qiskit/advanced/ignis/5b_interleaved_rb.ipynb

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    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
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    "## Interleaved Randomized Benchmarking\n",
    "---\n",
    "\n",
    "* **Last Updated:** August 19, 2019\n",
    "* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.2, qiskit-aer 0.2\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "**Interleaved Randomized Benchmarking** is a variant of the Randomized Benchmarking (RB) method that is used for benchmarking individual Clifford gates via randomization. The protocol consists of interleaving random gates between the given Clifford gate of interest. The protocol estimates the gate error of the given Clifford. \n",
    "The method is based on the paper *\"Efficient measurement of quantum gate error by interleaved randomized benchmarking\"*(https://arxiv.org/abs/1203.4550).\n",
    "\n",
    "This notebook gives an example for how to use the ``ignis.verification.randomized_benchmarking`` module in order to perform interleaved RB."
   ]
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   "source": [
    "#Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "#Import the RB Functions\n",
    "import qiskit.ignis.verification.randomized_benchmarking as rb\n",
    "\n",
    "#Import Qiskit classes classes\n",
    "import qiskit\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Select the Parameters of the Interleaved RB Run\n",
    "\n",
    "First, wee need to choose the regular RB parameters:\n",
    "\n",
    "- **nseeds:** The number of seeds. For each seed you will get a separate list of output circuits in rb_circs.\n",
    "- **length_vector:** The length vector of Clifford lengths. Must be in ascending order. RB sequences of increasing length grow on top of the previous sequences.\n",
    "- **rb_pattern:** A list of the form [[i,j],[k],...] which will make simultaneous RB sequences where Qi,Qj are a 2-qubit RB sequence and Qk is a 1-qubit sequence, etc. The number of qubits is the sum of the entries. For 'regular' RB the qubit_pattern is just [[0]],[[0,1]].\n",
    "- **length_multiplier:** If this is an array it scales each rb_sequence by the multiplier.\n",
    "- **seed_offset:** What to start the seeds at (e.g. if we want to add more seeds later).\n",
    "- **align_cliffs:**  If true adds a barrier across all qubits in rb_pattern after each set of Cliffords.\n",
    "\n",
    "As well as another parameter for interleaved RB:\n",
    "- **interleaved_gates:** A list of gates of Clifford elements that will be interleaved. The length of the list would equal the length of the rb_pattern.\n",
    "\n",
    "In this example we have 3 qubits Q0,Q1,Q2. \n",
    "We are running 2Q RB (on qubits Q0,Q2) and 1Q RB (on qubit Q1) simultaneously, \n",
    "where there are twice as many 1Q Clifford gates."
   ]
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   "source": [
    "#Number of qubits\n",
    "nQ = 3\n",
    "#There are 3 qubits: Q0,Q1,Q2.\n",
    "#Number of seeds (random sequences)\n",
    "nseeds = 5\n",
    "#Number of Cliffords in the sequence (start, stop, steps)\n",
    "nCliffs = np.arange(1,200,20)\n",
    "#2Q RB on Q0,Q2 and 1Q RB on Q1\n",
    "rb_pattern = [[0,2],[1]]\n",
    "#Do three times as many 1Q Cliffords\n",
    "length_multiplier = [1,3]\n",
    "#Interleaved Clifford gates (2-qubits and 1-qubit)\n",
    "interleaved_gates = [['h 0', 'cx 0 1'],['x 0']]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Interleaved RB sequences\n",
    "\n",
    "We generate RB sequences. We start with a small example (so it doesn't take too long to run).\n",
    "\n",
    "In order to generate the RB sequences **rb_circs**, which is a list of lists of quantum circuits, \n",
    "we run the function `rb.randomized_benchmarking_seq`.\n",
    "\n",
    "This function returns:\n",
    "\n",
    "- **rb_original_circs:** A list of lists of circuits for the original RB sequences (separate list for each seed).\n",
    "- **xdata:** The Clifford lengths (with multiplier if applicable).\n",
    "\n",
    "As well as:\n",
    "- **rb_interleaved_circs**: A list of lists of circuits for the interleaved RB sequences (separate list for each seed)."
