American_single_double_option.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "8f65649e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "92d139ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global Variables\n",
    "\n",
    "ratedCap = 8 #MW\n",
    "costPerMW = 0.9 #£1.1mil per MW\n",
    "initElecPrice = 5 #£/mwh \n",
    "capFactor = 0.3 #Output is 30% of RC\n",
    "availability = 0.95 #Assume turbines are on 95% of the time\n",
    "numberOfTurbines=100\n",
    "\n",
    "inflationRate = 1.05\n",
    "riskAdjRate = 0.07 #risk-adjusted discount rate\n",
    "riskFreeRate = 0.03\n",
    "\n",
    "assetLife = 20\n",
    "gearBoxLife = 10\n",
    "\n",
    "\n",
    "\n",
    "impCostGearbox = 100 # millions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95c617f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Legacy Function Definitions\n",
    "\n",
    "def findSetupCost(ratedCap, numberOfTurbines, costPerMW):\n",
    "    setupCost = numberOfTurbines * costPerMW * ratedCap\n",
    "    return setupCost\n",
    "\n",
    "def netYieldCalculation(numberOfTurbines, ratedCap, capFactor, availability):\n",
    "    grossYield = numberOfTurbines * ratedCap * capFactor * 8760 #8760 hours in a year\n",
    "    netYield = availability * grossYield\n",
    "    return netYield\n",
    "\n",
    "#assetLife, initElecPrice, inflationRate,riskAdjRate,numberOfTurbines\n",
    "def cashFlowCalculation(*args):\n",
    "    discountedCashFlows = np.zeros(assetLife)\n",
    "\n",
    "    \n",
    "    for i in range(0,assetLife):\n",
    "        netYield = netYieldCalculation(numberOfTurbines, ratedCap, capFactor, availability)\n",
    "        discountedCashFlows[i] = pow(10,-6)* 0.7 * np.random.normal(initElecPrice,3) * pow(inflationRate,i) * netYield / pow((1 + riskAdjRate), i)\n",
    "        \n",
    "    return discountedCashFlows\n",
    "\n",
    "\n",
    "#assetLife, initElecPrice, inflationRate,riskAdjRate,numberOfTurbines\n",
    "def baseCashFlowCalculation(*args):\n",
    "    discountedBaseCashFlows = np.zeros(assetLife)\n",
    "\n",
    "    \n",
    "    \n",
    "    for i in range(0,assetLife):\n",
    "        netYield = netYieldCalculation(numberOfTurbines, ratedCap, capFactor, availability)\n",
    "        discountedBaseCashFlows[i] = pow(10,-6)* 0.7 * initElecPrice * pow(inflationRate,i) * netYield / pow((1 + riskAdjRate), i)\n",
    "        \n",
    "    return discountedBaseCashFlows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c012a6d",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82db363",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1acd2572",
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83c4a5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation Environment Functions\n",
    "\n",
    "def TurbineDeathSimulation(scale, loc):\n",
    "    \"\"\"Takes the baseCashFlows and simulates how turbine failures would change overall project npv\"\"\"\n",
    "    \n",
    "    discountedBaseCashFlows = baseCashFlowCalculation()\n",
    "    x= np.floor(np.random.normal(loc = loc , scale = scale , size = numberOfTurbines)).astype('int')\n",
    "    unique, counts = np.unique(x, return_counts=True)\n",
    "    y = np.asarray((unique, 1 - np.cumsum(counts)/numberOfTurbines)).T\n",
    "\n",
    "    if np.any(y<0):\n",
    "        y = np.asarray([ [ i[0]-min(y[:,0]) , i[1] ] for i in y ])\n",
    "        \n",
    "        if np.any(y>assetLife-1):\n",
    "            y = y[y[:,0] < assetLife]\n",
    "            y[-1][1] = 0\n",
    "\n",
    "    elif np.any(y>assetLife-1):\n",
    "        y = np.asarray([ [ i[0] - ( max(y[:,0]) - (assetLife-1) ) , i[1] ] for i in y ]) \n",
    "\n",
    "        if np.any(y<0):\n",
    "            startValue = y[0][1]\n",
    "            y = y[y[:,0] > -1]\n",
    "            y[0][1] = startValue\n",
    "            \n",
    "            \n",
    "    \n",
    "    probArray = np.ones(assetLife)\n",
    "    for count,i in enumerate(y):\n",
    "        probArray[i[0].astype('int')] = i[1]\n",
    "    probArray = [0 if count>y[-1][0] else i for count,i in enumerate(probArray)]    \n",
    "    \n",
    "    output = discountedBaseCashFlows * probArray\n",
    "    \n",
    "    return output[output > 0]\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "def SigmaCalculator(testFunction,arguments):\n",
    "    \"\"\"Takes the price fluctuated cash flows and calculates the mean of the standard deviation of the\n",
    "    logrithemic returns ,from it\"\"\"\n",
    "    \n",
    "    sigma_array = []\n",
    "    for _ in range(0,10000):\n",
    "        \n",
    "        discountedCashFlows = testFunction(arguments)\n",
    "        logrithmicReturns = np.zeros(len(discountedCashFlows) - 1)\n",
    "        \n",
    "        for g in enumerate(discountedCashFlows):\n",
    "            if g[0]<len(discountedCashFlows) - 1:\n",
    "                logrithmicReturns[g[0]] = np.log( discountedCashFlows[g[0]+1]/discountedCashFlows[g[0]] )\n",
    "\n",
    "        sigma_array.append(np.std(logrithmicReturns))\n",
    "\n",
    "    sigma = np.mean(sigma_array)\n",
    "    return sigma\n",
    "       \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc24253",
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c7885bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option Valuation Functions\n",
    "def GearboxVal(tree , steps , p , delta , assetLife , impCostGearbox):\n",
    "    \"\"\" Gives us the npv of the gear box replacement option applied \"\"\"   \n",
    "\n",
    "    count = 0\n",
    "    optionTree = np.copy(tree)\n",
    "\n",
    "    for i in range(0,steps):\n",
    "            gain = ( pow(inflationRate,assetLife+gearBoxLife) - 1 ) / ( pow(inflationRate,assetLife) - 1 )\n",
    "            case0 = (optionTree[i,steps-1] * gain) - impCostGearbox\n",
    "            optionTree[i,steps-1] = max(optionTree[i,steps-1] ,case0 )\n",
    "\n",
    "    for i in range(steps-1, 0 ,-1):\n",
    "        for j in range(0,steps-1):\n",
    "\n",
    "            case1 = ( p*optionTree[j , i] + (1-p)*optionTree[j+1 , i] ) * np.exp(-1* riskFreeRate * delta)\n",
    "\n",
    "            extraYears = max(np.floor(delta*i) + gearBoxLife - assetLife , 0) \n",
    "            gain = ( pow(inflationRate,assetLife+extraYears) - 1 ) / ( pow(inflationRate,assetLife) - 1 ) \n",
    "\n",
    "            case2 = (optionTree[j , i-1] * gain) - impCostGearbox\n",
    "\n",
    "            optionTree[j , i-1] = max(case1 , case2)    \n",
    "\n",
    "\n",
    "\n",
    "    return optionTree\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def AbandonmentVal(optionTree , steps , p , delta , abdValue):\n",
    "    \"\"\" Gives us the npv of the Abandonment option applied \"\"\"\n",
    "\n",
    "    option_values = np.copy(optionTree)\n",
    "\n",
    "    for i in range(0,steps):\n",
    "        option_values[i , steps-1] = max( option_values[i , steps-1] , abdValue )\n",
    "\n",
    "    for i in range(steps-1, 0 ,-1):\n",
    "        for j in range(0,steps-1):\n",
    "            case1 = ( p*option_values[j , i] + (1-p)*option_values[j+1 , i] ) * np.exp(-1* riskFreeRate * delta)\n",
    "            case2 = (option_values[j , i -1] / option_values[j , i -1] ) * abdValue\n",
    "\n",
    "            option_values[j , i -1 ] = max(case1 , case2)\n",
    "\n",
    "\n",
    "    return option_values    \n",
    "        \n",
    "\n",
    "                                      \n",
    "                                      \n",
    "                                      \n",
    "def InitCalc(cashFlows ,steps , impCostGearbox , abdValue):\n",
    "    \"\"\" Brings together all the other option calculations \"\"\"\n",
    "    sigma2 = 0.085\n",
    "    S0 = sum(cashFlows)\n",
    "    assetLife = len(cashFlows)\n",
    "    delta = assetLife / steps\n",
    "    u = np.exp(sigma2 * np.sqrt(delta)) \n",
    "    d = 1/u\n",
    "    p = ( np.exp(riskFreeRate * delta) - d ) / (u - d)\n",
    "    tree = np.zeros((steps, steps))\n",
    "    \n",
    "\n",
    "    #Tree Initial Seeding  \n",
    "    for i in range(0,steps):\n",
    "        tree[0 , i] = S0 * pow(u,i)  \n",
    "\n",
    "\n",
    "    #Tree Propagator     \n",
    "    for i in range(0,steps-1):\n",
    "        if i < steps-1:\n",
    "            for j in range(0,steps):\n",
    "                if j < steps-1:\n",
    "                    tree[i+1 , j+1] = tree[i,j] * d\n",
    "    tree[tree == 0] = None\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    optionTree = GearboxVal(tree , steps , p , delta , assetLife , impCostGearbox) \n",
    "    AoptionTree = AbandonmentVal(optionTree , steps , p , delta , abdValue)\n",
    "    \n",
    "    return tree , optionTree , AoptionTree\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "7ad7ee21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing single failure scenario trees\n",
    "initElecPrice = 20\n",
    "\n",
    "#sigma = SigmaCalculator(cashFlowCalculation,())\n",
    "tree,optionTree,AoptionTree = InitCalc(TurbineDeathSimulation(scale = 1,loc = 10) ,steps = 31 , impCostGearbox = 1000000 , abdValue = 500)\n",
    "#optionTree[0,0] - tree[0,0] # For option value\n",
    "#pd.DataFrame(tree) # Base tree depending on failure scenario\n",
    "#pd.DataFrame(optionTree) # Base tree depending on failure scenario , with gearbox replacement option applied\n",
    "#pd.DataFrame(AoptionTree) # Base tree depending on failure scenario , with gearbox replacement + Abandonment option applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "10b6dfbe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244.31092547355198\n",
      "-475.68907452644805\n",
      "255.68907452644802\n",
      "-464.31092547355195\n"
     ]
    }
   ],
   "source": [
    "\n",
    "European_option_value = AoptionTree[0,0]-tree[0,0]\n",
    "setupCost = findSetupCost(ratedCap, numberOfTurbines, costPerMW)\n",
    "European_project_value = European_option_value - setupCost\n",
    "print(tree[0,0])\n",
    "print(tree[0,0]-setupCost)\n",
    "print(European_option_value)\n",
    "print(European_project_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "db0269da",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>992.750274</td>\n",
       "      <td>1050.504159</td>\n",
       "      <td>1111.439386</td>\n",
       "      <td>1175.729252</td>\n",
       "      <td>1243.556470</td>\n",
       "      <td>1315.113681</td>\n",
       "      <td>1390.603998</td>\n",
       "      <td>1470.241566</td>\n",
       "      <td>1554.252170</td>\n",
       "      <td>1642.873862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412.377507</td>\n",
