh4esmvXLoff0PhdV6xYIWIttOZWu3ZtadiwoQDy3XffiSQny/L27SXN/rnedZfI\nrl1y8uQevbyuAAAgAElEQVRJ87yXX35ZJCZG5OWXRXr1EmnZUiQ6WnLd3Ar/fUJDRb78ssCz69ev\nnwDi7+8vTZo0kTvuuMNpeSqMFStWSL9+/SQnJ0fuvPNO8fDwEEC+/fZb85gaNWrIiBEjxM/PT8aN\nG+f0Oq+//roA0r9/f6lfv75MnDhRALn//vsFkMDAQAFk8uTJMnPmTAFk4MCBAki9evUEkG+++abQ\nfA4ZMkQA8fPzc9gff+KE1AW5CeT2KlXkv/ffL9uffVb+bN1ajts9v9yICJFx40S2bpXMw4dlIMih\n2rULfd4JINNABkVGisX2mwHiBpK9ebPMbt9eZoHsBMm1qycCIr6+Il26iPTuLTG1askfSkleo0Yi\ntWqJREaK1KwpJ729JcVicTgvG+T3hg2lka2cHDt2TEREevToYZYdo914+eWXJTc3V6Kjo6VJkyaS\nnp4uN998s3nc0qVL5dChQwLI+PHjZf/+/QLIDTfcIIAcOXLE4Tn27NlT2rRpIyIin3zyiYwYMULm\nzZsngGzdutXh2DvvvFOee+45ERFp3769dOvWTYAtUo7tcElF5c+S7KvoLdrXV5JWrBB3paR27dpy\n9OhRh4J//PhxhwfcuHFjadWqlUNj06JFCwHMCt+qVStZunSpmd65c2fz8+TJk6UAeXmyfORIOWhX\n4GJAfmjaVOSVV0QGDJA4T0+HAnkeJLNpU5GHHhKZMUPku+8k5u235SEfH3nWx0decHOTSddcI190\n6ya3gwysUUO2v/ee/LdnT3kJJKdZs4KVy9NTYps2lWUg5/Kl5YLMDwuT/9oak/hjx0QWL5ZzvXvL\naSeVdCZIFZCUlJR8t5onft7esuCWWyQnMNAqYO7uIs88I8u/+koA+f3336V79+7SqlUrmTx5srzw\nwgsCyKuvviqAZGZmysiRI81n+vvvv4uIyLhx48Tb21t8fX0lPDzcTN+3b5+89NJLYi/qn332mQDy\nySefOPyWTZs2FUAWLFggZ8+eNffXq1dPbrnlFvP/3bt3i4jIq6++KiEgOaNHi3h5WcVRKZkDMhxk\nNEhMrVpOG7JcpWQbyHSQb3v3lltBtj/0kMh11104buxYkdxc8/m99tprAsh1110nt912m7Rr165g\neSqEs2fPSlRUlADy999/S7Vq1WTYsGESHBwsw4cPFxGRrKwsUUrJhAkTJCIiQh588EGn5fXV0aOl\niZubPNe/v9wYESHvPv+8ALJ65UqpAjKsY0fp7u0ts3v1konNmsnAatXki2eeET+QR//v/yTYYpE3\nhg8XOXRIJDOzwFfY15nU1FSRlBSRqVPlXJ06hQsxSF5goOTaypXTzc9P5F//koW1asmX1auLvPmm\nPNqkibjblQGLxSLx8fHi7+8vAQEBIiIyduxYM/2m9u3ls5Ej5RWQbfkFppjtvJub/A2yuFEjaR0U\nZAotIJs3bxYRkRYtWkjVqlXl1ltvld9//12aNWsmt912m/zyyy8CyJw5c0RE5MEHHzTP3bRpk+Tk\n5Mj9998vmzZtksTERAFEKSXe3t6Sa1eGREQeffRRqVKliuTk5EhERIQAUrVqVQEkLi6u0DJ03333\nia+v76UVFeB64AngGDDObnsZ+Ks8M1IeW1vbj30a5BuQH++7TwJBnn76aQHkjz/+uPBEc3Pl9pAQ\necfTUz4HeRvkWpBnn31WAKlSpYoAUqdOHZk/f74AZk/Q2F566SUREVm6dKkcPHhQ5NQpkR49LlSK\nRo3k14ceEjcwe9UiIv3795fW1auLbNggedu2SUZSktMf/a+//pKBAwdK586dJSEhQcaPHy+AtG7d\nWkQuNKaHDh2SO2rXltEgb4BsHTNGJDFRtmzZIoA0DA2VDxs3lk89PeV1i0WuAendu7cplmvXrhUR\nkW3btgkgP02bJtNvvlm+8vWVc7aKdgjk6IIFDvlLW71aNtlVsp9APnnySRER+eCDDwSQEydOSN++\nfc1n1rZtW/Hx8TEFIDY2Vtq0aWP2hpcsWSIi1t5X69atZdGiRdK8eXO5/vrrBZDo6GgBpG7dumYn\nYe3atQLIgAEDHH6fO++8UwCZOXOm2dsbO3as7Nu3T8aNGydG7y87O1tERD788EMBZN68ebL/119F\nHnlEspw1ND4+IsOGiSxYILJ1q8ihQ/L2iy+a3/vwww8LIMuWLRPJy7N2FDw8rOf+3/9Z94nIDz/8\nIIbV9sADD0j16tWdlgNnGNYEIE899ZQAMmvWLOndu7c0atRIRMTsVM2YMUMaNGggAwcOFMnIEFm5\nUmT8eJEbbhCpWtVpg5lzEY2rw+buLtK2rfU+P/5Y5Kuv5LHQUBluschjIMn9+on4+5vHnwLZHhgo\na0F+B9kYGirPgdwQEGB9Tnl5Ips2iTzyiEjt2iIBAbLZx0c+a91aJDlZRETatGkjvXv3NuuW0QAD\ncvPNN4uItXGPiooSkQuWmdH42jfo52Ji5NfnnpPnmzeXB+vWlTEdO4rs2iVy+LDI0aMiR47I4d9+\nk2qGKN10k7z00kuilJLOnTuLt7e3ea2RI0dKaGioPPTQQ+bvNmLECAkODpZhw4ZJYGCgZGRkiIjI\nt99+a5538OBBh986NzfXtHI6duxYoCy8//77AsjcuXMFkL59+8r1118vU6dOLbIMzZ4922jTLqmo\n3AS8BJyw/TW2cUD98sxIeWzXuLlJvK+vQyHPBUmrVUu+BDkwaJDVWhgyRCQszGmliL3uOmlkExRA\nqlWrJrNnzxZAatasKWA14/39/eXxxx8XEZHAwEB57p57RGw9ryR3d3mnTh2R7Gzzhx49erT5Y+7f\nv182btxY5A/ujDfffNMsyCIiS5YsEUA2btzoIHirV68WEZGMjAyxWCzSqVMnWb9+vXzzzTdSvXp1\nAWT48OFy4MABAeTee++VxMREs5HbuHGjjB07Vvz9/eWm8HDZ7u5ufZZubiLPPCPy7bciQ4dKnq3B\nTQ8KEpk/X/x8fWXs2LEiIvLkk0+Kl5eX5Obmyr333mvmLSgoSEJDQ2XBggWm0Ht6epqNwaxZs0RE\npFatWjJkyBDz3nNzcyU0NNTMrz2Geyo4OFgAs2I/8cQTAsikSZPMnuGqVatERCQ5OVm2bNni4NKb\nM2eO2SBFR0fLiRMnpK6bmzwF8hXI5+7u8mmnTiLJyTJ58mTTqhKxulyNexw0aJAAsmbNGhERSclO\nkVfXPyQpIT7WcvbooyJ5eXLixAlxd3eXjz/+WF588UWxWCymwBXH8OHDJSIiQry9vSUgIEDA6haZ\nPmaMTAfJqVtXsgMC5ABIQuvW8mO1arIjKEgkn5UsIBkeHnLQ3V1OBQXJP0pJpnGMxSJ5AQEiderI\n7qAgWQAy181Nsjp1kszoaMm0nZ/t5ycHQVKDghzcrkVtxxs1kj+ff17cQIYOHSqA+Pj4yOnTpwWQ\nRx55pNB7v+GGG6RLly7yww8/yB9//CHR0dEydOhQERH597//LYB069ZNrrvuOqtrU0SGDRsmXbp0\nERExXds33nijAA7eivj4eBkxYoT5/2OPPeY0D8Y5v/zyi0yfPl0ACQkJkbZt25rnXnPNNaKUMjuf\n9t/t4eEhDzzwgMM1N23aJFOnTnXqZrZZFPL0008XSFu0aJEA0qRJEwehKgl5eXnlLipFRn+JyBpg\njVJqtogcKerYy4HDQF0RnrrnHjyXL+fGxEQ6KoX/sWPcCzB3rsPx/wA/ANuAGwMDGZCaSuTvv7PB\nw4MhwE84DtRHREQQGxtLq1atiI2NJSkpiaysLAJTUxm9aBFkZJDXpg2td+zgrv79wd29wDItAPXr\n1zeXi7gY/P39AQgMDASsA6tgDYXOzs7mmmuu4eDBg+Z+Hx8funfvTseOHc3trbfe4sSJE4SGhlKn\nTh1uu+02vv76awA6dbJGzEdGRhIYGEh6ejq/Z2fz9A030GvNGkbn5sI771zIkJsb/8nNpd3s2XTt\n04fwZ54xQ1qPHDlCVFQUFovFYUHFpKQk6tataw4+rl+/nqysLLp3787//vc/EhISOHXqFMeOHaNp\n06bmeRaLhW7dujF37lzGjh3r8FxCQ0MJDAwkMTERPz8/wsLC+Oeff8zZ0ykpKeZAuzHAXKVKlQLh\n2EawgYhw5MgRbrvtNg7l5nLrypU0b96cTp060SYykuEBATz11FMMGTKEDh06ANbIQSO4wQiPNaJ8\nJsVN4gfvrciioUzo9rl14pubGxGTJ7Nr1y7q1KnDZ599Rl5eHqdOnaJGjRrFloWkpCRCQkKIiopi\n8+bNXNu4MVFTp/LQ1KnWIBDbgHo9awGhl3GiUtC6NXTtyqzDh7F06MA3K1eSnJJCz549efXVV3n8\n4Yf5atYsElNSzAgrtXcvZ9au5aabbsKjQQM8sC5t07JFC9yUYmD79qSlpbH7n3+wbNtmnWy6Zw+Z\nycn8sGgRUQ0a8Of+/Vw3ZAgvb9rEyqNHmVK7NrlcmLTYqFEjgoODSU1NdYiQyk9YWBj79+/n4Ycf\npk2bNiQmJprlyXh2LVu2dHgnyUcffWRGXDZv3hx/f39GjBjBunXr2L59uxlZmZyc7LDihTFgnp8X\nX3yRdevW0aVLFzPI4PTp0/Tu3ducG2REsRnh4wDt21vfjB4eHs6rr77qcM327dub6fkxog5vvPHG\nAmlGHnfv3s3IkSMd2priKGxyblkoafSXl1JqhlJqhVLqF2Mr99yUkdzcXDIyM6ndvTuNZszgt9de\n43xCAmmrVvEgsLF7d3juOfjoI1LWraMu8DgwC5h33XU0BrI6dSIoO5ulwFdAt9xcsvftowoQalvh\ntG3btgQFBZGUlETyvn2sBMIzMuDaa9n0xhsczs42G+j881TKgiEqVapUATBXXDXmvHzyySccOHCA\nJk2amOcsW7bMYYVT45zQ0FDc3NxYunQpDz74IIsWLSImJgalFBEREQQEBADWORf1mzVjDPD9U09x\nuHt3fnB3Z4qPD3NfeYWngdC6dQFrRTFEZf/+/WZhN/Jtfx9GI7By5UoAOnfujKenJwkJCYwcORIP\nDw/uuOMOh/Oef/55pk+f7jDZE6wV45577gGscwmMtbaqV6+On58fqamp5koBxvwdZ9i/pqB///5s\n374db29vbrjhBsLCwvD19SUjI4OkpCSys7PNyY5gFRWjQTMaGR8fH/Zk7GH5meXkkccy3x3sXTIN\nPD1h6lQYMYIGtWvj4eFhnlvSRUCTk5OpWrUqbdq04XZgeVwcvPceSoSZwEcjRvDRq6/SGEibN4/J\nrVrxSJ06cPo0/Pkn8u67jPvlF75Yvpz4kyeJiIgwG6PU1FTcvL2tAmSjUaNGjBw50iHyqFWrViiL\nBaUU48aNY9++ffy0bh106QJPPw2zZrH1iScYAMRMmMAjwHd16rA4JoaMjAxzQUR3d2vftnHjxgAE\nBATg4eFR6L2HhoYSFxdHXFwchw8fJj09vYCoGBFvBj4+PmY57NixI6mpqdx6661mutHJS0pKKpGo\n9OvXj/fffx+llJlvcF6+7COwWrZsyfjx41m2bFmJOg/56dixY4F9dW31DyjQ4aoISioq87F26F8A\nnrLbLktatmxJv379eOGFF/ALDsa/a1c+d3dnSbt28Oab8PDDnMn3gz7++ON8sHAhnqtXs6lfP7KA\nIVjjqMd88AHJwNyff+Yb4HYvL4KrVMH/+HEC+/enIbDLwwOWL2fN9u3AhR8//zyVsmAIVH5LxVgE\n8ZprrjF754VhiIr9IocDBw4kPT2d2bNnExYWZn39re074EIF3VmlCv1On+axyEjGnjvH//3nP7i7\nu5u9f2OxzKysLHbv3m3OCzJePGSQX1R8fHxo0KABoaGhLF68mMWLF/Pmm2/SrFkzh/OaNWvG/9ni\n8/Pz0Ucf8ddffzFnzhzz3sLCwkzrITY2lqpVqzpdht7AEBVvb2/mzJnDd999x4wZM8wOgSEqRrhm\nflExGhR7S2XeqXlkiXWGepZkMa9+LCxcaJ3kOXs23HorJCWZDUxsbGyBeSDOSE5OJrRKFUbv2sUS\nICg1Fdq0QW3ezJc33cTry5axMz6eo76++N99N1uaNmW5UmC7x9OnT5OcnExsbKwZFm6U1eTk5AK/\nWXHcddddREZG8vHHHzvsN5bIv/HGG/Hy8jLn5Xh6erJx40YCAgK47777aNasWYkngIaGhpKcnIyI\nmBN6jfJkNPD5rdD8KKWoUaMGv//+Oy+99JK5jIshKkYPvjBRsadx48bmgpAWi4U1a9Ywa9YsM93e\nUrFYLLzxxhsOVnhJMOq6cZ/2GL9b1apVHQSuoiipqOSIyEcisllEthpbUScopT5TSp1SSjl98YKy\nMlUpFaOU+lsp1cYuradSap8t7Vln5xeGm5tbgQerlCIkJISEhAQ+/fRTUlJSzLfAGURFRfGvf/0L\nLBbkqaeoD3wWEsI64KSPD2lAYG4ug4Hu//kPS9euZc6ff+J94AB7gFsBqVqV3bt3ExUVZTbeztxf\npSW/+8vo0e3btw8PD48iJ9sZ2FsqBl26dCE8PJyEhASz12O4dcBaKapUqcKpU6c4efIk3bp146GH\nHiI7O5svvvjCXDTRsFT27t1LdnY2LVu2BAquhWQvKufOnaNdu3a4ubkREhJivpxo4MCBF/18WrRo\nQZ06dcx7M5bkSElJMSd1FoUhKq1bt8bDw4O+ffsydOhQM93X15ezZ8+a93Ps2DFzMurp06cJDw/H\nYrE4LM0zMGwgnsr65kdP5cnAsIHQq5d1lnNEBKxeDX37UsO2NM+gQYNo1qyZ03XdsrOzze9LPnOG\n5//6i0Zr15Lj4YFMmgSbNkG7drz66qscP36cjz76iCZNmqCUIiAggKSkJCZOnEhycrL5nI8dO0ZC\nQoKDpZKUlHTRouLh4UGXLl3MuR3Z2dls376dXbt2ERAQQFRUFL179+aAbRLg+++/T5cuXfj555+J\njo5mx44dDlZQUdiXXWNJGaM8XXvttcTFxRWwZgujQ4cOvPzyy2Z5P3DgAOfOneORRx7h3//+tzlL\nvzimTZtGUFAQPXr0oHPnznTv3t1Ms7dUSsvu3buLtGIPHTpkziGqaEoqKkuUUo8opaorpaoZWzHn\nzAZ6FpF+G1Dfto0CPgJQSrkB02zpTYDBSqkmhV0kPw0bNnTqagoJCeHXX39l5MiRzJ07t8ACdoa7\nB6yWTs2OHTk4ahSdgFsbNKBuSAj/feYZJgDSoAGeeXlkAUduuYVOQFx2ttnzs2/cDSFwhagopcwK\n1rFjR/ONfUVh3+AaGG6wb775xuxJtmjRwlwio3HjxoSGhppLUYSGhjJt2jTi4+PNNcTAKiqJiYmm\nT9kQlfyz8f39/R0aLWPlACNv/v7+DpPrLhbj3oyxFsNSKe6agYGBeHt7c9111zlNz2+pZGdnm5+N\n9+d4eXk5WCqNfRvTs1pPLFjoWa0njXxtbpl27awiEB4Oa9YQ/uWXgLWR3L9/P9OnTy/w/W3atOGt\nt94CYOTJk7SNjYWqVXHfsAE1Zox11QGsrsRhw4Zxyy23MN82YzowMJCkpCSeeeYZpk+fbk5WNN76\nGBERYXaAEhMTC7wCuSTUq1ePo0ePcu7cOZ566ilat27N999/T9OmTVFK8d577+Ht7c21117Lo48+\nyq+//urQeSkp9qJiYN+DL41byegYGZOlu3fvztSpU0tUp8A6IfHMmTOm27tGjRqmBZF/Lb/SEBIS\nUuR91alTx1zVoqIpqagMw+ru2oB1QcmtwJaiThCR34DCX8MGd2JdR0xE5Hegqm0pmPZYZ+4fEpEs\nYK7t2BJR2FIsoaGh5sDZ0aNHC1gq9qLi4+PD+vXruf3224ELiyyOfPttXhVB7dvHC//+N0FeXqwa\nPBhjtaj4+PgCjVd0dDQffvgh/fr1K+ktFEp+UTG+E0reszd6Tfl7T23atGHw4MEOgjhy5EhycnJo\n0qQJYWFhxMTEkJ2dTWhoKBaLxeGZgbXyiAirVq3Cy8vL7OW9++67DBs2jD59+jjch8GAAQOAC1aU\n0bsuLUOHDuXtt98mMDDwoiwVi8XC6tWreeGFF5ym5xcVuLBYYkpKCsHBwXh7e5sz0o2OxJjIMfQJ\n7sOYyDGOF4yKsrrAAMubb2I0CeHh4bz55ptGBCZgHajduXMnO3fuJC8mhifPnycPYN48q0DlY/bs\n2axYscJcWNH+t/r6669NS8XAfhwtMTHxoi0VsIqKiLBgwQI++OADAIeAi9q1a/Pjjz+W+fW2zt5P\n48wtdDHkF5WoqKgyXU8pRb169ahWrVqpnuWVTIlERUTqONnqFn9mkURinf9iEGvbV9h+pyilRiml\ntiiltoDVdeEMo8GC4kXFwBgQT0hIKOCL942IIOP8eYfF606cOFFAVJRSPProow6DwKXFuAf7no/h\nDunfv3+JrnHvvfeycOHCYhtYA6OnFhYWZi4c6KynaJ+vFStW0LRpU3MAtmHDhsyePdt8LoaoNGzY\nkKZNmxZwydkHGpSGJk2a8MwzzwCYUWHx8fEluucOHToU+lsVJirG0jDBwcEODYhhMQe6B/Ji9IsE\nugdSgJ49oUcPSE9ntG3XY489RmJiosOyKvaLOOY89RSewJ5rr7WeWwLsy/fu3buZP3++g3BHRESY\nv0tZRAXgmWeewd/f37RC7MfGbr75Zlq1anXR17bHWfkra/3y9vbG29vbfBFYWUUFrOM6+QMGrgZK\nuvT9fc72i8gX5Zudi0dEZgAzABo3biyPPfaY0+PsC6K9qHh5eSEiTs19Q1Sg4HvGDVPT3o+5Z88e\nMjMzS9xgXyxRUVFs2LCBa6+91tz3wQcfsHfvXqe9N2cEBARYx44ukrCwMDOssbDvMkQlISHBqWVm\nnGc0Xjt37nTojdtbKuVFYGAghw4dQkTK3FDYi0poaCgJCQkcOXLEXB/NsFTAOsZQUtcJL74IK1bw\nrJ8f1V9/HYvt+SQlJZlWqREUUP3wYTxjYsgEdg4cSEmHe43y26FDB3bu3Mk///xD3bp1zfIbERFh\n3kdWVlaZROX48eP06dOH22+/nU2bNtG8efOLvlZRGHW5Xr165jphZbVUwFqnT5w4ga+vb7l0Aj/8\n8MMSBV1UNkrq/rrWbuuEdUZ9nzJ+