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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of GST's fiducials is to generate an informationally-complete set of states and measurements. However, it's possible to _reduce_ the number of fiducials necessary to do so.\n",
    "\n",
    "In this tutorial, we use the `pygsti.alg.find_sufficient_fiducial_pairs` function to reduce the number of fiducials necessary for GST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pygsti\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Follow Algorithm tutorial to generate LGST gatesets\n",
    "gs_target = pygsti.io.load_gateset(\"tutorial_files/Example_Gateset.txt\")\n",
    "ds = pygsti.io.load_dataset(\"tutorial_files/Example_Dataset.txt\", cache=True)\n",
    "fiducialList = pygsti.io.load_gatestring_list(\"tutorial_files/Example_FiducialList.txt\")\n",
    "\n",
    "#Run LGST to get an initial estimate for the gates in gs_target based on the data in ds\n",
    "specs = pygsti.construction.build_spam_specs(fiducialGateStrings=fiducialList)\n",
    "gs_lgst = pygsti.do_lgst(ds, specs, targetGateset=gs_target, svdTruncateTo=4, verbosity=1)\n",
    "\n",
    "#Gauge optimize the result to match the target gateset\n",
    "gs_lgst_after_gauge_opt = pygsti.optimize_gauge(gs_lgst, \"target\", targetGateset=gs_target)\n",
    "\n",
    "#Contract the result to CPTP\n",
    "gs_clgst = pygsti.contract(gs_lgst_after_gauge_opt, \"CPTP\")\n",
    "\n",
    "#Get lists of gate strings for successive iterations of LGST to use\n",
    "specs  = pygsti.construction.build_spam_specs(fiducialGateStrings=fiducialList)\n",
    "germList = pygsti.io.load_gatestring_list(\"tutorial_files/Example_GermsList.txt\")\n",
    "maxLengthList = json.load(open(\"tutorial_files/Example_maxLengths.json\",\"r\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Get sufficient set of fiducial pairs, meaning that with these fidicual pairs and the given set of germs,\n",
    "# the number of gateset parameters which are amplified when all pairs are used are also amplified when using\n",
    "# the returned subset.\n",
    "fidPairs = pygsti.alg.find_sufficient_fiducial_pairs(gs_target, fiducialList, fiducialList, germList, verbosity=1)\n",
    "print(fidPairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Test a specific set of fiducial pairs: see how many gateset parameter are amplified\n",
    "#FPR.find_sufficient_fiducial_pairs(gs_target, fiducialList, germList, testPairList=[(0,0),(0,1),(1,0)], verbosity=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lsgstListOfLists = pygsti.construction.make_lsgst_lists(gs_target.gates.keys(), fiducialList, fiducialList,\n",
    "                                                        germList, maxLengthList, fidPairs)\n",
    "\n",
    "gs_lsgst_list = pygsti.do_iterative_mc2gst(ds, gs_clgst, lsgstListOfLists, verbosity=2,\n",
    "                                         minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),\n",
    "                                         returnAll=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute a few additional quantities needed to generate the report\n",
    "Ls = maxLengthList\n",
    "gateStrDict = { (L,germ):pygsti.construction.repeat_with_max_length(germ,L,False) for L in Ls for germ in germList }\n",
    "\n",
    "#remove duplicates by replacing duplicate strings with None\n",
    "runningList = []\n",
    "for L in Ls:\n",
    "    for germ in germList:\n",
    "        if gateStrDict[(L,germ)] in runningList:\n",
    "            gateStrDict[(L,germ)] = None\n",
    "        else: runningList.append( gateStrDict[(L,germ)] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#optimize each gateset to the target\n",
    "gs_lsgst_list = [ pygsti.optimize_gauge(gs, \"target\", targetGateset=gs_target) for gs in gs_lsgst_list ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res = pygsti.report.Results()\n",
    "res.init_Ls_and_germs(\"chi2\", gs_target, ds, gs_clgst, maxLengthList, germList,\n",
    "                    gs_lsgst_list, lsgstListOfLists, fiducialList, fiducialList, \n",
    "                    pygsti.construction.repeat_with_max_length, False, fidPairs)\n",
    "\n",
    "res.create_full_report_pdf(filename=\"tutorial_files/Example_report_FR.pdf\", verbosity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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