       "      <td>438.074527</td>\n",
       "      <td>...</td>\n",
       "      <td>884.069568</td>\n",
       "      <td>935.921232</td>\n",
       "      <td>990.633701</td>\n",
       "      <td>1048.362863</td>\n",
       "      <td>1109.273079</td>\n",
       "      <td>1173.537641</td>\n",
       "      <td>1241.339259</td>\n",
       "      <td>1312.870573</td>\n",
       "      <td>1388.334688</td>\n",
       "      <td>1467.945749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386.241333</td>\n",
       "      <td>...</td>\n",
       "      <td>786.296887</td>\n",
       "      <td>832.838721</td>\n",
       "      <td>881.952995</td>\n",
       "      <td>933.779936</td>\n",
       "      <td>988.467394</td>\n",
       "      <td>1046.171252</td>\n",
       "      <td>1107.055869</td>\n",
       "      <td>1171.294533</td>\n",
       "      <td>1239.069950</td>\n",
       "      <td>1310.574756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>698.337418</td>\n",
       "      <td>740.102358</td>\n",
       "      <td>784.180314</td>\n",
       "      <td>830.697426</td>\n",
       "      <td>879.786688</td>\n",
       "      <td>931.588325</td>\n",
       "      <td>986.250183</td>\n",
       "      <td>1043.928144</td>\n",
       "      <td>1104.786559</td>\n",
       "      <td>1168.998716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>619.206232</td>\n",
       "      <td>656.673723</td>\n",
       "      <td>696.220845</td>\n",
       "      <td>737.961062</td>\n",
       "      <td>782.014007</td>\n",
       "      <td>828.505815</td>\n",
       "      <td>877.569478</td>\n",
       "      <td>929.345217</td>\n",
       "      <td>983.980874</td>\n",
       "      <td>1041.632327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>548.017255</td>\n",
       "      <td>581.618622</td>\n",
       "      <td>617.089659</td>\n",
       "      <td>654.532427</td>\n",
       "      <td>694.054538</td>\n",
       "      <td>735.769451</td>\n",
       "      <td>779.796797</td>\n",
       "      <td>826.262706</td>\n",
       "      <td>875.300168</td>\n",
       "      <td>927.049400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>483.973347</td>\n",
       "      <td>514.096624</td>\n",
       "      <td>545.900682</td>\n",
       "      <td>579.477326</td>\n",
       "      <td>614.923352</td>\n",
       "      <td>652.340816</td>\n",
       "      <td>691.837327</td>\n",
       "      <td>733.526343</td>\n",
       "      <td>777.527487</td>\n",
       "      <td>823.966890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>426.357374</td>\n",
       "      <td>453.351648</td>\n",
       "      <td>481.856774</td>\n",
       "      <td>511.955328</td>\n",
       "      <td>543.734375</td>\n",
       "      <td>577.285715</td>\n",
       "      <td>612.706141</td>\n",
       "      <td>650.097708</td>\n",
       "      <td>689.568018</td>\n",
       "      <td>731.230526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>374.524179</td>\n",
       "      <td>398.703501</td>\n",
       "      <td>424.240801</td>\n",
       "      <td>451.210352</td>\n",
       "      <td>479.690467</td>\n",
       "      <td>509.763717</td>\n",
       "      <td>541.517165</td>\n",
       "      <td>575.042607</td>\n",
       "      <td>610.436832</td>\n",
       "      <td>647.801891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>327.893359</td>\n",
       "      <td>349.540259</td>\n",
       "      <td>372.407606</td>\n",
       "      <td>396.562205</td>\n",
       "      <td>422.074494</td>\n",
       "      <td>449.018741</td>\n",
       "      <td>477.473257</td>\n",
       "      <td>507.520608</td>\n",
       "      <td>539.247855</td>\n",
       "      <td>572.746790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>285.942764</td>\n",
       "      <td>305.311415</td>\n",
       "      <td>325.776786</td>\n",
       "      <td>347.398963</td>\n",
       "      <td>370.241299</td>\n",
       "      <td>394.370594</td>\n",
       "      <td>419.857283</td>\n",
       "      <td>446.775633</td>\n",
       "      <td>475.203947</td>\n",
       "      <td>505.224792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>248.202651</td>\n",
       "      <td>265.521715</td>\n",
       "      <td>283.826191</td>\n",
       "      <td>303.170119</td>\n",
       "      <td>323.610479</td>\n",
       "      <td>345.207352</td>\n",
       "      <td>368.024089</td>\n",
       "      <td>392.127486</td>\n",
       "      <td>417.587974</td>\n",
       "      <td>444.479816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>214.250423</td>\n",
       "      <td>229.725613</td>\n",
       "      <td>246.086078</td>\n",
       "      <td>263.380419</td>\n",
       "      <td>281.659884</td>\n",
       "      <td>300.978508</td>\n",
       "      <td>321.393269</td>\n",
       "      <td>342.964244</td>\n",
       "      <td>365.754779</td>\n",
       "      <td>389.831669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>183.705954</td>\n",
       "      <td>197.522280</td>\n",
       "      <td>212.133850</td>\n",
       "      <td>227.584317</td>\n",
       "      <td>243.919771</td>\n",
       "      <td>261.188808</td>\n",
       "      <td>279.442674</td>\n",
       "      <td>298.735400</td>\n",
       "      <td>319.123960</td>\n",
       "      <td>340.668427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>156.230359</td>\n",
       "      <td>168.551257</td>\n",
       "      <td>181.589326</td>\n",
       "      <td>195.380985</td>\n",
       "      <td>209.967543</td>\n",
       "      <td>225.392706</td>\n",
       "      <td>241.702560</td>\n",
       "      <td>258.945700</td>\n",
       "      <td>277.173364</td>\n",
       "      <td>296.439583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.495816</td>\n",
       "      <td>154.110828</td>\n",
       "      <td>166.409824</td>\n",
       "      <td>179.423019</td>\n",
       "      <td>193.189374</td>\n",
       "      <td>207.750332</td>\n",
       "      <td>223.149597</td>\n",
       "      <td>239.433251</td>\n",
       "      <td>256.649883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.409437</td>\n",
       "      <td>140.347294</td>\n",
       "      <td>151.944177</td>\n",
       "      <td>164.218213</td>\n",
       "      <td>177.205809</td>\n",
       "      <td>190.946265</td>\n",
       "      <td>205.481023</td>\n",
       "      <td>220.853781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.947423</td>\n",
       "      <td>127.225491</td>\n",
       "      <td>138.154819</td>\n",
       "      <td>149.726967</td>\n",
       "      <td>161.975105</td>\n",
       "      <td>174.936500</td>\n",
       "      <td>188.650449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.102138</td>\n",
       "      <td>114.712796</td>\n",
       "      <td>125.006111</td>\n",
       "      <td>135.911711</td>\n",
       "      <td>147.457657</td>\n",
       "      <td>159.679288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.902762</td>\n",
       "      <td>102.780114</td>\n",
       "      <td>112.464238</td>\n",
       "      <td>122.736802</td>\n",
       "      <td>133.615895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.465707</td>\n",
       "      <td>91.404502</td>\n",
       "      <td>100.497119</td>\n",
       "      <td>110.168422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.117343</td>\n",
       "      <td>80.575897</td>\n",
       "      <td>89.074316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.692235</td>\n",
       "      <td>70.314151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.256886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0           1           2           3           4           5   \\\n",
       "0   677.219331  717.006438  758.989444  803.288012  850.028313  899.343372   \n",
       "1          NaN  637.875252  675.560809  715.328543  757.291950  801.570691   \n",
       "2          NaN         NaN  600.505708  636.197357  673.863315  713.611221   \n",
       "3          NaN         NaN         NaN  565.008380  598.808214  634.480035   \n",
       "4          NaN         NaN         NaN         NaN  531.286215  563.291059   \n",
       "5          NaN         NaN         NaN         NaN         NaN  499.247151   \n",
       "6          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "7          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "8          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "9          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "10         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "11         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "12         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "13         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "14         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "15         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "16         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "17         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "18         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "19         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "20         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "21         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "22         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "23         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "24         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "25         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "26         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "27         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "28         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "29         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "30         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "            6            7            8            9   ...           21  \\\n",
       "0   951.373443  1006.266403  1064.178164  1125.273112  ...  2180.328628   \n",
       "1   848.290932   897.585697   949.595237  1004.467427  ...  1952.453502   \n",
       "2   755.554569   799.813016   846.512727   895.786721  ...  1747.449661   \n",
       "3   672.125934   711.853547   753.776363   798.014040  ...  1563.021570   \n",
       "4   597.070833   632.722361   670.347728   710.054571  ...  1397.104088   \n",
       "5   529.548834   561.533384   595.292628   630.923385  ...  1247.839350   \n",
       "6   468.803859   497.489476   527.770629   559.734408  ...  1113.555959   \n",
