onskydata/Intro_to_DESI_SV_spectra.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to DESI SV Spectra\n",
    "\n",
    "The goal of this notebook is to demonstrate how to read in and manipulate DESI SV spectra using on-sky data. Specifically, we will use the February/March 2020 mini-SV-2 runs taken as part of DESI _commissioning_.\n",
    "\n",
    "If you identify any errors or have requests for additional functionality please create a new issue at https://github.com/desihub/tutorials/issues or send a note to desi-data@desi.lbl.gov.\n",
    "\n",
    "Note that this tutorial specifically deals with on-sky data from SV (or, currently, mini-SV). To learn how to work with Main Survey data look at the _Introduction to DESI Spectra_ tutorial instead. (e.g. https://github.com/desihub/tutorials/blob/master/Intro_to_DESI_spectra.ipynb).\n",
    "\n",
    "Last updated April 2022 using the 22.2 software release."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "\n",
    "### Using NERSC\n",
    "\n",
    "The easiest way to get started is to use the jupyter server at NERSC so that you don't need to\n",
    "install any code or download any data locally.\n",
    "\n",
    "If you need a NERSC account, see https://desi.lbl.gov/trac/wiki/Computing/AccessNersc\n",
    "\n",
    "Then do the one-time jupyter configuration described at https://desi.lbl.gov/trac/wiki/Computing/JupyterAtNERSC\n",
    "\n",
    "From a NERSC command line, checkout a copy of the tutorial code, *e.g.* from cori.nersc.gov\n",
    "```console\n",
    "mkdir -p $HOME/desi/\n",
    "cd $HOME/desi/\n",
    "git clone https://github.com/desihub/tutorials\n",
    "```\n",
    "And then go to https://jupyter.nersc.gov, login, navigate to where you checked out this package (*e.g.* `$HOME/desi/tutorials`), and double-click on `Intro_to_DESI_SV_spectra.ipynb`.\n",
    "\n",
    "This tutorial has been tested using the \"DESI 20.8\" kernel installed at NERSC.  To get an equivalent environment from a cori command line:\n",
    "```console\n",
    "source /global/common/software/desi/desi_environment.sh 20.8\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "from glob import glob\n",
    "import fitsio\n",
    "from collections import defaultdict\n",
    "\n",
    "# ADM Note that we use the commissioning targeting mask, as we're working with mini-SV data from commissioning.\n",
    "from desitarget.cmx.cmx_targetmask import cmx_mask  \n",
    "import desispec.io\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running locally and any of these fail, \n",
    "you should go back through the [installation instructions](https://desi.lbl.gov/trac/wiki/Pipeline/GettingStarted/Laptop) and/or email `desi-data@desi.lbl.gov` if you get stuck.\n",
    "If you are running from jupyter.nersc.gov and have problems, double check that your kernel is \"DESI 22.2\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment variables and data\n",
    "\n",
    "DESI uses environment variables to define the base directories for where to find data.  The below paths are for NERSC, but if you are running locally or want to access a different dataset, change these as needed to wherever your dataset is.\n",
    "\n",
    "Spectro production runs are grouped under `$DESI_SPECTRO_REDUX`, with `$SPECPROD` indicating which run to use, such that the data are under `$DESI_SPECTRO_REDUX/$SPECPROD`.  *e.g.* during operations, official productions will be in `$DESI_SPECTRO_REDUX=/global/cfs/cdirs/desi/spectro/redux` and `$SPECPROD` would be the name for individual data assemblies, *e.g.* `$SPECPROD=DA1`.  In this case, we'll use `$SPECPROD=daily`, which corresponds to the daily reductions for mini-SV-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: DESI_SPECTRO_REDUX=/global/cfs/cdirs/desi/spectro/redux\n",
      "env: SPECPROD=daily\n"
     ]
    }
   ],
   "source": [
    "%set_env DESI_SPECTRO_REDUX=/global/cfs/cdirs/desi/spectro/redux\n",
    "%set_env SPECPROD=daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`desispec.io.specprod_root` can handle the environment variable path wrangling for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/desi/spectro/redux/daily\n"
     ]
    }
   ],
   "source": [
    "reduxdir = desispec.io.specprod_root()\n",
    "print(reduxdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$DESI_SPECTRO_REDUX=/global/cfs/cdirs/desi/spectro/redux\n",
      "$SPECPROD=daily\n",
      "OK: /global/cfs/cdirs/desi/spectro/redux/daily exists\n"
     ]
    }
   ],
   "source": [
    "#- Do check that these are set correctly before proceeding\n",
    "def check_env():\n",
    "    for env in ('DESI_SPECTRO_REDUX', 'SPECPROD'):\n",
    "        if env in os.environ:\n",
    "            print('${}={}'.format(env, os.getenv(env)))\n",
    "        else:\n",
    "            print('Required environment variable {} not set!'.format(env))\n",
    "\n",
    "    reduxdir = desispec.io.specprod_root()\n",
    "    if not os.path.exists(reduxdir):\n",
    "        print(\"ERROR: {} doesn't exist; check $DESI_SPECTRO_REDUX/$SPECPROD\".format(reduxdir))\n",
    "    else:\n",
    "        print('OK: {} exists'.format(reduxdir))\n",
    "\n",
    "check_env()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Model for the spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directory structure\n",
    "\n",
    "Spectra from individual exposures are in the `exposures` directory.  But since SV will focus on targeting individual _tiles_, the relevant directory and file structure is: \n",
    "\n",
    "```\n",
    "$DESI_SPECTRO_REDUX/$SPECPROD/tiles/$TILE/$DATE/*-$SPECTROGRAPH-$TILE-$DATE.fits\n",
    "```\n",
    "\n",
    "where:\n",
    "\n",
    "* `$TILE` is the number of the relevant SV (or mini-SV) tile. For example, for mini-SV-2, see the list of tiles on the mini-SV-2 [wiki page](https://desi.lbl.gov/trac/wiki/TargetSelectionWG/miniSV2#Fieldcenters).\n",
    "* `$DATE` is the date expressed as YYYYMMDD, for example 20200229 for year=2020, month=february, day=29.\n",
    "* `$SPECTROGRAPH` corresponds to the DESI spectrograph used to observe the targets (0-9).\n",
    "\n",
    "The files we will focus on for this tutorial correspond to `$TILE=70003` and `$DATE=20200226` and `$SPECTROGRAPH=0`. For example:\n",
    "\n",
    "```\n",
    "$DESI_SPECTRO_REDUX/$SPECPROD/tiles/70003/20200226/coadd-0-70003-20200226.fits\n",
    "$DESI_SPECTRO_REDUX/$SPECPROD/tiles/70003/20200226/zbest-0-70003-20200226.fits\n",
    "```\n",
    "where the first file contains the (coadded) spectra and the second file contains information on the best-fit redshifts from the [redrock](https://github.com/desihub/redrock) code.\n",
    "\n",
    "Let's poke around in these directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/desi/spectro/redux/daily/tiles\n",
      "['1', '10', '104', '105', '106', '107', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '12', '120', '121', '122', '123', '124', '13', '131', '132', '133', '136', '137', '138', '139', '14', '140', '141', '142', '143', '144', '145', '147', '148', '149', '15', '150', '151', '152', '158', '16', '163', '164', '165', '166', '167', '168', '169', '17', '170', '174', '175', '176', '177', '178', '179', '18', '180', '185', '186', '187', '19', '190', '191', '192', '193', '194', '195', '196', '197', '198', '2', '20', '201', '202', '203', '204', '205', '206', '207', '208', '209', '21', '210', '211', '212', '213', '214', '215', '217', '218', '219', '220', '221', '222', '223', '224', '225', '228', '229', '23', '230', '231', '232', '24', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '255', '256', '257', '258', '259', '260', '261', '262', '271', '272', '273', '274', '275', '276', '277', '278', '279', '28', '280', '281', '282', '283', '284', '285', '286', '287', '288', '29', '298', '299', '3', '30', '300', '301', '302', '303', '304', '305', '306', '307', '308', '309', '31', '310', '311', '312', '313', '314', '315', '316', '317', '318', '319', '32', '320', '321', '325', '326', '327', '328', '329', '33', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '342', '343', '344', '345', '347', '352', '353', '354', '355', '356', '357', '358', '359', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '374', '375', '379', '380', '381', '382', '383', '384', '385', '386', '387', '388', '389', '39', '390', '391', '392', '393', '394', '395', '396', '4', '40', '401', '406', '407', '408', '409', '41', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '42', '420', '421', '422', '423', '424', '425', '428', '429', '43', '433', '436', '437', '439', '44', '440', '45', '46', '47', '48', '481', '482', '5', '50', '51', '511', '512', '52', '525', '526', '53', '541', '55', '555', '56', '57', '571', '58', '585', '586', '59', '6', '60', '61', '63075', '63159', '63160', '63161', '63162', '63163', '63164', '63165', '63166', '63167', '63168', '63169', '63170', '63171', '63211', '63224', '63225', '63226', '63227', '63228', '63229', '63230', '63231', '63232', '63233', '63234', '63235', '63236', '65008', '66', '66000', '66003', '66014', '66019', '67', '67142', '67230', '68', '68000', '68001', '68002', '69', '7', '70', '70002', '70003', '70004', '70005', '70006', '70500', '70502', '70506', '70508', '70510', '70511', '70512', '70513', '71', '77', '78', '79', '8', '80254', '80605', '80606', '80607', '80608', '80609', '80610', '80611', '80612', '80613', '80614', '80615', '80616', '80617', '80618', '80619', '80620', '80621', '80622', '80623', '80624', '80626', '80627', '80628', '80629', '80632', '80633', '80635', '80636', '80637', '80638', '80639', '80640', '80641', '80642', '80643', '80644', '80645', '80646', '80647', '80648', '80649', '80650', '80651', '80652', '80653', '80654', '80655', '80656', '80657', '80658', '80659', '80660', '80661', '80662', '80663', '80664', '80665', '80666', '80669', '80670', '80673', '80674', '80675', '80676', '80677', '80678', '80679', '80680', '80681', '80682', '80683', '80684', '80685', '80686', '80687', '80688', '80689', '80690', '80691', '80692', '80693', '80694', '80695', '80696', '80697', '80698', '80699', '80700', '80701', '80702', '80703', '80705', '80706', '80707', '80708', '80709', '80710', '80711', '80712', '80719', '80720', '80721', '80722', '80724', '80726', '80728', '80729', '80730', '80731', '80732', '80733', '80734', '80735', '80736', '80737', '80738', '80740', '80741', '80742', '80769', '80782', '80783', '80784', '80785', '80786', '80787', '80788', '80789', '80790', '80804', '80816', '80856', '80857', '80858', '80859', '80860', '80861', '80862', '80863', '80866', '80869', '80871', '80873', '80875', '80876', '80879', '80882', '80885', '80886', '80887', '80888', '80889', '80890', '80891', '80892', '80893', '80894', '80895', '80896', '80897', '80898', '80899', '80900', '80901', '80902', '80938', '80944', '80949', '80950', '80951', '80952', '80954', '80955', '80956', '80958', '80959', '80960', '80962', '80963', '80966', '80968', '80970', '81000', '81001', '81002', '81003', '81004', '81005', '81006', '81007', '81008', '81009', '81010', '81011', '81012', '81013', '81014', '81015', '81022', '81056', '81057', '81058', '81059', '81060', '81061', '81062', '81063', '81064', '81065', '81066', '81067', '81068', '81069', '81072', '81073', '81074', '81075', '81088', '81096', '81097', '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '93', '94', '95', '96', '97', '98', '99', 'archive', 'cumulative', 'temp']\n"
     ]
    }
   ],
   "source": [
    "basedir = os.path.join(os.getenv(\"DESI_SPECTRO_REDUX\"), os.getenv(\"SPECPROD\"), \"tiles\")\n",
    "subdir = sorted(os.listdir(basedir))\n",
    "print(basedir)\n",
    "print(subdir)\n",
    "tileid = 70003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003\n",
      "['20200219', '20200224', '20200226', '20200304']\n"
     ]
    }
   ],
   "source": [
    "basedir = os.path.join(basedir, str(tileid))\n",
    "subdir = sorted(os.listdir(basedir))\n",
    "print(basedir)\n",
    "print(subdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226\n",
      "['/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-3-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-7-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-7-70003-20200226.log', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-3-70003-20200226.log', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-0-70003-20200226.log', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-9-70003-20200226.log', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-6-70003-20200226.log', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-0-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-9-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-6-70003-20200226.fits']\n",
      "['/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/zbest-7-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/zbest-0-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/zbest-6-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/zbest-3-70003-20200226.fits', '/global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/zbest-9-70003-20200226.fits']\n"
     ]
    }
   ],
   "source": [
    "basedir = os.path.join(basedir, subdir[2])\n",
    "coaddfiles = glob(os.path.join(basedir, \"*coadd*\"))\n",
    "zbestfiles = glob(os.path.join(basedir, \"*zbest*\"))\n",
    "print(basedir)\n",
    "print(coaddfiles)\n",
    "print(zbestfiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### spectra file format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the Data Model for the coadded spectra themselves?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  file: /global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-0-70003-20200226.fits\n",
       "  mode: READONLY\n",
       "  extnum hdutype         hduname[v]\n",
       "  0      IMAGE_HDU       \n",
       "  1      BINARY_TBL      FIBERMAP\n",
       "  2      IMAGE_HDU       BRZ_WAVELENGTH\n",
       "  3      IMAGE_HDU       BRZ_FLUX\n",
       "  4      IMAGE_HDU       BRZ_IVAR\n",
       "  5      IMAGE_HDU       BRZ_MASK\n",
       "  6      IMAGE_HDU       BRZ_RESOLUTION"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tile, date, spectrograph = \"70003\", \"20200226\", \"0\"\n",
