two-qubit-state-tomo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-Qubit State Tomography #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing a few Python 2/3 compatability features and configuring Jupyter Notebook to use inline plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "from future.builtins import next\n",
    "from IPython.display import display, Math, Latex\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we import a few Python standard library packages that will make our lives much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools as it\n",
    "import pickle\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also import the scientific computing libraries that we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import ipyparallel\n",
    "import qinfer as qi\n",
    "import qutip as qt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finish the preamble by importing and configuring plotting support."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notation ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use QuTiP to define the Pauli group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Id, X, Y, Z = qt.qeye(2), qt.sigmax(), qt.sigmay(), qt.sigmaz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then expand the unitary operators $U_{\\theta,\\mu} = \\left.\\theta\\mu\\right)_x$, using the identity that $e^{-i \\theta \\sigma_\\mu} = \\cos(\\theta / 2)  \\mathbb{1} - \\sin(\\theta / 2) \\sigma_\\mu$. We will use these below to define measurements of our system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pi_x   = np.cos(np.pi/2.) * Id - 1.j * np.sin(np.pi/2.) * X\n",
    "pi_x_2 = np.cos(np.pi/4.) * Id - 1.j * np.sin(np.pi/4.) * X\n",
    "pi_y_2 = np.cos(np.pi/4.) * Id - 1.j * np.sin(np.pi/4.) * Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll define the action of a unitary $U$ on an operator $X$ as $U \\bullet X \\mathrel{:=} U X U^\\dagger$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def act(U, H):\n",
    "    return U * H * U.dag()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Single Qubit Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start analyzing the single-qubit data by defining a basis for representing Hermitian operators as well as a QInfer model and prior distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <strong>TomographyBasis:</strong>\n",
       "                dims=$2$\n",
       "            <p>\n",
       "                \\begin{equation}\n",
       "                    \n",
       "                𝟙 =                   \n",
       "                \\left(\\begin{matrix}\n",
       "                    0.707&0\\\\0&0.707\n",
       "                \\end{matrix}\\right)\n",
       "                ,\n",
       "                \\sigma_x =                   \n",
       "                \\left(\\begin{matrix}\n",
       "                    0&0.707\\\\0.707&0\n",
       "                \\end{matrix}\\right)\n",
       "                ,\n",
       "                \\sigma_y =                   \n",
       "                \\left(\\begin{matrix}\n",
       "                    0&-0.707\\mathrm{i}\\\\0.707\\mathrm{i}&0\n",
       "                \\end{matrix}\\right)\n",
       "                ,\n",
       "                \\sigma_z =                   \n",
       "                \\left(\\begin{matrix}\n",
       "                    0.707&0\\\\0&-0.707\n",
       "                \\end{matrix}\\right)\n",
       "                \n",
       "                \\end{equation}\n",
       "            </p>\n",
       "            "
      ],
      "text/plain": [
       "<TomographyBasis pauli_basis dims=[2] at 0x10d305d10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basis = qi.tomography.pauli_basis(1)\n",
    "model = qi.BinomialModel(qi.tomography.TomographyModel(basis))\n",
    "prior = qi.tomography.GinibreDistribution(basis)\n",
    "basis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Critically, our new basis is a basis of Hermitian operators, such that the representation of any other Hermitian operator is a real vector in this basis. If we attempt to expand a non-Hermitian operator, however, this basis will only represent the Hermitian part. For instance, the operator $U_{\\pi / 2, y} = \\frac{1}{\\sqrt{2}} (\\mathbb{1} - i \\sigma_y)$ defined above is not Hermitian due to the factor of $i$ in the coefficient of $\\sigma_y$. To wit, representing $U_{\\pi / 2, y}$ in our tomography basis keeps only the identity component: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basis.state_to_modelparams(pi_y_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is wholly consistent, however, with the use of our tomography basis to represent Born's rule. That is, we are interested in the probability $\\Tr(E \\rho)$ of observing a measurement effect $E$ for the hypothetical state $\\rho$. In our case, $E$ is some rotation of a fixed *fiducial measurement*, which we take to be $E_0 = |0\\rangle \\langle0|$ by convention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & 0.0\\\\0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 1.  0.]\n",
       " [ 0.  0.]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiducial_measurement = basis.modelparams_to_state(np.array([1,0,0,1])/np.sqrt(2))\n",
    "fiducial_measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then define our measurements in terms of the unitary rotations $U$ which transform $E_0$ into each measurement effect. For later convienence in making plots, we also assign a name to each measurement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "single_measurement_list = [Id, pi_x, pi_x_2, pi_y_2]\n",
    "single_measurement_labels = [r'\\mathbb{1}', r'U_{\\pi, x}', r'U_{\\pi / 2, x}', r'U_{\\pi / 2, y}']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this in place, we can then convert each of the measurements defined above by unitary transformations into measurement effects expanded in our tomography basis. In particular, we let $E_i = U^\\dagger_i E_0 U_i$ for each unitary operator $U_i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expparams = np.empty((len(single_measurement_list),), dtype=model.expparams_dtype)\n",
    "\n",
    "expparams['meas'] = [\n",
