{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to generate error bars for 2Q-GST\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory limit was =  3221225472\n",
      "Memory limit is now =  2684354560.0\n"
     ]
    }
   ],
   "source": [
    "import pygsti\n",
    "import time\n",
    "\n",
    "#If we were using MPI\n",
    "# from mpi4py import MPI\n",
    "# comm = MPI.COMM_WORLD\n",
    "comm = None\n",
    "\n",
    "#Load the 2-qubit results (if you don't have this directory, run the 2Q-GST example)\n",
    "results = pygsti.io.load_results_from_dir(\"example_files/My2QExample\", \"StandardGST\")\n",
    "\n",
    "#Set a memory limit\n",
    "print(\"Memory limit was = \", results.estimates['CPTP'].parameters.get('memLimit',\"none given\"))\n",
    "results.estimates['CPTP'].parameters['memLimit'] = 2.5*(1024.0)**3 # 2.5GB\n",
    "print(\"Memory limit is now = \", results.estimates['CPTP'].parameters['memLimit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaltree generation (deriv) w/mem limit = 2.47GB\n",
      "bulk_evaltree: created initial tree (1082 strs) in 1s\n",
      "bulk_evaltree: split tree (1 subtrees) in 0s\n",
      " mem(1 subtrees, 1,1 param-grps, 1 proc-grps) in 1s = 7853.61GB (7853.61GB fc)\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over (1920,1920) params (taken as 1920,4 param groups of ~1,480 params).\n",
      " Memory estimate = 2.03GB (cache=1082, wrtLen1=1, wrtLen2=480, subsPerProc=1).\n",
      "rank 0: 94.5588s: block 0/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 131.424s: block 1/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 168.311s: block 2/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 204.173s: block 3/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 239.922s: block 4/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 274.424s: block 5/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 309.599s: block 6/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 344.601s: block 7/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 380.99s: block 8/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 416.213s: block 9/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 451.373s: block 10/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 486.383s: block 11/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 522.758s: block 12/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 557.807s: block 13/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 592.904s: block 14/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 628.007s: block 15/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 664.472s: block 16/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 699.313s: block 17/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 734.498s: block 18/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 769.855s: block 19/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 806.698s: block 20/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 842s: block 21/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 877.752s: block 22/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 913.219s: block 23/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 949.306s: block 24/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 984.357s: block 25/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1019.19s: block 26/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1054.28s: block 27/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1090.41s: block 28/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1126.91s: block 29/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1162.49s: block 30/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1197.98s: block 31/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1234.56s: block 32/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1269.9s: block 33/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1305.11s: block 34/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1340.37s: block 35/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1376.68s: block 36/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1412.1s: block 37/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1447.11s: block 38/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1482.88s: block 39/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1519.37s: block 40/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1554.64s: block 41/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1590.87s: block 42/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1628.39s: block 43/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1664.2s: block 44/4803, sub-tree 0/1, sub-tree-len = 1082\n",
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      "rank 0: 1733.53s: block 46/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1768.03s: block 47/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1803.87s: block 48/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1838.46s: block 49/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1873.87s: block 50/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 1910.03s: block 51/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 3837.1s: block 52/4803, sub-tree 0/1, sub-tree-len = 1082\n",
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      "rank 0: 4697.37s: block 76/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 4732.63s: block 77/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 4767.74s: block 78/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 4802.98s: block 79/4803, sub-tree 0/1, sub-tree-len = 1082\n",
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      "rank 0: 5941.48s: block 111/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 5977.28s: block 112/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6011.99s: block 113/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6047.07s: block 114/4803, sub-tree 0/1, sub-tree-len = 1082\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rank 0: 6082.19s: block 115/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6118.64s: block 116/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6154.54s: block 117/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6189.9s: block 118/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6225.23s: block 119/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6261.84s: block 120/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6297.34s: block 121/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6332.55s: block 122/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6367.79s: block 123/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6404.29s: block 124/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6440.17s: block 125/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6475.38s: block 126/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6510.54s: block 127/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6547.22s: block 128/4803, sub-tree 0/1, sub-tree-len = 1082\n",