    "dirname = os.path.join(os.getenv(\"DESI_SPECTRO_REDUX\"), os.getenv(\"SPECPROD\"), \"tiles\", tile, date)\n",
    "filename = \"coadd-{}-{}-{}.fits\".format(spectrograph, tile, date)\n",
    "specfilename = os.path.join(dirname, filename)\n",
    "DM = fitsio.FITS(specfilename)\n",
    "DM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HDU 0 is blank.  The others should be used by name, not by number since the order could vary.\n",
    "\n",
    "`FIBERMAP` stores the mapping of the imaging information used to target and place a fiber on the source.\n",
    "\n",
    "The other HDUs contain the wavelength arrays, flux, inverse variance (ivar), mask (0 is good), and spectral resolution data coadded across each of the \"B\", \"R\", and \"Z\" cameras.\n",
    "\n",
    "Let's start by looking at the fibermap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TARGETID', '>i8'),\n",
       " ('PETAL_LOC', '>i2'),\n",
       " ('DEVICE_LOC', '>i4'),\n",
       " ('LOCATION', '>i4'),\n",
       " ('FIBER', '>i4'),\n",
       " ('FIBERSTATUS', '>i4'),\n",
       " ('TARGET_RA', '>f8'),\n",
       " ('TARGET_DEC', '>f8'),\n",
       " ('PMRA', '>f4'),\n",
       " ('PMDEC', '>f4'),\n",
       " ('PMRA_IVAR', '>f4'),\n",
       " ('PMDEC_IVAR', '>f4'),\n",
       " ('REF_EPOCH', '>f4'),\n",
       " ('LAMBDA_REF', '>f4'),\n",
       " ('FA_TARGET', '>i8'),\n",
       " ('FA_TYPE', '|u1'),\n",
       " ('OBJTYPE', '<U3'),\n",
       " ('FIBERASSIGN_X', '>f4'),\n",
       " ('FIBERASSIGN_Y', '>f4'),\n",
       " ('NUMTARGET', '>i2'),\n",
       " ('PRIORITY', '>i4'),\n",
       " ('SUBPRIORITY', '>f8'),\n",
       " ('OBSCONDITIONS', '>i4'),\n",
       " ('NUMOBS_MORE', '>i4'),\n",
       " ('RELEASE', '>i2'),\n",
       " ('BRICKID', '>i4'),\n",
       " ('BRICKNAME', '<U8'),\n",
       " ('BRICK_OBJID', '>i4'),\n",
       " ('MORPHTYPE', '<U4'),\n",
       " ('TARGET_RA_IVAR', '>f4'),\n",
       " ('TARGET_DEC_IVAR', '>f4'),\n",
       " ('EBV', '>f4'),\n",
       " ('FLUX_G', '>f4'),\n",
       " ('FLUX_R', '>f4'),\n",
       " ('FLUX_Z', '>f4'),\n",
       " ('FLUX_IVAR_G', '>f4'),\n",
       " ('FLUX_IVAR_R', '>f4'),\n",
       " ('FLUX_IVAR_Z', '>f4'),\n",
       " ('MW_TRANSMISSION_G', '>f4'),\n",
       " ('MW_TRANSMISSION_R', '>f4'),\n",
       " ('MW_TRANSMISSION_Z', '>f4'),\n",
       " ('FRACFLUX_G', '>f4'),\n",
       " ('FRACFLUX_R', '>f4'),\n",
       " ('FRACFLUX_Z', '>f4'),\n",
       " ('FRACMASKED_G', '>f4'),\n",
       " ('FRACMASKED_R', '>f4'),\n",
       " ('FRACMASKED_Z', '>f4'),\n",
       " ('FRACIN_G', '>f4'),\n",
       " ('FRACIN_R', '>f4'),\n",
       " ('FRACIN_Z', '>f4'),\n",
       " ('NOBS_G', '>i2'),\n",
       " ('NOBS_R', '>i2'),\n",
       " ('NOBS_Z', '>i2'),\n",
       " ('PSFDEPTH_G', '>f4'),\n",
       " ('PSFDEPTH_R', '>f4'),\n",
       " ('PSFDEPTH_Z', '>f4'),\n",
       " ('GALDEPTH_G', '>f4'),\n",
       " ('GALDEPTH_R', '>f4'),\n",
       " ('GALDEPTH_Z', '>f4'),\n",
       " ('FLUX_W1', '>f4'),\n",
       " ('FLUX_W2', '>f4'),\n",
       " ('FLUX_W3', '>f4'),\n",
       " ('FLUX_W4', '>f4'),\n",
       " ('FLUX_IVAR_W1', '>f4'),\n",
       " ('FLUX_IVAR_W2', '>f4'),\n",
       " ('FLUX_IVAR_W3', '>f4'),\n",
       " ('FLUX_IVAR_W4', '>f4'),\n",
       " ('MW_TRANSMISSION_W1', '>f4'),\n",
       " ('MW_TRANSMISSION_W2', '>f4'),\n",
       " ('MW_TRANSMISSION_W3', '>f4'),\n",
       " ('MW_TRANSMISSION_W4', '>f4'),\n",
       " ('ALLMASK_G', '>i2'),\n",
       " ('ALLMASK_R', '>i2'),\n",
       " ('ALLMASK_Z', '>i2'),\n",
       " ('FIBERFLUX_G', '>f4'),\n",
       " ('FIBERFLUX_R', '>f4'),\n",
       " ('FIBERFLUX_Z', '>f4'),\n",
       " ('FIBERTOTFLUX_G', '>f4'),\n",
       " ('FIBERTOTFLUX_R', '>f4'),\n",
       " ('FIBERTOTFLUX_Z', '>f4'),\n",
       " ('WISEMASK_W1', '|u1'),\n",
       " ('WISEMASK_W2', '|u1'),\n",
       " ('MASKBITS', '>i2'),\n",
       " ('FRACDEV', '>f4'),\n",
       " ('FRACDEV_IVAR', '>f4'),\n",
       " ('SHAPEDEV_R', '>f4'),\n",
       " ('SHAPEDEV_E1', '>f4'),\n",
       " ('SHAPEDEV_E2', '>f4'),\n",
       " ('SHAPEDEV_R_IVAR', '>f4'),\n",
       " ('SHAPEDEV_E1_IVAR', '>f4'),\n",
       " ('SHAPEDEV_E2_IVAR', '>f4'),\n",
       " ('SHAPEEXP_R', '>f4'),\n",
       " ('SHAPEEXP_E1', '>f4'),\n",
       " ('SHAPEEXP_E2', '>f4'),\n",
       " ('SHAPEEXP_R_IVAR', '>f4'),\n",
       " ('SHAPEEXP_E1_IVAR', '>f4'),\n",
       " ('SHAPEEXP_E2_IVAR', '>f4'),\n",
       " ('REF_ID', '>i8'),\n",
       " ('REF_CAT', '<U2'),\n",
       " ('GAIA_PHOT_G_MEAN_MAG', '>f4'),\n",
       " ('GAIA_PHOT_G_MEAN_FLUX_OVER_ERROR', '>f4'),\n",
       " ('GAIA_PHOT_BP_MEAN_MAG', '>f4'),\n",
       " ('GAIA_PHOT_BP_MEAN_FLUX_OVER_ERROR', '>f4'),\n",
       " ('GAIA_PHOT_RP_MEAN_MAG', '>f4'),\n",
       " ('GAIA_PHOT_RP_MEAN_FLUX_OVER_ERROR', '>f4'),\n",
       " ('GAIA_PHOT_BP_RP_EXCESS_FACTOR', '>f4'),\n",
       " ('GAIA_ASTROMETRIC_EXCESS_NOISE', '>f4'),\n",
       " ('GAIA_DUPLICATED_SOURCE', '|b1'),\n",
       " ('GAIA_ASTROMETRIC_SIGMA5D_MAX', '>f4'),\n",
       " ('GAIA_ASTROMETRIC_PARAMS_SOLVED', '|b1'),\n",
       " ('PARALLAX', '>f4'),\n",
       " ('PARALLAX_IVAR', '>f4'),\n",
       " ('PHOTSYS', '<U1'),\n",
       " ('CMX_TARGET', '>i8'),\n",
       " ('PRIORITY_INIT', '>i8'),\n",
       " ('NUMOBS_INIT', '>i8'),\n",
       " ('HPXPIXEL', '>i8'),\n",
       " ('BLOBDIST', '>f4'),\n",
       " ('FIBERFLUX_IVAR_G', '>f4'),\n",
       " ('FIBERFLUX_IVAR_R', '>f4'),\n",
       " ('FIBERFLUX_IVAR_Z', '>f4'),\n",
       " ('DESI_TARGET', '>i8'),\n",
       " ('BGS_TARGET', '>i8'),\n",
       " ('MWS_TARGET', '>i8'),\n",
       " ('NUM_ITER', '>i8'),\n",
       " ('FIBER_X', '>f8'),\n",
       " ('FIBER_Y', '>f8'),\n",
       " ('MEAN_DELTA_X', '>f8'),\n",
       " ('MEAN_DELTA_Y', '>f8'),\n",
       " ('FIBER_RA', '>f8'),\n",
       " ('FIBER_DEC', '>f8'),\n",
       " ('RMS_DELTA_X', '>f8'),\n",
       " ('RMS_DELTA_Y', '>f8'),\n",
       " ('FIRST_FIBER', '>i8'),\n",
       " ('LAST_FIBER', '>i8'),\n",
       " ('NUM_FIBER', '>i8')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm = fitsio.read(specfilename, 'FIBERMAP')\n",
    "fm.dtype.descr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TARGETID` is the unique mapping from target information to a fiber. So, if you wanted to look up full imaging information for a spectrum, you can map back to target files using `TARGETID`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are only looking at a single spectrograph this should correspond to a single petal in the DESI focal plane. I wonder if that's true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1554be8a8a30>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fm[\"TARGET_RA\"],fm[\"TARGET_DEC\"],'b.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This certainly looks like one petal to me.  Let's repeat, color coding by spectrograph number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ADM as of mini-SV-2 we only have spectrographs 0, 3, 6, 7, 9.\n",
    "for spectrograph in \"0\", \"3\", \"6\", \"7\", \"9\":\n",
    "    filename = \"coadd-{}-{}-{}.fits\".format(spectrograph, tile, date)\n",
    "    specfilename = os.path.join(dirname, filename)\n",
    "    DM = fitsio.FITS(specfilename)\n",
    "    fm = fitsio.read(specfilename, 'FIBERMAP')\n",
    "    plt.plot(fm[\"TARGET_RA\"],fm[\"TARGET_DEC\"], '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in addition to having multiple tiles, we also have multiple exposures of the same tile resulting in multiple spectra of the same targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  file: /global/cfs/cdirs/desi/spectro/redux/daily/tiles/70003/20200226/coadd-9-70003-20200226.fits\n",
       "  mode: READONLY\n",
       "  extnum hdutype         hduname[v]\n",
       "  0      IMAGE_HDU       \n",
       "  1      BINARY_TBL      FIBERMAP\n",
       "  2      IMAGE_HDU       BRZ_WAVELENGTH\n",
       "  3      IMAGE_HDU       BRZ_FLUX\n",
       "  4      IMAGE_HDU       BRZ_IVAR\n",
       "  5      IMAGE_HDU       BRZ_MASK\n",
       "  6      IMAGE_HDU       BRZ_RESOLUTION"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remaining extensions store the wavelength, flux, inverse variance on the flux, mask and resolution matrix coadded for the B, R and Z arms of the spectrograph. Let's check that the full wavelength coverage across all 3 arms of each of the DESI spectrographs is the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength coverage of spectrograph 9: 3600.0 to 9824.0 Angstroms\n",
      "wavelength coverage of spectrograph 7: 3600.0 to 9824.0 Angstroms\n",
      "wavelength coverage of spectrograph 6: 3600.0 to 9824.0 Angstroms\n",
      "wavelength coverage of spectrograph 3: 3600.0 to 9824.0 Angstroms\n",
      "wavelength coverage of spectrograph 0: 3600.0 to 9824.0 Angstroms\n"
     ]
    }
   ],
   "source": [
    "for spectrograph in \"9\", \"7\", \"6\", \"3\", \"0\":\n",
    "    filename = \"coadd-{}-{}-{}.fits\".format(spectrograph, tile, date)\n",
    "    specfilename = os.path.join(dirname, filename)\n",
    "    wave = fitsio.read(specfilename, 'BRZ_WAVELENGTH')\n",
    "    print(\"wavelength coverage of spectrograph {}: {:.1f} to {:.1f} Angstroms\".format(spectrograph, np.min(wave), np.max(wave)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in and Displaying spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we understand the Data Model, let's plot some spectra. To start, let's use the file we've already been manipulating (for spectrograph 0) and read in the flux to go with the wavelengths we already have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = fitsio.read(specfilename,'BRZ_FLUX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the wavelength arrays are 1-D (every spectrum in the spectral file is mapped to the same binning in wavelength) but the flux array (and flux_ivar, mask etc. arrays) are 2-D, because they contain multiple spectra:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7781,)\n",
      "(500, 7781)\n"
     ]
    }
   ],
   "source": [
    "print(wave.shape)\n",
    "print(flux.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot one of the spectra from this file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1554bd7a5220>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectrum = 23\n",
    "# ADM make the figure 20-by-5 in size.\n",
    "plt.figure(figsize=(20, 5))\n",
    "# ADM some reasonable plot limits.\n",
    "xmin, xmax, ymin, ymax = np.min(wave), np.max(wave), np.min(flux[spectrum][0:100]), np.max(flux[spectrum][0:100])\n",
    "plt.axis([xmin, xmax, ymin, ymax])\n",
    "plt.plot(wave, flux[spectrum], 'b-', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A DESI-specific spectrum reader\n",
    "\n",
    "Note that, for illustrative purposes, we discussed the Data Model in detail and read in the required files individually from that Data Model. But, the DESI data team has also developed standalone functions in `desispec.io` to facilitate reading in the plethora of information in the spectral files. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:spectra.py:285:read_spectra: iotime 0.556 sec to read coadd-0-70003-20200226.fits at 2022-04-29T07:33:17.495027\n"
     ]
    }
   ],
   "source": [
    "specobj = desispec.io.read_spectra(specfilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wavelengths and flux in each band are then available as dictionaries in the `wave` and `flux` attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brz': array([3600. , 3600.8, 3601.6, ..., 9822.4, 9823.2, 9824. ])}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specobj.wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brz': array([[ 1.03085494e+00,  5.34286082e-01, -9.64657187e-01, ...,\n",
       "         -3.80556762e-01, -2.18447313e-01, -8.33308101e-02],\n",
       "        [-2.55843133e-01, -8.46773267e-01, -1.68721449e+00, ...,\n",
       "          4.10736144e-01,  1.08134806e-01, -4.81233299e-02],\n",
       "        [ 5.07653379e+00,  6.74898148e+00,  4.82973862e+00, ...,\n",
       "          1.69361949e+00,  1.59529316e+00,  1.25684929e+00],\n",
       "        ...,\n",
       "        [ 1.06593895e+00, -8.84459436e-01,  3.35761309e-01, ...,\n",
       "          1.08722314e-01,  2.57558078e-02, -3.28279063e-02],\n",
       "        [ 1.64347768e+00, -1.54139948e+00, -1.17252338e+00, ...,\n",
       "         -3.61708133e-03,  1.42026439e-01,  1.00789659e-01],\n",
       "        [ 6.03420258e-01,  2.17040747e-01, -2.72437215e+00, ...,\n",
       "         -3.16569030e-01, -2.02280059e-02, -5.89266233e-03]])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specobj.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, to plot the (zeroth-indexed) 24th spectrum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1554bd79d2e0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectrum = 23\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.axis([xmin, xmax, ymin, ymax])\n",
    "plt.plot(specobj.wave[\"brz\"], specobj.flux[\"brz\"][spectrum], 'b-', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which should look very similar to one of the first plots we made earlier in the tutorial. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fibermap information is available as a table in the `fibermap` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=500</i>\n",
