Bacon_Shor_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bacon-Shor code\n",
    "==============\n",
    "\n",
    "A simulation of the Bacon-Shor code is carried out based on the work by Egan [1].  The aim work is to validate the noise model for use on the Steane code.  \n",
    "\n",
    "The Bacon-Shor code with ancilla is set up following the Extended Figure One of Egan's paper.\n",
    "\n",
    "First, the logical zero or logical one state are encoded.  Errors in logical state preparation are found by evaluating  parities.  These resuls are with Egan's paper.\n",
    "\n",
    "Errors are introduced by applying X or Z Pauli gates to specified qubits.  The population of the ancilla qubits are compared with those found by Egan.\n",
    "\n",
    "To have a good fit with Egan's results the two gate depolarising error parameter needs to be approxmately double that of quoted by Egan.  These higher errors may be more suitable for the Steane code simulation because there is more noise from cross talk for a complex circuit.\n",
    "\n",
    "This is part of an MSc project on the Steane code.  Please see https://goldsmdn.github.io/ for full details.\n",
    "\n",
    "[1] Egan L, Debroy DM, Noel C, Risinger A, Zhu D, Biswas D, et al. Fault-Tolerant Operation of a Quantum Error-Correction Code. arXiv Available from: http://arxiv.org/abs/2009.11482\n",
    "\n",
    "This worksheet is run with two gate de-polarising noise of 0.011."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary function modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import(\n",
    "    QuantumCircuit,\n",
    "    QuantumRegister,\n",
    "    ClassicalRegister,\n",
    "    execute,\n",
    "    Aer\n",
    "    )\n",
    "\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors import pauli_error, depolarizing_error\n",
    "from qiskit.compiler import transpile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from helper_functions import (\n",
    "    find_individual_ancilla_values,\n",
    "    find_ancilla_values,\n",
    "    find_parity,\n",
    "    get_noise,\n",
    "    mean_of_list,\n",
    "    calculate_standard_error\n",
    "    )\n",
    "\n",
    "from circuits import BaconShorCodeLogicalQubit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define constants so the process flow can be controlled from one place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SINGLE_GATE_SET = ['id', 'ry', 'rx']\n",
    "TWO_GATE_SET = ['rxx']\n",
    "BASIS_GATE_SET = SINGLE_GATE_SET + TWO_GATE_SET\n",
    "\n",
    "Z_LOGICAL = True\n",
    "\n",
    "BLOCKS = 3\n",
    "DATA_QUBITS = BLOCKS ** 2\n",
    "ANCILLAS = 2\n",
    "ANCILLA_QUBITS = ANCILLAS ** 2\n",
    "MEASURE_Z = True\n",
    "\n",
    "ITERATIONS = 25\n",
    "#25 iterations will take several minutes.  It might be better to run with fewer.\n",
    "\n",
    "NOISE = True                             #Test with noise\n",
    "SHOTS = 10000                            #Number of shots to run    \n",
    "MEASURE_NOISE = 0.0046                   #Measurement noise not relevant\n",
    "SINGLE_GATE_DEPOLARISING = 0.000366      #Single gate noise   \n",
    "TWO_GATE_DEPOLARISING = 0.011            #Two gate noise\n",
    "\n",
    "SIMULATOR = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run circuit and calculate results for logical_one and logical_zero. Iterate a number of times to find standard error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for logical one False\n",
      "The difference is 0.1143\n",
      "The standard_deviation is 0.0035 and the standard error is 0.0007\n",
      "Results for logical one True\n",
      "The difference is -0.1166\n",
      "The standard_deviation is 0.0023 and the standard error is 0.0005\n"
     ]
    }
   ],
   "source": [
    "values_list = []\n",
    "#values for graph\n",
    "values_graph = []\n",
    "standard_error_graph = []\n",
    "labels_graph = []\n",
    "\n",
    "for logical_one in [False, True]:\n",
    "    print(f'Results for logical one {logical_one}') \n",
    "    if logical_one:\n",
    "        labels_graph.append('|1L>')\n",
    "    else:\n",
    "        labels_graph.append('|0L>')\n",
    "    values_list = []\n",
    "    for iteration in range(ITERATIONS):\n",
    "        qubit = BaconShorCodeLogicalQubit(1, DATA_QUBITS, ANCILLA_QUBITS, ANCILLAS, \n",
    "                                          BLOCKS, logical_one, Z_LOGICAL)\n",
    "        qubit.encoding_nft()\n",
    "        qubit.encoding_ft()\n",
    "        if NOISE:\n",
    "            noise_model = get_noise(MEASURE_NOISE, SINGLE_GATE_DEPOLARISING, TWO_GATE_DEPOLARISING, \n",
