two-qubit-state-tomo_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from future.builtins import next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import qinfer as qi\n",
    "import qutip as qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools as it\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "basis = qi.tomography.pauli_basis(2)\n",
    "model = qi.BinomialModel(qi.tomography.TomographyModel(basis))\n",
    "prior = qi.tomography.GinibreDistribution(basis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "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}1.0 & 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 = [[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": 677,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiducial_measurement = qt.ket2dm(qt.tensor([qt.basis(2, 0)] * 2))\n",
    "fiducial_measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def PreRotationMeasurements(single_qubit_ops, core_measurement, n_qubits=2, include_conjugate=True):\n",
    "    \"\"\"Generate a set of measurement operators based on a set of rotations of a core measurement,\n",
    "    optionally including the conjugate of the same measurements as well.  Easily generates the sets in\n",
    "    http://journals.aps.org/pra/pdf/10.1103/PhysRevA.81.062325 using [I, PiX, PiX2, PiY2], M = |00><00|,\n",
    "    include_conjugate=True\"\"\"\n",
    "\n",
    "    assert (n_qubits == 2) #May optionally generalize later\n",
    "\n",
    "    FullMeasurementList = []\n",
    "    \n",
    "    for m1 in single_qubit_ops:\n",
    "        for m2 in single_qubit_ops:\n",
    "            FullMeasurementList.append(np.dot(np.conj(np.kron(m1, m2).T), np.dot(core_measurement, np.kron(m1, m2))))\n",
    "\n",
    "    if (include_conjugate):\n",
    "        FullMeasurementList.extend([np.conj(m) for m in FullMeasurementList])\n",
    "\n",
    "    return FullMeasurementList\n",
    "\n",
    "#Define Tomographic Measurements\n",
    "Id = np.eye(2, dtype=complex)\n",
    "X = np.array([[0.0, 1.0],\n",
    "              [1.0, 0.0]])\n",
    "Y = np.array([[0.0, -1.0j],\n",
    "              [1.0j, 0.0]])\n",
    "Z = np.array([[1.0, 0.0],\n",
    "              [0.0, -1.0]])\n",
    "\n",
    "PiX = np.cos(np.pi/2.) * Id - 1.j * np.sin(np.pi/2.) * X\n",
    "PiX2 = np.cos(np.pi/4.) * Id - 1.j * np.sin(np.pi/4.) * X\n",
    "PiY2 = np.cos(np.pi/4.) * Id - 1.j * np.sin(np.pi/4.) * Y\n",
    "\n",
    "# beta_vec=[0.32182797213542436,0.20388382857043799,0.26690981910827039,0.17047277839951108]\n",
    "beta_vec=[0.25,.25,.25,.25]\n",
    "CoreMeasurement = (np.kron(Id, Id) + np.kron(Z, Id) + np.kron(Id,Z) + np.kron(Z,Z))\n",
    "\n",
    "CoreMeasurement=np.multiply(CoreMeasurement,beta_vec)\n",
    "\n",
    "SingleMeasurementList = [Id, PiX, PiX2, PiY2]\n",
    "FullMeasurementList = PreRotationMeasurements(SingleMeasurementList, CoreMeasurement, \n",
    "                                              n_qubits=2, include_conjugate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meas_ops=[]\n",
    "meas_ops += [qt.Qobj(op) for op in FullMeasurementList]\n",
    "\n",
    "expparams = np.empty((len(meas_ops),),dtype=model.expparams_dtype)\n",
    "\n",
    "expparams['meas'] = [basis.state_to_modelparams(op) for op in meas_ops]\n",
