remake_plots_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-colorblind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_directory(path):\n",
    "    fits_list = []\n",
    "    \n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            if file.endswith(\".fits.gz\"):\n",
    "                element = str(path) + str(file)\n",
    "                fits_list.append(element)\n",
    "    return fits_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is pointing to where my files are located on my computer\n",
    "fits_list = append_directory('/raid5/homes/sshamsi/sas/mangawork/manga/sandbox/galaxyzoo3d/v1_0_0/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Verbose\n"
     ]
    },
    {
     "ename": "BrokenPipeError",
     "evalue": "[Errno 32] Broken pipe",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self=<marvin.core.exceptions.MarvinSentry object>, version='2.4.0')\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 self.client.context.merge({'user': {'name': os.getlogin(),\n\u001b[0m        \u001b[0;36mself.client.context.merge\u001b[0m \u001b[0;34m= <bound method Context.merge of <Context: {}>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mos.getlogin.join\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mos.uname\u001b[0m \u001b[0;34m= <built-in function uname>\u001b[0m\n\u001b[1;32m     48\u001b[0m                                                     'system': '_'.join(os.uname())}})\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 25] Inappropriate ioctl for device",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-a67bc25e139d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xmode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Verbose'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mspiral_galaxy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSpiralGalaxy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mspiral_galaxy\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mSpiralGalaxy\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/spirals/GZ3D_spiral_analysis/spiral_galaxy.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmarvin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMaps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mmarvin.tools.maps\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mMaps\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmulti_dot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# time - 483 ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmarvin\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextern\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmarvin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMarvinUserWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMarvinError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mmarvin.core.exceptions\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mMarvinUserWarning\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mMarvinError\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneral\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneral\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgetDbMachine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_yaml_loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbrain\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0mms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMarvinSentry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarvin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mms\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mMarvinSentry\u001b[0m \u001b[0;34m= <class 'marvin.core.exceptions.MarvinSentry'>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mversion\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mmarvin.__version__\u001b[0m \u001b[0;34m= '2.4.0'\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self=<marvin.core.exceptions.MarvinSentry object>, version='2.4.0')\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mee\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m                 warnings.warn('cannot initiate Sentry error reporting: {0}.'.format(str(ee)),\n\u001b[0;32m---> 51\u001b[0;31m                               UserWarning)\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mUserWarning\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mee\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/warnings.py\u001b[0m in \u001b[0;36m_showwarnmsg\u001b[0;34m(msg=<warnings.WarningMessage object>)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m             sw(msg.message, msg.category, msg.filename, msg.lineno,\n\u001b[0;32m--> 110\u001b[0;31m                msg.file, msg.line)\n\u001b[0m        \u001b[0;36mmsg.file\u001b[0m \u001b[0;34m= None\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mmsg.line\u001b[0m \u001b[0;34m= None\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0m_showwarnmsg_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36m_showwarning\u001b[0;34m(message=UserWarning('cannot initiate Sentry error reporting: [Errno 25] Inappropriate ioctl for device.'), category=<class 'UserWarning'>, filename='/home/sshamsi/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py', lineno=51, file=None, line=None)\u001b[0m\n\u001b[1;32m   2094\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNullHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2096\u001b[0;31m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mlogger.warning\u001b[0m \u001b[0;34m= <bound method factory.<locals>.warning of <SDSSLogger py.warnings (WARNING)>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36ms\u001b[0m \u001b[0;34m= '/home/sshamsi/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py:51: UserWarning: cannot initiate Sentry error reporting: [Errno 25] Inappropriate ioctl for device.\\n  UserWarning)\\n'\u001b[0m\n\u001b[1;32m   2097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2098\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcaptureWarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36mwarning\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, msg='%s', *args=('/home/sshamsi/miniconda3/envs/marvin/lib/python3...] Inappropriate ioctl for device.\\n  UserWarning)\\n',), **kwargs={})\u001b[0m\n\u001b[1;32m   1380\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1381\u001b[0m         \"\"\"\n\u001b[0;32m-> 1382\u001b[0;31m         \u001b[0mLog\u001b[0m \u001b[0;34m'msg % args'\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mseverity\u001b[0m \u001b[0;34m'WARNING'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mLog\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mseverity\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\n\u001b[1;32m   1383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m         \u001b[0mTo\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0mexception\u001b[0m \u001b[0minformation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mwith\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36mwarning\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, msg='%s', *args=('/home/sshamsi/miniconda3/envs/marvin/lib/python3...] Inappropriate ioctl for device.\\n  UserWarning)\\n',), **kwargs={})\u001b[0m\n\u001b[1;32m   1388\u001b[0m         \"\"\"\n\u001b[1;32m   1389\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misEnabledFor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWARNING\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1390\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWARNING\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mself._log\u001b[0m \u001b[0;34m= <bound method Logger._log of <SDSSLogger py.warnings (WARNING)>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mglobal\u001b[0m \u001b[0;36mWARNING\u001b[0m \u001b[0;34m= 30\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mmsg\u001b[0m \u001b[0;34m= '%s'\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36margs\u001b[0m \u001b[0;34m= ('/home/sshamsi/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/core/exceptions.py:51: UserWarning: cannot initiate Sentry error reporting: [Errno 25] Inappropriate ioctl for device.\\n  UserWarning)\\n',)\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mkwargs\u001b[0m \u001b[0;34m= {}\u001b[0m\n\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36m_log\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, level=30, msg='%s', args=('/home/sshamsi/miniconda3/envs/marvin/lib/python3...] Inappropriate ioctl for device.\\n  UserWarning)\\n',), exc_info=None, extra=None, stack_info=False)\u001b[0m\n\u001b[1;32m   1512\u001b[0m         record = self.makeRecord(self.name, level, fn, lno, msg, args,\n\u001b[1;32m   1513\u001b[0m                                  exc_info, func, extra, sinfo)\n\u001b[0;32m-> 1514\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mself.handle\u001b[0m \u001b[0;34m= <bound method SDSSLogger.handle of <SDSSLogger py.warnings (WARNING)>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mrecord\u001b[0m \u001b[0;34m= <LogRecord: py.warnings, 30, /home/sshamsi/miniconda3/envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/sdsstools/logger.py\u001b[0m in \u001b[0;36mhandle\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, record=<LogRecord: py.warnings, 30, /home/sshamsi/minic...envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">)\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetMessage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36msuper.handle\u001b[0m \u001b[0;34m= \u001b[0;36mundefined\u001b[0m\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mrecord\u001b[0m \u001b[0;34m= <LogRecord: py.warnings, 30, /home/sshamsi/miniconda3/envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_last_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36mhandle\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, record=<LogRecord: py.warnings, 30, /home/sshamsi/minic...envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">)\u001b[0m\n\u001b[1;32m   1522\u001b[0m         \"\"\"\n\u001b[1;32m   1523\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisabled\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1524\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallHandlers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mself.callHandlers\u001b[0m \u001b[0;34m= <bound method Logger.callHandlers of <SDSSLogger py.warnings (WARNING)>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mrecord\u001b[0m \u001b[0;34m= <LogRecord: py.warnings, 30, /home/sshamsi/miniconda3/envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">\u001b[0m\n\u001b[1;32m   1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0maddHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36mcallHandlers\u001b[0;34m(self=<SDSSLogger py.warnings (WARNING)>, record=<LogRecord: py.warnings, 30, /home/sshamsi/minic...envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">)\u001b[0m\n\u001b[1;32m   1584\u001b[0m                 \u001b[0mfound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfound\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1585\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevelno\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mhdlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1586\u001b[0;31m                     \u001b[0mhdlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mhdlr.handle\u001b[0m \u001b[0;34m= <bound method Handler.handle of <StreamHandler stderr (INFO)>>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mrecord\u001b[0m \u001b[0;34m= <LogRecord: py.warnings, 30, /home/sshamsi/miniconda3/envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">\u001b[0m\n\u001b[1;32m   1587\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpropagate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m                 \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m    \u001b[0;31m#break out\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/logging/__init__.py\u001b[0m in \u001b[0;36mhandle\u001b[0;34m(self=<StreamHandler stderr (INFO)>, record=<LogRecord: py.warnings, 30, /home/sshamsi/minic...envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">)\u001b[0m\n\u001b[1;32m    892\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mself.emit\u001b[0m \u001b[0;34m= <function colored_formatter at 0x7fc2fdc16440>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mrecord\u001b[0m \u001b[0;34m= <LogRecord: py.warnings, 30, /home/sshamsi/miniconda3/envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">\u001b[0m\n\u001b[1;32m    895\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/sdsstools/logger.py\u001b[0m in \u001b[0;36mcolored_formatter\u001b[0;34m(record=<LogRecord: py.warnings, 30, /home/sshamsi/minic...envs/marvin/lib/python3.7/warnings.py, 110, \"%s\">)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__stdout__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{}{}\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__stdout__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36msys.__stdout__.flush\u001b[0m \u001b[0;34m= <built-in method flush of _io.TextIOWrapper object at 0x7fc32ecd0e50>\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe"
     ]
    }
   ],
   "source": [
    "%xmode Verbose\n",
    "\n",
    "from spiral_galaxy import SpiralGalaxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_usable_galaxies(fits_list):\n",
    "    usable_galaxy_list = []\n",
    "    load_failure = []\n",
    "    \n",
    "    for path in fits_list:\n",
    "        try:\n",
    "            try:\n",
    "                gal = SpiralGalaxy(path)\n",
    "                \n",
    "            except:\n",
    "                gal = SpiralGalaxy(path)\n",
    "                \n",
    "        except Exception as e:\n",
    "            load_failure.append((path.split('/')[-1].split('_')[0], e))\n",
    "            continue\n",
    "            \n",
    "        if gal.check_usability(threshold=5, pix_percentage=1.0):\n",
    "            usable_galaxy_list.append(gal)\n",
    "            \n",
    "    return usable_galaxy_list, load_failure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = np.load('usable_path_list.npy')\n",
    "\n",
    "usable_path_list = []\n",
    "\n",
    "for path in paths:\n",
    "    path_parts = path.split('/')\n",
    "    \n",
    "    path_parts[1] = ''\n",
    "    path_parts[2] = 'home'\n",
    "    \n",
    "    path = '/'.join(path_parts)\n",
    "    \n",
    "    usable_path_list.append(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sshamsi/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/utils/general/general.py:936: MarvinUserWarning: drpall file not found. Downloading it.\n",
      "  warnings.warn('{0} file not found. Downloading it.'.format(name), MarvinUserWarning)\n"
     ]
    },
    {
     "ename": "MarvinMissingDependency",
     "evalue": "sdss_access is not installed.\nYou can submit this error to Marvin GitHub Issues (https://github.com/sdss/marvin/issues/new).\nFill out a subject and some text describing the error that just occurred.\nIf able, copy and paste the full traceback information into the issue as well.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMarvinMissingDependency\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/spirals/GZ3D_spiral_analysis/spiral_galaxy.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file_path)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmangaid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MANGAID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmangaid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhamap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memline_gflux_ha_6564\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhbmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memline_gflux_hb_4862\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/maps.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input, filename, mangaid, plateifu, mode, data, release, drpall, download, nsa_source, bintype, template, template_kin)\u001b[0m\n\u001b[1;32m     98\u001b[0m                                   \u001b[0mmangaid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmangaid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplateifu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplateifu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m                                   \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrelease\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m                                   drpall=drpall, download=download)\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0mNSAMixIn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnsa_source\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnsa_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input, filename, mangaid, plateifu, mode, data, release, drpall, download)\u001b[0m\n\u001b[1;32m    116\u001b[0m         MMAMixIn.__init__(self, input=input, filename=filename, mangaid=mangaid,\n\u001b[1;32m    117\u001b[0m                           \u001b[0mplateifu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplateifu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrelease\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m                           download=download)\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0mCacheMixIn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/mixins/mma.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input, filename, mangaid, plateifu, mode, data, release, drpall, download, ignore_db)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'local'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_doLocal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'remote'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_doRemote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/mixins/mma.py\u001b[0m in \u001b[0;36m_doLocal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_origin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'db'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m                 \u001b[0mfullpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getFullPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfullpath\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfullpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/maps.py\u001b[0m in \u001b[0;36m_getFullPath\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0mpath_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'path_type'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMaps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getFullPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/marvin/lib/python3.7/site-packages/marvin/tools/mixins/mma.py\u001b[0m in \u001b[0;36m_getFullPath\u001b[0;34m(self, pathType, url, **pathParams)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mMarvinMissingDependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sdss_access is not installed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_release\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMarvinMissingDependency\u001b[0m: sdss_access is not installed.\nYou can submit this error to Marvin GitHub Issues (https://github.com/sdss/marvin/issues/new).\nFill out a subject and some text describing the error that just occurred.\nIf able, copy and paste the full traceback information into the issue as well."
