{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Note: this tutorial needs updating and has not been recently tested for basic functionality: use at own risk" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import os\n", "import sys\n", "import time\n", "import json\n", "\n", "import pygsti\n", "\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading tutorial_files/Example_Dataset.txt: 100%\n", "Writing cache file (to speed future loads): tutorial_files/Example_Dataset.txt.cache\n" ] } ], "source": [ "#Load example quantities from files\n", "gs_target = pygsti.io.load_gateset(\"tutorial_files/Example_Gateset.txt\")\n", "gs_mc2gst = pygsti.io.load_gateset(\"tutorial_files/Example_MC2GST_Gateset.txt\")\n", "\n", "ds = pygsti.io.load_dataset(\"tutorial_files/Example_Dataset.txt\", cache=True)\n", "\n", "fiducials = pygsti.io.load_gatestring_list(\"tutorial_files/Example_FiducialList.txt\")\n", "germs = pygsti.io.load_gatestring_list(\"tutorial_files/Example_GermsList.txt\")\n", "maxLengths = json.load(open(\"tutorial_files/Example_maxLengths.json\",\"r\"))\n", "\n", "specs = pygsti.construction.build_spam_specs(fiducials)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parametric Bootstrapping\n", "Here we do parametric bootstrapping, as indicated by the 'parametric' argument below.\n", "The output is eventually stored in the \"mean\" and \"std\" GateSets, which hold the mean and standard deviation values of the set of bootstrapped gatesets (after gauge optimization). It is this latter \"standard deviation Gateset\"\n", "which holds the collection of error bars. Note: due to print setting issues, the outputs that are printed here will not necessarily reflect the true accuracy of the estimates made.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating DataSets: \n", "0 Generating parametric dataset.\n", "1 Generating parametric dataset.\n", "2 Generating parametric dataset.\n", "3 Generating parametric dataset.\n", "4 Generating parametric dataset.\n", "5 Generating parametric dataset.\n", "6 Generating parametric dataset.\n", "7 Generating parametric dataset.\n", "8 Generating parametric dataset.\n", "9 Generating parametric dataset.\n", "Creating GateSets: \n", "Running MLGST Iteration 0 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24430657954\n", " 1.19541285105\n", " 0.972320887627\n", " 0.924565187278\n", " 0.051646837852\n", " 0.0235729617374\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 53.752 (92 data params - 40 model params = expected mean of 52; p-value = 0.407044)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 53.9388\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 53.752 (92 data params - 40 model params = expected mean of 52; p-value = 0.407044)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 53.9388\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 136.04 (168 data params - 40 model params = expected mean of 128; p-value = 0.296731)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 136.06\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 406.903 (441 data params - 40 model params = expected mean of 401; p-value = 0.40868)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 407.508\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 812.725 (817 data params - 40 model params = expected mean of 777; p-value = 0.181528)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 814.023\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1182.5 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.323728)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1184.23\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1526.74 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.624697)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1528.65\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1918.09 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.565623)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1920.41\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2253.9 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.806931)\n", " Completed in 1.1s\n", " 2*Delta(log(L)) = 2256.51\n", " Iteration 9 took 1.2s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2605.37 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.894892)\n", " Completed in 2.9s\n", " 2*Delta(log(L)) = 2608.27\n", " Iteration 10 took 3.2s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1303.77 below upper bound of -4.60018e+06\n", " 2*Delta(log(L)) = 2607.54 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.889285)\n", " Completed in 1.9s\n", " 2*Delta(log(L)) = 2607.54\n", " Final MLGST took 1.9s\n", " \n", "Iterative MLGST Total Time: 9.4s\n", "Running MLGST Iteration 1 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24462644244\n", " 1.16391558424\n", " 0.954007097645\n", " 0.92144715149\n", " 0.0282314704911\n", " 0.0205782809412\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 63.7117 (92 data params - 40 model params = expected mean of 52; p-value = 0.127914)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 63.7499\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 63.7117 (92 data params - 40 model params = expected mean of 52; p-value = 0.127914)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 63.7499\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 119.606 (168 data params - 40 model params = expected mean of 128; p-value = 0.689576)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 119.723\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 398.006 (441 data params - 40 model params = expected mean of 401; p-value = 0.53284)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 398.085\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 807.775 (817 data params - 40 model params = expected mean of 777; p-value = 0.215506)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 808.819\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1176.93 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.365858)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1178.31\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1522.5 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.653494)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1524.22\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1874.24 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.810402)\n", " Completed in 0.8s\n", " 2*Delta(log(L)) = 1876.28\n", " Iteration 8 took 0.9s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2254.17 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.805836)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 2256.6\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2667.6 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.652713)\n", " Completed in 2.1s\n", " 2*Delta(log(L)) = 2670.47\n", " Iteration 10 took 2.3s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1334.89 below upper bound of -4.6007e+06\n", " 2*Delta(log(L)) = 2669.78 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.641607)\n", " Completed in 2.9s\n", " 2*Delta(log(L)) = 2669.78\n", " Final MLGST took 2.9s\n", " \n", "Iterative MLGST Total Time: 9.7s\n", "Running MLGST Iteration 2 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24487669817\n", " 1.15380695435\n", " 0.9568980082\n", " 0.911484529351\n", " 0.0436469558895\n", " 0.0260466370097\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 46.4266 (92 data params - 40 model params = expected mean of 52; p-value = 0.691931)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 46.4652\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 46.4266 (92 data params - 40 model params = expected mean of 52; p-value = 0.691931)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 46.4652\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 124.97 (168 data params - 40 model params = expected mean of 128; p-value = 0.559264)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 125.483\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 441.78 (441 data params - 40 model params = expected mean of 401; p-value = 0.0782058)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 443.224\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 846.689 (817 data params - 40 model params = expected mean of 777; p-value = 0.0414261)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 848.695\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1245.57 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.0420003)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1248.66\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1625.46 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.075647)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 1628.84\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2034.48 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.046588)\n", " Completed in 0.9s\n", " 2*Delta(log(L)) = 2038.23\n", " Iteration 8 took 1.0s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2447.25 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.0257897)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 2451.45\n", " Iteration 9 took 1.8s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2783.11 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.121147)\n", " Completed in 2.4s\n", " 2*Delta(log(L)) = 2787.59\n", " Iteration 10 took 2.7s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1393.4 below upper bound of -4.60025e+06\n", " 2*Delta(log(L)) = 2786.79 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.111542)\n", " Completed in 3.6s\n", " 2*Delta(log(L)) = 2786.79\n", " Final MLGST took 3.6s\n", " \n", "Iterative MLGST Total Time: 10.9s\n", "Running MLGST Iteration 3 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.2448605943\n", " 1.21338143204\n", " 0.974133743467\n", " 0.927811130122\n", " 0.0349618129799\n", " 0.00276832343227\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 66.5848 (92 data params - 40 model params = expected mean of 52; p-value = 0.0839743)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 67.1017\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 66.5848 (92 data params - 40 model params = expected mean of 52; p-value = 0.0839743)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 67.1017\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 126.098 (168 data params - 40 model params = expected mean of 128; p-value = 0.530968)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 126.62\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 377.592 (441 data params - 40 model params = expected mean of 401; p-value = 0.793768)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 378.374\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 766.929 (817 data params - 40 model params = expected mean of 777; p-value = 0.594703)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 767.893\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1108.93 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.860668)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1110.19\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1500.64 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.786334)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1502.41\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1911.73 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.605691)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1913.92\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2292.57 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.614628)\n", " Completed in 1.2s\n", " 2*Delta(log(L)) = 2295.13\n", " Iteration 9 took 1.4s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2663.2 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.674733)\n", " Completed in 1.9s\n", " 2*Delta(log(L)) = 2666.11\n", " Iteration 10 took 2.2s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1332.69 below upper bound of -4.60053e+06\n", " 2*Delta(log(L)) = 2665.38 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.66392)\n", " Completed in 3.0s\n", " 2*Delta(log(L)) = 2665.38\n", " Final MLGST took 3.0s\n", " \n", "Iterative MLGST Total Time: 9.5s\n", "Running MLGST Iteration 4 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24546178062\n", " 1.16653406048\n", " 0.981667764959\n", " 0.879092800621\n", " 0.0576399380988\n", " 0.0301734937346\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 65.4214 (92 data params - 40 model params = expected mean of 52; p-value = 0.100014)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 65.5603\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 65.4214 (92 data params - 40 model params = expected mean of 52; p-value = 0.100014)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 65.5603\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 146.928 (168 data params - 40 model params = expected mean of 128; p-value = 0.120916)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 147.258\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 422.888 (441 data params - 40 model params = expected mean of 401; p-value = 0.216898)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 424.087\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 794.254 (817 data params - 40 model params = expected mean of 777; p-value = 0.325864)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 795.972\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1204.97 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.180104)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1207.03\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1576.6 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.282129)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1579.07\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1928.75 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.497294)\n", " Completed in 0.8s\n", " 2*Delta(log(L)) = 1931.51\n", " Iteration 8 took 0.9s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2300.49 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.569234)\n", " Completed in 1.5s\n", " 2*Delta(log(L)) = 2303.62\n", " Iteration 9 took 1.6s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2720.5 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.371414)\n", " Completed in 2.4s\n", " 2*Delta(log(L)) = 2724.07\n", " Iteration 10 took 2.7s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1361.66 below upper bound of -4.59984e+06\n", " 2*Delta(log(L)) = 2723.31 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.357127)\n", " Completed in 3.7s\n", " 2*Delta(log(L)) = 2723.31\n", " Final MLGST took 3.7s\n", " \n", "Iterative MLGST Total Time: 10.8s\n", "Running MLGST Iteration 5 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24437399366\n", " 1.15913106375\n", " 0.96471164286\n", " 0.925272228881\n", " 0.0417459810904\n", " 0.0115152340233\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 47.5845 (92 data params - 40 model params = expected mean of 52; p-value = 0.648018)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 47.8345\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 47.5845 (92 data params - 40 model params = expected mean of 52; p-value = 0.648018)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 47.8345\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 121.065 (168 data params - 40 model params = expected mean of 128; p-value = 0.655296)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 121.199\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 425.539 (441 data params - 40 model params = expected mean of 401; p-value = 0.191372)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 425.903\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 801.086 (817 data params - 40 model params = expected mean of 777; p-value = 0.267078)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 801.362\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1184.72 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.307605)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1185.66\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1554.89 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.42487)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 1556.09\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1969.1 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.257212)\n", " Completed in 0.8s\n", " 2*Delta(log(L)) = 1970.77\n", " Iteration 8 took 0.9s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2316.13 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.477754)\n", " Completed in 0.8s\n", " 2*Delta(log(L)) = 2318.09\n", " Iteration 9 took 1.0s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2725.19 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.347671)\n", " Completed in 2.4s\n", " 2*Delta(log(L)) = 2727.63\n", " Iteration 10 took 2.7s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1363.45 below upper bound of -4.60109e+06\n", " 2*Delta(log(L)) = 2726.9 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.339209)\n", " Completed in 5.1s\n", " 2*Delta(log(L)) = 2726.9\n", " Final MLGST took 5.1s\n", " \n", "Iterative MLGST Total Time: 11.4s\n", "Running MLGST Iteration 6 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24373571951\n", " 1.16655847582\n", " 0.951729162665\n", " 0.921746887733\n", " 0.0477682961697\n", " 0.012527219858\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 84.9713 (92 data params - 40 model params = expected mean of 52; p-value = 0.0026406)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 85.2475\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 84.9713 (92 data params - 40 model params = expected mean of 52; p-value = 0.0026406)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 85.2475\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 156.521 (168 data params - 40 model params = expected mean of 128; p-value = 0.0439993)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 156.738\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 428.934 (441 data params - 40 model params = expected mean of 401; p-value = 0.161632)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 429.111\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 826.048 (817 data params - 40 model params = expected mean of 777; p-value = 0.10827)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 827.05\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1203.35 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.188813)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1204.73\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1570.13 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.322161)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1571.83\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1993.53 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.149558)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1995.71\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2435.44 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.0376067)\n", " Completed in 1.5s\n", " 2*Delta(log(L)) = 2438.12\n", " Iteration 9 took 1.6s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2801.01 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.0796543)\n", " Completed in 2.7s\n", " 2*Delta(log(L)) = 2804.01\n", " Iteration 10 took 3.0s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1401.61 below upper bound of -4.60012e+06\n", " 2*Delta(log(L)) = 2803.23 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.0753764)\n", " Completed in 3.0s\n", " 2*Delta(log(L)) = 2803.23\n", " Final MLGST took 3.0s\n", " \n", "Iterative MLGST Total Time: 10.5s\n", "Running MLGST Iteration 7 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.2446716307\n", " 1.16332597655\n", " 0.924480987796\n", " 0.901584474949\n", " 0.0412758255531\n", " 0.0231098191713\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 53.1539 (92 data params - 40 model params = expected mean of 52; p-value = 0.429505)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 53.1606\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 53.1539 (92 data params - 40 model params = expected mean of 52; p-value = 0.429505)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 53.1606\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 130.176 (168 data params - 40 model params = expected mean of 128; p-value = 0.429799)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 130.011\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 392.006 (441 data params - 40 model params = expected mean of 401; p-value = 0.616538)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 392.599\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 782.758 (817 data params - 40 model params = expected mean of 777; p-value = 0.435418)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 783.828\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1126.75 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.75928)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1128.21\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1487.9 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.848059)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 1489.67\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1863.05 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.856196)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1865.13\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2222.55 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.909508)\n", " Completed in 1.5s\n", " 