       "<table id=\"table23453717846528\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>TARGETID</th><th>PETAL_LOC</th><th>DEVICE_LOC</th><th>LOCATION</th><th>FIBER</th><th>FIBERSTATUS</th><th>TARGET_RA</th><th>TARGET_DEC</th><th>PMRA</th><th>PMDEC</th><th>PMRA_IVAR</th><th>PMDEC_IVAR</th><th>REF_EPOCH</th><th>LAMBDA_REF</th><th>FA_TARGET</th><th>FA_TYPE</th><th>OBJTYPE</th><th>FIBERASSIGN_X</th><th>FIBERASSIGN_Y</th><th>NUMTARGET</th><th>PRIORITY</th><th>SUBPRIORITY</th><th>OBSCONDITIONS</th><th>NUMOBS_MORE</th><th>RELEASE</th><th>BRICKID</th><th>BRICKNAME</th><th>BRICK_OBJID</th><th>MORPHTYPE</th><th>TARGET_RA_IVAR</th><th>TARGET_DEC_IVAR</th><th>EBV</th><th>FLUX_G</th><th>FLUX_R</th><th>FLUX_Z</th><th>FLUX_IVAR_G</th><th>FLUX_IVAR_R</th><th>FLUX_IVAR_Z</th><th>MW_TRANSMISSION_G</th><th>MW_TRANSMISSION_R</th><th>MW_TRANSMISSION_Z</th><th>FRACFLUX_G</th><th>FRACFLUX_R</th><th>FRACFLUX_Z</th><th>FRACMASKED_G</th><th>FRACMASKED_R</th><th>FRACMASKED_Z</th><th>FRACIN_G</th><th>FRACIN_R</th><th>FRACIN_Z</th><th>NOBS_G</th><th>NOBS_R</th><th>NOBS_Z</th><th>PSFDEPTH_G</th><th>PSFDEPTH_R</th><th>PSFDEPTH_Z</th><th>GALDEPTH_G</th><th>GALDEPTH_R</th><th>GALDEPTH_Z</th><th>FLUX_W1</th><th>FLUX_W2</th><th>FLUX_W3</th><th>FLUX_W4</th><th>FLUX_IVAR_W1</th><th>FLUX_IVAR_W2</th><th>FLUX_IVAR_W3</th><th>FLUX_IVAR_W4</th><th>MW_TRANSMISSION_W1</th><th>MW_TRANSMISSION_W2</th><th>MW_TRANSMISSION_W3</th><th>MW_TRANSMISSION_W4</th><th>ALLMASK_G</th><th>ALLMASK_R</th><th>ALLMASK_Z</th><th>FIBERFLUX_G</th><th>FIBERFLUX_R</th><th>FIBERFLUX_Z</th><th>FIBERTOTFLUX_G</th><th>FIBERTOTFLUX_R</th><th>FIBERTOTFLUX_Z</th><th>WISEMASK_W1</th><th>WISEMASK_W2</th><th>MASKBITS</th><th>FRACDEV</th><th>FRACDEV_IVAR</th><th>SHAPEDEV_R</th><th>SHAPEDEV_E1</th><th>SHAPEDEV_E2</th><th>SHAPEDEV_R_IVAR</th><th>SHAPEDEV_E1_IVAR</th><th>SHAPEDEV_E2_IVAR</th><th>SHAPEEXP_R</th><th>SHAPEEXP_E1</th><th>SHAPEEXP_E2</th><th>SHAPEEXP_R_IVAR</th><th>SHAPEEXP_E1_IVAR</th><th>SHAPEEXP_E2_IVAR</th><th>REF_ID</th><th>REF_CAT</th><th>GAIA_PHOT_G_MEAN_MAG</th><th>GAIA_PHOT_G_MEAN_FLUX_OVER_ERROR</th><th>GAIA_PHOT_BP_MEAN_MAG</th><th>GAIA_PHOT_BP_MEAN_FLUX_OVER_ERROR</th><th>GAIA_PHOT_RP_MEAN_MAG</th><th>GAIA_PHOT_RP_MEAN_FLUX_OVER_ERROR</th><th>GAIA_PHOT_BP_RP_EXCESS_FACTOR</th><th>GAIA_ASTROMETRIC_EXCESS_NOISE</th><th>GAIA_DUPLICATED_SOURCE</th><th>GAIA_ASTROMETRIC_SIGMA5D_MAX</th><th>GAIA_ASTROMETRIC_PARAMS_SOLVED</th><th>PARALLAX</th><th>PARALLAX_IVAR</th><th>PHOTSYS</th><th>CMX_TARGET</th><th>PRIORITY_INIT</th><th>NUMOBS_INIT</th><th>HPXPIXEL</th><th>BLOBDIST</th><th>FIBERFLUX_IVAR_G</th><th>FIBERFLUX_IVAR_R</th><th>FIBERFLUX_IVAR_Z</th><th>DESI_TARGET</th><th>BGS_TARGET</th><th>MWS_TARGET</th><th>NUM_ITER</th><th>FIBER_X</th><th>FIBER_Y</th><th>MEAN_DELTA_X</th><th>MEAN_DELTA_Y</th><th>FIBER_RA</th><th>FIBER_DEC</th><th>RMS_DELTA_X</th><th>RMS_DELTA_Y</th><th>FIRST_FIBER</th><th>LAST_FIBER</th><th>NUM_FIBER</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int16</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int64</th><th>uint8</th><th>bytes3</th><th>float32</th><th>float32</th><th>int16</th><th>int32</th><th>float64</th><th>int32</th><th>int32</th><th>int16</th><th>int32</th><th>bytes8</th><th>int32</th><th>bytes4</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>uint8</th><th>uint8</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int64</th><th>bytes2</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bool</th><th>float32</th><th>bool</th><th>float32</th><th>float32</th><th>bytes1</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>35191248222423832</td><td>0</td><td>523</td><td>523</td><td>368</td><td>512</td><td>214.44605767832988</td><td>51.84406836443377</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>9007199254742016</td><td>1</td><td>TGT</td><td>46.74537</td><td>-394.10425</td><td>0</td><td>4000</td><td>0.21990202658387958</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>2840</td><td>PSF</td><td>51333153000.0</td><td>54979980000.0</td><td>0.009342048</td><td>0.07065777</td><td>0.5183535</td><td>3.890564</td><td>436.4544</td><td>228.05014</td><td>95.41433</td><td>0.9727245</td><td>0.981544</td><td>0.9896342</td><td>4.790217e-07</td><td>4.101142e-08</td><td>-3.1664158e-07</td><td>0.0064982125</td><td>0.0010419904</td><td>0.0</td><td>0.9999985</td><td>0.99999803</td><td>1.0000007</td><td>3</td><td>4</td><td>3</td><td>585.86066</td><td>309.23462</td><td>109.99242</td><td>422.08148</td><td>194.14775</td><td>59.241077</td><td>631.47375</td><td>552.9918</td><td>174.71582</td><td>-227.9145</td><td>6.346824</td><td>1.1792632</td><td>0.0020697613</td><td>2.4200346e-05</td><td>0.99841803</td><td>0.9990282</td><td>0.99979264</td><td>0.9999217</td><td>0</td><td>0</td><td>0</td><td>0.055031408</td><td>0.40371677</td><td>3.0301442</td><td>0.055031408</td><td>0.40371677</td><td>3.0301442</td><td>163</td><td>163</td><td>2818</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>9007199254742016</td><td>3410</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>47.367</td><td>-396.32</td><td>-0.6622857142857143</td><td>2.601142857142857</td><td>0.0</td><td>0.0</td><td>0.662518786795622</td><td>2.601146780062319</td><td>368</td><td>368</td><td>1</td></tr>\n",
       "<tr><td>35191248222423864</td><td>0</td><td>522</td><td>522</td><td>478</td><td>0</td><td>214.4849311311757</td><td>51.82944600322742</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>1024</td><td>1</td><td>TGT</td><td>40.692936</td><td>-398.06967</td><td>0</td><td>500</td><td>0.8262228440133043</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>2872</td><td>PSF</td><td>81617840000.0</td><td>86225625000.0</td><td>0.008901713</td><td>0.49187332</td><td>1.5277255</td><td>3.5018857</td><td>541.32837</td><td>271.42923</td><td>140.84044</td><td>0.9739933</td><td>0.98240626</td><td>0.9901204</td><td>0.0054773903</td><td>0.002733698</td><td>0.0027740435</td><td>1.1730433e-06</td><td>0.0</td><td>0.0019555849</td><td>1.0000014</td><td>0.99999994</td><td>1.0000983</td><td>3</td><td>4</td><td>4</td><td>585.86066</td><td>309.23462</td><td>151.29567</td><td>422.08148</td><td>194.14775</td><td>77.41433</td><td>36.369946</td><td>24.431581</td><td>121.56135</td><td>103.24252</td><td>6.380593</td><td>1.1775938</td><td>0.0019730425</td><td>2.3882649e-05</td><td>0.99849254</td><td>0.999074</td><td>0.9998024</td><td>0.9999254</td><td>0</td><td>0</td><td>0</td><td>0.3829669</td><td>1.1894693</td><td>2.7265277</td><td>0.38342485</td><td>1.1908429</td><td>2.729172</td><td>32</td><td>0</td><td>2306</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>1024</td><td>3200</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>40.627</td><td>-397.68</td><td>0.0027142857142857147</td><td>-0.0008571428571428572</td><td>0.0</td><td>0.0</td><td>0.0040178174601214955</td><td>0.005503245795502349</td><td>478</td><td>478</td><td>1</td></tr>\n",
       "<tr><td>35191248222424145</td><td>0</td><td>509</td><td>509</td><td>353</td><td>0</td><td>214.4915008097018</td><td>51.85985927893284</td><td>-13.968278</td><td>-0.8086239</td><td>49.491528</td><td>53.194283</td><td>2015.5</td><td>5400.0</td><td>42949673511</td><td>3</td><td>TGT</td><td>39.58871</td><td>-389.73666</td><td>0</td><td>100</td><td>0.6052875618272948</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3153</td><td>PSF</td><td>2646198600000000.0</td><td>2231917300000000.0</td><td>0.00838432</td><td>80.17179</td><td>104.47831</td><td>105.898254</td><td>51.180035</td><td>40.88282</td><td>32.569424</td><td>0.97548616</td><td>0.9834203</td><td>0.99069196</td><td>1.5303195e-10</td><td>7.86668e-10</td><td>3.615868e-08</td><td>0.20385678</td><td>0.022875372</td><td>0.17757826</td><td>0.9999988</td><td>0.9999983</td><td>0.9999978</td><td>3</td><td>4</td><td>3</td><td>585.86066</td><td>309.23462</td><td>109.99242</td><td>422.08148</td><td>194.14775</td><td>59.241077</td><td>113.301414</td><td>32.438644</td><td>30.973074</td><td>307.66504</td><td>6.247066</td><td>1.1526513</td><td>0.0020091694</td><td>2.4345865e-05</td><td>0.9985801</td><td>0.99912775</td><td>0.9998139</td><td>0.9999297</td><td>0</td><td>0</td><td>0</td><td>62.126644</td><td>80.962234</td><td>82.06258</td><td>62.126644</td><td>80.962234</td><td>82.06258</td><td>163</td><td>162</td><td>2818</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1607815239118702848</td><td>G2</td><td>17.510342</td><td>846.1814</td><td>17.727562</td><td>155.63768</td><td>17.08302</td><td>166.45024</td><td>1.2317004</td><td>0.13897403</td><td>True</td><td>0.13220444</td><td>True</td><td>0.029689165</td><td>114.10905</td><td>N</td><td>42949673511</td><td>2675</td><td>1</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>42949672960</td><td>0</td><td>0</td><td>3</td><td>39.526</td><td>-389.355</td><td>0.0032857142857142855</td><td>0.00028571428571428574</td><td>0.0</td><td>0.0</td><td>0.005644213218610975</td><td>0.00595219047314276</td><td>353</td><td>353</td><td>1</td></tr>\n",
       "<tr><td>35191248222424151</td><td>0</td><td>510</td><td>510</td><td>397</td><td>0</td><td>214.41865376577314</td><td>51.870547368086314</td><td>7.427095</td><td>-13.272983</td><td>2.284936</td><td>2.890394</td><td>2015.5</td><td>5400.0</td><td>9007199254742049</td><td>1</td><td>TGT</td><td>50.953167</td><td>-386.89896</td><td>0</td><td>4000</td><td>0.8138218675528706</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3159</td><td>PSF</td><td>123526810000000.0</td><td>126902845000000.0</td><td>0.009091353</td><td>2.5900178</td><td>10.446622</td><td>29.741884</td><td>409.31555</td><td>135.22571</td><td>65.76285</td><td>0.97344667</td><td>0.9820348</td><td>0.989911</td><td>0.000608428</td><td>0.0002055512</td><td>0.0005091595</td><td>0.031109964</td><td>0.2512311</td><td>0.009555142</td><td>1.0000014</td><td>1.0000015</td><td>1.0000001</td><td>3</td><td>4</td><td>3</td><td>585.86066</td><td>223.88733</td><td>109.99242</td><td>422.08148</td><td>146.75131</td><td>59.241077</td><td>77.174095</td><td>44.10757</td><td>138.98195</td><td>-355.0471</td><td>6.330381</td><td>1.1813867</td><td>0.0020752603</td><td>2.4288007e-05</td><td>0.9984605</td><td>0.99905425</td><td>0.99979824</td><td>0.9999238</td><td>0</td><td>0</td><td>0</td><td>2.0049026</td><td>8.086609</td><td>23.022846</td><td>2.0049026</td><td>8.086609</td><td>23.022846</td><td>33</td><td>32</td><td>2818</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1607818327199113600</td><td>G2</td><td>19.985926</td><td>180.19847</td><td>20.632896</td><td>13.372794</td><td>18.845543</td><td>42.05697</td><td>1.6218333</td><td>0.06363857</td><td>True</td><td>0.59443814</td><td>True</td><td>0.54718876</td><td>6.3175335</td><td>N</td><td>9007199254742049</td><td>3460</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>50.878</td><td>-386.521</td><td>0.004285714285714286</td><td>0.0010000000000000002</td><td>0.0</td><td>0.0</td><td>0.006279217421667403</td><td>0.004765950662174936</td><td>397</td><td>397</td><td>1</td></tr>\n",
       "<tr><td>35191248222424269</td><td>0</td><td>520</td><td>520</td><td>471</td><td>0</td><td>214.62058871620775</td><td>51.812722022487186</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2015.5</td><td>5400.0</td><td>36028797018968064</td><td>1</td><td>TGT</td><td>19.45111</td><td>-402.54523</td><td>0</td><td>10000</td><td>0.9711294333962012</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3277</td><td>PSF</td><td>195370500000.0</td><td>11672692000.0</td><td>0.0071527828</td><td>2.959089</td><td>4.558193</td><td>4.7454715</td><td>453.50574</td><td>166.11905</td><td>103.54302</td><td>0.9790489</td><td>0.9858383</td><td>0.99205375</td><td>0.00022999183</td><td>0.00021171843</td><td>0.016263649</td><td>0.007845098</td><td>0.0051499275</td><td>0.021935232</td><td>0.9988676</td><td>0.99915993</td><td>0.9972266</td><td>3</td><td>3</td><td>3</td><td>627.9309</td><td>233.07008</td><td>115.97874</td><td>451.07172</td><td>148.70905</td><td>60.579525</td><td>10.019026</td><td>17.174576</td><td>50.743763</td><td>529.5972</td><td>6.2922864</td><td>1.1677842</td><td>0.0018624759</td><td>2.2360142e-05</td><td>0.99878854</td><td>0.99925584</td><td>0.9998412</td><td>0.99994004</td><td>0</td><td>0</td><td>0</td><td>2.2962644</td><td>3.5371754</td><td>3.6825044</td><td>2.2962644</td><td>3.5371754</td><td>3.6825044</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1607814272748939264</td><td>G2</td><td>21.170067</td><td>35.41784</td><td>nan</td><td>nan</td><td>0.0</td><td>nan</td><td>nan</td><td>0.0</td><td>True</td><td>17147.998</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>36028797018968064</td><td>3420</td><td>4</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>19.407</td><td>-402.153</td><td>0.0005714285714285715</td><td>0.0002857142857142856</td><td>0.0</td><td>0.0</td><td>0.003779644730092272</td><td>0.005554920598635309</td><td>471</td><td>471</td><td>1</td></tr>\n",
       "<tr><td>35191248222424420</td><td>0</td><td>521</td><td>521</td><td>459</td><td>0</td><td>214.56930303829608</td><td>51.82409752092173</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>1024</td><td>1</td><td>TGT</td><td>27.47765</td><td>-399.45853</td><td>0</td><td>500</td><td>0.9016111263712968</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3428</td><td>DEV</td><td>16067965000.0</td><td>21471707000.0</td><td>0.0073891957</td><td>0.1528418</td><td>1.5511683</td><td>5.3151093</td><td>227.54474</td><td>96.41734</td><td>36.896187</td><td>0.978364</td><td>0.9853737</td><td>0.99179214</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.07198834</td><td>0.06388924</td><td>0.054332186</td><td>0.93597347</td><td>0.9417486</td><td>0.9283005</td><td>3</td><td>4</td><td>4</td><td>585.86066</td><td>309.23462</td><td>151.29567</td><td>422.08148</td><td>194.14775</td><td>77.41433</td><td>26.642195</td><td>15.86967</td><td>35.629463</td><td>60.945168</td><td>5.090147</td><td>0.9750706</td><td>0.0017977568</td><td>2.3137256e-05</td><td>0.99874854</td><td>0.9992313</td><td>0.99983597</td><td>0.9999381</td><td>0</td><td>0</td><td>0</td><td>0.054637652</td><td>0.5545092</td><td>1.9000372</td><td>0.054637652</td><td>0.5545092</td><td>1.9000372</td><td>0</td><td>0</td><td>0</td><td>1.0</td><td>1.0</td><td>1.0814636</td><td>-0.16734639</td><td>0.17973742</td><td>289.80856</td><td>291.95023</td><td>312.5199</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>1024</td><td>3200</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>27.424</td><td>-399.07</td><td>0.002714285714285714</td><td>0.00014285714285714298</td><td>0.0</td><td>0.0</td><td>0.004765950662174936</td><td>0.0056694670951384085</td><td>459</td><td>459</td><td>1</td></tr>\n",