    "    basis.state_to_modelparams(act(op.dag(), fiducial_measurement))\n",
    "    for op in single_measurement_list\n",
    "]\n",
    "expparams['n_meas'] = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.00000000e+00,   0.00000000e+00,   1.00000000e+00],\n",
       "       [  0.00000000e+00,   0.00000000e+00,  -1.00000000e+00],\n",
       "       [  0.00000000e+00,   1.00000000e+00,   1.57009246e-16],\n",
       "       [ -1.00000000e+00,   0.00000000e+00,   1.57009246e-16]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(2) * expparams['meas'][:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To performance test over this choice, we define a heuristic that cycles over each of the four allowed measurement operators above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cycle_heuristic(expparams):\n",
    "    \n",
    "    def heuristic(updater):\n",
    "        return partial(next, it.cycle(expparams[:, np.newaxis]))\n",
    "    \n",
    "    return heuristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jcolless/anaconda/lib/python2.7/site-packages/qinfer/utils.py:268: ApproximationWarning: Numerical error in covariance estimation causing positive semidefinite violation.\n",
      "  warnings.warn('Numerical error in covariance estimation causing positive semidefinite violation.', ApproximationWarning)\n"
     ]
    }
   ],
   "source": [
    "perf = qi.perf_test_multiple(50,\n",
    "    model, 500, prior,\n",
    "    len(expparams), cycle_heuristic(expparams),\n",
    "    progressbar=qi.IPythonProgressBar\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113d63690>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qk4QghAMpL0V9BLz9Kp6Kar95BGZN8dSQIDr5ujHn6wz25ckcBVGVJAQhHIxq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\nQgCSEIQQQlSQhCCEEAKA/wfh7A5YFmJTyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d53b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.median(perf['loss'], axis=0))\n",
    "plt.plot(np.mean(perf['loss'], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GS_Single_Qubit_data = pickle.load(open(\"GS_Single_Qubit_data\",\"r\"))\n",
    "exp_data=GS_Single_Qubit_data[\"Measurements\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 32)\n"
     ]
    }
   ],
   "source": [
    "print(np.array(exp_data).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n",
      "Qobj data =\n",
      "[[ 1.  0.]\n",
      " [ 0.  0.]]\n",
      "[[ 0.70710678  0.          0.          0.70710678]]\n"
     ]
    }
   ],
   "source": [
    "n_shots = 100\n",
    "number_of_batches = 1\n",
    "expparams['n_meas'] = n_shots // number_of_batches\n",
    "\n",
    "true_state = qt.Qobj([[1, 0],[0, 0]])\n",
    "\n",
    "true_mps = basis.state_to_modelparams(true_state)[np.newaxis, :]\n",
    "# exp_data=np.round(np.multiply(GS_2[0:32],expparams['n_meas'][0]*number_of_batches))\n",
    "\n",
    "print(true_state)\n",
    "print(true_mps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ideal_data = [0] * 4\n",
    "\n",
    "for batch in range(number_of_batches):\n",
    "    i=0\n",
    "    for experiment in expparams[:, np.newaxis]:\n",
    "        datum = model.simulate_experiment(true_mps, experiment)\n",
    "        ideal_data[i]+=datum\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11063c090>,\n",
       "  <matplotlib.axis.XTick at 0x11064f210>,\n",
       "  <matplotlib.axis.XTick at 0x100b01290>,\n",
       "  <matplotlib.axis.XTick at 0x1106afa10>],\n",
       " <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uqNSlUiZzJMQ2x19Yt7N1+9ka/P/9b+jYFe/2X2NitCa6SF1SqUulTNNmmKt+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d30d210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixed_data=[0, 0, 0, 0 ]\n",
    "for i in range(100):\n",
    "    fixed_data += exp_data[i][0:4]\n",
    "\n",
    "plt.plot(fixed_data)\n",
    "plt.xticks(range(4), map(\"${}$\".format, single_measurement_labels), fontsize='x-large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x110922f90>,\n",
       "  <matplotlib.axis.XTick at 0x1109dbf50>,\n",
       "  <matplotlib.axis.XTick at 0x110a295d0>,\n",
       "  <matplotlib.axis.XTick at 0x110a29c10>],\n",
       " <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EFGrS3bBjM/yw3sUVehZH7kMPUFunefHro3x1ujH4vVfGSGNw0S4k7D3A8K7B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d30d250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fixed_data)\n",
    "plt.plot(np.divide(ideal_data, 1))\n",
    "\n",
    "plt.xticks(range(4), map(\"${}$\".format, single_measurement_labels), fontsize='x-large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jcolless/anaconda/lib/python2.7/site-packages/qinfer/utils.py:268: ApproximationWarning: Numerical error in covariance estimation causing positive semidefinite violation.\n",
      "  warnings.warn('Numerical error in covariance estimation causing positive semidefinite violation.', ApproximationWarning)\n"
     ]
    }
   ],
   "source": [
    "updater = qi.SMCUpdater(model, 10000, prior)\n",
    "\n",
    "# Pick out \"rows\" of the data corresponding to looping\n",
    "# over the four measurements defined above.\n",
    "for data_repetition, _ in zip(np.round(n_shots * np.array(exp_data)[:, :4] / number_of_batches), range(50)):\n",
    "    for experiment, datum in zip(expparams[:, np.newaxis], data_repetition):\n",
    "        updater.update(datum, experiment)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rho_est = basis.modelparams_to_state(updater.est_mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8611696376304074"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(rho_est ** 2).tr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.918 & (-0.055-0.055j)\\\\(-0.055+0.055j) & 0.082\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 0.91776296+0.j         -0.05541753-0.05466105j]\n",
       " [-0.05541753+0.05466105j  0.08223704+0.j        ]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rho_est"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "particle_purities = np.array([\n",
    "    (rho ** 2).tr()\n",