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      "rank 0: 6618.17s: block 130/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6653.65s: block 131/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6690.67s: block 132/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6725.87s: block 133/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6761.19s: block 134/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6796.28s: block 135/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6832.49s: block 136/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6867.64s: block 137/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6903.53s: block 138/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6938.71s: block 139/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 6975.65s: block 140/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 7010.96s: block 141/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 7045.97s: block 142/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 7081.56s: block 143/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 7118.21s: block 144/4803, sub-tree 0/1, sub-tree-len = 1082\n",
      "rank 0: 7154.8s: block 145/4803, sub-tree 0/1, sub-tree-len = 1082\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-dda12a7d9471>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# initialize a factory for the 'go0' gauge optimization within the 'default' estimate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mcrfact\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CPTP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_confidence_region_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stdgaugeopt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'final'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcrfact\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_hessian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#optionally use multiple processors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mcrfact\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_hessian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'intrinsic error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyGSTi/pygsti/objects/confidenceregionfactory.py\u001b[0m in \u001b[0;36mcompute_hessian\u001b[0;34m(self, comm, memLimit, approximate)\u001b[0m\n\u001b[1;32m    238\u001b[0m                                  \u001b[0mminProbClip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprobClipInterval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m                                  \u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemLimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemLimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m                                  opLabelAliases=aliases)\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m             nonMarkRadiusSq = max(2 * (_tools.logl_max(model, dataset)\n",
      "\u001b[0;32m~/pyGSTi/pygsti/tools/likelihoodfns.py\u001b[0m in \u001b[0;36mlogl_hessian\u001b[0;34m(model, dataset, circuit_list, minProbClip, probClipInterval, radius, poissonPicture, check, comm, memLimit, opLabelAliases, smartc, verbosity)\u001b[0m\n\u001b[1;32m    853\u001b[0m         \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmySliceTupList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m         for (slice1, slice2, hprobs, dprobs12) in model.bulk_hprobs_by_block(\n\u001b[0;32m--> 855\u001b[0;31m                 evalSubTree, mySliceTupList, True, blkComm):\n\u001b[0m\u001b[1;32m    856\u001b[0m             \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyGSTi/pygsti/objects/matrixforwardsim.py\u001b[0m in \u001b[0;36mbulk_hprobs_by_block\u001b[0;34m(self, evalTree, wrtSlicesList, bReturnDProbs12, comm)\u001b[0m\n\u001b[1;32m   2702\u001b[0m             hProdCache = self._compute_hproduct_cache(\n\u001b[1;32m   2703\u001b[0m                 \u001b[0mevalTree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprodCache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdProdCache1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdProdCache2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2704\u001b[0;31m                 scaleCache, comm, wrtSlice1, wrtSlice2)\n\u001b[0m\u001b[1;32m   2705\u001b[0m             \u001b[0mhGs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevalTree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinal_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhProdCache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyGSTi/pygsti/objects/matrixforwardsim.py\u001b[0m in \u001b[0;36m_compute_hproduct_cache\u001b[0;34m(self, evalTree, prodCache, dProdCache1, dProdCache2, scaleCache, comm, wrtSlice1, wrtSlice2)\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m             \u001b[0mdLdRa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdL1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdR2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1125\u001b[0;31m             \u001b[0mdLdRb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdL2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdR1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1126\u001b[0m             \u001b[0mdLdR_sym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdLdRa\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdLdRb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# error bars in reports require the presence of a fully-initialized\n",
    "# \"confidence region factory\" within the relevant Estimate object.\n",
    "# In most cases \"fully-initialized\" means that a Hessian has been \n",
    "# computed and projected onto the non-gauge space.\n",
    "start = time.time()\n",
    "\n",
    "# initialize a factory for the 'go0' gauge optimization within the 'default' estimate\n",
    "crfact = results.estimates['CPTP'].add_confidence_region_factory('stdgaugeopt', 'final')\n",
    "crfact.compute_hessian(comm=comm) #optionally use multiple processors\n",
    "crfact.project_hessian('intrinsic error')\n",
    "\n",
    "end = time.time()\n",
    "print(\"Total time=%f hours\" % ((end - start) / 3600.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note above cell was executed for demonstration purposes, and was **keyboard-interrupted intentionally** since it would have taken forever on a single processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write results back to disk\n",
    "results.write()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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