       "7          NaN   439.873503   467.025653   495.690500  ...   992.750274   \n",
       "8          NaN          NaN   412.377507   438.074527  ...   884.069568   \n",
       "9          NaN          NaN          NaN   386.241333  ...   786.296887   \n",
       "10         NaN          NaN          NaN          NaN  ...   698.337418   \n",
       "11         NaN          NaN          NaN          NaN  ...   619.206232   \n",
       "12         NaN          NaN          NaN          NaN  ...   548.017255   \n",
       "13         NaN          NaN          NaN          NaN  ...   483.973347   \n",
       "14         NaN          NaN          NaN          NaN  ...   426.357374   \n",
       "15         NaN          NaN          NaN          NaN  ...   374.524179   \n",
       "16         NaN          NaN          NaN          NaN  ...   327.893359   \n",
       "17         NaN          NaN          NaN          NaN  ...   285.942764   \n",
       "18         NaN          NaN          NaN          NaN  ...   248.202651   \n",
       "19         NaN          NaN          NaN          NaN  ...   214.250423   \n",
       "20         NaN          NaN          NaN          NaN  ...   183.705954   \n",
       "21         NaN          NaN          NaN          NaN  ...   156.230359   \n",
       "22         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "23         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "24         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "25         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "26         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "27         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "28         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "29         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "30         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "\n",
       "             22           23           24           25           26  \\\n",
       "0   2302.577405  2431.510106  2567.490245  2710.901085  2862.146707   \n",
       "1   2062.326858  2178.212056  2300.436109  2429.343799  2565.298634   \n",
       "2   1846.189688  1950.336929  2060.185562  2176.045748  2298.244498   \n",
       "3   1651.745694  1745.333089  1844.048393  1948.170622  2057.993951   \n",
       "4   1476.817581  1560.904997  1649.604398  1743.166781  1841.856782   \n",
       "5   1319.446588  1394.987515  1474.676285  1558.738690  1647.412787   \n",
       "6   1177.870548  1245.722777  1317.305292  1392.821208  1472.484674   \n",
       "7   1050.504159  1111.439386  1175.729252  1243.556470  1315.113681   \n",
       "8    935.921232   990.633701  1048.362863  1109.273079  1173.537641   \n",
       "9    832.838721   881.952995   933.779936   988.467394  1046.171252   \n",
       "10   740.102358   784.180314   830.697426   879.786688   931.588325   \n",
       "11   656.673723   696.220845   737.961062   782.014007   828.505815   \n",
       "12   581.618622   617.089659   654.532427   694.054538   735.769451   \n",
       "13   514.096624   545.900682   579.477326   614.923352   652.340816   \n",
       "14   453.351648   481.856774   511.955328   543.734375   577.285715   \n",
       "15   398.703501   424.240801   451.210352   479.690467   509.763717   \n",
       "16   349.540259   372.407606   396.562205   422.074494   449.018741   \n",
       "17   305.311415   325.776786   347.398963   370.241299   394.370594   \n",
       "18   265.521715   283.826191   303.170119   323.610479   345.207352   \n",
       "19   229.725613   246.086078   263.380419   281.659884   300.978508   \n",
       "20   197.522280   212.133850   227.584317   243.919771   261.188808   \n",
       "21   168.551257   181.589326   195.380985   209.967543   225.392706   \n",
       "22   142.495816   154.110828   166.409824   179.423019   193.189374   \n",
       "23          NaN   129.409437   140.347294   151.944177   164.218213   \n",
       "24          NaN          NaN   116.947423   127.225491   138.154819   \n",
       "25          NaN          NaN          NaN   105.102138   114.712796   \n",
       "26          NaN          NaN          NaN          NaN    93.902762   \n",
       "27          NaN          NaN          NaN          NaN          NaN   \n",
       "28          NaN          NaN          NaN          NaN          NaN   \n",
       "29          NaN          NaN          NaN          NaN          NaN   \n",
       "30          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "             27           28           29           30  \n",
       "0   3021.653145  3189.869573  3367.269570  3554.352437  \n",
       "1   2708.683875  2859.903599  3019.383835  3187.573757  \n",
       "2   2427.126588  2563.055526  2706.414566  2857.607782  \n",
       "3   2173.828538  2296.001389  2424.857279  2560.759709  \n",
       "4   1945.953412  2055.750843  2171.559229  2293.705573  \n",
       "5   1740.949571  1839.613673  1943.684102  2053.455026  \n",
       "6   1556.521479  1645.169678  1738.680262  1837.317857  \n",
       "7   1390.603998  1470.241566  1554.252170  1642.873862  \n",
       "8   1241.339259  1312.870573  1388.334688  1467.945749  \n",
       "9   1107.055869  1171.294533  1239.069950  1310.574756  \n",
       "10   986.250183  1043.928144  1104.786559  1168.998716  \n",
       "11   877.569478   929.345217   983.980874  1041.632327  \n",
       "12   779.796797   826.262706   875.300168   927.049400  \n",
       "13   691.837327   733.526343   777.527487   823.966890  \n",
       "14   612.706141   650.097708   689.568018   731.230526  \n",
       "15   541.517165   575.042607   610.436832   647.801891  \n",
       "16   477.473257   507.520608   539.247855   572.746790  \n",
       "17   419.857283   446.775633   475.203947   505.224792  \n",
       "18   368.024089   392.127486   417.587974   444.479816  \n",
       "19   321.393269   342.964244   365.754779   389.831669  \n",
       "20   279.442674   298.735400   319.123960   340.668427  \n",
       "21   241.702560   258.945700   277.173364   296.439583  \n",
       "22   207.750332   223.149597   239.433251   256.649883  \n",
       "23   177.205809   190.946265   205.481023   220.853781  \n",
       "24   149.726967   161.975105   174.936500   188.650449  \n",
       "25   125.006111   135.911711   147.457657   159.679288  \n",
       "26   102.780114   112.464238   122.736802   133.615895  \n",
       "27    83.465707    91.404502   100.497119   110.168422  \n",
       "28          NaN    74.117343    80.575897    89.074316  \n",
       "29          NaN          NaN    66.692235    70.314151  \n",
       "30          NaN          NaN          NaN    63.256886  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(optionTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5294acea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
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       "      <th>29</th>\n",
       "      <th>30</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>679.526769</td>\n",
       "      <td>718.348425</td>\n",
       "      <td>759.737351</td>\n",
       "      <td>803.684297</td>\n",
       "      <td>850.226036</td>\n",
       "      <td>899.435164</td>\n",
       "      <td>951.412494</td>\n",
       "      <td>1006.281325</td>\n",
       "      <td>1064.183148</td>\n",
       "      <td>1125.274510</td>\n",
       "      <td>...</td>\n",
       "      <td>2180.328628</td>\n",
       "      <td>2302.577405</td>\n",
       "      <td>2431.510106</td>\n",
       "      <td>2567.490245</td>\n",
       "      <td>2710.901085</td>\n",
       "      <td>2862.146707</td>\n",
       "      <td>3021.653145</td>\n",
       "      <td>3189.869573</td>\n",
       "      <td>3367.269570</td>\n",
       "      <td>3554.352437</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>641.680788</td>\n",
       "      <td>677.822375</td>\n",
       "      <td>716.619386</td>\n",
       "      <td>757.994075</td>\n",
       "      <td>801.931179</td>\n",
       "      <td>848.463569</td>\n",
       "      <td>897.661649</td>\n",
       "      <td>949.625326</td>\n",
       "      <td>1004.477870</td>\n",
       "      <td>...</td>\n",
       "      <td>1952.453502</td>\n",
       "      <td>2062.326858</td>\n",
       "      <td>2178.212056</td>\n",
       "      <td>2300.436109</td>\n",
       "      <td>2429.343799</td>\n",
       "      <td>2565.298634</td>\n",
       "      <td>2708.683875</td>\n",
       "      <td>2859.903599</td>\n",
       "      <td>3019.383835</td>\n",
       "      <td>3187.573757</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>606.710381</td>\n",
       "      <td>639.963510</td>\n",
       "      <td>676.064305</td>\n",
       "      <td>714.840153</td>\n",
       "      <td>756.203982</td>\n",
       "      <td>800.133985</td>\n",
       "      <td>846.658868</td>\n",
       "      <td>895.846806</td>\n",
       "      <td>...</td>\n",
       "      <td>1747.449661</td>\n",
       "      <td>1846.189688</td>\n",
       "      <td>1950.336929</td>\n",
       "      <td>2060.185562</td>\n",
       "      <td>2176.045748</td>\n",
       "      <td>2298.244498</td>\n",
       "      <td>2427.126588</td>\n",
       "      <td>2563.055526</td>\n",
       "      <td>2706.414566</td>\n",
       "      <td>2857.607782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.007846</td>\n",
       "      <td>605.003780</td>\n",
       "      <td>638.185833</td>\n",
       "      <td>674.249582</td>\n",
       "      <td>713.008678</td>\n",
       "      <td>754.365805</td>\n",
       "      <td>798.291990</td>\n",
       "      <td>...</td>\n",
       "      <td>1563.021570</td>\n",
       "      <td>1651.745694</td>\n",
       "      <td>1745.333089</td>\n",
       "      <td>1844.048393</td>\n",
       "      <td>1948.170622</td>\n",
       "      <td>2057.993951</td>\n",
       "      <td>2173.828538</td>\n",
       "      <td>2296.001389</td>\n",
       "      <td>2424.857279</td>\n",
       "      <td>2560.759709</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547.214557</td>\n",
       "      <td>573.357125</td>\n",
       "      <td>603.229476</td>\n",
       "      <td>636.343323</td>\n",
       "      <td>672.374938</td>\n",
       "      <td>711.122863</td>\n",
       "      <td>...</td>\n",
       "      <td>1397.104088</td>\n",
       "      <td>1476.817581</td>\n",
       "      <td>1560.904997</td>\n",
       "      <td>1649.604398</td>\n",
       "      <td>1743.166781</td>\n",
       "      <td>1841.856782</td>\n",
       "      <td>1945.953412</td>\n",
       "      <td>2055.750843</td>\n",
       "      <td>2171.559229</td>\n",