    "    for rho in basis.modelparams_to_state(updater.particle_locations)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x112390090>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zJmbOnOmVUERE5J8GdCNBa2srampqcOPGDYii6Hz/kUce8VowGhg+T4eIlMzjovPdd99h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a76510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(particle_purities, weights=updater.particle_weights, bins=40)\n",
    "plt.xlabel(r'$\\operatorname{Tr}(\\rho^2)$')\n",
    "plt.ylabel(r'$\\Pr(\\rho^2 | \\mathsf{data})$')\n",
    "plt.xlim(xmax=1)\n",
    "\n",
    "ylim = plt.ylim()\n",
    "plt.vlines(np.mean(particle_purities), *ylim, label=r'$\\mathbb{E}[\\operatorname{Tr}(\\rho^2)]$')\n",
    "plt.vlines((rho_est ** 2).tr(), *ylim, linestyle='--', label=r'$\\operatorname{Tr}(\\mathbb{E}[\\rho]^2)$')\n",
    "plt.ylim(*ylim)\n",
    "plt.legend(ncol=2, bbox_to_anchor=(1, 1.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rho_x, rho_y, rho_z = np.sqrt(2) * updater.particle_locations.T[1:]\n",
    "rho_r = np.sqrt(rho_x ** 2 + rho_y ** 2 + rho_z ** 2)\n",
    "rho_theta = np.arccos(rho_z / rho_r)\n",
    "rho_phi = np.arctan2(rho_y, rho_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pq7OzUxkZGfrqV7+qf/iHf1B6+vCb5Tz33HOTDgiBQEAulyu87XK5dOLECdMYt9s9bEwg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\nYPL/A6ICghVJU5WmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112290910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(8, 8))\n",
    "\n",
    "plt.sca(axes[0][0])\n",
    "plt.scatter(rho_x, rho_y)\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(r'$\\rho_x$')\n",
    "plt.ylabel(r'$\\rho_y$')\n",
    "plt.gca().set_aspect('equal')\n",
    "\n",
    "plt.sca(axes[0][1])\n",
    "plt.scatter(rho_z, rho_y)\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.gca().set_aspect('equal')\n",
    "\n",
    "plt.sca(axes[1][1])\n",
    "plt.scatter(rho_z, rho_x)\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(r'$\\rho_z$')\n",
    "plt.ylabel(r'$\\rho_x$')\n",
    "plt.gca().set_aspect('equal')\n",
    "\n",
    "fig.delaxes(axes[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.42299992e-03,   2.69167626e-02,   9.95989185e-02,\n",
       "          2.38265774e-01,   2.79465314e-01,   2.23051226e-01,\n",
       "          1.00715510e-01,   2.31136247e-02,   3.29669544e-03,\n",
       "          1.53175053e-04]),\n",
       " array([ 0.02975121,  0.03593521,  0.04211921,  0.04830321,  0.0544872 ,\n",
       "         0.0606712 ,  0.0668552 ,  0.0730392 ,  0.0792232 ,  0.08540719,\n",
       "         0.09159119]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBD8AGIbgBwDDEPwAYBiCHwAM8/8Adyi1/RUBEEMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11311edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rho_theta / np.pi, weights=updater.particle_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.38071969e-06,   7.70665776e-04,   2.93573954e-02,\n",
       "          2.42682377e-01,   4.72957196e-01,   2.27100470e-01,\n",
       "          2.63187553e-02,   7.99933628e-04,   7.77869385e-06,\n",
       "          4.74402217e-08]),\n",
       " array([ 0.5911659 ,  0.62714376,  0.66312162,  0.69909948,  0.73507734,\n",
       "         0.77105521,  0.80703307,  0.84301093,  0.87898879,  0.91496665,\n",
       "         0.95094451]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7WV0ul86ePSvLsnT27FmVl5erpKSk4OY0Xc5cspOzu7tbd9xxhyRpzJgxOn78uE6cOFFw\n85kup1OKptwvdxuEeDyeMqanp0enTp3SmjVrtGzZMn3zzTe29y2UrBetW7dOy5YtS36iN185Lzp3\n7py6urp07733XvW++cx5US7m027WBx54QEeOHNGTTz6p559/XvPmzVNJSUnBzWm6nBcVyjU6fvx4\nfffdd5L+Kdnjx48rHo8X3Hymy3lRtuezqNbcMxkaGtKhQ4e0evVqnT9/XqtWrdLEiRPzHeuy0mUN\nBAJat26dvF6vBgYG9PLLLysQCGjq1Kl5zfvDDz9o0qRJKi8vz2uOTC6Xs9Dm88cff9T48eP14osv\nqre3V+vWrdPkyZPzlieddDnLysoKak4bGhq0detWLV26VOPGjdOtt96a839h2HGlnE7MZ9GUu53b\nIPh8Po0cOVKlpaUqLS3VlClTdPjwYfl8voz7FkrWQCCQHDtq1Cjdc889ikajjvzFsZPzovb2ds2c\nOfOa9s1nzov7S87Pp92sO3bsUENDg1wulyorKzV69GgdPXq04OY0Xc4JEyYU1DVaVlaW/OCkZVla\ntGiRRo8erfPnzxfUfKbLeXF/KbvzWXg/3tKwexuE3377TUNDQzp37pyi0ajGjBlja99CyXr27Fmd\nOXNGknT27Fn99NNPyf9Rz0dO6Z93Tezfvz/luVzO6XBy5nI+7Wb1+/36+eefJUknTpzQ0aNHNXr0\n6IKb03Q5C+0aPX36tAYHByVJX375paZMmaKysrKCm890OZ2az6L6ENPevXv17rvvJm+D8Mgjj6Tc\nBkH65y1xO3bsUElJie6//349+OCDafctxKy9vb1av369pH+WbmbOnOloVjs5v/76a3V1dWnJkiUZ\n9y20nLmeTztZ4/G4Nm/enPydBw8//LBmzZqVdt9Cy1lo1+iBAwe0adMmSVJVVZWeeuqp5LJcIc1n\nupxOzWdRlTsAwJ6iWZYBANhHuQOAgSh3ADAQ5Q4ABqLcAcBAlDsAGIhyBwADUe4AYKD/A0IaPJ7r\n69g/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b48350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rho_phi / np.pi, weights=updater.particle_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.91776296,  0.08223704,  0.55466105,  0.55541753]]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.underlying_model.likelihood(1, updater.est_mean()[None, :], expparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0 & 0.0\\\\0.0 & 1.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 1.  0.]\n",
       " [ 0.  1.]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_measurement_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x111ede150>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IvnElq74/zaoGvejXzJ/4aJvRJQkXJQHvoLo29KVRQB2WqIbYA2xFU/vJBN1O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bac290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fixed_data / 100, label='Exp. Data')\n",
    "plt.plot(model.underlying_model.likelihood(1, updater.est_mean()[None, :], expparams)[0, 0, :], label='Reconstructed f/ Est.')\n",
    "\n",
    "plt.xticks(range(4), map(\"${}$\".format, single_measurement_labels), fontsize='x-large')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-Qubit Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "basis = qi.tomography.pauli_basis(2)\n",
    "model = qi.BinomialModel(qi.tomography.TomographyModel(basis))\n",
    "fid_prior = qi.tomography.GinibreDistribution(basis, rank=2)\n",
    "# prior = qi.tomography.GADFLIDistribution(\n",
    "#     fid_prior,\n",
    "#     mean=fiducial_state\n",
    "# )\n",
    "prior = fid_prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 1.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiducial_measurement = basis.modelparams_to_state(np.array([1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1])/np.sqrt(4))\n",