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "i = 0\n",
    "\n",
    "gal_objs = []\n",
    "\n",
    "for path in usable_path_list:\n",
    "    gal = SpiralGalaxy(path)\n",
    "    gal_objs.append(gal)\n",
    "    \n",
    "    print(i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n",
      "100\n",
      "101\n",
      "102\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "108\n",
      "109\n",
      "110\n",
      "111\n",
      "112\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "117\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "123\n",
      "124\n",
      "125\n",
      "126\n",
      "127\n",
      "128\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "145\n",
      "146\n",
      "147\n",
      "148\n",
      "149\n",
      "150\n",
      "151\n",
      "152\n",
      "153\n",
      "154\n",
      "CPU times: user 56.8 s, sys: 1.4 s, total: 58.2 s\n",
      "Wall time: 58.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "i = 0\n",
    "\n",
    "for gal in gal_objs:\n",
    "    gal.form_global_df()\n",
    "    \n",
    "    print(i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in gal_objs:\n",
    "    gal.df['Mass'] = gal.mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in gal_objs:\n",
    "    gal.df['$\\lg{Mass}$'] = gal.lgmass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in gal_objs:\n",
    "    map_ha = gal.maps.getMap('emline_gflux', channel='ha_6564')\n",
    "    map_hb = gal.maps.getMap('emline_gflux', channel='hb_4862')\n",
    "\n",
    "    ha_snr = map_ha.snr.flatten()\n",
    "    hb_snr = map_hb.snr.flatten()\n",
    "\n",
    "    gal.df['S/N $H_{\\\\alpha}$'] = ha_snr\n",
    "    gal.df['S/N $H_{\\\\beta}$'] = hb_snr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make total SFR Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/raid5/homes/sshamsi/galaxy_zoo/GZ3D_spiral_analysis/newer_Galaxy.py:134: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  sfr = (self.delta * (ha_flux**3.36) * (hb_flux**-2.36)) / self.spax_area\n"
     ]
    }
   ],
   "source": [
    "tot_sfrs = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df = gal.df\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df[(df['$r/r_e$'] <= 1) & (df['$r/r_e$'] >= 0.1)]\n",
    "    df = df[(df.Comp == 0) & (df.AGN == 0) & (df.Seyfert == 0) & (df.Liner == 0)]\n",
    "    df = df[(df['S/N $H_{\\\\alpha}$'] >= 3) & (df['S/N $H_{\\\\beta}$'] >= 3)]\n",
    "    \n",
    "    sfr, stdv = gal.get_sfr(df.index, avg=False)\n",
    "    \n",
    "    tot_sfrs.append(sfr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot_sfr_spirals = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df = gal.df\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df[(df['$r/r_e$'] <= 1) & (df['$r/r_e$'] >= 0.1)]\n",
    "    df = df[(df.Comp == 0) & (df.AGN == 0) & (df.Seyfert == 0) & (df.Liner == 0)]\n",
    "    df = df[(df['S/N $H_{\\\\alpha}$'] >= 3) & (df['S/N $H_{\\\\beta}$'] >= 3)]\n",
    "    df = df[df['Spiral Arm'] == 1]\n",
    "    \n",
    "    sfr, stdv = gal.get_sfr(df.index, avg=False)\n",
    "    \n",
    "    tot_sfr_spirals.append(sfr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pc_tot_sfr = []\n",
    "\n",
    "for i in range(len(tot_sfr_spirals)):\n",
    "    pc = tot_sfr_spirals[i] * 100 / tot_sfrs[i]\n",
    "    pc_tot_sfr.append(pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAI4CAYAAADnKmagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XtcVHXCx/HvCGqgCFMNomigtuBlvaaAppmX0ryUaLa2hW7t09aWaI9patllTfOS5ivFx916MsVLrbVe2mzNkChdFSwTW8WXdwFTJPGCooI6zx+tPLFcHHQu/JjP+/Xa16s5vzPnfGc5zXzndM5vLHa73S4AAAAAxqnh6QAAAAAAbgxlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwlK+nA5gsNzff7fu0Wv116lSB2/eLqodjAb/E8YBf4njAL3E8mM9mCyh3jDPzhvH19fF0BFQRHAv4JY4H/BLHA36J46F6o8wDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACGoswDAAAAhqLMAwAAAIaizAMAAACG8vV0AADwZsHTUzyy3xMT7vXIfgEAzsWZeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUL6eDuAM69at07Zt25SRkaE9e/bo/PnzGjhwoGbNmlXuc65cuaKVK1dq9erV2rt3ry5duiSbzabWrVtr9OjRatKkiRtfAQAAAFB51aLML1iwQHv27JG/v79CQkJ08ODBCtc/f/68nn32WW3dulUtWrTQoEGDVLt2beXk5Oi7777T4cOHKfMAAACo8qpFmZ84caJCQkIUFhamtLQ0DR8+vML1X331VW3dulV/+tOfNGzYsFLjRUVFrooKAAAAOE21KPMxMTEOr7tr1y599tln6tevX5lFXpJq1qzprGgAAACAy1SLMl8Zn332mSSpf//+ys/PV3Jyso4fP66goCDFxMQoLCzMwwkBAAAAx3hdmf/hhx8kST/++KN69+6t06dPF49ZLBY9+uijmjRpknx8fDwVEQAAAHCI15X5kydPSpKmT5+u3r17a/To0QoJCdHOnTv12muvafny5br11lsVHx9/3W1Zrf7y9XV/6bfZAty+T1RNHAu4URw71R9/Y/wSx0P15XVl/urVq5Kkpk2bas6cOcVn4Dt37qy5c+cqNjZWH3zwgZ5++mnVqlWrwm2dOlXg8rz/yWYLUG5uvtv3i6qHYwE3g2OneuP9Ab/E8WC+ir6Med2PRtWrV0+S1KNHj1KX0jRv3lyNGjXS+fPnrzu9JQAAAOBpXlfmr80fHxBQ9jeca2X/4sWLbssEAAAA3AivK/NdunSRJO3bt6/UWGFhoY4cOSJJCg0NdWsuAAAAoLK8rszff//9Cg4O1j/+8Q/t3LmzxNj8+fOVn5+v6Oho2Ww2DyUEAAAAHFMtboBNSkpSUlKSJCk3N1eStGPHDk2YMEGSZLVaNX78eEmSv7+/pk+frqefflq//e1vdf/996t+/fpKT0/Xd999p9tuu02TJ0/2zAsBAAAAKqFalPmMjAytWrWqxLKsrCxlZWVJ+vmSmWtlXpLuvvtuffLJJ/qf//kfbd68WefOndPtt9+uYcOG6dlnn1X9+vXdmh8AAAC4ERa73W73dAhTeWKaJ6aXwjUcC9VD8PQUj+z3xIR7PbJfuAfvD/gljgfzMTUlAAAAUA1R5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAEP5ejqAM6xbt07btm1TRkaG9uzZo/Pnz2vgwIGaNWuWQ89/+eWX9cknn0iS1q9fr7CwMFfGBQAAAJyiWpT5BQsWaM+ePfL391dISIgOHjzo8HOTk5P1ySefyN/fXwUFBS5MCQAAADhXtSjzEydOVEhIiMLCwpSWlqbhw4c79Ly8vDy98sor6tevn3766SelpaW5OCkAAADgPNXimvmYmBiFh4fLYrFU6nmvvPKKJOnVV191RSwAAADAparFmfkbsXLlSiUlJWn+/PmyWq2ejgMAAABUWrU4M19ZR48e1dSpU/Xggw+qd+/eno4DAAAA3BCvOzN/9epVTZgwQf7+/po0adJNbctq9Zevr4+TkjnOZgtw+z5RNXEsOIflhb97OoLbBU9P8di+7bMHemzfnuCNx5e3/Y1NwOdF9eV1ZX7RokVKS0vTu+++q8DAwJva1qlT7p/9xmYLUG5uvtv3i6qHYwGm4rit/vgbVy18Xpivoi9jXnWZzaFDhzRnzhwNHjxY3bt393QcAAAA4KZ41Zn5AwcOqLCwUCtXrtTKlSvLXOf++++XJM2fP5/r6QEAAFCleVWZDw0N1cMPP1zm2Ndff63c3Fz17dtXdevWVWhoqJvTAQAAAJXjVWW+RYsWmjp1apljcXFxys3N1ZgxYxQWFubmZAAAAEDlVYsyn5SUpKSkJElSbm6uJGnHjh2aMGGCJMlqtWr8+PEeywcAAAC4QrUo8xkZGVq1alWJZVlZWcrKypL08+U1lHkAAABUN9WizMfHxys+Pv6mtrFkyRInpQEAAADcw6umpgQAAACqE8o8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoX08HAADAHYKnp3g6AgA4HWfmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ/l6OoAzrFu3Ttu2bVNGRob27Nmj8+fPa+DAgZo1a1apdQ8fPqz169dr06ZNOnLkiE6ePKl69eqpbdu2GjFihGJiYjzwCgAAAIDKqxZlfsGCBdqzZ4/8/f0VEhKigwcPlrvuO++8o88//1x33nmnunfvrsDAQB06dEjJyclKTk7Wyy+/rOHDh7sxPQAAAHBjqkWZnzhxokJCQhQWFqa0tLQKy3i3bt301FNPqWXLliWWp6Wl6cknn9TMmTPVt29fBQcHuzo2AAAAcFOqxTXzMTExCg8Pl8Viue66gwcPLlXkJSkqKkpRUVEqKirS999/74qYAAAAgFNVizLvLL6+P/+HCh8fHw8nAQAAAK6PMv9vR48e1ZYtW+Tn56dOnTp5Og4AAABwXdXimvmbVVhYqLFjx6qwsFDjxo1TYGCgQ8+zWv3l6+v+s/g2W4Db94mqiWMBJuK4rf74G1c9/E2qL68v81euXNG4ceO0fft29evXT7///e8dfu6pUwUuTFY2my1Aubn5bt8vqh6OBZiK47b6429ctfB5Yb6Kvox59WU214r8unXr9MADD+itt95y6CZaAAAAoCrw2jJfVFSkMWPGaO3atRowYIBmz55dfAMsAAAAYAKvbK+FhYV6/vnntWHDBg0aNEjTpk1TjRpe+70GAAAAhvK6BltYWKiRI0dqw4YNevjhhynyAAAAMFa1ODOflJSkpKQkSVJubq4kaceOHZowYYIkyWq1avz48ZKk1157TV9//bWsVqvq16+v+fPnl9peVFSUoqOj3ZQeAAAAuDHVosxnZGRo1apVJZZlZWUpKytLkhQaGlpc5rOzsyVJp06dKrPIS9LIkSMp8wAAAKjyqkWZj4+PV3x8vEPrLlmyxMVpAAAAAPfgYnEAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFCUeQAAAMBQlHkAAADAUJR5AAAAwFAO/2jUlStXVFhYKD8/vxLLt2zZog0bNsjPz0+PPPKIGjdu7PSQAAAAAEpz+Mz8jBkzFBUVpfz8/OJla9eu1ZNPPqmlS5fqvffe09ChQ3Xs2DGXBAUAAABQksNl/ttvv1V0dLQCAgKKlyUkJKhevXqaMWOGxo0bp/z8fH3wwQcuCQoAAACgJIcvszl27Jjat29f/DgrK0uHDh3Sc889p4ceekiStG3bNm3cuNH5KQEAAACU4vCZ+XPnzqlu3brFj7/77jtZLBZ169ateNmvfvUrHT9+3LkJAQAAAJTJ4TJvs9mUnZ1d/HjLli265ZZb1KpVq+JlBQUF8vV1+GQ/AAAAgJvgcPNu166dkpOT9dVXX6l27dr64osvFBMTo5o1axavk52drfr167skKAAAAICSHC7zTz/9tDZs2KBnn31WklSjRg398Y9/LB6/dOmSvv32W/Xp08f5KQEAAACU4nCZj4yM1IoVK7R69WpJ0gMPPKA2bdoUj+/evVsxMTEaMGCA81MCAAAAKKVSF7hHRkZq/PjxZY61b99e8+fPd0ooAAAAANd3w3ernjlzRgUFBWrQoIEz8wAAqrng6SmejgAA1YbDs9lI0vnz5zV9+nTdfffdiomJUa9evYrH0tPT9dRTT2nXrl1ODwkAAACgNIfLfH5+voYNG6ZFixYpODhYzZo1k91uLx6PiIjQt99+q88++8wlQQEAAACU5HCZX7Bggfbt26fp06dr1apV6tu3b4lxPz8/RUVFaevWrU4PCQAAAKA0h8v8l19+qa5du2rQoEHlrtOwYUPl5OQ4JRgAAACAijlc5o8fP67IyMgK1/H391d+fv5NhwIAAABwfQ6X+Tp16igvL6/CdbKzs2W1Wm86FAAAAIDrc7jMt27dWl999ZXOnTtX5viJEyf0zTff6K677nJaOAAAAADlc7jMDx8+XKdPn9Yf/vAHHThwoMTYgQMHNHr0aF26dElxcXFODwkAAACgNId/NKpbt24aOXKkEhISNGDAAPn6/vzU6OhonT17Vna7XWPHjlWHDh1cFhYAAADA/6vUL8COHDlSHTt21JIlS5Senq7Tp0/LYrGoe/fuGjFihDp37uyqnAAAAAD+Q6XKvCTFxMQoJibGFVkAAAAAVILD18wDAAAAqFoo8wAAAIChyr3Mpnnz5qpRo4bWrl2rJk2aqHnz5rJYLNfdoMVi0e7du50aEgAAAEBp5Zb5Tp06SZL8/PxKPAYAAABQNZRb5pcsWVLhYwAAAACexTXzAAAAgKEcLvOHDh1yaL2kpKQbDgMAAADAcQ6X+SFDhmjNmjXljhcVFWnKlCmKj493SjAAAAAAFXO4zN9yyy2aMGGCJk6cqIsXL5YYy8zM1G9+8xstXbpUrVu3dnpIAAAAAKU5XObXrFmjTp06adWqVRoyZIj27t0rSfrss88UGxurjIwMPfnkk1q+fLnLwgIAAAD4f+XOZvOfbDabFi9erPnz52vBggV65JFHFB0drW+++UZWq1Vz5szRPffc48qsZVq3bp22bdumjIwM7dmzR+fPn9fAgQM1a9ascp+zfft2LViwQOnp6bp48aLCwsI0ZMgQxcXFycfHx43pAQAAgBvncJmXfv5BqJEjR8pqteqNN94oLvKrVq1S/fr1XZWxQgsWLNCePXvk7++vkJAQHTx4sML1k5KSNGrUKNWuXVsPPPCAAgMD9dVXX2natGnavn275s6d66bkAAAAwM2p9NSUK1as0FtvvSUfHx+FhoYqLy9P48aNU25urivyXdfEiRP1xRdfaPv27Xr99dcrXPfcuXN65ZVXVKNGDSUmJurNN9/U+PHjtWbNGrVv315ffPGF1q5d657gAAAAwE1yuMyfO3dO//3f/63XXntNgYGBWrx4sT7//HM98sgjSktL00MPPaSvv/7alVnLFBMTo/DwcFksluuuu27dOuXl5al///4lbtStXbu2Ro8eLUn68MMPXZYVAAAAcCaHy3xsbKz+8Y9/6J577tHq1avVsWNH1apVS5MnT9bbb7+tS5cu6ZlnntG0adNcmfembN26VZLUrVu3UmOdOnWSn5+fvv/+exUWFro7GgAAAFBpDpf5Y8eOafz48frLX/4iq9VaYqxfv35avXq1WrVqpcTERKeHdJZrP3wVHh5easzX11eNGjXS5cuXlZWV5eZkAAAAQOU5fAPs8uXL1aZNm3LHGzdurI8++kizZ892SjBXOHfunCQpICCgzPG6detKks6ePevQ9qxWf/n6un/2G5ut7PzwPhwLAKoi3puqHv4m1ZfDZb6iIl+8MV9fjR8//qYCmeTUqQK379NmC1Bubr7b94uqh2MBQFXFe1PVwueF+Sr6Mlbp2WxMdu3Me35+2Qf0tTP39erVc1smAAAA4EZVap55Sdq5c6c2bdqknJycMm8UtVgsevPNN50SztmaNGmif/3rXzp8+LB+/etflxi7fPmysrOz5evrq8aNG3soIQAAAOA4h8u83W7XhAkT9Omnn8put8tischutxePX3tclct8TEyM/v73v2vjxo0aMGBAibFt27bpwoUL6tSpk2rVquWhhAAAAIDjHL7MZunSpVqzZo0eeugh/e1vf5PdbteIESP00UcfacyYMapTp4769++vpKQkV+a9KX379pXVatXatWv1ww8/FC+/dOmS3nnnHUnSo48+6ql4AAAAQKU4fGZ+1apVatKkiaZPn168LCAgQO3atVO7du3UtWtXPfLII+rSpYuGDBnikrBlSUpKKv4Cce1XaHfs2KEJEyZIkqxWa/FNuXXr1tWUKVM0atQoDR8+XP369VNgYKCSk5N16NAh9enTR/369XNbdgAAAOBmOFzmDx06pEGDBpVYduXKleJ/btmypXr06KHly5e7tcxnZGRo1apVJZZlZWUVzxUfGhpaYoad3r17a8mSJfrzn/+s9evX69KlSwoLC9PEiRMVFxfn0C/JAgAAAFVBpW6A/eX87H5+fjpz5kyJ8bCwMG3atMk5yRwUHx+v+Pj4Sj3nrrvu0nvvveeiRAAAAIB7OHzNfHBwsHJycoofN27cWLt27SqxzpEjR+Tv7++8dAAAAADK5XCZb9OmTYnyfs8992jnzp2aP3++9u3bp2XLlmnDhg1q27atS4ICAAAAKMnhMt+nTx9duXKl+Fr0//qv/1LDhg01b948Pfjgg3rjjTcUEBCgF154wWVhAQAAAPw/h6+Z7927t3r37l38OCgoSKtXr9aKFSuUmZmp0NBQDRo0SMHBwS4JCgAAAKCkSv8C7C8FBATo97//vbOyAAAAAKgEhy+zAQAAAFC1lHtmftu2bTe80U6dOt3wcwEAAAA4ptwyfzM/oJSRkXHDgQAAAAA4ptwy/9xzz/FrqAAAAEAVVm6Zr+yvqgIAAABwL26ABQAAAAxFmQcAAAAMVal55u12u9atW6dNmzYpJydHhYWFpdaxWCxavHix0wICAAAAKJvDZb6wsFBPPfWU0tLSZLfbZbFYZLfbi8evPeamWQAAAMA9HL7M5t1331Vqaqr++Mc/auvWrbLb7Ro5cqQ2btyo2bNnq0GDBurfv7/+9a9/uTIvAAAAgH9zuMx/8cUXatmypUaNGqWgoKDi5TabTf3799fixYv11VdfcYkNAAAA4CYOl/nMzEx16NCh+LHFYtHly5eLHzdu3Fj33nuvVq1a5dyEAAAAAMrkcJn39fVV7dq1ix/XqVNHeXl5JdZp2LChsrKynJcOAAAAQLkcvgE2JCREOTk5xY/Dw8O1Y8eOEutkZGQoMDDQeekAuF3w9BSP7fvEhHs9tm8AAEzk8Jn5Dh066Pvvvy9+3Lt3b+3du1cvv/yyUlJSNGPGDG3evFlRUVEuCQoAAACgJIfPzA8YMEDHjh1Tdna2GjVqpBEjRmjDhg3629/+ppUrV8putyssLExjx451ZV4AAAAA/+ZwmY+OjlZ0dHTxYz8/P3344YfasGGDjhw5okaNGqlHjx7y8/NzSVAAAAAAJVXqF2BLPdnXV3369HFWFgAAAACV4PA18wAAAACqluuemd+zZ4/OnTunDh06qEaNn7t/UlKSNmzYUGrdtm3batiwYc5PCQAAAKCUCst8Tk6OHnnkEfXo0UMdO3YsXr5nz54yfxxq7dq16tGjh+rXr+/8pAAAAABKqLDMr1mzRkVFRRo1alSpMYvFooULFxY/PnPmjJ5//nmtWbNGf/jDH5yfFAAAAEAJFZb5zZs3q3nz5mrWrFmZ4507dy7xuH379tq8eTNlHgAAAHCDCm+A3b9/v1q3bu3wxn71q19p//79Nx0KAAAAwPVVeGb+9OnTuvXWW0stL+9XXq1Wq86cOeOcZAAAAAAqVGGZr1Wrli5cuFBqeVRUVJmF/uLFi/L1vamp6wEAAAA4qMLLbGw2m/bt2+fwxvbt26fg4OCbDgUAAADg+ios8+3atdO2bduUk5Nz3Q3l5OQoLS1N7du3d1o4AAAAAOWrsMzHxsaqqKhIL774oi5evFjuepcuXdL48eN15coVDRo0yOkhAQAAAJRWYZmPiYlRr169lJqaqtjYWH3yySfKyspSYWGhCgsLlZ2drY8//lixsbFKTU1Vr169FBMT467sAAAAgFe77t2qM2bMUHx8vLZs2aJXXnmlzHXsdrs6d+6sGTNmOD0gAAAAgLJdt8zXrVtX77//vj799FOtWLFCO3fu1OXLl39+sq+v2rZtq6FDh+rBBx9UjRoVnugHAAAA4EQOzSNZo0YNDRo0SIMGDdKVK1eK55IPDAyUj4+PSwMCAAAAKFulJ4X38fEp84ekAAAAALgX18UAAAAAhvLan2tNSUlRYmKi9u/fr9OnT8tms6lVq1Z64oknmCsfAAAARvDKMv/WW2/pf//3fxUUFKTevXvLarUqMzNTycnJWr9+vWbMmKGHHnrI0zEBAACACnldmc/NzdXChQt1++2369NPP9Vtt91WPLZ161aNGDFCc+fOpcwDAACgyvO6a+Z//PFHXb16VW3atClR5KWffySrTp06ysvL81A6AAAAwHFeV+bDwsJUs2ZN/fDDD6VK+7Zt23T+/Hl16dLFQ+kAAAAAx3ndZTZBQUEaO3aspk+frv79+6t3794KCgoqvmb+7rvv1uTJkz0dEwAAALgui91ut1fmCcnJycrIyFBOTo6KiopKb9Bi0Ztvvum0gK6SlJSkl156qfgHsKSfz9rHx8dr4MCBDm3j8uUr8vXlR7PgGpYX/u7pCABwQ+yzHfscBXDzHD4zf/ToUT3zzDPav3+/Kur/JpT59957T3PmzFFcXJwef/xx3X777Tp48KDefvttjR07VhkZGXrxxRevu51TpwrckLYkmy1Aubn5bt8vAACO4nOqaqE7mM9mCyh3zOEyP2XKFO3bt09DhgzRoEGDVL9+ffn4mHdWOjU1VbNmzdJ9992niRMnFi9v1aqVEhIS1KdPH33wwQd69NFH1bhxYw8mBQAAACrmcJnfunWrunbtqqlTp7oyj8ulpKRIkqKjo0uN+fn5qU2bNvryyy+1e/duyjwAAACqNIdns6lZs6YiIiJcmcUtCgsLJanc6SevLa9Zs6bbMgEAAAA3wuEy36FDB+3bt8+VWdzirrvukiStWLFCOTk5Jca+/vprbd++XbVr11b79u09EQ8AAABwmMOX2YwaNUqPPfaY1q5dq/79+7syk0v17dtXH3/8sTZv3qwHHnhA9913n26//XYdOHBAKSkpstvteuGFF2S1Wj0dFQAAAKhQpaamTE9P1x/+8AdFRESoVatWqlu3bukNWix67rnnnBrS2YqKirRs2TJ9/vnn2r9/vy5evKjAwEC1adNGcXFx6tq1q0Pb8cSd4dyR7j2Cp6d4OgIA3JATE+71dAT8At3BfBXNZuNwmc/Pz9fIkSOVmppa4XoWi0UZGRmVS2goyjxciTIPwFSU+aqF7mA+p0xN+eabbyo1NVVdunTRgw8+aOzUlAAAAEB14XCZT0lJUfv27bVw4UJX5gEAAADgIIdns7l48SIzvAAAAABViMNlvmXLlsrOznZlFgAAAACV4HCZf/bZZ5WcnKxvv/3WlXkAAAAAOMjha+Zzc3PVo0cP/e53v9OAAQPUqlUrBQSUfWftoEGDnBYQAAAAQNkcnpqyefPmslgs+uXqFoulxDp2u52pKV2M6aW8B1NTAjAVU1NWLXQH8zllaspp06Y5JQwAAAAA53C4zMfGxroyBwAAAIBKcvgGWAAAAABVC2UeAAAAMJTDl9n06tXLofUsFouSkpJuOBAAAAAAxzhc5sub9CY/P1/5+T/fIR0cHCxfX4c3CQAAAOAmONy8k5OTyx07cuSIpkyZogsXLuj99993SjAAAAAAFXPKNfNhYWFKSEhQTk6OEhISnLFJAAAAANfhtBtga9eurS5dumjt2rXO2iQAAACACjh1NhtfX1/l5uY6c5MAAAAAyuG0Mp+Xl6cvv/xSDRo0cNYmAQAAAFTA4Rtgy7sW/sqVKzp+/Lg2bNig/Px8jRkzxmnhAAAAAJTvpsv8NXXr1tUf//hHPfXUUzcdCgAAAMD1OVzmExMTy1xeo0YN1atXT02bNmWOeQAAAMCNHG7fUVFRrswBAAAAoJKcOpsJ7apQAAAgAElEQVQNAAAAAPep8Mz81atXb2ijNWrwHQEAAABwtQrLfKtWrSq9QYvFot27d99wIAAAAACOqbDMV2bO+IKCAp0+ffqmAwEAAABwTIVlPjk5+bobKCoq0tKlS/XnP/9ZkhQaGuqcZAAAAAAqdFNzSf7jH//Q22+/rezsbAUEBGjcuHGKi4tzVjYAAAAAFbihMr99+3bNnDlT6enp8vHxUVxcnJ577jkFBgY6Ox8AAACAclSqzGdmZmrWrFn68ssvZbfb1adPH73wwgu64447XJUPAAAAQDkcKvOnT59WQkKC/vrXv6qoqEjt2rXThAkT1K5dO1fnAwAAAFCOCst8YWGhFi9erPfee09nz57VHXfcoRdeeEF9+vRxVz4AAAAA5aiwzPft21fHjh1TYGCgXnrpJT322GPy8fFxVzYAAAAAFaiwzP/444+yWCyy2+1auHChFi5ceN0NWiwWffXVV04LCAAAAKBs171m3m6368yZMzpz5ow78gAAAABwUIVlfs+ePe7KAQAAAKCSang6AAAAAIAbQ5kHAAAADEWZBwAAAAxVqV+ArW62bNmipUuXaseOHTpz5oyCgoIUGRmp4cOHq3v37p6OBwAAAFTIa8v8zJkz9f777yskJEQ9e/aU1WpVXl6edu3apdTUVMo8AAAAqjyvLPMrVqzQ+++/r9jYWE2ePFm1atUqMV5UVOShZAAAAIDjvO6a+cLCQs2ZM0cNGzYss8hLUs2aNT2QDAAAAKgcrzsz/89//lN5eXkaMWKEatSooZSUFO3du1e1a9dWmzZt1L59e09HBAAAABzidWX+hx9+kCTVrl1bsbGx2rt3b4nxTp06ae7cubr11ls9EQ8AAABwmNeV+ZMnT0qS3n//fTVr1kzLli1TixYtlJ2drZkzZ2rTpk0aPXq0lixZct1tWa3+8vX1cXXkUmy2ALfvEwAARwVPT/HYvu2zB3ps31UZ3aH68royb7fbJUk+Pj5asGCBGjVqJEmKjIxUQkKC+vbtq7S0NH3//ffXveTm1KkCl+f9TzZbgHJz892+XwAATMBnZGl0B/NV9GXM626ADQj4+f+Mli1bFhf5a/z8/NS1a1dJ0s6dO92eDQAAAKgMryvzTZo0kfT/pf4/1atXT5J06dIlt2UCAAAAboTXlfnOnTvLYrHowIEDunr1aqnxffv2SVKps/YAAABAVeN1ZT40NFQ9evTQjz/+qMTExBJjmzZt0qZNm1SvXj1169bNQwkBAAAAx3jdDbCS9NprrykjI0PTpk1TSkqKWrRooaNHjyopKUk+Pj6aMmVKuZfhAAAAAFWFV5b5kJAQrVy5UvPnz1dycrK+/fZb1alTRz169NDTTz+tNm3aeDoiAAAAcF0W+7W5GlFpnpjmiemlvIcn52kGAFOdmHCvpyNUOXQH8zE1JQAAAFANUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABDUeYBAAAAQ1HmAQAAAENR5gEAAABD+Xo6ACoveHqKx/Z9YsK9Hts3AADX46nPSD4f4SmcmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZ/7c1a9YoMjJSkZGR+vjjjz0dBwAAALguyrykY8eO6Y033pC/v7+nowAAAAAO8/oyb7fbNXHiRAUFBWnYsGGejgMAAAA4zOvLfGJiorZu3app06ZxZh4AAABG8eoyf+DAAc2ePVvDhw9Xp06dPB0HAAAAqBSvLfOXL1/WuHHj1KBBA40ZM8bTcQAAAIBK8/V0AE+ZP3++MjIytHz5ct1yyy03tA2r1V++vj5OTla12WwBHtmv5YW/e2S/kmSfPdBj+wYAmMFTn4+Oqur5cOO8ssynp6frL3/5i5544gm1b9/+hrdz6lSBE1M5xtP/Mubm5nt0/57gja8ZAFA5VfmzwmYLqNL5cH0V9T+vu8zm8uXLevHFFxUeHq7nn3/e03EAAACAG+Z1Z+YLCgp0+PBhSVLr1q3LXGfSpEmaNGmShg8frpdfftmN6QAAAADHeV2Zr1Wrlh5++OEyx3bv3q3du3frrrvuUpMmTW7qEhwAAADA1byuzN9yyy2aOnVqmWPz5s3T7t27FRsbq6FDh7o5GQAAAFA5XnfNPAAAAFBdUOYBAAAAQ3ndZTYViY+PV3x8vKdjAAAAAA7hzDwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKF9PB/CEU6dOKSkpSSkpKdq7d69ycnJUs2ZNRUREaPDgwRoyZIhq1OB7DgAAAKo2ryzz69at0+uvvy6bzabo6Gg1bNhQP/30k7788ktNmjRJGzdu1DvvvCOLxeLpqAAAAEC5vLLMh4eHa8GCBbr33ntLnIEfM2aMhg4dqi+++ELr169Xnz59PJgSAAAAqJhXXkvSuXNn9ezZs9SlNDabTcOGDZMkpaWleSIaAAAA4DCvLPMV8fX9+T9W+Pj4eDgJAAAAUDHK/C9cvnxZa9askSR169bNw2kAAACAinnlNfPlmT17tvbu3avu3bs7VOatVn/5+nrXGfzg6SmejuB2NluApyMAAKo4T35WWF74u0f2a5890CP7lbzzNZeHMv9viYmJWrhwoZo2baqZM2c69JxTpwpcnKo0iqX75ebmezoCAKCK88bPCl6z+1TU/7jMRtLSpUs1depU3XnnnUpMTFRQUJCnIwEAAADX5fVn5hctWqRp06YpIiJCixYt0m233ebpSAAAAIBDvLrMv/vuu5o9e7ZatGihhQsX6tZbb/V0JAAAAMBhXlvm58+fr7lz56pVq1ZauHAhl9YAAADAOF5Z5letWqW5c+fKx8dHHTt21JIlS0qtExoaqsGDB3sgHQAAAOAYryzz2dnZkqQrV65o8eLFZa4TFRVFmQcAAECV5pVlPj4+XvHx8Z6OAQAAANwUpqYEAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAMRZkHAAAADEWZBwAAAAxFmQcAAAAM5evpAAAAAKYLnp7i6Qhu542vuSrizDwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoyjwAAABgKMo8AAAAYCjKPAAAAGAoX08H8JTjx4/rnXfe0caNG3X69GkFBwerV69eGjlypAIDAz0dDwAAALguryzzmZmZGjZsmE6ePKlevXqpadOm2rlzpxITE7Vx40Z9+OGHslqtno4JAAAAVMgry/yf/vQnnTx5UpMmTVJcXFzx8mnTpmnRokWaM2eOJk+e7MGEAAAAwPV53TXzmZmZ2rRpk0JDQ/XYY4+VGIuPj5e/v78+/fRTFRQUeCghAAAA4BivK/OpqamSpK5du6pGjZIvv27duurQoYMuXLig9PR0T8QDAAAAHOZ1Zf7gwYOSpPDw8DLHw8LCJEmHDh1yVyQAAADghnjdNfPnzp2TJAUEBJQ5fm15fn7+dbdls5W9DVezzx7okf3Cvfg7AwCA6/G6M/MAAABAdeF1Zb5u3bqSyj/zfm15eWfuAQAAgKrC68p806ZNJUmHDx8uc/zIkSOSpCZNmrgrEgAAAHBDvK7MR0dHS5I2bdqkq1evlhg7d+6ctm/fLj8/P7Vt29YT8QAAAACHeV2Zv+OOO9S1a1cdPXpUy5YtKzE2b948FRQU6MEHH5S/v7+HEgIAAACOsdjtdrunQ7hbZmamhg0bppMnT6pXr15q1qyZ0tPTlZqaqvDwcH300UeyWq2ejgkAAABUyCvLvCQdO3ZMc+fO1caNG3X69GnZbDb17t1bI0eOVGBgoKfjAQAAANfltWXeJMePH9c777xT/MUjODhYvXr14otHNXTq1CklJSUpJSVFe/fuVU5OjmrWrKmIiAgNHjxYQ4YMKfXLxZK0fft2LViwQOnp6bp48aLCwsI0ZMgQxcXFycfHxwOvBK6yZs0avfjii5KkKVOmaOjQoaXW+eqrr7Rw4ULt3r1bV69e1Z133qnf/va3io2NdXdcuMiWLVu0dOlS7dixQ2fOnFFQUJAiIyM1fPhwde/evcS6vD9UbykpKUpMTNT+/fuLT062atVKTzzxhNq3b19qfY6H6sfn9ddff93TIVC+zMxMDR06VNu3b1fnzp3Vo0cPXbhwQZ9//rmSkpLUv39/+fn5eTomnGT16tV69dVXlZ+fr7vuukvdunVTgwYNtG3bNq1bt0779+9X3759ZbFYip+TlJSkp556SidOnFCfPn3UqVMnHThwQH//+9+1f/9+PfDAAx58RXCmY8eO6ZlnnpGvr6+KiorUs2dPtWrVqsQ6S5cu1Ysvvqjz589rwIABat26tXbu3KnVq1eroKBAXbt29VB6OMvMmTP12muvqaCgQPfee6+6dOmi+vXrKzMzUxaLRXfffXfxurw/VG9vvfWWJk+erPz8fPXo0UMxMTGqXbu2kpOT9fHHH6tx48Zq3rx58focD9WUHVXak08+aY+IiLAnJiaWWP7mm2/aIyIi7K+88oqHksEVNm/ebN+wYYP9ypUrJZafOHHC3r17d3tERIR93bp1xcvz8/PtMTEx9latWtl37txZvPzixYv23/zmN/aIiAj7Z5995rb8cJ2rV6/aR4wYYe/Vq5d9+vTp9oiICPuKFStKrJOVlWX/9a9/bY+KirJnZWUVLz99+rS9d+/e9oiICPv27dvdHR1O9Ne//tUeERFhHz9+vP3SpUulxgsLC4v/mfeH6u3EiRP25s2b27t06WL/6aefSoxt2bLFHhERYe/Zs2fxMo6H6svrZrMxSWZmpjZt2qTQ0FA99thjJcbi4+Pl7++vTz/9VAUFBR5KCGfr3LmzevbsWepSGpvNpmHDhkmS0tLSipevW7dOeXl56t+/v1q3bl28vHbt2ho9erQk6cMPP3RDcrhaYmKitm7dqmnTppU729bf/vY3FRYW6rHHHlOjRo2KlwcGBurpp5+WJH300UduyQvnKyws1Jw5c9SwYUNNnjxZtWrVKrVOzZo1i/+Z94fq7ccff9TVq1fVpk0b3XbbbSXGYmJiVKdOHeXl5RUv43iovijzVVhqaqokqWvXrqXKXd26ddWhQwdduHBB6enpnogHN/P19ZWkEtc0bt26VZLUrVu3Uut36tRJfn5++v7771VYWOiekHCJAwcOaPbs2Ro+fLg6depU7noVHQ/33HNPiXVgnn/+85/Ky8vTfffdpxo1aiglJUXvvvuuFi9erO+//77U+rw/VG9hYWGqWbOmfvjhhxKlXZK2bdum8+fPq0uXLsXLOB6qL19PB0D5Dh48KEkKDw8vczwsLEybNm3SoUOH1LlzZzcmg7tdvnxZa9askVTyjfjQoUOSyj5GfH191ahRI+3bt09ZWVlq1qyZW7LCuS5fvqxx48apQYMGGjNmTIXrVnQ8BAcHy9/fX8ePH9eFCxe418ZAP/zwg6Sfz6TGxsZq7969JcY7deqkuXPn6tZbb5XE+0N1FxQUpLFjx2r69Onq37+/evfuraCgIGVmZio5OVl33323Jk+eXLw+x0P1RZmvws6dOydJCggIKHP82vL8/Hy3ZYJnzJ49W3v37lX37t1LlPnrHSN169aVJJ09e9b1IeES8+fPV0ZGhpYvX65bbrmlwnUdOR4KCgqUn59PmTfQyZMnJUnvv/++mjVrpmXLlqlFixbKzs7WzJkztWnTJo0ePVpLliyRxPuDN/jd736nRo0a6aWXXtKKFSuKl4eFhSk2NrbE5TccD9UXl9kAVVxiYqIWLlyopk2baubMmZ6OAzdKT0/XX/7yl3KnmIN3sf97JmkfHx8tWLBAHTt2VJ06dRQZGamEhASFhIQoLS2tzEtuUD299957GjVqlGJjY5WUlKQdO3Zo5cqVaty4scaOHctnhpegzFdh174ll3fm/dry8r5lw3xLly7V1KlTdeeddyoxMVFBQUElxq93jFw7E1OvXj3XBoXTXb58WS+++KLCw8P1/PPPO/QcR48H3jPMdO3v1rJlyxI3OEuSn59f8bSjO3fulMT7Q3WXmpqqWbNmqWfPnpo4caIaN24sPz8/tWrVSgkJCapfv74++OADZWVlSeJ4qM4o81VY06ZNJUmHDx8uc/zIkSOSpCZNmrgrEtxo0aJFeuONNxQREaHExETZbLZS61z725d1jFy+fFnZ2dny9fVV48aNXR0XTlZQUKDDhw/rwIEDat26tSIjI4v/l5CQIEmaNGmSIiMjNXXqVEkVHw8nTpxQQUGBQkJCuMTGUNf+vuV9GbtWwi5dulRifd4fqqeUlBRJUnR0dKkxPz8/tWnTRlevXtXu3bslcTxUZ5T5Kuzav6CbNm3S1atXS4ydO3dO27dvl5+fn9q2beuJeHChd999V9OmTVOLFi20ePHiUtOOXRMTEyNJ2rhxY6mxbdu26cKFC2rfvn2ZU9ihaqtVq5YefvjhMv/XsmVLSdJdd92lhx9+uPgSnIqOh2+++abEOjBP586dZbFYdODAgVKfCZK0b98+SSo+a8/7Q/V2bdaZ/5zJ5ppry69NV8rxUI15eqJ7VIwfjfI+CQkJ9oiICHtsbKz91KlTFa6bn59vj46O5kdAvMzcuXPL/NGozMxMfjSqmnvmmWfsERER9g8++KDE8o0bN9ojIyPtHTt2tJ89e9Zut/P+UN2tXbvWHhERYe/SpYv9+PHjJcZSUlLskZGR9tatW9vz8vLsdjvHQ3Vmsdv/fUcNqqTMzEwNGzZMJ0+eVK9evdSsWTOlp6crNTVV4eHh+uijj2S1Wj0dE06yatUqTZgwQT4+Pnr88cfL/M/poaGhGjx4cPHjpKQkjRo1SrVr11a/fv0UGBio5ORkHTp0SH369NE777wji8XizpcBF5s3b54SEhI0ZcoUDR06tMTYkiVLNGXKFAUFBalfv36qWbOmvvjiCx0/flxPPvmkxo8f76HUcIbjx49r2LBhOnbsmDp37qwWLVro6NGjSkpKksVi0dtvv60+ffoUr8/7Q/V19epV/f73v9fmzZtVp04d3Xfffbr99tt14MABpaSkyG6366WXXtKIESOKn8PxUD1R5g1w7NgxzZ07Vxs3btTp06dls9nUu3dvjRw5UoGBgZ6OBye6VtIqEhUVVTz13DXfffed/vznP2vHjh26dOmSwsLCNGTIEMXFxZX4kSlUDxWVeUlKTk7WwoULtWvXLtntdjVr1kyPP/64YmNjPZAWzpaXl6f58+crOTlZubm5qlOnjjp27Kinn35abdq0KbU+7w/VV1FRkZYtW6bPP/9c+/fv18WLFxUYGKg2bdooLi6u+KboX+J4qH4o8wAAAIChuAEWAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4AAAAwFGUeAAAAMBRlHgAAADAUZR4A3Cg1NVWRkZGaN2+ep6O4TM+ePdWzZ09Px7hpcXFxioyMdPl+5s2bp8jISKWmprp8XwCqH19PBwDgPT799FO99957ys7OVqNGjfTMM8+of//+pdb76aef1L9/fw0ePFjjx4+v1D4mTJigVatWacOGDWrUqNFNZ543b54SEhKUmJio6Ojom97ejTh79qw++OADJScnKzMzU0VFRbJarWrQoIE6dOigBx98UC1btiyVuTyhoaFKTk4ufvyfhbVGjRoKCAhQZGSkYmNjFRsbK4vF4vwXdhMuXbqkpUuXat26dTp48KAuXbqkoKAgBQcHq127durbt6+ioqI8HdPlLly4oG7duik/P18DBgzQ7NmzPR0JgJtR5gG4xYYNGzRu3Di1bdtWw4YN0zfffKMxY8aoTp06uvfee0usO3nyZAUFBWn06NGeCVuF5OTk6NFHH9XRo0fVuHFjDRw4UFarVWfOnNGuXbu0ePFi1a5du0SZvyYqKqrMQhsQEFDmvkaOHClJunz5so4cOaKkpCSlpaXpX//6l1599VWHMy9atMjhdW/E+fPnFRcXp127dslms+n++++XzWZTQUGB9uzZoxUrVujs2bM3XeZnzJihCxcuOCm1a3z++efKz8+XxWLR+vXrderUKVmtVk/HAuBGlHkAbvHhhx8qPDxcy5cvl6+vr5599ln17NlTy5YtK1Hm161bp/Xr12vp0qW65ZZbPBe4ipg7d66OHj2qIUOGaOrUqaXOkJ84cUK5ubllPjcqKkrx8fEO7+s/1/3uu+/0+OOPa/ny5XriiSfUuHFjh7Zzxx13OLzPG7F48WLt2rVLXbt21YIFC1SrVq0S42fOnNGBAwduej8NGza86W242v+1d/dhNd//A8efNbkpYiahub7uLjnuprKtVFNndHGaSWRyV20jGsZs2JW7jWJuhtUld0WM6HAiicgYRY4OuTc3ZTNmG1uEUej3R9c5VzknTrL87Ho9rqt/Pjfv9+fzqevq9X5/Xq/3JzExEUtLSz788ENWrFjB5s2bCQkJedGXJYSoQpIzL4SoElevXqVt27ZUq1Yyh1CnTh2aNWvG1atXDcfk5+czY8YMBg4cSOfOnSvch6OjI0lJSQC8++67ODo64ujoaJS/fenSJSZMmICnpyft27fHw8ODCRMmcOnSpTLHKZVKQ7rK0KFDDe2VTkvJy8tj3rx5+Pv74+rqSvv27fH29mbKlClcu3atwvfwuKNHjwIl+dumUl0aNmxIu3btKt2PKS4uLrRo0YLi4mJOnTpl9nmmcuY1Gg2Ojo5oNBqysrIYMmQITk5OODs7M3z48AoF3/pnEhgYaBTIA9StWxdnZ+cy20rnpSclJeHn50fHjh1xc3Pjyy+/NDkgMpUzX7rm4fjx4wwfPpy33noLR0dHfv31VwCysrKYMmUKKpUKZ2dnOnbsyHvvvUd0dDT37983+z6f5ty5c+Tk5ODm5sawYcOwsrJCrVaXe7yjoyNDhgzhzz//JDw8HE9PTxQKBRqNBihJUXN0dOTy5ct8//33qFQqOnTogFKpZMmSJRQXFwOwfft2+vXrR6dOnXBzc+Prr7/m3r17Rv1lZ2czYsQI3nnnHdq3b4+7uzv9+/d/YgqYEKLiZGZeCFElGjduzJkzZ3j06BGWlpbcvn2bS5cu0alTJ8MxM2fOpEaNGowfP/6Z+hg1ahTp6emcPXuWoUOHYmtrC5RNKzl+/DghISHcuXMHpVJJq1atyM3NJTk5md27d7Ny5Uo6duwIlATwu3fvRqvV0qdPHxwcHIz63LVrF+vXr+ftt9/G2dkZKysrzp8/j1qtZs+ePWzatAl7e/tnuh+AevXqASWDBoVC8cztVJZ+EFZZe/fuZffu3Xh6ejJgwAAuXrzIjz/+yIkTJ9i2bRv169d/ahuln0lFrVq1iszMTFQqFZ6enuh0OjQaDVqtFrVabVb/ADk5OSxduhQXFxf69u3L33//jZWVFQDLly8nLy8PJycnunbtSmFhIUeOHCEqKopDhw6xatUqXnnllQpf++MSExMB6NOnD/Xq1UOpVJKWlkZ2dna5g+H8/Hw++OADrK2t8fHxwcLCgtdee63MMXPmzEGr1eLt7Y27uzs//PADCxYsoKioiLp16zJ//ny6detG586dyczMZO3atTx8+JCvvvrK0Ma+ffsIDQ2ldu3aKJVK7O3tyc/PJzc3l3Xr1hlSuoQQlSfBvBCiSgQGBvLJJ58wcOBAnJyc2L9/P7du3SIwMBAoCfK2bt3KypUrsbGxeaY+Ro8ezZUrVzh79ixBQUFGBbDFxcVMnDiR27dvM3fuXN5//33DvtTUVMaNG8eECRNITU3F0tKS4OBgCgoKDMG8qQLY3r17ExwcbDRDnJGRwbBhw1i8eHGZIKeiVCoVOp2OyZMnc/LkSTw8PFAoFGblRWu1WpOr5vTp08es4uDDhw+Tm5uLlZWVYYBTWenp6cTGxuLm5mbYNn/+fJYtW8amTZsYNmzYU9tQqVQkJyezaNEirly5gpeXF23btqVhw4ZPPXf//v0kJiaWqTGIjIwkPj6eefPmERkZadZ9ZGRk8NVXXzFgwACjfdOnT+f11183epOycOFCYmJiSEtLQ6VSmdVPee7fv09ycjJ16tShe/fuQE9fKhcAAApsSURBVMnvNS0tjQ0bNpQbzJ87d47evXsTGRlZ7gDt1KlTJCcnGwaho0ePpnv37sTGxlKzZk00Gg0tW7YEoLCwED8/PzZt2sSYMWMMAwO1Ws2jR49Ys2YNbdq0KdP+X3/9Val7F0KUJWk2Qogq0a1bN2bPnk1BQQEJCQlYWFgwZ84clEolBQUFTJ06lX79+tGlSxfS0tLo0aMHCoUCpVLJhg0bnss1HDlyhNzcXJycnMoE8lASILq4uJCXl4dOpzO7TXt7e5OpHh4eHrRq1YqMjIxKXfOgQYMIDQ3lwYMHxMbGEhISgqurK0qlksmTJ3P27Nlyz9VqtURHRxv9XLlyxeTxUVFRREVFsWDBAsaOHUtISIhhAGROoGwOlUpVJpAH6N+/PwAnTpwwqw1vb2/Cw8OpWbMmCQkJhIaG4unpiYeHB+PHj+fw4cPlnvv4yj9QEqzWqVOHlJQUCgsLzboGhUJhMpAHaNq0qcmUqODgYKBkQFFZ27dv5+bNm6hUKkNtiaenJ3Z2dqSlpXHz5k2T51lZWTFx4sQnvmkZOXJkmbdJtra2KJVK/vnnHwIDAw2BPED16tVRqVQUFRWZTJWqUaOG0TZz334IIcwjM/NCiCqjX+bwcbNnzwZKcnZPnTrFp59+io+PD9OmTWPnzp1MnToVe3t7o1VvKur06dMA5S4x6erqik6n4/Tp07z55ptmtVlcXExycjJJSUmcPXuWW7du8fDhQ8N+ferFs7KwsOCzzz7j448/JiMjg5ycHE6fPs2xY8dQq9VoNBqmT59uCIhLGzVqVIUKYB/PZbawsCAiIoK+fftW6h5Ka9++vdG2xo0bA5QbgJoydOhQAgICyMzM5OjRo5w5c4ajR4+SkpJCSkoKYWFhJldDKm91H4VCgVar5eLFi2alMz3pTcXdu3dZvXo1u3bt4tKlS9y5c8eQbw4lRcuVpU+x8ff3N2yrVq0avXr1Ii4uji1btjB06FCj8xwcHIzSah7XoUMHo236wZyp+gx94F+6RqRXr17s3LmT/v3707NnT1xdXXF2dqZRo0Zm3J0QoiIkmBdCvFAHDhxg48aNLFmyhDp16hAXF4eNjQ2zZ8/G2toaV1dXMjMzWb58eaWD+YKCAoByZ5nt7OzKHGeOWbNmER8fj52dHR4eHtjb2xtmSpOSksqdBa8oW1tbVCqVIT3j7t27LFu2jJiYGGbMmIFSqaRBgwaV6uOnn34ytJ2Tk0N4eDjTpk2jSZMmRrPpz0pfx1Cafpb40aNHFWqrVq1adOvWjW7dugElKR9qtZqIiAgWL16Mj4+PUWBeXiCrf3bm/u7Le9ZFRUUEBQVx/PhxWrdujUqlon79+oZ7jI6ONnv2vzwXL15Ep9PRokWLMjUnUDJgjouLQ61Wmwzm9X/jT1K7dm2jbaUL1x+nz/9/8OCBYZuPjw9Lly4lLi4OjUZjeLvWrl07xo8fj7u7+1OvQwhhHgnmhRAvzJ07d5g8eTK9evXC29sbgNzcXJo3b461tTVQMjusUCjIysqqdH/6QKS8pRz1200FM6bcuHGDNWvW0Lp1axISEozOS0lJqcTVPpm1tTVjx45Fq9Wi0+k4cuQIPj4+z63tLl26EBMTg7+/P5MmTWLHjh3UqlXrubT/b6levTqDBg0iJyeH5ORksrKyjIL5GzdumDz3+vXrQPlr8D+uvI9o7d69m+PHj+Pv78+sWbPK7Pvjjz+ey0ou+sA4Nze33C/Unjt3jiNHjhit6lOVH//y8vLCy8uLu3fvcuzYMfbu3WtIi9q8eTOtWrWqsmsR4r9MgnkhxAszf/587t+/T3h4eJntj89cVmQ5P0vLklIgU7O8+sBOq9WaPPfQoUNA2VSCJ7V3+fJlHj16hLu7u1Egf+3aNcNShf8mfbFw6TSO56VNmzYEBASwfv16Vq1axciRI597H/+GJz0TrVaLn59fmW0FBQWcOXOGGjVqlMkHfxa//PILgKEotbQn5fKbq7CwkC1btmBpaVnul3mvXbtGRkYGarXaKJh/EaytrXFzc8PNzQ1bW1u+++479u3bJ8G8EM+JFMAKIV6I7Oxs1q1bx5QpU8qszNKyZUsuXLjA5cuXgZJAS6fTmf2PX79sYen16/VcXFxo3rw5Op2OHTt2lNm3Y8cOsrOzadasGS4uLma1p1+qUqfTlcmT179xKJ128KxWrFjB+fPnTe7Lzs7m0KFDVKtWzSjd4nkJCwujevXqxMXFVSin/d+UkJBATk6OyX0XL140/G5NreiSnJxsqJ3Qi4qKoqCgAF9fX5PFzBWh/5t4fMB4+fJl5s2bV6m2AdLS0sjPz8fDw4PIyEgiIiKMfhYtWoS1tTXbt2+vUMrY83T48GGTf//6NyPyQTghnh+ZmRdCVLl79+4RHh6Oj48PPXr0KLPvo48+Ytu2bQQFBdG9e3cOHDjArVu3GD58uFltu7m5ERsby5QpU/Dx8cHGxgZbW1sGDx6MhYUF33zzDSEhIYwbN46UlBRatGhBXl4e6enp2NjYMGfOHMNsPJQUxVpaWvLtt99y/vx5Q853WFgYdnZ2+Pr6sm3bNvz8/HB3d6egoIADBw5QvXp1FAoFZ86cqdSz2rp1K3PnzjXkR9vZ2XH37l0uXLhAVlYWxcXFTJo0qVJr2T+Jvb09AwYMYPXq1axYseKZvwHwPO3fv5/p06fj4OCAs7MzjRs3prCwkJ9//pmMjAyKiooYMmSIySJVT09PAgMD6dmzJ3Z2duh0OnQ6HQ4ODnz++eeVvjZvb2/+97//sXLlSs6dO4dCoeC3335jz549eHl5mRwUVoS+8DUgIKDcY2rXrk2PHj3QaDQkJyczaNCgSvX5LGbOnMnvv/+Os7MzDg4OWFlZcerUKbKysnBwcMDX17fKr0mI/yoJ5oUQVW7RokXk5+czdepUo30KhYKoqCgWLlzI2rVradSoETNnzqRr165mte3p6cmkSZNITEwkPj6eoqIiHBwcGDx4MABvvPEGGzduJCYmhoMHD7Jnzx5effVVfH19CQsLo0WLFmXaa9myJbNnzyYuLo5169YZUn7CwsIAiIiIoGnTpqSmprJ27Vrq16+PUqlkzJgxjBkzpjKPCSgpsN27dy9ZWVkcOnSI69evU1xcjL29Pb6+vgQGBj7T13IrIjQ0FLVazZo1awgKCqp0oW1lffHFF3Tu3JkDBw5w7Ngx0tPTefDgAQ0aNMDLy4u+ffsaajAeFxwcTPfu3YmPjyc1NRVra2v8/f0ZN27cU1d5MYe1tbVhzXqtVkt2djZNmzYlLCyMkJAQUlNTn7ntvLw8tFotDRo0KPf+9AICAtBoNCQmJr6QYD40NJT09HROnjzJwYMHsbCwoEmTJowYMYKgoCDq1q1b5dckxH+VRfG/kWgphBBC/D8SFRVFdHQ0q1evLndpUiGEeBlJzrwQQgghhBAvKQnmhRBCCCGEeElJMC+EEEIIIcRLSnLmhRBCCCGEeEnJzLwQQgghhBAvKQnmhRBCCCGEeElJMC+EEEIIIcRLSoJ5IYQQQgghXlISzAshhBBCCPGSkmBeCCGEEEKIl9T/ARr7Av8cWYlcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.hist(pc_tot_sfr, bins = 20)\n",
    "\n",
    "plt.xlabel('% total SFR in Spiral Arms', labelpad=20, size=20)\n",
    "plt.ylabel('Num Galaxies', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "plt.savefig('sfr_in_spirals_hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make average SFR Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_sfrs = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df = gal.df\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df[(df['$r/r_e$'] <= 1) & (df['$r/r_e$'] >= 0.1)]\n",
    "    df = df[(df.Comp == 0) & (df.AGN == 0) & (df.Seyfert == 0) & (df.Liner == 0)]\n",
    "    df = df[(df['S/N $H_{\\\\alpha}$'] >= 3) & (df['S/N $H_{\\\\beta}$'] >= 3)]\n",
    "    \n",
    "    sfr, stdv = gal.get_sfr(df.index, avg=True)\n",
    "    \n",
    "    avg_sfrs.append(sfr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_sfr_spirals = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df = gal.df\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df[(df['$r/r_e$'] <= 1) & (df['$r/r_e$'] >= 0.1)]\n",
    "    df = df[(df.Comp == 0) & (df.AGN == 0) & (df.Seyfert == 0) & (df.Liner == 0)]\n",
    "    df = df[(df['S/N $H_{\\\\alpha}$'] >= 3) & (df['S/N $H_{\\\\beta}$'] >= 3)]\n",
    "    df = df[df['Spiral Arm'] == 1]\n",
    "    \n",
    "    sfr, stdv = gal.get_sfr(df.index, avg=True)\n",
    "    \n",
    "    avg_sfr_spirals.append(sfr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_avg_sfr = []\n",
    "\n",
    "for i in range(len(tot_sfr_spirals)):\n",
    "    pc = avg_sfr_spirals[i] / avg_sfrs[i]\n",
    "    pc_avg_sfr.append(pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.hist(pc_avg_sfr, bins = 25)\n",
    "\n",
    "plt.xlabel(r'Average $\\frac{SFR_{spiral}}{SFR_{total}}$', labelpad=20, size=20)\n",
    "plt.ylabel('Num Galaxies', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "plt.savefig('sfr_in_spirals_hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we will bin our galaxies into mass groups and plot the total % SFR in Spiral Arms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in gal_objs:\n",
    "    gal.df['MaNGA ID'] = gal.mangaid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "mangaid_list = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    mangaid_list.append(gal.mangaid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df_list.append(gal.df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df = pd.concat(df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df = spax_df.dropna()\n",
    "spax_df = spax_df[(spax_df.Comp == 0) & (spax_df.AGN == 0) & (spax_df.Seyfert == 0) & (spax_df.Liner == 0)]\n",
    "spax_df = spax_df[(spax_df['$r/r_e$'] <= 1) & (spax_df['$r/r_e$'] >= 0.1)]\n",
    "spax_df = spax_df[(spax_df['S/N $H_{\\\\alpha}$'] >= 3) & (spax_df['S/N $H_{\\\\beta}$'] >= 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Radius</th>\n",
       "      <th>$H_{\\alpha}$</th>\n",
       "      <th>$\\sigma H_{\\alpha}$</th>\n",
       "      <th>$H_{\\beta}$</th>\n",
       "      <th>$\\sigma H_{\\beta}$</th>\n",
       "      <th>Comp</th>\n",
       "      <th>AGN</th>\n",
       "      <th>Seyfert</th>\n",
       "      <th>Liner</th>\n",
       "      <th>$r/r_e$</th>\n",
       "      <th>Spiral Arm</th>\n",
       "      <th>Mass</th>\n",
       "      <th>$\\lg{Mass}$</th>\n",
       "      <th>S/N $H_{\\alpha}$</th>\n",
       "      <th>S/N $H_{\\beta}$</th>\n",
       "      <th>MaNGA ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>11.997665</td>\n",
       "      <td>0.786094</td>\n",
       "      <td>0.030061</td>\n",
       "      <td>0.171968</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979344</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.150328</td>\n",
       "      <td>5.854283</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>11.793067</td>\n",
       "      <td>0.697728</td>\n",
       "      <td>0.028429</td>\n",
       "      <td>0.149700</td>\n",
       "      <td>0.027367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.962643</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.542965</td>\n",
       "      <td>5.470157</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>11.802633</td>\n",
       "      <td>0.541168</td>\n",
       "      <td>0.029345</td>\n",
       "      <td>0.095138</td>\n",
       "      <td>0.023748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.963424</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>18.441414</td>\n",
       "      <td>4.006091</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>11.957914</td>\n",
       "      <td>0.792545</td>\n",
       "      <td>0.029416</td>\n",
       "      <td>0.133767</td>\n",
       "      <td>0.027642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.976099</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.942406</td>\n",
       "      <td>4.839194</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>11.349176</td>\n",
       "      <td>0.861909</td>\n",
       "      <td>0.028596</td>\n",
       "      <td>0.172492</td>\n",
       "      <td>0.026646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.926409</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.141342</td>\n",
       "      <td>6.473502</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>10.941167</td>\n",
       "      <td>0.899165</td>\n",
       "      <td>0.032200</td>\n",
       "      <td>0.179711</td>\n",
       "      <td>0.028678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.893104</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>27.924206</td>\n",
       "      <td>6.266562</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>10.756754</td>\n",
       "      <td>0.832745</td>\n",
       "      <td>0.029660</td>\n",
       "      <td>0.152309</td>\n",
       "      <td>0.025783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.878051</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>28.076826</td>\n",