dxxgP7Jc07avsP3F4ufnV+hKqYVZKnXr1nVqpYCjqOSPqjJE\n5eDBg2ZDsmWL1SNYlvGA4rj++utNCwCsvdqyuhNKgr2QFGepgHN3nHGe8bzd3d0dQkeN9PKMYKlS\npYo5P6G0KwAb+Pn5maJiuDYKE5WLGkO78UZo3x6vs2cZFRBgli37cT9DVB63/X0P8KxbcmeBYVF3\n6dKF5cuXo5TioYceMq2V8PBwh3pQmjGV4OBg04108803M2zYML766qtyf0+MUZc7dOiAUgovL69C\n6/3FYKzi3KpVq3KZv+Hr6+vQhlwtlNT99W+7bSTQBvAv7rxiWAzcZ4sCuw5IEZETwB9AfaVUHaWU\nJzDIdmyZsBcVY+Kij48PNWrUKFRU7K2T/L17o+LHxMRQu3ZtPD09L4moVBQXKyo33XRTodfIP6Zi\n0KZNG8LDwx0ssbJiP/5UVlHx9fXl/PnzxMXFERERQZ06dTh48KC5KrW9++ui5yUZFvYHHxBka5jt\nV+49cuQIw4B22dlkVa3KRLiogdnhw4fTo0cPxo0bxw033EBOTg5PPvkk4eHhBAYG4uvr6/C7lMZS\nMcYRALp27YqnpydDhgwp8FKtsuLp6cnAgQPp27cv4eHh5urBZcVoB4z5M5rSUdpf+yxQp6gDlFJz\ngI1AQ6VUrFLqAaXUw0qph22HLAUOATHAf4FHAEQkB3gM6xSRPcC3IrKrlPk0se8F5+TksHfvXqpU\nqULXrl2dNoC2ezA/9+zpGMhmVOjjx48THBxMRESEGZN/tYqKUSkHDx7stCGxj+5yRocOHYiPjy+X\naBkDe1EpD/cXWF2eERERNG7cmN27d5uWSkhISOksFYABAyAsDP76i2jb2JW9qKTv3s1/bJ833X03\naVwYXC4JtWrVYvny5ebvaLFYsFgs1KxZ0wzasO9clXZwuUmTJoSHhxeYY1TezJ07l/79+zssDV9W\nVq5cyY4dO0ocRqxxTknHVJZgXc8GwA1oDHxb1DkiMriYdAEeLSRtKVbRKTeMcLyePXuyZ88eduzY\nQZUqVXj++ZK9cyy/NWPfQFWrVo1q1aqZg6nlEZd+uWE0Rn5+foU2mEop0tPTC+2l165dG4vF4rIx\nJ2cY7ofw8PAyr2pgiMr58+eJiIggKiqKr776ynxne5ksFS8v6yz0J58k8tNPUdiJyo4dPLF8OaHA\nz8AamzhfjKgUxl133WW6fewt89KKivECu/K2TgqjX79+Tuf0lIaLnZCocU5J3/z4rt3nHOCIiMS6\nID8uo1OnTixfvpzq1aszadIkjh07Vug6O/YYFk1+IiIiaNu2LVu3bqVatWo88sgjLFlifWt6UUtM\nXKnYz1IviqJmrEdHR5vuwkuFYamU1fUFOPjtr7nmGvPaa9euNf36pbZUAB5+GCZOxPuvv5gOhKxd\nC8uWwXffEZWbyw43N+7OzeVW2+B0ecxLMKLkwBrpZwQjlGZMBaziXZ6WZnGMHz/+kn2XpmSUSFRE\nZI1SKhzrQD1chm99LA6lFD169HB4pWpJBtGKMoX/9a9/sXXrVrKzs2nfvj033XRToa6dKx37CYVl\nwVg88FJh/MblLSpt27Y1G97169fToEEDc9AYSikqfn7w9ddIr148nJ1tzmERd3emAas6dybl1185\ncOCAOUu+vAkICCAjI+Oqm1uhKT9KZKMqpQYAm4G7sb6nfpNS6i5XZsxVBAYGmpMa7V8TXBqM62Rm\nZgLW93kb1kplo1q1algsljKLyqXGVZZK/fr1qV27Nr6+vuTm5nL//fcDF9xepXa13XIL6qef+MHD\ngwM1asBrr7F1wQL+DdzQy7rW8IEDB6hSpYpLXEyGUGlR0ZSWkpbK54FrRWSYiNyHddb7i67LlmuZ\nMjVpDMcAACAASURBVGUKUHb3QcuWLfn000+ZOnUqYLWGXLGU9OWAm5sb1atXL9USGBWJq0TFzc0N\ni8Vivvd7+PDhAGWzVAy6dePxmjV5pWtXeOEFft69G7gwwTU9Pd1lS3IYlrYWFU1pKemYikVE7Bdw\nSqT0kWMVTt26dfnrr7/KPKCulOKBBx4op1xd/nz//fdXXBBCw4YNGTNmDH379i3ztQzrw37l2rFj\nx5rrodkfU9agAOPVCgBr1qyhadOm1K5dGzc3N3Jzc10WYWhYKqUdU9FoSioqy5RSy4E5tv8HUs7R\nWZeawtYI0xROccuJX464u7szadKkcrmWEYDRu3dvc9+QIUMcjinOUknNSWVK3BRGR452vmyLDUNU\ncnJyWL9+Pffddx9KKXMip6vem6HdX5qyUqSoKKXqAeEi8pRSqh9gvHZsI/C1qzOn0VxOXHvttfz4\n448Oy5rnpzhLZVLcJH5I/AFBmBA9wekxYA0XjouL488//yQ9Pd2cSxUQEEBaWlqpltopCVpUNGWl\nOBfWZCAVQES+E5FxIjIOWGhL02iuKnr16lVkyHhRIcUOb4E8s4y9GXsLvU5QUBDJycmsWbMGsC5n\nD7Bv3z5Onz7tsrE7PaaiKSvFiUq4iOzIv9O2r7ZLcqTRXMEUNfmxwFsgT80r9DqG+2vNmjU0atTI\nHMuqXr16uc0gd4a2VDRlpThRKWrKbtnfOqXRVDKKslScvgWyEKpWrcr58+f5+eefC11GyBXogXpN\nWSlOVLYopUbm36mUehDri7o0Go0dRQ3UF/oWSCcYS69nZWVdUlHR7i9NWSku+msMsFApNYQLItIO\n8ATKHqOp0VQyihuoHxM5BoVidORop+kGffv25cCBA2RlZXHHHXeUez4LQ7u/NGWlSFERkZNAR6VU\nV8BYdvRHEfnF5TnTaK5AigspNt4CWRzh4eG899575Zq3kqBFRVNWSrr216/Ary7Oi0ZzxVNekx8r\nCsP9pcdUNKXlip0Vr9FcjpTLMi0VSGg5LquvuTrRoqLRlCNXuqXSsWNHVq1aRYcOHSo6K5orlJIu\n06LRaEqAYaGUxzvTKwKlFDfffHNFZ0NzBaMtFY2mHLn++ut544036NSpU0VnRaOpEFwqKkqpnkqp\nfUqpGKXUs07Sn1JKbbdtO5VSuUqpara0w0qpHba0La7Mp0ZTXnh6ejJ+/Pgr1v2l0ZQVl7m/lFJu\nwDSgOxAL/KGUWiwiu41jROQ/wH9sx98BjBWRM3aX6Soip12VR41Go9GUL660VNoDMSJySESygLnA\nnUUcP5gLS+trNBqN5grElaISCRyz+z/Wtq8ASilfoCfwP7vdAqxUSm1VSo0q7EuUUqOUUluUUlsS\nEhLKIduuITUnldeOvEZqTmpFZ0WjKYAun5ry4nIZqL8DWJ/P9XWjiLQCbgMeVUp1dnaiiMwQkXYi\n0u5yfn/6pLhJLE5czOQ4/cYAzeWHLp+a8sKVohL3/+2deXxV5Zn4v0+49yYEchMSs5AEIjtiHUSZ\nFheW9jetAiJO1VFK6SxMxd9nbEXtYqdK49ZWW4raRbSOtdPWXVHAgE5LEddWZFTQCALKEhJCE5LL\nErKQd/649xzOOffcLTmXLLxfPvlw77nnvOddnvd93uV5nxcYZvleHrnmxtU4pr6UUjWR/+sJn9/y\n2TTE8aSQyjkaGs3JRsunxkvSqVTeBsaIyAgRCRBWHCudN4lILjAdeMFybZCI5BifgS8BW9IY17SS\nyjkaGs3JRsunxkvSplSUUh3AdcBLQDXwlFLqAxG5VkSutdz6j8DLSqkjlmvFwGsi8h7wV8JOLNem\nK67J0J0551TO0dBoTjb9QT71mlDvIa1rKkqpKqXUWKXUKKXUXZFry5VSyy33PKqUutrx3E6l1MTI\n35nGsz1Jd+acjXM0BKEkUEJpoDQNMdT0FXpbA5jKOS+9Fb0m1HvoLQv1vZruzDkbDcjC4oUMyxzG\nntY9WvBPcXpjA7i4bDGXFlzK4rLFPR2VlNFrQr0LrVSSoDtzzkYDsrRmKfvb9mvBP8XprQ2gcc5L\n0Bfs6aikjF4T6l1opZIEXZ1ztjYgrza/qgVfoxvANNAf1oT6E1qpJEFX55ytDUgnnQgC9E/B723r\nBL0V3QB6T39YE+pPaKWSJF2Zc7Y2IJmSydTcqX1C8LuiIGKtE2hlY6evNIB9rdz68ppQf0MrlSRJ\ndc451BHimQPP8IW8L5gNSGVFZZcFP5VK3t0GIdWF5HjrBL1xUbqn6QsNYF8rt768JtTf0EolTRiV\nEjAbkO4IfiqVfFnNMl5oeIF/2fovKSuWriwkx1on6K2L0j3NyW4AU+1knMxyO5kjor42+uqraKWS\nBqyVcl3TOq4svLJbDUgqldy4V6HY1bqL23bdltK7urKQHGudQC9KJ0e6G7tURx0ns9xO5oior42+\n+ipaqeB9pfa6UqYS3pP1T9KqWs3vG5o3pNTT7MpCcqx1glN9UTpZuerOyDIRXRl1nKxyO5kjIq/e\npUc7idFKhdQXmRMJlteV0hneJQWXxHz/VUVXkWEpVoVKSal1dSHZbZ2gryxKp4tkesbdHVkmoisd\nnETl5lXDejJHRNZ3tapWflf3u5TDCHWE+Oet/6xHOwk45ZWKtQezpmENN+24yawssRqFRI2F142p\nM7zVjatjvv+M7DOYmnvifPSuKDWngkimEYm1TtAXFqWToTvrEqsaVvHOoXdcw/zezu/ZRpavNL9i\nk8Hu0tUOTrxy82oa6WSOZK3vAjimjqUcxg92/YDdrbv1GmECTnmlYust0cb65vXcW3NvzOFyssNo\nrxtTI7xZQ2YlfH9lRSXDM4d3Wak5FUS8RiRRY9tfrHK6sy7RSSd37rrTNcy9bXtt1xSK9c3rPZsK\n62oHx1pu1jJ2yv87h95JStm6yUmycfNiZHRG9hlMCU4xv78ZejMlpVB9tJrXml8zvxsjKz0dFs0p\nr1ScPRiAtY1reWjfQ65D82SH7LEa064KoRHe6sbVCd8f9AX57bjfeqLUEinR/rT4Gatsurou4cNn\nfq9tr7U9Z532MjbFWnGbCrPGL9QR4tZPbmXJp0vM+LrFP9QRor2znZn5M7ssC9Yydsr/nbvuTKr8\nY8lJMp0vr2RsYMZA83Oq021P1j9JJ53md0G4qugqz+W/Pyipfq9UEhWS0VuyYlQat6F5d4fshhDe\ns+eeLglPsu93U2pdEdh4SrSri5+9teLEaiCS7UhY03VG9hmUBErM3zpUh+05a5gKxaCMQVHhOY0s\nrPFbVrOMqoNVvNj4ohlft/gvq1nG2oNr8YmvS6NFZxl/Lvg5U/58+Khrr0tY/vHkJFHna2Noo2eL\n+V8t/iqZkgmkVndDHSEOHT9kPgswLXcaCuW5oYGzDLtTV3qqnvUrpVLbVhuVgcn0JBaXLWZG7gyb\nwC0qXeQ6NC8LlFEcKO7S1JK1cr108CVeaHghZeHpznpNMnnhjEc8JdbVhdbeOLqJ1/Alq8id6bpl\n+C2m0YTzOae3hWWjljEjd0ZMIwtr/KoaqqhqqDLvq2qsYk3jmqj4e2Hx5Czjv4b+aspfSaCEdtVu\n/har/LsiJ0Ze3rn7zqSfTbYDmWrdWVazjA3NGygOFCMIFZkV/KDiB67p6k5D7lZebpaBqVgV9kQ9\n61dKpamjyZaByVaqoC/I0lFLTYH7Qt4XePrA0ywsXhg1NF9Ws4w9rXsYljks5ekE5zy7QtmEJ1kB\nSHW9JtQR4qYdN0UZJNQcq4kSTmc84lXERI2tm/B7bUZ6MiyRkmmM3NI1OTiZOQVzXJ9znrEzZuAY\nlo5aGtPIwhq/dtrpoMO8r1218+C+B6Osm7prXWX00J1lbMjfLcNvSUrZpjq6t+ZlXVsdfvED4Smn\nSwouiflcMlacsepOMlOfda11TM+dzqPjHiXoC7qmqzsNubO8Htz3oKtlYCpWhT1hVNCvlApgy8BE\nZoROQTIEDmBlw0r+a/9/2Ybm1nnwurY69rXtcw0nFm7rN1bhiWcpZMWYMgDivteI1z177mF983rT\nysgwSLhux3U24YwliLEqYqLG1k34vTDtTPSOVDDy6JL8S+I2fIkUeawGPN5zi8sW287YCXWEGJQx\niPJAedw9P378tvUav/j5WvHXbGszx9QxT6ZqjR66NT6G/E0OTk6q55/qCMGpQA2l0kknK/62IuV1\nL6uMxJpuS2rqkzayJMt81pmurk6HucmgHz9bW7ZG7Tl77sBzrG5YndBQwlnPvrfze7aRjnM9zksG\nVFZWeh5oT/HDX/6wMv/L+RzpOMIboTeYnT+blw++zHGOA1CeWc5F+ReZ9/94z49Z1bCKgx0HmZ43\nncyMTIoCRdxXcx8ddPDpsU+5IPcCTvOfBsAva35JdUs1EBbw9s52ZuTNiAonFoX+QmraatjWso0M\nMlAoMiWT8sxyPmn9BAhPebx3+D2uLro6Zjhu8Z80eBL37LmHSYMnkZmRaf6+smElO47tQKGinm8+\n3oxCmel8sv5J1/RlZmSa+eNk0uBJNHU0sbhsse336qPV/Gzvz6LysThQzIsNL8Ysk1SI9Y5EhDpC\nZl4tq1nGqoZVZEgGoweO5uOWj5ldMJvLCy+3PRMvDwBbujIlk+9XfJ/T/KfFfW7nsZ08Vv+YGf/t\nx7azrmkd5wfP58xBZ9ry1JCdj1s+5pKCSxibPZatLVsBmFMwh/3t+/mo5UQjVttWy2WnXUarao2Z\npmTztuV4CxfmXsjNw26mtbPVJmexyt9Jsvc58zKDDNpUm6kwdx7bSXVLNeub1jMzf6YZVqy6mYyM\nxLon1BHi5YMvU9tWG1Nerel6uPZh1zgkwqjHVhkszSyltq026t53Dr9jKovjHGdd0zo+OPpBVNvj\nzMPm483sb9vP66HX2dC8gTUH1/Bxy8c0dTSx/t71tZWVlQ8ljGiSpHWkIiIXi8hWEdkuIje7/D5D\nRJpF5N3I35Jkn41FQAK0qBZWNqyk6mBVTDPCWD2beFMGbj2/VIeZi8sWM7dgLhcNucjs4VxTeo2t\n57mvbV/CvQrO91buqnQddSiUzWrFDSOdXenZxur5xcrH7pp2JnqH0etzm9ozMHqllbsqbXk4O392\n1Kiiq2tdpYHShM85e5OvNr8a17WPddSzuGwxs4bMYnb+bNPM3IqRH101bY/VQ3f26IO+INeXXc99\nNfd1aR+TFSOvywJl5tSggdEpMmTZOh0Ua5ouKh2O+my87/f7f+96jzFSG+IbYj7zZuhN2+jAmq6u\n1B9nPZ6dP5tpudPY37Y/yjLQh4/Wzlbb80c6j7i2PdbpVSP/1hxcw/MNz7P24FrzvqrGKjKsZnEe\nkDalIiIDgF8CM4EJwDwRmeBy66tKqbMjf7en+GwUU4JTeCv0lrl2UH202vwtGdPgeILhNoxPdbHO\nEMLvDPsOFw+5mJbjLTxe/zhFgSLzng46bHsV3ML7/f7fm0Nja4NkCJc1XgYBApw7+FybFYs1nd3d\ntGmNZ7x8TNa0M1GDfkn+JVFn1BiNnnNqzyDWwWmtqpU7d93J9WXX2xq+rq51JfOcNY+MkWu8PLE2\nYEFfkDtG3MG3yr9lW8w2MPIjlcbcms9umwVj7VG5Z889niwIWxelFxYvpDRQGrdDZFjHuU3TGUo9\n3rSmUUYtnS1xO4v17fXmM/HMqLtSf5ztx+qG1eQMyImyDDQMI4wRkxM3mVlctpjyQHnULIX1e7tq\nx5fvK8JD0jlS+SywXSm1UynVBjwBzE3ns3m+vLA2t6wd1LfXu1rgxGr04gmGm81/Vxfrgr4gvgwf\nLze9zIuNL1KRWWGz/IFwb2xe9TzXSnu086j52a1BcjZYAEMzh/LTkT810+e2QTJRzzbefKw13fHy\n0c20061hS5SPLx580Wx0SgIlHO44bDYEsXY+xzo4LYMM9rTtiWnokWitK9QR4r6a+7i+7Hpq2mqS\nGr1a82hq7tSUjR6MPNrbtteWDkFStm5y5rNzRPlG8xvcuOPGE3Ur0ri+0PACLx1M7DnAGvd4BhzG\novTPan7GqIGjzN+ddQPCjaN1PbKutY5pudNsSv35huddrTWtZftW6C2mBKfYFNL3dn7PJifG+61m\n1E4PHJC6EY1b++FmGeg0jHDLC6cRQ9AX5Ecjf2SbBXHiFz8dTR0NSUU2SWK/rfuUAXss3/cCn3O5\n73wReR+oAb6llPoghWcRkWuAawBKSkrYu2Uvhw8ftt2TQQYIzDlvDi1bW9ikNgFwVu1ZbGjawOS8\nydS313PvgXuZXzSfb078Jrs372b3vt1s2L+Bwb7BADxY+yAbmjYwLW8aOw7uYOLEiRzbeoyz9p0I\nZ0vLFp759BnaVBvPZDzDFQVX0PpxK0qd6B0c7jjMg7UP8u7hd+mgAxHhL+P+Qvmecj488qEt7tsj\n/wAzvOr3q/njp380hX5C9gS2t2ynTbWRmZHJlXOutMXr/OD57Di2g+q2ar796beZXzif2r/V8p8z\n/pMf/vmHzOiYwaaGTeY7ZzOb7Q3bEREmTpzIe++9Z8b/wdoHWd+0HoD9eftZNHQRIkJgTIBn33iW\n1s5WnpanObPmTGb4ZrDvwD4zfCM8Z54dVUf5du232dC0gZohNfzq4l+x4q0VZj4a4Y3IGmHG8ZPW\nT1gTWMORrUdQSvEhH3LTBzfR2N5o64kdlaM8nvs4lzVfhlKKzxz7DM9++ixtqo2ABDhr0Fn875H/\nJXtsNke2HeFpTrxr+b7lNDafCO9bO77Fny7/ky0/DAzZqBlSQ9H4Ig5WH6RTdXKEIyyrWcai0kVO\nuWXixIl8vv7z7Kvfx2Udl3HkwBFbnmziRJ59+6Vw/uzN28uioeGwdh7byTOfPkM77QwaO4ij245y\nfs75AFGya32nEX9DDt8/8r4tn0cOHMnEiRNprm7mcPOJutRIo/nZL372jdvH4a2HbXlx0/ab+Nno\nn9ne+Xze8zz95tPszQ3H3cgrIy0iworcFTRWN5phrWY1PvHRocJWbmcPOpv8QD4ZozL40zt/QilF\nQALsz95P45FGc6rocO5h/qf+f1gTWMOhrYd4Vj1LJ50MDQy1yfmDtQ9yrPgYR7cd5Yg6QlNOE+cO\nOJcZHTP49pZv81HTRwiCEkXeuDxG1Yxi06FNFPmL2N++35SJ1azmyNYj/OriX5n5atQfo64/duAx\nvlL4FXL8OVH1Cezt0FF1FBHhS3lf4qk3n2Jy7mRECbOZDQ0n7j0n5xx2lO2gaWuTGdZDtQ+ZsmHN\n/yJfEdu2bIuSWYAZeTPYlL2pOOqH7qCUSssfcAXwsOX7