   ]
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   "cell_type": "code",
   "execution_count": 3,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making the n=2 Clifford Table\n"
     ]
    }
   ],
   "source": [
    "rb_opts = {}\n",
    "rb_opts['length_vector'] = nCliffs\n",
    "rb_opts['nseeds'] = nseeds\n",
    "rb_opts['rb_pattern'] = rb_pattern\n",
    "rb_opts['length_multiplier'] = length_multiplier\n",
    "#rb_opts['align_cliffs'] = True\n",
    "rb_opts['interleaved_gates'] = interleaved_gates\n",
    "rb_original_circs, xdata, rb_interleaved_circs = rb.randomized_benchmarking_seq(**rb_opts)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "As an example, we print the circuit corresponding to the first original and interleaved RB sequences:"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "          ┌───┐ ┌─────┐┌───┐          ┌───┐     ┌───┐ ░      ┌───┐┌───┐     »\n",
      "qr_0: |0>─┤ H ├─┤ Sdg ├┤ H ├──■───────┤ X ├─────┤ Z ├─░──────┤ Z ├┤ X ├─────»\n",
      "          ├───┤ └┬───┬┘└─░─┘  │  ┌───┐└─┬─┘┌───┐└─░─┘ ░ ┌───┐├───┤└─┬─┘┌───┐»\n",
      "qr_1: |0>─┤ H ├──┤ Y ├───░────┼──┤ H ├──┼──┤ X ├──░─────┤ H ├┤ S ├──┼──┤ Y ├»\n",
      "         ┌┴───┴┐ ├───┤   ░  ┌─┴─┐└───┘  │  └───┘┌───┐ ░ └───┘├───┤  │  └───┘»\n",
      "qr_2: |0>┤ Sdg ├─┤ H ├──────┤ X ├───────■───────┤ Z ├─░──────┤ Z ├──■───────»\n",
      "         └─────┘ └───┘      └───┘               └───┘ ░      └───┘          »\n",
      " cr_0: 0 ═══════════════════════════════════════════════════════════════════»\n",
      "                                                                            »\n",
      " cr_1: 0 ═══════════════════════════════════════════════════════════════════»\n",
      "                                                                            »\n",
      " cr_2: 0 ═══════════════════════════════════════════════════════════════════»\n",
      "                                                                            »\n",
      "«               ┌───┐ ┌───┐      ┌───┐┌─┐\n",
      "«qr_0: ──■──────┤ H ├─┤ S ├──────┤ H ├┤M├\n",
      "«        │   ░ ┌┴───┴┐├───┤   ┌─┐└───┘└╥┘\n",
      "«qr_1: ──┼───░─┤ Sdg ├┤ H ├───┤M├──────╫─\n",
      "«      ┌─┴─┐ ░ └┬───┬┘├───┤┌─┐└╥┘      ║ \n",
      "«qr_2: ┤ X ├────┤ H ├─┤ S ├┤M├─╫───────╫─\n",
      "«      └───┘    └───┘ └───┘└╥┘ ║       ║ \n",
      "«cr_0: ═════════════════════╬══╬═══════╩═\n",
      "«                           ║  ║         \n",
      "«cr_1: ═════════════════════╩══╬═════════\n",
      "«                              ║         \n",
      "«cr_2: ════════════════════════╩═════════\n",
      "«                                        \n"
     ]
    }
   ],
   "source": [
    "#Original RB circuits\n",
    "print (rb_original_circs[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "name": "stdout",
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     "text": [
      "          ┌───┐ ┌─────┐┌───┐          ┌───┐   ┌───┐ ░      ┌───┐           ░ »\n",
      "qr_0: |0>─┤ H ├─┤ Sdg ├┤ H ├──■───────┤ X ├───┤ Z ├─░──────┤ H ├──■────────░─»\n",
      "          ├───┤ └┬───┬┘└─░─┘  │  ┌───┐└─┬─┘ ░ ├───┤ ░ ┌───┐└─░─┘  │  ┌───┐ ░ »\n",
      "qr_1: |0>─┤ H ├──┤ Y ├───░────┼──┤ X ├──┼───░─┤ H ├───┤ X ├──░────┼──┤ X ├───»\n",
      "         ┌┴───┴┐ ├───┤   ░  ┌─┴─┐└───┘  │   ░ ├───┤ ░ └───┘  ░  ┌─┴─┐└───┘ ░ »\n",
      "qr_2: |0>┤ Sdg ├─┤ H ├──────┤ X ├───────■─────┤ Z ├─░───────────┤ X ├──────░─»\n",
      "         └─────┘ └───┘      └───┘             └───┘ ░           └───┘      ░ »\n",
      " cr_0: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      " cr_1: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      " cr_2: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      "«         ┌───┐┌─────┐┌───┐┌───┐             ┌─┐                    \n",
      "«qr_0: ───┤ X ├┤ Sdg ├┤ H ├┤ X ├─────■───────┤M├────────────────────\n",
      "«       ░ ├───┤└┬───┬┘├───┤└─┬─┘ ░   │  ┌───┐└╥┘  ░  ┌─────┐┌───┐┌─┐\n",
      "«qr_1: ─░─┤ H ├─┤ S ├─┤ Y ├──┼───░───┼──┤ X ├─╫───░──┤ Sdg ├┤ H ├┤M├\n",
      "«       ░ ├───┤ └───┘ └───┘  │   ░ ┌─┴─┐└───┘ ║ ┌───┐└─┬─┬─┘└───┘└╥┘\n",
      "«qr_2: ───┤ Z ├──────────────■─────┤ X ├──────╫─┤ H ├──┤M├────────╫─\n",
      "«         └───┘                    └───┘      ║ └───┘  └╥┘        ║ \n",
      "«cr_0: ═══════════════════════════════════════╩═════════╬═════════╬═\n",
      "«                                                       ║         ║ \n",
      "«cr_1: ═════════════════════════════════════════════════╩═════════╬═\n",
      "«                                                                 ║ \n",
      "«cr_2: ═══════════════════════════════════════════════════════════╩═\n",
      "«                                                                   \n"
     ]
    }
   ],
   "source": [
    "#Interleaved RB circuits\n",
    "print (rb_interleaved_circs[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the noise model\n",
    "\n",
    "We define a noise model for the simulator. To simulate decay, we add depolarizing error probabilities to the CNOT and U gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "noise_model = NoiseModel()\n",
    "p1Q = 0.002\n",
