{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pylab as plt\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "pd.set_option('precision', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cumulative marriage percentages of white women born in 1920-1924 \n",
    "ages = np.array([i for i in range(15,39)])\n",
    "R = np.array([1.8, 4.60, 9.60, 17.6, 27.50, 38.10, 48.3, 57.5, 65.3, 71.6, 76.60, \n",
    "     80.60, 83.30, 85.20, 86.70, 87.90, 88.70, 89.40, 90.00, 90.5, 90.9, 91.2, 91.40, 91.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GompertzParameterEstimate(R):\n",
    "#input R: the vector holding the values of marriage percentages with respect to the ages in ascending order.\n",
    "    R = 0.01*R\n",
    "    R = R/(1-R)\n",
    "    m = len(R)\n",
    "    n = round(m/3)\n",
    "    S1 = np.sum(np.log(R[0:n]))\n",
    "    S2 = np.sum(np.log(R[n:2*n]))\n",
    "    S3 = np.sum(np.log(R[2*n:m]))\n",
    "    b = np.power((S3-S2)/(S2-S1),1/n)\n",
    "    k = np.exp(1/n*(S1+(S2-S1)/(1-math.pow(b,n))))\n",
    "    a = np.exp((S2-S1)*(b-1)/math.pow((1-math.pow(b,n)),2))\n",
    "    return (k,a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k= 13.2593102086\n",
      "a= 0.0012922054 \n",
      "b= 0.8544560118 \n",
      "A= 1.0462011797 \n"
     ]
    }
   ],
   "source": [
    "k,a,b  = GompertzParameterEstimate(R)\n",
    "print('k= %2.10f\\na= %2.10f \\nb= %2.10f \\nA= %2.10f '%(k,a,b,math.log(a)*math.log(b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution of the derived differential equation.\n",
    "def P(t):   \n",
    "    bt = np.power(b,t)\n",
    "    r = k*np.power(a,bt)\n",
    "    return 100*1/(1+1/r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "#predicted values by the model\n",
    "preds = np.array([P(t) for t in range(24)])\n",
    "plt.plot(ages,preds,'-r',ages,R,'--b')\n",
    "plt.legend(('Predicted', 'Actual'),\n",
    "           loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now work on the data presented in the paper. We have two files man.csv and women.csv converning the cumulative marriage\n",
    "fractions for the ages between 20 and 50 with 5-year intervals. We here demostrate for man.csv data but the process is same for women.csv as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Age</th>\n",
       "      <th>1940-1944</th>\n",
       "      <th>1945-1949</th>\n",
       "      <th>1950-1954</th>\n",
       "      <th>1955-1959</th>\n",
       "      <th>1960-1964</th>\n",
       "      <th>1965-1969</th>\n",
       "      <th>1970-1974</th>\n",
       "      <th>1975-1979</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>21.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>66.1</td>\n",
       "      <td>65.5</td>\n",
       "      <td>57.6</td>\n",
       "      <td>49.7</td>\n",
       "      <td>43.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>83.1</td>\n",
       "      <td>80.1</td>\n",
       "      <td>73.3</td>\n",
       "      <td>66.9</td>\n",
       "      <td>63.8</td>\n",
       "      <td>61.6</td>\n",
       "      <td>59.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>88.8</td>\n",
       "      <td>86.1</td>\n",
       "      <td>80.5</td>\n",
       "      <td>75.4</td>\n",
       "      <td>74.6</td>\n",
       "      <td>75.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>91.2</td>\n",
       "      <td>89.3</td>\n",
       "      <td>84.2</td>\n",
       "      <td>81.2</td>\n",
       "      <td>80.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>92.7</td>\n",
       "      <td>91.3</td>\n",
       "      <td>86.8</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>94.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  1940-1944  1945-1949  1950-1954  1955-1959  1960-1964  1965-1969  \\\n",
       "0   20       21.1       22.3       21.9       17.8       14.2       12.2   \n",
       "1   25       66.1       65.5       57.6       49.7       43.9       39.6   \n",
       "2   30       83.1       80.1       73.3       66.9       63.8       61.6   \n",
       "3   35       88.8       86.1       80.5       75.4       74.6       75.3   \n",
       "4   40       91.2       89.3       84.2       81.2       80.6        0.0   \n",
       "5   45       92.7       91.3       86.8       84.5        0.0        0.0   \n",
       "6   50       94.0       92.5       88.6        0.0        0.0        0.0   \n",
       "\n",
       "   1970-1974  1975-1979  \n",