    "                                    SINGLE_GATE_SET, TWO_GATE_SET)\n",
    "        qubit.logical_measure()\n",
    "        qt = transpile(qubit, basis_gates = BASIS_GATE_SET)\n",
    "\n",
    "        if NOISE:\n",
    "            result = execute(qt, SIMULATOR, noise_model = noise_model, shots = SHOTS).result()\n",
    "        else:\n",
    "            result = execute(qt, SIMULATOR, shots = SHOTS).result()\n",
    "\n",
    "        counts = result.get_counts(qt)\n",
    "        parity_values = find_parity(counts, DATA_QUBITS)\n",
    "        if logical_one:\n",
    "            #matching Egan's calculations\n",
    "            ideal = -1 \n",
    "            measure = -1 * parity_values.get('1') / SHOTS\n",
    "            difference = ideal - measure\n",
    "            values_list.append(difference) \n",
    "        else:\n",
    "            #logical zero\n",
    "            ideal = 1\n",
    "            measure = 1 * parity_values.get('0') / SHOTS\n",
    "            difference = ideal - measure\n",
    "            values_list.append(difference) \n",
    "    mean = mean_of_list(values_list)     \n",
    "    values_graph.append(mean)\n",
    "    standard_deviation, standard_error = calculate_standard_error(values_list)\n",
    "    standard_error_graph.append(standard_error)\n",
    "    \n",
    "    print(f'The difference is {mean:.4f}')\n",
    "    print(f'The standard_deviation is {standard_deviation:.4f} and the standard error is {standard_error:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph constants\n",
    "YMIN = -0.4\n",
    "YMAX = 0.4\n",
    "YTICK = 0.2\n",
    "XMIN = -0.2\n",
    "XMAX = 0.2\n",
    "SIZE = 20\n",
    "\n",
    "x = np.arange(len(labels_graph))  # the bar locations\n",
    "width = 0.03  # the width of the bars\n",
    "bar_width = 5 #width of error bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects0 = ax.bar(x - width/2, values_graph[0], width, label = labels_graph[0], \n",
    "                color = '#428dff')\n",
    "rects1 = ax.bar(x + width/2, values_graph[1], width, label = labels_graph[1], \n",
    "                color = '#428dff')\n",
    "plt.errorbar(-width/2, values_graph[0], xerr = 0, yerr = standard_error_graph[0], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "plt.errorbar( width/2, values_graph[1], xerr = 0, yerr = standard_error_graph[1], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "\n",
    "ax.set_ylabel('<P ideal> - <P measured>')\n",
    "ax.set_title('Errors in logical state preparation')\n",
    "ax.legend()\n",
    "plt.ylim(YMIN, YMAX)\n",
    "plt.yticks(np.arange(YMIN, YMAX, YTICK))\n",
    "plt.xlim(XMIN, XMAX)\n",
    "ax.axes.xaxis.set_ticks([])\n",
    "\n",
    "# print labels on top of or on bottom of bars\n",
    "for i in range(len(labels_graph)):\n",
    "    plt.text(x = i * width - 1.25 * width/2, y = 1.25 * values_graph[i], s = labels_graph[i], size = 10)\n",
    "\n",
    "fname = \"Bacon_Shor_errors_in_logical_state_graph1.png\"\n",
    "plt.savefig(fname)"
   ]
  },
  {
   "attachments": {
    "Egan%20Figure%202b%20v2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the actual observations from Egan for comparison.  The blue columns fault tolerant bars are comparable.\n",
    "![Egan%20Figure%202b%20v2.png](attachment:Egan%20Figure%202b%20v2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1634.16x927.08 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output = 'mpl', filename = 'Bacon_Shor_encoding_circuit.jpg', fold = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out transpiled circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2175.96x927.08 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qt.draw(output='mpl', filename = 'Bacon_Shor_encoding_circuit_transpiled.jpg', fold = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run with different X, Z errors as per Egan paper and find the ancilla populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For case I\n",
      "Summary of results for case I\n",
      " The mean for index 0 with label Ancilla qubit 10 is 0.1474\n",
      " The standard error for index 0 is 0.0006\n",
      " The mean for index 1 with label Ancilla qubit 11 is 0.1581\n",
      " The standard error for index 1 is 0.0007\n",
      " The mean for index 2 with label Ancilla qubit 12 is 0.0850\n",
      " The standard error for index 2 is 0.0006\n",
      " The mean for index 3 with label Ancilla qubit 13 is 0.0979\n",
      " The standard error for index 3 is 0.0005\n",
      "For case Z2\n",
      "Summary of results for case Z2\n",