       "      <td>2293.705573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>524.327637</td>\n",
       "      <td>545.699172</td>\n",
       "      <td>571.632481</td>\n",
       "      <td>601.381177</td>\n",
       "      <td>634.430882</td>\n",
       "      <td>...</td>\n",
       "      <td>1247.839350</td>\n",
       "      <td>1319.446588</td>\n",
       "      <td>1394.987515</td>\n",
       "      <td>1474.676285</td>\n",
       "      <td>1558.738690</td>\n",
       "      <td>1647.412787</td>\n",
       "      <td>1740.949571</td>\n",
       "      <td>1839.613673</td>\n",
       "      <td>1943.684102</td>\n",
       "      <td>2053.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>507.849705</td>\n",
       "      <td>523.078517</td>\n",
       "      <td>544.107829</td>\n",
       "      <td>569.825675</td>\n",
       "      <td>...</td>\n",
       "      <td>1113.555959</td>\n",
       "      <td>1177.870548</td>\n",
       "      <td>1245.722777</td>\n",
       "      <td>1317.305292</td>\n",
       "      <td>1392.821208</td>\n",
       "      <td>1472.484674</td>\n",
       "      <td>1556.521479</td>\n",
       "      <td>1645.169678</td>\n",
       "      <td>1738.680262</td>\n",
       "      <td>1837.317857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>507.071680</td>\n",
       "      <td>521.760436</td>\n",
       "      <td>...</td>\n",
       "      <td>992.750274</td>\n",
       "      <td>1050.504159</td>\n",
       "      <td>1111.439386</td>\n",
       "      <td>1175.729252</td>\n",
       "      <td>1243.556470</td>\n",
       "      <td>1315.113681</td>\n",
       "      <td>1390.603998</td>\n",
       "      <td>1470.241566</td>\n",
       "      <td>1554.252170</td>\n",
       "      <td>1642.873862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>884.069568</td>\n",
       "      <td>935.921232</td>\n",
       "      <td>990.633701</td>\n",
       "      <td>1048.362863</td>\n",
       "      <td>1109.273079</td>\n",
       "      <td>1173.537641</td>\n",
       "      <td>1241.339259</td>\n",
       "      <td>1312.870573</td>\n",
       "      <td>1388.334688</td>\n",
       "      <td>1467.945749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>786.312556</td>\n",
       "      <td>832.838721</td>\n",
       "      <td>881.952995</td>\n",
       "      <td>933.779936</td>\n",
       "      <td>988.467394</td>\n",
       "      <td>1046.171252</td>\n",
       "      <td>1107.055869</td>\n",
       "      <td>1171.294533</td>\n",
       "      <td>1239.069950</td>\n",
       "      <td>1310.574756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>698.584484</td>\n",
       "      <td>740.141710</td>\n",
       "      <td>784.180314</td>\n",
       "      <td>830.697426</td>\n",
       "      <td>879.786688</td>\n",
       "      <td>931.588325</td>\n",
       "      <td>986.250183</td>\n",
       "      <td>1043.928144</td>\n",
       "      <td>1104.786559</td>\n",
       "      <td>1168.998716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>621.031293</td>\n",
       "      <td>657.235871</td>\n",
       "      <td>696.319674</td>\n",
       "      <td>737.961062</td>\n",
       "      <td>782.014007</td>\n",
       "      <td>828.505815</td>\n",
       "      <td>877.569478</td>\n",
       "      <td>929.345217</td>\n",
       "      <td>983.980874</td>\n",
       "      <td>1041.632327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>556.466735</td>\n",
       "      <td>585.368757</td>\n",
       "      <td>618.354936</td>\n",
       "      <td>654.780626</td>\n",
       "      <td>694.054538</td>\n",
       "      <td>735.769451</td>\n",
       "      <td>779.796797</td>\n",
       "      <td>826.262706</td>\n",
       "      <td>875.300168</td>\n",
       "      <td>927.049400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>511.845302</td>\n",
       "      <td>529.757452</td>\n",
       "      <td>553.443130</td>\n",
       "      <td>582.287016</td>\n",
       "      <td>615.546679</td>\n",
       "      <td>652.340816</td>\n",
       "      <td>691.837327</td>\n",
       "      <td>733.526343</td>\n",
       "      <td>777.527487</td>\n",
       "      <td>823.966890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.133652</td>\n",
       "      <td>510.006458</td>\n",
       "      <td>526.732356</td>\n",
       "      <td>549.866616</td>\n",
       "      <td>578.851142</td>\n",
       "      <td>612.706141</td>\n",
       "      <td>650.097708</td>\n",
       "      <td>689.568018</td>\n",
       "      <td>731.230526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>507.711071</td>\n",
       "      <td>522.843653</td>\n",
       "      <td>545.448584</td>\n",
       "      <td>575.042607</td>\n",
       "      <td>610.436832</td>\n",
       "      <td>647.801891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>504.494295</td>\n",
       "      <td>517.393990</td>\n",
       "      <td>539.247855</td>\n",
       "      <td>572.746790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>505.224792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0           1           2           3           4           5   \\\n",
       "0   679.526769  718.348425  759.737351  803.684297  850.226036  899.435164   \n",
       "1          NaN  641.680788  677.822375  716.619386  757.994075  801.931179   \n",
       "2          NaN         NaN  606.710381  639.963510  676.064305  714.840153   \n",
       "3          NaN         NaN         NaN  575.007846  605.003780  638.185833   \n",
       "4          NaN         NaN         NaN         NaN  547.214557  573.357125   \n",
       "5          NaN         NaN         NaN         NaN         NaN  524.327637   \n",
       "6          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "7          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "8          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "9          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "10         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "11         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "12         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "13         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "14         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "15         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "16         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "17         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "18         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "19         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "20         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "21         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "22         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "23         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "24         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "25         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "26         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "27         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "28         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "29         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "30         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "            6            7            8            9   ...           21  \\\n",
       "0   951.412494  1006.281325  1064.183148  1125.274510  ...  2180.328628   \n",
       "1   848.463569   897.661649   949.625326  1004.477870  ...  1952.453502   \n",
       "2   756.203982   800.133985   846.658868   895.846806  ...  1747.449661   \n",
       "3   674.249582   713.008678   754.365805   798.291990  ...  1563.021570   \n",
       "4   603.229476   636.343323   672.374938   711.122863  ...  1397.104088   \n",
       "5   545.699172   571.632481   601.381177   634.430882  ...  1247.839350   \n",
       "6   507.849705   523.078517   544.107829   569.825675  ...  1113.555959   \n",
       "7          NaN   500.000000   507.071680   521.760436  ...   992.750274   \n",
       "8          NaN          NaN   500.000000   500.000000  ...   884.069568   \n",
       "9          NaN          NaN          NaN   500.000000  ...   786.312556   \n",
       "10         NaN          NaN          NaN          NaN  ...   698.584484   \n",
       "11         NaN          NaN          NaN          NaN  ...   621.031293   \n",
       "12         NaN          NaN          NaN          NaN  ...   556.466735   \n",
       "13         NaN          NaN          NaN          NaN  ...   511.845302   \n",
       "14         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "15         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "16         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "17         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "18         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "19         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "20         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "21         NaN          NaN          NaN          NaN  ...   500.000000   \n",
       "22         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "23         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "24         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "25         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "26         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "27         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "28         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "29         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "30         NaN          NaN          NaN          NaN  ...          NaN   \n",
       "\n",
       "             22           23           24           25           26  \\\n",
       "0   2302.577405  2431.510106  2567.490245  2710.901085  2862.146707   \n",
       "1   2062.326858  2178.212056  2300.436109  2429.343799  2565.298634   \n",
       "2   1846.189688  1950.336929  2060.185562  2176.045748  2298.244498   \n",
       "3   1651.745694  1745.333089  1844.048393  1948.170622  2057.993951   \n",
       "4   1476.817581  1560.904997  1649.604398  1743.166781  1841.856782   \n",
       "5   1319.446588  1394.987515  1474.676285  1558.738690  1647.412787   \n",
       "6   1177.870548  1245.722777  1317.305292  1392.821208  1472.484674   \n",
       "7   1050.504159  1111.439386  1175.729252  1243.556470  1315.113681   \n",
       "8    935.921232   990.633701  1048.362863  1109.273079  1173.537641   \n",
       "9    832.838721   881.952995   933.779936   988.467394  1046.171252   \n",