       "0       10.1        8.7  \n",
       "1       35.9       24.3  \n",
       "2       59.7        0.0  \n",
       "3        0.0        0.0  \n",
       "4        0.0        0.0  \n",
       "5        0.0        0.0  \n",
       "6        0.0        0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Marital history of man for selected birth cohorts. This is the row data to be interpolated.\n",
    "Df_Man = pd.read_csv('man.csv')\n",
    "display(Df_Man)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We interpolate the data for the intermediate ages and write the results to 'ManComplete.csv' (WomenComplete.csv) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_man = Df_Man.values\n",
    "interp_man = pd.DataFrame(columns=Df_Man.columns)\n",
    "for j in range(len(df_man[0,:])):\n",
    "    m = df_man[:,j]\n",
    "    ml = np.array([])\n",
    "    for i in range(len(m)-1):\n",
    "        if m[i+1]!=0:\n",
    "            x = np.linspace(m[i],m[i+1],6)\n",
    "            ml = np.append(ml,x[:-1])\n",
    "        else:\n",
    "            x = np.zeros(6)\n",
    "            ml = np.append(ml,x[:-1])\n",
    "    \n",
    "    nn = m[np.max(np.nonzero(m))]\n",
    "    if ml[-1]!=0:\n",
    "        ml = np.append(ml,nn)\n",
    "    else:\n",
    "        ml[np.max(np.nonzero(ml))+1] = nn\n",
    "        ml = np.append(ml,0)\n",
    "\n",
    "    interp_man[interp_man.columns[j]] = ml\n",
    "    interp_man.to_csv('ManComplete.csv',index=False)\n",
    "    #interp_man.to_csv('WomenComplete.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Age</th>\n",
       "      <th>1940-1944</th>\n",
       "      <th>1945-1949</th>\n",
       "      <th>1950-1954</th>\n",
       "      <th>1955-1959</th>\n",
       "      <th>1960-1964</th>\n",
       "      <th>1965-1969</th>\n",
       "      <th>1970-1974</th>\n",
       "      <th>1975-1979</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.30</td>\n",
       "      <td>21.90</td>\n",
       "      <td>17.80</td>\n",
       "      <td>14.20</td>\n",
       "      <td>12.20</td>\n",
       "      <td>10.10</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.94</td>\n",
       "      <td>29.04</td>\n",
       "      <td>24.18</td>\n",
       "      <td>20.14</td>\n",
       "      <td>17.68</td>\n",
       "      <td>15.26</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.58</td>\n",
       "      <td>36.18</td>\n",
       "      <td>30.56</td>\n",
       "      <td>26.08</td>\n",
       "      <td>23.16</td>\n",
       "      <td>20.42</td>\n",
       "      <td>14.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>48.1</td>\n",
       "      <td>48.22</td>\n",
       "      <td>43.32</td>\n",
       "      <td>36.94</td>\n",
       "      <td>32.02</td>\n",
       "      <td>28.64</td>\n",
       "      <td>25.58</td>\n",
       "      <td>18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>57.1</td>\n",
       "      <td>56.86</td>\n",
       "      <td>50.46</td>\n",
       "      <td>43.32</td>\n",
       "      <td>37.96</td>\n",
       "      <td>34.12</td>\n",
       "      <td>30.74</td>\n",
       "      <td>21.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  1940-1944  1945-1949  1950-1954  1955-1959  1960-1964  1965-1969  \\\n",
       "0  20.0       21.1      22.30      21.90      17.80      14.20      12.20   \n",
       "1  21.0       30.1      30.94      29.04      24.18      20.14      17.68   \n",
       "2  22.0       39.1      39.58      36.18      30.56      26.08      23.16   \n",
       "3  23.0       48.1      48.22      43.32      36.94      32.02      28.64   \n",
       "4  24.0       57.1      56.86      50.46      43.32      37.96      34.12   \n",
       "\n",
       "   1970-1974  1975-1979  \n",
       "0      10.10       8.70  \n",
       "1      15.26      11.82  \n",
       "2      20.42      14.94  \n",
       "3      25.58      18.06  \n",
       "4      30.74      21.18  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for man\n",
    "interp_man.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We estimate the corresponding paramaters and write to 'ManParams.csv' (WomenParams.csv). Note that we should use the same number of data points for parameter estimation if we are to compare those parameters for different birth cohorts.  For example, if we opt to estimate the parameters over the ages from 20 to 35, we can consider the first 6 birth cohorts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Params</th>\n",