AuAXjnuCwODI51nAx8k+6/bn8/uUb4j7\nX1ZRlnrk94+osrIyVVhYaP4NKhikCgsLVXZBtvIN8amckhz1xBNPqJySHOUb4lPZBdmqsLBQ5Rfm\n28IrLi1WTzzxhBmeMxzfEJ/yF/nV5fdfHvVO6z3Gfaf/8HSVVZQVM/7W8HKKc2zXs/OzzTCziu3p\nzC7IVln59nDzC/NVWVmZLf5uf857BuYPjM7X/CxVWlaqLr//cuUrtMQpkm/xwjPybEjhEPO5QFFA\nLX10aVQaB+YPNO8vLCxUOSU5asQPR9jeGSvP7vrNXbZ0GnllxDGrKMsWVnZ+thpUMMgWLyOspY8u\njYq/VTYCRQH13V9/V/kL/bb8TpQXTnm03vfTR3/qGp6RDqv8ONNnDc/5TqccGs8Y9xUNLYqZr5lF\nmTHz35re4tJiNeZHY8z7YsniTx/9adyyzM4Px+uR3z9i1pPsguwomcwvzI8pG/HiZfzmrOf+Ir+6\n4v4r1NDSoa4yYZVZt7pkLQsj/jklOVH3WOtLrPusMlJaVqquuP+KuGk0wrrrkbts9/mH+NXA/IEq\nuyBb5Z2WpxiA8rLtT+dIpQYYZvleHrlmVWghy+cqEfmViJyWzLNu5FXkUVBZEDWHmEEGd428i8s+\ndxmjx47mD/V/4CuFXzF7cTuP7eS2T28ze/qD/99gTv/Z6bR2thKQAJWnhxd0NzRvMHtEM/JmcOXM\nKxk7dqytB/Dh4Q+5a89ddNJJZkYmt8+5ndYLT4xUrO8yEBGC44LceN6NNvcwTvwZfm6fczvzzp1H\n5aeVZm+78vRKCn2FLNm1hPr2enadt4tVq1ahlLKNLCBs6z89dzrXll3LxIkTKR1ZyqN1jyIifK3o\na64927Fjx7J833JbOFY+P+TzfGPaN9h22jZbnllHFoc7DvP43x7non+4KCrPbth+A3XtdeGykgya\npzTzm+d+Y6YRIEuyaFNtTMubxqKhi/ik9RN+EvgJgYqAGZYglPhL2N++n+JAMbVttYgIbee1sWrV\nKg61H+KxA49xWf5lPN/4vPn/1OBUfp79cwIVAfz4OWvQWWw6HO6VNh9v5ljnMVvcVq1axfJ9y81R\nq1LKlI0MySBzSib/9pt/45WmV8z4OrHmrVsP0nrfitwVjLp3FJ2q0yy/RaWLTFlqp528cXksnb6U\nTjpt8iUI0/OmmyOCEWeOYP3g9VyQcwF3773bJmtGuRkjlXjlfvags9k1bJct/w0uyLmA68qvA8Ij\ngreL32bU8FGu6RzqH8odI+7grIlnsWLgCg60H4i6xxkvow63d7bzRugNigPF7G/bn1A2jHwz8vWF\nvBd4avhTTM2dapbR8n3LzbIEKA2U8uu5v+aTCz9xHa2DXS6sabTWdUGYNHgSH3/mY8qXljM1dyrz\nC+ezZNcSW9ytsnH2hLNjyoZRlgdKDlDXVme2S+fnnE9gQMBs34ywdhftZn3TerIysmjtbGVa3jTm\nF87nmzu+Scv3W1zf0VXSqVTeBsaIyAjCCuFq4CvWG0SkBNivlFIi8lnCazwNQFOiZ90ozikmOCFo\ns+0GmJE7gy+P+jIA64rW8c6AdygtKGVJRdjYbOWnK8nMyiRAAEH4dd2vCYwLNzCCsClvEznFOeSH\n8mlVrWRKJovHLSYjI4NJkybZ3rVq1yqyB2cDMLdgLhMGTwDLLdZ3Wbmk4BK+ce43OLjrICsbVpIp\nmVGeVIcHhjNh8AQmnD+BD8o+YFXDKuYUzOHyisupPlpNm7+NgWogLze9zNXjrkah2CybGTz0hKLI\nlExuGHeDObe8rmgd7w54Nxx+wXAzT6xkjctiM/ZwrLwr7zIgYwBXnH+FGacvln3RXGMI+oLctus2\nNvo2cn/t/SyZdOId1UeraaWVwYTD9oufeSXzGD9yPK8Xv84rza+Y9/rw8T7v82jwUfJ8efyD+gfW\njl+LIGElLpksHb2UqsYqpgenc9MnN9FJp5kfKw+sZKNvI/WZ9fx80s+5bsd17M3cS2lBKbOYxSpZ\nxdTcqbwVeotBahBHOIIPHznkmObf80rmoUoUm9lM9tBsNstmbq24lS27tpiyMa9kHqXDS23pj4VT\nftw4HjrOmvFrzDQa5XcO5/Bh2YesaljFrIJZfLniy1R+WhklX5tlM9njshmfPT5cDkM3UhuoJXOw\n/b6ZQ2byYcaHfLHsi6Zs/+Ssn3Dbrtt4vfl12mk3y+i2cbfx1IGnWCWrmJIzhbcOvWWubw3JHcLo\nitHcV3MfXx/9dTbv2IyMj/ZvBnCYw/y54M+sq13HsdHHyCPPtrETwvXo8ooT5tBT/34qpx09jYVb\nF5KtsmmllVm5s/hBxQ8I+oKcwzl8sOsDXpAXzGcCBBicO5jRFaMBwnEr/jrHzzuOiDC6fDRBX5Ab\nxt/Alq0nyvKBcQ+QF8izldPXQ1/nne3vmIrHL34uLbyU1QNWm+Ud6ghx+0e3kzn+RB5vYxvbm7cz\ncPxANstmfjfodxzyHSKbbFsZGYw6a5RNhqzrdoZMPTD7ARZuXWjGNy+Yx6vNr9raN4B7LrqH23bd\nxpuhNxmgBpjvHzBuAAOyBiSUwVRI20K9UqoDuA54CagGnlJKfSAi14rItZHbrgC2iMh7wP3A1SqM\n67OJ3pmVkWVbhLa6VIDYZsTOxbJFpYts31tUi+smMCdG+BAeHc3Onx11j+1dBLggeAGz82ebC3uG\nyfFTZzxFif+E76gMMril4hbzu3NB0M0Kzc0CzLlYuaZhjflbVWOVmSdW01zroqUb7ao9ynzVuvgb\nz+z6yfonbY3IUP9QM35ulo5ttPF66HVebHwRIMo8+9ycc7m14lbWNa+zGS84dyf/67Z/tS3mG+bE\nAzMGRnVKANvidzzXJc4Ngl54Z7YaJDhlzykHTkeWRhyfrH8y5k71AAFm5M4AiFqwN7xNzCqYZV6b\nlT+L8dnjzXffNeIumyeAN0NvmibuVQeronzrWVEoqhqqWNu41iwv07AmEi+3RW/nBsnggKAtr2cN\nmWVb3M/357OheYPpN83Y3OzL8FHVWGVaWjo9HZQGSqPevbpxte37UP/QqOMo3DxQwwmT6FbVyqbD\nJ9YxnRsUjTCcYboZVBiyZ7V8Xdmwkuu3X2+GF/QFbVZlzvd7SVr3qSilqpRSY5VSo5RSd0WuLVdK\nLY98/oVS6kyl1ESl1BSl1Bvxnk0GQ9B/MeoXzC2Ya7pUgPh7J6yNwsz8ma4FZbUugWgrFqeTwNUN\nq53Rs71rZsFM7h99P7effrsZR6MxKssq4/EzHjcttOYUzOHcnHPNcJyuyd3s9J0KzFlBn6x/0ux9\nQlg5fHfHdwl1hGymuVbrogABJg6aaLOf94vfZr7qtH6K5fEZos1qDcUZ6ghxrPOYq9WPwR+b/siV\nhVfynWHfibK4cXYUAJuyaLAYvBimnLdW3MpXi78a9U6FYlhgWFyLv3SdG5Ooo+JUXk5HlnDCo65V\nPjvooMRfYsrhvw/9d9Y1rYtpsWbdF2Ok0fpuawegVbWyoXmDTWHPyJ2BH79rGttpt23oMzwKzCyY\nydJRS10VcyzrTaNOrmhYYZsGr2+vx/CbtrZxre2z0w2K09NBvHdnkMGC4gU2eTd2vavIPzfc5Hp3\n225TuTk7Ym6+3axlc2nBpTbLV4XitdBrtvi7WYSmg37lpuW4Om4OD8uyyqJ6ivH2TjgbBeP7rz5V\nwwAAEvNJREFUwIyBMXtEzp5DIkF3uoNJ1AAl68I+ljsNpwJzVtCriq6Kquh72/faNgXubt1tVgxB\nmFkwk0fGPWK6s4ETPVcDp/I28sMt361xNBRnqCN8wt6G5g0U+WOb0BsjJLdRgbOjcE3pNTErktHo\nGs9Ze90QVpo/GvmjmO45vB6ZWEmmo+LE6sgSwh51x2ePt8mnINxQfoMpW4n8hBn7YqwdICtWM3Ej\nrhBWMM8deI6lo5ZGKTsDQWzycUvFLQllPpbJutveE6vJvVWBWT/DiX0vNW017G/bH1PBOmXWsJ4z\n0rts77KY+2us+6qsGOVlKDc3P2CxyseQPWsH0cA6++A0YXfuV/OKfuWm5fZf3l5Ze1FtTHcpVlcX\nTrcVTncaxvfcAbm82PiiOaduuN9wc+1wRvYZruG7uYOJ5/LDSqJ7Y7nTMO53usewuigpzyxnX9s+\n092Hwe7W3VGbrBSK4YHh3D3ybtM9x4G2A4zNHsuN5Tfa4ud0WXLHiDtiugsJdYR4tflVxmSPMcO5\n+ZObef/I+ygUbZ1tFPoLOdxpNxOHcGN/S8UtpkuNO3fdyfrm9eENng4XIuWZ5Xx09CN2te6KCmd6\n7nTmF8834/P2obdp6WwhdDzcCZhTMCfKxUkqbke6gpEew72GkZeLyxfzwL4HbOl0UppZSl1bHdta\ntlGRWcG9o+6ltbOV39T9huGZw9l+bDsKxUdHP+LHI35M0BeM6WYmWawuiJznwxiuTUZnjaaqsSqq\n9z4jdwafGfQZUz7mFc1Lqn44y8BaF+ra6pgSnMKe1j1MzZ1qulrx48cnPvMzODYDdraz5ciWhO5W\nrO8enjXc5nrIOp3rFz9Tc6eacjdzyEwOHz9M0/Em854sybIphN2tu1lQvIDXm183y+PGYTfavjvL\nZ2NoIw/UPhCVt5100tHZYcbfiPfNw27mbx1/Y1vLNuqfrD92yzdvSXo2KBH9aqQS6ggl3HCW6jRF\nrPlsZ0/id3W/445dd0R5Nk63V17rznq3ueVEpzguLltMWaDM9g63g6MyJdPWW4/Xc3VzWRLroCjn\neR/OE/baaefsQWczLDAsKk7WEZLb+SLOtFtPxHRbc7PG56xBZ0VN91hJ94mWRnpeC71mG4Gublwd\nlU43jLU5Y/rXKPfmjuaoXjF4cwS2sYPbieGKx+q12Zn/XZk+dJaBs04OlIFcWnAplRWVZtpmFcxi\nZv5M87ObV+hk3K1Y3+3cKGpt2If6h1JZUWnK0mUFl9l26ANMGjTJNrJUqKh1uguCF8Rd071z952u\noyNjatot3oaMHPvkmKcujPuVUknk5sLNeiKeK5B489nOqYRDnYdcPRt31/24E6dSsO6sT+RvyE3B\nBX1B7h55t20azOhdxdp1nwin54FYB0W5xcfthL0FJQv40cgf2fL7wuCFNsUdy+DAinU60W3NzRqf\ndU3rmF88P+Z0Tzpxpmdf6z6m5U5j1pBZSaUT7I2Hbfe4xUoL7AeBdXddKOgLcsvwW6KUv5vXZmf+\ne6GkncpgQckCWwNq9ZtmfHYevV0aKOWZA8/whbwvpCT3bkYlxhqhtQO2unG1Lf8zyOC68utclZvT\n8CXWkRvVR6upa6szv1vz3zk1bcX0dh7L90sX6VdKxekHykmsk/FiuQKJN59tPTGyk07eDL3pOhrp\nrvtxK26Ld38J/cX8/bzgeXErQDxDBad1T2VFpVn5U21orEoknssS6yjL6l7GecKec33o0oJLuW/0\nfTbF7TQ4cFrSGFgNIZyNWKodgHSdrOdMTwcdBAcEWd24OiqdyXRSnK5prHTSaeaVFw27dWTv5h4p\nXv6nglvexxttOf2mWT9b1y2N9gBISe6ta0qGqxyncQ0QU76dys26TmfUIeeRGwZOC8oyf1ncUXa6\n6VdKJdeXG7N3YW2QDZ89iY4qTaQQrGaLVlNB6xkhXkwrGMRbvIPw3Gw8EhkqWAWxq5XfqfjiWX65\njbKsJp3OqalYPWmnwYEgUX68kiHVDkC6TtZzpseYwoh1PZnwrL6kzhl8jvmbm8+zruIc2Z8XPM8T\nuXejO2feOzFkfdvRbeZZJbE8RcfCWs8vGnIRcwvmsrB4YUzF55TveEY5iTo7TquuJacviWtUkW76\nlVIp8hfFFCinK+/1zesTHlWaSCGsblztajLo3LTolblpvP00mZLJgpIFcZ9P1JPzQhCdow8jrsb/\nRiNYfbQ65ijLuR5gjaObgnOOtATBeqomJDeqSLYD4DxJ0+uT9dxGjsZoze16MuFZ07V05FJzTQOI\nyquu4hzZ52TkpNXM2s2pY3dGW9Z1ia5MVRv1/DvDvsOtFbfy8P6HYyq+VOQ7UWfHzYKyJ+lXSmWA\nDIgpUG6nLto2gMXomcZTCM51FQPnGSFeLerG20+TbG+wOwoumYbZOfpYVLrINY7OjZlZkmWGD6Sc\nX8ZIy+rq23k2RjKjimTyZ1nNMvtJmh6slbnFw20KI9b1ZMIz0mX0imPlVVeJt6bhJW4dxO6OtJzr\nEj7xpTxVHWsdy6mwU20PkunspGufVFfoV0olHtY1EIN22in0FcYtrHgCYC1s687bdDQyBrH20yQr\nTN1RcIka5lijD7c4OqdkFpQs6NZ0kjHS+vHIH3frILVE+WOd4jHw4yd0POTp2kqskWNXR5TOdBlu\n0b1a7wNvp3rj4dZB7O5IK55nh66G56WBTqJ6nm5rxFQ4ZZQKhAtmRu4M20LZ6QNP75aGNwp7yfAl\nUWeEpAO3xuFkCFMyDbPb6CNWHL0639tJsgepdRU31zclmSWmC5C+RDqUwMnoMbt1ELtbrrE8O3gR\nnhftQW9SGok4pZSK4cfIalO+8dDGlBbk3MK8teJWJgcnn5ReWk+RTMPsNvpwYp1CszZAXjb8bv6w\nvKrgTtc35w4+N+7u696O10rgZDV+zg5id8vV63WJkzVq642cUkrFINnjbFOlN81rek0yDXMyFck6\nxWVtgLxs+N38YXlVwcsCZeYmtJkFMyn0F6Z1bSXd9KUesBWjg+hlw+11/e3P7UE8Tkml4nacrRf0\n1QqaDMk2zPEqUrwptHT37Lyq4M5NaKlsPtV4j5cNt9f1tz+3B/E4JZXKqTw07Q7JVOB4FSnRFFc6\ne3ZeVHDrOep1bXW8Hno9pc2nGu85VRvu3kw6D+nq1SwuW4wgXF92fU9Hpc9gunXoIlcVXcXLB1+m\nVbW69uq7G3666e7mU43mVOCUHKmA7uH0BD01QvTKnUp3N59qNKcCp6xS0fQMPbF46ZU7FS82n2o0\n/Z1TdvpL0zOc7Ckup3HAPxX+U7caf+e0qZ5G1WjspHWkIiIXi8hWEdkuIje7/D5fRN4Xkc0i8oaI\nTLT89mnk+rsisjGd8dT0X7ze2dxTm081mr5C2pSKiAwAfgnMBCYA80RkguO2T4DpSqmzgDuAhxy/\nf14pdbZSanK64qnp33i9s1mj0cQnnSOVzwLblVI7lVJtwBPAXOsNSqk3lFIHI1/fAqKPjdNouoE2\nH0+NdJ0Rozl1SOeaShmwx/J9L/C5OPcvBNZYvivgjyJyHHhQKeUcxWg0SaHXPZJnWc0yVjeEj3RY\nUrGkp6Oj6YP0CusvEfk8YaXyXcvlC5VSZxOePvsPEZkW49lrRGSjiGw8cODASYitpq+h1z2SIxVv\nzhpNLNKpVGqAYZbv5ZFrNkTk74CHgblKqQbjulKqJvJ/PbCC8HRaFEqph5RSk5VSkwsLCz2MvkZz\nauG1UYPm1CSdSuVtYIyIjBCRAHA1sNJ6g4gMB54DFiiltlmuDxKRHOMz8CVgSxrjqtF4Rl9dl9BG\nDRovSJtSUUp1ANcBLwHVwFNKqQ9E5FoRuTZy2xKgAPiVw3S4GHhNRN4D/gq8qJRam664ajRekq6z\n69ONNmrQeIEoFX3Gel9l8uTJauNGvaVF03NUH61m4daFtKpWMiWTR8Y90qca51BHiPtq7uP6suv1\nGtQpgoi84+W2jV6xUK/R9Bf6+rqENmrQdBetVDQaD9HrEppTHa1UNBoP0esSmlMd7VBSo/EYvdlS\ncyqjlYpG4zG9/bAxjSad6OkvjUaj0XiGVioajUaj8QytVDQajUbjGVqpaDQajcYztFLRaDQajWdo\npaLRaDQaz9BKRaPRaDSeoZWKRqPRaDxDKxWNRqPReIZWKhqNRqPxDK1UNBqNRuMZWqloNBqNxjO0\nUtFoNBqNZ2ilotFoNBrPSKtSEZGLRWSriGwXkZtdfhcRuT/y+/sick6yz2o0Go2m95E2pSIiA4Bf\nAjOBCcA8EZnguG0mMCbydw3wQArPajQajaaXkc6RymeB7UqpnUqpNuAJYK7jnrnAf6swbwF5IjI0\nyWc1Go1G08tI58mPZcAey/e9wOeSuKcsyWcBEJFrCI9yAFpFZEs34tybOQ34W09HIo3o9PVtdPr6\nLuO8DKzPHyeslHoIeAhARDYqpSb3cJTSQn9OG+j09XV0+vouIrLRy/DSqVRqgGGW7+WRa8nc40/i\nWY1Go9H0MtK5pvI2MEZERohIALgaWOm4ZyXwtYgV2BSgWSlVm+SzGo1Go+llpG2kopTqEJHrgJeA\nAcAjSqkPROTayO/LgSpgFrAdOAr8a7xnk3jtQ96npNfQn9MGOn19HZ2+vounaROllJfhaTQajeYU\nRu+o12g0Go1naKWi0Wg0Gs/o1UpFRIaJyJ9F5EMR+UBEro9czxeR/xGRjyP/D7E8872Ia5etInKR\n5fq5IrI58tv9IiI9kSZLfFJKm4icLiItIvJu5G+5JaxelbZInGKl78rI904Rmex4pk+UXSROKaWv\nH5XfT0Tko4hbpRUikmd5pj+Un2v6+lL5xUnbHZF0vSsiL4tIqeUZ78pOKdVr/4ChwDmRzznANsJu\nW+4Bbo5cvxm4O/J5AvAekAmMAHYAAyK//RWYAgiwBpjZx9J2OrAlRli9Km0J0ncG4c1W64HJlvv7\nTNl1MX39pfy+BPgi1+/ui3Wvi+nrM+UXJ21Byz3fBJano+x69UhFKVWrlNoU+XwIqCa8234u8NvI\nbb8FLot8ngs8oZRqVUp9Qtiq7LMSdv0SVEq9pcI59d+WZ3qELqTNld6YNoidPqVUtVJqq8sjfabs\noEvpc6UPpu9lpVRH5La3CO8hg/5TfrHS50pvTF+ctIUstw0CDCstT8uuVysVKyJyOjAJ+AtQrML7\nWQDqgOLI53huX/a6XO8VJJk2gBGRoesrIjI1cq1Xpw2i0heLPll2kHT6oP+V378R7r1C/yw/a/qg\nD5afM20icpeI7AHmA0sit3ladn1CqYjIYOBZYLFD2xLRoH3WLjqFtNUCw5VSZwM3Ao+JSPCkRrYL\nxEtffyCF9PWr8hOR7wMdwB96Km5ekEL6+lz5uaVNKfV9pdQwwum6Lh3v7fVKRUT8hDPmD0qp5yKX\n90eGZsbwsz5yPZbblxrsw9he4fYllbRFhqYNkc/vEJ73HEsvTRvETF8s+lTZQWrp60/lJyL/AlwC\nzI90fKAflZ9b+vpa+SUhm38ALo989rbsvFocSscf4cWh/wbudVz/CfbF7Hsin8/EvuC0k9gLTrP6\nWNoKLWkZGSnc/N6Ytnjps/y+HvtCdp8puy6mr1+UH3Ax8CFQ6LjeL8ovTvr6TPnFSdsYy+dvAM+k\no+x6tGCTyJwLCU//vA+8G/mbBRQAfwI+Bv5oFG7kme8T7kVsxWKpAEwGtkR++wURbwJ9JW2EexUf\nRO7bBMzprWlLkL5/JDw32wrsB17qa2XXlfT1o/LbTnj+3bi2vJ+Vn2v6+lL5xUnbs5F4vg+sIrx4\n73nZaTctGo1Go/GMXr+motFoNJq+g1YqGo1Go/EMrVQ0Go1G4xlaqWg0Go3GM7RS0Wg0Go1naKWi\n0aSAiByPuOrYIiJPi0h25PobXQzvdBHZ4m0sNZqeQysVjSY1WpRSZyulPgO0Acbx2Of3bLQ0mt6B\nVioaTdd5FRgNICKHI///o4j8ScIMFZFtIlIiIgMiZ3W8HTnTYpEzMBE5U0T+GhkJvS8iY05yejSa\nbuPr6QhoNH0REfEBM4G11utKqRUicjnwH4RdfvxAKVUnItcAzUqpvxeRTOB1EXkZuzPUa4H7lFJ/\nEJEAMOCkJEaj8RCtVDSa1BgoIu9GPr8K/JfLPd8g7NriLaXU45FrXwL+TkSuiHzPBcYQPkDJ4E3g\n+yJSDjynlPrY89hrNGlGKxWNJjVaVNj9eTzKgU6gWEQylFKdhB3yfUMp9ZL1xsh5FwAopR4Tkb8A\ns4EqEVmklFrnaew1mjSj11Q0Gg+JTIs9AswjfOLejZGfXgL+f8QlOSIyVkQGOZ4dCexUSt0PvAD8\n3UmLuEbjEXqkotF4y38CryqlXhOR94C3ReRF4GHC55xvEhEBDhB9NOs/AQtEpJ3wqZ8/PHnR1mi8\nQXsp1mg0Go1n6OkvjUaj0XiGVioajUaj8QytVDQajUbjGVqpaDQajcYztFLRaDQajWdopaLRaDQa\nz9BKRaPRaDSe8X9WsZkmQtiCogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c0a56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 3580.13623047,  3580.96191406,  3581.78564453, ...,  6961.44775391,\n",