    "expparams['n_meas'] = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "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": 539,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jcolless/anaconda/lib/python2.7/site-packages/qinfer/smc.py:285: ApproximationWarning: Extremely small n_ess encountered (6.74172039524). Resampling is likely to fail. Consider adding particles, or resampling more often.\n",
      "  ApproximationWarning\n",
      "/Users/jcolless/anaconda/lib/python2.7/site-packages/qinfer/smc.py:285: ApproximationWarning: Extremely small n_ess encountered (9.13020333115). Resampling is likely to fail. Consider adding particles, or resampling more often.\n",
      "  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": 540,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11824ca90>]"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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CBBdJ/EegQkKwzrwQ57F7MZ/MRx0/oUnbc555CHOkISLaRUNMHETHQvt0yj9eAJ8tQk2a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180eb750>"
      ]
     },
     "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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Updater Loop ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50.   4.  27.  27.   4.   0.   3.   2.  27.   2.  14.  14.  27.   2.  15.\n",
      "  15.  50.   4.  27.  27.   4.   1.   3.   2.  27.   3.  15.  14.  27.   2.\n",
      "  15.  15.]\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0 & 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 = [[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": 688,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expparams['n_meas'] = 50\n",
    "number_of_batches = 1\n",
    "\n",
    "true_state = qt.tensor(qt.Qobj([[1, 0],[0, 0]]),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(exp_data)\n",
    "true_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eA8USolgKqkWtxVWLbjQcCrnkQNWk6LZ57SBka4tOeoXV3PgiB+MoZdVMl0JK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\nE5RAVygUijxBCXSFQqHIE/4f1iVvbhSMXh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bbc2750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c536510>]"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y8B2Bj2F3FfccJoec9XdmSyvKO6d/H5KtztEff/0V1J79OY+R89gvvQjA4lOP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\nCApBEAQhLyIoBEEQhLyIoBAEQRDy8v8DZsV9+h64CxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c17e6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ideal_data)\n",
    "plt.plot(exp_data)\n",
    "plt.show()\n",
    "plt.plot(np.abs(ideal_data-exp_data)/3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jcolless/anaconda/lib/python2.7/site-packages/qinfer/smc.py:285: ApproximationWarning: Extremely small n_ess encountered (2.42199080985). Resampling is likely to fail. Consider adding particles, or resampling more often.\n",
      "  ApproximationWarning\n"
     ]
    }
   ],
   "source": [
    "updater = qi.SMCUpdater(model, 5000, prior)\n",
    "\n",
    "for batch in range(number_of_batches):\n",
    "    i=0\n",
    "    for experiment in expparams[:, np.newaxis]:\n",
    "        datum=np.round(exp_data[i]/number_of_batches)\n",
    "        updater.update(datum, experiment)\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "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}0.629 & (-0.127-0.014j) & (0.054+0.093j) & (0.009-0.044j)\\\\(-0.127+0.014j) & 0.119 & (-0.041-0.045j) & (0.039+1.124\\times10^{-04}j)\\\\(0.054-0.093j) & (-0.041+0.045j) & 0.171 & (-0.036-0.036j)\\\\(0.009+0.044j) & (0.039-1.124\\times10^{-04}j) & (-0.036+0.036j) & 0.082\\\\\\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",
       "[[ 0.62861495+0.j         -0.12681858-0.01406297j  0.05443726+0.0934675j\n",
       "   0.00905773-0.04419037j]\n",
       " [-0.12681858+0.01406297j  0.11855300+0.j         -0.04056348-0.04483257j\n",
       "   0.03851000+0.00011239j]\n",
       " [ 0.05443726-0.0934675j  -0.04056348+0.04483257j  0.17069401+0.j\n",
       "  -0.03616614-0.03596709j]\n",
       " [ 0.00905773+0.04419037j  0.03851000-0.00011239j -0.03616614+0.03596709j\n",
       "   0.08213804+0.j        ]]"
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_state = basis.modelparams_to_state(updater.est_mean())\n",
    "est_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "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}1.0 & 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 = [[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": 687,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5       ,  0.04847909, -0.07169135,  0.31731047, -0.06881028,\n",
       "       -0.00680501,  0.13105656, -0.12479183,  0.05672536,  0.03937975,\n",
       "       -0.00556914,  0.06367069,  0.26528405, -0.03956664, -0.11278846,\n",
       "        0.24255185])"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updater.est_mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.22044605e-16,  -4.84790920e-02,   7.16913482e-02,\n",
       "         1.82689526e-01,   6.88102787e-02,   6.80501189e-03,\n",
       "        -1.31056563e-01,   1.24791828e-01,  -5.67253589e-02,\n",
       "        -3.93797509e-02,   5.56913729e-03,  -6.36706922e-02,\n",
       "         2.34715954e-01,   3.95666419e-02,   1.12788463e-01,\n",
       "         2.57448149e-01])"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(true_mps - updater.est_mean())[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117954950>]"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T3DKC24SbcRn+iFlgMBi5FzVBwoT2F/j2StyTIIwxxrYl4zNhe9CgsSYjd8FtJinDL3om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\nNEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117516ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(true_mps[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 16 artists>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9HtNa9yS6mgu6nW0tXc7cZrZZPZ6jbqPL32ddzTaYa2FhYahxk55jOATcWt++FfjCKssc\nAbZGxJaIeDmwqx5HXSYrfhn4bxPmkSRNaNJzDPuAjIj3AH8MBEBELFBdlnpjZp6NiL3AA1SXq96T\nmY/X4/9pRFxNdSjpKeDvTphH6oT1zj919S9MCSYshsx8FnjTKvOfBm7smz4MnHcpama+c5LtS5Ka\n5yufJUkFi0GSVPBN9CSNzdfyXJp8xiBJKlgMkqSCxSBJKlgMkqSCxSBJKlgMkqSCxSBJKlgMkqSC\nxSBJKlgMkqSCxSBJKlgMkqSCxSBJKlgMkqSCxSBJKkz0eQwR8Urg88BrqD6zOTLze6ssdw/wNuBM\nZr5u1PGSpNmZ9BnDh4GHMnMr8FA9vZrPAtsnGC9JmpFJi2EncG99+17g7astlJkPA98dd7wkaXYm\n/WjPDZl5qr79DLBhWuMjYjewGyAz6fV6o2Ydyvz8/NTWPYmu5oJ2s52eYGyb+9PHc3RdzQXdzTZu\nrnWLISIeBK5Y5a47+icyczkilkdOMOT4zFwEFuvJ5aWlpXE3taZer8e01j2JruaCbmdbS5uZu7zP\nupqtq7mgu9kGcy0sLAw1bt1iyMw3X+i+iDgdEVdm5qmIuBI4M9RWXzTpeElSwyY9x3AIuLW+fSvw\nhRmPlyQ1bNJi2Ae8JSL+B/DmepqIWIiIwysLRcRvA78H/GxEnIiI96w1XpLUnolOPmfms8CbVpn/\nNHBj3/TNo4yXJLXHVz5LkgoWgySpYDFIkgoWgySpYDFIkgoWgySpYDFIkgoWgySpYDFIkgoWgySp\nYDFIkgqXLS+P/REKbbooQ0tSB1y23gIX6zOGy6b1LyK+Ps31X2q5upytq7nMdmnl6nK2C+Ra18Va\nDJKkKbEYJEkFi+F8i+sv0oqu5oLuZutqLjDbOLqaC7qbbaxcF+vJZ0nSlPiMQZJUmOijPS8lEbEd\n2A/MAQcysxOfPx0Rm4HPARuoLtNdzMz97aZ6UUTMAY8CJzPzbW3nWRERrwAOAK+j2m/vzszfazcV\nRMTfB26jyvRHwN/JzP/bUpZ7gLcBZzLzdfW8VwKfB14DPAVEZn6vI9n+GfA3gOeB/0m17/5X27n6\n7vsg8Engz2fm0ixzrZUtIn4VeB9wDvhiZn5ovXX5jIEXfrndCewAtgE3R8S2dlO94CzwwczcBvwC\n8L4OZQP4e8CTbYdYxX7gy5n5WuAqOpAxIjYC7weuqX9w54BdLUb6LLB9YN6HgYcycyvwUD3dhs9y\nfravAK/LzNcD3wI+MutQrJ5r5Q+4twJ/MutAfT7LQLaIuAHYCVyVmX+RqrjWZTFUrgWOZuaxzHwe\nOEi1M1uXmacy8xv17eeofsFtbDdVJSI2AX+d6i/zzoiIPwv8VeDTAJn5/Kz/slzDPPCnI2Ie+Eng\n6baCZObDwHcHZu8E7q1v3wu8faahaqtly8zfycyz9eQjwKYu5Kr9BvAhWnzx7QWyvRfYl5n/r17m\nzDDrshgqG4HjfdMn6Mgv334R8Rrg54HfbznKin9J9cPw47aDDNgCfAf4TET8QUQciIjL2w6VmSep\n/mL7E+AU8P3M/J12U51nQ2aeqm8/Q3UIs4veDXyp7RAAEbGT6lDqY21nWcXPAH8lIn4/Iv5LRPyl\nYQZZDBeJiPgzwL8HPpCZP+hAnpVjmV9vO8sq5oE3AJ/KzJ8H/jftHRJ5QUT8Oaq/yLcAC8DlEfG3\n2k11YZm5TAfffiYi7qA6xPpbHcjyk8BHgY+1neUC5oFXUh2G/gdARsS6r362GCongc1905vqeZ0Q\nET9BVQq/lZn3tZ2n9peBmyLiKapDb38tIv5Nu5FecAI4kZkrz6z+HVVRtO3NwLcz8zuZ+SPgPuAX\nW8406HREXAlQ/z/UoYdZiYh3UZ1gfUddXG37C1RF/1j9s7AJ+EZEXNFqqhedAO7LzOXM/BrVs/ve\neoO8KqlyBNgaEVuoCmEXcEu7kSp1u38aeDIz/0XbeVZk5keoT/5FxC8Bv5aZnfjrNzOfiYjjEfGz\nmfnfgTcBT7Sdi+oQ0i/Uf2X+H6pcj7Yb6TyHgFuBffX/X2g3zovqKwc/BLwxM3/Ydh6AzPwj4FUr\n03U5XNPGVUkX8B+AG4D/HBE/A7wcWDebL3CrRcSNVMfM54B7MvOftBwJgIi4HvivVJc2rhzL/2hm\nHm4vVamvGLp0uerVVCfFXw4co7q0ceaXXQ6KiH8M/E2qQyF/ANy2cmKwhSy/DfwS1V+Qp4F/SPWL\nJIFXA39Mdbnqaidb28j2EeBPAc/Wiz2SmXvazpWZn+67/ylaKoYL7LN/DdwDXE11me+vZeZ/Wm9d\nFoMkqeA5BklSwWKQJBUsBklSwWKQJBUsBklSwWKQJBUsBklSwWKQJBX+P0/FTfPPdwdEAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11796d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(16), (true_mps - updater.est_mean())[0, :])\n",
    "# plt.xticks(np.arange(16) + 0.5, map(\"${}$\".format, basis.labels), rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: we omit the traceful part (modelparam 0) when taking the hull. The convex hull algorithm (quickhull) is fast in the number of points, but very slow in the number of dimensions, so this call will take a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "faces, vertices = updater.region_est_hull(modelparam_slice=np.s_[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GS_2\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "f = h5py.File('Fiducials.hdf5','r')\n",
    "\n",
    "for key in f.keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GS_2=[]\n",
    "\n",
    "for data in f[\"GS_2\"]:\n",
    "    GS_2.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 993.79707576   75.32122286  533.00841825  532.12228622   80.19494905\n",
      "    9.74745237   54.05405405   48.73726185  543.19893664   42.53433762\n",
      "  277.80239256  282.67611874  536.55294639   49.62339389  301.28489145\n",
      "  295.52503323  990.25254763   82.41027913  543.64200266  541.86973859\n",
      "   77.53655295   10.19051839   55.3832521    45.19273372  548.95879486\n",
      "   54.05405405  301.72795747  276.03012849  545.41426673   43.42046965\n",
      "  295.08196721  294.19583518]\n"
     ]
    }
   ],
   "source": [
    "print(np.multiply(GS_2,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b4e0550>]"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kw/L6AXK1VPABRUQKVLUfIJeGLQFT2y9r7aztmT+fX2nKmra8acqatrxpypq2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\nm+TYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4e0490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.multiply(GS[0:15],1000))\n",
    "# plt.show()\n",
    "plt.plot(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}