       "10   740.141710   784.180314   830.697426   879.786688   931.588325   \n",
       "11   657.235871   696.319674   737.961062   782.014007   828.505815   \n",
       "12   585.368757   618.354936   654.780626   694.054538   735.769451   \n",
       "13   529.757452   553.443130   582.287016   615.546679   652.340816   \n",
       "14   500.133652   510.006458   526.732356   549.866616   578.851142   \n",
       "15   500.000000   500.000000   500.000000   507.711071   522.843653   \n",
       "16   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "17   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "18   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "19   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "20   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "21   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "22   500.000000   500.000000   500.000000   500.000000   500.000000   \n",
       "23          NaN   500.000000   500.000000   500.000000   500.000000   \n",
       "24          NaN          NaN   500.000000   500.000000   500.000000   \n",
       "25          NaN          NaN          NaN   500.000000   500.000000   \n",
       "26          NaN          NaN          NaN          NaN   500.000000   \n",
       "27          NaN          NaN          NaN          NaN          NaN   \n",
       "28          NaN          NaN          NaN          NaN          NaN   \n",
       "29          NaN          NaN          NaN          NaN          NaN   \n",
       "30          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "             27           28           29           30  \n",
       "0   3021.653145  3189.869573  3367.269570  3554.352437  \n",
       "1   2708.683875  2859.903599  3019.383835  3187.573757  \n",
       "2   2427.126588  2563.055526  2706.414566  2857.607782  \n",
       "3   2173.828538  2296.001389  2424.857279  2560.759709  \n",
       "4   1945.953412  2055.750843  2171.559229  2293.705573  \n",
       "5   1740.949571  1839.613673  1943.684102  2053.455026  \n",
       "6   1556.521479  1645.169678  1738.680262  1837.317857  \n",
       "7   1390.603998  1470.241566  1554.252170  1642.873862  \n",
       "8   1241.339259  1312.870573  1388.334688  1467.945749  \n",
       "9   1107.055869  1171.294533  1239.069950  1310.574756  \n",
       "10   986.250183  1043.928144  1104.786559  1168.998716  \n",
       "11   877.569478   929.345217   983.980874  1041.632327  \n",
       "12   779.796797   826.262706   875.300168   927.049400  \n",
       "13   691.837327   733.526343   777.527487   823.966890  \n",
       "14   612.706141   650.097708   689.568018   731.230526  \n",
       "15   545.448584   575.042607   610.436832   647.801891  \n",
       "16   504.494295   517.393990   539.247855   572.746790  \n",
       "17   500.000000   500.000000   500.000000   505.224792  \n",
       "18   500.000000   500.000000   500.000000   500.000000  \n",
       "19   500.000000   500.000000   500.000000   500.000000  \n",
       "20   500.000000   500.000000   500.000000   500.000000  \n",
       "21   500.000000   500.000000   500.000000   500.000000  \n",
       "22   500.000000   500.000000   500.000000   500.000000  \n",
       "23   500.000000   500.000000   500.000000   500.000000  \n",
       "24   500.000000   500.000000   500.000000   500.000000  \n",
       "25   500.000000   500.000000   500.000000   500.000000  \n",
       "26   500.000000   500.000000   500.000000   500.000000  \n",
       "27   500.000000   500.000000   500.000000   500.000000  \n",
       "28          NaN   500.000000   500.000000   500.000000  \n",
       "29          NaN          NaN   500.000000   500.000000  \n",
       "30          NaN          NaN          NaN   500.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(AoptionTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "62da9be0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>30</th>\n",
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       "      <td>293.200171</td>\n",
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       "      <td>424.557440</td>\n",
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       "      <td>1289.000735</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
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       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>...</td>\n",
       "      <td>759.588781</td>\n",
       "      <td>800.840456</td>\n",
       "      <td>844.332422</td>\n",
       "      <td>890.186346</td>\n",
       "      <td>938.530501</td>\n",
       "      <td>989.500125</td>\n",
       "      <td>1043.237803</td>\n",
       "      <td>1099.893861</td>\n",
       "      <td>1159.626791</td>\n",
       "      <td>1222.603692</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
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       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
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       "      <td>720.462000</td>\n",
       "      <td>759.588781</td>\n",
       "      <td>800.840456</td>\n",
       "      <td>844.332422</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>...</td>\n",
       "      <td>614.764374</td>\n",
       "      <td>648.150939</td>\n",
       "      <td>683.350658</td>\n",
       "      <td>720.462000</td>\n",
       "      <td>759.588781</td>\n",
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       "      <td>844.332422</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>...</td>\n",
       "      <td>553.061933</td>\n",
       "      <td>583.097568</td>\n",
       "      <td>614.764374</td>\n",
       "      <td>648.150939</td>\n",
       "      <td>683.350658</td>\n",
       "      <td>720.462000</td>\n",
       "      <td>759.588781</td>\n",
       "      <td>800.840456</td>\n",
       "      <td>844.332422</td>\n",
       "      <td>890.186346</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>...</td>\n",
       "      <td>497.552420</td>\n",
       "      <td>524.573449</td>\n",
       "      <td>553.061933</td>\n",
       "      <td>583.097568</td>\n",
       "      <td>614.764374</td>\n",
       "      <td>648.150939</td>\n",
       "      <td>683.350658</td>\n",
       "      <td>720.462000</td>\n",
       "      <td>759.588781</td>\n",
       "      <td>800.840456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>...</td>\n",
       "      <td>447.614265</td>\n",
       "      <td>471.923257</td>\n",
       "      <td>497.552420</td>\n",
       "      <td>524.573449</td>\n",
       "      <td>553.061933</td>\n",
       "      <td>583.097568</td>\n",
       "      <td>614.764374</td>\n",
       "      <td>648.150939</td>\n",
       "      <td>683.350658</td>\n",
       "      <td>720.462000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202.484593</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>...</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>424.557440</td>\n",
       "      <td>447.614265</td>\n",
       "      <td>471.923257</td>\n",
       "      <td>497.552420</td>\n",
       "      <td>524.573449</td>\n",
       "      <td>553.061933</td>\n",
       "      <td>583.097568</td>\n",
       "      <td>614.764374</td>\n",
       "      <td>648.150939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>...</td>\n",
       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>424.557440</td>\n",
       "      <td>447.614265</td>\n",
       "      <td>471.923257</td>\n",
       "      <td>497.552420</td>\n",
       "      <td>524.573449</td>\n",
       "      <td>553.061933</td>\n",
       "      <td>583.097568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>343.610642</td>\n",
       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>424.557440</td>\n",
       "      <td>447.614265</td>\n",
       "      <td>471.923257</td>\n",
       "      <td>497.552420</td>\n",
       "      <td>524.573449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>343.610642</td>\n",
       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>424.557440</td>\n",
       "      <td>447.614265</td>\n",
       "      <td>471.923257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>343.610642</td>\n",
       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "      <td>402.688283</td>\n",
       "      <td>424.557440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>343.610642</td>\n",
       "      <td>362.271416</td>\n",
       "      <td>381.945617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "      <td>325.911094</td>\n",
       "      <td>343.610642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>202.484593</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "      <td>293.200171</td>\n",
       "      <td>309.123258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>172.778459</td>\n",
       "      <td>182.161695</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>202.484593</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "      <td>263.772366</td>\n",
       "      <td>278.097290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>155.437095</td>\n",
       "      <td>163.878558</td>\n",
       "      <td>172.778459</td>\n",
       "      <td>182.161695</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>202.484593</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "      <td>237.298160</td>\n",
       "      <td>250.185325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>139.836243</td>\n",
       "      <td>147.430456</td>\n",
       "      <td>155.437095</td>\n",
       "      <td>163.878558</td>\n",
       "      <td>172.778459</td>\n",
       "      <td>182.161695</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>202.484593</td>\n",
       "      <td>213.481107</td>\n",
       "      <td>225.074818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>125.801211</td>\n",
       "      <td>132.633211</td>\n",
       "      <td>139.836243</td>\n",
       "      <td>147.430456</td>\n",
       "      <td>155.437095</td>\n",
       "      <td>163.878558</td>\n",
       "      <td>172.778459</td>\n",
       "      <td>182.161695</td>\n",
       "      <td>192.054515</td>\n",
       "      <td>202.484593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>113.174842</td>\n",
       "      <td>119.321130</td>\n",
       "      <td>125.801211</td>\n",
       "      <td>132.633211</td>\n",
       "      <td>139.836243</td>\n",
       "      <td>147.430456</td>\n",
       "      <td>155.437095</td>\n",
       "      <td>163.878558</td>\n",
       "      <td>172.778459</td>\n",
       "      <td>182.161695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>101.815752</td>\n",
       "      <td>107.345152</td>\n",
       "      <td>113.174842</td>\n",