       "         6963.05322266,  6964.65478516], dtype=float32),\n",
       " array([ 0.55113399,  0.71094912,  0.38548875, ...,  0.94836283,\n",
       "         1.12514913,  1.10390759], dtype=float32),\n",
       " 22.500017328085544)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppxf_miles_deredshifted(2,'no',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''adding a code that de-redshifts things for you'''\n",
    "def ppxf_miles_deredshifted(vr, plot, bs):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    file = '../spec-1777-53857-0076.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "    z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    flux = t['flux'][mask]\n",
    "    var = 1/np.sqrt(t['ivar'][mask])\n",
    "    var_scaled=var/np.median(flux)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(var, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(flux, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    galaxy = flux/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    noise = var_scaled\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    "    \n",
    "    file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(ssp)\n",
    "        plt.title('Template')\n",
    "        plt.show()\n",
    "    \n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = h2['CDELT1']\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        #ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, 0)\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    z_final=0\n",
    "    vel = c*np.log(1 + z_final)   # Initial estimate of the galaxy velocity in km/s\n",
    "    print('input vel', vel)\n",
    "    start = [vel, 100.]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    t = clock()\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, bias=bs, plot=True, moments=4,\n",
    "              degree=-1, vsyst=dv, velscale_ratio=vr)#was degree=12\n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    print('Best-fitting redshift z:', (z + 1)*(1 + pp.sol[0]/c) - 1)\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    plt.xlim([2000,2300])\n",
    "    plt.ylim([0,2])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0]\n",
    "\n",
    "\n"
   ]
  },
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    "'''try putting everything into log-space'''\n",
    "def ppxf_miles_log(vr, plot, bs):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    file = '../spec-1777-53857-0076.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "    z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    flux = t['flux'][mask]\n",
    "    var = 1/np.sqrt(t['ivar'][mask])\n",
    "    var_scaled=var/np.median(flux)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(var, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(flux, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    galaxy = flux/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    noise = var_scaled\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    "    \n",
    "    file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(ssp)\n",
    "        plt.title('Template')\n",
    "        plt.show()\n",
    "    \n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = h2['CDELT1']\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        #ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, 0)\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    z_final=0\n",
    "    vel = c*np.log(1 + z_final)   # Initial estimate of the galaxy velocity in km/s\n",
    "    print('input vel', vel)\n",
    "    start = [vel, 100.]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    t = clock()\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, bias=bs, plot=True, moments=4,\n",
    "              degree=-1, vsyst=dv, velscale_ratio=vr)#was degree=12\n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    print('Best-fitting redshift z:', (z + 1)*(1 + pp.sol[0]/c) - 1)\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    plt.xlim([1500,2500])\n",
    "    plt.ylim([0,2])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0]\n",
    "\n",
    "\n"
   ]
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    {
     "data": {
      "text/plain": [
       "'try to just import the text file separately'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''try to just import the text file separately'''"
   ]
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     "text": [
      "#K OBJECT   '249'  Name of the object observed\n",
      "#K OBSERVAT 'KPNO'  observatory\n",
      "#K TELESCOP 'kpcdf'  telescope name\n",
      "#K DETECTOR 'f3kb'  detector name\n",
      "#K APERTURE '200 microns'  aperture\n",
      "#K OBJNAME  HD 249\n",
      "#K OBJRA    00:07:22.56\n",
      "#K OBJDEC   +26:27:02.2\n",
      "#K OBJBMAG  8.330000E+00\n",
      "#K OBJVMAG  7.330000E+00\n",
      "#K SPTYPE   K1IV\n",
      "#K OBJRV    1.260000E+01\n",
      "#K HDNAME   HD 249\n",
      "#K BDNAME   BD+25 5073\n",
      "#K HIPNAME  HIP 607\n",
      "#K SAONAME  SAO 73755\n",
      "#K VHELIO   -4.82757688627675E+00\n",
      "#K VLSR     -6.11122200078475E-01\n",
      "#K VSUN     20.    18.    30.  1900.\n",
      "#K CMIN1    3.452802E+03\n",
      "#K CMAX1    4.777408E+03\n",
      "#K PICKTYPE K1IV\n",
      "#K NCOMBINE 5\n",
      "#K TEFF     4723  2001MNRAS.326..981C (Cenarro)\n",
      "#K LOGG     2.400000E+00  2001MNRAS.326..981C (Cenarro)\n",
      "#K FEH      -3.200000E-01  2001MNRAS.326..981C (Cenarro)\n",
      "#K COVERAGE 3465.0-9469.0\n",
      "#K GAPS     6789.4-6810.6\n",
      "\n",
      "#      wavelength        spectrum\n",
      "#       angstroms                \n",
      "\n",
      "            3465.       0.2237947\n",
      "           3465.4       0.1839945\n",
      "           3465.8       0.1502364\n",
      "           3466.2       0.1490312\n",
      "           3466.6       0.1726146\n",
      "            3467.       0.1946185\n",
      "           3467.4       0.2169859\n",
      "           3467.8       0.2416768\n",
      "           3468.2       0.2501735\n",
      "           3468.6       0.2478547\n",
      "            3469.       0.2390553\n",
      "           3469.4       0.2362803\n",
      "           3469.8        0.254901\n",
      "           3470.2        0.276743\n",
      "           3470.6       0.2815582\n",
      "            3471.       0.2679066\n",
      "           3471.4       0.2507015\n",
      "           3471.8       0.2181635\n",
      "           3472.2       0.1928788\n",
      "           3472.6       0.1797997\n",
      "            3473.       0.1840423\n",
      "           3473.4       0.1827767\n",
      "           3473.8       0.1698347\n",
      "           3474.2       0.1549253\n",
      "           3474.6        0.137639\n",
      "            3475.       0.1228766\n",
      "           3475.4       0.1142217\n",
      "           3475.8       0.1062431\n",
      "           3476.2       0.1036205\n",
      "           3476.6       0.1043395\n",
      "            3477.       0.1273054\n",
      "           3477.4       0.1650475\n",
      "           3477.8       0.1931138\n",
      "           3478.2       0.2086292\n",
      "           3478.6       0.2202476\n",
      "            3479.         0.22527\n",
      "           3479.4        0.219677\n",
      "           3479.8       0.2120939\n",
      "           3480.2        0.204714\n",
      "           3480.6       0.2032873\n",
      "            3481.       0.2125959\n",
      "           3481.4       0.2150957\n",
      "           3481.8       0.2200806\n",
      "           3482.2       0.2196377\n",
      "           3482.6       0.2115644\n",
      "            3483.       0.1810069\n",
      "           3483.4       0.1621382\n",
      "           3483.8       0.1809208\n",
      "           3484.2       0.1943485\n",
      "           3484.6       0.1923435\n",
      "            3485.       0.1832364\n",
      "           3485.4       0.1752904\n",
      "           3485.8       0.1890806\n",
