redrock/RedrockBOSSDemo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Redrock on BOSS data\n",
    "\n",
    "This tutorial demonstrates running redrock on BOSS data.\n",
    "Some pieces are a bit cryptic because of code organized for parallelism\n",
    "rather than interactive use.  In general one would use the `rrboss` script,\n",
    "but this tutorial pokes under the hood a bit to see the pieces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from redrock.external import boss\n",
    "from redrock.zfind import zfind\n",
    "\n",
    "from redrock.targets import Spectrum, Target, DistTargetsCopy\n",
    "from redrock.templates import load_dist_templates\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read a subset of the data from spPlate-3678-55208.fits.\n",
    "* If fiberid is not specified, all fibers from the plate are read.\n",
    "* If use_frames is True, the individual exposures will be discovered from the spPlate header. The corresponding spCFrame files should be in the same directory as the spPlate file.\n",
    "* BEWARE: The targets list might not have the same order as the input fiberid\n",
    "\n",
    "**NOTE**: BOSS/eBOSS is publicly accessible and can be downloaded\n",
    "from https://data.sdss.org/sas/dr14/eboss/spectro/redux/\n",
    "\n",
    "They are also available at NERSC: `/global/cfs/cdirs/cosmo/data/sdss/dr14/eboss/spectro/redux/v5_10_0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG: read /global/cfs/cdirs/cosmo/data/sdss/dr14/eboss/spectro/redux/v5_10_0/3678/spPlate-3678-55208.fits \n"
     ]
    }
   ],
   "source": [
    "if 'NERSC_HOST' in os.environ:\n",
    "    spplate = \"/global/cfs/cdirs/cosmo/data/sdss/dr14/eboss/spectro/redux/v5_10_0/3678/spPlate-3678-55208.fits\"\n",
    "else:\n",
    "    spplate=\"data/spPlate-3678-55208.fits\"\n",
    "targets,meta = boss.read_spectra([spplate,], use_frames=False, fiberid=[36,51,52,54,60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG: Read templates from /global/common/software/desi/cori/desiconda/20211217-2.0.0/code/redrock-templates/0.7.2\n",
      "DEBUG: Using default redshift range -0.0050-1.6997 for rrtemplate-galaxy.fits\n",
      "DEBUG: Using default redshift range 0.0500-5.9934 for rrtemplate-qso.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-A.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-B.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-CV.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-F.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-G.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-K.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-M.fits\n",
      "DEBUG: Using default redshift range -0.0020-0.0020 for rrtemplate-star-WD.fits\n",
      "Read and broadcast of 10 templates: 0.1 seconds\n",
      "Rebinning templates: 23.7 seconds\n"
     ]
    }
   ],
   "source": [
    "dtargets = DistTargetsCopy(targets)\n",
    "dwave = dtargets.wavegrids()\n",
    "dtemplates = load_dist_templates(dwave)\n",
    "templates = dict()\n",
    "for dt in dtemplates:\n",
    "    templates[dt.template.full_type] = dt.template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing the redshifts scans\n",
    "\n",
    "Define the templates and to the redshift scans over those templates.\n",
    "\n",
    "This might take a few minutes, please be patient! Your computer is working hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing redshifts\n",
      "  Scanning redshifts for template GALAXY\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 7.3 seconds\n",
      "  Scanning redshifts for template QSO\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 4.2 seconds\n",
      "  Scanning redshifts for template STAR:::A\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.5 seconds\n",
      "  Scanning redshifts for template STAR:::B\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.5 seconds\n",
      "  Scanning redshifts for template STAR:::CV\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.4 seconds\n",
      "  Scanning redshifts for template STAR:::F\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.5 seconds\n",
      "  Scanning redshifts for template STAR:::G\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.4 seconds\n",
      "  Scanning redshifts for template STAR:::K\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.4 seconds\n",
      "  Scanning redshifts for template STAR:::M\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.4 seconds\n",
      "  Scanning redshifts for template STAR:::WD\n",