       "      <td>5.907425</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>10.807388</td>\n",
       "      <td>0.662477</td>\n",
       "      <td>0.026681</td>\n",
       "      <td>0.087502</td>\n",
       "      <td>0.019808</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.882184</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.829538</td>\n",
       "      <td>4.417412</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>11.733803</td>\n",
       "      <td>0.779113</td>\n",
       "      <td>0.033132</td>\n",
       "      <td>0.120216</td>\n",
       "      <td>0.028803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957805</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>23.515188</td>\n",
       "      <td>4.173698</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>10.920948</td>\n",
       "      <td>0.797542</td>\n",
       "      <td>0.033273</td>\n",
       "      <td>0.139530</td>\n",
       "      <td>0.030301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.891454</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>23.969484</td>\n",
       "      <td>4.604856</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>10.292976</td>\n",
       "      <td>0.882672</td>\n",
       "      <td>0.028746</td>\n",
       "      <td>0.170586</td>\n",
       "      <td>0.027513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.840194</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.705425</td>\n",
       "      <td>6.200134</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>9.885184</td>\n",
       "      <td>0.942623</td>\n",
       "      <td>0.030157</td>\n",
       "      <td>0.179143</td>\n",
       "      <td>0.026481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.806906</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>31.256712</td>\n",
       "      <td>6.765010</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>9.725309</td>\n",
       "      <td>0.920885</td>\n",
       "      <td>0.029873</td>\n",
       "      <td>0.148579</td>\n",
       "      <td>0.024873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.793856</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.826530</td>\n",
       "      <td>5.973423</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>9.825460</td>\n",
       "      <td>0.782165</td>\n",
       "      <td>0.031834</td>\n",
       "      <td>0.096305</td>\n",
       "      <td>0.022553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.802031</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.570114</td>\n",
       "      <td>4.270170</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>11.746320</td>\n",
       "      <td>0.914587</td>\n",
       "      <td>0.030153</td>\n",
       "      <td>0.192304</td>\n",
       "      <td>0.028246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.958827</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.332022</td>\n",
       "      <td>6.808211</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>10.739362</td>\n",
       "      <td>0.879698</td>\n",
       "      <td>0.032883</td>\n",
       "      <td>0.163791</td>\n",
       "      <td>0.025839</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876631</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.752737</td>\n",
       "      <td>6.338825</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>9.888632</td>\n",
       "      <td>0.886313</td>\n",
       "      <td>0.032711</td>\n",
       "      <td>0.167295</td>\n",
       "      <td>0.027289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.807188</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>27.095070</td>\n",
       "      <td>6.130381</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>9.237394</td>\n",
       "      <td>0.917669</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.180422</td>\n",
       "      <td>0.028002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.754029</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.115129</td>\n",
       "      <td>6.443089</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8.829899</td>\n",
       "      <td>0.954733</td>\n",
       "      <td>0.033126</td>\n",
       "      <td>0.185635</td>\n",
       "      <td>0.030887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.720766</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>28.820913</td>\n",
       "      <td>6.010154</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8.700462</td>\n",
       "      <td>0.931854</td>\n",
       "      <td>0.031758</td>\n",
       "      <td>0.171861</td>\n",
       "      <td>0.029542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.710200</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>29.342697</td>\n",
       "      <td>5.817459</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8.861277</td>\n",
       "      <td>0.841962</td>\n",
       "      <td>0.033357</td>\n",
       "      <td>0.118438</td>\n",
       "      <td>0.025746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.723327</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>25.241179</td>\n",
       "      <td>4.600220</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>9.297294</td>\n",
       "      <td>0.667234</td>\n",
       "      <td>0.032965</td>\n",
       "      <td>0.083894</td>\n",
       "      <td>0.022731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758918</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>20.240828</td>\n",
       "      <td>3.690757</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>11.994724</td>\n",
       "      <td>1.014992</td>\n",
       "      <td>0.032660</td>\n",
       "      <td>0.229246</td>\n",
       "      <td>0.029881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979104</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>31.077599</td>\n",
       "      <td>7.671884</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>10.816847</td>\n",
       "      <td>1.037158</td>\n",
       "      <td>0.032406</td>\n",
       "      <td>0.219370</td>\n",
       "      <td>0.028014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.882956</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.005161</td>\n",
       "      <td>7.830865</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>9.758495</td>\n",
       "      <td>0.996838</td>\n",
       "      <td>0.030895</td>\n",
       "      <td>0.195095</td>\n",
       "      <td>0.026537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.796565</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.265450</td>\n",
       "      <td>7.351828</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>8.862591</td>\n",
       "      <td>0.982216</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.193597</td>\n",
       "      <td>0.027458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.723434</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.116337</td>\n",
       "      <td>7.050580</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>8.182669</td>\n",
       "      <td>0.999042</td>\n",
       "      <td>0.032829</td>\n",
       "      <td>0.213680</td>\n",
       "      <td>0.027730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.667934</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.431665</td>\n",
       "      <td>7.705595</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>7.775597</td>\n",
       "      <td>1.023632</td>\n",
       "      <td>0.035172</td>\n",
       "      <td>0.223833</td>\n",
       "      <td>0.031314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.634705</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>29.103226</td>\n",
       "      <td>7.148062</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>7.684854</td>\n",
       "      <td>0.948965</td>\n",
       "      <td>0.035155</td>\n",
       "      <td>0.189639</td>\n",
       "      <td>0.029769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.627298</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.993769</td>\n",
       "      <td>6.370412</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>7.921321</td>\n",
       "      <td>0.848381</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.172392</td>\n",
       "      <td>0.029323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.646600</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.880238</td>\n",
       "      <td>5.879050</td>\n",
       "      <td>1-109308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>17.653207</td>\n",
       "      <td>2.195584</td>\n",
       "      <td>0.041686</td>\n",
       "      <td>0.463635</td>\n",
       "      <td>0.033666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.898223</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>52.669350</td>\n",
       "      <td>13.771405</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987</th>\n",
       "      <td>17.199372</td>\n",
       "      <td>2.537671</td>\n",
       "      <td>0.044783</td>\n",
       "      <td>0.552040</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875131</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>56.666401</td>\n",
       "      <td>16.688622</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>16.809930</td>\n",
       "      <td>3.077898</td>\n",
       "      <td>0.043314</td>\n",
       "      <td>0.690206</td>\n",
       "      <td>0.031990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.855316</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>71.060196</td>\n",
       "      <td>21.575853</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>16.489442</td>\n",
       "      <td>3.588930</td>\n",
       "      <td>0.050621</td>\n",
       "      <td>0.809271</td>\n",
       "      <td>0.036186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.839009</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>70.898148</td>\n",
       "      <td>22.364519</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>16.241991</td>\n",
       "      <td>3.917448</td>\n",
       "      <td>0.050110</td>\n",
       "      <td>0.878297</td>\n",
       "      <td>0.034714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.826418</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>78.177238</td>\n",
       "      <td>25.301003</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>16.070952</td>\n",
       "      <td>3.974187</td>\n",
       "      <td>0.047228</td>\n",
       "      <td>0.873234</td>\n",
       "      <td>0.033072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.817715</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>84.149704</td>\n",
       "      <td>26.404215</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>15.978777</td>\n",
       "      <td>3.738355</td>\n",
       "      <td>0.048255</td>\n",
       "      <td>0.801883</td>\n",
       "      <td>0.034041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.813025</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.470467</td>\n",
       "      <td>23.556086</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>15.966834</td>\n",
       "      <td>3.209375</td>\n",
       "      <td>0.044689</td>\n",
       "      <td>0.684992</td>\n",
       "      <td>0.030846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.812418</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>71.815353</td>\n",
       "      <td>22.206532</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>16.035301</td>\n",
       "      <td>2.539776</td>\n",
       "      <td>0.045811</td>\n",
       "      <td>0.560699</td>\n",
       "      <td>0.034769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.815901</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>55.439896</td>\n",
       "      <td>16.126286</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>19.253894</td>\n",
       "      <td>2.188377</td>\n",
       "      <td>0.043221</td>\n",
       "      <td>0.485161</td>\n",
       "      <td>0.033618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979668</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>50.631763</td>\n",
       "      <td>14.431700</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>18.755371</td>\n",
       "      <td>2.225327</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>0.476171</td>\n",
       "      <td>0.032106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.954303</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>54.025909</td>\n",
       "      <td>14.831236</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>18.313603</td>\n",
       "      <td>2.437381</td>\n",
       "      <td>0.042256</td>\n",
       "      <td>0.537481</td>\n",
       "      <td>0.031211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931825</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>57.681820</td>\n",
       "      <td>17.221140</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>17.932785</td>\n",
       "      <td>2.962603</td>\n",
       "      <td>0.043152</td>\n",
       "      <td>0.684808</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912448</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.654411</td>\n",
       "      <td>21.017509</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>17.616872</td>\n",
       "      <td>3.432966</td>\n",
       "      <td>0.051686</td>\n",
       "      <td>0.818733</td>\n",
       "      <td>0.039460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.896374</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>66.419205</td>\n",
       "      <td>20.748230</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4066</th>\n",
       "      <td>17.369403</td>\n",
       "      <td>3.730800</td>\n",
       "      <td>0.050078</td>\n",
       "      <td>0.886000</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.883783</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>74.500244</td>\n",
       "      <td>23.567005</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4067</th>\n",
       "      <td>17.193336</td>\n",
       "      <td>3.778134</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.868089</td>\n",
       "      <td>0.034127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.874824</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>79.465782</td>\n",
       "      <td>25.436825</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4068</th>\n",
       "      <td>17.090876</td>\n",
       "      <td>3.506205</td>\n",
       "      <td>0.049529</td>\n",
       "      <td>0.803545</td>\n",
       "      <td>0.035151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.869611</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>70.790695</td>\n",
       "      <td>22.859985</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4069</th>\n",
       "      <td>17.063351</td>\n",
       "      <td>3.110394</td>\n",
       "      <td>0.045417</td>\n",
       "      <td>0.690574</td>\n",
       "      <td>0.032122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.868210</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.485344</td>\n",
       "      <td>21.498419</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>17.111120</td>\n",
       "      <td>2.670440</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>0.567858</td>\n",
       "      <td>0.035072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.870641</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>60.181644</td>\n",
       "      <td>16.191349</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4139</th>\n",
       "      <td>19.429425</td>\n",
       "      <td>2.580920</td>\n",
       "      <td>0.048611</td>\n",
       "      <td>0.544338</td>\n",
       "      <td>0.036988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988600</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>53.092915</td>\n",
       "      <td>14.716763</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4140</th>\n",
       "      <td>19.056252</td>\n",
       "      <td>2.987011</td>\n",
       "      <td>0.044976</td>\n",
       "      <td>0.684523</td>\n",
       "      <td>0.032585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.969612</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>66.413887</td>\n",
       "      <td>21.007029</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>18.744373</td>\n",
       "      <td>3.481735</td>\n",
       "      <td>0.045086</td>\n",
       "      <td>0.823852</td>\n",
       "      <td>0.033027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953743</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.223938</td>\n",
       "      <td>24.944590</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>18.496890</td>\n",
       "      <td>3.723005</td>\n",
       "      <td>0.046612</td>\n",
       "      <td>0.881419</td>\n",
       "      <td>0.033707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.941151</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>79.872459</td>\n",
       "      <td>26.149103</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4143</th>\n",
       "      <td>18.316413</td>\n",
       "      <td>3.881938</td>\n",
       "      <td>0.050978</td>\n",
       "      <td>0.929229</td>\n",
       "      <td>0.037766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931968</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>76.149979</td>\n",
       "      <td>24.604906</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>18.204935</td>\n",