      "           3486.2        0.223365\n",
      "           3486.6       0.2559542\n",
      "            3487.       0.2716507\n",
      "           3487.4       0.2779671\n",
      "           3487.8        0.263234\n",
      "           3488.2       0.2373404\n",
      "           3488.6       0.2129436\n",
      "            3489.        0.180603\n",
      "           3489.4       0.1565922\n",
      "           3489.8        0.136146\n",
      "           3490.2       0.1202059\n",
      "           3490.6       0.1164339\n",
      "            3491.        0.126258\n",
      "           3491.4       0.1544928\n",
      "           3491.8       0.1776924\n",
      "           3492.2       0.1676193\n",
      "           3492.6       0.1464826\n",
      "            3493.       0.1345028\n",
      "           3493.4       0.1538745\n",
      "           3493.8       0.1863322\n",
      "           3494.2       0.2167614\n",
      "           3494.6        0.233654\n",
      "            3495.       0.2188312\n",
      "           3495.4       0.1907678\n",
      "           3495.8       0.1695596\n",
      "           3496.2       0.1535007\n",
      "           3496.6       0.1452865\n",
      "            3497.       0.1406736\n",
      "           3497.4       0.1341261\n",
      "           3497.8       0.1449577\n",
      "           3498.2       0.1778601\n",
      "           3498.6       0.2180892\n",
      "            3499.       0.2592924\n",
      "           3499.4       0.2821687\n",
      "           3499.8       0.2900239\n",
      "           3500.2       0.2800083\n",
      "           3500.6       0.2656958\n",
      "            3501.       0.2666223\n",
      "           3501.4       0.2728923\n",
      "           3501.8       0.2637581\n",
      "           3502.2       0.2467567\n",
      "           3502.6        0.251421\n",
      "            3503.       0.2875851\n",
      "           3503.4       0.3298244\n",
      "           3503.8       0.3377472\n",
      "           3504.2       0.3208342\n",
      "           3504.6       0.2792492\n",
      "            3505.       0.2470236\n",
      "           3505.4       0.2356673\n",
      "           3505.8       0.2359048\n",
      "           3506.2       0.2265246\n",
      "           3506.6       0.2222137\n",
      "            3507.       0.2403028\n",
      "           3507.4       0.2619495\n",
      "           3507.8       0.2793784\n",
      "           3508.2       0.2845171\n",
      "           3508.6       0.2792132\n",
      "            3509.       0.2673903\n",
      "           3509.4       0.2383705\n",
      "           3509.8      0.05247688\n",
      "           3510.2       0.1106671\n",
      "           3510.6       0.1939268\n",
      "            3511.       0.2179586\n",
      "           3511.4       0.2418342\n",
      "           3511.8       0.2350563\n",
      "           3512.2       0.2197905\n",
      "           3512.6       0.2036914\n",
      "            3513.       0.1783718\n",
      "           3513.4       0.1619114\n",
      "           3513.8       0.1534509\n",
      "           3514.2       0.1407298\n",
      "           3514.6       0.1322921\n",
      "            3515.       0.1406333\n",
      "           3515.4       0.1634194\n",
      "           3515.8       0.2017915\n",
      "           3516.2       0.2488492\n",
      "           3516.6        0.274818\n",
      "            3517.       0.2849613\n",
      "           3517.4       0.3032084\n",
      "           3517.8        0.323991\n",
      "           3518.2       0.3257058\n",
      "           3518.6       0.3049528\n",
      "            3519.       0.2876077\n",
      "           3519.4       0.2649575\n",
      "           3519.8       0.2389622\n",
      "           3520.2        0.221801\n",
      "           3520.6       0.2193829\n",
      "            3521.       0.1916531\n",
      "           3521.4       0.1678144\n",
      "           3521.8       0.1773153\n",
      "           3522.2       0.1988549\n",
      "           3522.6       0.2029093\n",
      "            3523.       0.1820972\n",
      "           3523.4       0.1528268\n",
      "           3523.8       0.1274243\n",
      "           3524.2       0.1033201\n",
      "           3524.6      0.09961557\n",
      "            3525.       0.1208215\n",
      "           3525.4       0.1278949\n",
      "           3525.8       0.1165805\n",
      "           3526.2       0.1077574\n",
      "           3526.6       0.1238535\n",
      "            3527.       0.1669055\n",
      "           3527.4       0.2015615\n",
      "           3527.8        0.227168\n",
      "           3528.2       0.2486073\n",
      "           3528.6       0.2556764\n",
      "            3529.       0.2626027\n",
      "           3529.4        0.262048\n",
      "           3529.8        0.257443\n",
      "           3530.2       0.2466603\n",
      "           3530.6       0.2476281\n",
      "            3531.       0.2499982\n",
      "           3531.4       0.2387703\n",
      "           3531.8       0.2181329\n",
      "           3532.2       0.2024205\n",
      "           3532.6       0.1942346\n",
      "            3533.       0.1956341\n",
      "           3533.4       0.2095219\n",
      "           3533.8       0.2375351\n",
      "           3534.2       0.2748899\n",
      "           3534.6        0.292852\n",
      "            3535.       0.2801091\n",
      "           3535.4       0.2703209\n",
      "           3535.8       0.2635933\n",
      "           3536.2       0.2637388\n",
      "           3536.6       0.2597572\n",
      "            3537.       0.2594972\n",
      "           3537.4       0.2522319\n",
      "           3537.8       0.2361535\n",
      "           3538.2       0.2403739\n",
      "           3538.6       0.2681705\n",
      "            3539.       0.2975199\n",
      "           3539.4       0.3215407\n",
      "           3539.8       0.3269179\n",
      "           3540.2       0.2996543\n",
      "           3540.6       0.2653788\n",
      "            3541.       0.2495292\n",
      "           3541.4       0.2335027\n",
      "           3541.8       0.2321603\n",
      "           3542.2        0.230522\n",
      "           3542.6       0.2366749\n",
      "            3543.        0.249599\n",
      "           3543.4       0.2562505\n",
      "           3543.8       0.2723345\n",
      "           3544.2       0.2915367\n",
      "           3544.6       0.2905698\n",
      "            3545.       0.2776209\n",
      "           3545.4       0.2580195\n",
      "           3545.8       0.2560754\n",
      "           3546.2       0.2612836\n",
      "           3546.6       0.2696688\n",
      "            3547.        0.250614\n",
      "           3547.4       0.2241185\n",
      "           3547.8       0.2090184\n",
      "           3548.2       0.2108987\n",
      "           3548.6       0.2319237\n",
      "            3549.       0.2610782\n",
      "           3549.4       0.2797731\n",
      "           3549.8       0.2738878\n",
      "           3550.2        0.255272\n",
      "           3550.6       0.2467932\n",
      "            3551.        0.234722\n",
      "           3551.4       0.2310055\n",
      "           3551.8       0.2370192\n",
      "           3552.2       0.2366713\n",
      "           3552.6       0.2260566\n",
      "            3553.       0.2061495\n",
      "           3553.4       0.1899495\n",
      "           3553.8       0.1856907\n",
      "           3554.2       0.1769154\n",
      "           3554.6       0.1717474\n",
      "            3555.       0.1923843\n",
      "           3555.4       0.2254726\n",
      "           3555.8       0.2537726\n",
      "           3556.2       0.2573639\n",
      "           3556.6       0.2233312\n",
      "            3557.       0.2200532\n",
      "           3557.4       0.2316418\n",
      "           3557.8       0.2182532\n",
      "           3558.2       0.1858765\n",
      "           3558.6       0.1617963\n",
      "            3559.       0.1655278\n",
      "           3559.4       0.1837602\n",
      "           3559.8       0.1983972\n",
      "           3560.2       0.2022321\n",
      "           3560.6       0.1962473\n",
      "            3561.       0.1970386\n",
      "           3561.4       0.2036896\n",
      "           3561.8       0.2011552\n",
      "           3562.2       0.2136959\n",
      "           3562.6       0.2459891\n",
      "            3563.       0.2608154\n",
      "           3563.4       0.2650039\n",
      "           3563.8       0.2527314\n",
      "           3564.2       0.2111991\n",
      "           3564.6       0.1654224\n",
      "            3565.       0.1239327\n",
      "           3565.4      0.09055673\n",
      "           3565.8      0.08671577\n",
      "           3566.2       0.1021736\n",
      "           3566.6       0.1195043\n",
      "            3567.       0.1473963\n",
      "           3567.4        0.180153\n",
      "           3567.8       0.1865484\n",
      "           3568.2       0.1849187\n",
      "           3568.6       0.1719866\n",
      "            3569.        0.143483\n",
      "           3569.4       0.1061573\n",
