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   "source": [
    "As noted in Tutorial 00, `pygsti` can provide many reports or presentation slides.\n",
    "\n",
    "In this tutorial, we look at how to customize some of the output of `pygsti`'s report generating module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
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   "source": [
    "#Import the GST module -- you probably want this at the beginning of every notebook\n",
    "import pygsti\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating reports is a method of the `pygsti.report.Results` object. Below, we create such an object, and then initialize it with the appropriate fiducials, germs, maximum lengths, etc.\n",
    "\n",
    "It's important to note that the `pygsti.report.Results` object requires an argument to indicate which objective function you wish to score the estimate using. It's recommended that, if you ran MC2GST, you use the `\"chi2\"` argument, while if you ran MLGST, you use `\"logL\"`. Otherwise, you will score your estimate based on an objective function you weren't optimizing over, so your scores may be rather weird!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating reports is a method of the `pygsti.report.Results` object. Below, we create such an object, and then initialize it with the appropriate fiducials, germs, maximum lengths, etc.\n",
    "\n",
    "It's important to note that the `pygsti.report.Results` object requires an argument to indicate which objective function you wish to score the estimate using. It's recommended that, if you ran MC2GST, you use the `\"chi2\"` argument, while if you ran MLGST, you use `\"logL\"`. Otherwise, you will score your estimate based on an objective function you weren't optimizing over, so your scores may be rather weird!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll make smaller reports that don't contain any confidence intervals for the estimates,\n",
    "nor any appendices.\n",
    "\n",
    "NOTE: In order for you to create PowerPoint files, you'll have to have `python-pptx` installed on your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": false,
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   "source": [
    "# Follow Algorithm tutorial to generate LSGST gatesets\n",
    "gs_target = pygsti.io.load_gateset(\"tutorial_files/Example_Gateset.txt\")\n",
    "gs_target.set_basis(\"gm\",2)\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 MC2GST 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\"))\n",
    "lsgstListOfLists = [ pygsti.io.load_gatestring_list(\"tutorial_files/Example_LSGSTlist%d.txt\" % l) for l in maxLengthList]\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 )\n",
    "\n",
    "go_gatesets = [ pygsti.optimize_gauge(gs, \"target\", targetGateset=gs_target, gateWeight=1, spamWeight=0.001) for gs in gs_lsgst_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = pygsti.report.Results()\n",
    "res.init_Ls_and_germs(\"chi2\", gs_target, ds, gs_clgst, maxLengthList, germList,\n",
    "                    go_gatesets, lsgstListOfLists, fiducialList, fiducialList, \n",
    "                    pygsti.construction.repeat_with_max_length, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Make smaller reports (no confidence intervals or appendicies)\n",
    "res.create_full_report_pdf(filename=\"tutorial_files/Example_reportB.pdf\", verbosity=2, confidenceLevel=None,\n",
    "                        debugAidsAppendix=False, gaugeOptAppendix=False,\n",
    "                        pixelPlotAppendix=False, whackamoleAppendix=False)\n",
    "\n",
    "res.create_brief_report_pdf(filename=\"tutorial_files/Example_briefB.pdf\", verbosity=2, confidenceLevel=None)\n",
    "\n",
    "res.create_presentation_pdf(filename=\"tutorial_files/Example_slidesB.pdf\", verbosity=2, confidenceLevel=None,\n",
    "                         debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)\n",
    "\n",
    "res.create_presentation_ppt(filename=\"tutorial_files/Example_slidesB.ppt\", verbosity=2, confidenceLevel=None,\n",
    "                         debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res.create_full_report_pdf(filename=\"tutorial_files/Example_report.pdf\", verbosity=2, confidenceLevel=95,\n",
    "                        debugAidsAppendix=True, gaugeOptAppendix=True,\n",
    "                        pixelPlotAppendix=True, whackamoleAppendix=True)\n",
    "\n",
    "res.create_brief_report_pdf(filename=\"tutorial_files/Example_brief.pdf\", verbosity=2, confidenceLevel=95)\n",
    "\n",
    "res.create_presentation_pdf(filename=\"tutorial_files/Example_slides.pdf\", verbosity=2, confidenceLevel=95,\n",
    "                         debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True)\n",
    "\n",
    "res.create_presentation_ppt(filename=\"tutorial_files/Example_slides.ppt\", verbosity=2, confidenceLevel=95,\n",
    "                         debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pygsti.report.latex as LU\n",
    "from imp import reload\n",
    "reload(LU)\n",
    "LU.latex_value(1.2e30,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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