       "      <td>3.809508</td>\n",
       "      <td>0.055537</td>\n",
       "      <td>0.892221</td>\n",
       "      <td>0.040023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.926296</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.594658</td>\n",
       "      <td>22.292799</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>18.163727</td>\n",
       "      <td>3.461078</td>\n",
       "      <td>0.049513</td>\n",
       "      <td>0.791914</td>\n",
       "      <td>0.036130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.924199</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>69.902092</td>\n",
       "      <td>21.918278</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4218</th>\n",
       "      <td>19.624439</td>\n",
       "      <td>4.083565</td>\n",
       "      <td>0.052521</td>\n",
       "      <td>1.010040</td>\n",
       "      <td>0.035964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998522</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.750954</td>\n",
       "      <td>28.084816</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4219</th>\n",
       "      <td>19.440063</td>\n",
       "      <td>4.478929</td>\n",
       "      <td>0.049010</td>\n",
       "      <td>1.077462</td>\n",
       "      <td>0.035760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.989141</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>91.388191</td>\n",
       "      <td>30.130371</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4220</th>\n",
       "      <td>19.320615</td>\n",
       "      <td>4.591545</td>\n",
       "      <td>0.050835</td>\n",
       "      <td>1.090058</td>\n",
       "      <td>0.038737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983063</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>90.322166</td>\n",
       "      <td>28.140167</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4221</th>\n",
       "      <td>19.267301</td>\n",
       "      <td>4.309638</td>\n",
       "      <td>0.047929</td>\n",
       "      <td>0.984319</td>\n",
       "      <td>0.034730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980351</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>89.917435</td>\n",
       "      <td>28.341719</td>\n",
       "      <td>1-96075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88036 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Radius  $H_{\\alpha}$  $\\sigma H_{\\alpha}$  $H_{\\beta}$  \\\n",
       "1764  11.997665      0.786094             0.030061     0.171968   \n",
       "1765  11.793067      0.697728             0.028429     0.149700   \n",
       "1766  11.802633      0.541168             0.029345     0.095138   \n",
       "1834  11.957914      0.792545             0.029416     0.133767   \n",
       "1835  11.349176      0.861909             0.028596     0.172492   \n",
       "1836  10.941167      0.899165             0.032200     0.179711   \n",
       "1837  10.756754      0.832745             0.029660     0.152309   \n",
       "1838  10.807388      0.662477             0.026681     0.087502   \n",
       "1905  11.733803      0.779113             0.033132     0.120216   \n",
       "1906  10.920948      0.797542             0.033273     0.139530   \n",
       "1907  10.292976      0.882672             0.028746     0.170586   \n",
       "1908   9.885184      0.942623             0.030157     0.179143   \n",
       "1909   9.725309      0.920885             0.029873     0.148579   \n",
       "1910   9.825460      0.782165             0.031834     0.096305   \n",
       "1976  11.746320      0.914587             0.030153     0.192304   \n",
       "1977  10.739362      0.879698             0.032883     0.163791   \n",
       "1978   9.888632      0.886313             0.032711     0.167295   \n",
       "1979   9.237394      0.917669             0.030472     0.180422   \n",
       "1980   8.829899      0.954733             0.033126     0.185635   \n",
       "1981   8.700462      0.931854             0.031758     0.171861   \n",
       "1982   8.861277      0.841962             0.033357     0.118438   \n",
       "1983   9.297294      0.667234             0.032965     0.083894   \n",
       "2047  11.994724      1.014992             0.032660     0.229246   \n",
       "2048  10.816847      1.037158             0.032406     0.219370   \n",
       "2049   9.758495      0.996838             0.030895     0.195095   \n",
       "2050   8.862591      0.982216             0.030583     0.193597   \n",
       "2051   8.182669      0.999042             0.032829     0.213680   \n",
       "2052   7.775597      1.023632             0.035172     0.223833   \n",
       "2053   7.684854      0.948965             0.035155     0.189639   \n",
       "2054   7.921321      0.848381             0.031562     0.172392   \n",
       "...         ...           ...                  ...          ...   \n",
       "3986  17.653207      2.195584             0.041686     0.463635   \n",
       "3987  17.199372      2.537671             0.044783     0.552040   \n",
       "3988  16.809930      3.077898             0.043314     0.690206   \n",
       "3989  16.489442      3.588930             0.050621     0.809271   \n",
       "3990  16.241991      3.917448             0.050110     0.878297   \n",
       "3991  16.070952      3.974187             0.047228     0.873234   \n",
       "3992  15.978777      3.738355             0.048255     0.801883   \n",
       "3993  15.966834      3.209375             0.044689     0.684992   \n",
       "3994  16.035301      2.539776             0.045811     0.560699   \n",
       "4061  19.253894      2.188377             0.043221     0.485161   \n",
       "4062  18.755371      2.225327             0.041190     0.476171   \n",
       "4063  18.313603      2.437381             0.042256     0.537481   \n",
       "4064  17.932785      2.962603             0.043152     0.684808   \n",
       "4065  17.616872      3.432966             0.051686     0.818733   \n",
       "4066  17.369403      3.730800             0.050078     0.886000   \n",
       "4067  17.193336      3.778134             0.047544     0.868089   \n",
       "4068  17.090876      3.506205             0.049529     0.803545   \n",
       "4069  17.063351      3.110394             0.045417     0.690574   \n",
       "4070  17.111120      2.670440             0.044373     0.567858   \n",
       "4139  19.429425      2.580920             0.048611     0.544338   \n",
       "4140  19.056252      2.987011             0.044976     0.684523   \n",
       "4141  18.744373      3.481735             0.045086     0.823852   \n",
       "4142  18.496890      3.723005             0.046612     0.881419   \n",
       "4143  18.316413      3.881938             0.050978     0.929229   \n",
       "4144  18.204935      3.809508             0.055537     0.892221   \n",
       "4145  18.163727      3.461078             0.049513     0.791914   \n",
       "4218  19.624439      4.083565             0.052521     1.010040   \n",
       "4219  19.440063      4.478929             0.049010     1.077462   \n",
       "4220  19.320615      4.591545             0.050835     1.090058   \n",
       "4221  19.267301      4.309638             0.047929     0.984319   \n",
       "\n",
       "      $\\sigma H_{\\beta}$  Comp  AGN  Seyfert  Liner   $r/r_e$  Spiral Arm  \\\n",
       "1764            0.029375   0.0  0.0      0.0    0.0  0.979344       False   \n",
       "1765            0.027367   0.0  0.0      0.0    0.0  0.962643       False   \n",
       "1766            0.023748   0.0  0.0      0.0    0.0  0.963424       False   \n",
       "1834            0.027642   0.0  0.0      0.0    0.0  0.976099       False   \n",
       "1835            0.026646   0.0  0.0      0.0    0.0  0.926409        True   \n",
       "1836            0.028678   0.0  0.0      0.0    0.0  0.893104        True   \n",
       "1837            0.025783   0.0  0.0      0.0    0.0  0.878051       False   \n",
       "1838            0.019808   0.0  0.0      0.0    0.0  0.882184       False   \n",
       "1905            0.028803   0.0  0.0      0.0    0.0  0.957805       False   \n",
       "1906            0.030301   0.0  0.0      0.0    0.0  0.891454       False   \n",
       "1907            0.027513   0.0  0.0      0.0    0.0  0.840194        True   \n",
       "1908            0.026481   0.0  0.0      0.0    0.0  0.806906        True   \n",
       "1909            0.024873   0.0  0.0      0.0    0.0  0.793856       False   \n",
       "1910            0.022553   0.0  0.0      0.0    0.0  0.802031       False   \n",
       "1976            0.028246   0.0  0.0      0.0    0.0  0.958827       False   \n",
       "1977            0.025839   0.0  0.0      0.0    0.0  0.876631        True   \n",
       "1978            0.027289   0.0  0.0      0.0    0.0  0.807188       False   \n",
       "1979            0.028002   0.0  0.0      0.0    0.0  0.754029       False   \n",
       "1980            0.030887   0.0  0.0      0.0    0.0  0.720766        True   \n",
       "1981            0.029542   0.0  0.0      0.0    0.0  0.710200        True   \n",
       "1982            0.025746   0.0  0.0      0.0    0.0  0.723327        True   \n",
       "1983            0.022731   0.0  0.0      0.0    0.0  0.758918       False   \n",
       "2047            0.029881   0.0  0.0      0.0    0.0  0.979104        True   \n",
       "2048            0.028014   0.0  0.0      0.0    0.0  0.882956        True   \n",
       "2049            0.026537   0.0  0.0      0.0    0.0  0.796565        True   \n",
       "2050            0.027458   0.0  0.0      0.0    0.0  0.723434        True   \n",
       "2051            0.027730   0.0  0.0      0.0    0.0  0.667934       False   \n",
       "2052            0.031314   0.0  0.0      0.0    0.0  0.634705        True   \n",
       "2053            0.029769   0.0  0.0      0.0    0.0  0.627298        True   \n",
       "2054            0.029323   0.0  0.0      0.0    0.0  0.646600        True   \n",
       "...                  ...   ...  ...      ...    ...       ...         ...   \n",
       "3986            0.033666   0.0  0.0      0.0    0.0  0.898223       False   \n",
       "3987            0.033079   0.0  0.0      0.0    0.0  0.875131       False   \n",
       "3988            0.031990   0.0  0.0      0.0    0.0  0.855316       False   \n",
       "3989            0.036186   0.0  0.0      0.0    0.0  0.839009       False   \n",
       "3990            0.034714   0.0  0.0      0.0    0.0  0.826418       False   \n",
       "3991            0.033072   0.0  0.0      0.0    0.0  0.817715       False   \n",
       "3992            0.034041   0.0  0.0      0.0    0.0  0.813025       False   \n",
       "3993            0.030846   0.0  0.0      0.0    0.0  0.812418       False   \n",
       "3994            0.034769   0.0  0.0      0.0    0.0  0.815901       False   \n",
       "4061            0.033618   0.0  0.0      0.0    0.0  0.979668        True   \n",
       "4062            0.032106   0.0  0.0      0.0    0.0  0.954303        True   \n",
       "4063            0.031211   0.0  0.0      0.0    0.0  0.931825        True   \n",
       "4064            0.032583   0.0  0.0      0.0    0.0  0.912448       False   \n",
       "4065            0.039460   0.0  0.0      0.0    0.0  0.896374       False   \n",
       "4066            0.037595   0.0  0.0      0.0    0.0  0.883783       False   \n",
       "4067            0.034127   0.0  0.0      0.0    0.0  0.874824       False   \n",
       "4068            0.035151   0.0  0.0      0.0    0.0  0.869611       False   \n",
       "4069            0.032122   0.0  0.0      0.0    0.0  0.868210       False   \n",
       "4070            0.035072   0.0  0.0      0.0    0.0  0.870641       False   \n",
       "4139            0.036988   0.0  0.0      0.0    0.0  0.988600        True   \n",
       "4140            0.032585   0.0  0.0      0.0    0.0  0.969612        True   \n",
       "4141            0.033027   0.0  0.0      0.0    0.0  0.953743        True   \n",
       "4142            0.033707   0.0  0.0      0.0    0.0  0.941151        True   \n",
       "4143            0.037766   0.0  0.0      0.0    0.0  0.931968        True   \n",
       "4144            0.040023   0.0  0.0      0.0    0.0  0.926296       False   \n",
       "4145            0.036130   0.0  0.0      0.0    0.0  0.924199       False   \n",
       "4218            0.035964   0.0  0.0      0.0    0.0  0.998522        True   \n",
       "4219            0.035760   0.0  0.0      0.0    0.0  0.989141        True   \n",
       "4220            0.038737   0.0  0.0      0.0    0.0  0.983063        True   \n",
       "4221            0.034730   0.0  0.0      0.0    0.0  0.980351        True   \n",
       "\n",
       "              Mass  $\\lg{Mass}$  S/N $H_{\\alpha}$  S/N $H_{\\beta}$  MaNGA ID  \n",
       "1764  2.226830e+11    11.347687         26.150328         5.854283  1-109308  \n",
       "1765  2.226830e+11    11.347687         24.542965         5.470157  1-109308  \n",
       "1766  2.226830e+11    11.347687         18.441414         4.006091  1-109308  \n",
       "1834  2.226830e+11    11.347687         26.942406         4.839194  1-109308  \n",
       "1835  2.226830e+11    11.347687         30.141342         6.473502  1-109308  \n",
       "1836  2.226830e+11    11.347687         27.924206         6.266562  1-109308  \n",
       "1837  2.226830e+11    11.347687         28.076826         5.907425  1-109308  \n",
       "1838  2.226830e+11    11.347687         24.829538         4.417412  1-109308  \n",
       "1905  2.226830e+11    11.347687         23.515188         4.173698  1-109308  \n",
       "1906  2.226830e+11    11.347687         23.969484         4.604856  1-109308  \n",
       "1907  2.226830e+11    11.347687         30.705425         6.200134  1-109308  \n",
       "1908  2.226830e+11    11.347687         31.256712         6.765010  1-109308  \n",
       "1909  2.226830e+11    11.347687         30.826530         5.973423  1-109308  \n",
       "1910  2.226830e+11    11.347687         24.570114         4.270170  1-109308  \n",
       "1976  2.226830e+11    11.347687         30.332022         6.808211  1-109308  \n",
       "1977  2.226830e+11    11.347687         26.752737         6.338825  1-109308  \n",
       "1978  2.226830e+11    11.347687         27.095070         6.130381  1-109308  \n",
       "1979  2.226830e+11    11.347687         30.115129         6.443089  1-109308  \n",
       "1980  2.226830e+11    11.347687         28.820913         6.010154  1-109308  \n",
       "1981  2.226830e+11    11.347687         29.342697         5.817459  1-109308  \n",
       "1982  2.226830e+11    11.347687         25.241179         4.600220  1-109308  \n",
       "1983  2.226830e+11    11.347687         20.240828         3.690757  1-109308  \n",
       "2047  2.226830e+11    11.347687         31.077599         7.671884  1-109308  \n",
       "2048  2.226830e+11    11.347687         32.005161         7.830865  1-109308  \n",
       "2049  2.226830e+11    11.347687         32.265450         7.351828  1-109308  \n",
       "2050  2.226830e+11    11.347687         32.116337         7.050580  1-109308  \n",
       "2051  2.226830e+11    11.347687         30.431665         7.705595  1-109308  \n",
       "2052  2.226830e+11    11.347687         29.103226         7.148062  1-109308  \n",
       "2053  2.226830e+11    11.347687         26.993769         6.370412  1-109308  \n",
       "2054  2.226830e+11    11.347687         26.880238         5.879050  1-109308  \n",
       "...            ...          ...               ...              ...       ...  \n",
       "3986  7.329890e+10    10.865097         52.669350        13.771405   1-96075  \n",
       "3987  7.329890e+10    10.865097         56.666401        16.688622   1-96075  \n",
       "3988  7.329890e+10    10.865097         71.060196        21.575853   1-96075  \n",
       "3989  7.329890e+10    10.865097         70.898148        22.364519   1-96075  \n",
       "3990  7.329890e+10    10.865097         78.177238        25.301003   1-96075  \n",
       "3991  7.329890e+10    10.865097         84.149704        26.404215   1-96075  \n",
       "3992  7.329890e+10    10.865097         77.470467        23.556086   1-96075  \n",
       "3993  7.329890e+10    10.865097         71.815353        22.206532   1-96075  \n",
       "3994  7.329890e+10    10.865097         55.439896        16.126286   1-96075  \n",
       "4061  7.329890e+10    10.865097         50.631763        14.431700   1-96075  \n",
       "4062  7.329890e+10    10.865097         54.025909        14.831236   1-96075  \n",
       "4063  7.329890e+10    10.865097         57.681820        17.221140   1-96075  \n",
       "4064  7.329890e+10    10.865097         68.654411        21.017509   1-96075  \n",
       "4065  7.329890e+10    10.865097         66.419205        20.748230   1-96075  \n",
       "4066  7.329890e+10    10.865097         74.500244        23.567005   1-96075  \n",
       "4067  7.329890e+10    10.865097         79.465782        25.436825   1-96075  \n",
       "4068  7.329890e+10    10.865097         70.790695        22.859985   1-96075  \n",
       "4069  7.329890e+10    10.865097         68.485344        21.498419   1-96075  \n",
       "4070  7.329890e+10    10.865097         60.181644        16.191349   1-96075  \n",
       "4139  7.329890e+10    10.865097         53.092915        14.716763   1-96075  \n",
       "4140  7.329890e+10    10.865097         66.413887        21.007029   1-96075  \n",