       "<tr><td>35191248222424545</td><td>0</td><td>507</td><td>507</td><td>495</td><td>0</td><td>214.59811552037903</td><td>51.83949866088522</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>1024</td><td>1</td><td>TGT</td><td>22.943327</td><td>-395.21274</td><td>0</td><td>500</td><td>0.973433252063359</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3553</td><td>DEV</td><td>180297370000.0</td><td>211564130000.0</td><td>0.006903227</td><td>0.9611883</td><td>5.430288</td><td>12.958144</td><td>430.88446</td><td>152.5851</td><td>42.741318</td><td>0.97977245</td><td>0.986329</td><td>0.9923299</td><td>0.0028529738</td><td>0.0011885962</td><td>0.002190915</td><td>0.01586761</td><td>0.014152854</td><td>0.0141761275</td><td>0.99648046</td><td>0.99637127</td><td>0.9954583</td><td>4</td><td>5</td><td>3</td><td>777.52014</td><td>366.24078</td><td>109.99242</td><td>561.54517</td><td>234.63875</td><td>59.241077</td><td>24.06449</td><td>12.884567</td><td>25.37766</td><td>130.89162</td><td>5.795505</td><td>1.0864435</td><td>0.0017787046</td><td>2.1772465e-05</td><td>0.9988308</td><td>0.99928176</td><td>0.99984676</td><td>0.9999421</td><td>0</td><td>0</td><td>0</td><td>0.4580131</td><td>2.587571</td><td>6.174648</td><td>0.45807865</td><td>2.5878143</td><td>6.175212</td><td>0</td><td>0</td><td>0</td><td>1.0</td><td>1.0</td><td>0.5990056</td><td>-0.12249275</td><td>-0.1619985</td><td>5190.2</td><td>1562.0414</td><td>1866.3104</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>1024</td><td>3200</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>22.896</td><td>-394.828</td><td>0.0024285714285714284</td><td>0.0024285714285714284</td><td>0.0</td><td>0.0</td><td>0.0050849919230164815</td><td>0.005542047068934521</td><td>495</td><td>495</td><td>1</td></tr>\n",
       "<tr><td>35191248222424614</td><td>0</td><td>525</td><td>525</td><td>398</td><td>0</td><td>214.3295899853732</td><td>51.849373799210866</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>9007199254742016</td><td>1</td><td>TGT</td><td>64.98388</td><td>-392.80252</td><td>0</td><td>4000</td><td>0.08261503016466931</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3622</td><td>DEV</td><td>130512806000.0</td><td>103781080000.0</td><td>0.009155999</td><td>1.0374372</td><td>4.7821026</td><td>19.989508</td><td>186.85315</td><td>72.86764</td><td>20.237263</td><td>0.9732604</td><td>0.98190826</td><td>0.9898396</td><td>0.07280203</td><td>0.042181607</td><td>0.07330275</td><td>0.029915025</td><td>0.027447842</td><td>0.033020124</td><td>0.9748432</td><td>0.97712046</td><td>0.9793046</td><td>3</td><td>4</td><td>3</td><td>585.86066</td><td>309.23462</td><td>111.25983</td><td>422.08148</td><td>194.14775</td><td>60.642483</td><td>66.88532</td><td>39.37154</td><td>-8.305798</td><td>-138.17685</td><td>4.697166</td><td>0.8935888</td><td>0.0016378114</td><td>2.1838963e-05</td><td>0.9984495</td><td>0.9990475</td><td>0.9997968</td><td>0.9999233</td><td>0</td><td>0</td><td>0</td><td>0.3236258</td><td>1.4917643</td><td>6.2356744</td><td>0.32389936</td><td>1.4929394</td><td>6.2401032</td><td>128</td><td>2</td><td>768</td><td>1.0</td><td>1.0</td><td>1.2793514</td><td>0.15801483</td><td>-0.08904728</td><td>1756.903</td><td>3366.074</td><td>2108.6274</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>9007199254742016</td><td>3410</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>64.901</td><td>-392.42</td><td>0.004428571428571429</td><td>-0.0002857142857142856</td><td>0.0</td><td>0.0</td><td>0.005916079783099617</td><td>0.0058309518948453</td><td>398</td><td>398</td><td>1</td></tr>\n",
       "<tr><td>35191248222424683</td><td>0</td><td>524</td><td>524</td><td>356</td><td>0</td><td>214.3712716571634</td><td>51.85367231915112</td><td>0.28622606</td><td>-1.8559401</td><td>0.13659196</td><td>0.20833178</td><td>2015.5</td><td>5400.0</td><td>36028797018968097</td><td>1</td><td>TGT</td><td>58.43127</td><td>-391.56836</td><td>0</td><td>5000</td><td>0.41577533249226306</td><td>65535</td><td>99</td><td>8001</td><td>590822</td><td>2144p517</td><td>3691</td><td>PSF</td><td>20589887000000.0</td><td>21456650000000.0</td><td>0.009289361</td><td>4.026837</td><td>6.108062</td><td>7.2819943</td><td>274.7827</td><td>214.65195</td><td>49.99294</td><td>0.97287625</td><td>0.98164713</td><td>0.9896924</td><td>1.7916667e-05</td><td>1.3363281e-05</td><td>0.00018468997</td><td>0.33889103</td><td>0.0059655486</td><td>0.3132881</td><td>0.99996084</td><td>0.99997777</td><td>0.8229634</td><td>3</td><td>4</td><td>3</td><td>436.27136</td><td>309.23462</td><td>69.423004</td><td>311.60797</td><td>194.14775</td><td>34.852478</td><td>21.236103</td><td>35.099133</td><td>20.55431</td><td>-144.31076</td><td>6.3027434</td><td>1.1569926</td><td>0.0020609219</td><td>2.3985369e-05</td><td>0.998427</td><td>0.9990337</td><td>0.9997938</td><td>0.99992216</td><td>0</td><td>0</td><td>0</td><td>3.1362238</td><td>4.7571454</td><td>5.6714396</td><td>3.136265</td><td>4.7574153</td><td>5.672227</td><td>162</td><td>130</td><td>768</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1607816781016090880</td><td>G2</td><td>20.636858</td><td>99.06601</td><td>20.35025</td><td>5.830006</td><td>19.821177</td><td>9.160764</td><td>1.8576816</td><td>0.0</td><td>True</td><td>2.9157043</td><td>True</td><td>-1.2970991</td><td>1.2732724</td><td>N</td><td>36028797018968097</td><td>3464</td><td>4</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>58.354</td><td>-391.188</td><td>0.0038571428571428576</td><td>0.00028571428571428574</td><td>0.0</td><td>0.0</td><td>0.005250850271282601</td><td>0.005855400437691199</td><td>356</td><td>356</td><td>1</td></tr>\n",
       "<tr><td>35191248226617964</td><td>0</td><td>518</td><td>518</td><td>470</td><td>0</td><td>214.76189199412664</td><td>51.798574854368795</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>9007199254742016</td><td>1</td><td>TGT</td><td>-2.715021</td><td>-406.3697</td><td>0</td><td>4000</td><td>0.24820468319351363</td><td>65535</td><td>99</td><td>8001</td><td>590823</td><td>2148p517</td><td>2668</td><td>REX</td><td>86706080000.0</td><td>89417335000.0</td><td>0.008369433</td><td>0.20934753</td><td>1.4645604</td><td>7.2784386</td><td>512.3916</td><td>206.73953</td><td>69.615814</td><td>0.97552913</td><td>0.9834495</td><td>0.9907084</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.010238778</td><td>0.006377295</td><td>0.011832016</td><td>0.99464446</td><td>0.9961385</td><td>0.9903827</td><td>3</td><td>4</td><td>3</td><td>608.9563</td><td>290.77057</td><td>113.58766</td><td>421.69617</td><td>181.59448</td><td>59.73579</td><td>29.229172</td><td>15.676769</td><td>2.4349356</td><td>676.5237</td><td>6.19524</td><td>1.1470841</td><td>0.0018169022</td><td>2.2244076e-05</td><td>0.99858266</td><td>0.9991293</td><td>0.9998142</td><td>0.99992985</td><td>0</td><td>0</td><td>0</td><td>0.13250725</td><td>0.9269986</td><td>4.6069136</td><td>0.13250725</td><td>0.9269986</td><td>4.6069136</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.3253221</td><td>0.0</td><td>0.0</td><td>6474.425</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td>N</td><td>9007199254742016</td><td>3410</td><td>2</td><td>11424</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>-2.751</td><td>-405.975</td><td>0.003428571428571429</td><td>-0.0015714285714285713</td><td>0.0</td><td>0.0</td><td>0.005756983337031395</td><td>0.005916079783099616</td><td>470</td><td>470</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
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       "<tr><td>611652015377876161</td><td>0</td><td>39</td><td>39</td><td>124</td><td>512</td><td>215.01538425501187</td><td>52.87347798707264</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>4294967296</td><td>4</td><td>SKY</td><td>-39.40727</td><td>-128.49597</td><td>0</td><td>0</td><td>0.916087610594768</td><td>63</td><td>0</td><td>8001</td><td>594363</td><td>2151p527</td><td>1217</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.010223993</td><td>-0.0008797595</td><td>-0.048865918</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td></td><td>4294967296</td><td>0</td><td>-1</td><td>11425</td><td>21.0</td><td>9101.544</td><td>2460.5415</td><td>192.20097</td><td>4294967296</td><td>0</td><td>0</td><td>3</td><td>-33.517</td><td>-130.045</td><td>-5.8588571428571425</td><td>1.6271428571428572</td><td>0.0</td><td>0.0</td><td>5.858858884507411</td><td>1.6271524645395536</td><td>124</td><td>124</td><td>1</td></tr>\n",
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       "<tr><td>611652019031113948</td><td>0</td><td>44</td><td>44</td><td>440</td><td>0</td><td>214.63349851312955</td><td>52.91762393067374</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>4294967296</td><td>4</td><td>SKY</td><td>16.900475</td><td>-117.76331</td><td>0</td><td>0</td><td>0.9741072768627668</td><td>63</td><td>0</td><td>8001</td><td>595234</td><td>2145p530</td><td>220</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>-0.02461417</td><td>-0.034209017</td><td>-0.14746661</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td></td><td>4294967296</td><td>0</td><td>-1</td><td>11425</td><td>31.030293</td><td>8659.154</td><td>2298.3567</td><td>149.57674</td><td>4294967296</td><td>0</td><td>0</td><td>3</td><td>16.886</td><td>-117.691</td><td>0.000857142857142857</td><td>-0.0015714285714285715</td><td>0.0</td><td>0.0</td><td>0.008</td><td>0.006792853387410708</td><td>440</td><td>440</td><td>1</td></tr>\n",
       "<tr><td>611652022671769822</td><td>0</td><td>6</td><td>6</td><td>412</td><td>0</td><td>214.7711018222889</td><td>53.16392537568099</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>4294967296</td><td>4</td><td>SKY</td><td>-3.1995244</td><td>-57.458134</td><td>0</td><td>0</td><td>0.9934278036994962</td><td>63</td><td>0</td><td>8001</td><td>596102</td><td>2147p532</td><td>222</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>-0.012936313</td><td>-0.00065925147</td><td>0.009387987</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td></td><td>4294967296</td><td>0</td><td>-1</td><td>11425</td><td>35.927174</td><td>8800.269</td><td>2346.1511</td><td>199.86691</td><td>4294967296</td><td>0</td><td>0</td><td>3</td><td>-3.192</td><td>-57.419</td><td>0.002428571428571429</td><td>-0.0025714285714285726</td><td>0.0</td><td>0.0</td><td>0.00795523188132906</td><td>0.011501552690211626</td><td>412</td><td>412</td><td>1</td></tr>\n",
       "<tr><td>611652022671770069</td><td>0</td><td>4</td><td>4</td><td>403</td><td>0</td><td>214.75501043297203</td><td>53.21662340263464</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5400.0</td><td>4294967296</td><td>4</td><td>SKY</td><td>-0.82327896</td><td>-44.622955</td><td>0</td><td>0</td><td>0.9504507026384935</td><td>63</td><td>0</td><td>8001</td><td>596102</td><td>2147p532</td><td>469</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>-0.0016212885</td><td>0.020510893</td><td>-0.016234271</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td></td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>True</td><td>0.0</td><td>True</td><td>0.0</td><td>0.0</td><td></td><td>4294967296</td><td>0</td><td>-1</td><td>11425</td><td>41.0</td><td>9138.918</td><td>2415.1436</td><td>187.34349</td><td>4294967296</td><td>0</td><td>0</td><td>3</td><td>-0.815</td><td>-44.595</td><td>0.0025714285714285717</td><td>-0.003285714285714285</td><td>0.0</td><td>0.0</td><td>0.007764387566686396</td><td>0.011898379241368488</td><td>403</td><td>403</td><td>1</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=500>\n",
       "     TARGETID      PETAL_LOC DEVICE_LOC ... FIRST_FIBER LAST_FIBER NUM_FIBER\n",
       "      int64          int16     int32    ...    int64      int64      int64  \n",
       "------------------ --------- ---------- ... ----------- ---------- ---------\n",
       " 35191248222423832         0        523 ...         368        368         1\n",
       " 35191248222423864         0        522 ...         478        478         1\n",
       " 35191248222424145         0        509 ...         353        353         1\n",
       " 35191248222424151         0        510 ...         397        397         1\n",
       " 35191248222424269         0        520 ...         471        471         1\n",
       " 35191248222424420         0        521 ...         459        459         1\n",
       " 35191248222424545         0        507 ...         495        495         1\n",
       " 35191248222424614         0        525 ...         398        398         1\n",
       " 35191248222424683         0        524 ...         356        356         1\n",
       " 35191248226617964         0        518 ...         470        470         1\n",
       "               ...       ...        ... ...         ...        ...       ...\n",
       "611652015377874998         0         90 ...         270        270         1\n",
       "611652015377875327         0         82 ...         273        273         1\n",
       "611652015377875573         0         73 ...         290        290         1\n",
       "611652015377875634         0         62 ...         291        291         1\n",
       "611652015377875886         0         55 ...         297        297         1\n",
       "611652015377876161         0         39 ...         124        124         1\n",
       "611652019031113764         0         50 ...         126        126         1\n",
       "611652019031113948         0         44 ...         440        440         1\n",
       "611652022671769822         0          6 ...         412        412         1\n",
       "611652022671770069         0          4 ...         403        403         1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specobj.fibermap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;TARGETID&apos; dtype=&apos;int64&apos; length=500&gt;\n",
       "<table>\n",
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       "<tr><td>35191248222423864</td></tr>\n",
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       "<tr><td>...</td></tr>\n",
       "<tr><td>611652015373680702</td></tr>\n",
       "<tr><td>611652015373681790</td></tr>\n",
       "<tr><td>611652015377874998</td></tr>\n",
       "<tr><td>611652015377875327</td></tr>\n",
       "<tr><td>611652015377875573</td></tr>\n",
       "<tr><td>611652015377875634</td></tr>\n",
       "<tr><td>611652015377875886</td></tr>\n",