2*Delta(log(L)) = 2224.93\n", " Iteration 9 took 1.6s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2638.71 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.785336)\n", " Completed in 2.6s\n", " 2*Delta(log(L)) = 2641.57\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1320.43 below upper bound of -4.60018e+06\n", " 2*Delta(log(L)) = 2640.87 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.776542)\n", " Completed in 3.6s\n", " 2*Delta(log(L)) = 2640.87\n", " Final MLGST took 3.6s\n", " \n", "Iterative MLGST Total Time: 10.9s\n", "Running MLGST Iteration 8 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24432387856\n", " 1.14904678757\n", " 0.957002356215\n", " 0.902434260566\n", " 0.0467116103808\n", " 0.0121332141202\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 48.3534 (92 data params - 40 model params = expected mean of 52; p-value = 0.618095)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 48.3879\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 48.3534 (92 data params - 40 model params = expected mean of 52; p-value = 0.618095)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 48.3879\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 107.182 (168 data params - 40 model params = expected mean of 128; p-value = 0.909384)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 107.339\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 380.231 (441 data params - 40 model params = expected mean of 401; p-value = 0.765092)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 380.855\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 751.167 (817 data params - 40 model params = expected mean of 777; p-value = 0.740782)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 752.742\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1123.92 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.777541)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1126.07\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1503.69 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.769699)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 1506.18\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1895.57 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.702138)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1898.45\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2245.01 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.8413)\n", " Completed in 1.2s\n", " 2*Delta(log(L)) = 2248.19\n", " Iteration 9 took 1.4s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2615.57 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.866724)\n", " Completed in 2.5s\n", " 2*Delta(log(L)) = 2619.08\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1309.19 below upper bound of -4.60057e+06\n", " 2*Delta(log(L)) = 2618.38 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.858136)\n", " Completed in 3.2s\n", " 2*Delta(log(L)) = 2618.38\n", " Final MLGST took 3.2s\n", " \n", "Iterative MLGST Total Time: 10.3s\n", "Running MLGST Iteration 9 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.2446590126\n", " 1.17232819284\n", " 0.955349676809\n", " 0.94638204726\n", " 0.0308787960176\n", " 0.0111799222352\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 43.4461 (92 data params - 40 model params = expected mean of 52; p-value = 0.794856)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 43.5368\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 43.4461 (92 data params - 40 model params = expected mean of 52; p-value = 0.794856)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 43.5368\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 123.869 (168 data params - 40 model params = expected mean of 128; p-value = 0.586763)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 123.693\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 403.458 (441 data params - 40 model params = expected mean of 401; p-value = 0.456145)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 403.939\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 798.056 (817 data params - 40 model params = expected mean of 777; p-value = 0.29244)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 799.142\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1127.66 (1201 data params - 40 model params = expected mean of 1161; p-value = 0.753259)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 1128.96\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1494.28 (1585 data params - 40 model params = expected mean of 1545; p-value = 0.818719)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 1495.97\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1896.96 (1969 data params - 40 model params = expected mean of 1929; p-value = 0.69425)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 1899.08\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2310.74 (2353 data params - 40 model params = expected mean of 2313; p-value = 0.509377)\n", " Completed in 1.3s\n", " 2*Delta(log(L)) = 2313.29\n", " Iteration 9 took 1.5s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2716.23 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.393496)\n", " Completed in 2.6s\n", " 2*Delta(log(L)) = 2719.2\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 1359.25 below upper bound of -4.60053e+06\n", " 2*Delta(log(L)) = 2718.5 (2737 data params - 40 model params = expected mean of 2697; p-value = 0.381706)\n", " Completed in 2.8s\n", " 2*Delta(log(L)) = 2718.5\n", " Final MLGST took 2.8s\n", " \n", "Iterative MLGST Total Time: 10.0s\n" ] } ], "source": [ "#The number of simulated datasets & gatesets made for bootstrapping purposes. \n", "# For good statistics, should probably be greater than 10.\n", "numGatesets=10\n", "\n", "param_boot_gatesets = pygsti.drivers.make_bootstrap_gatesets(\n", " numGatesets, ds, 'parametric', fiducials, fiducials, germs, maxLengths,\n", " inputGateSet=gs_mc2gst, startSeed=0, returnData=False,\n", " verbosity=2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spam weight 0\n", "Spam weight 1\n", "Spam weight 2\n", "Spam weight 3\n", "Spam weight 4\n", "Spam weight 5\n", "Spam weight 6\n", "Spam weight 7\n", "Spam weight 8\n", "Spam weight 9\n", "Spam weight 10\n", "Spam weight 11\n", "Spam weight 12\n", "Best SPAM weight is 0.0001\n" ] }, { "data": { "image/png": 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215UnrnH4ms8mTcr/uvLF4cPwl7/4n0/PcfijrMbhOacPPWS9l/ib0/J83/U3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JAAAA\nVElEQVRAFweKiIiIiCRAibOIiIiISAKUOIuIiIiIJECJs4iIiIhIApQ4i4iIiIgkQImziIiIiEgC\nlDiLiIiIiCRAibOIiIiISAKUOIuIiIiIJOD/AeneLZMbw2QAAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gauge_opt_pboot_gatesets = pygsti.drivers.gauge_optimize_gs_list(param_boot_gatesets, gs_mc2gst,\n", " plot=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parametric bootstrapped error bars, with 10 resamples\n", "\n", "Error in rho vec:\n", "Fully Parameterized spam vector with length 4\n", " 0\n", " 0\n", " 0.01\n", " 0\n", "\n", "\n", "Error in E vec:\n", "Fully Parameterized spam vector with length 4\n", " 0\n", " 0\n", " 0\n", " 0\n", "\n", "\n", "Error in Gi:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0 0 0 0\n", " 0 0 0 0\n", " 0 0 0 0\n", "\n", "\n", "Error in Gx:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0 0 0.01 0.02\n", " 0.02 0.02 0 0\n", " 0.01 0.02 0 0\n", "\n", "\n", "Error in Gy:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0.01 0.01 0.02 0.02\n", " 0 0.02 0 0.02\n", " 0 0.02 0.02 0.01\n", "\n" ] } ], "source": [ "pboot_mean = pygsti.drivers.to_mean_gateset(gauge_opt_pboot_gatesets, gs_mc2gst)\n", "pboot_std = pygsti.drivers.to_std_gateset(gauge_opt_pboot_gatesets, gs_mc2gst)\n", "\n", "#Summary of the error bars\n", "print(\"Parametric bootstrapped error bars, with\", numGatesets, \"resamples\\n\")\n", "print(\"Error in rho vec:\") \n", "print(pboot_std['rho0'], end='\\n\\n')\n", "print(\"Error in E vec:\")\n", "print(pboot_std['E0'], end='\\n\\n')\n", "print(\"Error in Gi:\")\n", "print(pboot_std['Gi'], end='\\n\\n')\n", "print(\"Error in Gx:\")\n", "print(pboot_std['Gx'], end='\\n\\n')\n", "print(\"Error in Gy:\")\n", "print(pboot_std['Gy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Non-parametric Bootstrapping\n", "Here we do non-parametric bootstrapping, as indicated by the 'nonparametric' argument below.\n", "The output is again eventually stored in the \"mean\" and \"std\" GateSets, which hold the mean and standard deviation values of the set of bootstrapped gatesets (after gauge optimization). It is this latter \"standard deviation Gateset\"\n", "which holds the collection of error bars. Note: due to print setting issues, the outputs that are printed here will not necessarily reflect the true accuracy of the estimates made.\n", "\n", "(Technical note: ddof = 1 is by default used when computing the standard deviation -- see numpy.std -- meaning that we are computing a standard deviation of the sample, not of the population.)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating DataSets: \n", "0 Generating non-parametric dataset.\n", "1 Generating non-parametric dataset.\n", "2 Generating non-parametric dataset.\n", "3 Generating non-parametric dataset.\n", "4 Generating non-parametric dataset.\n", "5 Generating non-parametric dataset.\n", "6 Generating non-parametric dataset.\n", "7 Generating non-parametric dataset.\n", "8 Generating non-parametric dataset.\n", "9 Generating non-parametric dataset.