       "      <td>119.321130</td>\n",
       "      <td>125.801211</td>\n",
       "      <td>132.633211</td>\n",
       "      <td>139.836243</td>\n",
       "      <td>147.430456</td>\n",
       "      <td>155.437095</td>\n",
       "      <td>163.878558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.571174</td>\n",
       "      <td>101.815752</td>\n",
       "      <td>107.345152</td>\n",
       "      <td>113.174842</td>\n",
       "      <td>119.321130</td>\n",
       "      <td>125.801211</td>\n",
       "      <td>132.633211</td>\n",
       "      <td>139.836243</td>\n",
       "      <td>147.430456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.596746</td>\n",
       "      <td>96.571174</td>\n",
       "      <td>101.815752</td>\n",
       "      <td>107.345152</td>\n",
       "      <td>113.174842</td>\n",
       "      <td>119.321130</td>\n",
       "      <td>125.801211</td>\n",
       "      <td>132.633211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.878554</td>\n",
       "      <td>91.596746</td>\n",
       "      <td>96.571174</td>\n",
       "      <td>101.815752</td>\n",
       "      <td>107.345152</td>\n",
       "      <td>113.174842</td>\n",
       "      <td>119.321130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.403398</td>\n",
       "      <td>86.878554</td>\n",
       "      <td>91.596746</td>\n",
       "      <td>96.571174</td>\n",
       "      <td>101.815752</td>\n",
       "      <td>107.345152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.158760</td>\n",
       "      <td>82.403398</td>\n",
       "      <td>86.878554</td>\n",
       "      <td>91.596746</td>\n",
       "      <td>96.571174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.132765</td>\n",
       "      <td>78.158760</td>\n",
       "      <td>82.403398</td>\n",
       "      <td>86.878554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.314151</td>\n",
       "      <td>74.132765</td>\n",
       "      <td>78.158760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.692235</td>\n",
       "      <td>70.314151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.256886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0           1           2           3           4           5   \\\n",
       "0   309.123258  325.911094  343.610642  362.271416  381.945617  402.688283   \n",
       "1          NaN  293.200171  309.123258  325.911094  343.610642  362.271416   \n",
       "2          NaN         NaN  278.097290  293.200171  309.123258  325.911094   \n",
       "3          NaN         NaN         NaN  263.772366  278.097290  293.200171   \n",
       "4          NaN         NaN         NaN         NaN  250.185325  263.772366   \n",
       "5          NaN         NaN         NaN         NaN         NaN  237.298160   \n",
       "6          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "7          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "8          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "9          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "10         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "11         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "12         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "13         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "14         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "15         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "16         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "17         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "18         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "19         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "20         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "21         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "22         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "23         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "24         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "25         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "26         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "27         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "28         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "29         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "30         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "            6           7           8           9   ...          21  \\\n",
       "0   424.557440  447.614265  471.923257  497.552420  ...  938.530501   \n",
       "1   381.945617  402.688283  424.557440  447.614265  ...  844.332422   \n",
       "2   343.610642  362.271416  381.945617  402.688283  ...  759.588781   \n",
       "3   309.123258  325.911094  343.610642  362.271416  ...  683.350658   \n",
       "4   278.097290  293.200171  309.123258  325.911094  ...  614.764374   \n",
       "5   250.185325  263.772366  278.097290  293.200171  ...  553.061933   \n",
       "6   225.074818  237.298160  250.185325  263.772366  ...  497.552420   \n",
       "7          NaN  213.481107  225.074818  237.298160  ...  447.614265   \n",
       "8          NaN         NaN  202.484593  213.481107  ...  402.688283   \n",
       "9          NaN         NaN         NaN  192.054515  ...  362.271416   \n",
       "10         NaN         NaN         NaN         NaN  ...  325.911094   \n",
       "11         NaN         NaN         NaN         NaN  ...  293.200171   \n",
       "12         NaN         NaN         NaN         NaN  ...  263.772366   \n",
       "13         NaN         NaN         NaN         NaN  ...  237.298160   \n",
       "14         NaN         NaN         NaN         NaN  ...  213.481107   \n",
       "15         NaN         NaN         NaN         NaN  ...  192.054515   \n",
       "16         NaN         NaN         NaN         NaN  ...  172.778459   \n",
       "17         NaN         NaN         NaN         NaN  ...  155.437095   \n",
       "18         NaN         NaN         NaN         NaN  ...  139.836243   \n",
       "19         NaN         NaN         NaN         NaN  ...  125.801211   \n",
       "20         NaN         NaN         NaN         NaN  ...  113.174842   \n",
       "21         NaN         NaN         NaN         NaN  ...  101.815752   \n",
       "22         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "23         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "24         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "25         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "26         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "27         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "28         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "29         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "30         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "\n",
       "            22           23           24           25           26  \\\n",
       "0   989.500125  1043.237803  1099.893861  1159.626791  1222.603692   \n",
       "1   890.186346   938.530501   989.500125  1043.237803  1099.893861   \n",
       "2   800.840456   844.332422   890.186346   938.530501   989.500125   \n",
       "3   720.462000   759.588781   800.840456   844.332422   890.186346   \n",
       "4   648.150939   683.350658   720.462000   759.588781   800.840456   \n",
       "5   583.097568   614.764374   648.150939   683.350658   720.462000   \n",
       "6   524.573449   553.061933   583.097568   614.764374   648.150939   \n",
       "7   471.923257   497.552420   524.573449   553.061933   583.097568   \n",
       "8   424.557440   447.614265   471.923257   497.552420   524.573449   \n",
       "9   381.945617   402.688283   424.557440   447.614265   471.923257   \n",
       "10  343.610642   362.271416   381.945617   402.688283   424.557440   \n",
       "11  309.123258   325.911094   343.610642   362.271416   381.945617   \n",
       "12  278.097290   293.200171   309.123258   325.911094   343.610642   \n",
       "13  250.185325   263.772366   278.097290   293.200171   309.123258   \n",
       "14  225.074818   237.298160   250.185325   263.772366   278.097290   \n",
       "15  202.484593   213.481107   225.074818   237.298160   250.185325   \n",
       "16  182.161695   192.054515   202.484593   213.481107   225.074818   \n",
       "17  163.878558   172.778459   182.161695   192.054515   202.484593   \n",
       "18  147.430456   155.437095   163.878558   172.778459   182.161695   \n",
       "19  132.633211   139.836243   147.430456   155.437095   163.878558   \n",
       "20  119.321130   125.801211   132.633211   139.836243   147.430456   \n",
       "21  107.345152   113.174842   119.321130   125.801211   132.633211   \n",
       "22   96.571174   101.815752   107.345152   113.174842   119.321130   \n",
       "23         NaN    91.596746    96.571174   101.815752   107.345152   \n",
       "24         NaN          NaN    86.878554    91.596746    96.571174   \n",
       "25         NaN          NaN          NaN    82.403398    86.878554   \n",
       "26         NaN          NaN          NaN          NaN    78.158760   \n",
       "27         NaN          NaN          NaN          NaN          NaN   \n",
       "28         NaN          NaN          NaN          NaN          NaN   \n",
       "29         NaN          NaN          NaN          NaN          NaN   \n",
       "30         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "             27           28           29           30  \n",
       "0   1289.000735  1359.003664  1432.808304  1510.621120  \n",
       "1   1159.626791  1222.603692  1289.000735  1359.003664  \n",
       "2   1043.237803  1099.893861  1159.626791  1222.603692  \n",
       "3    938.530501   989.500125  1043.237803  1099.893861  \n",
       "4    844.332422   890.186346   938.530501   989.500125  \n",
       "5    759.588781   800.840456   844.332422   890.186346  \n",
       "6    683.350658   720.462000   759.588781   800.840456  \n",
       "7    614.764374   648.150939   683.350658   720.462000  \n",
       "8    553.061933   583.097568   614.764374   648.150939  \n",
       "9    497.552420   524.573449   553.061933   583.097568  \n",
       "10   447.614265   471.923257   497.552420   524.573449  \n",
       "11   402.688283   424.557440   447.614265   471.923257  \n",
       "12   362.271416   381.945617   402.688283   424.557440  \n",
       "13   325.911094   343.610642   362.271416   381.945617  \n",
       "14   293.200171   309.123258   325.911094   343.610642  \n",
       "15   263.772366   278.097290   293.200171   309.123258  \n",