    "fiducial_measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.990 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.070 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.100 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.006\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 0.99033414  0.          0.          0.        ]\n",
       " [ 0.          0.07023314  0.          0.        ]\n",
       " [ 0.          0.          0.10012892  0.        ]\n",
       " [ 0.          0.          0.          0.00576184]]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FullMeasurementList=[]\n",
    "single_measurement_list = [Id, pi_x, pi_x_2, pi_y_2]\n",
    "single_measurement_labels = [r'\\mathbb{1}', r'U_{\\pi, x}', r'U_{\\pi / 2, x}', r'U_{\\pi / 2, y}']\n",
    "\n",
    "for item1 in single_measurement_list:\n",
    "    for item2 in single_measurement_list:\n",
    "        FullMeasurementList.append(qt.tensor(item1,item2))\n",
    "\n",
    "for item1 in single_measurement_list:\n",
    "    for item2 in single_measurement_list:\n",
    "        FullMeasurementList.append((qt.tensor(item1,item2)).conj())\n",
    "\n",
    "beta_vec = np.load(\"GS_cal_betas.npy\",\"r\")  \n",
    "# beta_vec=[0.25,.25,.25,.25]\n",
    "fiducial_measurement = (beta_vec[0]*qt.tensor(Id, Id) + beta_vec[1]*qt.tensor(Z, Id) + beta_vec[2]*qt.tensor(Id,Z) + beta_vec[3]*qt.tensor(Z,Z))\n",
    "fiducial_measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expparams = np.empty((len(FullMeasurementList),), dtype=model.expparams_dtype)\n",
    "\n",
    "expparams['meas'] = [\n",
    "    basis.state_to_modelparams(act(op.dag(), fiducial_measurement))\n",
    "    for op in FullMeasurementList\n",
    "]\n",
    "expparams['n_meas']= 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "perf = qi.perf_test_multiple(50,\n",
    "    model, 2000, prior,\n",
    "    len(expparams), cycle_heuristic(expparams),\n",
    "    progressbar=qi.IPythonProgressBar\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11233cbd0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PcsWot5AxHPXI6E78de0BFq7dy5INudwwKJrzOoc0XdvHsMGFk2FzJuUf/JvK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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x100dea5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.median(perf['loss'], axis=0))\n",
    "plt.plot(np.mean(perf['loss'], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GS_Two_Qubit_data = np.load(\"GS.npy\",\"r\")\n",
    "# exp_data=GS_Two_Qubit_data[\"Measurements\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 1.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]\n",
       " [ 0.  0.  0.  0.]]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_shots = 1000\n",
    "number_of_batches = 1000\n",
    "expparams['n_meas'] = n_shots // number_of_batches\n",
    "\n",
    "true_state = qt.Qobj([[1, 0, 0, 0],[0, 0, 0, 0],[0, 0, 0, 0],[0, 0, 0, 0]])\n",
    "\n",
    "true_mps = basis.state_to_modelparams(true_state)[np.newaxis, :]\n",
    "# exp_data=np.round(np.multiply(GS_2[0:32],expparams['n_meas'][0]*number_of_batches))\n",
    "\n",
    "true_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 32)\n",
      "(100, 32)\n",
      "(32,)\n",
      "[ 0.99052003  0.09727439  0.52586172  0.52926123  0.07074867  0.00591315\n",
      "  0.0329489   0.0395867   0.55252029  0.06231337  0.30390637  0.2930666\n",
      "  0.55598727  0.0561174   0.29979088  0.28883762  0.99143358  0.11358204\n",
      "  0.53787663  0.55281351  0.08080395  0.00646357  0.04653325  0.03603298\n",
      "  0.55571186  0.05175505  0.30473486  0.27731067  0.54088354  0.05283148\n",
      "  0.26516751  0.29567854]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(exp_data).shape)\n",
    "print(np.array((E1_Two_Qubit_data.tolist()*100)).reshape(100,32).shape)\n",
    "print(np.array(E1_Two_Qubit_data).shape)\n",
    "\n",
    "new_data=np.array((GS_Two_Qubit_data.tolist()*100)).reshape(100,32)\n",
    "print(new_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1142bb390>]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8dInZNQmCxAxMkGmH5SrkpLZvCjGjo7crmPZov/rFUp0TlRvIYUomQE/4QWzY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\nwHRw/0qBa01ApRH5kO5Bp91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ktPV0jKaaoFFcX/Fernrc3y6izgzPoGM78qGX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142bb350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal_data = [0] * 32\n",
    "\n",
    "for batch in range(number_of_batches):\n",
    "    i=0\n",
    "    for experiment in expparams[:, np.newaxis]:\n",
    "        datum = model.simulate_experiment(true_mps, experiment)\n",
    "        ideal_data[i]+=datum\n",
    "        i+=1\n",
    "\n",
    "\n",
    "plt.plot(np.divide(ideal_data,1000.))\n",
    "plt.plot(GS_Two_Qubit_data)\n",
    "# plt.xticks(range(4), map(\"${}$\".format, single_measurement_labels), fontsize='x-large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "updater = qi.SMCUpdater(model, 5000, prior)\n",
    "\n",
    "# Pick out \"rows\" of the data corresponding to looping\n",
    "# over the four measurements defined above.\n",
    "for data_repetition in np.round(n_shots * np.array(new_data) / number_of_batches):\n",
    "    for experiment, datum in zip(expparams[:, np.newaxis], data_repetition):\n",
    "        updater.update(datum, experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rho_est = basis.modelparams_to_state(updater.est_mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.934 & (0.012-0.147j) & (-0.059-0.055j) & (-0.020-0.159j)\\\\(0.012+0.147j) & 0.027 & (0.008-0.011j) & (0.024-0.005j)\\\\(-0.059+0.055j) & (0.008+0.011j) & 0.010 & (0.010+0.009j)\\\\(-0.020+0.159j) & (0.024+0.005j) & (0.010-0.009j) & 0.030\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[ 0.93353487+0.j          0.01189607-0.14714816j -0.05922075-0.05510404j\n",
       "  -0.01985598-0.1586937j ]\n",
       " [ 0.01189607+0.14714816j  0.02733045+0.j          0.00797157-0.01085906j\n",
       "   0.02418450-0.00548054j]\n",
       " [-0.05922075+0.05510404j  0.00797157+0.01085906j  0.00958082+0.j\n",
       "   0.01005178+0.0087289j ]\n",
       " [-0.01985598+0.1586937j   0.02418450+0.00548054j  0.01005178-0.0087289j\n",
       "   0.02955386+0.j        ]]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rho_est\n",