\n", "Creating GateSets: \n", "Running MLGST Iteration 0 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24550397209\n", " 1.20335083144\n", " 0.975668667345\n", " 0.917454523185\n", " 0.0670503056945\n", " 0.019102495855\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 98.6739 (92 data params - 40 model params = expected mean of 52; p-value = 0.000100717)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 99.1174\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 98.6739 (92 data params - 40 model params = expected mean of 52; p-value = 0.000100717)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 99.1174\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 296.362 (168 data params - 40 model params = expected mean of 128; p-value = 2.22045e-15)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 298.374\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 898.783 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 903.33\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1704.29 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1712.24\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2458.62 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2468.5\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3270.34 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3281.93\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4037.88 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.1s\n", " 2*Delta(log(L)) = 4051.09\n", " Iteration 8 took 1.2s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4745.54 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.5s\n", " 2*Delta(log(L)) = 4760.02\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5442.75 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.4s\n", " 2*Delta(log(L)) = 5458.42\n", " Iteration 10 took 2.7s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2727.7 below upper bound of -4.59866e+06\n", " 2*Delta(log(L)) = 5455.39 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 1.9s\n", " 2*Delta(log(L)) = 5455.39\n", " Final MLGST took 1.9s\n", " \n", "Iterative MLGST Total Time: 9.2s\n", "Running MLGST Iteration 1 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24583719101\n", " 1.17077453673\n", " 0.957852209477\n", " 0.916561097732\n", " 0.0669317955101\n", " 0.0231638779774\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 91.4608 (92 data params - 40 model params = expected mean of 52; p-value = 0.000598068)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 91.5623\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 91.4608 (92 data params - 40 model params = expected mean of 52; p-value = 0.000598068)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 91.5623\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 248.357 (168 data params - 40 model params = expected mean of 128; p-value = 9.96094e-10)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 248.944\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 812.875 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 814.97\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1544.53 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1548.06\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2219.02 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2223.87\n", " Iteration 6 took 0.4s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3037.52 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 3043.97\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3821.3 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 0.9s\n", " 2*Delta(log(L)) = 3829.33\n", " Iteration 8 took 1.0s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4510.98 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.4s\n", " 2*Delta(log(L)) = 4520.25\n", " Iteration 9 took 1.5s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5255.37 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.5s\n", " 2*Delta(log(L)) = 5266.14\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2631.68 below upper bound of -4.59916e+06\n", " 2*Delta(log(L)) = 5263.37 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 3.9s\n", " 2*Delta(log(L)) = 5263.37\n", " Final MLGST took 3.9s\n", " \n", "Iterative MLGST Total Time: 11.1s\n", "Running MLGST Iteration 2 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24587928058\n", " 1.1653069138\n", " 0.954081292751\n", " 0.914239273392\n", " 0.0316175274274\n", " 0.0180748763317\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 93.2764 (92 data params - 40 model params = expected mean of 52; p-value = 0.000386683)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 93.5569\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 93.2764 (92 data params - 40 model params = expected mean of 52; p-value = 0.000386683)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 93.5569\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 264.548 (168 data params - 40 model params = expected mean of 128; p-value = 1.53207e-11)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 265.35\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 852.824 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 855.449\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1597.59 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1602.3\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2393.22 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2400.04\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3153.62 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3161.98\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4004.14 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 4014.42\n", " Iteration 8 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4804.24 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.3s\n", " 2*Delta(log(L)) = 4816.31\n", " Iteration 9 took 1.5s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5494.64 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.6s\n", " 2*Delta(log(L)) = 5507.87\n", " Iteration 10 took 2.9s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2752.41 below upper bound of -4.59897e+06\n", " 2*Delta(log(L)) = 5504.81 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.0s\n", " 2*Delta(log(L)) = 5504.81\n", " Final MLGST took 2.0s\n", " \n", "Iterative MLGST Total Time: 8.9s\n", "Running MLGST Iteration 3 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24572611089\n", " 1.21940117097\n", " 0.976875017673\n", " 0.930877541327\n", " 0.0513818811616\n", " 0.0316019098629\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 111.101 (92 data params - 40 model params = expected mean of 52; p-value = 3.55272e-06)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 112.248\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 111.101 (92 data params - 40 model params = expected mean of 52; p-value = 3.55272e-06)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 112.248\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 262.519 (168 data params - 40 model params = expected mean of 128; p-value = 2.61924e-11)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 264.725\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 831.582 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 834.727\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1533.73 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1539.41\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2240.67 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2247.65\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3087.76 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 3096.53\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3910.6 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 3921.18\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4629.46 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.1s\n", " 2*Delta(log(L)) = 4641.38\n", " Iteration 9 took 1.2s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5300.1 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.6s\n", " 2*Delta(log(L)) = 5313.11\n", " Iteration 10 took 2.9s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2655.12 below upper bound of -4.59909e+06\n", " 2*Delta(log(L)) = 5310.23 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 4.9s\n", " 2*Delta(log(L)) = 5310.23\n", " Final MLGST took 4.9s\n", " \n", "Iterative MLGST Total Time: 12.1s\n", "Running MLGST Iteration 4 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24587972369\n", " 1.17514363414\n", " 0.982174972059\n", " 0.879560441108\n", " 0.0645746514061\n", " 0.0323833315662\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 134.098 (92 data params - 40 model params = expected mean of 52; p-value = 3.52406e-09)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 134.856\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 134.098 (92 data params - 40 model params = expected mean of 52; p-value = 3.52406e-09)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 134.856\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 279.637 (168 data params - 40 model params = expected mean of 128; p-value = 2.55018e-13)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 280.865\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 811.836 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 814.022\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1464.25 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1468.44\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2326.65 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2333.11\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3246.52 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3255.2\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3999.89 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 4009.93\n", " Iteration 8 took 1.0s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4823.55 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 4835.4\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5592.79 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.2s\n", " 2*Delta(log(L)) = 5606.12\n", " Iteration 10 took 2.5s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2801.54 below upper bound of -4.59866e+06\n", " 2*Delta(log(L)) = 5603.09 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 4.3s\n", " 2*Delta(log(L)) = 5603.09\n", " Final MLGST took 4.3s\n", " \n", "Iterative MLGST Total Time: 11.3s\n", "Running MLGST Iteration 5 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24471574745\n", " 1.1691929877\n", " 0.960719984385\n", " 0.924282540953\n", " 0.0658750484814\n", " 0.0395139838088\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 113.27 (92 data params - 40 model params = expected mean of 52; p-value = 1.92039e-06)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 114.272\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 113.27 (92 data params - 40 model params = expected mean of 52; p-value = 1.92039e-06)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 114.272\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 250.232 (168 data params - 40 model params = expected mean of 128; p-value = 6.21891e-10)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 251.777\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 856.142 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 860.296\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1555.05 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1562.33\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2267.44 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2276.69\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3107.74 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3118.96\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3994.81 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 0.9s\n", " 2*Delta(log(L)) = 4008.18\n", " Iteration 8 took 1.0s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4781.84 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 4796.74\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5598.09 