      " The mean for index 0 with label Ancilla qubit 10 is 0.1480\n",
      " The standard error for index 0 is 0.0007\n",
      " The mean for index 1 with label Ancilla qubit 11 is 0.1595\n",
      " The standard error for index 1 is 0.0007\n",
      " The mean for index 2 with label Ancilla qubit 12 is 0.9141\n",
      " The standard error for index 2 is 0.0006\n",
      " The mean for index 3 with label Ancilla qubit 13 is 0.9013\n",
      " The standard error for index 3 is 0.0007\n",
      "For case X5\n",
      "Summary of results for case X5\n",
      " The mean for index 0 with label Ancilla qubit 10 is 0.8525\n",
      " The standard error for index 0 is 0.0006\n",
      " The mean for index 1 with label Ancilla qubit 11 is 0.8410\n",
      " The standard error for index 1 is 0.0007\n",
      " The mean for index 2 with label Ancilla qubit 12 is 0.0852\n",
      " The standard error for index 2 is 0.0006\n",
      " The mean for index 3 with label Ancilla qubit 13 is 0.0989\n",
      " The standard error for index 3 is 0.0006\n",
      "For case Y1\n",
      "Summary of results for case Y1\n",
      " The mean for index 0 with label Ancilla qubit 10 is 0.8525\n",
      " The standard error for index 0 is 0.0006\n",
      " The mean for index 1 with label Ancilla qubit 11 is 0.1586\n",
      " The standard error for index 1 is 0.0007\n",
      " The mean for index 2 with label Ancilla qubit 12 is 0.9145\n",
      " The standard error for index 2 is 0.0007\n",
      " The mean for index 3 with label Ancilla qubit 13 is 0.0985\n",
      " The standard error for index 3 is 0.0005\n",
      "For case Y5\n",
      "Summary of results for case Y5\n",
      " The mean for index 0 with label Ancilla qubit 10 is 0.8529\n",
      " The standard error for index 0 is 0.0007\n",
      " The mean for index 1 with label Ancilla qubit 11 is 0.8408\n",
      " The standard error for index 1 is 0.0007\n",
      " The mean for index 2 with label Ancilla qubit 12 is 0.9150\n",
      " The standard error for index 2 is 0.0006\n",
      " The mean for index 3 with label Ancilla qubit 13 is 0.9009\n",
      " The standard error for index 3 is 0.0006\n"
     ]
    }
   ],
   "source": [
    "#initialise lists for graphs results and calculations\n",
    "values_graph = [[] for i in range(DATA_QUBITS + 1,DATA_QUBITS + 1 + ANCILLA_QUBITS)]\n",
    "standard_error_graph = [[] for i in range(DATA_QUBITS + 1,DATA_QUBITS + 1 + ANCILLA_QUBITS)]\n",
    "index_list = [str(i) for i in range(DATA_QUBITS + 1,DATA_QUBITS + 1 + ANCILLA_QUBITS)]\n",
    "labels_graph = ['Ancilla qubit ' + str(i) for i in range(DATA_QUBITS + 1, DATA_QUBITS + 1 + ANCILLA_QUBITS)]\n",
    "\n",
    "cases_list = []\n",
    "\n",
    "runs = {'I':{},\n",
    "        'Z2':{'test_z_qubit': 1},\n",
    "        'X5':{'test_x_qubit': 4},\n",
    "        'Y1':{'test_x_qubit': 0,'test_z_qubit': 0},\n",
    "        'Y5':{'test_x_qubit': 4,'test_z_qubit': 4}\n",
    "       }\n",
    "\n",
    "#Egan labels the qubits starting at 1 and quiskit uses 0, so need to adjust accordingly\n",
    "for cases, parameters in runs.items():\n",
    "    print(\"For case\", cases)\n",
    "    values_list = [[] for i in range(DATA_QUBITS + 1,DATA_QUBITS + 1 + ANCILLA_QUBITS)]\n",
    "    for iteration in range(ITERATIONS):\n",
    "        #print(f'Iteration {iteration}')\n",
    "        test_x = True\n",
    "        if 'test_x_qubit' in parameters:\n",
    "            x_bit = parameters.get('test_x_qubit')\n",
    "        else:\n",
    "            test_x = False\n",
    "        test_z = True\n",
    "        if 'test_z_qubit' in parameters:\n",
    "            z_bit = parameters.get('test_z_qubit')\n",
    "        else:\n",
    "            test_z = False\n",
    "        qubit = BaconShorCodeLogicalQubit(1, DATA_QUBITS, ANCILLA_QUBITS, ANCILLAS, \n",
    "                                          BLOCKS, logical_one, Z_LOGICAL)\n",
    "        qubit.reset_stabilizers()\n",
    "        qubit.encoding_nft()\n",
    "        qubit.encoding_ft()\n",
    "        if test_x:\n",
    "            qubit.x_testing(0, x_bit)\n",
    "        if test_z:\n",
    "            qubit.z_testing(0, z_bit)\n",
    "        qubit.x_stabilizers()\n",
    "        qubit.z_stabilizers()\n",
    "        if NOISE:\n",
    "            noise_model = get_noise(MEASURE_NOISE, SINGLE_GATE_DEPOLARISING, TWO_GATE_DEPOLARISING, \n",
    "                                    SINGLE_GATE_SET, TWO_GATE_SET)\n",
    "        qubit.logical_measure()\n",
    "\n",
    "        qt = transpile(qubit, basis_gates = BASIS_GATE_SET)\n",