       "<tr><td>611652015377876161</td></tr>\n",
       "<tr><td>611652019031113764</td></tr>\n",
       "<tr><td>611652019031113948</td></tr>\n",
       "<tr><td>611652022671769822</td></tr>\n",
       "<tr><td>611652022671770069</td></tr>\n",
       "</table>"
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      "text/plain": [
       "<Column name='TARGETID' dtype='int64' length=500>\n",
       " 35191248222423832\n",
       " 35191248222423864\n",
       " 35191248222424145\n",
       " 35191248222424151\n",
       " 35191248222424269\n",
       " 35191248222424420\n",
       " 35191248222424545\n",
       " 35191248222424614\n",
       " 35191248222424683\n",
       " 35191248226617964\n",
       " 35191248226617968\n",
       " 35191248226618068\n",
       "               ...\n",
       "611652015373680702\n",
       "611652015373681790\n",
       "611652015377874998\n",
       "611652015377875327\n",
       "611652015377875573\n",
       "611652015377875634\n",
       "611652015377875886\n",
       "611652015377876161\n",
       "611652019031113764\n",
       "611652019031113948\n",
       "611652022671769822\n",
       "611652022671770069"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specobj.target_ids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also functions for getting the number of spectra and selecting a subset of spectra.  All of the information that could be read in from the different extensions of the spectral file can be retrieved from the `specobj` object. Here's what's available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_bands',\n",
       " '_ftype',\n",
       " '_get_slice',\n",
       " '_single',\n",
       " 'bands',\n",
       " 'exp_fibermap',\n",
       " 'extra',\n",
       " 'extra_catalog',\n",
       " 'fibermap',\n",
       " 'flux',\n",
       " 'ftype',\n",
       " 'ivar',\n",
       " 'mask',\n",
       " 'meta',\n",
       " 'num_spectra',\n",
       " 'num_targets',\n",
       " 'resolution_data',\n",
       " 'scores',\n",
       " 'scores_comments',\n",
       " 'select',\n",
       " 'target_ids',\n",
       " 'update',\n",
       " 'wave',\n",
       " 'wavelength_grid']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(specobj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about if we only want to plot spectra of certain target classes? For mini-SV-2 (which is part of DESI _commissioning_) the targeting information is stored in the `CMX_TARGET` entries of the fibermap array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name = CMX_TARGET\n",
       "dtype = int64\n",
       "class = Column\n",
       "n_bad = 0\n",
       "length = 500"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specobj.fibermap[\"CMX_TARGET\"].info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and which target corresponds to which targeting bit is stored in the commisioning (cmx) mask (we imported this near the beginning of the notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmx_mask:\n",
       "  - [STD_GAIA,         0, \"Gaia stars used for dithering (and other) tests\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 2400, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2400, 'MORE_ZGOOD': 2400}, 'numobs': 100}]\n",
       "  - [SV0_STD_FAINT,    1, \"SV-like STD_FAINT class is set (very early SV selection)\", {'obsconditions': 'DARK|GRAY', 'priorities': {'UNOBS': 2600, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2600, 'MORE_ZGOOD': 2600}, 'numobs': 100}]\n",
       "  - [SV0_STD_BRIGHT,   2, \"SV-like STD_BRIGHT class is set (very early SV selection)\", {'obsconditions': 'BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 2600, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2600, 'MORE_ZGOOD': 2600}, 'numobs': 100}]\n",
       "  - [STD_TEST,         3, \"Very bright stars for early tests\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 2800, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2800, 'MORE_ZGOOD': 2800}, 'numobs': 1}]\n",
       "  - [STD_CALSPEC,      4, \"Matches to CALSPEC stars\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 3000, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3000, 'MORE_ZGOOD': 3000}, 'numobs': 100}]\n",
       "  - [STD_DITHER,       5, \"Gaia stars specifically for dithering tests\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 2400, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2400, 'MORE_ZGOOD': 2400}, 'numobs': 1}]\n",
       "  - [SV0_MWS_CLUSTER,  6, \"SV-like Open and Globular clusters for the MWS\", {'obsconditions': 'BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1600, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1600, 'MORE_ZGOOD': 1600}, 'numobs': 100}]\n",
       "  - [SV0_MWS_CLUSTER_VERYBRIGHT,  7, \"Bright SV-like Open and Globular clusters for the MWS\", {'obsconditions': 'TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1601, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1601, 'MORE_ZGOOD': 1601}, 'numobs': 100}]\n",
       "  - [SV0_BGS,          8, \"SV-like BGS bit is set (very early SV selection)\", {'obsconditions': 'BRIGHT', 'priorities': {'UNOBS': 2100, 'MORE_ZWARN': 2100, 'MORE_ZGOOD': 1000, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0}, 'numobs': 1}]\n",
       "  - [SV0_MWS,          9, \"SV-like MWS bit is set (very early SV selection)\", {'obsconditions': 'BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1500, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1500, 'MORE_ZGOOD': 1500}, 'numobs': 100}]\n",
       "  - [SV0_LRG,         10, \"SV-like LRG bit is set (very early SV selection)\", {'obsconditions': 'DARK', 'priorities': {'UNOBS': 3200, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3200, 'MORE_ZGOOD': 3200}, 'numobs': 2}]\n",
       "  - [SV0_ELG,         11, \"SV-like ELG bit is set (very early SV selection)\", {'obsconditions': 'DARK|GRAY', 'priorities': {'UNOBS': 3000, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3000, 'MORE_ZGOOD': 3000}, 'numobs': 1}]\n",
       "  - [SV0_QSO,         12, \"SV-like QSO bit is set (very early SV/RF selection)\", {'obsconditions': 'DARK', 'priorities': {'UNOBS': 3400, 'MORE_ZGOOD': 3500, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3400}, 'numobs': 4}]\n",
       "  - [SV0_WD,          13, \"SV-like WD bit is set (very early MWS_WD selection)\", {'obsconditions': 'BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 2998, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2998, 'MORE_ZGOOD': 2998}, 'numobs': 100}]\n",
       "  - [SV0_QSO_Z5,      14, \"SV-like QSO bit is set (specifically for the QSO_Z5 selection from SV\", {'obsconditions': 'DARK', 'priorities': {'UNOBS': 3600, 'MORE_ZGOOD': 3700, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3600}, 'numobs': 4}]\n",
       "  - [BACKUP_BRIGHT,   16, \"Bright Gaia targets for poor conditions\", {'obsconditions': 'TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 10, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 10, 'MORE_ZGOOD': 10}, 'numobs': 1}]\n",
       "  - [BACKUP_FAINT,    17, \"Fainter Gaia targets for poor conditions\", {'obsconditions': 'BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 10, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 10, 'MORE_ZGOOD': 10}, 'numobs': 1}]\n",
       "  - [M31_STD_BRIGHT,  18, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1008, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1008, 'MORE_ZGOOD': 1008}, 'numobs': 100}]\n",
       "  - [M31_H2PN,        19, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1007, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1007, 'MORE_ZGOOD': 1007}, 'numobs': 100}]\n",
       "  - [M31_GC,          20, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1006, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1006, 'MORE_ZGOOD': 1006}, 'numobs': 100}]\n",
       "  - [M31_QSO,         21, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1005, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1005, 'MORE_ZGOOD': 1005}, 'numobs': 100}]\n",
       "  - [M31_VAR,         22, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1004, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1004, 'MORE_ZGOOD': 1004}, 'numobs': 100}]\n",
       "  - [M31_BSPL,        23, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1003, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1003, 'MORE_ZGOOD': 1003}, 'numobs': 100}]\n",
       "  - [M31_M31cen,      24, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1002, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1002, 'MORE_ZGOOD': 1002}, 'numobs': 100}]\n",
       "  - [M31_M31out,      25, \"M31 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1001, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1001, 'MORE_ZGOOD': 1001}, 'numobs': 100}]\n",
       "  - [ORI_STD_BRIGHT,  26, \"Orion first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1008, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1008, 'MORE_ZGOOD': 1008}, 'numobs': 100}]\n",
       "  - [ORI_QSO,         27, \"Orion first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1005, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1005, 'MORE_ZGOOD': 1005}, 'numobs': 100}]\n",
       "  - [ORI_ORI,         28, \"Orion first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1003, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1003, 'MORE_ZGOOD': 1003}, 'numobs': 100}]\n",
       "  - [ORI_HA,          29, \"Orion first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1002, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1002, 'MORE_ZGOOD': 1002}, 'numobs': 100}]\n",
       "  - [ROS_STD_BRIGHT,  30, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1008, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1008, 'MORE_ZGOOD': 1008}, 'numobs': 100}]\n",
       "  - [ROS_QSO,         31, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1005, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1005, 'MORE_ZGOOD': 1005}, 'numobs': 100}]\n",
       "  - [SKY,             32, \"Blank sky locations\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {}, 'numobs': -1}]\n",
       "  - [STD_FAINT,       33, \"SV-like standard stars for dark/gray conditions\", {'obsconditions': 'DARK|GRAY', 'priorities': {}, 'numobs': -1}]\n",
       "  - [STD_BRIGHT,      35, \"SV-like standard stars for BRIGHT conditions\", {'obsconditions': 'BRIGHT', 'priorities': {}, 'numobs': -1}]\n",
       "  - [BAD_SKY,         36, \"Blank sky locations that are imperfect but still useable\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 0, 'OBS': 0, 'DONE': 0, 'MORE_ZWARN': 0, 'MORE_ZGOOD': 0, 'DONOTOBSERVE': 1}, 'numobs': 0}]\n",
       "  - [SUPP_SKY,        37, \"SKY is based on Gaia-avoidance (SKY will be set, too)\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {}, 'numobs': -1}]\n",
       "  - [ROS_ROSM17,      38, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1004, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1004, 'MORE_ZGOOD': 1004}, 'numobs': 100}]\n",
       "  - [ROS_ROS1,        39, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1003, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1003, 'MORE_ZGOOD': 1003}, 'numobs': 100}]\n",
       "  - [ROS_HA,          40, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1002, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1002, 'MORE_ZGOOD': 1002}, 'numobs': 100}]\n",
       "  - [ROS_ROS2,        41, \"Rosette first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1001, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1001, 'MORE_ZGOOD': 1001}, 'numobs': 100}]\n",
       "  - [M33_STD_BRIGHT,  42, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1008, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1008, 'MORE_ZGOOD': 1008}, 'numobs': 100}]\n",
       "  - [M33_H2PN,        43, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1007, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1007, 'MORE_ZGOOD': 1007}, 'numobs': 100}]\n",
       "  - [M33_GC,          44, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1006, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1006, 'MORE_ZGOOD': 1006}, 'numobs': 100}]\n",
       "  - [M33_QSO,         45, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1005, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1005, 'MORE_ZGOOD': 1005}, 'numobs': 100}]\n",
       "  - [M33_M33cen,      46, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1002, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1002, 'MORE_ZGOOD': 1002}, 'numobs': 100}]\n",
       "  - [M33_M33out,      47, \"M33 first light program\", {'obsconditions': 'DARK|GRAY|BRIGHT|BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 1001, 'DONE': 10, 'OBS': 10, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 1001, 'MORE_ZGOOD': 1001}, 'numobs': 100}]\n",
       "  - [MINI_SV_LRG,     53, \"LRGs for Mini SV tests (NORTH+SOUTH with noresolve)\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 3410, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3410, 'MORE_ZGOOD': 3410}, 'numobs': 2}]\n",
       "  - [MINI_SV_ELG,     54, \"ELGs for Mini SV tests (NORTH+SOUTH with noresolve)\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 3001, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3001, 'MORE_ZGOOD': 3001}, 'numobs': 1}]\n",
       "  - [MINI_SV_QSO,     55, \"QSOs (RF) for Mini SV tests (NORTH+SOUTH with noresolve)\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 3420, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 3420, 'MORE_ZGOOD': 3420}, 'numobs': 4}]\n",
       "  - [MINI_SV_BGS_BRIGHT, 56, \"BGS (bright) for Mini SV tests (NORTH+SOUTH with noresolve)\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 2101, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2101, 'MORE_ZGOOD': 2101}, 'numobs': 1}]\n",
       "  - [SV0_MWS_FAINT,   57, \"Faint stars for Mini SV tests\", {'obsconditions': 'BACKUP|TWILIGHT12|TWILIGHT18', 'priorities': {'UNOBS': 5, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 5, 'MORE_ZGOOD': 5}, 'numobs': 1}]\n",