      "           3569.8      0.07881447\n",
      "           3570.2      0.07615851\n",
      "           3570.6       0.1001413\n",
      "            3571.        0.130466\n",
      "           3571.4       0.1440309\n",
      "           3571.8       0.1473938\n",
      "           3572.2       0.1590986\n",
      "           3572.6       0.1938946\n",
      "            3573.       0.2095838\n",
      "           3573.4       0.2181523\n",
      "           3573.8       0.2005217\n",
      "           3574.2        0.190608\n",
      "           3574.6       0.1975525\n",
      "            3575.       0.1966178\n",
      "           3575.4        0.198506\n",
      "           3575.8       0.2086416\n",
      "           3576.2       0.2131294\n",
      "           3576.6       0.2137541\n",
      "            3577.       0.2089141\n",
      "           3577.4        0.195738\n",
      "           3577.8       0.1735743\n",
      "           3578.2       0.1393432\n",
      "           3578.6       0.1113886\n",
      "            3579.       0.1129565\n",
      "           3579.4       0.1173788\n",
      "           3579.8       0.1179946\n",
      "           3580.2       0.1023306\n",
      "           3580.6      0.07789931\n",
      "            3581.      0.06080822\n",
      "           3581.4      0.05473271\n",
      "           3581.8      0.06290084\n",
      "           3582.2      0.07946591\n",
      "           3582.6      0.09642256\n",
      "            3583.       0.1129811\n",
      "           3583.4       0.1226742\n",
      "           3583.8       0.1233735\n",
      "           3584.2       0.1214361\n",
      "           3584.6       0.1003098\n",
      "            3585.        0.081767\n",
      "           3585.4      0.07174655\n",
      "           3585.8       0.0752776\n",
      "           3586.2       0.0870335\n",
      "           3586.6      0.09038439\n",
      "            3587.      0.09043048\n",
      "           3587.4      0.09910025\n",
      "           3587.8       0.1159977\n",
      "           3588.2       0.1334832\n",
      "           3588.6       0.1333528\n",
      "            3589.       0.1239775\n",
      "           3589.4        0.117061\n",
      "           3589.8       0.1217145\n",
      "           3590.2       0.1442263\n",
      "           3590.6       0.1987786\n",
      "            3591.       0.2529998\n",
      "           3591.4       0.2751618\n",
      "           3591.8       0.2744383\n",
      "           3592.2       0.2586486\n",
      "           3592.6       0.2299012\n",
      "            3593.       0.1956356\n",
      "           3593.4       0.1656517\n",
      "           3593.8       0.1766173\n",
      "           3594.2       0.2032678\n",
      "           3594.6       0.2298533\n",
      "            3595.       0.2496705\n",
      "           3595.4       0.2692551\n",
      "           3595.8       0.2873478\n",
      "           3596.2       0.3020696\n",
      "           3596.6       0.3187133\n",
      "            3597.       0.3226124\n",
      "           3597.4       0.2983727\n",
      "           3597.8       0.2753063\n",
      "           3598.2       0.2849377\n",
      "           3598.6       0.2985682\n",
      "            3599.       0.2902697\n",
      "           3599.4       0.2976351\n",
      "           3599.8       0.3220284\n",
      "           3600.2       0.3456661\n",
      "           3600.6       0.3484126\n",
      "            3601.       0.3470244\n",
      "           3601.4       0.3114817\n",
      "           3601.8       0.2589766\n",
      "           3602.2       0.2277725\n",
      "           3602.6        0.213434\n",
      "            3603.       0.2163726\n",
      "           3603.4       0.2267369\n",
      "           3603.8       0.2221424\n",
      "           3604.2       0.2227322\n",
      "           3604.6       0.2059587\n",
      "            3605.       0.1785787\n",
      "           3605.4       0.1689238\n",
      "           3605.8       0.1805861\n",
      "           3606.2       0.2028731\n",
      "           3606.6       0.2189048\n",
      "            3607.       0.2384674\n",
      "           3607.4       0.2476516\n",
      "           3607.8       0.2195926\n",
      "           3608.2       0.1651583\n",
      "           3608.6       0.1157103\n",
      "            3609.      0.09896167\n",
      "           3609.4       0.1130203\n",
      "           3609.8       0.1277034\n",
      "           3610.2       0.1452915\n",
      "           3610.6       0.1665927\n",
      "            3611.       0.2035536\n",
      "           3611.4       0.2365703\n",
      "           3611.8       0.2544851\n",
      "           3612.2       0.2510825\n",
      "           3612.6       0.2369582\n",
      "            3613.       0.2122993\n",
      "           3613.4       0.1932033\n",
      "           3613.8       0.2041087\n",
      "           3614.2       0.2269311\n",
      "           3614.6         0.25965\n",
      "            3615.       0.2853957\n",
      "           3615.4       0.2976454\n",
      "           3615.8       0.2996657\n",
      "           3616.2       0.2914811\n",
      "           3616.6       0.2764427\n",
      "            3617.       0.2517304\n",
      "           3617.4       0.2172826\n",
      "           3617.8       0.1721842\n",
      "           3618.2        0.121779\n",
      "           3618.6      0.08619858\n",
      "            3619.      0.07416099\n",
      "           3619.4      0.08396834\n",
      "           3619.8       0.1210816\n",
      "           3620.2       0.1659854\n",
      "           3620.6       0.2047042\n",
      "            3621.       0.2130613\n",
      "           3621.4        0.218083\n",
      "           3621.8       0.2455762\n",
      "           3622.2       0.2842768\n",
      "           3622.6       0.3080238\n",
      "            3623.       0.3002432\n",
      "           3623.4       0.2709626\n",
      "           3623.8       0.2463115\n",
      "           3624.2       0.2352269\n",
      "           3624.6       0.2420848\n",
      "            3625.       0.2736051\n",
      "           3625.4        0.304976\n",
      "           3625.8       0.3401816\n",
      "           3626.2       0.3731161\n",
      "           3626.6       0.3724629\n",
      "            3627.       0.3690357\n",
      "           3627.4       0.3446843\n",
      "           3627.8       0.3247154\n",
      "           3628.2       0.3200029\n",
      "           3628.6       0.3310273\n",
      "            3629.       0.3380142\n",
      "           3629.4       0.3238766\n",
      "           3629.8       0.2985391\n",
      "           3630.2       0.2440473\n",
      "           3630.6       0.1746846\n",
      "            3631.       0.1257529\n",
      "           3631.4      0.09927821\n",
      "           3631.8       0.1101822\n",
      "           3632.2       0.1437422\n",
      "           3632.6       0.1792581\n",
      "            3633.       0.2017717\n",
      "           3633.4       0.2262827\n",
      "           3633.8       0.2332379\n",
      "           3634.2       0.2100734\n",
      "           3634.6       0.1931891\n",
      "            3635.       0.1977611\n",
      "           3635.4       0.2207602\n",
      "           3635.8       0.2596655\n",
      "           3636.2       0.2665264\n",
      "           3636.6       0.2591618\n",
      "            3637.       0.2604458\n",
      "           3637.4       0.2647272\n",
      "           3637.8       0.2657239\n",
      "           3638.2       0.2756668\n",
      "           3638.6       0.3036077\n",
      "            3639.       0.3256351\n",
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      "           9333.8       0.6589555\n",
      "           9334.2       0.6575894\n",
      "         9334.601       0.6477916\n",
      "            9335.       0.6523496\n",
      "           9335.4       0.6682127\n",
      "           9335.8       0.6895134\n",
      "           9336.2       0.7117515\n",
      "         9336.601       0.6882715\n",
      "            9337.       0.6895295\n",
      "           9337.4       0.7034519\n",
      "           9337.8       0.6911599\n",
      "           9338.2       0.6876758\n",
      "         9338.601       0.6688253\n",
      "            9339.       0.6319361\n",
      "           9339.4       0.6496435\n",
      "           9339.8       0.6772529\n",
      "           9340.2       0.6848962\n",
      "         9340.601       0.6766151\n",
      "            9341.       0.6839363\n",
      "           9341.4       0.6827654\n",
      "           9341.8       0.6918585\n",
      "           9342.2       0.6823149\n",
      "         9342.601       0.6414276\n",
      "            9343.       0.6098529\n",
      "           9343.4       0.6037338\n",
      "           9343.8       0.5932983\n",
      "           9344.2       0.6072991\n",
      "         9344.601        0.660351\n",
      "            9345.       0.6358936\n",
      "           9345.4       0.6324089\n",
      "           9345.8        0.674566\n",
      "           9346.2       0.7027118\n",
      "         9346.601        0.692794\n",