    "# print((rho_est ** 2).tr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "particle_purities = np.array([\n",
    "    (rho ** 2).tr()\n",
    "    for rho in basis.modelparams_to_state(updater.particle_locations)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118da9f50>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p6br44ov16quvhqxTAID4FHDo1NTUBPT5cJQ/Nom5OgDgh+nDazt27JB05tY1Z1+fdejQ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\nOgAAyxA6AADLEDoAAMsQOgAAy/w/8wXdnC7dqy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136b0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(particle_purities, weights=updater.particle_weights, bins=40)\n",
    "plt.xlabel(r'$\\operatorname{Tr}(\\rho^2)$')\n",
    "plt.ylabel(r'$\\Pr(\\rho^2 | \\mathsf{data})$')\n",
    "plt.xlim(xmax=1)\n",
    "\n",
    "ylim = plt.ylim()\n",
    "plt.vlines(np.mean(particle_purities), *ylim, label=r'$\\mathbb{E}[\\operatorname{Tr}(\\rho^2)]$')\n",
    "plt.vlines((rho_est ** 2).tr(), *ylim, linestyle='--', label=r'$\\operatorname{Tr}(\\mathbb{E}[\\rho]^2)$')\n",
    "plt.ylim(*ylim)\n",
    "plt.legend(ncol=2, bbox_to_anchor=(1, 1.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]], shape = (16, 16), type = super, isherm = True, superrep = choi\\begin{equation*}\\left(\\begin{array}{*{11}c}8.786\\times10^{-06} & (5.324\\times10^{-07}-3.918\\times10^{-06}j) & (3.960\\times10^{-06}+2.327\\times10^{-06}j) & (-2.583\\times10^{-07}-1.279\\times10^{-05}j) & (5.324\\times10^{-07}+3.918\\times10^{-06}j) & \\cdots & (5.957\\times10^{-05}-5.728\\times10^{-05}j) & (-2.583\\times10^{-07}+1.279\\times10^{-05}j) & (1.126\\times10^{-05}-1.836\\times10^{-05}j) & (5.957\\times10^{-05}+5.728\\times10^{-05}j) & 3.182\\times10^{-04}\\\\(5.324\\times10^{-07}+3.918\\times10^{-06}j) & -8.079\\times10^{-06} & (4.468\\times10^{-06}-4.018\\times10^{-06}j) & (6.184\\times10^{-07}+2.847\\times10^{-06}j) & (-2.336\\times10^{-05}+6.207\\times10^{-06}j) & \\cdots & (1.405\\times10^{-05}+9.005\\times10^{-06}j) & (-4.315\\times10^{-05}+2.573\\times10^{-05}j) & (4.780\\times10^{-06}-8.053\\times10^{-06}j) & (4.536\\times10^{-05}+3.664\\times10^{-05}j) & (3.329\\times10^{-05}+1.236\\times10^{-05}j)\\\\(3.960\\times10^{-06}-2.327\\times10^{-06}j) & (4.468\\times10^{-06}+4.018\\times10^{-06}j) & -2.945\\times10^{-06} & (-2.221\\times10^{-07}+4.029\\times10^{-07}j) & (-3.140\\times10^{-05}+1.912\\times10^{-05}j) & \\cdots & (3.336\\times10^{-08}+1.686\\times10^{-06}j) & (5.961\\times10^{-05}+7.680\\times10^{-06}j) & (-7.897\\times10^{-06}+3.495\\times10^{-05}j) & (8.414\\times10^{-06}-8.977\\times10^{-06}j) & (2.108\\times10^{-05}+5.142\\times10^{-06}j)\\\\(-2.583\\times10^{-07}+1.279\\times10^{-05}j) & (6.184\\times10^{-07}-2.847\\times10^{-06}j) & (-2.221\\times10^{-07}-4.029\\times10^{-07}j) & 2.238\\times10^{-06} & (-4.315\\times10^{-05}+2.573\\times10^{-05}j) & \\cdots & (-2.661\\times10^{-06}+3.734\\times10^{-06}j) & (9.628\\times10^{-05}+9.801\\times10^{-05}j) & (5.034\\times10^{-05}+2.677\\times10^{-05}j) & (1.819\\times10^{-05}-1.101\\times10^{-05}j) & (-1.293\\times10^{-05}+4.238\\times10^{-06}j)\\\\(5.324\\times10^{-07}-3.918\\times10^{-06}j) & (-2.336\\times10^{-05}-6.207\\times10^{-06}j) & (-3.140\\times10^{-05}-1.912\\times10^{-05}j) & (-4.315\\times10^{-05}-2.573\\times10^{-05}j) & -8.079\\times10^{-06} & \\cdots & (4.536\\times10^{-05}-3.664\\times10^{-05}j) & (6.184\\times10^{-07}-2.847\\times10^{-06}j) & (4.567\\times10^{-06}-1.626\\times10^{-05}j) & (1.405\\times10^{-05}-9.005\\times10^{-06}j) & (3.329\\times10^{-05}-1.236\\times10^{-05}j)\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\(5.957\\times10^{-05}+5.728\\times10^{-05}j) & (1.405\\times10^{-05}-9.005\\times10^{-06}j) & (3.336\\times10^{-08}-1.686\\times10^{-06}j) & (-2.661\\times10^{-06}-3.734\\times10^{-06}j) & (4.536\\times10^{-05}+3.664\\times10^{-05}j) & \\cdots & -2.779\\times10^{-06} & (1.819\\times10^{-05}-1.101\\times10^{-05}j) & (7.926\\times10^{-06}-4.149\\times10^{-07}j) & (2.938\\times10^{-06}-3.928\\times10^{-06}j) & (-3.048\\times10^{-06}+2.576\\times10^{-07}j)\\\\(-2.583\\times10^{-07}-1.279\\times10^{-05}j) & (-4.315\\times10^{-05}-2.573\\times10^{-05}j) & (5.961\\times10^{-05}-7.680\\times10^{-06}j) & (9.628\\times10^{-05}-9.801\\times10^{-05}j) & (6.184\\times10^{-07}+2.847\\times10^{-06}j) & \\cdots & (1.819\\times10^{-05}+1.101\\times10^{-05}j) & 2.238\\times10^{-06} & (3.290\\times10^{-06}+1.207\\times10^{-06}j) & (-2.661\\times10^{-06}-3.734\\times10^{-06}j) & (-1.293\\times10^{-05}-4.238\\times10^{-06}j)\\\\(1.126\\times10^{-05}+1.836\\times10^{-05}j) & (4.780\\times10^{-06}+8.053\\times10^{-06}j) & (-7.897\\times10^{-06}-3.495\\times10^{-05}j) & (5.034\\times10^{-05}-2.677\\times10^{-05}j) & (4.567\\times10^{-06}+1.626\\times10^{-05}j) & \\cdots & (7.926\\times10^{-06}+4.149\\times10^{-07}j) & (3.290\\times10^{-06}-1.207\\times10^{-06}j) & -3.216\\times10^{-06} & (3.532\\times10^{-06}-5.045\\times10^{-06}j) & (-2.831\\times10^{-06}+2.417\\times10^{-06}j)\\\\(5.957\\times10^{-05}-5.728\\times10^{-05}j) & (4.536\\times10^{-05}-3.664\\times10^{-05}j) & (8.414\\times10^{-06}+8.977\\times10^{-06}j) & (1.819\\times10^{-05}+1.101\\times10^{-05}j) & (1.405\\times10^{-05}+9.005\\times10^{-06}j) & \\cdots & (2.938\\times10^{-06}+3.928\\times10^{-06}j) & (-2.661\\times10^{-06}+3.734\\times10^{-06}j) & (3.532\\times10^{-06}+5.045\\times10^{-06}j) & -2.779\\times10^{-06} & (-3.048\\times10^{-06}-2.576\\times10^{-07}j)\\\\3.182\\times10^{-04} & (3.329\\times10^{-05}-1.236\\times10^{-05}j) & (2.108\\times10^{-05}-5.142\\times10^{-06}j) & (-1.293\\times10^{-05}-4.238\\times10^{-06}j) & (3.329\\times10^{-05}+1.236\\times10^{-05}j) & \\cdots & (-3.048\\times10^{-06}-2.576\\times10^{-07}j) & (-1.293\\times10^{-05}+4.238\\times10^{-06}j) & (-2.831\\times10^{-06}-2.417\\times10^{-06}j) & (-3.048\\times10^{-06}+2.576\\times10^{-07}j) & 3.756\\times10^{-06}\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]], shape = (16, 16), type = super, isherm = True, superrep = choi\n",
       "Qobj data =\n",
       "[[  8.78562749e-06 +0.00000000e+00j   5.32396222e-07 -3.91847572e-06j\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\|\\Sigma\\rho\\|_{Tr} = 0.0013$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov_superop=basis.covariance_mtx_to_superop(updater.est_covariance_mtx())\n",