       "  - [STD_DITHER_GAIA, 58, \"Gaia stars for dithering tests outside of the Legacy Surveys footprint\", {'obsconditions': 'DARK|GRAY|BRIGHT', 'priorities': {'UNOBS': 2400, 'DONE': 2, 'OBS': 1, 'DONOTOBSERVE': 0, 'MORE_ZWARN': 2400, 'MORE_ZGOOD': 2400}, 'numobs': 1}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmx_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the indexes of all standard stars in the spectral file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2  12  44  98 134 145 171 177 180 181 194 205 207 264 265 321 333 338\n",
      " 343 360 368]\n"
     ]
    }
   ],
   "source": [
    "stds = np.where(specobj.fibermap[\"CMX_TARGET\"] & cmx_mask.mask(\"STD_FAINT|STD_BRIGHT|SV0_STD_FAINT|SV0_STD_BRIGHT\"))[0]\n",
    "print(stds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where were these located on the original plate-fiber mapping?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1554bd6f5940>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fm = specobj.fibermap   #- shorthand\n",
    "plt.plot(fm[\"TARGET_RA\"],fm[\"TARGET_DEC\"],'b.', alpha=0.1)\n",
    "plt.plot(fm[\"TARGET_RA\"][stds],fm[\"TARGET_DEC\"][stds],'kx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the spectra of the first 9 of these standard stars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print()\n",
    "plt.figure(figsize=(12, 9))\n",
    "for panel, std in enumerate(stds[:9]):\n",
    "    plt.subplot(3, 3, panel+1)\n",
    "    plt.plot(specobj.wave['brz'], specobj.flux[\"brz\"][std], 'b-', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These seem star-like. Let's zoom in on some of the Balmer series for the zeroth standard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C0psnGAHnHK+Lzq9yPpDmS6VD7A9F0Trog06tQr8nlLKh0VsNI7tvTX3j543EYhzdzgBqCs0osxnQeo5GfvedFXJWpOh5Ouw+M4j9Z0dXwiHOHeTIbw7lJmWF8zvsCyPPNDI5Liu34cIFDiwosYBzpJW41u0KoNRuBGMM375+KS5eKERkE/Vava4Abrl7lzzZSrgCERRZ9dBpVHGvOdg6DABC5Neqlx3bswNe1BSaYNCqUWozoG1QcFgTnV6l7GKsC+t4lwvLym2oyDPBOfPdhMblVLcbw74UrYZFyUmqhhPSOVC2rT7S4UQoEpOTTKIxHnfTUWITZA1AfFKbNxiBOxBBqd0oRH7dQbmcUigaw9un088AH/SGUCBucwJAbaEZLQNeHO8UItgNPe5459cTRHO/Bye6Jtd+Oxe5/+0z+PO2zOxkvHayB49NoO7rKyeESjHjdWDc1TyAfWcHxzyGmBzjyR48wQiOdbpkeZE073mDEVn6pqwI0dTnAefA2po8RGM85Q33mw198o3t6d7xHb1+bxDhKEd5ngFVBaaEyO+54/we7xI01RNJBu92BuQ5/B8H2vHBu3emTPBOxh/fbMJeUc7y2N42PH2ocwIjJmYCafd9gJzf6SHViR32h+IiA7//8Fr8+pa1qC4wA0hP99vlDKDMZpB/lt4v8a79+8+ewM7mAXzuof041DYsP+/yh2EzaGE3auNec6BtGFaDBvMcZhRbBWes1xXEmX4vaguF8VUXjDTmGPJKF9HINoKKIe65RIKRKBp73FhabkNFvpB41TGUPdHfcDSG9/x+B377+umkv5eTwlJ8v9ICeEqx1SYlrUhRbqdfSAxcVGIFAJTa9HBYhcVLKW2QPqvUrkeRVY9gJAZXICJXY2gd9MVFbcZiQCF7AIAahwltg34c7XSKY3Sj2xWUa5J2DPnR7wlh0BtKqSV3B6becjnb6Rz24/WTvdjR1D/uIvj4/nb8+rXTU5YeBMJRDPnCODvgS9mMREkkGpNvcN9s6IuLEAbCUdz4m+1y45m7njqGT/1l7zmf6dzvCcrygunCE4zgLzta5BtWaedN2lVTYjFo4QlE8PpJYUx2o1Z2fpWBAk9wxJ4kyYMkN+tNUvEhEo1hW2M/rlxSgmKrfpS8KpEj7U55vi2zG1FTaMKpbjcu+7838KtXG+HyR6Ao7z6nkW7cExN7UxGJxtDrDsAfjsIfimJX0yB2NQ/ieJpNfzjn+MmLp/Dw7jYAwO/fbMKPnj855eRoIrOMRH5J9jAtDKVYXJz+MPKMI06ISaeBUaeWk9/SqfjQ7QygLG/E+ZUiDUq91rbGPvzrUCc+tqUWDqsOt967W05OcwUisBo0sBu1cRHIg23DWF2VB5WKoUh0frucfrQO+jC/2AIAqMg3yk7cYELkd8AbRIFZD7tRmzLy29jjQSTGsazchsp8qepA9iQlnOhywROMyB2Qjne64qIpUuQ3VTkw6Xw2iJHfaIzLC5LkyEjO5Ac2VGFpmQ3rawtQZBG+T6XuV/qsElHzCwiRYaUz+nYa9T8j0Ric/jDyTSPXXV2hGaFoDK+eEBbj5n4vOoZ8WFImOOSHFJnozUkW1Eg0hut+tQ2X/OSNcza6EYnGcMufduHj9+/Bh/70juxgvnSsO+kNwYAnBKc/jEPtw1P6XKWTs6t5YNzj97cOY9gXxjXLSjHoDeGw4vMf2NmCQ+1ObGsUnPeWAS+GfGH88tX0JRVzkYd2ncXH798zbbtKnHN8/bFDuOvpY7jsp2/gX4c64QqEYdapoVGPXmqkhLdtp/uxosKOynyjHOVVzpVK2UNDjwdaNcOGWqG5RTKN/8G2YbgDEVy8qAj1RZYxnd+Wfi+2/mY7viNWhSizG1BTaIY7GEFznxc7mvrhCoRRYNJBo8p+D3isZhzuwEg2f/uQD6FIDD987oRczjEZve4gpPvGAW9Q/l7ebBh7N0XC6Q8jEuPocvrlRlQdw/60ovHEzOFU+C3haAz3bD+Tltx0LjOjzm8gEk2q0Rr2hUfVgQSAQrMOJp16XOfXHRBkAmX2EedXap+pjOLe/VYzKvKM+Na1i/HXT56HUpsBH713N14+3gN3QBiDzaCRoxVOfxinul1YXZUHALKztffsECIxjnlFQuS3WEy84pxjyBsvHO/3hOCw6OCw6FI6v9I2+7JyOyrFklvZpPuVdGJSBPabTxzGt544LP9+JPKb3PmVzmenMyBPwNIkLUVcJLnI/GILnvvihVheYU8e+RWd3zK7MU4WITleKgZsT0P6IN2IFSpkDzViJP9ktxsmnRrRGEe/J4SlZTYAiNspSDZ5bzvdj7ZBPxiAOx4+EFe26VzhmcNdODvgw3/esBSAoFXvdgZw24P78OietrhjOefyNTGe9GA8lI1PdjYN4D/+cQRfeiR1Es4rJ3qgVTN8+/olULGRzx/yhvDr14QdjJYBL3rcAQQjMRSadXhgZ0vGFuVsjP73uILgPP3a6RPlgZ1n8fzRbnz6wjpU5Bvxv8+fhNMfTqr3BYRosNMfxsG2YWyqK5BlEEC8U6t01o92OFFfZJF3yJIlvb3Z0Ae1iuH8+Q7ML7bgdK8nZaSxU0xePSbOweV5Rvzbpmr89H2rcP2KMrQN+oVdQaMWZv24DVFnnbHWypNi8MGsU6N9yI8DrUO4+61mvHoy9W6AspvmoHekXv2badqzFEnscgbg9IflRlSJssNM0jnsxztp3CATI0jrYb8nhB1NA/j+M8fx2hjXxbnAjDq/nI9EzALhKL7y6EEc7XDC5Y/X/EpIJagSS7Pc/rf98pYlEB8NlFCrGKx6TVwU91S3G+fNK4RBq0Z1oQlPfm4LCsw6PH+0Cy5/RJY9SK/52UunwAFcvawUAOAQnS0p8lRfJER+i6x6hCJCJDGxZMiAJ4hCi06sXBDvHA77QnjpWDeOdDhh1qlRU2CCw6KHTq3KKtnDflH3LE18ncN+HBY1u8DIAuRJkpwGxCcQNvR44jKNpcivdNNQoJAhFJpHa35l2YMi8tvrHtGknTevEDtO96dMhJEYTPJ5tY6RSiNXLS2RH88rskCnUeFIh+DMMoak0aTH97Uj36TFE5/bAmB8J3yuVSGIxTh++/ppLCyx4ONbalFdYMKpHjeOiTKRxCicKxBBSPwb040UpUL63uc5zHj6UCf+9k4rnjrUiV53AHtaBvGLV0YSHf2hKJ4+2Inz5hWiqsCE1VV5eEP8/Id2nYUnGMGli4rQMuBDs5gM9d2tS2HUqvGDZ49PeGyBcDTOudrVPIAVd72UdSWlJFlSurKgifLn7c3YWFeAO69dgneJibvtQ76kgQ1AiPyGIjGEIjFsTHR+RaepzG6QnwtGotjTMojN9YXyja9U7oxzHheVXFOVB7tRi/oiM9yBSMpKO9I8oFUz6DUq5Ju0yDPp8N51lagvMqPL6cegNwSbQSPLn7KZQCQqr7HRGMdX/35ILh8nBVkuWVSM9iE/joo/j1UOVNlNc8Abkm18X+tQWlroAcWaIe0cAsDrJ1PPB5zzONtJJa060Do0ysZiMY7PPrQPt967G75QBAfbhnH7X/cjPMfm2pkkGuPydzngCcrXz5n+8W+Sh30hfOy+3TMy1wXCUbnJmJJINIb//OdRtIjjfepgx7hSJ2AWEt4krdDdbzXjyQMd+NfhTgz7w8hLMUHWFJrEJAfBADqH/Xj2cBce2HlWPqZLEQ1UYjNq5S/V6Q+j1x3EghKL/HuzXoOl5Xac6nbDFRD6z0vO78G2YTyw6yw+urkWyyvs8vFmnRqHxYieFPmVnLCmPo/sdMmaX28IhWY9HEm6lf2/507itgf34cFdZ7GkzAaVikGlYqjIN6I9jcjvsC80IwJ1KdO3zx1EKBLDgDeEUCQmlzbrdgagVQtbgsmkD05/WNY9N/S40Sg6v8srbPKEKEV+8xXOqE5cjJROVY8rAJtBkMWUiJH+HldAfv0HN1ZjyBfGT186hXA0lrKkj5Qoo3R+S6wG6DWCSVy/shxqcdAlNgMKzTp4ghHoNCosKrGOihA6fWG8fKwHN66uQJndiPoiM945M3b0Ya4VFN92uh+NvR7cful8qFQMC0usaOh2y4vqYELykXRtzisy43C7c0qZxFLXvxtXVyAYiWGewwzOgRePduO7Tx3DL15plN//7rea0e0K4AuXLQAAbKwrxLEOJwLhKPa3DmFRiRVXLytFKBLDzibhO1pbnY87Ll+A10/1yRrUdHjuSBfW/PfLcVHvpw52IDTGtTdbSLbZMg3OL+ccfe4gVlXaoVIxebdkf+tw0mQ3AHHO5IbaAqHphaT5dQfBGFBVYJKfO9A6jEA4hi31DnlukG68XznRi40/eAXPHu7CkQ6nnPA8v1iQLCXKtL7y94Pwh6Ky4/fT963CN65ZDKYQ91YVmBDjQsTUZtTOCecXAF48JiR6vn26H0/sb8fje4Uk0RNdLuSbtFhbkw9/OIrtjSO7IalQlnUc9ITQ7wlhbbWQbLgjyc290xeOi8ZLkd9gJCbfJG+eV4g9LYNwp3Ce32jow0U/eR3NfR60D/mw9K4XsD+J0/O5v+7HT186FffcPw924FC7E8FIDDtOD+Ce7Wfw7JEunKQk5ZS4A0K+jUmnxqA3JDuO6Ti/bzb04Y1TfdMayZd4dE8b3v27HTio2IEFhHX0wV1n8bfdrRjwBPGlRw/il6+ML2GbUeeXQdhe6hz243dvCFuPh9uc8IWiKbfGLpjvQMuAT774JY1uY69HvtsY2Qo3xL3WZtTKW+7S5Leg2BJ3zOJSKxp63AhFYrAZtLLD/H8vnYLDosdXr1oYd3yxzYBojKPEpodVnNQl5/ekopqBXO3BE0KhRSfUCFYs/q5AGE8f6sS6mnwUW/W4SJysAaAiz5hW5Pfrjx/G7X/bP+5xU6HHJWi0HBYd3IFInBb5QOsQItEY+j1BLBQT1ZI54y5/GLWFZph0apzqdqOhx4OKPCMWFFtHtNJiomB+wg5AkTW+kUWXM4BS8Xu2ijcjXc4ABjwhmHVqbF1Zhg9tqsbv32jC5v/3Gq76+Vtxmfydw368dKxbTkxUOr8qFZN15quq7KhzCDc3pXaDLI+ozDNifrFlVPmkZ490IRSN4b3rKgEAm+YVYm/L0JjR3cY5pns7Kka+L18iRMUXlVpwpt8rT0aJNz7SwiedE2Xzl2R8/bFD+HqKMnJS178PbKjCpYuKcN/HN2B+sQW/fPW0fBO2/+wQupx+/OHNJly/ogwb6wRd6OoqOyIxjhNdLhxud2JFxch3+/qpXmjVDOV5Rnx0Sy3mOcz48Yunko4hkX8caMfn/rof/nBU/i5jMY5XRM14l1Mo0/i1xw5lhYZfjvwOZl724AtFEQjHUChGZJeWC85vKBJLHfkVpWkLSyzIN+tg0WtkfW+fJ4gCkw55Rq383I7T/VAxYNM84XstsRnkyO/+1iHEOPDlRw+Cc+DiRcJ8Wl8sfM+P7mmTna1nDnfiyf0dONrplIMU168owycvqIsbn9RtVIj8amHWq6d6mqYdo1aNF44JzYae2C84vZJ9Hu9yYWm5DVWiZOTt08KN32CKKj6AsLZKQYCzgz6EojFctawUNoNGdrKVfPufR/C+P+yUg1XKahzSGv7BjVWIxDj2piif1tQrVPQ43O7EfvGGpzHhRjIa4+hxBeIcNF8ogh+9cBIrKuyw6DV47kgXXhUrvkhJzMRopEoP9UUWRGJcvl5a0nB+JUmk0vdJxp1PHsFXHj04pXFK68+fEqoHSYGvbY392H66H5wLjcnGS6qcUefXoFXjWKcTP3rhJDgX7gD3iQaRTPYAADevrYTNoMG921sAAG819sOsEyYhSZMibYkW2/Rxr7UZNHLk97SYJbxAjARILCqxIhzl8vFS5HdH0wDet65SdnAlpO02SfIAAMVWwRmTErq0aoYBTwiBcBTuYAQOi152HiVZwD8PdMAfjuKurUux+9tX4AuXzZffryLPiPYhP+5+qwmfeXBvyvPZ1OsZ96KbLP5QFO/7ww7c/lfBub5CdHiUWb4H2obR5xESIpaJi10y3a/TH4bdpMWCYguOdTpxqtuNhSUWlOcZ0O0KIBrjGPaFoNeoYNTGLzDFVkNcJ6ceV0CWtzDGUJZnRNewEPktsAjd2v7rXctwzbJSOTIvRSYB4M/bzuAzD+1Di6h7VDq/ADDPYYHDokex1YCF4i5BiU2PAlGCUZFvRH2RBW1DPvhDIxr2Nxt6UZFnlM/DproCeIKRMcuiJU7o2U5LvxdFVr0cAVtYYkUkxrGtUYgAJWYKS87WRQuKYDdqseN06kj4wbZhPLavHa8naAnvfqsJJ7tdYilDA0rtBtz38Y2oKTTjuhVl6PcE4bDooFUz7GsdwsPvtCIYieJb1y6W32NlZR4Aoe7vgDeElZUjzu+xTheqCkxQqxh0GhXet74KJ7pcaTVLefpgJ+ocZlTkGeXjD7UPy4+7nQEc63Ti8X3teKsh/RJ804Vkmy39mXfEpQjqSNOYEVlSsgYXgHDzCkC+SbHoNXKnzX53EA6LPk4KsaNpACsq8+RIcpFVL88NJ7tcsOo1CEVjKDDrsLxc2K0rtRnwyQvq8PShTlz/q+0IRqI4IC7uPS5BLpVn0iZNyJO6Pkp/gyVFBDubsBm1ONQ2jKMdTrxwtBsGrQoNvW60D/lwrNOFNVX5qBQbSUmSpLEjvwFUF5igVTOc6hbm0TK7AdevLMOLx7rhC8UnT+5pGUTroE9ODlbOCfvODkGrZjhvXiGA1J3mpJ3ck91unOySdpXio8SD3hBiPH737IGdZ9HjCuK7W5figvkO/ONgh1wuT3KciNFIUs36hPWyJY3cgL0tgv92agw/hHOOl4934+lDnWM2HYvFOHafSV1yUtpJe/5IF57Y147vPX0MvlBEDmSd6HLhyf1Cd9w+d3Bc6cOMOr9GrRp7Wobw1MFOfOaiebhiaYmsG7WbdElfY9ZrcMvGajx/tAutAz5sb+zHtSvKUOcwxzm/hWYd9Jp4x8mmKFvW2OOBQauSEyUkFpVa4463G7XgXLizvG5F2ajxSBO60vmVnpNKedU5zBjwBON0pZJeeNAbAuccf3unFcsrbPLCrNxuq8w3ot8TxM9fbsRLx3tG9bYHhAuqY9iPYV84Zf3dqbCnZRB7WoZwuN0Ju1ErbyNKiSHVBSYcbBuWJ6pl4mKTTFsnJb0sr7BjT8sQTvW4sbDEivI8I6IxLmt2C8y6uPMAjI78djsDKFVou8vsBnQ5/RjwhlAgXkNatQp/+Mg6PHrbeTDp1GhW3MG2DnrB+UgN2PyE6+4b1yzCHz68FoCwFW43alFk0cuLelWBCfOLhfrTF/3kdVz3y22IRIXt8/PnF8rjlyZ4pfQhFuP4yD3v4NZ7d+Oe7Wfku+a5QsuAF3ViUiAwYjvSIpooe+gXr/9imx6b5xViR1Pqu/GfitHWfk9QniCb+jz44XMn8edtZ9DjDKAk4eb2hpWCfX5siyBN2tcyhGePdGFTXaEctQOEa8Rh0ePvewVpwopKoWa3dBNdq/ibpKiiss1uKhp6PFhVaUeZ3SBfo6+c6IFaxWA3atHp9Mv1v2e7eLx0Iw5MT8Kb9PcpE0gl6UMq2YMUWJAqN1j0GgQjMYSjMfR5gsJ3JDq/3qCg39xSXyi/vthqkCsVnOhy44qlJfjMRfPwqQvroBKjlYwx/OcNS/GLD6xG66APe84MybXbe1zBuEY3iZTYDLKcy2bQwjIHIr95Ri10GhVu/O3bCEZi+OzF88E58OtXTyMa47h8SXHcGqhWMQyOUeKvy+lHmd2AfJNOLk/psOhx0+oK+EJRvHC0Gz97uQH/PNCBbmdArtf8vNhYpt8TlKtkNPR4UGITmhPpNaqU5dakndxT3S45uDOUsMZJ9jbsC8PpD8MVCOMPbzbhkkVF2FBbgMsWF4NzYe3dUJufE86vVFFjogwrks0BIMaFZNR+T2hMXbcnGMHJbhfUKoaGbnfKz+5xBdHvCSES43jt5OjdAomXT/Tg/X/cmXRdjMU4Gno8uH5FGVSM4auPHcL9O1qw+8xg3M7Fmw19WCUWKNjRNLbscErOL2Psy4yxY4yxo4yxhxljhrGON+jUCEViKLMb8O+X1MuTI5C8A5DER7fUQqNS4ebfvw2nP4wLFzhw6aJi7GwegC8UQbczEJfsJmEzKJzfXg/qiyzyFo7E/OKR56wGjbxFV1UwEsVTMuL8jiyYNoMGeo1KnhwWFFvRr8iMLTTr5O3Afk8QXc4ATna7cfOayqR/rzQ5+cNRcJ58S2HQG0JQvHFIpxTcRHnnzADUKoadd16G1756McrFKhSS83vN8lKcHfDJd4nLK8aJ/Bq1+I/rluA3H1qD2y6ahw9sqJLfs3PYjyFfaJQjCkDu4sY5lyUWpfZE5zeAoSSLGGMMdQ5z3NaYFCk42DYMu1ELbULEZ16RBevFxfhjW2rxxtcugUatkp3fynwjVlbaoVEJCTKnety4Z/sZuAIRnD/fIb9Pic2A2kJTXJWDIV8I2xr7cbB1CN9/5vioKOdMMFGbBSBPamf6vXLEFBCi5NLCtrjUigFPKG4ClHSbBSYdzp9fiI5hf9JrdW/LILaf7pcdGynh4iVxW3VPy6AQ+U2w8YUlVjzzhQvw7xfXY111Pva3DqGpz4vrVsbftDLGsLrKjiFfGBoVw+JSq3BtiDYsSV0AYEWFHUatOmW2+D8OtOPJ/e1wBcLoGPZjYak1rs32ayf7sKE2H/VFZnQ7A/L1NtvOr3QjXmozoCvNrpkTQbJ7KUkVGJE+pJI9bKwrwH/esBTXLBcSiqVqCt5gRI7oWwyCFOJohxORGMdG0TYBYUemzx1EjyuAblcAi0utuPO6JfjcJfNHfdaVS0ug06jw971tstSq1x3AgDcYV+tbiVrF5CipzaiFWTfzmt+J2qtOo8Jzd1yAyxYX49JFRfjolhoAQr1th0WHVZVCIqBNlJysrc6TI7+tA75R10W3KDMrMOvkSKDDoseG2gJU5Bnx3aeO4VevNuLHL5yUt8uLrXo8f7RbrPYSRK3DLN9ElIuNqCryjSmlQJLO+FRc5DfB+VXYU9ugD/dtb8GwL4yvXil0dr1kcREYE5LVV1fl4US3e9ykt0g0hpPd6dUvzjY6h/248Eev4/mj3RN+rdM/InuQkIJdY0kfDrYOI8aBK5YUwx2MjKpQ9fyRLhzrdMo3HmoVw4tHUzu/Um5RshrSrYM++MNRXLTQgf99z0rcIe6U93tCsvMuBTP+bVM1KvKMY+40AlNwfhljFQDuALCec74cgBrAB8d6jVmnBmPAd65fCpNOE+f8pkp4A4TyM3/99CYYdWro1CpcMN+BCxc4EIrEcLjdKdT4tSdxfo0auc7v6V7PKL0vIEgxpAXdZtDKUYrrlpeNikICCudX8V6MCTWAJQOtL7YgFInJiSWFFr3cIKPLGZDD98mca0CQPQDAhtp8AJA1jUqUmbPpbE9MlF3Ng1hZaUehRY9Ci5CwBwDHOpzQqBiuFKsh3Pv2GQCQNb3SVve+s4O48MevYUCM5NnFUkE3rCzHf1y3BPOKLPLf2TEsRH7zzaOvgSKrHqFoDC5/RJZYlMRFfo3o8wgLYH6SRazOYZYz+jnnsjMiRQXGQqNWye8pdYKrzDehptCMQ3ddhVe+cjGsBo3cUnmzIioFAO/fUIXtp/vxwlEhCiJN2D+8eQVe/erF+MqV8Xry6WYyNvunt5pxwY9ex5BXSHapVTi/Oo1Ktp2LFxYhEuNx7cQHvEHkm3TQqFXYIt4YJKvB/OieNlj0Gnz7+iUARtrRvihqF6XGFiVJbHx5hR0atQrravIR40Kpu2vE6ixKpB2WRaVWGERpTZ1DsGFl5Fcrvtc7Kbbf/vhmM378wilZ4rSoxCrvTsTE+tWrKvNQZjei2xmQt3Zn2/mVnNO1NXlpd82c0Pt7U0d+UwU2dBoVPnlBnbxjJ2mA3YEI+hSyh3CUy3OcMqJ/3rxCRGIcv3+jCQCwpCz5fAoIteM31RXgX4dH6m/3jhP5BSDXXbcZNPL4ZorJ2CsgJPn96db1uO/jG5Fn0qHOYUY0xnHpomI5Il6Zb4LdqMWycjuGvCG5RvnvxHMJiLpadxBlovMr3dcWWfVQqRhuWlMOTzCCRSVWdDoD+NvuVmhUDLdfOh+tg4LMYsATQpFFLwcspFr8lfmmlJFfaTex0xlAp/g4UZqhrE18dsCHZw534vz5hVhRKexAFlsNeOiTm/D1qxdheYUdoUhMzvt56Vg3Hk/SKfKJ/e247pfbxqx7nK0c63QhEuNyUuFEkDW/Cp/m8iXFAMZOett7dhCMCUnmQLz0IRbj+Opjh/BfTx/H0U4nVAx495oKvNHQC38o+Y23dPOUTA4o7aovKrXhvesq8ZmL6wEI8+qgNwydWiXr/C9aUIQt9YXY2TwwZhOmqcoeNACMjDENABOAMSv7G7Rq7P/OlbhejMzYTVrZARor8gsIW2MvfPEivPjli1Bo0csTndCFK5B0YZQ6Bjn9QpRmQYl11DHAyPatzajFwhIL8kxavHttRdJjFxRboNeosLg0fqKVnFutmqFWjCTtERfQMrsBC0osYExwZKW+6gtTjGdlZR7ev74SP3v/atgMmqTOb8fwyOKV6civLxTB4fZhbKobceak6MiAN4Riqx7ra/KxdVU5mvu80KlVKDDrUGjRyUlPD+w8i7ZBP453ueAKJK/1Kd2wdIryjWSRX2U5s2SJjeV5BnAulDxKFsGZ5zCjfciHYCSKPk8QgXBMvkMcz/lV4hAjWlKyiFmvgUGrxrtWlSMYiWFhiUXWfkt8+sJ5WFZuw3f+eQzDvpDcqKHYakB9kQV3XL4g7c/PIGnbbDTG8avXGtEx7Mc/DghaqjpFOThAuIErtuqxWGwEokxw6XePdNCb5zCj1GYYdTfuD0Xx3JEuXLeiFAtLrNCqGZr6POhxBXCwbVi+yYpxjIr8KllXI9wobqwrkK8ZJSvFRVH6X/hbRkd+pfc41ZO8nXfHsB/droDsmC8ssaLYqpfnmFAkhsp8I0rFHQk58uvOvDQpkaY+Dz7z4N6kUV2p9OLaauE8ZXrO6E8S+V1VmQcVG/t7UyJpybtdAQTCMRTbRvTl0pyptP0t9YUoNOvwt3daAUC+BlNx6SJhK1yrZlhWbouTW6VC0v3OYrWHCa2xyZDq1EsODSBEwm9eW4FCsw7uYAStgz54gpG4HY9+TxDRGEeZ3SifI7WKyYGqz10yH7/50Br89dOboGLAWw19WFxmleVIbzX2CRF8q16uxCT9X5lvTOr8Ch3lgnGBIa2ajUrKU0Z+D7cPo7HXgy31jrhjzp/vEPTfYrUmKQL529dP4+cvNyCRIx3OUTribMIfiuK7Tx3FFT97c5QcQQqopdLz97oC+MLDB5LmMkjOb02hSa7KdPHC4jHfDxA03ItKrPLcq9yh7nT64QtFsbtlEK+c6EF9kQXvXlOBQDiWtARoNMblUqINPYKE4qmDHbLk81S3G4xBzsMx6zUwatXodwcxJAbOPnfJfHzn+iUotRtw3rxCoU/DGHk1k3Z+OecdAH4KoBVAFwAn5/ylxOMYY7cxxvYyxvb29fWNis5J3bNSJbwpMes18oJVYtPDZtDgcLsTg95QXGtjCSmKK91RzE8S+QWAJZLza9BiXpEFB7971SjnVuLKpSXY/R9XjFpgpZ/zTSP63n8e7BATu4ww6TSoL7LgaIcLDT1uOCz6pJFKADDq1Pjxe1ehqsCExWW2FM6v4Aha9JqMRX7PDnix8Qev4H+ePYFwlOO8eSNbjAatWm5RWmwzgDGGH71nBRaXWlFdaAJjDIVmoZybLxSRt6xPdLnAefKbG6tB2H5rG/Rh0Jd8EZIcSml7E4iP/JYqytslO5/ziiyIcWFrTIrCXS1us07E+b1sSTE+c9E8eSKVeP/6KgAYNfECQhTx+zctR78niFdP9MoTT3ES52wmSMdmlfba2jsEdyACFRPq4wIj0VKJ/7huCR761Cb5mh/whuALRRCOxoQtZTESyJjQdOCtxr44DftLx7vhDUVx89pKaNUqVBeY0NznwUvHhevny1cslJMgk0mbJIptBnz6wjq5vFkia6ryYTNo5La4ALCywg6tmsXp/gEhWZFzIblWiSsQlhOyHtnTBrNOjYo8o2z70jxTmW9Cmd0Afzgqb+EpI7/BSDQuCTNTvH6yFy8e60nq2I5EfoWFKtPlzqRqK0bdiC62utCEF790kSxrGA/JuTwjRv6LrQZZCtHY64HNoIlrNKFRq3DdijKEokKTEikZORWXLhYW9CVlNlQXmAS5lC885jwgRZqFag8z6/xOdo1N5IolJajMN+ICxbX/5SsX4q6ty+Q5U3I8jnQ45Qo1Ug32MrtBvoktNOvk6LG0kyfJIAAhcFNo0aOm0IQj7UI1jUKzDuXiTUu5HPk1YtAbGpXP0u8JIRrjuGTRyFhXV+WNivz2uYW