       "4141  7.329890e+10    10.865097         77.223938        24.944590   1-96075  \n",
       "4142  7.329890e+10    10.865097         79.872459        26.149103   1-96075  \n",
       "4143  7.329890e+10    10.865097         76.149979        24.604906   1-96075  \n",
       "4144  7.329890e+10    10.865097         68.594658        22.292799   1-96075  \n",
       "4145  7.329890e+10    10.865097         69.902092        21.918278   1-96075  \n",
       "4218  7.329890e+10    10.865097         77.750954        28.084816   1-96075  \n",
       "4219  7.329890e+10    10.865097         91.388191        30.130371   1-96075  \n",
       "4220  7.329890e+10    10.865097         90.322166        28.140167   1-96075  \n",
       "4221  7.329890e+10    10.865097         89.917435        28.341719   1-96075  \n",
       "\n",
       "[88036 rows x 16 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spax_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df['Mass Bin'] = pd.qcut(spax_df['Mass'], q=10, labels=range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Radius</th>\n",
       "      <th>$H_{\\alpha}$</th>\n",
       "      <th>$\\sigma H_{\\alpha}$</th>\n",
       "      <th>$H_{\\beta}$</th>\n",
       "      <th>$\\sigma H_{\\beta}$</th>\n",
       "      <th>Comp</th>\n",
       "      <th>AGN</th>\n",
       "      <th>Seyfert</th>\n",
       "      <th>Liner</th>\n",
       "      <th>$r/r_e$</th>\n",
       "      <th>Spiral Arm</th>\n",
       "      <th>Mass</th>\n",
       "      <th>$\\lg{Mass}$</th>\n",
       "      <th>S/N $H_{\\alpha}$</th>\n",
       "      <th>S/N $H_{\\beta}$</th>\n",
       "      <th>MaNGA ID</th>\n",
       "      <th>Mass Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>11.997665</td>\n",
       "      <td>0.786094</td>\n",
       "      <td>0.030061</td>\n",
       "      <td>0.171968</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979344</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.150328</td>\n",
       "      <td>5.854283</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>11.793067</td>\n",
       "      <td>0.697728</td>\n",
       "      <td>0.028429</td>\n",
       "      <td>0.149700</td>\n",
       "      <td>0.027367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.962643</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.542965</td>\n",
       "      <td>5.470157</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>11.802633</td>\n",
       "      <td>0.541168</td>\n",
       "      <td>0.029345</td>\n",
       "      <td>0.095138</td>\n",
       "      <td>0.023748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.963424</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>18.441414</td>\n",
       "      <td>4.006091</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>11.957914</td>\n",
       "      <td>0.792545</td>\n",
       "      <td>0.029416</td>\n",
       "      <td>0.133767</td>\n",
       "      <td>0.027642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.976099</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.942406</td>\n",
       "      <td>4.839194</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>11.349176</td>\n",
       "      <td>0.861909</td>\n",
       "      <td>0.028596</td>\n",
       "      <td>0.172492</td>\n",
       "      <td>0.026646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.926409</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.141342</td>\n",
       "      <td>6.473502</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>10.941167</td>\n",
       "      <td>0.899165</td>\n",
       "      <td>0.032200</td>\n",
       "      <td>0.179711</td>\n",
       "      <td>0.028678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.893104</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>27.924206</td>\n",
       "      <td>6.266562</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>10.756754</td>\n",
       "      <td>0.832745</td>\n",
       "      <td>0.029660</td>\n",
       "      <td>0.152309</td>\n",
       "      <td>0.025783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.878051</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>28.076826</td>\n",
       "      <td>5.907425</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>10.807388</td>\n",
       "      <td>0.662477</td>\n",
       "      <td>0.026681</td>\n",
       "      <td>0.087502</td>\n",
       "      <td>0.019808</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.882184</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.829538</td>\n",
       "      <td>4.417412</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>11.733803</td>\n",
       "      <td>0.779113</td>\n",
       "      <td>0.033132</td>\n",
       "      <td>0.120216</td>\n",
       "      <td>0.028803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957805</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>23.515188</td>\n",
       "      <td>4.173698</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>10.920948</td>\n",
       "      <td>0.797542</td>\n",
       "      <td>0.033273</td>\n",
       "      <td>0.139530</td>\n",
       "      <td>0.030301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.891454</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>23.969484</td>\n",
       "      <td>4.604856</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>10.292976</td>\n",
       "      <td>0.882672</td>\n",
       "      <td>0.028746</td>\n",
       "      <td>0.170586</td>\n",
       "      <td>0.027513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.840194</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.705425</td>\n",
       "      <td>6.200134</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>9.885184</td>\n",
       "      <td>0.942623</td>\n",
       "      <td>0.030157</td>\n",
       "      <td>0.179143</td>\n",
       "      <td>0.026481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.806906</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>31.256712</td>\n",
       "      <td>6.765010</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>9.725309</td>\n",
       "      <td>0.920885</td>\n",
       "      <td>0.029873</td>\n",
       "      <td>0.148579</td>\n",
       "      <td>0.024873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.793856</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.826530</td>\n",
       "      <td>5.973423</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>9.825460</td>\n",
       "      <td>0.782165</td>\n",
       "      <td>0.031834</td>\n",
       "      <td>0.096305</td>\n",
       "      <td>0.022553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.802031</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>24.570114</td>\n",
       "      <td>4.270170</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>11.746320</td>\n",
       "      <td>0.914587</td>\n",
       "      <td>0.030153</td>\n",
       "      <td>0.192304</td>\n",
       "      <td>0.028246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.958827</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.332022</td>\n",
       "      <td>6.808211</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>10.739362</td>\n",
       "      <td>0.879698</td>\n",
       "      <td>0.032883</td>\n",
       "      <td>0.163791</td>\n",
       "      <td>0.025839</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876631</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.752737</td>\n",
       "      <td>6.338825</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>9.888632</td>\n",
       "      <td>0.886313</td>\n",
       "      <td>0.032711</td>\n",
       "      <td>0.167295</td>\n",
       "      <td>0.027289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.807188</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>27.095070</td>\n",
       "      <td>6.130381</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>9.237394</td>\n",
       "      <td>0.917669</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.180422</td>\n",
       "      <td>0.028002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.754029</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.115129</td>\n",
       "      <td>6.443089</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8.829899</td>\n",
       "      <td>0.954733</td>\n",
       "      <td>0.033126</td>\n",
       "      <td>0.185635</td>\n",
       "      <td>0.030887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.720766</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>28.820913</td>\n",
       "      <td>6.010154</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8.700462</td>\n",
       "      <td>0.931854</td>\n",
       "      <td>0.031758</td>\n",
       "      <td>0.171861</td>\n",
       "      <td>0.029542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.710200</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>29.342697</td>\n",
       "      <td>5.817459</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8.861277</td>\n",
       "      <td>0.841962</td>\n",
       "      <td>0.033357</td>\n",
       "      <td>0.118438</td>\n",
       "      <td>0.025746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.723327</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>25.241179</td>\n",
       "      <td>4.600220</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>9.297294</td>\n",
       "      <td>0.667234</td>\n",
       "      <td>0.032965</td>\n",
       "      <td>0.083894</td>\n",
       "      <td>0.022731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758918</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>20.240828</td>\n",
       "      <td>3.690757</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>11.994724</td>\n",
       "      <td>1.014992</td>\n",
       "      <td>0.032660</td>\n",
       "      <td>0.229246</td>\n",
       "      <td>0.029881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979104</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>31.077599</td>\n",
       "      <td>7.671884</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>10.816847</td>\n",
       "      <td>1.037158</td>\n",
       "      <td>0.032406</td>\n",
       "      <td>0.219370</td>\n",
       "      <td>0.028014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.882956</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.005161</td>\n",
       "      <td>7.830865</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>9.758495</td>\n",
       "      <td>0.996838</td>\n",
       "      <td>0.030895</td>\n",
       "      <td>0.195095</td>\n",
       "      <td>0.026537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.796565</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.265450</td>\n",
       "      <td>7.351828</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>8.862591</td>\n",
       "      <td>0.982216</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.193597</td>\n",
       "      <td>0.027458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.723434</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>32.116337</td>\n",
       "      <td>7.050580</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>8.182669</td>\n",
       "      <td>0.999042</td>\n",
       "      <td>0.032829</td>\n",
       "      <td>0.213680</td>\n",
       "      <td>0.027730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.667934</td>\n",
       "      <td>False</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>30.431665</td>\n",
       "      <td>7.705595</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>7.775597</td>\n",
       "      <td>1.023632</td>\n",
       "      <td>0.035172</td>\n",
       "      <td>0.223833</td>\n",
       "      <td>0.031314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.634705</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>29.103226</td>\n",
       "      <td>7.148062</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>7.684854</td>\n",
       "      <td>0.948965</td>\n",
       "      <td>0.035155</td>\n",
       "      <td>0.189639</td>\n",
       "      <td>0.029769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.627298</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.993769</td>\n",
       "      <td>6.370412</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>7.921321</td>\n",
       "      <td>0.848381</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.172392</td>\n",
       "      <td>0.029323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.646600</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226830e+11</td>\n",
       "      <td>11.347687</td>\n",
       "      <td>26.880238</td>\n",
       "      <td>5.879050</td>\n",
       "      <td>1-109308</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>17.653207</td>\n",
       "      <td>2.195584</td>\n",
       "      <td>0.041686</td>\n",
       "      <td>0.463635</td>\n",
       "      <td>0.033666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.898223</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>52.669350</td>\n",
       "      <td>13.771405</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987</th>\n",
       "      <td>17.199372</td>\n",
       "      <td>2.537671</td>\n",
       "      <td>0.044783</td>\n",
       "      <td>0.552040</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875131</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>56.666401</td>\n",
       "      <td>16.688622</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>16.809930</td>\n",
       "      <td>3.077898</td>\n",
       "      <td>0.043314</td>\n",
       "      <td>0.690206</td>\n",
       "      <td>0.031990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.855316</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>71.060196</td>\n",
       "      <td>21.575853</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>16.489442</td>\n",
       "      <td>3.588930</td>\n",
       "      <td>0.050621</td>\n",
       "      <td>0.809271</td>\n",
       "      <td>0.036186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.839009</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>70.898148</td>\n",
       "      <td>22.364519</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>16.241991</td>\n",
       "      <td>3.917448</td>\n",
       "      <td>0.050110</td>\n",
       "      <td>0.878297</td>\n",
       "      <td>0.034714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.826418</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>78.177238</td>\n",
       "      <td>25.301003</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>16.070952</td>\n",
       "      <td>3.974187</td>\n",
       "      <td>0.047228</td>\n",
       "      <td>0.873234</td>\n",
       "      <td>0.033072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.817715</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>84.149704</td>\n",
       "      <td>26.404215</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>15.978777</td>\n",
       "      <td>3.738355</td>\n",
       "      <td>0.048255</td>\n",
       "      <td>0.801883</td>\n",
       "      <td>0.034041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.813025</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.470467</td>\n",
       "      <td>23.556086</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>15.966834</td>\n",
       "      <td>3.209375</td>\n",
       "      <td>0.044689</td>\n",
       "      <td>0.684992</td>\n",
       "      <td>0.030846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.812418</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>71.815353</td>\n",
       "      <td>22.206532</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>16.035301</td>\n",
       "      <td>2.539776</td>\n",
       "      <td>0.045811</td>\n",
       "      <td>0.560699</td>\n",
       "      <td>0.034769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.815901</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>55.439896</td>\n",
       "      <td>16.126286</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>19.253894</td>\n",
       "      <td>2.188377</td>\n",
       "      <td>0.043221</td>\n",
       "      <td>0.485161</td>\n",
       "      <td>0.033618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979668</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>50.631763</td>\n",
       "      <td>14.431700</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>18.755371</td>\n",
       "      <td>2.225327</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>0.476171</td>\n",
       "      <td>0.032106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.954303</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>54.025909</td>\n",
       "      <td>14.831236</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>18.313603</td>\n",
       "      <td>2.437381</td>\n",
       "      <td>0.042256</td>\n",
       "      <td>0.537481</td>\n",
       "      <td>0.031211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931825</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>57.681820</td>\n",
       "      <td>17.221140</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>17.932785</td>\n",
       "      <td>2.962603</td>\n",
       "      <td>0.043152</td>\n",
       "      <td>0.684808</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912448</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.654411</td>\n",