      "    Progress:   0 %\n",
      "    Progress:  10 %\n",
      "    Progress:  20 %\n",
      "    Progress:  30 %\n",
      "    Progress:  40 %\n",
      "    Progress:  50 %\n",
      "    Finished in: 0.5 seconds\n",
      "  Finding best fits for template GALAXY\n",
      "    Finished in: 2.5 seconds\n",
      "  Finding best fits for template QSO\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::A\n",
      "    Finished in: 1.4 seconds\n",
      "  Finding best fits for template STAR:::B\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::CV\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::F\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::G\n",
      "    Finished in: 1.6 seconds\n",
      "  Finding best fits for template STAR:::K\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::M\n",
      "    Finished in: 1.3 seconds\n",
      "  Finding best fits for template STAR:::WD\n",
      "    Finished in: 1.3 seconds\n"
     ]
    }
   ],
   "source": [
    "zscan, zfit = zfind(dtargets, dtemplates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the output\n",
    "\n",
    "The zscan dictionary contains the all the redshift scan information for each target and each template.\n",
    "\n",
    "For example, here's a plot of the $\\chi^2$ vs $z$ for the first target.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\chi^2$')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targetid = targets[0].id\n",
    "for template in dtemplates:\n",
    "    full_type = template.template.full_type\n",
    "    plt.plot(zscan[targetid][full_type]['redshifts'],\\\n",
    "         zscan[targetid][full_type]['zchi2'],label=full_type)\n",
    "\n",
    "plt.legend(loc=0,frameon=False)\n",
    "plt.ylim(4000,6000)\n",
    "plt.grid()\n",
    "plt.xlabel(\"redshift\",fontsize=24)\n",
    "plt.ylabel(\"$\\chi^2$\",fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! We find that the best fit is corresponds to a QSO template at redshift around 1.8. \n",
    "\n",
    "Let's confirm that by looking at the zfit table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Redrock thinks 3678552080036 is a QSO at redshift 1.871151350370875\n"
     ]
    }
   ],
   "source": [
    "print(\"Redrock thinks {} is a {} at redshift {}\".format(targets[0].id,zfit[0]['spectype'],zfit[0]['z']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now make a plot of the spectrum and its best fit. The best fit is obtained by evaluating the best fit template (in this case a QSO) at the best fit redshift. Let's list the template types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 GALAXY\n",
      "1 QSO\n",
      "2 STAR:::A\n",
      "3 STAR:::B\n",
      "4 STAR:::CV\n",
      "5 STAR:::F\n",
      "6 STAR:::G\n",
      "7 STAR:::K\n",
      "8 STAR:::M\n",
      "9 STAR:::WD\n"
     ]
    }
   ],
   "source": [
    "for i,t in enumerate(templates.values()):\n",
    "    print(i,t.full_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now evaluate the template 'QSO' at the best fit redshift:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "tid = targets[0].id\n",
    "t_qso = templates['QSO']\n",
    "\n",
    "## several minima are stored in the zfit table\n",
    "minumum_number = 0\n",
    "\n",
    "## select the target id and minumum number\n",
    "w = (zfit[:]['targetid']==tid) & (zfit[:]['znum']==minumum_number)\n",
    "\n",
    "## now get the coefficients\n",
    "coeff = zfit[w]['coeff'].reshape(-1)\n",
    "zbest = zfit[w]['z'][0]\n",
    "\n",
    "## compute the best fit:\n",
    "fit = t_qso.eval(coeff[:4],targets[0].spectra[0].wave,zbest)\n",
    "\n",
    "## remultiply by (1+z)\n",
    "fit *= (1+zbest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 6.0)"
      ]
     },
     "execution_count": 9,
     "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": [
    "wave=targets[0].spectra[0].wave\n",
    "flux = targets[0].spectra[0].flux\n",
    "plt.plot(wave,flux)\n",
    "plt.plot(wave,fit)\n",
    "plt.ylim(-1,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a bit of rebinning to smooth out the noise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1554bfdcce50>]"
      ]
     },
     "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": [
    "i = np.arange(len(fit),dtype=float)\n",
    "A = i - i[:,None]\n",
    "rebin = 5\n",
    "w = abs(A)<rebin\n",
    "A[w]=1.\n",
    "A[~w]=0\n",
    "A /= A.sum(axis=1).reshape(-1,1)\n",
    "plt.plot(wave,A.dot(flux))\n",
    "plt.plot(wave,A.dot(fit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}