       "      <th>1940-1944</th>\n",
       "      <th>1945-1949</th>\n",
       "      <th>1950-1954</th>\n",
       "      <th>1955-1959</th>\n",
       "      <th>1960-1964</th>\n",
       "      <th>1965-1969</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.36223</td>\n",
       "      <td>0.36341</td>\n",
       "      <td>0.30306</td>\n",
       "      <td>0.29722</td>\n",
       "      <td>0.29282</td>\n",
       "      <td>0.27701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>0.95558</td>\n",
       "      <td>0.93815</td>\n",
       "      <td>0.94303</td>\n",
       "      <td>0.93864</td>\n",
       "      <td>0.94986</td>\n",
       "      <td>0.96657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>k</td>\n",
       "      <td>912.74167</td>\n",
       "      <td>97.15599</td>\n",
       "      <td>55.37289</td>\n",
       "      <td>26.90764</td>\n",
       "      <td>57.79610</td>\n",
       "      <td>581.01011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>a</td>\n",
       "      <td>0.00034</td>\n",
       "      <td>0.00337</td>\n",
       "      <td>0.00570</td>\n",
       "      <td>0.00915</td>\n",
       "      <td>0.00337</td>\n",
       "      <td>0.00029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Params  1940-1944  1945-1949  1950-1954  1955-1959  1960-1964  1965-1969\n",
       "0      A    0.36223    0.36341    0.30306    0.29722    0.29282    0.27701\n",
       "1      b    0.95558    0.93815    0.94303    0.93864    0.94986    0.96657\n",
       "2      k  912.74167   97.15599   55.37289   26.90764   57.79610  581.01011\n",
       "3      a    0.00034    0.00337    0.00570    0.00915    0.00337    0.00029"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_params = pd.DataFrame(columns=Df_Man.columns[1:7])\n",
    "man_params.insert(0,column='Params',value=['A','b','k','a'])\n",
    "ManPartial = interp_man.iloc[:16,0:7]\n",
    "for i in range(1,7):\n",
    "    k,a,b  = GompertzParameterEstimate(ManPartial[ManPartial.columns[i]])\n",
    "    A = np.log(a)*np.log(b)\n",
    "    man_params[man_params.columns[i]] = [A,b,k,a]\n",
    "    man_params.to_csv('ManParams.csv',index=False,float_format='%6.5f')\n",
    "    #man_params.to_csv('WomenParams.csv',index=False,float_format='%6.5f')\n",
    "man_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "df = pd.read_csv('ManParams.csv')\n",
    "df  = man_params\n",
    "x_ticks_labels = df.columns[1:]\n",
    "x = np.arange(2,14,2)\n",
    "A = df.iloc[0][1:].values\n",
    "b = df.iloc[1][1:].values\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(x,A,color='green', marker='o', linewidth=2, markersize=12)\n",
    "plt.ylabel('Initial Marriage Potential')\n",
    " # Set number of ticks for x-axis\n",
    "ax.set_xticks(x)\n",
    " # Set ticks labels for x-axis\n",
    "ax.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=12)\n",
    "plt.savefig('A_Man')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "fig, ax = plt.subplots(1,1) \n",
    "ax.plot(x,b,color='green', marker='o', linewidth=2, markersize=12)\n",
    "plt.ylabel('Detoriation Rate')\n",
    " # Set number of ticks for x-axis\n",
    "ax.set_xticks(x)\n",
    " # Set ticks labels for x-axis\n",
    "ax.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=12)\n",
    "plt.savefig('b_Man')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "df = pd.read_csv('WomenParams.csv')\n",
    "x_ticks_labels = df.columns[1:]\n",
    "x = np.arange(2,14,2)\n",
    "A = df.iloc[0][1:].values\n",
    "b = df.iloc[1][1:].values\n",
    "fig, ax = plt.subplots(1,1) \n",
    "ax.plot(x,A,color='green', marker='o', linewidth=2, markersize=12)\n",
    "plt.ylabel('Initial Marriage Potential')\n",
    " # Set number of ticks for x-axis\n",
    "ax.set_xticks(x)\n",
    "\n",
    " # Set ticks labels for x-axis\n",
    "ax.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=12)\n",
    "plt.savefig('A_Women')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "fig, ax = plt.subplots(1,1) \n",
    "ax.plot(x,b,color='green', marker='o', linewidth=2, markersize=12)\n",
    "plt.ylabel('Detoriation Rate')\n",
    " # Set number of ticks for x-axis\n",
    "ax.set_xticks(x)\n",
    " # Set ticks labels for x-axis\n",
    "ax.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=12)\n",
    "plt.savefig('b_Women')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}