       "16   237.298160   250.185325   263.772366   278.097290  \n",
       "17   213.481107   225.074818   237.298160   250.185325  \n",
       "18   192.054515   202.484593   213.481107   225.074818  \n",
       "19   172.778459   182.161695   192.054515   202.484593  \n",
       "20   155.437095   163.878558   172.778459   182.161695  \n",
       "21   139.836243   147.430456   155.437095   163.878558  \n",
       "22   125.801211   132.633211   139.836243   147.430456  \n",
       "23   113.174842   119.321130   125.801211   132.633211  \n",
       "24   101.815752   107.345152   113.174842   119.321130  \n",
       "25    91.596746    96.571174   101.815752   107.345152  \n",
       "26    82.403398    86.878554    91.596746    96.571174  \n",
       "27    74.132765    78.158760    82.403398    86.878554  \n",
       "28          NaN    70.314151    74.132765    78.158760  \n",
       "29          NaN          NaN    66.692235    70.314151  \n",
       "30          NaN          NaN          NaN    63.256886  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4103e70a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b069474",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below here is for running the american option\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "875f8862",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Test American option\n",
    "\n",
    "initElecPrice = 30\n",
    "multiTreeOption = []\n",
    "steps = 31\n",
    "locVal = np.arange(1, assetLife-1, (assetLife-2) / steps )\n",
    "\n",
    "for n in range(0,len(locVal)): \n",
    "\n",
    "    averageOptionValue=[]\n",
    "\n",
    "    for _ in range(0,50):    \n",
    "        tree,optionTree,AoptionTree = InitCalc(TurbineDeathSimulation(scale = 1,loc = locVal[n]) ,steps = 31 , impCostGearbox = 100, abdValue = 500)        \n",
    "        averageOptionValue.append(AoptionTree[0,0]-tree[0,0])\n",
    "\n",
    "    multiTreeOption.append( np.mean(averageOptionValue) )\n",
    "\n",
    "multiTreeOption = np.asarray(multiTreeOption)\n",
    "print(multiTreeOption)\n",
    "# ######################\n",
    "# Estimating mean of standard deviation of logrithmic returns of multiTreeOption\n",
    "\n",
    "def substitute(multiTreeOption):\n",
    "    return multiTreeOption\n",
    "metaSigma = SigmaCalculator(substitute , (multiTreeOption))\n",
    "\n",
    "\n",
    "print(\"mean of logrithmic returns standard deviations is: \" + str(metaSigma))\n",
    "\n",
    "\n",
    "\n",
    "# ######################\n",
    "# Final tree seeding \n",
    "\n",
    "delta = assetLife / steps \n",
    "u = np.exp(metaSigma * np.sqrt(delta)) \n",
    "d = 1/u\n",
    "p = ( np.exp(riskFreeRate * delta) - d ) / (u - d)\n",
    "if p > 1:\n",
    "    p=0.8\n",
    "print(p)\n",
    "finalTree = np.zeros((steps, steps))\n",
    "\n",
    "if multiTreeOption[0] < multiTreeOption[-1]:  # When seeding largest values must be first as that is how BT works\n",
    "    finalTree[:,-1] = np.fliplr([multiTreeOption])\n",
    "else:\n",
    "    finalTree[:,-1] = multiTreeOption\n",
    "\n",
    "finalTree[finalTree == 0] = None\n",
    "\n",
    "#back propagation algorithem\n",
    "for i in range(steps-1, 0 ,-1):\n",
    "    for j in range(0,steps-1):\n",
    "\n",
    "        case1 = ( p*finalTree[j , i] + (1-p)*finalTree[j+1 , i] )           \n",
    "        finalTree[j , i-1] = case1\n",
    "\n",
    "\n",
    "\n",
    "pd.DataFrame(finalTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5cff9e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f05ae7cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e4a78fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[492.78969195 493.24267243 493.21158916 493.11372373 492.28341361\n",
      " 490.69553833 489.24043887 487.69999761 486.20469741 484.74243887\n",
      " 483.17559665 481.81114472 480.25818808 478.92053731 477.40940789\n",
      " 475.88245827 474.69428519 473.38857817 472.04580091 470.7340962\n",
      " 469.42505132 468.11268108 466.76336467 465.53473467 464.40084723\n",
      " 463.04320896 461.95824546 460.73313573 460.04185556 459.91553905\n",
      " 460.20125133]\n",
      "mean of logrithmic returns standard deviations is: 0.0011921226907372106\n",
      "[485.81155764 486.48940436 485.94511843 485.68334915 484.04319985\n",
      " 481.36729005 478.32890877 475.1981727  472.20451028 469.51980135\n",
      " 466.39478518 463.53120721 460.65057638 457.62314922 454.87365215\n",
      " 452.28179532 449.55789728 446.68388465 444.23783522 441.37909061\n",
      " 438.85156233 436.19714266 433.75919207 431.27175581 428.55164355\n",
      " 426.17000375 423.66210452 421.5068576  419.67650928 420.09234822\n",
      " 419.37837738]\n",
      "mean of logrithmic returns standard deviations is: 0.002257430220054912\n",
      "[478.39185633 479.69061549 478.46306062 478.60892049 475.90940978\n",
      " 471.90274476 467.43776497 462.93442706 458.56221284 454.07846439\n",
      " 449.49555733 445.22891595 440.72476541 437.03584842 432.59644675\n",
      " 428.66334426 424.22431875 420.17680205 416.26844858 412.16908736\n",
      " 408.33399035 404.61906785 400.50246306 396.87418085 392.91255828\n",
      " 389.21721512 385.58403431 382.12666984 379.60860217 380.68139184\n",
      " 379.7642739 ]\n",
      "mean of logrithmic returns standard deviations is: 0.0037738750148022825\n",
      "[473.27681274 473.78901887 472.56765811 472.14520189 468.88763067\n",
      " 463.12934763 456.83804926 450.24440468 444.47327276 438.69697901\n",
      " 432.6365275  427.0990541  421.66125056 415.62494944 409.90664638\n",
      " 404.64912622 398.88115316 393.61202491 388.17380717 382.71743095\n",
      " 377.83399756 372.6630901  367.46039617 362.17057012 357.33818989\n",
      " 352.04384625 347.71059417 342.80184918 339.79115937 340.97193132\n",
      " 339.36589993]\n",
      "mean of logrithmic returns standard deviations is: 0.0050281678434017695\n",
      "[464.58663327 465.43989408 464.52352862 466.16899098 460.87935328\n",
      " 453.78512678 445.44060122 438.19802199 431.02400245 423.28022163\n",
      " 416.29181742 408.4396372  401.03518975 394.46473159 387.86306745\n",
      " 380.22922526 373.86841579 367.13812394 360.04827155 353.84713193\n",
      " 347.22648916 341.19635123 333.85289909 327.72175468 321.59298981\n",
      " 315.44910851 309.60101124 303.56507029 299.61551167 299.57688749\n",
      " 300.14949471]\n",
      "mean of logrithmic returns standard deviations is: 0.0072785285924835815\n",
      "[457.34865971 458.7668054  458.87635891 459.86068662 452.839211\n",
      " 443.75765156 434.94199703 426.13843564 417.13496383 408.2577647\n",
      " 399.4896504  390.58858867 381.75595782 373.1949256  364.88319374\n",
      " 357.01782308 347.8759441  340.01551768 332.40433349 324.89726071\n",
      " 316.74801288 308.77268963 300.71910026 293.37372563 286.15360204\n",
      " 278.76940759 271.01188505 264.21723118 258.99569683 258.49586352\n",
      " 260.68142542]\n",
      "mean of logrithmic returns standard deviations is: 0.009865710910991771\n",
      "[447.99442259 451.02059699 450.0979698  451.09419977 445.35763105\n",
      " 434.15641746 424.74042135 413.04123162 403.39689367 392.85926216\n",
      " 381.49991017 372.49507955 362.4023428  352.21894164 343.05620294\n",
      " 333.08847427 323.370574   313.91750972 303.44462958 295.02573174\n",
      " 286.01740979 276.86354397 267.8840634  261.34978116 257.62175332\n",
      " 255.24411319 253.85957859 253.82935639 254.39632243 255.99318876\n",
      " 254.48415026]\n",
      "mean of logrithmic returns standard deviations is: 0.013045669532797094\n",
      "[443.6466608  446.55457414 442.87809065 446.31840445 437.05203166\n",
      " 425.51700305 413.12143449 400.92378671 389.21755536 377.89917971\n",
      " 365.83558064 354.46011951 341.59991379 331.47826343 320.51686367\n",
      " 309.92498199 297.87615365 287.89238623 278.03366291 273.15730572\n",
      " 268.56447405 268.23584075 267.91707951 269.60632313 272.17770249\n",
      " 274.88901086 276.96664921 280.44575318 281.93682057 282.44362103\n",
      " 282.10580066]\n",
      "mean of logrithmic returns standard deviations is: 0.017908377114720885\n",
      "[435.54485188 439.72355051 435.6561981  438.24406011 428.27670442\n",
      " 416.40307664 402.12232957 388.96057402 375.56389495 362.16899043\n",
      " 348.91500862 336.37076327 323.09575397 310.00949604 297.68621269\n",
      " 288.83585691 283.30210788 281.83561209 281.68005819 284.85117597\n",
      " 286.87193314 290.71400298 294.61852724 299.02682593 303.40421081\n",
      " 307.34590506 311.78126039 317.06817007 318.83292383 318.57140691\n",
      " 321.05309914]\n",
      "mean of logrithmic returns standard deviations is: 0.021627116034651856\n",
      "[428.13764013 432.211035   430.92386335 430.98655182 422.04103147\n",
      " 406.75229624 391.11824971 376.41515392 361.47764952 346.29216932\n",
      " 332.43199031 317.91077552 303.81917178 296.84084528 294.93636165\n",
      " 294.80388274 296.88865945 300.37123911 307.01322286 309.94780301\n",
      " 315.81850548 323.62529926 327.4905522  335.19870659 338.61071348\n",
      " 345.74643179 351.47139596 355.18314033 356.72662955 358.68388708\n",
      " 360.46735237]\n",
      "mean of logrithmic returns standard deviations is: 0.02385778550410478\n",
      "[421.39338368 425.4980989  425.06814158 421.93923179 413.16073324\n",
      " 396.40079215 379.88771806 364.13319637 347.47276575 330.93209687\n",
      " 316.91658257 308.66269273 304.30796501 305.71146107 312.893127\n",
      " 317.10228705 324.77028176 329.51759311 339.25863834 345.08405862\n",
      " 351.55031186 359.1523291  364.35619657 373.82702253 377.62128596\n",
      " 384.92985081 390.74086012 396.04204267 399.24434076 402.67813661\n",
      " 404.85049413]\n",
      "mean of logrithmic returns standard deviations is: 0.024879834814924055\n",
      "[415.84754733 415.87346851 416.66328249 411.94790125 404.25637825\n",
      " 385.80483781 368.54387439 352.13347324 333.44319589 319.61867746\n",
      " 317.9603232  319.91342458 323.01862517 331.75992115 338.11452326\n",
      " 344.41156452 357.47592713 363.01803698 374.04211746 379.64085924\n",
      " 390.07356296 397.00355381 404.07578647 411.02536398 416.37103539\n",
      " 424.41433915 431.99449927 439.44997672 442.28781597 442.30819509\n",
      " 441.41888744]\n",
      "mean of logrithmic returns standard deviations is: 0.02492091054534656\n",
      "[411.92408474 410.2510401  411.4895627  410.38991598 398.31325869\n",
      " 378.62061236 358.03858587 338.2309982  327.14480858 326.16579122\n",
      " 332.67203935 341.26523479 348.95194229 359.81173223 369.32768809\n",