    "display(qt.to_choi(cov_superop))\n",
    "display(Latex(r\"$\\|\\Sigma\\rho\\|_{{Tr}} = {:0.4f}$\".format(cov_superop.norm('tr'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x110a506d0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x112141750>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F6TKXWTERExFRSEXLizr0YiImIqKQ4s1aWkzEFLTfvvl3fF3pu/eg9lzaOwFP\nT4z8mzKIiIm4NSZiCtrXlfXY9e+acIdBRJ0EE7EWEzFRBDj153Vw1VT6NY8lqTcuGH9PB0VE5B0T\nsRYTMfHZ3wjgqqmE83BZuMMg0oWJWMtnIlZVFYsWLUJ8fDxyc3Pd4xsbGzF37lwMGTIE48aNw7x5\n8/DQQw9h+PDh7mm++OIL5OXlYcGCBeyn2MTqGl04dtoZ7jDapFQcgnA0tDuNtHWBmjwoRBEZTynf\nD9HY/jV2GZsA9ZIhIYqIqGMxEWv5fJhLURTk5ORg//79KC4udo9ft24dVFXF1KlT0b9/f9x1111Y\nuXIljh8/DgBoaGhAYWEhxo8fzyRMAROOBiinT7Y7+ErUZica6yEajrc/+EjURJ2JpUUnNb6GaKCr\nabpPnz6YMmUK1q5di5SUFFRUVGDr1q1YuHAh4uLiAAC33XYbPv/8c6xYsQJz587F6tWrkZCQgMzM\nzA5dACJ/1DlUuFTP16FaFIGuNv9fMlDvUKG2er2qIgQSAijLrMy6jOLMKQhVe0lFKhbIOP8vnQjH\naQjZqixh4Xu5O0i0JFi9dF8jHjt2LEpLS/HSSy+hsrLSo6arKApmzpyJxx57DAUFBdixYwfy8vJg\ntfIytFeqE+LsAU4KAQTYmxAAuFSp6S+WG3rbXKqEq63XkreRnPVQZVvlRdZ7z826jEJ1Qaiel1QC\niUxIF4T07C4j4KVUVWh6cI6S7vz04vFJy68j//Tp0/HQQw+hT58+bdZ0k5OTkZ6ejnfeeQe//OUv\ncemllxoVp2m0rBgE+7pUIeX5TtalDOrQJr18JjKjc/tR5L5y+Py+HdyeHZmMTsR79+7FmjVroKoq\nxowZg4yMDM33UkqsWbMGe/bsQWxsLHJycnDZZZe1O29dXR3y8/NRVVWFXr164eGHH0bXrl1x6tQp\nLF++HGVlZRg1ahSysrKCjt+v07Ti4mLYbDZUV1ejstLzUQmHw4Ht27cjNjYWBw8ebLdHpKKiIuTm\n5iI3NxdFRUX+R05ERJ2SRQjdgy+qqmLVqlV44oknkJ+fj+3bt6O8vFwzzZ49e1BRUYGCggLMmDED\nr776qs95N23ahJSUFBQUFCAlJQWbNm0CAMTExCAzMxNTpkwxbH3oTsRlZWXYvHkz5syZg6uvvhp2\nux2qqm3KeeONN6CqKpYsWYKvvvoKW7Zs8VpeWloali5diqVLlyItLS3wJejEpBCQaK7ByiCrBsLL\nZyIKh/NRFoIKAAAgAElEQVT7NvdIT4oQugdfysrKkJycjD59+sBqtWLEiBEoLS3VTLNr1y7ceOON\nEEJg0KBBqK+vR21tbbvzlpaWYuTIkQCAkSNHusfHxcVh8ODBsNlshq0PXU3TDocDdrsdI0eOxNCh\nQ/HDH/4Qc+bMwebNm3H77bcDAP7xj39g69atWLBgAfr16+e+uWvo0KFITk42LOBwM7QpTbEa1mjF\nay7h0Xyg8LyRKZJ0xDIasYqkYtE1TldZoo2y2hinG68Jt8vi59+/5aOzaWlpmspbTU0Nevbs6f5/\nz5498eWXX2rmr6mpwYUXXqiZpqampt15T5w4gcTERABAjx49cOLECf+C9oOuRLx+/Xo4HA5MmzbN\nHVRWVhbsdjtSU1Nx4YUXorCwELfccgt+9KMfAWheWTt37kRhYSEWLFgAhRsmmYBFEW3emBXoiUy4\n7xwOBbMuo4y7wLATWWmL55XcEFL83N+WLl3aQZHoI4SA6MATbJ+J+MCBA9iyZQvmz5+P+Pjzt/Lf\ncMMN2LlzJ+x2O37wgx/AZrPh7rvv1sybnZ2NRx55BB988AHGjx9vfPQU8aStCzzvZfWcRq9AHlEi\nImPpufarV1JSEqqrq93/r66uRlJSksc0x44d85jG5XJ5nbd79+6ora1FYmIiamtr0a1bN8Nibs1n\nIr7yyiuxYcOGNr+bM2dOu/MmJSVhzZo1gUVGIdM1Vn8TnD/TGqEzvzFLLxmbYMg0RJ2FkZdvBgwY\ngKNHj6KyshJJSUkoKSnB7NmzNdOkpqZiy5YtuOGGG/Dll1+iS5cuSExMRLdu3bzOm5qaim3btiEj\nIwPbtm3DsGHDDIu5NT7kS3x3dJjx1ZUUbfy9RtxuWRYL7r//fixevBiqquKmm25Cv379sHXrVgDN\n78AYOnQodu/ejdmzZ8NmsyEnJ6fdeQEgIyMD+fn5KC4udj++dM7MmTPR0NAAp9OJ0tJSPPXUU7jk\nkksCXgYh23vGqJM409jY7qNS1LGmvlASdDeIqQOS8IdfjzAooujD3pc6LyEE4mJjwx1GSG34+3e6\np737xxd3YCTmwBoxBe3S3sE3mxpRRjRjQqXOJNKeLAgWEzEF7emJPw53CETUicTwcUsNJmIiIgop\nvvdAi4mYiIhCik3TWkzEREQUUkbeNR0JmIjJtPZ9X4c6h8v3hAC62ixI6dO1gyMiIiOwRqylKxGr\nqopFixYhPj5e887PxsZGzJ07F0OGDMHOnTuRnp7u0f3Uiy++iMOHD7Nv4iiiVByCcDS0O420dfH5\nso46hwu1Z/QlYiLqPHiNWEtXZlQUBTk5OXj00UdRXFyM0aNHAwDWrVsHVVUxdepUXH311XjxxRdx\n7bXXon///gCAv/3tb/jss8+YhKOMcDRAOX2y3Wl8vbYymlTVO9Gkel8jMYqCXgncfyhyMA9r6d67\n+/Tp4+5RKSUlBRUVFdi6dSsWLlyIuLg4DB8+HKWlpbDb7Vi8eDEaGhqwcuVKTJgwwZ2Y6bwzThWt\nj72KAsRZw/cuZKXhOITq9BgvFSvULj3CEJH5CUc9RKs/pFQUSJv+56KbVBWN7Vb8/T9taXSqHn1b\nKAKIDeP2RXSOke+ajgR+nWaPHTsWpaWleOmll1BZWYnx48dj8ODB7u+zsrLwyCOPYOPGjSgvL0ff\nvn2Rnp5ueNBh42zUHHSlogDWwN6Io6ptHF6DqSaqToizbxeTQgCK/zUooTohXE1BBNGJGLC+AECo\nKoRslUXV1p0Ghp4qPWNoo9MpPwo0Zn0ZqtX+CAS7T0r3OhPwv4cgbWEmXF8mwmvEWn6fHk+fPh0H\nDx5ETEwMMjMzNd916dIFOTk52LhxI/bu3YtZs2ZFVPeHQlUhIM8P7TQnhpqQEgLNBxDB1336xPXl\nH8PXl6rCo0nI35ha749B7pPSy+eAYuP21S6Lon+IBn6fphUXF8Nms6G6uhqVlZW4+GLte0BTUlIw\ncOBA9OvXD7179/ZaTlFREYqKigB4dvRMRJGOySmasUas5VciLisrw+bNmzF37lxs3boVdrsdzz77\nrEet12KxwGJpv7s8Jl9jSSGAlk1h1C6uL/8Yv77Mt84FoGmaDga3r/bxGrGW7oq/w+GA3W7HyJEj\nMXToUDzwwAOoqKjA5s2bOzI+U5GKom0IC6LZXVGaV75mCKYZRrFCWmIgLTG8HqWHQetLKgqksGgH\nE1yOUQTON42eHYK6U9Xo7UtRgtzg29gfg94nBSxnh6CuDwPcH31QhNA9RAPdW8j69evhcDgwbdo0\nAECPHj2QlZUFu92O1NRUdx+OEc0aa1iDWjjvjvZGejlgeBtPgLQlmLKRNSrujjZwf6TQipZrv3rp\nOsIeOHAAW7Zswfz58xEfH+8ef8MNN2Dnzp3uR5Z8NUeTuRn1iJK0dfF5A7i0dTHktyJBjKKgvVvm\nY0xQwyYyUrTUdPUSUnb+W/rONDYiAhaDWuErLikaCCEQFxvYI1ed1b6jJ3RPm9K3ewdGYg5scyTT\nYmIlikyKCW/WCycmYiIiCim2TGsxERMRUUjxXdNaTMRERBRSrBFrMRETEVFI8RqxFhMxERGFFGvE\nWkzEREQUUhYmYg0mYiIiCim+0EPLZyJWVRWLFi1CfHw8cnNz3eMbGxsxd+5cDBkyBE1NTdi2bZvX\nMnJycjBq1ChDAiYios6NeVjLZyJWFAU5OTl49NFHUVxcjNGjRwMA1q1bB1VVMXXqVKiqinvuucdj\n3hdffBEVFRW49tprjY+ciIg6Jb60VUtX03SfPn0wZcoUrF27FikpKaioqMDWrVuxcOFCxMXFAQC6\ndNG+O3jjxo04dOgQnnnmGXTr1s34yKlDnG5SobZ6XagiBOJj/Nt16hwqXKrna0ctikBXW/h2Q6Pj\navCyvrr4ub4AQDhOQ0jtKz2lsEDa4r3MQdQ5CVaJNXRfIx47dixKS0vx0ksvobKyEuPHj8fgwYPb\nnPbzzz/HH//4R/zmN7/BpZdealSs4ed0QLQ46EohAKst4OJcqtT0f2oxwVPuqpTwzFP+v8fbpUq4\n2pqtjSSom+p0r38pREDdyxkdlyrbKi+wsoR0QUjPzh/C/RZ1h1P1WD2KAGxm6OGpxT4Z7P7oVM9v\n+4oArCbYHyMVV62WX3vS9OnTcfDgQcTExCAzM7PNab777jsUFBTg9ttvx/XXX29IkGYhZKveT4Ps\naEJ6+RxQWfL8EKma1//Z/nUjeUGNoqqA6mr+N5hiZPP22XII5nwKgGEbq2afDLK8lssU9PIh8vfH\nYAihf4gGflUpiouLYbPZUF1djcrKSlx88cWa7xsaGvDcc8/hyiuv9JqozykqKkJRUREAIC0tDWlp\naX6GTkTtaz5xkWGvUxNpmaAtxVR0J+KysjJs3rwZc+fOxdatW2G32/Hss89COdtXqqqqeOGFFyCE\nwOzZs31eA2DyxdmD5PnP1D4phLuKIaPlVDko4mwSNuG6Mujv17xNtPhMnQKvEWvpOjFxOByw2+0Y\nOXIkhg4digceeAAVFRXYvHmze5oNGzbg0KFDmDt3LuLjI/PmEik0DdNB7/gWRcB6dgj2+rBRTTmK\nEFAEWg0m2WkUK6QlBtISE9D14Y6gCAGLgGYIdH1JYYEUSqvBEkRwCqBYmv8NgiJw/pLA2cE01/is\nNsiYWMiY2KCuDwPaZTJi+aKpadVfnscY70M00HU0W79+PRwOB6ZNmwYA6NGjB7KysmC325Gamopv\nv/0WmzdvRnZ2NuLj43H8+HHN/DabzeOu6k7Jaov4Rj5/7472xqKINi+0hfuGNKPjCuTuaG+kLd6U\n25cpbsoKAd6cFTpc01pCyvZvJzhw4ACefvppzJ8/H0OGDNF8t3z5clRWViI+Ph779+/3WsbIkSMx\nc+ZMYyJuw5nGRvhYDCIiUxJCIC42NtxhhNTxugbd0/boGgGVOB98JuLOgImYiDqraEzEJ+pP6562\ne4LvS5179+7FmjVroKoqxowZg4yMDM33UkqsWbMGe/bsQWxsLHJycnDZZZe1O29dXR3y8/NRVVWF\nXr164eGHH0bXrl0BAO+++y6Ki4uhKAruu+8+XHPNNbqXpy3R0eZERESmYeQ1YlVVsWrVKjzxxBPI\nz8/H9u3bUV5erplmz549qKioQEFBAWbMmIFXX33V57ybNm1CSkoKCgoKkJKSgk2bNgEAysvLUVJS\nguXLl+PJJ5/EqlWroAb5iCATMRERhVTrm//aG3wpKytDcnIy+vTpA6vVihEjRqC0tFQzza5du3Dj\njTdCCIFBgwahvr4etbW17c5bWlqKkSNHAmi+vNpy/IgRIxATE4PevXsjOTkZZWVlQa0PJmIiIgqp\n5qcz9A2+1NTUoGfPnu7/9+zZEzU1NR7TXHjhhR7TtDfviRMnkJiYCKD5BuUTJ060+XtJSUkev+cv\nczwDQkREUcPfx7pa9vwXjndQCCE69NlnJmIiIgopf19HunTpUq/fJSUlobq62v3/6upqJCUleUxz\n7Ngxj2lcLpfXebt3747a2lokJiaitrbW3XlR69+rqanx+D1/sWmaiIhCS6r6Bx8GDBiAo0ePorKy\nEk6nEyUlJUhNTdVMk5qaik8++QRSShw6dAhdunRBYmJiu/OmpqZi27ZtAIBt27Zh2LBh7vElJSVo\nampCZWUljh49issvvzyo1cHHl4iIwigaH19qrD+le9rYhAt8TrN7926sXbsWqqripptuwh133IGt\nW7cCaO45UEqJVatW4e9//ztsNhtycnIwYMAAr/MCwKlTp5Cfn49jx455PL60ceNG/PWvf4WiKLj3\n3nsxdOhQf1eBBhMxEVEYRWUirjuhe9rYrt07MBJzCLhpuqCgAI899hicTqdm/L59+zBx4kT861//\nwoQJE/Dvf/876CCJzObbk40oqznd7vDtycZwh0lkTqpL/xAFAk7EWVlZqKurw9tvv+0e19DQgMLC\nQqSnp7tv+yaKRI1OFfVNst2h0RncQ/7h9n19E8pPOtodvq9vCneY1AkJqeoeokHAd00nJCQgOzsb\neXl5GDZsGC6//HKsXbsWCQkJmDBhQtDPVVHnVu9QobZxuUARAgk2/8//hKMeotXba6SiQNoS/C6r\nockzNkUIQztwiARNLokzrui45NPW9hrotko6REmC1Suox5euvvpq3HzzzbDb7cjMzMSnn36KvLw8\nWK2R+VSUaDqjOUOTQoGMiQuoLIdT1XQCpIjgerlRVanp21gJoicZV6uyAumZSJUSbR/DAzuwC1WF\nkK2aqdTASms7NnMkHCP/jlHB6XA/CiOFCLgrRDNvExGJiVgj6NO9yZMnQ0qJ/Px8ZGZmon///kbE\nZUpGNpuosnk3Pze00TOfX6SXz+Eui/xj6LpX1RbX2kx24JOyeQiSkC16CDfZDZvnFtFkYZmDgY8v\nRYKgq642mw3p6elYs2YNxo8fr3u+oqIiFBUVAQjPm1KIIp90v6tX8pSKzMRsJ4ZhZkgbssVigRAC\niqK/gs3kaywBaJo0zVIW+cfYdS8gzfqX7MDXBZL5RctNWHpF5sXcDiKF0u7//aEIeFwjDoaR1xID\nuSbc0aSiAGob4wLQ/CJ5zxtzzMDQa8IBrp/ORArh/lNKk/wNzzFZOObCRKzBROwHGRNnWANfMDdm\ndQZtJbvz4/0nbQmGrXveHR1BrDZDtgszn5xFJF4412Aipg4R6Y99xFoVeFTR25ym84qx+E5Eeqbp\nDCJ9ezUd1og1+IpLIqIwisZXXDqPfql7WmvfgR0YiTmwRkxERKHFGrEGEzEREYUWE7EGEzEREYUW\nE7EGEzEREYUUnyPWYiImIqLQ4pu1NJiIicLgv//6b3x3/ExA817cIw6/uWmAwRERhRCfctFgIiYK\ng++On8G+I6fCHQZReLBpWoOJmIiIQorXiLV8vk6moKAAjz32GJxOp2b8vn37MHHiRLzzzjuYOHEi\nvvxS+4D2Rx99hClTpqCiosLYiMnUlPL9sPz7bz4HpXx/uEMlonBhN4gaPmvEWVlZePTRR/H2229j\n4sSJAICGhgYUFhYiPT0dd955J2pra2G32/G73/0ONpsNlZWV+MMf/oApU6YgOTm5wxeCgmepOQzF\n2ej1e9UaC1eS776mRWM9RMNxw+KqqneiqZ0bO2IUBb0S2LBDgTt+xgmnlw7BrYpAjzhuX4aLkgSr\nl88acUJCArKzs/Hee++hrKwMALB27VokJCRgwoQJAIApU6bA5XJh3bp1kFLi97//PQYNGoSxY8d2\nbPSdVF2jCydOOz2GukZX2GJSnI1QHPXeh3aSdEdqUlU0uuB1aC9JeyMcp6E01mkG4TjdAdFHhtNN\nKuodLs1wuilyDqROVaJJRZuDtwTdHtFYB+X0Cc0gGus6IPJOTHXpH6KArlO9q6++GjfffDPsdjsy\nMzPx6aefIi8vD1Zr8+xxcXGYOXMmFi5ciJMnT+Kbb77BsmXLOjTwcHCq0qPrQmsA3da5VAlnW/t3\nADs9+U9IV5vXqAJd+6qq7fXX0K4Mg2BUXKqUbWyaQWyrqhPi7F2zUghAiawap1BdELJVAlGDWmMR\nRzqbwh2CqejucmTy5MmQUiI/Px+ZmZno31/bTDl48GD813/9F7Zv344pU6YgKSnJ8GDDrfXByHR5\nU0o+FhAG0svncDNrXEJKCDSfHAhur9GJNWIN3YnYZrMhPT0dMTExGD9+vMf3x48fx+7duxEbG4t/\n/vOfPssrKipCbm4ucnNzUVRU5F/URETUaUmXS/cQDfxqE7JYLBBCQFE88/eKFSvQt29fTJ06Fb/9\n7W/x//7f/0NqaqrXstLS0pCWluZ/xOQdOzIPi5ZdypvpL2DWuKQQ7pYbyW02OvHNWhqGXJz5+OOP\nsW/fPvzud79D37598Ytf/AIrV67E4MGD0bVrVyN+whQUAY9rxNT5SGHRNU4vs1wTbs2ouBTRMqW3\nHBdogVZTNZUbTSoWQG1jHJ0XJU3OegWdiI8dO4Y1a9Zg0qRJ6Nu3LwDgrrvuwueff47Vq1dj9uzZ\nQQdpFoHcmNUWS+uM3nI8dThpi4/oRGC0+BjdV7A6peb92vvjS/6SsV25ffkgmYg1gkrEUkoUFhZi\nwIABGDdu3PlCrVbMmjUL8+bNw/Dhw/GTn/wk6EAjSddY850dq9bYoL7vKDGKAo/qhcf3RIHjc8Jh\nwKZpDSFl579t8UxjIyJgMSKCUr4forHe53QyNgHqJUNCEJE5Pfbu/oDfNZ1y0QV47vboXXeRRgiB\nuNjwnOiGi2PHn3RPa7v+lx0YiTnwVJAMFc3JlYh0YtO0BhMxURhc3CMuLPMSmQKbpjWYiInCgP0J\nUzSLlueD9WIiJiKi0GLTtAYTMRERhRYTsQYTMRERhZTkNWINJmIyxKk/r4OrptLv+SxJvXHB+Hs6\nIKLoUbj9Gxw5eSageS/qFofsG35gcEREPoSoRlxXV4f8/HxUVVWhV69eePjhh9t82+PevXuxZs0a\nqKqKMWPGICMjw+f87777LoqLi6EoCu677z5cc801AIA333wTn3zyCerq6vD666/ripOJmAzhqqmE\n83BZuMOISkdOnsGB730/u01kGiFKxJs2bUJKSgoyMjKwadMmbNq0CZMnT9aGoqpYtWoVnnrqKfTs\n2RPz5s1DamoqLrnkEq/zl5eXo6SkBMuXL0dtbS2eeeYZvPDCC1AUBddddx3GjRvn11sldb2WqKCg\nAI899hicTqdm/L59+zBx4kRMnDgRJSUlmu+klFi4cCGWLFmiOxgKH+XIQVi+3tPuoBw5GO4wiSgC\nSFXVPQSjtLQUI0eOBACMHDkSpaWlHtOUlZUhOTkZffr0gdVqxYgRI9zTeZu/tLQUI0aMQExMDHr3\n7o3k5GSUlTVXRAYNGoTExES/4tSViLOyslBXV4e3337bPa6hoQGFhYVIT0/HpEmTsGrVKhw/ftz9\n/QcffIDDhw8jOzvbr4AoPITjNJTTJ9odhON0uMMkAgCcbHSh5rSzzeFko3+1LSPLIp1C1B/xiRMn\n3EmxR48eOHHihMc0NTU16Nmzp/v/PXv2RE1NTbvzt54nKSnJPU8gdDVNJyQkIDs7G3l5eRg2bBgu\nv/xyrF27FgkJCZgwYQIURcGuXbvwyiuv4PHHH8eRI0fw5ptvIicnx+8zg2jR6FQ9+n1QBBBr5buT\nyTyU0ycgXE6P8dJihRrfPQwRNXOqEk6vlSX/XndrZFkA921d/Eywubm57s+tu9B95plnNJXAc+6+\n+27N/4UQEEH0Ghbs/O3RfY346quvxs033wy73Y7MzEx8+umnyMvLg9XaXMTMmTPx2GOP4aOPPkJx\ncTF+8pOfYMSIER0SdCRQpecu3kaHTHRWtBzcVFVq+hAOdxeLwuWEUJvCGkNnw33bN39f6LF06VKv\n382fP9/rd927d0dtbS0SExNRW1uLbt26eUyTlJSE6upq9/+rq6uRlJTU7vyt56mpqXHPEwi/jmKT\nJ0+GlBL5+fnIzMxE//793d/17t0b06ZNw4oVK1BdXY2srKyAgzI1VT3bZGKy2+9VJ4SrCVA9ay+R\n4NzBreUQ1MHNyL+ju6zgy5NePkeUs9tqJG+v5IOq6h+CkJqaim3btgEAtm3bhmHDhnlMM2DAABw9\nehSVlZVwOp0oKSlBampqu/OnpqaipKQETU1NqKysxNGjR3H55ZcHHKdfd03bbDakp6djzZo1GD9+\nvMf3o0ePxltvvYVx48YhISGh3bKKiopQVFQEwLOpwdwkmnsvNddhUsjmuCDNFplZGfl3PLvuDSsv\nsrm3VYDba5SSTkdIficjIwP5+fkoLi52P34ENNdgX3nlFcybNw8WiwX3338/Fi9eDFVVcdNNN6Ff\nv37tzt+vXz9cf/31mDNnDhRFQVZWFpSzXbK+8cYb+PTTT+FwOPDggw9i9OjRmDBhQrtx+v34ksVi\ngRDC/aNtfe/tu5Y6V/JtSZw92Ia3ybA1KUTzQa2DrmFEHiP/jgLaBuVgSoJBJZnXuW3V/ZmiTqhe\n6HHBBRfgt7/9rcf4pKQkzJs3z/3/a6+9Ftdee63u+QHgjjvuwB133OExfvLkyR6PSPnC54j9ZVBH\n9IrwbFoN6nKgYo3omoXx68vAa8sGlhXua8IhwW016kmXyS7thRkTcZhE2k1GHY3rKzykpe1DhLfx\noWJVWrYdtPVdeMoCuK3qwUSsxURMAABpi4evXUPa4kMSC5lHOB9Rak+3WIspyyJ9+K5pLb8T8ahR\nozBq1Civ39vt9mDioTBRLxoc7hCIKEqwRqzFGjEREYUUE7EWEzEZwpLUO6Tz0XkXdYsLy7xEgVL9\nfKFHpGMiJkOwK8PwYTeG1NnwGrEWEzEREYUUm6a1mIiJiCikmIi1mIiJiCik2DStxURMREQhpbJG\nrMFETEREIcWmaS2fibigoADffvutpu9hANi3bx+WLFkCAHC1cyv6woULceWVVxoQKhGF0pGTDjS6\n2n8rdKxF4KJuthBFRJGCiVjLZyLOysrCo48+irfffhsTJ04EADQ0NKCwsBDp6em47bbb4HRq+xR1\nOp3Iy8uDzWYLqo9GIrP6vr4JTT6SVIxFoE9CTIgiMl6jS+K0M5K7Z6Bw4TViLZ+JOCEhAdnZ2cjL\ny8OwYcNw+eWXY+3atUhISMCECRM0teRzXn75ZZw6dcqdjCn61DtUqNLzIK4IgQRb538pfpNL4oyP\nROyv002e60wRAvEx/q0v5fQJCJfTY7y0WE377miKLqwRa+m6Rnz11Vfj5ptvht1uR2ZmJj799FOP\npupzPvzwQ3zyySdYsGABevbsaXjAYeVshGhxJicVBbDGBlaUKjVdpSkisJ5eOoJL1fauawkgLlVK\ntJ2ngkheqhOiZT+2SmC3ODicapvd1NnC3GuOKqVHXIGsL+FyQqhNhsRkZsJxGkJqL4tJYQmocxLD\ntwmDttVIxUSspXsrmzx5MqSUyM/PR2ZmJvr37+8xzYEDB/Daa68hKysLV1xxhaGBmoFQVQjI80MQ\nzSutd3rPA7B/pDw/BEt6+RxuQkoINJ8ciCAWVJXNy9VyCHb9U+gJ6YKQaqshsFcnGr1NGLWtRipV\nVXUP0UD3aZrNZkN6ejrWrFmD8ePHe3x/7NgxLF++HGlpaRgzZozP8oqKilBUVAQASEtLQ1pamh9h\nExFRZ8UasZZf7SUWiwVCCCiKtiLtcDjw3HPPoV+/frj33nt1lcXka14tu0k3R2N5MymEu8ovhZki\nI9Litto+yU4fNAy5cPHyyy+jrq4OTz75JCyWyO1kWyoK0PoacYAUAY9rxMEwcl8P5JpwSChWQ5rK\nW6/7c+PCTREtT4FajqO2SOF5rGlrnB6GbxMGbauRindNawWdiN977z3s2LEDjz/+OFRVxfHjxzXf\nx8XFIS4uQrpas8YatnOZ5casjtJWUjk/PrzCfVOWN/7eHe2NtLS9W3sb31lJW7xh+6NZt4lIpTo8\n7+qPZkHvmR9++CFcLpf75R6t3XnnnZgwYUKwP0OdTCQ8otSeGIvvEwo903QEox5RitURv55piFrj\nKy61hJSd/5a+M42NiIDFIKIoJIRAXGxgj0F2Vgd/laF72sGvburASMwhstqqiIjI9