    "\n",
    "        if NOISE:\n",
    "            result = execute(qt, SIMULATOR, noise_model = noise_model, shots = SHOTS).result()\n",
    "        else:\n",
    "            result = execute(qt, SIMULATOR, shots = SHOTS).result()\n",
    "\n",
    "        counts = result.get_counts(qt)\n",
    "\n",
    "        ancilla_values = find_ancilla_values(counts, ANCILLA_QUBITS)\n",
    "        #print('The population of different ancilla bitstrings are', ancilla_values)\n",
    "        #print()\n",
    "        individual_ancilla_values = find_individual_ancilla_values(ancilla_values, DATA_QUBITS, ANCILLA_QUBITS)\n",
    "        #print('By ancilla number the populations are ', individual_ancilla_values)\n",
    "        #print()\n",
    "        index = 0\n",
    "        for labels in index_list:\n",
    "            values_list[index].append(individual_ancilla_values.get(labels) / SHOTS)\n",
    "            index = index + 1\n",
    "    cases_list.append(cases)\n",
    "    index = 0\n",
    "    print(f'Summary of results for case {cases}')\n",
    "    for labels in index_list:\n",
    "        mean = mean_of_list(values_list[index])\n",
    "        print(f' The mean for index {index} with label {labels_graph[index]} is {mean:.4f}')\n",
    "        standard_deviation, standard_error = calculate_standard_error(values_list[index])\n",
    "        print(f' The standard error for index {index} is {standard_error:.4f}')\n",
    "        values_graph[index].append(mean)\n",
    "        standard_error_graph[index].append(standard_error)\n",
    "        index = index + 1\n",
    "    #print('values_graph', values_graph )\n",
    "    #print('standard_error_graph', standard_error_graph )         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input circuit can be drawn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3620.76x927.08 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output='mpl', filename = 'Bacon_Shor_full_circuit.jpg', fold = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The transpiled circuit can be drawn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3861.56x1890.28 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qt.draw(output='mpl', filename = 'Bacon_Shor_full_circuit_transpiled.jpg', fold = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot graph of results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph constants\n",
    "YMIN = 0\n",
    "YMAX = 1.0\n",
    "YTICK = 0.2\n",
    "WIDTH = 0.2\n",
    "GAP = .8\n",
    "\n",
    "x = np.arange(len(cases_list))  # the label locations\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(x - WIDTH * 3 / 2, values_graph[0], color = '#ff944c', width = WIDTH * GAP, \n",
    "        label = labels_graph[0])\n",
    "plt.bar(x - WIDTH / 2, values_graph[1], color = '#d45400', width = WIDTH * GAP, \n",
    "        label = labels_graph[1])\n",
    "plt.bar(x + WIDTH / 2, values_graph[2], color = '#ab75ff', width = WIDTH * GAP, \n",
    "        label = labels_graph[2])\n",
    "plt.bar(x + WIDTH * 3 /2, values_graph[3], color = '#5c00e8', width = WIDTH * GAP, \n",
    "        label = labels_graph[3])\n",
    "\n",
    "plt.errorbar(x -  WIDTH * 3 / 2, values_graph[0], xerr = 0 , yerr = standard_error_graph[0], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "plt.errorbar(x - WIDTH / 2, values_graph[1], xerr = 0, yerr = standard_error_graph[1], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "plt.errorbar(x + WIDTH / 2, values_graph[2], xerr = 0, yerr = standard_error_graph[2], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "plt.errorbar(x + WIDTH * 3 /2, values_graph[3], xerr = 0, yerr = standard_error_graph[3], \n",
    "             color = 'black', fmt = 'none', elinewidth = bar_width)\n",
    "\n",
    "plt.axhline(y = 0.5, color = 'black', linestyle = 'dashed')\n",
    "ax.set_ylabel('Ancilla Population')\n",
    "ax.set_xlabel('Introduced error')\n",
    "ax.set_title('Simulation of ancilla population for an introduced error')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(cases_list)\n",
    "ax.legend()\n",
    "\n",
    "plt.ylim(YMIN, YMAX)\n",
    "plt.yticks(np.arange(YMIN, YMAX, YTICK))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fname = \"Bacon_Shor_ancilla_graph1.png\"\n",
    "plt.savefig(fname)"
   ]
  },
  {
   "attachments": {
    "Egan%20Figure%204b.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the actual observations from Egan for comparison:\n",
    "\n",
    "![Egan%20Figure%204b.png](attachment:Egan%20Figure%204b.png)"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}