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.5s\n", " 2*Delta(log(L)) = 5614.95\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2805.9 below upper bound of -4.59901e+06\n", " 2*Delta(log(L)) = 5611.8 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.3s\n", " 2*Delta(log(L)) = 5611.8\n", " Final MLGST took 2.3s\n", " \n", "Iterative MLGST Total Time: 9.7s\n", "Running MLGST Iteration 6 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.2454706912\n", " 1.20039479438\n", " 0.962462040517\n", " 0.919961721698\n", " 0.0489976096626\n", " 0.0264468003084\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 92.3735 (92 data params - 40 model params = expected mean of 52; p-value = 0.000480853)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 92.6192\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 92.3735 (92 data params - 40 model params = expected mean of 52; p-value = 0.000480853)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 92.6192\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 237.363 (168 data params - 40 model params = expected mean of 128; p-value = 1.46772e-08)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 238.114\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 815.605 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 817.2\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1531.81 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1535.37\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2292 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2296.97\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3067 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3073.75\n", " Iteration 7 took 0.6s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3945.73 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 3954.56\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4820.88 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.4s\n", " 2*Delta(log(L)) = 4831.68\n", " Iteration 9 took 1.5s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5654.88 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.4s\n", " 2*Delta(log(L)) = 5667.44\n", " Iteration 10 took 2.7s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2832.11 below upper bound of -4.5984e+06\n", " 2*Delta(log(L)) = 5664.23 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.8s\n", " 2*Delta(log(L)) = 5664.23\n", " Final MLGST took 2.8s\n", " \n", "Iterative MLGST Total Time: 9.8s\n", "Running MLGST Iteration 7 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24510947557\n", " 1.17112066686\n", " 0.947794732077\n", " 0.887769813302\n", " 0.0520565945096\n", " 0.0147627734961\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 97.0157 (92 data params - 40 model params = expected mean of 52; p-value = 0.000153413)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 97.2526\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 97.0157 (92 data params - 40 model params = expected mean of 52; p-value = 0.000153413)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 97.2526\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 242.701 (168 data params - 40 model params = expected mean of 128; p-value = 4.03742e-09)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 243.217\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 743.182 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 745.083\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1422.2 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1425.56\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2120.16 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2124.8\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2948.96 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 2954.97\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3788.5 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 3796.44\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4527.6 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 4536.92\n", " Iteration 9 took 1.8s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5276.17 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.3s\n", " 2*Delta(log(L)) = 5287\n", " Iteration 10 took 2.5s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2642.14 below upper bound of -4.59896e+06\n", " 2*Delta(log(L)) = 5284.28 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 5.5s\n", " 2*Delta(log(L)) = 5284.28\n", " Final MLGST took 5.5s\n", " \n", "Iterative MLGST Total Time: 12.8s\n", "Running MLGST Iteration 8 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24447001028\n", " 1.16048285872\n", " 0.943896909169\n", " 0.912777176138\n", " 0.0717633774961\n", " 0.0366924472244\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 95.6424 (92 data params - 40 model params = expected mean of 52; p-value = 0.000216299)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 96.0506\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 95.6424 (92 data params - 40 model params = expected mean of 52; p-value = 0.000216299)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 96.0506\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 244.477 (168 data params - 40 model params = expected mean of 128; p-value = 2.61065e-09)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 245.07\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 710.537 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 711.619\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1369.09 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1371.56\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2147.18 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2151.29\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2962.63 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.7s\n", " 2*Delta(log(L)) = 2968.4\n", " Iteration 7 took 0.8s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3783.59 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 3791.07\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4526.21 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 4535.17\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5245.33 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.5s\n", " 2*Delta(log(L)) = 5255.66\n", " Iteration 10 took 2.8s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2626.47 below upper bound of -4.59911e+06\n", " 2*Delta(log(L)) = 5252.94 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.3s\n", " 2*Delta(log(L)) = 5252.94\n", " Final MLGST took 2.3s\n", " \n", "Iterative MLGST Total Time: 9.9s\n", "Running MLGST Iteration 9 \n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 4 of 6) = \n", " 4.24627607281\n", " 1.1828960755\n", " 0.960121416802\n", " 0.938909418001\n", " 0.0392610346821\n", " 0.0300820382837\n", " \n", " Singular values of target I_tilde (truncating to first 4 of 6) = \n", " 4.246313691\n", " 1.17235194083\n", " 0.953112718624\n", " 0.943760994228\n", " 3.49602251407e-16\n", " 1.72707620951e-16\n", " \n", "--- Iterative MLGST: Iter 01 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 96.0807 (92 data params - 40 model params = expected mean of 52; p-value = 0.000193936)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 96.5628\n", " Iteration 1 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 02 of 10 92 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 96.0807 (92 data params - 40 model params = expected mean of 52; p-value = 0.000193936)\n", " Completed in 0.0s\n", " 2*Delta(log(L)) = 96.5628\n", " Iteration 2 took 0.0s\n", " \n", "--- Iterative MLGST: Iter 03 of 10 168 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 215.585 (168 data params - 40 model params = expected mean of 128; p-value = 2.0478e-06)\n", " Completed in 0.1s\n", " 2*Delta(log(L)) = 216.178\n", " Iteration 3 took 0.1s\n", " \n", "--- Iterative MLGST: Iter 04 of 10 441 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 728.515 (441 data params - 40 model params = expected mean of 401; p-value = 0)\n", " Completed in 0.2s\n", " 2*Delta(log(L)) = 730.3\n", " Iteration 4 took 0.2s\n", " \n", "--- Iterative MLGST: Iter 05 of 10 817 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 1444.66 (817 data params - 40 model params = expected mean of 777; p-value = 0)\n", " Completed in 0.3s\n", " 2*Delta(log(L)) = 1449.14\n", " Iteration 5 took 0.3s\n", " \n", "--- Iterative MLGST: Iter 06 of 10 1201 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2208.27 (1201 data params - 40 model params = expected mean of 1161; p-value = 0)\n", " Completed in 0.4s\n", " 2*Delta(log(L)) = 2214.65\n", " Iteration 6 took 0.5s\n", " \n", "--- Iterative MLGST: Iter 07 of 10 1585 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 2995.31 (1585 data params - 40 model params = expected mean of 1545; p-value = 0)\n", " Completed in 0.6s\n", " 2*Delta(log(L)) = 3003.36\n", " Iteration 7 took 0.7s\n", " \n", "--- Iterative MLGST: Iter 08 of 10 1969 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 3797.02 (1969 data params - 40 model params = expected mean of 1929; p-value = 0)\n", " Completed in 1.0s\n", " 2*Delta(log(L)) = 3806.65\n", " Iteration 8 took 1.1s\n", " \n", "--- Iterative MLGST: Iter 09 of 10 2353 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 4573.87 (2353 data params - 40 model params = expected mean of 2313; p-value = 0)\n", " Completed in 1.6s\n", " 2*Delta(log(L)) = 4585.05\n", " Iteration 9 took 1.7s\n", " \n", "--- Iterative MLGST: Iter 10 of 10 2737 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Sum of Chi^2 = 5334.95 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.6s\n", " 2*Delta(log(L)) = 5347.63\n", " Iteration 10 took 2.9s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 56 params (taken as 1 param groups of ~56 params).\n", " Maximum log(L) = 2672.41 below upper bound of -4.59872e+06\n", " 2*Delta(log(L)) = 5344.82 (2737 data params - 40 model params = expected mean of 2697; p-value = 0)\n", " Completed in 2.0s\n", " 2*Delta(log(L)) = 5344.82\n", " Final MLGST took 2.0s\n", " \n", "Iterative MLGST Total Time: 9.5s\n" ] } ], "source": [ "#The number of simulated datasets & gatesets made for bootstrapping purposes. \n", "# For good statistics, should probably be greater than 10.