    "p2Q = 0.01\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(2*p1Q, 1), 'u3')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can execute the original and interleaved RB sequences either using a Qiskit Aer Simulator (with some noise model) or using an IBMQ provider, and obtain a list of results, `result_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating Original Circuits\n"
     ]
    }
   ],
   "source": [
    "#Original RB circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 200\n",
    "original_result_list = []\n",
    "original_qobj_list = []\n",
    "import time\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_original_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})\n",
    "    original_result_list.append(job.result())\n",
    "    original_qobj_list.append(qobj)    \n",
    "print(\"Finished Simulating Original Circuits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating Interleaved Circuits\n"
     ]
    }
   ],
   "source": [
    "#Interleaved RB circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 200\n",
    "interleaved_result_list = []\n",
    "interleaved_qobj_list = []\n",
    "import time\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_interleaved_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})\n",
    "    interleaved_result_list.append(job.result())\n",
    "    interleaved_qobj_list.append(qobj)    \n",
    "print(\"Finished Simulating Interleaved Circuits\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the results\n",
    "\n",
    "We fit the results of the original RB circuits and the interleaved RB circuits into an exponentially decaying function and obtain the *Errors per Clifford* $\\alpha$ and $\\alpha_C$ of the original and interleaved sequences, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Create the original and interleaved RB fitter\n",
    "original_rb_fit = rb.RBFitter(original_result_list, xdata, rb_opts['rb_pattern'])\n",
    "interleaved_rb_fit = rb.RBFitter(interleaved_result_list, xdata, rb_opts['rb_pattern'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the interleaved gate error fidelity\n",
    "\n",
    "From the values of $\\alpha$ and $\\alpha_C$ we obtain the gate error of the interleaved Clifford $c$, and $r_C=1-$(average gate fidelity of the interleaved Clifford $C$), is estimated by:\n",
    "$$ EPC^{est} = r_C^{est} = \\frac{(2^n-1)(1-\\alpha_C/\\alpha)}{2^n}$$\n",
    "and must lie in the range given by certain systematic error bounds: \n",
    "$$[r_C^{est}-E,r_C^{est}+E]$$ \n",
    "for each of the patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate the joint fitter\n",
    "joint_rb_fit = rb.InterleavedRBFitter(original_result_list, interleaved_result_list, xdata, rb_opts['rb_pattern'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pattern: 0 - 2 qubit interleaved RB: {'alpha': 0.9790150569295543, 'alpha_err': 0.0010670035132882818, 'alpha_c': 0.9659641705527663, 'alpha_c_err': 0.0027065173128870855, 'epc_est': 0.009997971648453674, 'epc_est_err': 0.002224733032806278, 'systematic_err': 0.021479442957214934, 'systematic_err_L': -0.01148147130876126, 'systematic_err_R': 0.03147741460566861}\n",
      "pattern: 1 - 1 qubit interleaved RB: {'alpha': 0.9975047962312747, 'alpha_err': 0.00017636673385384308, 'alpha_c': 0.9932153383197261, 'alpha_c_err': 0.0003455482992490731, 'epc_est': 0.00215009387812215, 'epc_est_err': 0.00019429005022227532, 'systematic_err': 0.00215009387812215, 'systematic_err_L': 0.0, 'systematic_err_R': 0.0043001877562443}\n"
     ]
    }
   ],
   "source": [
    "#Print the joint fitter parameters\n",
    "for patt_ind, pattern in enumerate(rb_pattern):\n",
    "    print ('pattern:', patt_ind, '-', len(pattern), 'qubit interleaved RB:', joint_rb_fit.fit_int[patt_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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l29jY8NatW43GOX/+PNvZ2fH06dP52rVrHBYWxpUrV+avvvpKLfPq1Su+d++e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\n2y0siouLGTx4cNBh1Fjo4zer9Bq+0MefQDbEv2QJXH758XtSY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde7e4b19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the joint RB data\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "for i in range(2):\n",
    "    ax = plt.subplot(1, 2, i+1)\n",
    "    pattern_ind = i\n",
    "\n",
    "    # Plot the essence by calling plot_rb_data\n",
    "    joint_rb_fit.plot_rb_data(pattern_ind, ax=ax, add_label=True, show_plt=False)\n",
    "\n",
    "    # Add title and label\n",
    "    ax.set_title('%d Qubit interleaved RB'%(len(rb_opts['rb_pattern'][i])), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T01:33:15.440972Z",
     "start_time": "2019-08-22T01:33:15.431512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.3</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:33:15 2019 EDT</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
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  "varInspector": {
   "cols": {
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    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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