      "            9347.       0.6985139\n",
      "           9347.4       0.6926779\n",
      "           9347.8       0.6807998\n",
      "           9348.2       0.6827085\n",
      "         9348.601       0.6784284\n",
      "            9349.       0.6665016\n",
      "           9349.4        0.651302\n",
      "           9349.8       0.6504241\n",
      "           9350.2       0.6438825\n",
      "         9350.601       0.6550928\n",
      "            9351.       0.6694301\n",
      "           9351.4       0.6712735\n",
      "           9351.8       0.6778457\n",
      "           9352.2        0.664456\n",
      "         9352.601       0.6285079\n",
      "            9353.       0.5898156\n",
      "           9353.4       0.5683581\n",
      "           9353.8       0.5913793\n",
      "           9354.2       0.6424096\n",
      "         9354.601       0.6710842\n",
      "            9355.       0.6734344\n",
      "           9355.4       0.7093126\n",
      "           9355.8       0.6860558\n",
      "           9356.2       0.6695104\n",
      "         9356.601       0.6260464\n",
      "            9357.       0.5917056\n",
      "           9357.4       0.5762205\n",
      "           9357.8       0.5708869\n",
      "           9358.2         0.56243\n",
      "         9358.601       0.5691305\n",
      "            9359.       0.6366344\n",
      "           9359.4       0.6470258\n",
      "           9359.8       0.6554309\n",
      "           9360.2       0.6657726\n",
      "         9360.601       0.6582345\n",
      "            9361.        0.650767\n",
      "           9361.4       0.6622205\n",
      "           9361.8       0.6402428\n",
      "           9362.2        0.608004\n",
      "         9362.601        0.616807\n",
      "            9363.       0.6578677\n",
      "           9363.4       0.6848615\n",
      "           9363.8       0.6819052\n",
      "           9364.2       0.6844389\n",
      "         9364.601       0.6459581\n",
      "            9365.       0.6703061\n",
      "           9365.4        0.704331\n",
      "           9365.8       0.6770773\n",
      "           9366.2       0.7051355\n",
      "         9366.601       0.7133884\n",
      "            9367.       0.6916819\n",
      "           9367.4       0.6731623\n",
      "           9367.8        0.649329\n",
      "           9368.2        0.637001\n",
      "         9368.601       0.6169078\n",
      "            9369.       0.5953832\n",
      "           9369.4       0.5885365\n",
      "           9369.8       0.6621916\n",
      "           9370.2       0.6792592\n",
      "         9370.601        0.663776\n",
      "            9371.       0.5791852\n",
      "           9371.4       0.5829256\n",
      "           9371.8       0.6567108\n",
      "           9372.2       0.6775919\n",
      "         9372.601       0.6434819\n",
      "            9373.       0.6278494\n",
      "           9373.4       0.6721892\n",
      "           9373.8       0.6772426\n",
      "           9374.2       0.6635138\n",
      "         9374.601        0.672453\n",
      "            9375.       0.6741982\n",
      "           9375.4        0.683783\n",
      "           9375.8       0.6915143\n",
      "           9376.2       0.6938936\n",
      "         9376.601       0.7019038\n",
      "            9377.       0.7066476\n",
      "           9377.4       0.6603722\n",
      "           9377.8       0.6458223\n",
      "           9378.2        0.724173\n",
      "         9378.601       0.7151935\n",
      "            9379.       0.7029885\n",
      "           9379.4       0.6822423\n",
      "           9379.8       0.6455173\n",
      "           9380.2       0.6191788\n",
      "         9380.601       0.6215307\n",
      "            9381.       0.6078737\n",
      "           9381.4       0.6352308\n",
      "           9381.8       0.6754785\n",
      "           9382.2       0.6797878\n",
      "         9382.601       0.6748512\n",
      "            9383.       0.6546367\n",
      "           9383.4       0.6590971\n",
      "           9383.8       0.6696582\n",
      "           9384.2       0.6663797\n",
      "         9384.601       0.6607217\n",
      "            9385.       0.6611159\n",
      "           9385.4       0.6714197\n",
      "           9385.8       0.6865997\n",
      "           9386.2       0.6696787\n",
      "         9386.601       0.6273668\n",
      "            9387.       0.6267589\n",
      "           9387.4       0.6516331\n",
      "           9387.8       0.6426388\n",
      "           9388.2       0.6481113\n",
      "         9388.601       0.6683822\n",
      "            9389.        0.675781\n",
      "           9389.4       0.6652954\n",
      "           9389.8       0.6613195\n",
      "           9390.2       0.6585645\n",
      "         9390.601       0.6558968\n",
      "            9391.       0.6650967\n",
      "           9391.4       0.6618181\n",
      "           9391.8       0.6431853\n",
      "           9392.2        0.635861\n",
      "         9392.601       0.6370206\n",
      "            9393.       0.6485608\n",
      "           9393.4       0.6637218\n",
      "           9393.8       0.6565364\n",
      "           9394.2       0.6507579\n",
      "         9394.601       0.6499211\n",
      "            9395.       0.6527799\n",
      "           9395.4       0.6610967\n",
      "           9395.8       0.6591185\n",
      "           9396.2       0.6558238\n",
      "         9396.601       0.6587889\n",
      "            9397.       0.6510512\n",
      "           9397.4       0.6396998\n",
      "           9397.8       0.6491507\n",
      "           9398.2       0.6725121\n",
      "         9398.601       0.6762415\n",
      "            9399.       0.6711577\n",
      "           9399.4       0.6923892\n",
      "           9399.8       0.6939768\n",
      "           9400.2       0.6838427\n",
      "         9400.601       0.6779937\n",
      "            9401.       0.6503499\n",
      "           9401.4       0.6322544\n",
      "           9401.8       0.6544981\n",
      "           9402.2       0.6707649\n",
      "         9402.601        0.678724\n",
      "            9403.       0.6921951\n",
      "           9403.4       0.6737293\n",
      "           9403.8       0.6594511\n",
      "           9404.2       0.6592093\n",
      "         9404.601       0.6463968\n",
      "            9405.       0.6466988\n",
      "           9405.4       0.6374344\n",
      "           9405.8       0.6271791\n",
      "           9406.2       0.6455974\n",
      "         9406.601       0.6485823\n",
      "            9407.       0.6335099\n",
      "           9407.4       0.6525054\n",
      "           9407.8       0.6568685\n",
      "           9408.2       0.6639612\n",
      "         9408.601       0.6679685\n",
      "            9409.       0.6895282\n",
      "           9409.4       0.6863479\n",
      "           9409.8       0.6519378\n",
      "           9410.2       0.6921358\n",
      "         9410.601       0.6919023\n",
      "            9411.        0.656573\n",
      "           9411.4       0.6775955\n",
      "           9411.8       0.6653105\n",
      "           9412.2       0.6672515\n",
      "         9412.601       0.6778593\n",
      "            9413.        0.641049\n",
      "           9413.4       0.5712478\n",
      "           9413.8       0.5711859\n",
      "           9414.2       0.5900423\n",
      "         9414.601       0.5902216\n",
      "            9415.       0.5834739\n",
      "           9415.4       0.6112583\n",
      "           9415.8       0.6618971\n",
      "           9416.2       0.6735417\n",
      "         9416.601       0.6537372\n",
      "            9417.       0.6688919\n",
      "           9417.4       0.6890374\n",
      "           9417.8       0.6248202\n",
      "           9418.2       0.6490981\n",
      "         9418.601       0.6707956\n",
      "            9419.       0.6755926\n",
      "           9419.4       0.6875088\n",
      "           9419.8        0.673344\n",
      "           9420.2       0.6719753\n",
      "         9420.601       0.6807651\n",
      "            9421.       0.6648616\n",
      "           9421.4       0.6509469\n",
      "           9421.8       0.6479205\n",
      "           9422.2       0.6737821\n",
      "         9422.601       0.6793352\n",
      "            9423.       0.6850218\n",
      "           9423.4        0.681665\n",
      "           9423.8       0.6806363\n",
      "           9424.2       0.6763955\n",
      "         9424.601       0.6554601\n",
      "            9425.        0.656612\n",
      "           9425.4        0.679903\n",
      "           9425.8       0.6821923\n",
      "           9426.2       0.6401749\n",
      "         9426.601       0.5992585\n",
      "            9427.       0.6564136\n",
      "           9427.4       0.6794317\n",
      "           9427.8       0.6305261\n",
      "           9428.2       0.6560903\n",
      "         9428.601       0.7023849\n",
      "            9429.       0.7237422\n",
      "           9429.4       0.7113758\n",
      "           9429.8       0.6820496\n",