\n", "numGatesets=10\n", "\n", "nonparam_boot_gatesets = pygsti.drivers.make_bootstrap_gatesets(\n", " numGatesets, ds, 'nonparametric', fiducials, fiducials, germs, maxLengths,\n", " targetGateSet=gs_mc2gst, startSeed=0, returnData=False, verbosity=2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spam weight 0\n", "Spam weight 1\n", "Spam weight 2\n", "Spam weight 3\n", "Spam weight 4\n", "Spam weight 5\n", "Spam weight 6\n", "Spam weight 7\n", "Spam weight 8\n", "Spam weight 9\n", "Spam weight 10\n", "Spam weight 11\n", "Spam weight 12\n", "Best SPAM weight is 0.0001\n" ] }, { "data": { "image/png": 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9eobg525GRgYZixZxfOtWPrWmpOskItt8h/DO8bs4OztbZ7hSlc5ldhjf72Nf\nreqLZcHxV66nG9Q7lqSk0v9cSUryfjyIpKT4Pv7rr30fD9Y+vqSk+D4+WPXIyrIeZ2WFXz0c2S6/\n/OK5HsGoh69rGsp6OHItWXJxXY/y1sPX9Qx1PRzZRo26eK5HeevheK28Xc8Q1iN7wQIB7XFWVZu/\nPc46q4arxER4/nnPvR9RUaUfv2xZ6T0fviQkQHb2hefeeih9eeABSE72vj0Y9XD0OGdlee79KF4P\nTyqqHjk5VqasLKvH+ZVXfPc4l5YxmPUI5HpC8N9XntSoceF183RNK+N95akertczJQUyM333OFfm\n94evHmdfKuP749gx39fTtR7eVFQ9XK/p1Km+r2ll/tz11eMc6p+7jh5nX9czVD93c3Ks10upi4Wv\nVvXFsqBjnP0XrrlENFugwjVbuOYS0WyBCtds4ZpLpEpk0zHOqjrwt8c55I3WcFjcvlnD4YeStwyh\nzhauuXxl0Gy+hWu2cM3lK4Nm8y1cs4VrLl8ZwiybTkenqgOdji5Q8fHWv8E++SR0GT75xPO/4kKd\nLVxzgWYLVLhmC9dcoNkCFa7ZwjUXVM1sSlVzOqsG+klepZRSKlB+z0ZQCv1drELJ3/ex9jgrpZRS\nSinlB204K6WUUkop5QdtOCullFJKqUrVvHlzxo4dG+oYZaYNZ6WUUkqpSvDVV19x5513Eh8fT3R0\nNM2bN+e2227jlVdece4THx+PzWZzLo0bN+bmm2/m3Xff9Vpux44dsdlsLFq0yOP2RYsWOcv74osv\nSmwXEZo2bYrNZmPw4MHlr6gfbDYbxphKOVcwacPZxcSJE0lOTiYjIyPUUZRSSqmwlpGRQXJyMhMn\nTgx1lCph8+bNdOnSha+++oqxY8cyb948xowZw/nz53nppZec+xlj6NChA2lpabz99ts8+uijHDly\nhMGDB7PAw81mvvnmG7Zv306rVq1IS0vzmSE6Opr09PQS6zdu3Ehubi5R/tysJ0j27t3L/PnzK+18\nwaJ3DnSRmpqqn+RVSiml/DBixAhGjBjhOhuB8mH27NnUqVOHrVu3Ehsb67YtNzfX7XmzZs0YMWKE\n8/k999xDmzZtSE1NLTG84a233qJJkyY8//zzDBs2jMOHD9OsWTOPGfr27cvSpUtJTU116+1NT0/n\nuuuu49ChQ+Wtpt9q1qxZaecKJu1xVkoppVSVVJH/IA522fv27aN9+/YlGs0Al112mc9jGzduTGJi\nIvv37y+7saz0AAAgAElEQVSxLSMjg+HDh9O/f39iY2O9/tfcGMPIkSPJzc1l48aNzvUFBQWsXLmS\nkSNH4u8UxR999BE2m42VK1fy1FNP0axZM2rXrs3w4cM5efIkZ8+eZcKECTRu3JjatWszZswYCgsL\n3cooPsb59ddfx2az8fnnn/PII4/QsGFDatWqxZ133smvv/7qV67KoA1npZRSSlVJVanhfPnll5Od\nnc3OnTvLfGxhYSHff/899evXd1u/adMmcnJyGDFiBJGRkQwcONDncI3WrVvTtWtXt8Z1ZmYmp06d\nYvjw4WXONWvWLLKyspgyZQr33nsvK1asYNy4cYwaNYqcnBxmzJjBwIEDWbx4MS+++KLbscXHNzue\njxs3jt27d/P000/z0EMP8d577zFhwoQyZ6soOlRDKaWUUqqCTZo0ib59+3LNNdfQtWtXbrrpJnr2\n7En37t2JiHBvjp07d45jx44BcPjwYf7nf/6Hn376qUQD8u2336ZVq1Z06dIFgLvuuou33nqLXbt2\nkZSU5DHHyJEjmT59OvPnz6dmzZqkp6fTs2dPGjZsGFC9srKysNmsftjc3FzS0tIYMGAAq1atAuDB\nBx/k3//+N4sXL+bxxx8vtbzLLruM1atXO58XFBQwf/58XnvtNWJiYgLKGEza46yUUkqpKik/H7Zt\n877s2lV6Gbt2eT42Pz+4WXv16sXmzZu54447+PLLL3nhhRe4/fbbadasGZmZmW77rlu3joYNG9Kw\nYUOuueYaVqxYwR/+8AeeffZZ5z6FhYUsX77cbSx07969qV+/vs9e5+HDh5OXl8eaNWs4ceIEa9as\nYeTIkQHVafTo0c5GM8C1114LwH333ee237XXXsuBAwdKLc8YwwMPPOC27qabbuL8+fMcPHgwoIzB\npj3OSimllKoSMjLch1B8+KG1eJOUBKWNjBg61HsDOzn5wuMRI6ylPDp37szy5cspLCxkx44dvPPO\nO6SmpjJ06FC2b99Ou3btALjuuuuYPXs2ADExMSQmJlK7dm23stasWcOxY8fo0qULe/fuBaxp5W69\n9VbS09OdxxfXuHFjunfvTnp6OseOHfM5Bd3Ro0c5f/6883lsbKxbr2+LFi3c9o+Li/O6vrCwkLy8\nPI9jvF0VP7Zu3boAYTPOWRvOSimllKoSijdeb7sNXDphS/BndrVlyzz3Lj/+ONhHGwRdREQEnTp1\nolOnTrRt25Z7772XZcuW8eSTTwLQoEEDunfv7rOM9PR0jDEMGjTIbb1jrPCmTZu44YYbPB47cuRI\nxo0bx8GDB+nXrx+XXnqpx/06dOjA4cOHneXOnDmTKVOmOLfXqFHD43He1vvz4cPyHFsZdKiGBzk5\n8OaboTv/m29aGTwJZbZwzQWaLVDhmi1cc4FmC1S4ZgvXXFB1s1WmqCjo2NH74mWYr5ukJM/HVtaU\nxp07dwbgyJEjfh9z8uRJMjMzGTlyJMuXL3dbli1bRsOGDX0O1xgyZAgAX3zxhc9hGv/85z/ZsGED\nGzZs4MMPP+Tuu+/2O2N1pQ3nYnJy4L774JZbQpfhllusDMV/KIU6W7jmAs0WqHDNFq65QLMFKlyz\nhWsuqJrZlHdZWVke1zs+COcYpuGP5cuXk5+fz/jx4xk8eHCJpV+/fixbtqzEFHAOsbGxzJ8/n+nT\np9OvXz+v5+nWrRs9evRwLi1btnRuq4p3/QsKEbnoF6AjIJmZ2dK9u8j+/RJy+/eLW5biz0MlXHOJ\naLZAhWu2cM0lotkCFa7ZwjWXSNXIlpmZLYAAHSUIv4uzs7P9zpCeHsQKVXDZV111lSQkJMh///d/\ny8KFC2XevHkycuRIiYiIkNatW8vx48dFRCQ+Pl4GDBjgs6xevXrJZZdd5nX7u+++K8YYyczMFBGR\n119/XYwxsmPHDp/lNm/eXAYNGlRqXTZs2CDGGHnvvffc1ns7z7Rp08Rmsznr6DjXmDFjSj3Wca5N\nmzaVmqs8srP9ex9XyzHOxpjmwFtAI+AcMEtElpd23GOPwfPPwy+/WIurqKjS/+Wza5fvT+E2aWIt\n3pw5A7t3X3j+5z9bH1oYOxYWLICUFGjc2HeGI0esxZtg1OPZZ62ehpQUmDEDFi+G+Hjv9fAkMRGi\no71vD6Qe8fFWFke2xx6DJ5/0fD2h7NfDk7LUw9P1PH3ad/kQ/PeVJ3/7m+9rWhnvq+L1KH49U1Jg\n0iTv1xMq5n1VnKMenq7nL79AZGTlvq88eeIJ39fTtR7eVMT3R/FrOnWqNY7U2zWtrJ+7v/zi/Xr+\n8kvlvq88+fOf4e67YfZs79czFD93Hddz6FDf561I5f2wXmWWPWfOHJYtW8batWtZuHAhBQUFtGzZ\nkocffpgpU6Y4P/xnjPHZm5ubm0tWVhajR4/2uk/v3r2Jjo4mLS2N/v37O8stTWnnLr5vWdb7c67y\nllkpfLWqq+oCXAb8xv64MXAIiPaxf0dAIFtAPC5JSaX/tZKU5PlYx5KS4vv4r7/2fTxY+/iSkuL7\n+GDVIyvLepyVFX71cGS7/PKL53oEox6+rmko6+HItWTJxXU9ylsPX9cz1PVwZBs16uK5HuWth+O1\n8nY9Q1mPBQtC1+OsVLBc1D3OIpIL5Nof/2iMOQrUAw77Oi4x0epxbtq05LbyfDLXwVevB0BCAmRn\nX3j+ww9W74JrD0hCgu8yHnjAffqc4oJRj8JCq5coK8tz70fxenhSUfXIybEyZWVZPc6vvOL5ekLZ\nr4e3fXxxrUcg1xOC/77ypEaNC6+bp2taGe8rT/VwvZ4pKZCZ6f16QuV+f3i6nk2bVv77ypNjx3xf\nT9d6eFNR9XC9plOn+r6mlflz19v19FYPVxX9/fHDD9Z/+nxdz1D93M3JsV4vpS4avlrV1WEBOgFf\nlrKPjnH2U7jmEtFsgQrXbOGaS0SzBSpcs4VrLpGqkS2UY5yVChZ/e5xD3rAV65vlJmAVVo9wEZDs\nYZ//AvYDZ4AtQBc/yq0HfA1cW8p+zm/WcPih5C1DqLOFay5fGTSbb+GaLVxz+cqg2XwL12zhmstX\nhnDL5m+Do7RFG84qlPx9H4fLdHSXAtuxGsdSfKMxZjgwB0gBOgA7gHXGmAYu+4wzxvzLGLPNGBNp\njLkEeAd4RkQ+9zeI48MOn3xSrvqUyyefeP5XXKizhWsu0GyBCtds4ZoLNFugwjVbuOaCqplNqerO\niJRop4aUMaYIGCgiq1zWbQE+F5E/2Z8b4HvgZRF53ks5GcBuEXnaj3N2BLKzs7Pp2LFjMKqhlFJK\nXRS2bdtGp06dADqJyLZAy9HfxSqU/H0fh0uPs1fGmJpY45Q/cqwTq7W/AbjeyzE3AEOBgS690O0r\nI69SSimllKqeqsKsGg2AGsCPxdb/CFzp6QAR2UQAdZs4cSJxcXFu60aMGMGIipwoUimllKoiMjIy\nyMjIcFt3/PjxEKVRqvJVhYazNwYP46HLIzU1Vf89pJRSSnnhqTPJ5V/cSlV7YT9UAzgKnMe6kYmr\nRpTshVZKKaWUUqpChH2Ps4icM8ZkAz2xpqxzfDiwJ/ByMM/lGKoRbsMzRCS8bjdpF665QLMFKlyz\nhWsu0GyBCtds4ZoLwi+bY9iGDtVQF5OAepyNMfcYYzYZY34wxlxuX/eIMeaOAMu71BjzW2PMNfZV\nCfbnLezP/wKMNcb8wRjTDngViAHeCOR83uzZc4b4+A7O+7qHUl5eHhMmpNCqVS9atBhIq1a9mDAh\nhby8PM2l2S6KbOGaS7NVv2zhmivcs/Xv35/4+A7s2XMm1FGUqjy+Jnn2tAAPAT8DU4HTQIJ9/Wjg\n47KWZz/2Fqwbn5wvtix22WcckIN1A5TPgM6BnMvL+TsCAlvFZlsr7dv3lhMnTgQ0gXYwnDhxQtq3\n7y0221qBIucS6mzhmkuzVb9s4ZpLs1W/bOGaq2pl26o3QFFVXkXeAGU8MEZEZtsbtw5bgasDKA8R\n+UREbCJSo9hyn8s+fxOReBGJFpHrRWRrIOfyJYobsBWNY+fO9kybNifYxftt6tQX2bXrQWKL1pBA\nAp1oQQIJxBatYdeuB0KWLVxzabbqly1cc2m26pctXHNVhWw7d7bHVjSOKG4IWQ6lKluZb4BijDkD\ntBORA8aYPOC3IrLPGNMW+FJEoisiaEVyTLq+FTiCjbEkIpc1ZPXqj0OSp2/fW7H9eJSF7KYvRc7p\nQ1aHOFu45tJs1S9buObSbNUvW7jmqkrZLqOIztZqvQFKKb766itmzJjB1q1b+fHHH6lfvz5JSUkk\nJyfz8MMPAxAfH8/BgwedxzRs2JArr7ySP//5zwwcONBjuR07dmT79u0sXLiQ+++/v8T2RYsWMWbM\nGAC2bNlC165d3baLCM2aNSM3N5eBAweycuXKYFW5yvD7Rj6+uqM9LcAu4A774zwuDNUYD2wra3nh\nsOD49xCIgKzCJnG0sv9bTCp5KZI4Wsn72DzuELps4ZpLs1W/bOGaS7NVv2zhmqtqZcsGAR2qUZpN\nmzZJZGSkXHHFFTJ79mxZtGiRTJ8+XXr06CGtW7d27hcfHy8dO3aU9PR0SUtLkxdeeEHatGkjxhh5\n7bXXSpS7e/duMcZIQkKCdO/e3eO5X3/9dTHGSExMjPzpT38qsX3Dhg1ijJHo6GgZNGhQ8Cpdhfg7\nVCOQWTX+AswzxkRhzaXc1RgzAngC+GMA5YWd/hTRwnaYz/7+r5Ccf+gfDtO3qMjjtlBmC9dcoNkC\nFa7ZwjUXaLZAhWu2cM0FVTeb8mz27NnUqVOHrVu3Ehsb67YtNzfX7XmzZs3cZve65557aNOmDamp\nqYwdO9Zt37feeosmTZrw/PPPM2zYMA4fPkyzZs08Zujbty9Lly4lNTXVbYaW9PR0rrvuOg4dOlTe\nalZ/vlrV3hbg98AerA/0FQHfA/cHUlY4LNj/yr0ZZABIOkgySFHl/xkvRfZz+9onFNnCNZdmq37Z\nwjWXZqt+2cI1V1XJlo71O/NmKq/H+Y03RPbv97xt/35re6AqsmwRkXbt2kmPHj1K3S8+Pl4GDBhQ\nYn2XLl0kMjKyxPpWrVrJxIkTJT8/X+Li4uSFF14osc/rr78uNptNVq5cKcYY2bBhg3Pb2bNnpU6d\nOvLXv/5Vmjdv7lePs6OHesWKFfLkk09K06ZNJTY2VoYNGyZ5eXmSn58v48ePl0aNGklsbKz88Y9/\nlHPnzpXI1KNHD2nUqJFERUVJ+/btZcGCBW77rF+/XowxMmvWLLf1b7zxhhhj5PXXXy81q78qsscZ\nEUkD0owxMUAtEfmpXK33MJGKc3oNFl52GWb16krPYIC8fv2Q3Fw8zdYpQF4IsoVrLtBsgQrXbOGa\nCzRboMI1W7jmgqqR7a7cXEYA24DKum/gLbfAfffB4sUQH39hfU7OhfXhWDbA5ZdfzpYtW9i5cyft\n27cv07GFhYV8//331K9f3239pk2byMnJYcSIEURGRjJw4EDS0tKYNGmSx3Jat25N165dycjIoGfP\nngBkZmZy6tQphg8fznPPPVemXLNmzaJWrVpMmTKFb7/9lnnz5hEZGUlBQQGnT59mxowZbN68mcWL\nF9O6dWsef/xx57Hz58+nQ4cO3HHHHURERPDee+/xwAMPADjHY/fu3ZsHHniAmTNnMmDAAH7zm99w\n+PBhJk6cyO9+9zuP47krnK9WtacFeAro4WH9pcBTZS0vHBaKjXFebbNJyoQJ5fnDpVyeGj9e1tg8\nj2sLZbZwzaXZql+2cM2l2apftnDNVZWyVfYY5/37Rbp3v9A7XPx5eVRk2R9++KHUrFlTIiIipFu3\nbjJ58mRZv359iZ7Y+Ph46dOnjxw9elSOHj0qO3bskLvuuktsNps88sgjbvs++OCDkpCQ4Hy+du1a\nsdlssnPnTrf9HD3OO3bskLlz50rdunWloKBAREQGDx4sffr0EREpc49zhw4d5Pz58871w4YNE5vN\nJnfccYfb/l27dpW2bdu6rcvPzy9Rbq9evaRdu3Zu606ePCmtW7eWa665RgoKCqRPnz5Sr149OXLk\nSKk5y8LfHudA3thFwFngz8XWNwbOl7W8cFgc36xbQdbYbNK7ffuQz4/Zu317WWOzOf8NVxQG2cI1\nl2arftnCNZdmq37ZwjVXVcq2tZIbziIXGrQrVoh07iySmSmSnV1yKdZ+9GjnTvdjMjOtMlesCF6j\n2eH//u//ZMiQIVKrVi2x2WxijJFGjRrJqlWrnPvEx8eLMcZtqVmzpowePdqtsXnu3Dlp0KCBTJ06\n1bmusLBQGjZsKFOmTHE7r2vDOTc3VyIiIuTdd9+V48ePS1RUlPz9738XkbI3nOfOneu2fs6cOWKz\n2eS9995zWz9+/Hi55JJLvJZ3/PhxOXr0qMycOVNsNpucPn3abfunn34qNWrUkGuvvVZsNpu8/fbb\npWYsq4puOA/HugnKG8AlUk0aznUiI+WKVq1k0aJF5b4A5XXixAlJmTBBesXHS3KzZtIrPl5SJkwI\n6Q/KcM6l2apftnDNpdmqX7ZwzRXu2RYtWiRXtGoldSIjK73hLCKSlSWlDslOSiq9nKQk78dnZZX1\nVfHPuXPnZOvWrTJ16lSJiYmRyMhI2b17t4hYDefrr79eNm7cKBs3bpQtW7bI8ePHS5Tx3nvviTFG\n3n33Xfnuu+/ku+++kz179sjQoUMlPj7ebV/XhrOISO/evWXYsGGyaNEiiYmJkZMnT4pIyYbzzz//\nLLm5uc7l1KlTInKh4bxy5UqP59m2bZvb+mnTponNZnN733766afSvXt3ufTSS93+SLDZbB57kx98\n8EExxkj//v39fp3LoqIbzo2A1lhT0222N5qrfMM5XKfAKSoqCnUEj8I1l4hmC1S4ZgvXXCKaLVDh\nmi1cc4mEbzZ/GxylLRdDj7Mnjg+6Pf300yLi/cOBxQ0fPtzZa1284Wmz2eR///d/nfsWbzgvWbJE\noqOj5brrrpOhQ4c69yvecG7evLlbubNnzxaRCw3n4j3Lxc/j4Gg4O/4A+PbbbyUyMlI6deokCxYs\nkLVr18pHH30kf/rTn8Rms8nhw4fdjs/Pz5ekpCSx2WySmJjocZhHeVXkhwPFPjZ6rzHmOmAp1l0D\nHwygLOUH1yljwkm45gLNFqhwzRauuUCzBSpcs4VrLgjvbJXJ9cN68fHQsaPnD/X5KynJvexJk2DZ\nsuCU7Y/Ona3bxxw5csTvY06ePElmZiYjR45k0KBBJbaPGzeOtLQ0brjB810dhwwZwrhx4/jiiy+Y\nPHmy1/P885//JD8/3/m8TZs2fmf0ZdWqVZw7d47Vq1fTuHFj5/p169Z53H/q1Kl8++23vPjiizz6\n6KNMnTqVF198MShZyiqQhrPzO1dEThhj+gIvAe8GLZVSSimlVDHFG81gfV28uPwN3IosGyArK4tb\nb721xPrV9plR2rVr53dZy5cvJz8/n/Hjx5e4C6CjzGXLlvHyyy8TEVGyqRcbG8v8+fM5ePAg/fr1\n83qebt26ed1Wnj/katSoAUCRy1zgv/76K3//+99L7Lt582ZeeuklHnvsMSZOnMiRI0dITU1l8ODB\nPvNVlEAazvcCxx1PRKQImGCM+Rdwc7CCKaWUUkq5+uQTzw1YRwP3k08Cb9xWZNkA48eP5/Tp0wwa\nNIh27dpRUFDApk2bWLp0KQkJCYwePdrvstLS0mjUqJHHRjNAcnIyS5Ys4YMPPqB///4AjuEwTqNG\njQq4Lp7KK229q9tvv53JkyfTt29fxowZw4kTJ1i4cCFNmjThp58uzHB85swZRo8eTfv27ZkxYwYA\nM2fOZPXq1dx7773s2LGDqKioctWjrMrccBaRN72sXwIsKXeiEJo4cSJxcXGMGDHC7Y49SimllHKX\nkZFBRkYGx48fL33nIPHV1ouPL1/DtiLLBpgzZw7Lli1j7dq1LFy4kIKCAlq2bMnDDz/MlClTqF27\nNmD15Prqzc3NzSUrK8tnQ7t3795ER0eTlpbmbDj700Nc2rmL71uW9a4SExNZvnw506ZNY9KkSTRt\n2pTx48dTq1Yt51zOAI8//jgHDhzg888/p2bNmgBERkbyxhtvcMMNN/D444/z0ksv+ZU3WIw/fxkY\nYyYAC0Qk3/7YGxGRvwYtXSUxxnQEsrOzs+nYsWOo4yillFJVxrZt2+jUqRNAJxHZFmg5+rtYhZK/\n72N/e5wnAmlAvv2xNwJUuYazUkoppZRSpfGr4SwirTw9VkoppZRS6mJhK28BxpgaxphrjDF1gxFI\nKaWUUkqpcFTmhrMx5iVjzP32xzWAT4FtwPfGmFuDG08ppZRSSqnwEMh0dHcCb9sfDwDigXbAPcBs\nwPNs21WAzqqhlFJK+ScUs2ooFWqBNJwbALn2x32BZSLyrTFmMfCnoCULgdTUVP0kr1JKKeUHRyeT\ny2wESlV7gYxx/hFIsg/T6ANssK+PAc4HK5hSSimllFLhJJAe5yXAUuAI1vRzH9rXXwt8E6RcSiml\nlFJKhZVA7hw43RjzNdACa5jGWfum88CzwQynlFJKKaVUuAikxxkRWe5hncdbcSullFJKKVUdlHse\nZ6WUUkoppS4GAfU4V1c6HZ1SSinlH52OTl2MtOHsQqejU0oppfyj09GF3vz584mJiWHUqFGhjnLR\nKNNQDWNMhDHmD8aYxhUVSCmllFJKle5vf/sbb76pHzGrTGVqOItIIfAqEFUxcZRSSiml/CMiVbJs\nVXUF8uHAL4Brgh1EKaWUUqo0eXl5pEyYQK9WrRjYogW9WrUiZcIE8vLywrpsh6ysLDp37kx0dDRt\n27ZlwYIFTJ8+HZvtQpNsyZIl9OzZk8aNGxMVFUX79u159dVX3cpp1aoVO3fuJCsrC5vNhs1mo0eP\nHs7tx48f55FHHqFly5ZERUXRtm1bnn/++RJ/EPzjH/+gc+fO1K5dm7i4OH7zm9/w8ssvl1oPR+Y9\ne/Zw9913U6dOHRo1asRTTz0FwPfff8/AgQOJi4ujSZMm/OUvfylRRkFBASkpKbRt25aoqChatmzJ\n5MmTKSgocNvPn9cDID4+nuTkZDZt2sS1115LdHQ0rVu35q233iq1Pv4KZIzz34C/GGNaANnAKdeN\nIvJlMIIppZRSSrnKy8tjyPXX8+fdu5leVITBuhPbunnzGLJxIys++4zY2NiwK9vhX//6F7/73e9