       "      <td>21.017509</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>17.616872</td>\n",
       "      <td>3.432966</td>\n",
       "      <td>0.051686</td>\n",
       "      <td>0.818733</td>\n",
       "      <td>0.039460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.896374</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>66.419205</td>\n",
       "      <td>20.748230</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4066</th>\n",
       "      <td>17.369403</td>\n",
       "      <td>3.730800</td>\n",
       "      <td>0.050078</td>\n",
       "      <td>0.886000</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.883783</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>74.500244</td>\n",
       "      <td>23.567005</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4067</th>\n",
       "      <td>17.193336</td>\n",
       "      <td>3.778134</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.868089</td>\n",
       "      <td>0.034127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.874824</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>79.465782</td>\n",
       "      <td>25.436825</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4068</th>\n",
       "      <td>17.090876</td>\n",
       "      <td>3.506205</td>\n",
       "      <td>0.049529</td>\n",
       "      <td>0.803545</td>\n",
       "      <td>0.035151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.869611</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>70.790695</td>\n",
       "      <td>22.859985</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4069</th>\n",
       "      <td>17.063351</td>\n",
       "      <td>3.110394</td>\n",
       "      <td>0.045417</td>\n",
       "      <td>0.690574</td>\n",
       "      <td>0.032122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.868210</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.485344</td>\n",
       "      <td>21.498419</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>17.111120</td>\n",
       "      <td>2.670440</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>0.567858</td>\n",
       "      <td>0.035072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.870641</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>60.181644</td>\n",
       "      <td>16.191349</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4139</th>\n",
       "      <td>19.429425</td>\n",
       "      <td>2.580920</td>\n",
       "      <td>0.048611</td>\n",
       "      <td>0.544338</td>\n",
       "      <td>0.036988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988600</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>53.092915</td>\n",
       "      <td>14.716763</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4140</th>\n",
       "      <td>19.056252</td>\n",
       "      <td>2.987011</td>\n",
       "      <td>0.044976</td>\n",
       "      <td>0.684523</td>\n",
       "      <td>0.032585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.969612</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>66.413887</td>\n",
       "      <td>21.007029</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>18.744373</td>\n",
       "      <td>3.481735</td>\n",
       "      <td>0.045086</td>\n",
       "      <td>0.823852</td>\n",
       "      <td>0.033027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953743</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.223938</td>\n",
       "      <td>24.944590</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>18.496890</td>\n",
       "      <td>3.723005</td>\n",
       "      <td>0.046612</td>\n",
       "      <td>0.881419</td>\n",
       "      <td>0.033707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.941151</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>79.872459</td>\n",
       "      <td>26.149103</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4143</th>\n",
       "      <td>18.316413</td>\n",
       "      <td>3.881938</td>\n",
       "      <td>0.050978</td>\n",
       "      <td>0.929229</td>\n",
       "      <td>0.037766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931968</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>76.149979</td>\n",
       "      <td>24.604906</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>18.204935</td>\n",
       "      <td>3.809508</td>\n",
       "      <td>0.055537</td>\n",
       "      <td>0.892221</td>\n",
       "      <td>0.040023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.926296</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>68.594658</td>\n",
       "      <td>22.292799</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>18.163727</td>\n",
       "      <td>3.461078</td>\n",
       "      <td>0.049513</td>\n",
       "      <td>0.791914</td>\n",
       "      <td>0.036130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.924199</td>\n",
       "      <td>False</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>69.902092</td>\n",
       "      <td>21.918278</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4218</th>\n",
       "      <td>19.624439</td>\n",
       "      <td>4.083565</td>\n",
       "      <td>0.052521</td>\n",
       "      <td>1.010040</td>\n",
       "      <td>0.035964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998522</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>77.750954</td>\n",
       "      <td>28.084816</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4219</th>\n",
       "      <td>19.440063</td>\n",
       "      <td>4.478929</td>\n",
       "      <td>0.049010</td>\n",
       "      <td>1.077462</td>\n",
       "      <td>0.035760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.989141</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>91.388191</td>\n",
       "      <td>30.130371</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4220</th>\n",
       "      <td>19.320615</td>\n",
       "      <td>4.591545</td>\n",
       "      <td>0.050835</td>\n",
       "      <td>1.090058</td>\n",
       "      <td>0.038737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983063</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>90.322166</td>\n",
       "      <td>28.140167</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4221</th>\n",
       "      <td>19.267301</td>\n",
       "      <td>4.309638</td>\n",
       "      <td>0.047929</td>\n",
       "      <td>0.984319</td>\n",
       "      <td>0.034730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980351</td>\n",
       "      <td>True</td>\n",
       "      <td>7.329890e+10</td>\n",
       "      <td>10.865097</td>\n",
       "      <td>89.917435</td>\n",
       "      <td>28.341719</td>\n",
       "      <td>1-96075</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88036 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Radius  $H_{\\alpha}$  $\\sigma H_{\\alpha}$  $H_{\\beta}$  \\\n",
       "1764  11.997665      0.786094             0.030061     0.171968   \n",
       "1765  11.793067      0.697728             0.028429     0.149700   \n",
       "1766  11.802633      0.541168             0.029345     0.095138   \n",
       "1834  11.957914      0.792545             0.029416     0.133767   \n",
       "1835  11.349176      0.861909             0.028596     0.172492   \n",
       "1836  10.941167      0.899165             0.032200     0.179711   \n",
       "1837  10.756754      0.832745             0.029660     0.152309   \n",
       "1838  10.807388      0.662477             0.026681     0.087502   \n",
       "1905  11.733803      0.779113             0.033132     0.120216   \n",
       "1906  10.920948      0.797542             0.033273     0.139530   \n",
       "1907  10.292976      0.882672             0.028746     0.170586   \n",
       "1908   9.885184      0.942623             0.030157     0.179143   \n",
       "1909   9.725309      0.920885             0.029873     0.148579   \n",
       "1910   9.825460      0.782165             0.031834     0.096305   \n",
       "1976  11.746320      0.914587             0.030153     0.192304   \n",
       "1977  10.739362      0.879698             0.032883     0.163791   \n",
       "1978   9.888632      0.886313             0.032711     0.167295   \n",
       "1979   9.237394      0.917669             0.030472     0.180422   \n",
       "1980   8.829899      0.954733             0.033126     0.185635   \n",
       "1981   8.700462      0.931854             0.031758     0.171861   \n",
       "1982   8.861277      0.841962             0.033357     0.118438   \n",
       "1983   9.297294      0.667234             0.032965     0.083894   \n",
       "2047  11.994724      1.014992             0.032660     0.229246   \n",
       "2048  10.816847      1.037158             0.032406     0.219370   \n",
       "2049   9.758495      0.996838             0.030895     0.195095   \n",
       "2050   8.862591      0.982216             0.030583     0.193597   \n",
       "2051   8.182669      0.999042             0.032829     0.213680   \n",
       "2052   7.775597      1.023632             0.035172     0.223833   \n",
       "2053   7.684854      0.948965             0.035155     0.189639   \n",
       "2054   7.921321      0.848381             0.031562     0.172392   \n",
       "...         ...           ...                  ...          ...   \n",
       "3986  17.653207      2.195584             0.041686     0.463635   \n",
       "3987  17.199372      2.537671             0.044783     0.552040   \n",
       "3988  16.809930      3.077898             0.043314     0.690206   \n",
       "3989  16.489442      3.588930             0.050621     0.809271   \n",
       "3990  16.241991      3.917448             0.050110     0.878297   \n",
       "3991  16.070952      3.974187             0.047228     0.873234   \n",
       "3992  15.978777      3.738355             0.048255     0.801883   \n",
       "3993  15.966834      3.209375             0.044689     0.684992   \n",
       "3994  16.035301      2.539776             0.045811     0.560699   \n",
       "4061  19.253894      2.188377             0.043221     0.485161   \n",
       "4062  18.755371      2.225327             0.041190     0.476171   \n",
       "4063  18.313603      2.437381             0.042256     0.537481   \n",
       "4064  17.932785      2.962603             0.043152     0.684808   \n",
       "4065  17.616872      3.432966             0.051686     0.818733   \n",
       "4066  17.369403      3.730800             0.050078     0.886000   \n",
       "4067  17.193336      3.778134             0.047544     0.868089   \n",
       "4068  17.090876      3.506205             0.049529     0.803545   \n",
       "4069  17.063351      3.110394             0.045417     0.690574   \n",
       "4070  17.111120      2.670440             0.044373     0.567858   \n",
       "4139  19.429425      2.580920             0.048611     0.544338   \n",
       "4140  19.056252      2.987011             0.044976     0.684523   \n",
       "4141  18.744373      3.481735             0.045086     0.823852   \n",
       "4142  18.496890      3.723005             0.046612     0.881419   \n",
       "4143  18.316413      3.881938             0.050978     0.929229   \n",
       "4144  18.204935      3.809508             0.055537     0.892221   \n",
       "4145  18.163727      3.461078             0.049513     0.791914   \n",
       "4218  19.624439      4.083565             0.052521     1.010040   \n",
       "4219  19.440063      4.478929             0.049010     1.077462   \n",
       "4220  19.320615      4.591545             0.050835     1.090058   \n",
       "4221  19.267301      4.309638             0.047929     0.984319   \n",
       "\n",
       "      $\\sigma H_{\\beta}$  Comp  AGN  Seyfert  Liner   $r/r_e$  Spiral Arm  \\\n",
       "1764            0.029375   0.0  0.0      0.0    0.0  0.979344       False   \n",
       "1765            0.027367   0.0  0.0      0.0    0.0  0.962643       False   \n",
       "1766            0.023748   0.0  0.0      0.0    0.0  0.963424       False   \n",
       "1834            0.027642   0.0  0.0      0.0    0.0  0.976099       False   \n",
       "1835            0.026646   0.0  0.0      0.0    0.0  0.926409        True   \n",
       "1836            0.028678   0.0  0.0      0.0    0.0  0.893104        True   \n",
       "1837            0.025783   0.0  0.0      0.0    0.0  0.878051       False   \n",
       "1838            0.019808   0.0  0.0      0.0    0.0  0.882184       False   \n",
       "1905            0.028803   0.0  0.0      0.0    0.0  0.957805       False   \n",
       "1906            0.030301   0.0  0.0      0.0    0.0  0.891454       False   \n",
       "1907            0.027513   0.0  0.0      0.0    0.0  0.840194        True   \n",
       "1908            0.026481   0.0  0.0      0.0    0.0  0.806906        True   \n",
       "1909            0.024873   0.0  0.0      0.0    0.0  0.793856       False   \n",
       "1910            0.022553   0.0  0.0      0.0    0.0  0.802031       False   \n",
       "1976            0.028246   0.0  0.0      0.0    0.0  0.958827       False   \n",
       "1977            0.025839   0.0  0.0      0.0    0.0  0.876631        True   \n",
       "1978            0.027289   0.0  0.0      0.0    0.0  0.807188       False   \n",
       "1979            0.028002   0.0  0.0      0.0    0.0  0.754029       False   \n",
       "1980            0.030887   0.0  0.0      0.0    0.0  0.720766        True   \n",
       "1981            0.029542   0.0  0.0      0.0    0.0  0.710200        True   \n",
       "1982            0.025746   0.0  0.0      0.0    0.0  0.723327        True   \n",
       "1983            0.022731   0.0  0.0      0.0    0.0  0.758918       False   \n",
       "2047            0.029881   0.0  0.0      0.0    0.0  0.979104        True   \n",
       "2048            0.028014   0.0  0.0      0.0    0.0  0.882956        True   \n",
       "2049            0.026537   0.0  0.0      0.0    0.0  0.796565        True   \n",
       "2050            0.027458   0.0  0.0      0.0    0.0  0.723434        True   \n",
       "2051            0.027730   0.0  0.0      0.0    0.0  0.667934       False   \n",
       "2052            0.031314   0.0  0.0      0.0    0.0  0.634705        True   \n",
       "2053            0.029769   0.0  0.0      0.0    0.0  0.627298        True   \n",
       "2054            0.029323   0.0  0.0      0.0    0.0  0.646600        True   \n",
       "...                  ...   ...  ...      ...    ...       ...         ...   \n",
       "3986            0.033666   0.0  0.0      0.0    0.0  0.898223       False   \n",
       "3987            0.033079   0.0  0.0      0.0    0.0  0.875131       False   \n",
       "3988            0.031990   0.0  0.0      0.0    0.0  0.855316       False   \n",
       "3989            0.036186   0.0  0.0      0.0    0.0  0.839009       False   \n",
       "3990            0.034714   0.0  0.0      0.0    0.0  0.826418       False   \n",
       "3991            0.033072   0.0  0.0      0.0    0.0  0.817715       False   \n",
       "3992            0.034041   0.0  0.0      0.0    0.0  0.813025       False   \n",
       "3993            0.030846   0.0  0.0      0.0    0.0  0.812418       False   \n",
       "3994            0.034769   0.0  0.0      0.0    0.0  0.815901       False   \n",
       "4061            0.033618   0.0  0.0      0.0    0.0  0.979668        True   \n",
       "4062            0.032106   0.0  0.0      0.0    0.0  0.954303        True   \n",
       "4063            0.031211   0.0  0.0      0.0    0.0  0.931825        True   \n",
       "4064            0.032583   0.0  0.0      0.0    0.0  0.912448       False   \n",
       "4065            0.039460   0.0  0.0      0.0    0.0  0.896374       False   \n",
       "4066            0.037595   0.0  0.0      0.0    0.0  0.883783       False   \n",
       "4067            0.034127   0.0  0.0      0.0    0.0  0.874824       False   \n",
       "4068            0.035151   0.0  0.0      0.0    0.0  0.869611       False   \n",
       "4069            0.032122   0.0  0.0      0.0    0.0  0.868210       False   \n",
       "4070            0.035072   0.0  0.0      0.0    0.0  0.870641       False   \n",
       "4139            0.036988   0.0  0.0      0.0    0.0  0.988600        True   \n",
       "4140            0.032585   0.0  0.0      0.0    0.0  0.969612        True   \n",
       "4141            0.033027   0.0  0.0      0.0    0.0  0.953743        True   \n",
       "4142            0.033707   0.0  0.0      0.0    0.0  0.941151        True   \n",
       "4143            0.037766   0.0  0.0      0.0    0.0  0.931968        True   \n",
       "4144            0.040023   0.0  0.0      0.0    0.0  0.926296       False   \n",
       "4145            0.036130   0.0  0.0      0.0    0.0  0.924199       False   \n",
       "4218            0.035964   0.0  0.0      0.0    0.0  0.998522        True   \n",
       "4219            0.035760   0.0  0.0      0.0    0.0  0.989141        True   \n",
       "4220            0.038737   0.0  0.0      0.0    0.0  0.983063        True   \n",
       "4221            0.034730   0.0  0.0      0.0    0.0  0.980351        True   \n",
       "\n",
       "              Mass  $\\lg{Mass}$  S/N $H_{\\alpha}$  S/N $H_{\\beta}$  MaNGA ID  \\\n",
       "1764  2.226830e+11    11.347687         26.150328         5.854283  1-109308   \n",
       "1765  2.226830e+11    11.347687         24.542965         5.470157  1-109308   \n",
       "1766  2.226830e+11    11.347687         18.441414         4.006091  1-109308   \n",