HjXtBYTMRER\nhZQ0+K10nR0TMRERhRSvEWsxERMRUUhJvspOg4mYiIhCSmXTtAYTMRERhRRv1tJiIiYiopDizVpa\nTMRERBRSbJrWCigR79+/H4sWLfL6/ZAhQ7BgwQJMmDABc+bMwfDhwwMOkIiIIgubprUCSsRXXHEF\nVqxY4TF+165dWLlyJcaOHRt0YEREFJlU3jWtEVAitlqt6NGjh2ZceXk5Xn/9ddx+++24/vrrDQmO\niIgiD68Raxlyjbi+vh7PPfccrrzySmRmZhpRZHRQnRAt+yxVeMmeKGxcTdr90RIT5oAiF1/ooRX0\nkV9VVRQUFEBRFMyePRvCBN3cdRZCSrjXlpTBdemmqmjudlAAQfSTfDYUt2D/nEaWZeQyAjgfnBHb\nrLU2MwUAAA8qSURBVFFlGXlyZtITvdb9s5jlkNG8P54NTrbViScZhTViraD3zDfffBOHDh3CkiVL\nEB8fr3u+oqIiFBUVAQDS0tKQlpYWbChRrjmpB5nOTS7yl9HIkzNDT/TIf0ae6EWYUCXiuro65Ofn\no6qqCr169cLDDz+Mrl27eky3d+9erFmzBqqqYsyYMcjIyPA5/7vvvovi4mIoioL77rsP11xzDRob\nG7F8+XJ8//33UBQF1113He655x6fcQaViLdv3473338fubm56Nu3r1/zMvmeraW0rLEERZxNUJG8\n0xu8jEYeIA0qy8htwtjtK/I1r68Wn4PFde5VqJqmN23ahJSUFGRkZGDTpk3YtGkTJk+erI1FVbFq\n1So89dRT6NmzJ+bNm4fU1FRccsklXucvLy9HSUkJli9fjtraWjzzzDN44YUXAADp6em46qqr4HQ6\n8fTTT2PPnj0YOnRou3EG3L739ddf4+WXX8akSZNwzTXXBFpMdFOskJYYSEtM8M2GigIoFkOabIU4\nP5ipLCOX0bQM3SYMLMtALbcJU+UqSwyk1QZptfH6cAeTqtQ9BKO0tBQjR44EAIwcORKlpaUe05SV\nlSE5ORl9+vSB1WrFiBEj3NN5m7+0tBQjRoxATEwMevfujeTkZJSVlSE2NhZXXXUVgOabmn/4wx+i\nurraZ5wB7Z0nT55035x144034vjx4x7TtL6rmoiICAjdCz1OnDiBxMREAM056cSJEx7T1NTUoGfP\nnu7/9+zZE19++WW789fU1GDgwIHueZKSklBTU6Mpt76+Hp9//jluueUWn3EGlIh3796NqqoqVFVV\nYcaMGW1O89ZbbwVSNBERRTh/X+iRm5vr/tz6suYzzzzTZmXw7rvv1vxfCBHUzcT+zO9yufDCCy/g\n5z//Ofr06eNz+oAS8ahRozBq1Cif0zEZExFRay6Hf4l46dKlXr+bP3++1++6d++O2tpaJCYmora2\nFt26dfOYJikpSdN8XF1djaSkpHbnbz1PTU2Nex4AeOWVV5CcnIxbb71V1/JF8MU2IiIyI6mquodg\npKamYtu2bQCAbdu2YdiwYR7TDBgwAEePHkVlZSWcTidKSkqQmpra7vypqakoKSlBU1MTKisrcfTo\nUVx++eUAgA0bNqChoQH33nuv7jiFlK2f6ut8zjQ2IgIWg4iikBACcbGx4Q4jpD4cdK3uaX92aHfA\nv3Pq1Cnk5+fj2LFjmsePampq8Morr2DevHkAmi+3rl27Fqqq4qabbsIdd9zR7vwAsHHjRvz1r3+F\noii49957MXToUFRXVyM7OxsXX3wxrNbmBudx48ZhzJgx7cbJRExEFEbRmIi3DGj/cZ6Wxv17TwdG\nYg7meaaBiIiiAntf0mIiJiKikGJ/xFpMxEREFFJ817QWEzEREYWUynt6NJiIiYgopFxMxBo+E/H+\n/fuxaNEir98PGTIE9913H+bNm4eHHnoIw4cPd3/3xRdfIC8vDwsWLMDgwYONiZiIiDo1tkxr+UzE\nV1xxBVasWOExfteuXVi5ciXGjh2L/v3746677sLKlSsxePBg9OjRAw0NDSgsLMT48eOZhInIUCfO\nOOH00iGAVRHoHsfGPjNjjVjL59ZqtVo9OnAoLy/H66+/jttvvx3XX389AOC2227D559/jhUrVmDu\n3LlYvXo1EhISkJmZ2TGRh8EZp4rWL3pRFCDOGjkvKBOOeohWCykVBdKW4H9ZZ05BqK5WZVkg4y4I\nKkZq38lGV7tJqlusJcQRGc+pSjR5fQKGB3mzY41Yy+/Txvr6enfPSy2TrKIomDlzJh577DEUFBRg\nx44dyMvLc79dJBKoKuCx75vlcTjVCdGy79kAu70TqgohtckTamCHNqG6IFSnx/iA9kFno+YEQSoK\nYA3iJQhOh3Z9WW3mKMsATlXCaWCSEo7THtuEFBZIW7z/wUUJVZXuNS0AKIqZ+nsMP9aItfyqyqmq\nioKCAiiKgtmzZ3v0RJGcnIz09HR8+umnyMjIwKWXXmpkrJFHVQHVBY9qdgCElBBo3ulFBG7kQlUh\nIM8PQa6z5vV1dghyfRlZlhkJ6YKQaqvB5XvGKCa9fKZmLql/iAZ+VZvefPNNHDp0CEuWLEF8vOfZ\nsMPhwPbt2xEbG4uDBw9CSum126iioiIUFRUB8OzWKno0J0/JXZWIoghrxFq6E/H27dvx/vvvIzc3\nF3379m1zmjfeeAOqqmLJkiV46qmnsGXLFvz85z9vc9roTb4tibNJOPhmKykE0LJ5lIjCpvkE+/xn\n0oqWmq5eupqmv/76a7z88suYNGkSrrnmmjan+cc//oGtW7ciJycH/fr1w5QpU7B+/XpUVFQYGnA4\nKUrzCtMMwdynpSiAYgmykHNlWSEtMZCWmICvDwNnb8wSFu1gRHxBkorSsmE66JikaFFWkCcuRpZl\nRs3bgdJq6Pw3fHUkRRGwnB14fdgTm6a1fB6xT5486b4568Ybb8Tx48c9pomNjUVhYSFuueUW/OhH\nPwLQXOPduXMnCgsLsWDBAigmOJgHK5LujvZG2hIMayiXiufBuq1xulhjjW3At9qMK8/IsgxgVVrW\nx9r6zj/SFm+q5QOMX0YKLTZNa/lMxLt370ZVVRWqqqowY8aMNqe56aabYLPZcPfdd2vGZ2dn45FH\nHsEHH3yA8ePHGxMxdRoy7gLTHcCjQSQ8nuQLnxPu3KKlpqsX+yMmIgqjaOyPePkFg3RPO+fUoQ6M\nxBx4WklERCHFGrEWEzEREYUUrxFrMRETEVFIOby8gjVaMRETEVFImeXNwGbBRExERCHFpmktJmIi\nIgop3qylxURMREQhxRqxFhMxERGFFGvEWroS8f79+7Fo0SKv3w8ZMgSHDx9Geno6MjIyNN+9+OKL\nOHz4cMT1TUxERIFhjVhLV2a84oorsGLFCo/xu3btwsqVKzF27FgIIfDiiy/i2muvRf/+/QEAf/vb\n3/DZZ58xCbdy/IwTTi+371sVgR58fV/U4TYRHlX1Tjhc7d/Da7Mo6JXA9W8k1oi1dG1dVqsVPXr0\n0IwrLy/H66+/jttvvx3XX389AKC0tBR2ux2LFy9GQ0MDVq5ciQkTJrgTMzVzqhJNXvf98G6hwlEP\noWqDk4oCaUsIU0TmVe9QobZxZq8IgQSbfx2EGLlNnGx0tZvUo+Fd1Ho5XCoaXb6m4sM2RmONWCug\n07z6+np3j0yZmZnu8VlZWXjkkUewceNGlJeXo2/fvkhPTzcsWOp4QlUhZKsjkxrY6YFwnPYoSwoL\npC0+8AAN4lQlzuUqRQTWY48qpZcz+/AeZJyqhNPAEz2HU0XrvK4IwBZob2SuJoiWfWdbYgIrhzot\n1oi1/E7EqqqioKAAiqJg9uzZEC36X+3SpQtycnKwePFi2Gw2PP/88xHR/aGG6tQeRILo+zfSCemC\nkJ4ZIdB9UFWlprP1YPp5bZlY+JKf9qnS828WzDoTsrn3ZgBAG2VHjHMtS5F2DDTAy/LrcIdgKn5n\nkTfffBOHDh3CkiVLEB/vWbNJSUnBwIED0a9fP/Tu3dtrOUVFRSgqKgLQ3HdxWlqav6GERfNB5Cwp\nI/cgYkLSy2cic+JWSvr4lYi3b9+O999/H7m5uejbt6/X6SwWCyyW9q9Ddabk25IUAmhZI6aQadkV\nPNd859W8D7X4HLEiednISLoT8ddff42XX34ZkyZNwjXXXNORMZmbYo3o81ypKB73psgAm9ak8DwZ\na2vc/2/v/llaWcI4jj+zeuON92JlY2efQlubaJE3YCX4AgQVJKWtCFobQQRLC/8Ugtim0MJO7WKR\nykJBSBOCylVydm8RT07iZs0mO/ok6/dTye4wbCab/e2OMzthRemK9tVlpOl/xAjW2FaN27o28Fes\nf0N1dEkjpFBBXKlU6oOz0um0lMtlX5mPo6oRrDYwKHhUqyYv8Y+1i6SXSPbsBddGOzum9ffodPGU\nZ/OcsH1+dT0oC0AooYL45uZGSqWSlEolWVhYaFnm+PjY6oHFGXNC46HTKUqfsXlOMD0pvMSAI+2m\nJ9XKAF/HeF7/T+j67/VVYvAxAPxAxhj5e2hI+zCgiFs9AAAUEcQAACgiiAEAUEQQAwCgiCAGAEAR\nQQwAgCKCGAAARQQxAACK2r7Op1AoyNraWuD+VColo6OjcnFxEVhmaWlJZmZmujpAAADirO2btarV\nqjw9Pfm2X11dyd7enmSzWZmYmJC3tzdfme3tbXl8fJTNzU0ZGRmxd9Qf8GYtAP2KN2uh7RPx4OCg\nb0GH+/t72d/fl9nZWZmamhIRkeHh4aYyJycnUiwWZX19/UtDGIgL8/okxv3VtM1zBsQb+lfpiAB8\nh47fNP/8/FxfiWlubq5lmevrazk6OpJsNivj4+NRjxGocatiGteCduK1eIZxf4nxmoNY3O6Wl3+t\nui2XLhxiJaUv9xbQ9qxihSAdXclc15VcLieO48jKyoqYFsu9PTw8SC6Xa3paRoDG7vSIC6RbrKq5\nwqiVua7UosREXp/VeN6fpdY9L9ISizbby3rbW+B6/gD/GA6dVdibN0Gu++c8MGJhzWr3fSWmCOeq\n9bZH7HX0azo4OJBisSgbGxuSTCZ9+19eXto+Lf+Wz+cln8+LiEgmk5FMJtPJocRG8MqxceG9f8Z4\nf8q4s3kT9LuOWsURb0AD/rZTI/A9Qgfx5eWlnJ2dyerqqoyNjfn2u64rW1tbYowJfFpu9JPDt1HP\n/uytPdqZ9xCOXp9nTP0C7vXKo+cPYb3tLX1/jTey9moEvleoIL67u5Pd3V2Zn5+XycnJlmUODw8/\nfVpGCxbDpGdzKWJ3dHNdg9ZuXGy2Vy+2vWP83aGRem0ttr1Nkbui/RVGr8J22yP22gZxpVKpdzen\n02kpl8u+Mre3t3J6eiqLi4uSTCZ9ZRKJhG9UNYBmnjMg4rbY1gUGZelhUBY61XYe8fn5uezs7Hxa\nSSqVkkKhELh/enpalpeXuzvCEJhHDKBfMY8YbYO4HxDEAPoVQQz6UAAAUEQQAwCgiCAGAEBRb7we\nJyLHmI+DTQGgLzi9OP8N3yoWg7UAAOhXdE0DAKCIIAYAQBFBDACAIoIYAABFBDEAAIoIYgAAFBHE\nAAAoIogBAFBEEAMAoIggBgBAEUEMAIAighgAAEUEMQAAighiAAAUEcQAACgiiAEAUEQQAwCgiCAG\nAEARQQwAgCKCGAAARf8D+e3uFQrVBZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a506d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(qt.visualization.hinton(cov_superop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This takes up lots of memory (>20 GB) and then seems to crash computer at presesnt lol\n",
    "\n",
    "# faces, vertices = updater.region_est_hull(modelparam_slice=np.s_[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a, c = updater.region_est_ellipsoid(modelparam_slice=np.s_[1:])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}