28w6LDk8dFO4HJCcskbpCM8w6NY52OGXtaMewf1SynnSDJUWbs4lwNIabf78DD+w8i9O9HryQIG+QoqXNKSK1v3ujCf861InH9rWN+t2wPwSrXgO9Ro0Csw6lNiHSX243pPQtOOc43O7EmmohAbUizxgX+ZXOJQAc7XBheYUd62ryoVOrsKdlEJxz/PKVRllmcrrXA18oCqteg8YeD/aeHcIXHzko52mc6najusAEk0J65LDq0OcJypLJ5RV2fOrCeQBGroXEsmhKpiJ7yAdwI4A6AOUAzIyxDycexzm/m3O+nnO+vqioKPHXWFdTAJtBM6qqQhqfj8WlNrwlFq9PFvmVtGZSBDZVpPWDG6vx3zcuG5VQk+pz7UkcdclJkyKggLB9d9XSkYl/RYUdRzucaOhxy3cw47G0zIaT3e5R4fvOYT+MWjWWldsyFsU50eVGrzuIv73TCrWKydpXCWlxkc6TSafBY/++GQ9+ciMAwGHRYcATwsvHe+ROPkc7hIs7le5vTXU+3jjVN6rVsPyZcuQ3KEd+lZrfcsXjZJFf6Wapqc8r39G/e01FyuNT4bDoced1S0ZphFdW2vH/bl6B2y6al/R1UgSjfcgvZ6Yni0zOBOnYrNJefTENrllWilVVefKkmhglLbYZsLDEKjsQA54g3vWbt/HD505gwBOSnWIA+LfzquEORPDw7lb5ucf3taMizyhrOYV2tF48vq8d84stWFJmxdqaPABjO78A8O3rl8bprpXYTVoc+O5VuFbhiF2+pBi77rx8VH3w1dV5qC4w4RuPH8Izim1yZflBdyCCBSXWuLbnB8REKinyKx0HxEeqHn6nFTf8elvGuzhK75cso1qSIy0otsCi12S8KL1wozP6ul5QYh2VZ5EKybls6hcWzmKrXk4yO93rkXMElGxdVQ5AiPomk6kpqXOYsamuAFcvK0WxVY+zAz5EY1yu5JIMaRfPbpr5yG+m1tjrV5Zh+zcvSzp+yW6l2vW+UFTOXZEcmFqHWXaSHSluMK4SpUarRXnRigo79p4dgjsYQZFVj7K8xMivcF4TbUDS+66tzodVrL60pMw2WvPrFhIiqwtM6HYFoFExrBI/OxGVimF5hR0H251ClR5xbWpOKFcplSvsyqLuqhKvn+zFiS4Xfvq+VagpNOGpgx041e3Gh//8DnrdAfk7OzvgBeccf97WLEsgBjxBPLJHmHOfPjgyn53ocuGe7Wfg9IVln6bYapCDhLUOM050ueJ8j91nBuVcBqc/jBUVeQCE3fODbcNyZ8vG3pHkf0CQpxm0aqyotGNvyyAaez34+SsNeGS34NxK3VNvWFWOAW8IT4iyFGlOPdntGuW/OSxCsC1ZvlBNoQn5Jm3SphgSU5E9XAHgDOe8j3MeBvAkgC0TfZNPXlCHV75ycdoTpJKFpRbZoUiq+RUjlTubB+QOaslwWPS4dXPtuJPnWEgLYJ5JGzdBXLVspFPYsnIbul0BHO8a/UWmYkmZFb5QdNRWTOewH+V5BtQ5zBnL3pYW6CuWFOPGVeWjJkvp71JuY1oNWnlCKzTr0eMK4K+7WlFuN6Ay3ygbYCpZy9ZVQieoVBpcqXxdnzuIblcQWjWTqzoAkCdVYHTlBgByUtOZfq+ceb+htgBbV5XH1VaeLIwx3LKxOunCDAB6jRrFVj06hn3odQdg1qlns03qhGw2xjk+f9l8XCg6lOV2g6yXTUS6Nk51e3C614PnjnShzxOMs4W11fk4b14B/rztDIKRKA63D2NbYz9u2VglR5PmFVnQ1OfBobZhfHhTNRhj2FgryG+kqNFkUatYnI0zxpI6bHqNGk98dguWltnw5UcPyqW2pEiYlOy6SLThIoswrgNtwkRbkW+Mc6hrC01xZQ6PdboQ40jaJGAqSM65K5nz6xVKCVr0GhSYdWN29ZoMA2I986kg7SxJTokgexDmjY5hf9I5fn1NPpZX2HDRgvRs+dHPbMbtl86X67UDY98EX76kBP/3vlVYXZk3G7KHjKyxYyHNmYfbh+XnpGjZwfZhWA0a1BWa5XOU6sb9ptXl2LqqHJeJ0oqVlXZ5p0sZ+ZW+Q0lL3T7kw0vHumWJkbJ9/drqfKytzkOBWQeXaD83/HobXjjaJTi/Fr0sS1leYY/bdUhkTXU+jnc6ZScfiO/S2a+oztQ1i5HfmKLOrpJH97Sh2KrHTavLcePqCuxoGsC/P7QP20/34+mDnWjq88Cq18AXiuJIhxP/8+wJ/O/zJwEAf9nRgmAkhls31wjl48Ro6wM7z+L7zxzH7pZBeef9R+9Zibu2Cq3rr15WipPdbvzoBeF93mkewAfv3onvP3tcLmUnycjev74KrYM+fPsfR8G5EF0vsurxIVEPvFwMAq2vycfRDpccuZZ02NK1drXoLz0pyuwOtA3B6QvjTL83LkcMgNw1d8gXHpUvxBjDmup82XlOxlSc31YA5zHGTExYUS4HcGKib6LTqFCcpiYskUUKaUIyXZkUbTzUNix3UJsupEmhwKyTnbhSmwErFNvk0pZ5OMon4PwKf2Oi9KFz2I8KMfmq3xOSF+ip0OcOQsWAP35kPX72gdWjfi/9jam+L0nzu7tlEJ+4oE7YwhYjhqnKHV21TMjCBpJLX4TtGBV63QH0uAIothrivkeLXiPXC022+NoMws1Ic58HrYM+lNoEB+7Xt6xJWspuOpD0bX3u4KSv9QwxIZs16zRYXmGXo6nKZLdEpEVUamTR4wrCHYiMciw+d8l8dLsC+NnLDfjpSw3IN2nx0S218u/ri4TObUatGjeLzTE+dn4tfn3LmlER2umkyKrH5y6Zj3CUy/VNpSiVJHVZKMoliuRkUBcKzTqYdJo4R03S2Up6+AZxwt85TimeiTJW5LdfvBFhjMW1cM8UQifLqTm/suxBnDOKrIa4xhJlSW4wVSqGZ75woZwAky7KXYSxZA9atQrvWVcJlYrNxk1rRtbYsZDmzKMdLrGk2Ui07HD7sKDbVjE5Op4q8lto0ePXt6yRfy9FBKXfXbuiDHdcNl9ezyTn94Wj3bjtwX1yol2nIq/j1x9ag1/dskb+foTKAS48f7RbLoVXLSarrk8heZBYU52HcJTLuQsqJtTJl1Bu03fOYuT34T2tuOm3b8eVWux2BvD6qV68b30lNGoVblpdDs4FO8kzaXHf24JzK8l6Hhajqdsa+7Hv7BDufbsFVy8txR2XL4BaxeTor9T7oH3IjzyjcI5XVNrl3KhbN9fg1s01+ONbzfjE/XtwxyMHEOPAm6f6sK9lUOh2Ks6B1ywX3v/xfe14fF87Gns9WFhiwYfPq8GP37tSlsWsq8lHKBqTE+WlCPGhNuFak+SmoUgMpTYD2gb9eGxfG2IccVIYQBH59SavFLWmKm/MRlJT0fy+A+BxAPsBHBHf6+7Jvt9kWKzQ6pWmSHgDgEiMy2V3potiheZXq1ahptCEd60uj4s0KUX86coeFpZYYdFr8ItXGtE26MOT+9txstuFjuEAKvIM8rZcJqK/fe4ACsz6lFF4hzhRptp+vnltJf794no8/8UL8akL56Ey3yhnCKeK/NoMWlwqXtTJFiHGGIrFkkZS2Z1EpAziZAYACMlWp7rdaB3wxRWunykq8k3oGBZkD7MleQAmbrPS972mWtDLJmpjleg0KtiNWrmBgPweCX/vhQsceO+6SvzxzWa81dCHz1xcHyd5kjKOb1pTId8w2Y1aeXt7JpHmDEmf2zHklxthbKorkCdjyYEIRWPyol5k1cvJI2uq8wAIpd9iMY7Toj4vnS5EE0FyfpPV5lRGZqfF+fUE45LdJoPkXLYO+KDXqEQ53IjDWZ7E9ieLUnefrvZ/piO/M7HGSnOmPxxFVb4Ja6rzcbBtGIFwFCe73FhVJQRspMiaw5reuVpeYYO09DksQh7MV65aJK8tRRah1q9UdeHFY0JptG6nH3qNCnkmofKS1aCVxyhF8Q60DsuyB2k3d33t+M4vALxxqhdVBUZUF5jiunRKTtjiUuusRX4557j/7RYAQkUKiSf2tyPGR2665xVZsHVVOW6/tB63nlcj2720y/z0wQ6YdGqoGPDx+3YjEI7iG9csgsOix/nzHXhBPNdKxzCZlJMxhru2LsNXrlyIQ23DGPaF8bWrFsITjODxfe1YWmaLkwF+6fIFWFJmw71vt+B0jxsLiq0w6tR4//qRnT1JizvsC8Nq0KDHFUSX048TXS6sqc4TO+cKdnb7pcIN7e/eaEKxVT9K1lJk0WHQFxIaXCWx4TXVY18TU6r2wDm/i3O+mHO+nHP+Ec75jNbzWSh2azPr1Ek1w0qdaWLIPNMoI78A8MwXLsDXr14Ud4zVoJU1MKkqTyRi0Krxx4+sw9lBLy788ev4yt8P4ZP370W/J4hyuxE14p3vEcV2zmTpcwfHTMZyJGh+E5lfbMG3rl0s391X5Y84msmMS+I9a4UIX2V+cse02Cpkzjf1eZJufSplF8m4cmkJDrU7sa91KK5U0kxRkWdE57AfPa7ArDq/wMRsVrIfnUaFpz9/Ab48Tmm2QrMO0RhHZb4Rq8TtsMRoIGMMP3nvSvz3jctw6aIi3Lq5Ju73y8vt+Pj5tfLEN5uU2Q2wG7U4Lnbpax/2oyJPyHx/9DOb5bqYWrHaCTByDWvVKhRZ9dCpVVgqNoDp9wTRMeyHNxTF0jIbupyBjCWeeYMROWs7qeZXUc/Wrmj+kwliMY5B79RlD5JzGYoKlR4Yi4+2lmYw8q+8gU933LNR7WG611jlbltlvlGOlr1yogeRGJcdDmluHS+pUMJq0GKeuNYlixYzxlCRZ0SMC3PE2QEfGno86HIGUJ5njAsaSbYlOb+tgz64A4KW+LLFxfjYltpxJWzFVkGGF+OCXKlelFdJNPYIsoE11XnocvoRjsbwyO5WWcM6E+xoGkBjrwcmnRqvii3SAeBA6xAWFFvktR4Afn3LGnz96sWy1hoALlpYBJ1aBW8oii31Qi8EVyAitG0X56pNdQVo7vPiTL8Xw76wLDNIVWpWrWK44/IF2HHnZdj+zcvwqQvnwahVwxuKxlXOAYRdmA9tqsaJLhe8oSgWJAnwFVr08nXxb5uEuf/vewTnfkNtARhjWFRiRanNgPesqxSasHhDuHxJyaide4dVD86FSkB5SQJfK6vsGEvJOqMd3jKN3aRFmd2QNNkNGNH8AiMdyKYLKSIpOTdWw+gGCoBwB1pVYBy3tJuS8+c78OAnN+GDG6pw19al8p1eeZ4Ri0utWFpmw69fOz3lwvXjRSYl/W26pYuUjuZYf+9Vy0qx687L4xooKCmy6LG7ZRC97mDSCGCZ3QC1isVFiZR84oI6bKkvRDTGZynya0Q4KtQYnq1KD1Ol1mFOKV2RkJyIZeU2eQsumaaWMYZbN9fivo9vjMveBQRH+66ty1LeCM0kjDEsVVRb6Rjyy6UZE5GcgkpF96xSuxGV+Ub5O+/3BOUI08dEqceOJHWPJ4MycUjp/Pa6AjjR5RIjv1K74YlFfqNi29FUUWpXIIxIjCf9rieCWsXkyh5SArHS+c1k5Fd5A59qxyiRma7zOxNo1Sp5nazMN+KmNRXQqVX4jyePABgpk1ZTaMLFC4uSVrVJhVRbO9XNhdTM6SfvWwnGhGhnYgdPYMRBT0xeKrIKFZO+965lo+aRZEiRwEWlVswvtqC53yvrvht63FhQYkGZ3Yh+TwjPHO7Et548gofFMnq7zwymrE+bKf6yowUFZh2+dMUCNPd7Zee8occjS6wSWVZuQ0WeERV5RtgMWlSLO8Gb6wvxuUvrccF8B+5QVMCRvk+pDfAtG6tx89oKXLqoeNR7K9Fr1Ciy6mHQqnHhAuEaWJkkwfCm1eUwidrrBcXJx7y5vhAOiw7vXy8EvR7d0woVG4nO37V1GX7zoTUw6TRyNTBl7pSE8qaqIEmPAJtBm7S3g8Scdn4B4KqlJTg/hUGadRqomDCpJrsLySTFVgPu+/gG3Lw2edc2ie/esBQPf/q8Cb//htoC/O97VuLj59fhptWCA1ieZ4RKxfCd65egY9iPe7afmdTYJfrGcX6vW1GG/3fzipQl4xKpKhAmN7WKybV1U5FMziBRbBPu8OY5zLhyyWgjuGVjNe68dnFKTbdaxfCLD6zGxroCXLAg/ck7U0gOEecYVQv4XEKKDi0rt+MDG6rwgfVVKRu5zBWWlNlwstuFaExoJ57S+RXtRtk69tbzavDJC+vk3/V7QnJW9tXLSoW6xxnS/SorUSgbjfzPsydw3a+2CS3gFbIHVyCctuTixWPdeP8fd2JbY3JHXarx65hi5BcYcTClGwZlV7Vkmt/JYhfbAJt16pRJnInM9q7NdKHctagqMOETF9TBFYig1GaQcxQMWjX+8omNE5IPvn99FT5yXk1Kx/TKpSW4cXU5LltcgjVVeXhw11kc63SN2t2TxtfpDGCewzwinZjg97FWdK4WipHfUCSGTz+wF4u+8zz2tAxiYYlV/uwn9gnO4QM7z+Ll40Lb3Qd2tkzo81Kxs2lAdrolAuEo3jjVh5tWV8i5KK+e6IE/FEXbkC+lE8eYsP5Lu3JSw57z5hVgXU0BHvrUprhdV6kJyJP72+Vz8bP3r8YVS0evq6m4fmUZVCx5aTmrQYsbV1eAMaQc853XLcHTn78AtYVmGLQqdDoDWFxqk3fvV1Ta5UpTm+oKYTdq49qaSyid31Q3sD9494qUf8ecv5X9rxuXp/ydSsVgM2pRYk2dpZ5Jxrt7AoTwfLIQ/UT47tZlqHWY5Ytvy3wHrlhSgt++fhrvWlU+qa39WIyPK3uwGrS4RczeTAcpemc3aqdWSUO8yG+7aF5SB3dVVZ7czzsVxTYD/v6ZzZMew1SoVCza5+oCCoxEeJaW2VBmN+JH7105yyOaOkvLbQiEYzjV7UafOxjn3CqRvldl5Pc960ZuhC16DfrcQbgCYRRb9bCbtNhcX4i3GvrAOZ+SfQCCJAMQHAVlVPdUtxs6tQrBSEy+8bIbtQhHOfzhaFoRMyn7/In97Um3l6UyalPV/AIj50mah9QqBpNODV8omlTyNFkYYyi26sfcFk02tnORfLMOLQM++dq9/dJ6PL6vHRvqCsZ55dhsri8c1fVSya2ba3Hr5loAwHvXVeF7/zqGzfMKcasiARaId2wWl1lh0qtxtMOVtgRD4oolJXjmcBc21xfKpf5eO9mL96ythFolbMNLevm3m/phNWjQ3O/Flx45AEBIIJtoYmUiJ7pcuOVPu/DnW9fHOZz7zw4hFI3hggWFqMw3YXGpFa+e6MWWegc4T12mFQCuVSRur63Jw6keF5ak6FNgM2hRX2RGU58XZp16Ujb1rlXlWFeTn3J37lvXLsZ1K0pT9jGw6DWyLc0vFnofbEih2f7qVQvjukAqUd5sp3J+N9SmvobPTWtWUF1gSln/b64ibI3E6y+/966luPrnb+FbTx7GQ5/cNOHFdNgvbF1m0jkrsujlRKipcOniYjT3e3HTmuRd97IdpcM0V2UP6SBtey+rmNvRXiXStttrJ4WmG6lK2o04v6nKKerQ7wni7IBPXsg21xfiHwc60NDjGTOZMB06hvzQqhnmF1nkBTwa4zjT78XHz6/FefWF8s2yZI9Ofzgt51fKJ3jxWDc8wcgoJ1CqYjGRpjGpkN5bWRXFLFZ8yXQAo9RmQDSDCYdzFal0pHTtWg1aPHvHBTMSMJL40KZq3LKxKum6ZdCq5RugmkIzCs16HO1wyTK8dKkqMOGJzwqV4mwGLa5dXoqtq8rjqv40i1IDzoHbL52Pe7afQZ87iPU1+djdMohAODql8yJ1tesU6xn/61AnNs0rwK7mAajYiLN2yaJi/Hlbs5xAnG6C/GcvrsenL0weJJJYXZWPpj4v6ostk7rpZoyNKUuzG7VxzYTGYkGxFUc7XFiXwkk16TQp5yjHJJJWlZzzzu/Dnz4PGvX0lTjLFirzTbjzuiX4zj+P4it/P4TLFhfj+aNduGxxCd67bmwpBgBF97HMRVdUKobKfOOEG5gksrzCjp8nKb02VzDpNMg3aTHkC094wp5LvG9dpVDQfgZLkk03C4qFlsv372gBgJSyhwXFFlgNmrgkTyUOix6NPR6cHfTKuyfSVt6Opv6pO7/DfpTnGZFn0so1wduHfAhFY6gvtsTtSimd31Tf1aluN5480I5vXL0YRzudWCJqn1842j1qPpG612VC9iCVNlPehFvFVuKZ5js3LEWMnF/km3VCG2pFFHC8hjLTwViOWL5JB1/Ij9pCE5aVC4lMjinsNBi0avz+w+tGPa+0h0sWFaG20ITO4QBqHSZ84v692Hd2KGUjnXSQ1tk+dxBOXxhfePgArllWikFvCCsq7PJaedFCB/7wZhMe2NECrZrFJbuNBWMM2nH8ndVVdjyxvz1t+eJ0sqTMin8eRMrI71hY9RroNCqEIrGUUeaxOOed31lsKDDjfGhjNVr6vXhw11m5nmGXM5CW89vrFsq7ZHpb/uY1FVCr5ry0fMpU5BsF5/cc1vxWFZjwkfNqxj9wDqHTqPCFyxbgzYY+VOSbUmoe37O2EtcsL01ZaN9h0WPv2SFY9BrZ+a3MN6G6wISdTQP4+Pl1Uxpnx5APFXnGuDJmUsKMVJVCQnZ+famT3p4/2oU/vtmMukIz3IEIPnJeDf7wZhOePdw5aj5p7vPCqFWnrAE7EaSmFsodkjqHecqVJJKxehypVK5w5dIS6DUqud56NlJg1qFj2I+aQjOWV9hHtZnPFEadGvkmLVSMYWGxVa476wlGoFExbD/dPyXnV1pnhaZNwuMXjnVDrWL41IUjc8C6mnyYdGq5Xm6y5PnJsrpKcDSzwfn98Hk1WF9bMKmACWMMRRa93DxqouSOZ5gDqFQM37lhKT57ST1Odbvx/NFuPLavDdEYT1m7l3MOVyCiiPxm1vn9vCLTNJepzDPhZJc7ZUkZInu54/IFuOPysa9jlYqNucNRnmeERsXw+w+vjdPvbakvxHNHusa00XToGPbjogVFsCnKmEndk6SOdBLKyK+SE10utA36cNWyUrls2k9fagAgdHJaVGpN2ha5sVfIlM9EEyGrnPA2cpP4h4+sw7m/dzd7XL2sFFcrSmZlI1JkrzbNCOhUWFOdj/K80c2U1lTnYXtjP755zeTfu1cR+ZWcX0CQKJ03b0Qfrdeocd68Qrx2sjftsqjpsqzchq9fvUguMTqbmHQauQnQZHBYdAhHY5OSb2TvrR4xaQotemyZ78DqqjwEwjFZx5SMh3e3YcMPXsGeFqGMzLmckDWbXLq4CFctG12rkMgN7rh8Pp6544JRWrjN9YVwBSJyy9HJEIxE0eMSkvHsRi28oSjC0Riaer1wWEYn2NqMgoOZ6Pz+8LkT+I9/CCWupHaz/Z4gdGoVFpZYYTdq4Q6M7iTZ0ONJWdZookiyB6U8SKtWQZPByBcx9ygwaWHQqmYkZ+Lej23A95Mk0m+pd+Bop3NKDWJ6XYLz2+8JokdspnHLxmpYDZpRyVkXiZWJFmbItiRUKobbL50/K9KWTFNZYJp0IizNKOcwUuLRsc7kC2soEsNvXmtEKBLDY3vbYNapc0omMpN8YEM1fvdvozVmRG6QZ9LJW6hKpNrWncOT7yolvbYq3yTXbHUHImjq88jF7ZUki/wGwlHsPjOIQW8IsRjHsD8MvbgNvqjUCp1GBZthdH3gYV8Ife5g2gk541FkMcCi18hJWAQBSOUsl8xY8CBZJHFjXQE4F+oNR6IxuQrKROjzjER+e8TI711bl2LnnZePSiS9fEkJDFoVNtRNPjJ6rvO9rcvwmw+tndRryfk9h6kvskCnUeFoR/Lub08d7ECnM4A6hznjlR4IghgfyeYk2dFkkKQIlflGuaan0x9GU59nlN4XgCzPUHZ529MyiGAkhhgXXjvkC2NjXQEWlVixZb6wHWszauAJRhCJxuTXSXWLxyrFNBE+cUEt/nn7+bRDQsSxaV4hPppQAm2mWV2VB7WKYd/ZITy46yxu+u3bcc1l0qFPdHj7PEF0uQLIN2lh0KqTltGrKjDh6PeunlBjkVyjyKqfdNdWCvOdw2jVKiwptaaM/N79VjOWlNnwg3cvx82/23FOJ2MRRDYi1cadivMrlU+qKjDBExRkCS0DXgz5wqP0vgDkbojKKK6ygcWgLwSnL4SaAhPu/dgGaERHVOrw5wlGZClFQ4/QsS5TTYSsBu2Uq8MQxHRg1gsdx/a2DMltj7vGaHyTCOccfZ4gDFoVAuEYGrrd40oPSO4zfdCZPcdZWm7HsU7nqG5OoUgMjb0eXLOsFGur83HxwiKsqcmbnUESRI6i06iQb9KizzN52UP7kA9aNUOJzSBLGvacGQSQOqPbntDi+K2GPhi0wnIw6A1hyBdGnklo0S5tAduMUsQ4gr+904r3/3EnjnY4Ydap03YACGIus76mAPvODmF/6zCAke6G6TDkCyMc5bL86XiXa8zOpsT0Qs7vOc6ychtcgYgcHZIYFAvTSyWE/vKJjbjz2iUzPj6CyHUcFj363ekvoom0DQk1ftViR0tA6FwFAGuqkusFhRbHQpS41x3AyW43rhBbh/eLnegSK5MotcLvnBnA7jOD+PveNswvsU65Qx1BzAXW1+YjpJD9SDWu00Ha3ZFavvtCUZSeA0lncxVyfs9x5onbnokliga8UktSSiwhiNmkyKqXE2HS4dUTPbjtgb341F/2oqHHjfahkda0koN6stuNhSUWWQOciDLyK5VEu0RshNEy4APnGF0lQkymcwXCGBAjXjEOLMyCeqEEMROsrxEqMiwWm9IMTCDyK9X4VdYKLybnd9Ygze85jrQYSi1PJQYz2JKUIIjJU2TV44C4jZoOD+w8iz0tgwhHYyjPM6B9yI/LRMfVptDLrqtJ3dfebtSiUXR6u8WSS1JESiqNmJfgOI/IHsIY8Iawqa4AHEKTBILIBUrtBrx7TQUuW1yM/3zq6IQiv1KZs2XlIw06KPI7e0wp8ssYy2OMPc4YO8kYO8EY25ypgRGZwa7Q6SkZkT1QhYdcgmw2+yiy6Ce0iPa4AthS78DFC4vw0rEe9LmDqCoQIr8GrQo6MUlmfU3qEknKyG+X6PzWFpph1qnR3O8FILSUVWJT3EgPeIKoLTTj75/ZjKuyvEHCXIbsNfv4+QdWY+uqchSadfIO6li09HvxmQf34pSYHDq/2CLbaKmd1t/ZYqqyh18CeIFzvhjAKgAnpj4kIpPYUnRzkrZrSPaQc5DNZhkOqx6+UBTe4OgGEsnodQdRYtPj0sXFcpeoynyh3A9jTG5isb42XefXjzyTVmjtatbJkd9EyYR0Iz3sC2PIF0LBNLQcJkZB9pqlFIpa/UA4ip+93AB3IHnzi3fODODFYz247+0zMOuEsmYO0XbOhUYTc5VJO7+MMRuAiwDcAwCc8xDnfDhD4yIyhEWnAWPJZQ9qFZMXNOLch2w2OymypF/uLBSJYdAbQrHVgEtFqQMAWfMLCDe8Dose1WPUv7QZtQhFYgiEo+gaDqDMLry+wKzDkNjaODHya9apoWJCK+VwlNON8zRD9prdFFn06PcGsbN5AL96tRHPHO5KetygV7CncHSklr70P8keZo+pRH7nAegDcB9j7ABj7M+MselvvE1MCJWKwWbQxhW0B4ABbwj5Ji0Vk88tyGazEGkh7HT68V//OjYqOVWJlBhXYtOjPM8oJ94oC72vr8nH1lVlY1ZgUFZu6HIG5BahyhyAxGoPQlRZizOiLKKQIr/TDdlrFlNo0aHfHURzn2APUnnBRIZ8IejUKhSYdXJpsyKrHlo1G3WDScwcU3F+NQDWAvg953wNAC+AbyUexBi7jTG2lzG2t6+vbwofR0wWm1EzSvYw6A1SslvuMa7Nkr3OPJLz+69Dnbjv7RY8sqc15bFSS1Rpu/SGlWVwWPRy9BgAfvzeVbhr67IxP1Oy/T53EF1O/4jzKy7GjI1IppTYDFp5sZcadBDTBq2xWYzDoocrEMGpbqGJ1O6W5M7voDeEQosOf/3UJnz/xuUAgKVlNiwrt1PwaRaZivPbDqCdc/6O+PPjEAw1Ds753Zzz9Zzz9UVFRVP4OGKy2AwjNT0lBr0hcn5zj3Ftlux15pGc32cOCdum208PpDy2V3R+i23Caz57yXy8/rWLJ7yILikTKjvsOzuEIV94VOTXZtBCneQ9bUYNOp3+uGOJaYPW2CxG2vnY0zIEQOi02OUc3e54yBtCvkmHJWU2LBDbgH/5yoV48rNbZm6wxCgm7fxyzrsBtDHGFolPXQ7geEZGRWQUuzG57IEiN7kF2Wx2km/SQcUAt5jwdqR9GE5f8uSZXlEXLLUiF1oVT1y3X1NggtWgwUvHuwFA1vzmiw5tYpkzCbtRC6lZpIMqxUwrZK/ZjXT9n+n3yvIjyRFWMugbHWhijFHUd5aZarWHLwD4K2PsMIDVAH445RERGcdm0CaRPYRIs5ebkM1mGWoVk0sOXr2sBDEO7GxOHv3tcQWE46cYdVWpGFZW2rGrWdiqlSK/hbLzm/z9lXWE882ULDsDkL1mKQ7F+nnDyjKYdeqkut8hb0i+qSSyhyk1ueCcHwSwPjNDIaYLm1ETV+0hHI1h2BembcschGw2Oymy6NHnDuKLly/EtsZ+7GjqxzXLR9fP7XEFUWzVZyRqtLIyD2+LEouyvITIb4oqMJLzazVooNeopzwGYmzIXrMX5c7HghIr1tbkY+/ZJJFfbwgFKXZSiNmD2hvnAILsYUTzO+SjGr8EkU2U2Q2oyDNiSZkVm+oKsP10f9LjelwBFFszIzdYVTm605Q0J+SnWKylGsI0dxC5jrJBVH2RGQuKrTg74AWXdEEQAk2uQIQiv1kItTfOAWwGLfzhKEKRGHQalaK1MWn2CCIb+M4NS+EPRcEYw4oKO95o6EM0xkclnQnd3FLX750IKyrzAAiOrlEnRHHz05Q9UGdIItcx69TQa1SIxDiqC8wozzPAF4rC6Q/L9jMsavdplzX7oMhvDqBsSwoAgx7J+SWDJIhsoM5hxtJyoQJDoUUPzoFhcYdGSY8rgBJbZhzPcrsBDosOpfaRBhlSRDdV8xup6xvNHUSuwxiTm8noNCpUiNKhjuGRig/SLivZS/ZBkd8cQFrIXP4wHBY9BsTILyW8EUT2IS2UA95QXIQ1GIliyBdGiTUzXaEYY/jweTXQqkdiIHajFl+/ehGuTaI3BkYivw6aOwgCi0qtcqOKctH57RwOYFm5ICmSd1mpmUXWQc5vDiDp9KRavyOyBzJIgsg2pJvSfk8QC8W6oADQ6xLLnGUo8gsAX7piYdzPjDHcfun8lMdLcwnNHQQB/PEj6+THI86vIvIrrrWk+c0+yPnNAZStTAEhosQYqLUiQWQhUha5dJMq0Sq2PS62ZSbyOxlkzS/lCxBE3K5JoVkHnUYV5/wOkuwhayHNbw4gLVhSo4uOIT9KrIakHZwIgphdZNmDJ975fWjXWdgMGqyvyZ+NYQEYaa4hRbkIghBQqRgq8ozxml/xBjZV0xhi9qDIbw6QmPDW3O/BvCLzbA6JIIgU5Jt0YAyyNh8Amvo8eOFYN26/ZP6kOrpliupCE/71+QuwTEzOIwhihPI8Q3zk1xuGRU81sbMRcn5zAKXsgXOO5j4vblhZNsujIggiGWoVQ75JhwFPECe6XPj+M8fR6w5Cp1bhY+fXzvbwsEJRH5ggiBHK7Ua81dgn/zzkC1EnxCyFZA85gF6jgk6tgssfwaA3BKc/jHlFltkeFkEQKSg06zDgCeGV4z3Y0TQABuCOyxfEdZUiCCK7KM8zotcdRCgSAyB1dyO9bzZCkd8cgDEmtzhu7vcCAMkeCCKLKbToMOgN4cyAF6U2A17+ysWzPSSCIMahIs8IzoV63FUFJgz5QpTslqVQ5DdHsBm1cPrDaO7zAADqHRT5JYhspdCsR783iDP9XtQ56EaVIOYC5QmNLgY8FPnNVsj5zRFsBi1c/jCa+7xCN5p8ytYmiGyl0CLIHlr6vaijXRqCmBOU5wnVUDqG/Ogc9qNj2I/6Ygo0ZSMke8gR7EYtOof90GvUqC00UZkzgshiCsw6uS53XSE5vwQxF6jIN8Jq0OC1k71yne7rV1ByeTZCkd8c4aplJWjs9eDNhl7MI8kDQWQ1yrbGJHsgiLmBXqPGhzZV4/mjXXhw11msrLSjluw3K5my88sYUzPGDjDGnsnEgIjp4YMbqrGqKg/hKKdktxyG7HVu4FAkyZDsIbchm51bfHxLHVSMoXXQh60ry2d7OEQKMhH5/SKAExl4H2IaUasYfnDTcug1KqyszJvt4RCzB9nrHEDKEFcxoCrfNMujIWYZstk5RKndgHetKgdjwPVUTz9rmZLzyxirBHA9gD9nZjjEdLK8wo4D370SVy8rme2hELMA2evcQZI9VBWYoNOQOi1XIZudm3x361L87VPnURvwLGaqs+ovAHwDQGzqQyFmApNOA8Yo2S1H+QXIXucEDosQ+SW9b87zC5DNzjnyTDpsri+c7WEQYzBp55cxdgOAXs75vnGOu40xtpcxtrevr2+sQwmCmCbIXucWNoMWeo0K86kTY85CNksQ08dUIr/nA3gXY6wFwCMALmOMPZR4EOf8bs75es75+qKioil8HEEQU4DsdQ6hUjE89KlN+Owl9bM9FGL2IJsliGli0s4v5/xOznkl57wWwAcBvMY5/3DGRkYQRMYge517bKgtiCt5RuQWZLMEMX1QJgVBEARBEASRM2Skwxvn/A0Ab2TivQiCmF7IXglibkE2SxCZhSK/BEEQBEEQRM5Azi9BEARBEASRM5DzSxAEQRAEQeQM5PwSBEEQBEEQOQM5vwRBEARBEETOQM4vQRAEQRAEkTOQ80sQBEEQBEHkDOT8EgRBEARBEDkDOb8EQRAEQRBEzkDOL0EQBEEQBJEzkPNLEARBEARB5Azk/BIEQRAEQRA5Azm/BEEQBEEQRM5Azi9BEARBEASRM5DzSxAEQRAEQeQMk3Z+GWNVjLHXGWMnGGPHGGNfzOTACILILGSzBDF3IHsliOlDM4XXRgB8lXO+nzFmBbCPMfYy5/x4hsZGEERmIZsliLkD2StBTBOTjvxyzrs45/vFx24AJwBUZGpgBEFkFrJZgpg7kL0SxPSREc0vY6wWwBoA72Ti/QiCmF7IZgli7kD2ShCZZcrOL2PMAuAJAF/inLuS/P42xthextjevr6+qX4cQRBTZCybJXsliOyC1liCyDxTcn4ZY1oIRvlXzvmTyY7hnN/NOV/POV9fVFQ0lY8jCGKKjGezZK8EkT3QGksQ08NUqj0wAPcAOME5/1nmhkQQxHRANksQcweyV4KYPqYS+T0fwEcAXMYYOyj+uy5D4yIIIvOQzRLE3IHslSCmiUmXOuOcbwfAMjgWgiCmEbJZgpg7kL0SxPRBHd4IgiAIgiCInIGcX4IgCIIgCCJnIOeXIAiCIAiCyBnI+SUIgiAIgiByBnJ+CYIgCIIgiJyBnF+CIAiCIAgiZyDnlyAIgiAIgsgZyPklCIIgCIIgcgZyfgmCIAiCIIicgZxfgiAIgiAIImcg55cgCIIgCILIGcj5JQiCIAiCIHIGcn4JgiAIgiCInIGcX4IgCIIgCCJnIOeXIAiCIAiCyBmm5Pwyxq5hjJ1ijJ1mjH0rU4MiCGJ6IJsliLkD2StBTA+Tdn4ZY2oAvwVwLYClAG5hjC3N1MAIgsgsZLMEMXcgeyWI6WMqkd+NAE5zzps55yEAjwC4MTPDIghiGiCbJYi5A9krQUwTU3F+KwC0KX5uF58jCCI7IZsliLkD2StBTBOaKbyWJXmOjzqIsdsA3Cb+6GGMnZrCZzoA9E/h9dMJjW3yZPP4pntsNdP43omMa7MZtleJXP5+p0o2jy+bxwZMz/iyyl6BabHZXPxeMwWNbfJM1/iS2uxUnN92AFWKnysBdCYexDm/G8DdU/gcGcbYXs75+ky8V6ahsU2ebB5fNo9tEoxrs5m0V4lsPofZPDYgu8eXzWMDsn98aTDjayyQ/ectm8dHY5s8Mz2+qcge9gBYwBirY4zpAHwQwNOZGRZBENMA2SxBzB3IXglimph05JdzHmGMfR7AiwDUAO7lnB/L2MgIgsgoZLMEMXcgeyWI6WMqsgdwzp8D8FyGxpIOGd2OzTA0tsmTzePL5rFNmFmwWSC7z2E2jw3I7vFl89iA7B/fuJC9JiWbx0djmzwzOj7G+Sj9PEEQBEEQBEGck1B7Y4IgCIIgCCJnmHXnlzGmZowdYIw9I/5cwBh7mTHWKP6frzh2JWNsJ2PsGGPsCGPMID6/Tvz5NGPsV4yxZCVipnV8jDEtY+wv4jhOMMbuVLzHtIwvydjeJ56bGGNsfcKxd4qff4oxdvV0j20i42OMXckY2yeOYx9j7LLpHt9Ezp34+2rGmIcx9rXpHttcIck5/D5j7DBj7CBj7CXGWLni2Bm9/tIdW5ZceynPm/j7Gb32Jvi9zuicPIHvdcbn42wnyblbzRjbJZ67vYyxjeLz2WATScemOH62bSLl+LLAJlJ9r7NiE0nGt0o8P0cYY/9ijNkUx87cOsE5n9V/AL4C4G8AnhF//jGAb4mPvwXgR+JjDYDDAFaJPxcCUIuPdwPYDKEu4vMArp2F8X0IwCPiYxOAFgC10zm+JGNbAmARgDcArFcctxTAIQB6AHUAmmbp3KUa3xoA5eLj5QA6FL+b1XOnOP4JAI8B+Np0j22u/EtyDm2K390B4A+zdf1NYGzZcO0lHdtsXXsTOHczPidPYGwzPh9n+78k5+4l6W8HcB2AN8TH2WATScemOH62bSLVucsGm0g1tlmxiSTj2wPgYvHxJwB8X3w8o+vErEZ+GWOVAK4H8GfF0zcC+Iv4+C8AbhIfXwXgMOf8EABwzgc451HGWBmECXAnF87SA4rXzOT4OAAzY0wDwAggBMA1XeNLNjbO+QnOebIC5zdCuOiDnPMzAE4D2DjT5y7V+DjnBzjnUv3KYwAMjDF9lpw7MMZuAtAsjk16btrO3VwgxTl0KQ4xY6Qg/4xefxMZW5Zce6nO24xfexMc34zOyRMc24zOx9lOirWMA5CibnaINYSzwSZSjU08/ibMsk2MMb5Zt4kxxjbjNpFifIsAvCU+fhnAe8THM7pOTKnaQwb4BYBvALAqnivhnHcBAOe8izFWLD6/EABnjL0IoAjCSfoxhHaP7YrXZ7IF5ETG9ziEL68Lwl3Vlznng0zYQp+O8SUbWyoqAOxKMobwNI1touNT8h4ABzjnQcbYdH23aY+NMWYG8E0AVwL4muJX03ndzQV+gSTnkDH2AwC3AnACuFR8eqavv4mMTcmsXXvJxjZL117a48PMz8kTGdtMz8fZzi8w+tx9CcCLjLGfQpBAbknyutmyiaRjyyKbSDo+ZIdNpBrbbNhEsvEdBfAuAE8BeB9GGrnM6Doxa5FfxtgNAHo55/vSfIkGwAUA/k38/92MscuRZgvIGRjfRgBRAOUQQvZfZYzNm47xTWJsqcaQLedOet0yAD8C8BnpqSSHzfS5+y8AP+ecexLfKtNjmyuMdQ4559/mnFcB+CuAz0svSfI203L9TWJs0utm9dpLMbYZvfYmMb4Zm5MnMbYZm4+znTHO3WchOEBVAL4M4J6E182mTaQaW7bYRKrxZYNNpBrbjNrEGOP7BIDbGWP7IDjFIeklKcYxLeObzcjv+QDexRi7DoABgI0x9hCAHsZYmRhVLQPQKx7fDuBNznk/ADDGngOwFsBDENo+SiRtATkD4/sQgBc452EAvYyxtwGsB7BtGsaXdGyc8w+nOD5Vm8z2aRjbZMYnbY/8A8CtnPMmxbhn+9xtAvBextiPAeQBiDHGAhA0Z9Nx7uYC6ZzDvwF4FsBdmNnrb6Jjy7ZrTzm2mb72JvO9ztScPNGxzeR8nO2kWsu2AviieMxjUGxNz7ZNjDG2rLCJMcY36zYxxthm2ibGstmrAIAxthCCLAKYaT+FZ0jUPJV/AC7BiBj6J4hPKPux+DgfwH4I4XoNgFcAXM9HBNTnYUQMfd0sjO+bAO4Tx2AGcBzAyuken3JsiufeQHxC2TLEC8mbMSIkn7FzN8b48sTxvSfJ62f13CX87nuIT7CY1nM3F/4l2MYCxfNfAPD4bF5/aY5t1q+9VGObzWsvzXM3K3NymmOblfk42/8lnLsTAC4RH18OYJ/4OBtsIunYEo6fTZtIde6ywSZSjW3WbCJhfMXi/yoI+t1PiD/P6Dox68aY5MQUAngVQKP4f4HiuA9DELkfheh0is+vF59rAvAbQGjeMZPjA2CBcJd1TLyovj4T40sY27sh3CUFAfQAeFFx3LfFzz8FRabkDJ+7pOMD8B0AXgAHFf+Kp3t86Z47xfHfQ/xkO63nbi78SziHT4jn4zCAfwGomM3rL52xZcm1l/K8zda1N4Hvdcbn5DS/11mZj7P9X8K5uwDAPggOxzsA1onPZ4NNJB1bwvGzaRMpx5cFNpHqe501m0gY3xcBNIj//lf5WZjBdYI6vBEEQRAEQRA5w6w3uSAIgiAIgiCImYKcX4IgCIIgCCJnIOeXIAiCIAiCyBnI+SUIgiAIgiByBnJ+CYIgCIIgiJyBnF+CIAiCIAgiZyDnlyAIgiAIgsgZyPklCIIgCIIgcob/D+ONP2tuDEbDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Balmer = [4102, 4341, 4861]\n",
    "halfwindow = 50\n",
    "plt.figure(figsize=(4*len(Balmer), 3))\n",
    "for i in range(len(Balmer)):\n",
    "    plt.subplot(1, len(Balmer), i+1)\n",
    "    plt.axis([Balmer[i]-halfwindow, Balmer[i]+halfwindow, 0, np.max(flux[stds[0]])])\n",
    "    plt.plot(wave, flux[stds[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Redshifts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directory from which we took these spectra also contains information on the best-fit redshifts for the spectra from the [redrock](https://github.com/desihub/redrock) code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TARGETID', '>i8'),\n",
       " ('CHI2', '>f8'),\n",
       " ('COEFF', '>f8', (10,)),\n",
       " ('Z', '>f8'),\n",
       " ('ZERR', '>f8'),\n",
       " ('ZWARN', '>i8'),\n",
       " ('NPIXELS', '>i8'),\n",
       " ('SPECTYPE', '<U6'),\n",
       " ('SUBTYPE', '<U20'),\n",
       " ('NCOEFF', '>i8'),\n",
       " ('DELTACHI2', '>f8'),\n",
       " ('NUMEXP', '>i4'),\n",
       " ('NUMTILE', '>i4')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zfilename = specfilename.replace('coadd', 'zbest')\n",
    "zs = fitsio.read(zfilename)\n",
    "zs.dtype.descr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, let's ensure that there are the same number of redshifts, targets, and spectra in the files. This may not be so in the DESI _Main Survey_, where there might be repeat observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500 redshifts\n",
      "500 targets\n",
      "500 spectra\n",
      "(500, 7781) shape of flux[\"brz\"]\n"
     ]
    }
   ],
   "source": [
    "print(zs.shape[0], 'redshifts')\n",
    "print(specobj.num_targets(), 'targets')\n",
    "print(specobj.num_spectra(), 'spectra')\n",
    "print(specobj.flux['brz'].shape, 'shape of flux[\"brz\"]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems logical: 5000 DESI fibers, 10 petals, so 500 entries per petal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `TARGETID` (which is intended to be unique for each source) is useful for mapping source spectra to redshift. Let's extract all sources that were targeted as SV-like quasars in mini-SV-2 (the bit-name `SV0_QSO`; not to be confused with the Main-Survey-like quasars that were targeted as `MINI_SV_QSO`) using the fibermap information from the spectral file, and plot the first 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132 QSOs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qsos = np.where(specobj.fibermap[\"CMX_TARGET\"] & cmx_mask[\"SV0_QSO\"])[0]\n",
    "print(len(qsos), 'QSOs')\n",
    "plt.figure(figsize=(25,15))\n",
    "xmin, xmax = np.min(wave), np.max(wave)\n",
    "for i in range(len(qsos))[0:9]:\n",
    "    plt.subplot(3,3,i+1)\n",
    "    ymin, ymax = np.min(flux[qsos[i]][30:50]), np.max(flux[qsos[i]][0:50])\n",
    "    plt.axis([xmin, xmax, ymin, ymax])\n",
    "    plt.plot(wave, flux[qsos[i]],'b', alpha=0.5)\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I definitely see some broad emission lines! Let's match these quasar targets to the redshift file on `TARGETID` to extract their best-fit redshifts from `redrock`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = defaultdict(list)\n",
    "for index, item in enumerate(zs[\"TARGETID\"]):\n",
    "    dd[item].append(index)\n",
    "zqsos = [index for item in fm[qsos][\"TARGETID\"] for index in dd[item] if item in dd]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That might be hard to follow at first glance, but all I did was use some \"standard\" python syntax to match the indices in `zs` (the ordering of objects in the `redrock` redshift file) to those for quasars in `fm` (the ordering of quasars in the fibermap file), on the unique `TARGETID`, such that the indices stored in `qsos` for `fm` point to the corresponding indices in `zqsos` for `zs`. This might help illustrate the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([35191248222424269, 35191248222424683, 35191248226618337,\n",
       "        35191248226618473, 35191248226618954, 35191248230812543,\n",
       "        35191248230812879]),\n",
       " array([35191248222424269, 35191248222424683, 35191248226618337,\n",
       "        35191248226618473, 35191248226618954, 35191248230812543,\n",
       "        35191248230812879]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zs[zqsos][\"TARGETID\"][0:7], np.array(fm[qsos][\"TARGETID\"][0:7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what best-fit template `redrock` assigned to each quasar target. This information is stored in the `SPECTYPE` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'STAR', 'QSO', 'QSO',\n",
       "       'GALAXY', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'QSO', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'GALAXY', 'GALAXY', 'GALAXY', 'QSO', 'GALAXY', 'QSO', 'GALAXY',\n",
       "       'STAR', 'QSO', 'QSO', 'GALAXY', 'QSO', 'GALAXY', 'QSO', 'QSO',\n",
       "       'GALAXY', 'GALAXY', 'GALAXY', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'QSO', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'QSO', 'QSO',\n",
       "       'GALAXY', 'GALAXY', 'QSO', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'GALAXY', 'GALAXY', 'QSO', 'QSO', 'QSO', 'GALAXY', 'QSO', 'GALAXY',\n",
       "       'GALAXY', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY', 'QSO', 'GALAXY',\n",
       "       'GALAXY', 'GALAXY', 'QSO', 'QSO', 'QSO', 'GALAXY', 'QSO', 'QSO',\n",
       "       'QSO', 'QSO', 'GALAXY', 'QSO', 'GALAXY', 'QSO', 'QSO', 'QSO',\n",
       "       'GALAXY', 'GALAXY', 'QSO', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'GALAXY', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'QSO', 'QSO', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'QSO', 'QSO',\n",
       "       'QSO', 'QSO', 'STAR', 'GALAXY', 'GALAXY', 'GALAXY', 'QSO',\n",
       "       'GALAXY', 'QSO', 'GALAXY', 'GALAXY', 'STAR', 'QSO', 'GALAXY',\n",
       "       'GALAXY', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'GALAXY',\n",
       "       'STAR'], dtype='<U6')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zs[zqsos][\"SPECTYPE\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or for standard stars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = defaultdict(list)\n",
    "for index, item in enumerate(zs[\"TARGETID\"]):\n",
    "    dd[item].append(index)\n",
    "zstds = [index for item in fm[stds][\"TARGETID\"] for index in dd[item] if item in dd]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For stars, we can also display the type of star that `redrock` fit (this is stored in the `SUBTYPE` column):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-STAR\n",
      "K-STAR\n",
      "A-STAR\n",
      "F-STAR\n",
      "F-STAR\n"
     ]
    }
   ],
   "source": [
    "zipper = zip(zs[zstds][\"SUBTYPE\"][10:15], zs[zstds][\"SPECTYPE\"][10:15])\n",
    "for sub, spec in zipper:\n",
    "    print(\"{}-{}\".format(sub,spec))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I just picked 5 correctly identified stars as an example. Note that the conversion to `utf-8` is simply for display purposes because the strings in `SUBTYPE` and `SPECTYPE` are stored as bytes instead of unicode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, back to our quasars. Let's plot the quasar targets that *are identified as quasars* , but add a label for the `SPECTYPE` and the redshift fit by `redrock`. I'll also add some median filtering and over-plot some (approximate) typical quasar emission lines at the redrock redshift (if those lines would fall in the DESI wavelength coverage):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.signal import medfilt\n",
    "\n",
    "# ADM we'll clip to z < 5, as redrock can misidentify low S/N sources as very-high-z quasars.\n",
    "qsoid = np.where( (zs[zqsos][\"SPECTYPE\"] == 'QSO') & (zs[zqsos][\"Z\"] < 5) )[0]\n",
    "qsolines = np.array([1216, 1546, 1906, 2800, 4853, 4960, 5008])\n",
    "\n",
    "wave = specobj.wave[\"brz\"]\n",
    "flux = specobj.flux[\"brz\"]\n",
    "plt.figure(figsize=(25, 15))\n",
    "for i in range(9):\n",
    "    plt.subplot(3,3,1+i)\n",
    "    spectype = zs[zqsos[qsoid[i]]][\"SPECTYPE\"]\n",
    "    z = zs[zqsos[qsoid[i]]][\"Z\"]\n",
    "    plt.plot(wave, medfilt(flux[qsos[qsoid[i]]], 15), 'b', alpha=0.5)\n",
    "    plt.title(\"{}, z={:.3f}\".format(spectype,z))\n",
    "    for line in qsolines:\n",
    "        if ((1+z)*line > np.min(wave)) & ((1+z)*line < np.max(wave)):\n",
    "            plt.axvline((1+z)*line, color='y', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix: code versions used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This tutorial last ran successfully to completion using the following versions of the following modules:\n",
      "python      : 3.9.7\n",
      "numpy       : 1.21.4\n",
      "scipy       : 1.7.3\n",
      "astropy     : 5.0\n",
      "yaml        : 6.0\n",
      "matplotlib  : 3.5.1\n",
      "fitsio      : 1.1.6\n",
      "healpy      : 1.15.0\n",
      "desiutil    : 3.2.5\n",
      "desispec    : 0.51.13\n",
      "desitarget  : 2.4.0\n",
      "desimodel   : 0.17.0\n",
      "speclite    : 0.15\n"
     ]
    }
   ],
   "source": [
    "from desitutorials import print_code_versions as pcv\n",
    "print(\"This tutorial last ran successfully to completion using the following versions of the following modules:\") \n",
    "pcv()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "DESI 22.2",
   "language": "python",
   "name": "desi-22.2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}