       "1834  2.226830e+11    11.347687         26.942406         4.839194  1-109308   \n",
       "1835  2.226830e+11    11.347687         30.141342         6.473502  1-109308   \n",
       "1836  2.226830e+11    11.347687         27.924206         6.266562  1-109308   \n",
       "1837  2.226830e+11    11.347687         28.076826         5.907425  1-109308   \n",
       "1838  2.226830e+11    11.347687         24.829538         4.417412  1-109308   \n",
       "1905  2.226830e+11    11.347687         23.515188         4.173698  1-109308   \n",
       "1906  2.226830e+11    11.347687         23.969484         4.604856  1-109308   \n",
       "1907  2.226830e+11    11.347687         30.705425         6.200134  1-109308   \n",
       "1908  2.226830e+11    11.347687         31.256712         6.765010  1-109308   \n",
       "1909  2.226830e+11    11.347687         30.826530         5.973423  1-109308   \n",
       "1910  2.226830e+11    11.347687         24.570114         4.270170  1-109308   \n",
       "1976  2.226830e+11    11.347687         30.332022         6.808211  1-109308   \n",
       "1977  2.226830e+11    11.347687         26.752737         6.338825  1-109308   \n",
       "1978  2.226830e+11    11.347687         27.095070         6.130381  1-109308   \n",
       "1979  2.226830e+11    11.347687         30.115129         6.443089  1-109308   \n",
       "1980  2.226830e+11    11.347687         28.820913         6.010154  1-109308   \n",
       "1981  2.226830e+11    11.347687         29.342697         5.817459  1-109308   \n",
       "1982  2.226830e+11    11.347687         25.241179         4.600220  1-109308   \n",
       "1983  2.226830e+11    11.347687         20.240828         3.690757  1-109308   \n",
       "2047  2.226830e+11    11.347687         31.077599         7.671884  1-109308   \n",
       "2048  2.226830e+11    11.347687         32.005161         7.830865  1-109308   \n",
       "2049  2.226830e+11    11.347687         32.265450         7.351828  1-109308   \n",
       "2050  2.226830e+11    11.347687         32.116337         7.050580  1-109308   \n",
       "2051  2.226830e+11    11.347687         30.431665         7.705595  1-109308   \n",
       "2052  2.226830e+11    11.347687         29.103226         7.148062  1-109308   \n",
       "2053  2.226830e+11    11.347687         26.993769         6.370412  1-109308   \n",
       "2054  2.226830e+11    11.347687         26.880238         5.879050  1-109308   \n",
       "...            ...          ...               ...              ...       ...   \n",
       "3986  7.329890e+10    10.865097         52.669350        13.771405   1-96075   \n",
       "3987  7.329890e+10    10.865097         56.666401        16.688622   1-96075   \n",
       "3988  7.329890e+10    10.865097         71.060196        21.575853   1-96075   \n",
       "3989  7.329890e+10    10.865097         70.898148        22.364519   1-96075   \n",
       "3990  7.329890e+10    10.865097         78.177238        25.301003   1-96075   \n",
       "3991  7.329890e+10    10.865097         84.149704        26.404215   1-96075   \n",
       "3992  7.329890e+10    10.865097         77.470467        23.556086   1-96075   \n",
       "3993  7.329890e+10    10.865097         71.815353        22.206532   1-96075   \n",
       "3994  7.329890e+10    10.865097         55.439896        16.126286   1-96075   \n",
       "4061  7.329890e+10    10.865097         50.631763        14.431700   1-96075   \n",
       "4062  7.329890e+10    10.865097         54.025909        14.831236   1-96075   \n",
       "4063  7.329890e+10    10.865097         57.681820        17.221140   1-96075   \n",
       "4064  7.329890e+10    10.865097         68.654411        21.017509   1-96075   \n",
       "4065  7.329890e+10    10.865097         66.419205        20.748230   1-96075   \n",
       "4066  7.329890e+10    10.865097         74.500244        23.567005   1-96075   \n",
       "4067  7.329890e+10    10.865097         79.465782        25.436825   1-96075   \n",
       "4068  7.329890e+10    10.865097         70.790695        22.859985   1-96075   \n",
       "4069  7.329890e+10    10.865097         68.485344        21.498419   1-96075   \n",
       "4070  7.329890e+10    10.865097         60.181644        16.191349   1-96075   \n",
       "4139  7.329890e+10    10.865097         53.092915        14.716763   1-96075   \n",
       "4140  7.329890e+10    10.865097         66.413887        21.007029   1-96075   \n",
       "4141  7.329890e+10    10.865097         77.223938        24.944590   1-96075   \n",
       "4142  7.329890e+10    10.865097         79.872459        26.149103   1-96075   \n",
       "4143  7.329890e+10    10.865097         76.149979        24.604906   1-96075   \n",
       "4144  7.329890e+10    10.865097         68.594658        22.292799   1-96075   \n",
       "4145  7.329890e+10    10.865097         69.902092        21.918278   1-96075   \n",
       "4218  7.329890e+10    10.865097         77.750954        28.084816   1-96075   \n",
       "4219  7.329890e+10    10.865097         91.388191        30.130371   1-96075   \n",
       "4220  7.329890e+10    10.865097         90.322166        28.140167   1-96075   \n",
       "4221  7.329890e+10    10.865097         89.917435        28.341719   1-96075   \n",
       "\n",
       "     Mass Bin  \n",
       "1764        9  \n",
       "1765        9  \n",
       "1766        9  \n",
       "1834        9  \n",
       "1835        9  \n",
       "1836        9  \n",
       "1837        9  \n",
       "1838        9  \n",
       "1905        9  \n",
       "1906        9  \n",
       "1907        9  \n",
       "1908        9  \n",
       "1909        9  \n",
       "1910        9  \n",
       "1976        9  \n",
       "1977        9  \n",
       "1978        9  \n",
       "1979        9  \n",
       "1980        9  \n",
       "1981        9  \n",
       "1982        9  \n",
       "1983        9  \n",
       "2047        9  \n",
       "2048        9  \n",
       "2049        9  \n",
       "2050        9  \n",
       "2051        9  \n",
       "2052        9  \n",
       "2053        9  \n",
       "2054        9  \n",
       "...       ...  \n",
       "3986        8  \n",
       "3987        8  \n",
       "3988        8  \n",
       "3989        8  \n",
       "3990        8  \n",
       "3991        8  \n",
       "3992        8  \n",
       "3993        8  \n",
       "3994        8  \n",
       "4061        8  \n",
       "4062        8  \n",
       "4063        8  \n",
       "4064        8  \n",
       "4065        8  \n",
       "4066        8  \n",
       "4067        8  \n",
       "4068        8  \n",
       "4069        8  \n",
       "4070        8  \n",
       "4139        8  \n",
       "4140        8  \n",
       "4141        8  \n",
       "4142        8  \n",
       "4143        8  \n",
       "4144        8  \n",
       "4145        8  \n",
       "4218        8  \n",
       "4219        8  \n",
       "4220        8  \n",
       "4221        8  \n",
       "\n",
       "[88036 rows x 17 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spax_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_total_sfrs(spax_df, mangaid_list, gal_objs):\n",
    "    unique_ids = spax_df['MaNGA ID'].unique()\n",
    "    \n",
    "    ncorr_sfrs, ncorr_sfr_stdvs = [], []\n",
    "    ncorr_spiral_sfrs, ncorr_spiral_sfr_stdvs = [], []\n",
    "    ncorr_nspiral_sfrs, ncorr_nspiral_sfr_stdvs = [], []\n",
    "    \n",
    "    for mangaid in unique_ids:\n",
    "        df = spax_df[spax_df['MaNGA ID'] == mangaid]\n",
    "        df_spiral = df[df['Spiral Arm'] == True]\n",
    "        df_nspiral = df[df['Spiral Arm'] == False]\n",
    "        \n",
    "        index = mangaid_list.index(mangaid)\n",
    "        \n",
    "        sfr, sfr_stdv = gal_objs[index].get_sfr(df.index, avg=False)\n",
    "        sfr_spiral, sfr_spiral_stdv = gal_objs[index].get_sfr(df_spiral.index, avg=False)\n",
    "        sfr_nspiral, sfr_nspiral_stdv = gal_objs[index].get_sfr(df_nspiral.index, avg=False)\n",
    "        \n",
    "        ncorr_sfrs.append(sfr)\n",
    "        ncorr_sfr_stdvs.append(sfr_stdv)\n",
    "        \n",
    "        ncorr_spiral_sfrs.append(sfr_spiral)\n",
    "        ncorr_spiral_sfr_stdvs.append(sfr_spiral_stdv)\n",
    "        \n",
    "        ncorr_nspiral_sfrs.append(sfr_nspiral)\n",
    "        ncorr_nspiral_sfr_stdvs.append(sfr_nspiral_stdv)\n",
    "        \n",
    "    tot_sfr = sum(ncorr_sfrs)\n",
    "    tot_sfr_stdv = np.sqrt(np.square(ncorr_sfr_stdvs).sum())\n",
    "    \n",
    "    tot_sfr_spiral = sum(ncorr_spiral_sfrs)\n",
    "    tot_sfr_stdv_spiral = np.sqrt(np.square(ncorr_spiral_sfr_stdvs).sum())\n",
    "    \n",
    "    tot_sfr_nspiral = sum(ncorr_nspiral_sfrs)\n",
    "    tot_sfr_stdv_nspiral = np.sqrt(np.square(ncorr_nspiral_sfr_stdvs).sum())\n",
    "    \n",
    "    return tot_sfr, tot_sfr_stdv, tot_sfr_spiral, tot_sfr_stdv_spiral, tot_sfr_nspiral, tot_sfr_stdv_nspiral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/raid5/homes/sshamsi/galaxy_zoo/GZ3D_spiral_analysis/newer_Galaxy.py:134: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  sfr = (self.delta * (ha_flux**3.36) * (hb_flux**-2.36)) / self.spax_area\n"
     ]
    }
   ],
   "source": [
    "bin_sfr, bin_sfr_stdv = [], []\n",
    "bin_sfr_spiral, bin_sfr_spiral_stdv = [], []\n",
    "bin_sfr_nspiral, bin_sfr_nspiral_stdv = [], []\n",
    "\n",
    "for label in range(10):\n",
    "    bin_df = spax_df[spax_df['Mass Bin'] == label]\n",
    "    \n",
    "    ans = make_total_sfrs(bin_df, mangaid_list, gal_objs)\n",
    "    \n",
    "    bin_sfr.append(ans[0])\n",
    "    bin_sfr_stdv.append(ans[1])\n",
    "    bin_sfr_spiral.append(ans[2])\n",
    "    bin_sfr_spiral_stdv.append(ans[3])\n",
    "    bin_sfr_nspiral.append(ans[4])\n",
    "    bin_sfr_nspiral_stdv.append(ans[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_sfr_spiral_pc = []\n",
    "bin_sfr_spiral_pc_stdv = []\n",
    "\n",
    "for i in range(10):\n",
    "    sfr_spiral_pc = 100 * bin_sfr_spiral[i] / bin_sfr[i]\n",
    "    \n",
    "    sfr_spiral_pc_stdv = sfr_spiral_pc * np.sqrt((bin_sfr_spiral_stdv[i] / bin_sfr_spiral[i])**2 +\n",
    "                                                 (bin_sfr_stdv[i]/bin_sfr[i])**2)\n",
    "    \n",
    "    bin_sfr_spiral_pc.append(sfr_spiral_pc)\n",
    "    bin_sfr_spiral_pc_stdv.append(sfr_spiral_pc_stdv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([36., 38., 40., 42., 44., 46., 48., 50., 52., 54.]),\n",
       " <a list of 10 Text yticklabel objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.errorbar(range(10), bin_sfr_spiral_pc, yerr=bin_sfr_spiral_pc_stdv)\n",
    "\n",
    "plt.xlabel('Mass Bins $lg(M_*/M_☉)$', labelpad=20, size=20)\n",
    "plt.ylabel('% Total SFR in Spiral Arms', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex_df = spax_df[spax_df['Mass Bin'] == 0]\n",
    "\n",
    "unique_ids = ex_df['MaNGA ID'].unique()\n",
    "    \n",
    "ncorr_sfrs, ncorr_sfr_stdvs = [], []\n",
    "ncorr_spiral_sfrs, ncorr_spiral_sfr_stdvs = [], []\n",
    "ncorr_nspiral_sfrs, ncorr_nspiral_sfr_stdvs = [], []\n",
    "\n",
    "for mangaid in unique_ids:\n",
    "    df = ex_df[ex_df['MaNGA ID'] == mangaid]\n",
    "    df_spiral = df[df['Spiral Arm'] == True]\n",
    "    df_nspiral = df[df['Spiral Arm'] == False]\n",
    "\n",
    "    index = mangaid_list.index(mangaid)\n",
    "\n",
    "    sfr, sfr_stdv = gal_objs[index].get_sfr(df.index, avg=False)\n",
    "    sfr_spiral, sfr_spiral_stdv = gal_objs[index].get_sfr(df_spiral.index, avg=False)\n",
    "    sfr_nspiral, sfr_nspiral_stdv = gal_objs[index].get_sfr(df_nspiral.index, avg=False)\n",
    "\n",
    "    ncorr_sfrs.append(sfr)\n",
    "    ncorr_sfr_stdvs.append(sfr_stdv)\n",
    "\n",
    "    ncorr_spiral_sfrs.append(sfr_spiral)\n",
    "    ncorr_spiral_sfr_stdvs.append(sfr_spiral_stdv)\n",
    "\n",
    "    ncorr_nspiral_sfrs.append(sfr_nspiral)\n",
    "    ncorr_nspiral_sfr_stdvs.append(sfr_nspiral_stdv)\n",
    "\n",
    "tot_sfr = sum(ncorr_sfrs)\n",
    "tot_sfr_stdv = np.sqrt(np.square(ncorr_sfr_stdvs).sum())\n",
    "\n",
    "tot_sfr_spiral = sum(ncorr_spiral_sfrs)\n",
    "tot_sfr_stdv_spiral = np.sqrt(np.square(ncorr_spiral_sfr_stdvs).sum())\n",
    "\n",
    "tot_sfr_nspiral = sum(ncorr_nspiral_sfrs)\n",
    "tot_sfr_stdv_nspiral = np.sqrt(np.square(ncorr_nspiral_sfr_stdvs).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1-24124'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_ids[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Radius</th>\n",
       "      <th>$H_{\\alpha}$</th>\n",
       "      <th>$\\sigma H_{\\alpha}$</th>\n",
       "      <th>$H_{\\beta}$</th>\n",
       "      <th>$\\sigma H_{\\beta}$</th>\n",
       "      <th>Comp</th>\n",
       "      <th>AGN</th>\n",
       "      <th>Seyfert</th>\n",
       "      <th>Liner</th>\n",
       "      <th>$r/r_e$</th>\n",
       "      <th>Spiral Arm</th>\n",
       "      <th>Mass</th>\n",
       "      <th>$\\lg{Mass}$</th>\n",
       "      <th>S/N $H_{\\alpha}$</th>\n",
       "      <th>S/N $H_{\\beta}$</th>\n",
       "      <th>MaNGA ID</th>\n",
       "      <th>Mass Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>5.319175</td>\n",
       "      <td>0.051064</td>\n",
       "      <td>0.016434</td>\n",
       "      <td>0.079698</td>\n",
       "      <td>0.023477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571339</td>\n",
       "      <td>False</td>\n",
       "      <td>2.872520e+09</td>\n",
       "      <td>9.458263</td>\n",
       "      <td>3.107225</td>\n",
       "      <td>3.394674</td>\n",
       "      <td>1-24124</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Radius  $H_{\\alpha}$  $\\sigma H_{\\alpha}$  $H_{\\beta}$  \\\n",
       "768  5.319175      0.051064             0.016434     0.079698   \n",
       "\n",
       "     $\\sigma H_{\\beta}$  Comp  AGN  Seyfert  Liner   $r/r_e$  Spiral Arm  \\\n",
       "768            0.023477   0.0  0.0      0.0    0.0  0.571339       False   \n",
       "\n",
       "             Mass  $\\lg{Mass}$  S/N $H_{\\alpha}$  S/N $H_{\\beta}$ MaNGA ID  \\\n",
       "768  2.872520e+09     9.458263          3.107225         3.394674  1-24124   \n",
       "\n",
       "    Mass Bin  \n",
       "768        0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spax_df[(spax_df['MaNGA ID'] == '1-24124') & (spax_df['Mass Bin'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We're going to make a S/N scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gal in gal_objs:\n",
    "    map_ha = gal.maps.getMap('emline_gflux', channel='ha_6564')\n",
    "    map_hb = gal.maps.getMap('emline_gflux', channel='hb_4862')\n",
    "    \n",
    "    ha_snr = map_ha.snr.flatten()\n",
    "    hb_snr = map_hb.snr.flatten()\n",
    "    \n",
    "    gal.df['S/N $H_{\\\\alpha}$'] = ha_snr\n",
    "    gal.df['S/N $H_{\\\\beta}$'] = hb_snr\n",
    "    \n",
    "    gal.df['% SFR Error'] = (gal.df['$\\sigma$SFR'] * 100) / gal.df['SFR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = []\n",
    "\n",
    "for gal in gal_objs:\n",
    "    df_list.append(gal.df)\n",
    "    \n",
    "spax_df = pd.concat(df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df = spax_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df = spax_df[(spax_df.Comp == 0) & (spax_df.AGN == 0) & (spax_df.Seyfert == 0) & (spax_df.Liner == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "spax_df = spax_df[(spax_df['$r/r_e$'] <= 1) & (spax_df['$r/r_e$'] >= 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_ha_snr = spax_df['S/N $H_{\\\\alpha}$'].tolist()\n",
    "global_hb_snr = spax_df['S/N $H_{\\\\beta}$'].tolist()\n",
    "global_sfr = spax_df['SFR'].tolist()\n",
    "global_pcerr_sfr = spax_df['% SFR Error'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc613ff5320>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.scatter(global_ha_snr, global_pcerr_sfr, label='$H_{\\\\alpha}$')\n",
    "plt.scatter(global_hb_snr, global_pcerr_sfr, label='$H_{\\\\beta}$')\n",
    "\n",
    "plt.xlabel('SNR', labelpad=20, size=20)\n",
    "plt.ylabel('% Error SFR', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "\n",
    "plt.ylim(0, 200)\n",
    "plt.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc613c6dbe0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.scatter(global_ha_snr, global_sfr, label='$H_{\\\\alpha}$')\n",
    "plt.scatter(global_hb_snr, global_sfr, label='$H_{\\\\beta}$')\n",
    "\n",
    "plt.xlabel('SNR', labelpad=20, size=20)\n",
    "plt.ylabel('SFR', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)\n",
    "\n",
    "plt.ylim(0, 10)\n",
    "plt.xlim(0, 40)\n",
    "\n",
    "plt.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0., 1000., 2000., 3000., 4000., 5000., 6000., 7000., 8000.]),\n",
       " <a list of 9 Text yticklabel objects>)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "\n",
    "plt.hist(global_ha_snr, bins=100, alpha=0.7)\n",
    "\n",
    "plt.xlabel('SNR', labelpad=20, size=20)\n",
    "plt.ylabel('Num Spaxels', labelpad=20, size=20)\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [1, 2, 4, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = np.sqrt(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.863703305156273"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('usable_path_list', usable_path_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}