o\n2rQpM2fOpLCwkJkzZ9KgQQOMMc79Xn31Va666iruuOMOIiIiyMzMZNy4cYgIDz30EABz587l4Ycf\nJjY2lmnTpiEiNG5sjaQ9c+YMN998Mz/88AMPPfQQLVq0YPPmzTzxxBPk5uY6G7EffvghI0eOpHfv\n3jz//PMA7N69m88++4wJEyb4rIsj7/Dhw0lKSuK5555j9erVzJ49m3r16vHaa6/Rs2dPnnvuOdLT\n03n00Ufp2rUrN954I2D16A8YMIDNmzfzwAMP0K5dO7766itSU1PZs2cPK1euLNPr4ci0Z88ehg4d\nyv3338/o0aNZvHgx9957L507dyYxMbFc188ZvCwLUORhOe/4WtbywmEBOgKSnZ0tSimllPJfdna2\nYLUxO0oF/y5+avx4WWuziUCJZY3NJikTJgRcj4os22HAgAFSq1Ytyc3Nda7bu3ev1KxZU2w2m3Nd\nfn5+iWP79Okjbdq0cVt31VVXSffu3UvsO3PmTImNjZW9e/e6rX/iiSekZs2acujQIREReeSRR6Ru\n3boB1WX69OlijJGHHnrIue78+fPSokULqVGjhrz44ovO9f/5z38kJiZG7r33Xue6t956SyIiImTz\n5s1u5b722mtis9nks88+c67z9/WIj48Xm80mmzZtcq77+eefJSoqSh599FGf9fH3fRzIUI1WHpYE\nl69KKaWUUkG3KTOT24uKPG7rU1TEpuXLYdu2gJZNy5f7LnvVqnJlLyoq4qOPPmLgwIHOnmGAhIQE\nfve737ntGxkZ6Xx84sQJjh07xs0338y+ffv8GjayfPlybrrpJuLi4jh27Jhz6dmzJ4WFhXz66acA\n1KlTh5MnT7Ju3bqA6mSM4f7773c+t9lsdO7cGRHh3nvvda6Pi4vjyiuvZN++fW4ZExMTueKKK9wy\ndu/eHRHh448/Duj1SEpKolu3bs7nDRo0KHHu8gjkltsHgnJmpZRSSik/iQiXnjuH8bLdADE//IB0\n6uR1H69lA5fay/Ba9rlziIjbkIqy+Omnnzhz5gxt2rQpsa34uk2bNpGSksKWLVs4ffr0hRzGcPz4\n8VKHjOzZs4evvvqKhg0blqyLMfz0008AjBs3jmXLltG3b1+aNm3KbbfdxrBhw7j99tsBq7H/888/\nux1fr149atas6XzesmVLt+1xcXFERUVRr169Eut/+eUXt4zffPNNqRnL+noUzwNQt25dfv311xLr\nAxHwPM7GmCSgJXCJ63oRKd+fZEoppZRSxRhjOFWzJoLnBq4Ap5o0wbz/ftnLBk71748cOeK97Jo1\nA240l8XevXvp1asXiYmJpKam0qJFCy655BJWr17NSy+9RJGXXnFXRUVF9O7dm8mTJ3ucHeSKK64A\noGHDhmzfvp1169axdu1a1q5dy5IlSxg1ahRLlizh+++/p1WrVhhjnH80fPzxx9x8883OsmrUqFGi\nfE/rwH2mkqKiIq6++mpSU1M9ZmzRogUA+/btK9Pr4c+5y6PMDWdjTALwDnA1uL1/HYk8J1ZKKaWU\nKocbBgxg3bx59PHQePzAZuPGoUMhwBuZ3XDnnb7LTk4OqFyHRo0aER0dzXfffVdi2549e5yPMzMz\nKSgoIDMzk2bNmjnXf/TRRyWO89aQb926NSdPnqR79+6l5oqIiKBfv37069cPgIceeogFCxbw5JNP\n0qxZMzZs2OC2/29/+9tSy/RH69at+fLLL0vNWJbXozIEMsZ5LrAfaAycBtoDNwNbgVuDliwEJk6c\nSHJyMhkZGaGOopRSSoW1jIwMkpOTmThxYqWdc9Ls2fwlMZG1Npuzt06AtTYbqYmJ/PesWWFZNljj\nf9YrsiUAACAASURBVHv27Mm7775Lbm6uc/13333HBx984HweEWH1abr2pB4/fpw33nijRJmXXnop\n//nPf0qsHzZsGJ999hnr168vse348eOcP38ewG3ohMPVV18NwNmzZ4mMjKRHjx5uS1xcnJ819m3Y\nsGEcOnSIhQsXltiWn5/vHJLh6EH25/WoDIEM1bge6CEiPxtjioAiEflfY8wTwMtAh6AmrER6y22l\nlFLKP6G45XZsbCwrPvuMOdOm8ZdVq4g5d47TNWtyQ3IyK2bNKtd0cRVZtsP06dNZv3493bp146GH\nHqKwsJB58+Zx9dVXs337dgBuu+02atasSf/+/XnggQfIy8vj9ddfp3Hjxm4NboBOnTrx6quvMnv2\nbNq0aUOjRo3o3r07jz76KKtWraJ///6MHj2aTp06cerUKb788ktWrlxJTk4O9erV449//CO//PIL\nPXr0oHnz5uTk5PDKK69wzTXXBGfqNh/uueceli5dykMPPcTHH3/MDTfcwPnz59m9ezfLli1j/fr1\ndOzYsUyvR6XwNeWGpwX4FUiwP94LdLc/bg2cLmt54bCg09EppZRSAanM6eiKKyoqCmZVKqXsjz/+\nWDp16iRRUVHStm1bWbx4sUyaNEliYmKc+7z//vtyzTXXSExMjCQkJMiLL74oS5YsEZvNJgcOHHDu\n9+OPP8qAAQMkLi5ObDab29R0p06dkqlTp8oVV1whUVFR0qhRI7nxxhslNTVVCgsLRURk5cqV0qdP\nH7nsssskKipK4uPjZdy4cfLjjz+WWo/p06eLzWaTY8eOua0fPXq01K5du8T+t956q/zmN79xW1dY\nWCgvvPCCXH311RIdHS3169eXLl26yKxZsyQvL6/Mr0erVq0kOTnZ47l79Ojhsz7+vo+NlHGwtDHm\n/wFzRORdY0w6UBeYBYwFOonIVeVsy1c6Y0xHIDs7O1t7nJVSSqkycOlx7iQi2wIt52L+XTxo0CB2\n7drFv//971BHuWj5+z4OZIzzLJfjnsKav/n/AX0B37eZUUoppZS6iJ09e9bt+Z49e1izZo1fH+RT\noRfIPM7rXB5/B7QzxtQDfpWydl8rpZRSSl1EEhISGDVqFAkJCeTk5PDqq68SFRXFo48+Gupoyg8B\nz+MMYIxpAYiIHApSHqWUUkqpaqtPnz784x//IDc3l8jISLp168YzzzxD69atQx1N+SGQeZwjgBSs\nYRm17OtOAn8FZojIuaAmVEoppZSqJhYtWhTqCKocAulx/iswGHgM+My+7npgOlAfeCgoyZRSSiml\nlAojgTScRwJ3ichal3VfGmMOAv9AG85KKaWUUqoaCmRWjbNAjof1OUBBecIopZRSSikVrgJpOL8C\nPGmMiXSssD+eat8WcsaYOGPM/xljthljvjTG/DHUmZRSSimlVNXm11ANY8zKYqt6AYeMMTvsz38L\nXAJ8FMRs5XECuElE8o0x0cBOY8wKEfk11MGUUkoppVTV5G+P8/FiywrgfeB7+/I+sNK+LeTsd0/M\ntz+Ntn81ocpTXhkZoU7gWbjmAs0WqHDNFq65QLMFKlyzhWsuCO9sSl0s/Go4i8i9/i4VHdhf9uEa\n24GDwAsi8kuoMwUqXH9Yhmsu0GyBCtds4ZoLNFugwjVbuOaC8M6m1MUikDHOQWeMuckYs8oYc9gY\nU2SMSfawz38ZY/YbY84YY7YYY7r4KlNEjovINVi3BP+9MaZhReVXSimllFL+O3DgADabjb///e+h\njlIm5bpzYBBdCmwHFmMNA3FjjBkOzAHGAl8AE4F1xpgrROSofZ9xwBhAgOtF5CyAiPxsjPkSuAlr\nOIlXu3d73xYVBUlJviuxaxfk53vf3qSJtXhz5oznDMePw7Zt1uPERIiOLrmPw5Ej1uJNKOvh6mKu\nh+v1rMr1cHUx18P1ekLVrUdxF2s9il9PCJ96+BIu10NVrvnz5xMTE8OoUaNCHSUgxlTBUbQiElYL\nUAQkF1u3BZjr8twAh4DHvJTRGKhlfxwHfAW093HOjoBAtoB4XJKSpFRJSZ6PdSwpKb6P//pr38eD\nyK23iqSney8jJcX38cGoxxVXiAwYYD0eMODC4sjlTz2+/tp3hrLWIz3dPQuI1KpV8dejvPWoVcv3\n9RSpvPeVr2taGe8r13p4up633lr57ytPmjXzXcbgwb6Pr6z3la/rKVI57yvXeni6pldc8f/bu/P4\nqKrzj+OfJ4CENYqyKEJlEQqKUoLWjSCgiAuItmrpBrjUgl2kFa1VAaVQoQruolaRVkV/WKq4glCU\nurWaiCsqoqlWi6hIBAQF8vz+uJM4mUySmZuZzCT5vl+veSVz7r3nPnfOzeSZM+eeW7fnVTyzZ2fH\neZXI+2517VnX77vR7TloUKEH/0MZ4F6r//8DAC8sLKz5BRN3dz/wwAN9yJAhmQ4jtK+++spLS0sz\nHYa7uxcWJnYemwcna9Yws1JgtLsviTxvBnwJfK+sLFJ+J5Dn7qfEqeMQ4Nayp8AN7v7navY5ACi8\n665C+vQZEHedTPbgTJoEc+cGv2dLz8fee8OoUbBkSeXl2dDzMWoUXHlldvaoRbdntvUMVtWmme4Z\nHDUK7rsv8+cVVD6O6PaE7Oqprao9IfM9zqNGwS23ZF+Pc2x7Qva87557btXtmcn33aKiIvLz8wHy\n3b2o8hqJKftfXFhYyIAB8f8XS0X9+vWjffv2/OMf/8h0KPVewudxdVl17ANoRjDl3P7JbJfkPir0\nOAN7R8q+G7PeLOC5FO1zAOAFBQU+cuTICo97auoOrAMjR2Y6gviyNS53xRZWtsaWrXG5K7awsjW2\nbI3LPTtiu+eeeyr9nywoKMhcj3M6/0enqe6VK1d6fn6+5+bmes+ePf2WW27xqVOnupmVr3PHHXf4\n0KFDvUOHDt68eXPv27ev33zzzRXq2W+//dzMKjyie583bdrkv/71r71Lly7evHlz79mzp8+aNatS\nD+/ChQs9Pz/f27Rp423btvV+/fr5tddeW+NxlMX89ttv+49+9CPPy8vz9u3b+2WXXebu7u+//76f\nfPLJ3rZtW+/UqZNfffXVFbYvLi52M/MFCxaUl40dO9Zbt27tH374oZ988sneunVrb9++vV9wwQVp\n75lOtMc5qTHO7r7DzA5KZps0MoIDTJm5c+fqU66IiEgVxowZw5gxYyqURfXU1b2FCyEmnmyu+6WX\nXuL4449nn332Yfr06ezcuZPp06ez1157VRjvO2/ePA488EBOPvlkmjZtykMPPcTEiRNxdyZMmADA\ntddeyy9+8QvatGnDpZdeirvTsWNHALZt20ZBQQEfffQREyZMoEuXLjz77LNcfPHFrF+/njlz5gDw\nxBNP8MMf/pBjjz2W2bNnA7BmzRqee+45fvWrX1V7LGXxnnHGGfTt25dZs2bxyCOPMGPGDNq1a8ct\nt9zCsGHDmDVrFvfccw+TJ0/m0EMP5aijjqq2ztLSUo477jgOO+wwrr76apYvX86cOXPo2bMn5557\nbvgXP1Wqy6rjPYC5wJXJbpdE/bE9zs2AHVQe93wn8PcU7TOrx1VlQad3XNkal7tiCytbY8vWuNwV\nW1jZGlu2xuWevbEl2lNX0yPU/+J0dsOnoe6RI0d669atff369eVl69at82bNmnlOTk552fbt2ytt\nO2LECO/Zs2eFsqrGOE+fPt3btGnj69atq1B+8cUXe7Nmzfy///2vu7uff/75vscee4Q6lmnTprmZ\n+YQJE8rLdu3a5V26dPEmTZr4VVddVV6+adMmb9mypY8fP768LF6P87hx4zwnJ8dnzJhRYV8DBgzw\nQw45JFSciUpLj3NEU+BMMzsWeBHYGpOI/yZEnVXyoJe7EBgGlI17tsjz61K5r0mTJpGXlxf3E3Um\nZVEoFWRrXKDYwsrW2LI1LlBsYWVrbNkaF2RfbAsXLmThwoWUlGTw3mfbt1eeBiVabQaYVzfoPITS\n0lJWrFjBqaeeWt4zDNC9e3eOP/54Hn744fKy5s2bl//+xRdfsGPHDgoKCli2bBmbN2+mTZs21e7r\n/vvvZ9CgQeTl5fHZZ5+Vlw8bNowrr7ySVatWMWbMGHbffXe2bNnC0qVLOe6445I+JjPjrLPOKn+e\nk5PDwIEDefDBBxk//ptbe+Tl5dG7d2/efffdhOqN7VkeNGgQd911V9LxpUOYxPlAoOws7RWzLNTQ\nCTNrBfTkm7v7dTezg4GN7v4BMAdYEEmgy6aja0nQ65wyGqohIiKSmLJOpjodqrFwYcU7wTzxRPCo\nSt++8Prr1dd52mlB8hzPqKjbSowZU6tPLxs2bGDbtm307Nmz0rLYsmeeeYapU6fy/PPP8+WXX5aX\nmxklJSU1Js5r167l1VdfpX37yrewMDM2bNgAwMSJE1m0aBEnnHAC++yzD8OHD+f0008vT6JLS0v5\n5JNPKmzfrl07mjVrVv68a9euFZbn5eWRm5tLu3btKpVv3Fjzvehyc3PZc889K5TtsccefP755zVu\nWxeSTpzdfUga4hgIrCRIvJ1gzmaABcCZ7v5/ZrYXcAXBVHOrgePc/ZN4lYmIiEgDFJu8Dh8eTKFU\nldzcmutctCh+7/Lvflf1NCZptG7dOo455hj69OnD3Llz6dKlC7vtthuPPPII11xzDaWlpTXWUVpa\nyrHHHstFF11UNgymgl69gn7P9u3bs3r1apYuXcpjjz3GY489xvz58xk7dizz58/ngw8+oFu3bpgZ\n7o6ZsXLlSgoKCsrratKkSaX645UBcWNJdNtsUasboJjZvoC7+4e1qcfdn6KGuxi6+03ATbXZT02y\ndaiGiIhItsmKoRq5uVDbb4qrGsqRSNKdhA4dOtCiRQveeeedSsvWrl1b/vtDDz3E119/zUMPPUTn\nzp3Ly1esWFFpu6puINKjRw+2bNnCkCE193U2bdqUE088kRNPPBGACRMmcOutt3LZZZfRuXNnli9f\nXmH9gw8+uMY6G7KkE2czywEuBX4LtI6UbSboJZ7h7jV/FMpSGqohIiKSmIwM1ajHcnJyGDZsGA88\n8ADr16+nU6dOALzzzjs8/vjj5es1bRqkZtE9yyUlJdx5552V6mzVqhWbNm2qVH766adz+eWXs2zZ\nMoYPH15hWUlJCa1bt6ZJkyZs3Lix0pCKfv36AfDVV1/RvHlzhg4dGu6AG6gwPc4zgLOA3wHPEIxL\nPhKYBuQCl6QqOBEREZEqpfPb4TTUPW3aNJYtW8YRRxzBhAkT2LlzJzfeeCP9+vVj9erVAAwfPpxm\nzZpx0kknce6557J582b+/Oc/07FjR9avX1+hvvz8fObNm8eMGTPo2bMnHTp0YMiQIUyePJklS5Zw\n0kknMW7cOPLz89m6dSuvvPIKixcvpri4mHbt2nH22WezceNGhg4dyr777ktxcTE33HAD/fv3p0+f\nPik//oYgTOI8Fjjbo+7iB7xsZh8SDKVQ4iwiIiLpV88S5wEDBvD4449zwQUXMGXKFLp06cL06dN5\n4403ePPNN4Fg/PHf/vY3Lr30UiZPnkynTp2YOHEie+65Z4UZLACmTJnC+++/z5/+9Cc2b97M4MGD\nGTJkCC1atGDVqlXMnDmTRYsW8de//pW2bdvSq1cvrrjiCvLy8gD4yU9+wq233srNN9/Mpk2b6NSp\nE2PGjGHq1Km1Os6qhpDElsdbL9FtMyXpW26b2XbgIHd/O6a8N7Da3au5oWd2KrvNZ0FBgcY4i4iI\nJCB6jPOqVatAt9wO7ZRTTuGNN97grbfeynQojVait9wO0+P8MvALIPaWMr+ILKu3NMZZREQkMRrj\nHE7Z2OEya9eu5dFHH60w77FkrzCJ84XAI2Z2DPAcwfRxRwBdgBNSGJuIiIhIg9K9e3fGjh1L9+7d\nKS4uZt68eeTm5jJ58uRMhyYJCDOP81Nm1gs4D/g2wcWBi4Gb3P2jFMcnIiIi0mCMGDGCe++9l/Xr\n19O8eXOOOOIIZs6cSY8ePTIdmiQgqcTZzJoCvwfucPcGdxGg5nEWERFJTFbM41wP3X777ZkOQWoh\nqcTZ3Xea2YXAX9IUT0ZpjLOIiEhiNMZZGqNq79ZXhRXA4FQHIiIiIiKSzcJcHPgYcKWZ9QMKga3R\nC2PmdxYRERERaRDCJM43RX7+Js4yB5qED0dEREREJDuFmVUjzPCOekEXB4qIiCRGFwdKY5TsrBrN\ngMeBn7v72vSElDm6OFBERCQxujhQGqOkeo/dfQdwUJpiERERERHJWmGGXdwFnJXqQEREREREslmY\niwObAmea2bHAi1SeVSPeRYMiIiIiIvVamMT5QKAo8nuvmGVeu3BEREREJBE333wzLVu2ZOzYsZkO\npdEIM6vGkHQEkg00q4aIiEhiNKtG5t100020b99eiXMdCtPjDICZ9QR6AKvcfZuZmbvX6x5nzaoh\nIiKSGM2qIY1R0hcHmtmeZrYCeBt4FNg7suh2M7s6lcGJiIiIlFuwAIqL4y8rLg6WZ2PdUZ588kkG\nDhxIixYt2H///bn11luZNm0aOTnfpGTz589n2LBhdOzYkdzcXA444ADmzZtXoZ5u3brx+uuv8+ST\nT5KTk0NOTg5Dhw4tX15SUsL5559P165dyc3NZf/992f27NnE9nHee++9DBw4kLZt25KXl8dBBx3E\nddddV+NxlMW8du1afvzjH7P77rvToUMHpkyZAsAHH3zA6NGjycvLY++992bOnDkVtt+xYwdTpkxh\n4MCB7L777rRu3ZqCggKefPLJCutNnTqVJk2asHLlygrl55xzDs2bN+fVV1+tMdZUCjOrxlxgB9AV\n+DKq/D5gRCqCEhEREalk8GA488zKCW5xcVA+eHB21h3x0ksvcfzxx/P5558zffp0zjrrLKZPn86D\nDz6ImZWvN2/ePPbbbz8uueQS5syZQ9euXZk4cSI333xz+TrXXnst++67L3369OHuu+/mrrvu4pJL\nLgFg27ZtFBQUcPfddzNu3Diuv/56jjrqKC6++GJ++9vfltfxxBNP8MMf/pA999yT2bNnM2vWLIYM\nGcJzzz1X47GUxXvGGWcAMGvWLA477DBmzJjBNddcw/Dhw9l3332ZNWsW+++/P5MnT+bpp58u3/6L\nL77gjjvuYMiQIcyePZvLL7+cTz/9lBEjRvDKK6+Ur3fZZZfRv39/zjrrLLZuDeajWLp0KbfffjvT\npk2jX79+YZoiPHdP6gGsBw6O/L4Z6B75vTuwJdn6suEBDAC8sLDQRUREJHGFhYVOMDnAAK+L/8Xv\nvec+ZEjwM97z2khn3e4+cuRIb926ta9fv768bN26dd6sWTPPyckpL9u+fXulbUeMGOE9e/asUHbg\ngQf6kCFDKq07ffp0b9Omja9bt65C+cUXX+zNmjXz//73v+7ufv755/see+wR6limTZvmZuYTJkwo\nL9u1a5d36dLFmzRp4ldddVV5+aZNm7xly5Y+fvz48rLS0lLfsWNHhTpLSkq8U6dOfvbZZ1cof+21\n17x58+b+s5/9zDdt2uSdO3f27373u75r165QsceT6Hkcpse5FRV7msu0A74KUZ+IiIhIYvbbD+64\nI+gFXrwYTjsNfvMb2LgRiooqPt54o+b63njjm/U3bgzqOu20oO4zzwz2td9+tQ67tLSUFStWMHr0\naDp27Fhe3r17d44//vgK6zZv3rz89y+++ILPPvuMgoIC3n33XTZv3lzjvu6//34GDRpEXl4en332\nWflj2LBh7Ny5k1WrVgGw++67s2XLFpYuXRrqmMyMs8765tYeOTk5DBw4EHdn/Pjx5eV5eXn07t2b\nd999t8K2TZsGl9q5O59//jlff/01AwcOpKioiGgHHHAAl19+ObfddhvHHXccGzduZMGCBRWGt9SV\nMBcH/hP4KXBZ5LmbWQ5wIbCyyq1EREREUmG//WDqVDj66OD5yJHx1+vbF15/vfq6TjstfoL9ve/B\nk0+mJGkG2LBhA9u2baNnz56VlsWWPfPMM0ydOpXnn3+eL7/8pq/SzCgpKaFNmzbV7mvt2rW8+uqr\ntG/fvtIyM2PDhg0ATJw4kUWLFnHCCSewzz77MHz4cE4//XSOO+44IEj2P/nkkwrbt2vXjmbNmpU/\n79q1a4XleXl55Obm0q5du0rlGzdurFC2YMEC5syZw5tvvsmOHTvKy7t3714p7smTJ3Pvvffywgsv\nMHPmTHr37l3ta5AuYRLnC4EVZjYQ2A2YDRxA0ON8ZApjq3Oajk5ERCQxGZ2OrrgYLr8c/vY3+OMf\ngyR6n30qr5ebW3NdixbB9u3fPP/oo6Duiy8OfqaoxzlR69at45hjjqFPnz7MnTuXLl26sNtuu/HI\nI49wzTXXUFpaWmMdpaWlHHvssVx00UWVLgYE6NUruA1H+/btWb16NUuXLuWxxx7jscceY/78+Ywd\nO5b58+fzwQcf0K1bN8wMd8fMWLlyJQUFBeV1NWnSpFL98cqACrHcddddjB8/nlNPPZULL7yQDh06\n0KRJE2bOnFmhZzr6dVm7di1AnV8QGC3MPM6vmVkv4BcEY5xbA4uBG939fymOr05pOjoREZHEZGw6\nurKL9coS2gEDajekom/finVfcEGQTKei7igdOnSgRYsWvPPOO5WWlSWEAA899BBff/01Dz30EJ07\ndy4vX7FiRaXtoi8ojNajRw+2bNnCkCE133qjadOmnHjiiZx44okATJgwgVtvvZXLLruMzp07s3z5\n8grrH3zwwTXWmYi//e1v9OjRg/vvv79CedmsHNHcnXHjxpGXl8ekSZOYMWMG3//+9xk9enRKYklG\nqMEh7l7i7jPc/XR3P8HdL63vSbOIiIhkudikGSqOea5qOrlM100w/nfYsGE88MADrF+/vrz8nXfe\n4fHHHy9/XjbuN7pnuaSkhDvvvLNSna1atWLTpk2Vyk8//XSee+45li1bVmlZSUkJu3btAqg0dAIo\nn6Xiq6++onnz5gwdOrTCIy8vL8Ejrl6TJk0qJf7/+te/4s7ocfXVV/P8889z2223ccUVV3DkkUcy\nYcKEuPGnW+gboIiIiIjUqaeeit/7W5bgPvVU+J7hdNYdMW3aNJYtW8YRRxzBhAkT2LlzJzfeeCP9\n+vVj9erVAAwfPpxmzZpx0kknce6557J582b+/Oc/07FjxwoJN0B+fj7z5s1jxowZ9OzZkw4dOjBk\nyBAmT57MkiVLOOmkkxg3bhz5+fls3bqVV155hcWLF1NcXEy7du04++yz2bhxI0OHDmXfffeluLiY\nG264gf79+9OnT59aHWtNTjrpJBYvXszo0aM58cQTeffdd7nllls44IAD2LJlS/l6a9asYcqUKYwf\nP54TTjgBCOa57t+/PxMmTOC+++5La5yVVDflRmN5oOnoREREQqnz6ejquZUrV3p+fr7n5ub6/vvv\n73fccYdfcMEF3rJly/J1Hn74Ye/fv7+3bNnSu3fv7ldddZXPnz/fc3Jy/D//+U/5eh9//LGPHDnS\n8/LyPCcnp8LUdFu3bvVLLrnEe/Xq5bm5ud6hQwc/6qijfO7cub5z5053d1+8eLGPGDHCO3Xq5Lm5\nub7ffvv5xIkT/eOPP67xOKZNm+Y5OTn+2WefVSgfN26ct23bttL6Rx99tB900EEVyq688krv1q2b\nt2jRwvPz8/3RRx/1cePGebdu3dw9mN7u0EMP9W9961v+xRdfVNj2uuuu85ycHF+0aFGNsSYi0fPY\nPM6g8cbGzAYAhYWFhRrjLCIikoSoMc757l5U0/pVacz/i0855RTeeOMN3nrrrUyH0mgleh7X/QR4\nIiIiIo3UV19VvOXF2rVrefTRRxO6kE8yL+kxzmb2D+BUd98UU94WeMDdh8bfUkRERKRx6969O2PH\njqV79+4UFxczb948cnNzmTx5cqZDkwSEuTjwaIL5m2PlAoNqFY2IiIhIAzZixAjuvfde1q9fT/Pm\nzTniiCOYOXMmPXr0yHRokoCEE2czOyjqaV8z6xT1vAkwAvgwVYGJiIiINDS33357pkOQWkimx3k1\nwdWGDvwjzvJtwC9TEZSIiIiISLZJJnHuBhjwLnAoEH3z8q+BDe6+K4Wx1TndcltERCQxGb3ltkiG\nJJw4u/t/Ir822Jk4dMttERGRxGTsltsiGRTqzoFm1ovgIsEOxCTS7n5F7cMSEREREckuYaajOwe4\nGfgUWE8w5rmMA0qcRUREJJQ1a9ZkOgRphBI978L0OF8KXOLus0JsKyIiIhLPpzk5Odt//OMf52Y6\nEGmccnJytpeWln5a3TphEuc9gEXhQhIRERGpzN3fN7PewF6ZjkUap9LS0k/d/f3q1gmTOC8ChgPz\nQkUlIiIiEkckaak2cRHJpDCJ8zvAdDM7DHgV2BG90N2vS0VgIiIiIiLZJEzi/DNgCzA48ojmgBJn\nEREREWlwkk6c3b1bOgIREREREclmoW9mYma7mVlvMws1F7SIiIiISH2SdOJsZi3N7HbgS+B1oGuk\n/Hoz+12K46sVM2thZsVmNjvTsYiIiIhI/Ramx/mPwMEEdw7cHlW+HDgjBTGl0iXA85kOQkRERETq\nvzCJ82jgF+7+NBXvGvg60CMlUaWAmfUEegOPZjoWEREREan/wiTO7YENccpbUTGRzrSrgIsBy3Qg\nIiIiIlL/hUmcXwROjHpeliyfDTwXJggzG2RmS8zsQzMrNbNRcdY5z8zeM7NtZva8mR1STX2jgLfc\n/Z2yojBxiYiIiIiUCTMjxu+Bx8ysb2T7X5vZAcDhVJ7XOVGtgNXAHcDfYhea2RnA1QRzSP8bmAQs\nNbNe7v5pZJ2JwDkEifxK4PtmdhrQBmhqZiXu/oeQ8YmIiIhIIxdmHuenzaw/8DuCOwcOB4qAw939\n1TBBuPvjwOMAZhavd3gScIu7/yWyzs8Jer3PBGZH6rgJuClqm99G1h0LHKCkWURERERqI9QczO6+\njqB3N+3MrBmQD8yM2r+b2XKCXm4RERERkbQLffMSM+sAdCBmnLS7v1LboGLsBTQBPo4p/5hgyZoW\n3AAAHn5JREFU1oxqufuCRHc0adIk8vLyKpSNGTOGMWPGJFqFiIhIg7Vw4UIWLlxYoaykpCRD0YjU\nvaQTZzPLBxYAfah80Z0TJLl1wUjxLB5z585lwIABqaxSRESkwYjXmVRUVER+fn6GIhKpW2F6nO8A\n3gbOIuj1TfcUdJ8Cu4COMeUdqNwLLSIiIiKSFmES5+7A96Kmeksrd99hZoXAMGAJlF9AOAy4LpX7\nKhuqoeEZIiIi1SsbtqGhGtKYmHtyHcZm9gDwV3evNG1c6CDMWgE9CYZfFAG/IZhSbqO7f2BmpxMM\nDzmXb6aj+z7wbXf/JAX7HwAUFhYWaqiGiIhIEqKGauS7e1Gm4xFJpzA9zmcDC8zsQOA1YEf0Qndf\nEqLOgQSJskceV0fKFwBnuvv/mdlewBUEQzZWA8elImkWEREREUlEmB7nkcBfgbZxFru719XFgSlT\n1uNcUFCgoRoiIiIJiB6qsWrVKlCPszQCYRLnYuBhYLq7N4iL8zRUQ0REJBwN1ZDGJKfmVSrZE5jb\nUJJmEREREZFEhEmcFwNDUh2IiIiIiEg2C3Nx4NvAH83sKOBVKl8cmNIp4uqSpqMTERFJjKajk8Yo\nzBjn96pZ7O7evXYh1T2NcRYREQlHY5ylMUm6x9ndu6UjEBERERGRbBZmjDMAZrabmfU2szDDPURE\nRERE6pWkk14zawlcD4yNFPUC3jWz64EP3f3KFMZXpzTGWUREJDEa4yyNUZgxztcCRwLnA48DB7n7\nu2Z2MjDN3b+T+jDTS2OcRUREwtEYZ2lMwgyzGA2c4e7Pm1l01v060CM1YYmIiIiIZJcwY5zbAxvi\nlLcCkuu+FhERERGpJ8Ikzi8CJ0Y9L0uWzwaeq3VEIiIiIiJZKMxQjd8Dj5lZ38j2vzazA4DDgcGp\nDK6u6eJAERGRxOjiQGmMkr44EMDMegC/Aw4GWgNFwCx3fzW14dUNXRwoIiISji4OlMYk1BzM7r4O\nOCfFsYiIiIiIZK2EEmcza5tohe7+RfhwRERERESyU6I9zpuoecYMi6zTpFYRiYiIiIhkoUQT5yFp\njUJEREREJMsllDi7+1PpDiQbaFYNERGRxGhWDWmMws6qMQg4F+gOnObuH5rZT4D33P3pFMeYdppV\nQ0REJBzNqiGNSdI3QDGz7wFLgW3AAKB5ZFEewRzPIiIiIiINTpg7B14K/NzdzwF2RJU/Q5BIi4iI\niIg0OGES597AqjjlJcDutQtHRERERCQ7hUmc1wM945QfBbxbu3BERERERLJTmMT5NuBaM/suwbzN\n+5jZj4CrgJtSGZyIiIiISLYIc8vtKwkS7hVAS4JhG18BV7n7DSmMrc5pOjoREZHEaDo6aYxCTUcH\nYGa7EQzZaA284e5bUhlYXdJ0dCIiIuFoOjppTML0OAPg7l8Db6QwFhERERGRrBVmjLOIiIiISKOj\nxFlEREREJAFKnEVEREREEpBU4mxmzczsDjPrlq6ARERERESyUVKJs7vvAE5NUywiIiIiIlkrzFCN\nB4HRqQ5ERERERCSbhZmObi0wxcyOBAqBrdEL3f26VAQmIiIiIpJNwiTOZwGbgPzII5oDSpxFRERE\npMFJOnF29wZ7YaBuuS0iIpIY3XJbGqPa3nK7G7DO3XemNKo6pltui4iIhKNbbktjkvTFgWbW0sxu\nB74EXge6RsqvN7PfpTg+EREREZGsEGZWjT8CBwNHA9ujypcDZ6QgJhERERGRrBPm4sDRwBnu/ryZ\nRY/zeB3okZqwRERERESyS5ge5/bAhjjlrQhm1RARERERaXDCJM4vAidGPS9Lls8Gnqt1RCIiIiIi\nWSjMUI3fA4+ZWd/I9r82swOAw4HBqQxORERERCRbJN3j7O5PA/0JkuZXgeHAx8Dh7l6Y2vBERERE\nRLJDmB5n3H0dcE6KYxERERERyVoJ9zibWY6ZXWRmz5jZC2Z2pZm1SGdwIiIiIiLZIpke598D04AV\nwDbg10BHYHzqw6o9MysGNhFcvLjR3YdlNiIRERERqc+SSZzHAhPd/VYAMzsGeMTMznL30rREVzul\nBOOut2U6EBERERGp/5K5OLAr8FjZE3dfTtCbu0+qg0oRI9x0eyIiIiIilSSTWDal4i22AXYAzVIX\nTkqVAk+a2b/M7IeZDkZERERE6rdkhmoYcKeZfRVVlgvMM7OtZQXufmqyQZjZIGAykA/sDYx29yUx\n65wHXAB0Al4GfunuL1RT7ZHuvt7MOgHLzexld3892dhERERERCC5HucFBLfaLol63AV8FFMWRitg\nNXAecW7bbWZnAFcDU4HvECTOS81sr6h1JprZS2ZWZGbN3X09QOTnowRJuYiIiIhIKAn3OLt72mbP\ncPfHgccBzMzirDIJuMXd/xJZ5+cEt/0+E5gdqeMm4KbI8pZm1trdt5hZa2AocF+64hcRERGRhi/U\nDVDqkpk1I+gtnllW5u5uZssJbvMdT0fg72bmQBPgVt3VUERERERqI+sTZ2AvguT345jyj4He8TZw\n9/cIbguelEmTJpGXl1ehbMyYMYwZMybZqkRERBqchQsXsnDhwgplJSVhR2mK1D/1IXGuihFnPHRt\nzJ07lwEDBqSyShERkQYjXmdSUVER+fm6jEgah/owz/GnwC6C4RfROlC5F1pEREREJC2yvsfZ3XeY\nWSEwDFgC5RcQDgOuS+W+yoZqaHiGiIhI9cqGbWiohjQm5p7S0Q7hgjBrBfQkGH5RBPwGWAlsdPcP\nzOx0gunwzgX+TTDLxveBb7v7JynY/wCgsLCwUEM1REREkhA1VCPf3YsyHY9IOmVLj/NAgkTZI4+r\nI+ULgDPd/f8iczZfQTBkYzVwXCqSZhERERGRRGRFj3OmlfU4FxQUaKiGiIhIAqKHaqxatQrU4yyN\ngBJnNFRDREQkLA3VkMakPsyqISIiIiKScUqcRUREREQSkC0XB2YFTUcnIiKSGE1HJ42RxjijMc4i\nIiJhaYyzNCYaqiEiIiIikgAlziIiIiIiCdAY5yga4ywiIpIYjXGWxkhjnNEYZxERkbA0xlkaEw3V\nEBERERFJgBJnEREREZEEKHEWEREREUmALg6MoosDRUREEqOLA6Ux0sWB6OJAERGRsHRxoDQmGqoh\nIiIiIpIAJc4iIiIiIglQ4iwiIiIikgAlziIiIiIiCdCsGlE0q4aIiEhiNKuGNEaaVQPNqiEiIhKW\nZtWQxkRDNUREREREEqDEWUREREQkAUqcRUREREQSoMRZRERERCQBSpxFRERERBKg6eiiaDo6ERGR\nxGg6OmmMNB0dmo5OREQkLE1HJ42JhmqIiIiIiCRAibOIiIiISAKUOIuIiIiIJECJs4iIiIhIApQ4\ni4iIiIgkQImziIiIiEgClDiLiIiIiCRAibOIiIiISAKUONcHCxdmOoL4sjUuUGxhZWts2RoXKLaw\nsjW2bI0Lsjs2kUZCiXOUSZMmMWrUKBZm25tTtsVTJlvjAsUWVrbGlq1xgWILK1tjy9a4IOtiW7hw\nIaNGjWLSpEmZDkWkzjTNdADZZO7cubrltoiISALGjBnDmDFjom+5LdLgKXGOtmZN1ctyc6Fv3+q3\nf+MN2L696uV77x08qrJtW/wYSkqgqCj4vU8faNGi6jr+97/gUZVMHke0xnwc0e1Zn48jWmM+juj2\nhPp7HLEa63HEtidkz3FUJ1vaQ6Shc/dG/wAGAF4I7lU9+vb1GvXtW/X24D51avXbv/Za9duD+9FH\nu99zT9V1TJ1a/fapOI5evdxHjgx+Hznym0dZXIkcx2uvVR9Dssdxzz0VYwH31q3T3x61PY7Wratv\nT/e6O6+qa9O6OK+ijyNeex59dN2fV/F07lx9HaeeWv32dXVeVdee7nVzXkUfR7w27dWrbs+reGbP\nzo7zKpH33eras67fd6Pas3DQIAccGOCe+f/peuiRzoe5e0YT92xgZgOAwsK77mJAnz7xV8pkD86k\nSTB3bvB7tvR87L03jBoFS5ZUXp4NPR+jRsGVV2Znj1p0e2Zbz2BVbZrpnsFRo+C++zJ/XkHl44hu\nT8iuntqq2hMy3+M8ahTcckv29TjHtidkz/vuuedW3Z4ZfN+NGqqR7+5FlVYQaUA0VCNanz5QmzHO\ntf0Kq0WL+PvPy0s8rpr+SSQiXceRjIZ8HMm0J2TvcSSroR5Hsu2ZrceRrIZ6HMm2J2TncYSRiuMQ\naeA0q4aIiIiISAKUONcHY8ZkOoL4sjUuUGxhZWts2RoXKLawsjW2bI0Lsjs2kUZCY5yJGuNcWKjp\n6ERERJKgMc7SmKjHWUREREQkAUqcRUREREQS0GATZzPbz8z+YWavm9nLZlbNHDwiIiIiItVryNPR\n3Qn83t2fNbPdga8yHI+IiIiI1GMNssfZzPoCX7v7swDuvsndSzMcltShhQsXZjoESSG1Z8OjNhWR\n+qhBJs7A/sBWM3vQzF40s4szHZDULf1TbljUng2P2lRE6qOsSJzNbJCZLTGzD82s1MxGxVnnPDN7\nz8y2mdnzZnZINVU2A44CJgBHAMea2bA0hS8iIiIijUBWJM5AK2A1cB5QaWJpMzsDuBqYCnwHeBlY\namZ7Ra0z0cxeMrMi4APgBXf/yN2/Bh4F+qf/MFIj1T0xtakvmW0TWbemdapbXtWybO+5Skd8YetM\ndXvWtJ7aM711JrtdOv9G62t7gt5zk11WH9pUJF2yInF298fdfYq7PwBYnFUmAbe4+1/c/U3g58CX\nwJlRddzk7t9x9wHAi0BHM8szsxygAFiT/iNJDb2JJ7cs29/E62uipcQ5vvranomur8Q5s/XpPVck\nu2X9rBpm1gzIB2aWlbm7m9ly4PB427j7LjP7PfDPSNEyd3+0mt3kAqxZkx25dUlJCUVFqbv5Um3q\nS2bbRNataZ3qlle1LF55ql/D2khHLGHrTHV71rSe2jO9dSa7XTr/RlNVngl6z63d32jU/87cGoMW\nqeey7pbbZlYKjHb3JZHnewMfAoe7+7+i1psFFLh73OQ5yX3+ELi7tvWIiIg0Yj9y93syHYRIOmV9\nj3M1jDjjoUNaCvwIKAa2p6hOERGRxiAX2I/gf6lIg1YfEudPgV1Ax5jyDsDHqdiBu38G6FOyiIhI\nOM9mOgCRupAVFwdWx913AIVA+XRyZmaR5/pDFREREZE6kRU9zmbWCujJNzNqdDezg4GN7v4BMAdY\nYGaFwL8JZtloSXBbbRERERGRtMuKiwPNbDCwkspjlhe4+5mRdSYCFxIM2VgN/NLdX6zTQEVERESk\n0cqKxFlEREREJNtl/RjnbGJmLcys2MxmZzoWqZ3IzXFeMLMiM3vFzM7OdEwSnpnta2Yrzex1M1tt\nZt/PdExSO2a22Mw2mtn/ZToWqR0zO8nM3jSzt8zsrEzHI1Ib6nFOgpn9gWAs9vvufmGm45HwIheY\nNnf37WbWAngdyHf3zzMcmoRgZp2ADu7+ipl1JLigeH9335bh0CSkyBC+1sBYdz890/FIOGbWBHgD\nGAxsJvjbPMzdN2U0MJGQ1OOcIDPrCfQGqrsDodQTHiibs7tF5Ge8271LPeDu6939lcjvHxNMY9ku\ns1FJbbj7U8CWTMchtXYo8Frkb3Qrwf/Q4zIck0hoSpwTdxVwMUquGozIcI3VwPvAn9x9Y6Zjktoz\ns3wgx90/zHQsIsI+BHf/LfMR0DlDsYjUWoNMnM1skJktMbMPzazUzEbFWec8M3vPzLaZ2fNmdkg1\n9Y0C3nL3d8qK0hW7xJfqNgVw9xJ37w90A35kZu3TFb9UlI72jGzTDlgAnJOOuCW+dLWnZFaK2jXe\n/0uNEZV6q0EmzkArginrziPOH6iZnQFcDUwFvgO8DCw1s72i1ploZi+ZWRHB2KwfmNm7BD3PZ5vZ\npek/DImS0jY1s+Zl5e7+CfAKMCi9hyBRUt6eZrYb8Hdgprv/qy4OQsql7e9TMqrW7UrQ27xv1PPO\nwP/SFbBIujX4iwPNrBQY7e5LosqeB/7l7r+OPDfgA+A6d692xgwzGwscoIsDMycVbRq5gGyru28x\nszzgaeAH7v56nRyElEvV36iZLQTWuPsVdRC2VCGV77lmdjRwnruflt6opSZh2zX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gauge_opt_npboot_gatesets = pygsti.drivers.gauge_optimize_gs_list(nonparam_boot_gatesets, gs_mc2gst,\n", " plot=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Non-parametric bootstrapped error bars, with 10 resamples\n", "\n", "Error in rho vec:\n", "Fully Parameterized spam vector with length 4\n", " 0\n", " 0\n", " 0.01\n", " 0\n", "\n", "\n", "Error in E vec:\n", "Fully Parameterized spam vector with length 4\n", " 0\n", " 0.01\n", " 0.01\n", " 0\n", "\n", "\n", "Error in Gi:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0 0 0 0\n", " 0 0 0 0\n", " 0 0 0 0\n", "\n", "\n", "Error in Gx:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0 0 0.01 0.01\n", " 0.01 0.03 0 0\n", " 0 0.02 0 0\n", "\n", "\n", "Error in Gy:\n", "Fully Parameterized gate with shape (4, 4)\n", " 0 0 0 0\n", " 0.01 0.01 0.03 0.01\n", " 0 0.03 0 0.03\n", " 0.01 0.01 0.02 0.01\n", "\n" ] } ], "source": [ "npboot_mean = pygsti.drivers.to_mean_gateset(gauge_opt_npboot_gatesets, gs_mc2gst)\n", "npboot_std = pygsti.drivers.to_std_gateset(gauge_opt_npboot_gatesets, gs_mc2gst)\n", "\n", "#Summary of the error bars\n", "print(\"Non-parametric bootstrapped error bars, with\", numGatesets, \"resamples\\n\")\n", "print(\"Error in rho vec:\")\n", "print(npboot_std['rho0'], end='\\n\\n')\n", "print(\"Error in E vec:\")\n", "print(npboot_std['E0'], end='\\n\\n')\n", "print(\"Error in Gi:\")\n", "print(npboot_std['Gi'], end='\\n\\n')\n", "print(\"Error in Gx:\")\n", "print(npboot_std['Gx'], end='\\n\\n')\n", "print(\"Error in Gy:\")\n", "print(npboot_std['Gy'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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juHTepYWdiZQVJWEiIiJFMHkyzHuknSVH1bPo4Km8ackULnj7BWmHJSlSx3wR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\nD2NPUQQDCQMAGEgYAMBAwgAABhIGADAIpIbuvf9lqxYAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loglog(npboot_std.to_vector(),pboot_std.to_vector(),'.')\n", "loglog(np.logspace(-4,-2,10),np.logspace(-4,-2,10),'--')\n", "xlabel('Non-parametric')\n", "ylabel('Parametric')\n", "xlim((1e-4,1e-2)); ylim((1e-4,1e-2))\n", "title('Scatter plot comparing param vs. non-param bootstrapping error bars.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }