{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd44c129",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sympy as smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6961a2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('datasets/IN4148_10K.csv', names=['vrawout','vraw1', 'vraw2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dd63db62",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Duty'] = dataset['vrawout']/256\n",
    "dataset['VRD'] = dataset['vraw1']/1024*5\n",
    "dataset['VD'] =  dataset['vraw2']/1024*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b1e01283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vrawout</th>\n",
       "      <th>vraw1</th>\n",
       "      <th>vraw2</th>\n",
       "      <th>Duty</th>\n",
       "      <th>VRD</th>\n",
       "      <th>VD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>0.019531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.034180</td>\n",
       "      <td>0.024414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.029297</td>\n",
       "      <td>0.029297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vrawout  vraw1  vraw2      Duty       VRD        VD\n",
       "0        0     14      4  0.000000  0.068359  0.019531\n",
       "1        1      7      5  0.003906  0.034180  0.024414\n",
       "2        2      0      0  0.007812  0.000000  0.000000\n",
       "3        3      0      0  0.011719  0.000000  0.000000\n",
       "4        4      6      6  0.015625  0.029297  0.029297"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3486249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vrawout</th>\n",
       "      <th>vraw1</th>\n",
       "      <th>vraw2</th>\n",
       "      <th>Duty</th>\n",
       "      <th>VRD</th>\n",
       "      <th>VD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>879</td>\n",
       "      <td>112</td>\n",
       "      <td>0.980469</td>\n",
       "      <td>4.291992</td>\n",
       "      <td>0.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>252</td>\n",
       "      <td>883</td>\n",
       "      <td>113</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>4.311523</td>\n",
       "      <td>0.551758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>253</td>\n",
       "      <td>887</td>\n",
       "      <td>112</td>\n",
       "      <td>0.988281</td>\n",
       "      <td>4.331055</td>\n",
       "      <td>0.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>890</td>\n",
       "      <td>113</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>4.345703</td>\n",
       "      <td>0.551758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>895</td>\n",
       "      <td>112</td>\n",
       "      <td>0.996094</td>\n",
       "      <td>4.370117</td>\n",
       "      <td>0.546875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vrawout  vraw1  vraw2      Duty       VRD        VD\n",
       "251      251    879    112  0.980469  4.291992  0.546875\n",
       "252      252    883    113  0.984375  4.311523  0.551758\n",
       "253      253    887    112  0.988281  4.331055  0.546875\n",
       "254      254    890    113  0.992188  4.345703  0.551758\n",
       "255      255    895    112  0.996094  4.370117  0.546875"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "315c47d7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams[\"figure.figsize\"]=8,8\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,6))\n",
    "\n",
    "\n",
    "ax.set_title(\"10K IN4148 in series\")\n",
    "duty = dataset['Duty']\n",
    "vrd = dataset['VRD']\n",
    "vd = dataset['VD']\n",
    "ax.plot(duty, vrd, label='V_{RD}')\n",
    "ax.plot(duty, vd, label='V_D')\n",
    "ax.set_xlabel(\"Duty\", fontsize=12)\n",
    "ax.set_ylabel(\"Voltage\", fontsize=12)\n",
    "ax.legend()\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "838653a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['ID'] = (dataset['VRD']- dataset['VD'])/10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d3d9022c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vrawout</th>\n",
       "      <th>vraw1</th>\n",
       "      <th>vraw2</th>\n",
       "      <th>Duty</th>\n",
       "      <th>VRD</th>\n",
       "      <th>VD</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>879</td>\n",
       "      <td>112</td>\n",
       "      <td>0.980469</td>\n",
       "      <td>4.291992</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>252</td>\n",
       "      <td>883</td>\n",
       "      <td>113</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>4.311523</td>\n",
       "      <td>0.551758</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>253</td>\n",
       "      <td>887</td>\n",
       "      <td>112</td>\n",
       "      <td>0.988281</td>\n",
       "      <td>4.331055</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>890</td>\n",
       "      <td>113</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>4.345703</td>\n",
       "      <td>0.551758</td>\n",
       "      <td>0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>895</td>\n",
       "      <td>112</td>\n",
       "      <td>0.996094</td>\n",
       "      <td>4.370117</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.000382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vrawout  vraw1  vraw2      Duty       VRD        VD        ID\n",
       "251      251    879    112  0.980469  4.291992  0.546875  0.000375\n",
       "252      252    883    113  0.984375  4.311523  0.551758  0.000376\n",
       "253      253    887    112  0.988281  4.331055  0.546875  0.000378\n",
       "254      254    890    113  0.992188  4.345703  0.551758  0.000379\n",
       "255      255    895    112  0.996094  4.370117  0.546875  0.000382"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2f35fb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vrawout</th>\n",
       "      <th>vraw1</th>\n",
       "      <th>vraw2</th>\n",
       "      <th>Duty</th>\n",
       "      <th>VRD</th>\n",
       "      <th>VD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>0.019531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.034180</td>\n",
       "      <td>0.024414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.029297</td>\n",
       "      <td>0.029297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vrawout  vraw1  vraw2      Duty       VRD        VD\n",
       "0        0     14      4  0.000000  0.068359  0.019531\n",
       "1        1      7      5  0.003906  0.034180  0.024414\n",
       "2        2      0      0  0.007812  0.000000  0.000000\n",
       "3        3      0      0  0.011719  0.000000  0.000000\n",
       "4        4      6      6  0.015625  0.029297  0.029297"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "159ed4ed",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams[\"figure.figsize\"]=8,8\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,6))\n",
    "\n",
    "\n",
    "ax.set_title(\"$I_D$ vs $V_D$\")\n",
    "\n",
    "ax.plot(vd, dataset['ID']*1000)\n",
    "ax.set_xlabel(\"Voltage ($V_D$)\", fontsize=12)\n",
    "ax.set_ylabel(\"Current (mA)\", fontsize=12)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "adf1b0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit, least_squares\n",
    "from numpy import exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "99929e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fmodel(v, isv, k):\n",
    "    return isv*(exp(v/k)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3e02c45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7.87675051e-09, 5.09995477e-02]),\n",
       " array([[5.85408665e-09, 3.62295898e-03],\n",
       "        [3.62295898e-03, 2.24458583e+03]]))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curve_fit(fmodel, dataset['VD'], dataset['ID'], (1e-8, 0.258), absolute_sigma=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "407b051c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle I_{D} = I_{S} \\left(e^{\\frac{V_{D}}{k}} - 1\\right)$"
      ],
      "text/plain": [
       "Eq(I_D, I_S*(exp(V_D/k) - 1))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sisv, svd, sid, sk = smp.symbols('I_S V_D I_D k')\n",
    "expr = smp.Eq(sid , sisv * (smp.exp(svd/sk) - 1))\n",
    "expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "70ee59d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - \\frac{I_{S} V_{D} e^{\\frac{V_{D}}{k}}}{k^{2}}$"
      ],
      "text/plain": [
       "-I_S*V_D*exp(V_D/k)/k**2"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j2  = smp.diff(expr.args[1], sk)\n",
    "j2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de3495c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle e^{\\frac{V_{D}}{k}} - 1$"
      ],
      "text/plain": [
       "exp(V_D/k) - 1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j1  = smp.diff(expr.args[1], sisv)\n",
    "j1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6193e02f",
   "metadata": {},
   "outputs": [],
   "source": [
    "j1f = smp.lambdify([svd, sisv, sk], j1)\n",
    "j2f = smp.lambdify([svd, sisv, sk], j2)\n",
    "def jacfun(x, isv, k):\n",
    "    return np.transpose(np.array([j1f(x, isv, k), j2f(x, isv, k)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "609f5f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.26403762e+03, -3.22713680e-20])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jacfun(2, 1e-24, 0.28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "006f2f03",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/splat/venvs/jupyter-notebook/lib/python3.8/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: overflow encountered in exp\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7.87674935e-09, 5.09995470e-02])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(fmodel, dataset['VD'], dataset['ID'], (1e-14, 0.258), absolute_sigma=True, method='trf')\n",
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "24245fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.65125763e-05, 4.73772483e+01])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.diag(pcov))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a4d56c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.85417434e-09, 3.62300050e-03],\n",
       "       [3.62300050e-03, 2.24460365e+03]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1f97b32e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams[\"figure.figsize\"]=8,8\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,6))\n",
    "\n",
    "x = np.linspace(0, 0.55, 1000)\n",
    "y = popt[0]*(np.exp(x/popt[1])-1)\n",
    "ax.set_title(\"$I_D$ vs $V_D$\")\n",
    "\n",
    "ax.plot(vd, dataset['ID']*1000, label='actual')\n",
    "ax.plot(x,y*1000, label = 'best fit', linewidth=3)\n",
    "ax.set_xlabel(\"Voltage ($V_D$)\", fontsize=12)\n",
    "ax.set_ylabel(\"Current (mA)\", fontsize=12)\n",
    "ax.legend()\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed24e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": 5
}