      "           9430.2       0.6313658\n",
      "         9430.601        0.595431\n",
      "            9431.       0.6212664\n",
      "           9431.4       0.6449547\n",
      "           9431.8         0.64474\n",
      "           9432.2       0.6313605\n",
      "         9432.601       0.5995381\n",
      "            9433.       0.6164823\n",
      "           9433.4       0.6409418\n",
      "           9433.8       0.6632742\n",
      "           9434.2        0.690567\n",
      "         9434.601       0.6805635\n",
      "            9435.       0.6572894\n",
      "           9435.4       0.6773463\n",
      "           9435.8       0.6954197\n",
      "           9436.2       0.6716641\n",
      "         9436.601       0.6650951\n",
      "            9437.       0.6782326\n",
      "           9437.4       0.6647946\n",
      "           9437.8       0.6373242\n",
      "           9438.2       0.6233978\n",
      "         9438.601       0.6271726\n",
      "            9439.       0.6260266\n",
      "           9439.4       0.6519226\n",
      "           9439.8       0.6765811\n",
      "           9440.2       0.6522759\n",
      "         9440.601       0.6007736\n",
      "            9441.        0.595089\n",
      "           9441.4        0.699235\n",
      "           9441.8        0.715864\n",
      "           9442.2       0.6971138\n",
      "         9442.601       0.7093893\n",
      "            9443.       0.6835654\n",
      "           9443.4       0.6338783\n",
      "           9443.8       0.6130722\n",
      "           9444.2       0.6363756\n",
      "         9444.601       0.6558314\n",
      "            9445.       0.6488012\n",
      "           9445.4       0.6457338\n",
      "           9445.8       0.6484822\n",
      "           9446.2       0.6520269\n",
      "         9446.601       0.6309673\n",
      "            9447.       0.6000338\n",
      "           9447.4       0.6172001\n",
      "           9447.8       0.6568979\n",
      "           9448.2       0.6896624\n",
      "         9448.601       0.6965675\n",
      "            9449.       0.6694197\n",
      "           9449.4       0.6667629\n",
      "           9449.8       0.6897784\n",
      "           9450.2       0.6838087\n",
      "         9450.601       0.6790466\n",
      "            9451.       0.6796861\n",
      "           9451.4       0.6817576\n",
      "           9451.8       0.6758556\n",
      "           9452.2       0.6454942\n",
      "         9452.601       0.6615915\n",
      "            9453.       0.6811246\n",
      "           9453.4       0.6893006\n",
      "           9453.8       0.6479111\n",
      "           9454.2       0.6928239\n",
      "         9454.601       0.7404575\n",
      "            9455.       0.6510027\n",
      "           9455.4         0.66418\n",
      "           9455.8       0.6669205\n",
      "           9456.2       0.6695144\n",
      "         9456.601       0.7072967\n",
      "            9457.       0.7275232\n",
      "           9457.4       0.7084412\n",
      "           9457.8       0.6984619\n",
      "           9458.2       0.6973535\n",
      "         9458.601       0.6973315\n",
      "            9459.       0.6936393\n",
      "           9459.4        0.651791\n",
      "           9459.8       0.6072087\n",
      "           9460.2       0.6316571\n",
      "         9460.601       0.6544248\n",
      "            9461.       0.6414469\n",
      "           9461.4       0.6547601\n",
      "           9461.8       0.6677119\n",
      "           9462.2        0.669195\n",
      "         9462.601       0.6625814\n",
      "            9463.        0.662059\n",
      "           9463.4         0.64693\n",
      "           9463.8       0.6435764\n",
      "           9464.2       0.6675007\n",
      "         9464.601       0.6748681\n",
      "            9465.          0.6801\n",
      "           9465.4       0.6997571\n",
      "           9465.8       0.7048227\n",
      "           9466.2       0.6910755\n",
      "         9466.601       0.6861589\n",
      "            9467.       0.6952686\n",
      "           9467.4       0.6991348\n",
      "           9467.8       0.7326682\n",
      "           9468.2       0.7150941\n",
      "         9468.601        0.684506\n",
      "            9469.       0.6798533\n",
      "\n",
      "K\n"
     ]
    }
   ],
   "source": [
    "file_dir = '../ppxf/indo_us/TEXT/249.txt'\n",
    "open(file_dir)\n",
    "\n",
    "\n",
    "file = open(file_dir, 'r') \n",
    "stel = file.read()\n",
    "print(stel)\n",
    "print(stel[1])\n",
    "\n",
    "\n",
    "#for i in range(len(stel)):\n",
    "#    if stel[i] !='#':\n",
    "#        print(stel[i])\n",
    "#    else:\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C6MiZwJqiWjbWn8UOC/s9IJs/NNLswOlz6Bv0oL61V07bKwkVbd6aPAs/8GAkaSJHhRAo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\nHMLCnWEYJg75/3nMYVvn1WM3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dcc6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMPLE  =                    T / Fits standard                                  BITPIX  =                  -32 / Bits per pixel                                 NAXIS   =                    1 / Number of axes                                 NAXIS1  =                15011 / Axis length                                    EXTEND  =                    T / File may contain extensions                    ORIGIN  = 'NOAO-IRAF FITS Image Kernel December 2001' / FITS file originator    DATE    = '2004-02-10T22:35:52' / Date FITS file was generated                  IRAF-TLM= '22:35:52 (10/02/2004)' / Time of last modification                   OBJECT  = '249     '           / Name of the object observed                    OBSERVAT= 'KPNO              '  /  observatory                                  TELESCOP= 'kpcdf             '  /  telescope name                               DETECTOR= 'f3kb              '  /  detector name                                APERTURE= '200 microns       '  /  aperture                                     CTYPE1  = 'LINEAR  '                                                            CD1_1   =                  0.4                                                  OBJNAME = 'HD 249  '                                                            OBJRA   = '00:07:22.56'                                                         OBJDEC  = '+26:27:02.2'                                                         OBJBMAG =                 8.33                                                  OBJVMAG =                 7.33                                                  SPTYPE  = 'K1IV    '                                                            OBJRV   =                 12.6                                                  HDNAME  = 'HD 249  '                                                            BDNAME  = 'BD+25 5073'                                                          HIPNAME = 'HIP 607 '                                                            SAONAME = 'SAO 73755'                                                           HJD     =      2452562.8117904                                                  VHELIO  =    -4.82757688627674                                                  VLSR    =   -0.611122200078474                                                  VSUN    = '   20.    18.    30.  1900.'                                         CMIN1   =             3452.802                                                  CMAX1   =             4777.408                                                  PICKTYPE= 'K1IV    '                                                            CRVAL1  =                3465.                                                  CRPIX1  =                   1.                                                  CDELT1  =                  0.4                                                  NCOMBINE=                    5                                                  COVERAGE= '3465.0-9469.0'                                                       GAPS    = '6789.4-6810.6'                                                       TEFF    =                 4723 / 2001MNRAS.326..981C (Cenarro)                  LOGG    =                 2.40 / 2001MNRAS.326..981C (Cenarro)                  FEH     =                -0.32 / 2001MNRAS.326..981C (Cenarro)                  PHYSREF = '2001MNRAS.326..981C (Cenarro)' / Reference                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           END                                                                             \n"
     ]
    }
   ],
   "source": [
    "'''trying the fits file way'''\n",
    "file_dir = '../ppxf/indo_us/IRAF/249.fits'\n",
    "stel=pyfits.open(file_dir)\n",
    "stel_head=stel[0].header\n",
    "intensity=stel[0].data\n",
    "\n",
    "xs=np.linspace(0,len(intensity)-1, len(intensity))\n",
    "wavelengths=[stel_head['CRVAL1']+stel_head['CDELT1']*x for x in xs]\n",
    "plt.plot(wavelengths, intensity, lw=0.25)\n",
    "plt.show()\n",
    "\n",
    "print(stel[0].header)\n",
    "\n",
    "\n",
    "\n",
    "#for i in range(len(stel)):\n",
    "#    if stel[i] !='#':\n",
    "#        print(stel[i])\n",
    "#    else:\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}