      " 377.40871797 388.69693319 400.3757974  408.48890693 416.07627638\n",
      " 424.9790873  432.16537554 443.53211013 449.94390387 457.24983376\n",
      " 465.57605989 473.15739628 478.85065495 487.76219034 485.86621385\n",
      " 482.93917685]\n",
      "mean of logrithmic returns standard deviations is: 0.025322769494287075\n",
      "[402.71967046 403.79483681 397.20449894 406.22892526 391.28111168\n",
      " 370.56525462 348.5421338  334.10161324 336.70060202 344.2550728\n",
      " 361.89512293 365.27922046 378.33762067 391.05607197 401.57257579\n",
      " 414.1726245  424.80861552 434.43829533 445.10714347 451.35534062\n",
      " 460.89816636 470.85591647 479.35299063 489.77855504 494.79418398\n",
      " 507.22184325 514.16593804 521.77045939 523.62992501 525.27303607\n",
      " 525.96816075]\n",
      "mean of logrithmic returns standard deviations is: 0.025940203292956807\n",
      "[396.33465343 399.99788508 394.85112635 394.84956238 382.61833199\n",
      " 361.0538101  345.68963901 344.48119004 353.55759664 365.96655851\n",
      " 383.19013868 391.66957969 408.24894048 421.08875054 433.60850151\n",
      " 449.43329834 462.35772484 469.75757207 481.59418867 491.0738294\n",
      " 500.71355923 509.31280162 517.36208236 529.65392898 535.76945573\n",
      " 545.91867281 556.07482982 560.44563913 569.47238917 567.3051654\n",
      " 565.74140149]\n",
      "mean of logrithmic returns standard deviations is: 0.023276266022621492\n",
      "[391.17167385 385.95469191 392.63867163 394.5154513  375.57783277\n",
      " 353.42869864 347.74318214 354.98360094 375.91245762 392.49916622\n",
      " 411.65414157 428.26098185 443.49842835 457.44510727 471.37241147\n",
      " 481.02615956 495.5178636  500.83232426 512.94215525 527.02253695\n",
      " 539.43513648 549.32004615 557.90837519 567.38923023 577.58095116\n",
      " 584.40866144 594.06124255 601.37118495 606.39787595 611.15002197\n",
      " 610.59859202]\n",
      "mean of logrithmic returns standard deviations is: 0.02440067038860056\n",
      "[387.09162844 382.95633947 382.24994507 384.77215361 368.0265636\n",
      " 355.19905462 362.46545939 377.5011794  398.12894818 418.72089653\n",
      " 441.29716961 448.26589049 472.19533484 487.67043535 504.05134314\n",
      " 510.17880296 530.83191201 542.28892147 556.7284631  563.01981593\n",
      " 578.31579004 587.39822691 600.13022763 607.49812996 615.28363156\n",
      " 629.38932628 635.59379138 644.73398422 643.93317045 651.77956026\n",
      " 649.93404876]\n",
      "mean of logrithmic returns standard deviations is: 0.02267160391416447\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[379.83710644 384.26758841 381.37678019 376.7266806  365.75182185\n",
      " 365.0963367  382.68186271 402.7829757  427.01423722 446.53393176\n",
      " 466.6577225  484.21623628 502.46309431 518.27616756 535.68397772\n",
      " 550.37560911 565.97495878 581.09145832 588.2645537  603.96565246\n",
      " 614.87733848 624.20993317 635.87978321 646.348772   654.91252733\n",
      " 666.42350413 675.68409791 686.50666024 692.72822714 693.98525222\n",
      " 688.7720505 ]\n",
      "mean of logrithmic returns standard deviations is: 0.019709952694027318\n",
      "[377.95371806 380.9268009  375.44349172 379.5634812  365.33820709\n",
      " 371.66746684 404.32464454 428.93112179 456.45156177 479.48864915\n",
      " 496.27975381 518.84909604 529.79484116 559.63098769 569.57830751\n",
      " 588.71240824 601.67750959 613.09362671 627.12923597 636.7354716\n",
      " 647.00405015 666.598341   675.32069546 686.95162036 693.20317532\n",
      " 709.19911051 715.32486679 727.9666326  735.34772829 736.47832425\n",
      " 735.16172414]\n",
      "mean of logrithmic returns standard deviations is: 0.023477150617317904\n",
      "[377.95291005 383.56165623 382.15652461 380.37940634 371.8690312\n",
      " 395.63321236 427.20674431 448.0900285  485.75184403 505.18256913\n",
      " 535.87251158 549.46107965 569.02234165 588.56684943 605.28333534\n",
      " 623.29135649 633.51342652 648.26713551 665.1332149  674.35140681\n",
      " 691.28992416 701.94295043 715.11874336 724.95520842 736.87405258\n",
      " 749.14458183 759.64362048 770.83269108 777.52781111 771.25120939\n",
      " 776.87591516]\n",
      "mean of logrithmic returns standard deviations is: 0.0229490313028146\n",
      "[387.90510561 389.55904392 383.79724148 390.70023225 385.08791095\n",
      " 416.59178566 457.80931647 486.49844991 512.47619285 540.41299596\n",
      " 556.43644895 576.83930948 594.90468372 619.02922387 637.09047088\n",
      " 652.53476994 670.23654341 683.13256672 699.50497792 719.29592583\n",
      " 725.6941083  740.52661625 754.81166724 764.03054947 779.2653445\n",
      " 789.02432465 798.03985172 809.59631151 820.65501035 807.29486258\n",
      " 807.74195307]\n",
      "mean of logrithmic returns standard deviations is: 0.024476240596632913\n",
      "[391.44607579 406.88239033 387.81127031 393.18258669 404.90556329\n",
      " 447.10231194 473.88618066 504.88292942 528.30520647 568.57160745\n",
      " 588.95377869 609.89002792 639.59064835 658.79393987 671.21154446\n",
      " 692.70236631 708.39521583 719.14244944 738.01787999 750.08230288\n",
      " 765.42630011 774.78126781 794.74724361 803.31534824 822.18769986\n",
      " 830.54715364 838.01781771 851.75467137 851.56348646 870.37523827\n",
      " 857.88937762]\n",
      "mean of logrithmic returns standard deviations is: 0.026206716659558982\n",
      "[399.20856916 401.59248092 398.28323838 406.286753   425.60297534\n",
      " 452.08693902 507.87799677 528.04608917 569.65690424 592.19316677\n",
      " 618.71299311 636.45603077 659.95364236 686.94535539 703.49958123\n",
      " 724.82703124 737.78184573 755.57278573 773.41429461 788.27484434\n",
      " 802.52405369 812.38173921 830.97385881 845.86816824 855.30217929\n",
      " 869.08357654 882.8461619  893.85440617 903.76115038 896.87901134\n",
      " 898.59674066]\n",
      "mean of logrithmic returns standard deviations is: 0.024373650865872218\n",
      "[418.20733116 415.300969   423.17915305 416.76788605 445.43862136\n",
      " 480.02922576 521.64360263 561.71184805 596.65632075 624.94669975\n",
      " 643.58817117 674.66709685 700.09830929 723.5424043  736.52422548\n",
      " 761.91149455 780.76237017 790.74149787 811.05526599 823.98817371\n",
      " 837.92871352 854.58861203 869.93479746 884.89968596 900.57884874\n",
      " 913.74234328 924.08297743 932.40715301 942.09522429 946.94323228\n",
      " 946.87520849]\n",
      "mean of logrithmic returns standard deviations is: 0.024059803576269283\n",
      "[439.78128848 421.32673777 422.42135644 437.03839464 456.18838167\n",
      " 499.36560246 554.6033548  580.81415735 619.10642239 651.60699344\n",
      " 683.04043771 708.45535289 729.58648442 753.24550246 772.80060113\n",
      " 796.18856669 810.3186493  827.95950509 848.23714902 864.25060633\n",
      " 880.148746   890.77692255 907.71959545 921.23485671 940.43725066\n",
      " 951.21135106 962.3406683  972.97189498 977.85861521 980.89057091\n",
      " 989.07340075]\n",
      "mean of logrithmic returns standard deviations is: 0.02684006142070378\n",
      "[ 444.9157436   435.04324666  443.43395096  446.08265063  477.21865807\n",
      "  528.67673878  574.11853938  609.34817839  655.98939328  681.69390896\n",
      "  713.02411861  744.32546429  760.10146381  781.55994486  806.13119605\n",
      "  826.56403878  851.0515987   864.09296527  890.9983551   897.03963287\n",
      "  918.2248259   934.02027576  943.3262534   959.49654569  978.63602419\n",
      "  993.4740653  1008.01310714 1020.00465263 1031.4684904  1023.04641251\n",
      " 1025.74342192]\n",
      "mean of logrithmic returns standard deviations is: 0.026503073610018083\n",
      "[ 471.70509541  460.78631358  476.76333305  461.96672766  502.14276747\n",
      "  553.97129633  607.15007565  642.64406909  680.12641466  711.90604781\n",
      "  735.52305287  765.77184018  797.72013871  810.58934531  835.30582613\n",
      "  865.19057768  883.18498587  901.77049016  923.50963727  929.98419745\n",
      "  949.50889347  969.77403672  984.24883042 1003.26564613 1015.21837863\n",
      " 1029.19840516 1044.85311636 1055.96517484 1067.24895899 1065.10193155\n",
      " 1065.5373002 ]\n",
      "mean of logrithmic returns standard deviations is: 0.02862248175722715\n"
     ]
    }
   ],
   "source": [
    "# Plot multiple values from American option######################\n",
    "# calculates out option values for different failure situations and stores in multiTreeOption array\n",
    "plotarr = []\n",
    "for QQ in range(1,28):\n",
    "    initElecPrice = QQ * 2\n",
    "    multiTreeOption = []\n",
    "    steps = 31\n",
    "    locVal = np.arange(1, assetLife-1, (assetLife-2) / steps )\n",
    "\n",
    "    for n in range(0,len(locVal)): \n",
    "\n",
    "        averageOptionValue=[]\n",
    "\n",
    "        for _ in range(0,50):    \n",
    "            tree,optionTree,AoptionTree = InitCalc(TurbineDeathSimulation(scale = 1,loc = locVal[n]) ,steps = 31 , impCostGearbox = 100, abdValue = 500)        \n",
    "            averageOptionValue.append(AoptionTree[0,0]-tree[0,0])\n",
    "\n",
    "        multiTreeOption.append( np.mean(averageOptionValue) )\n",
    "\n",
    "    multiTreeOption = np.asarray(multiTreeOption)\n",
    "    print(multiTreeOption)\n",
    "    # ######################\n",
    "    # Estimating mean of standard deviation of logrithmic returns of multiTreeOption\n",
    "\n",
    "    def substitute(multiTreeOption):\n",
    "        return multiTreeOption\n",
    "    metaSigma = SigmaCalculator(substitute , (multiTreeOption))\n",
    "\n",
    "\n",
    "    print(\"mean of logrithmic returns standard deviations is: \" + str(metaSigma))\n",
    "\n",
    "\n",
    "\n",
    "    # ######################\n",
    "    # Final tree seeding \n",
    "\n",
    "    delta = assetLife / steps \n",
    "    u = np.exp(metaSigma * np.sqrt(delta)) \n",
    "    d = 1/u\n",
    "    p = ( np.exp(riskFreeRate * delta) - d ) / (u - d)\n",
    "    if p > 1:\n",
    "        p=0.99\n",
    "    finalTree = np.zeros((steps, steps))\n",
    "\n",
    "    if multiTreeOption[0] < multiTreeOption[-1]:  # When seeding largest values must be first as that is how BT works\n",
    "        finalTree[:,-1] = np.fliplr([multiTreeOption])\n",
    "    else:\n",
    "        finalTree[:,-1] = multiTreeOption\n",
    "\n",
    "    finalTree[finalTree == 0] = None\n",
    "\n",
    "    #back propagation algorithem\n",
    "    for i in range(steps-1, 0 ,-1):\n",
    "        for j in range(0,steps-1):\n",
    "\n",
    "            case1 = ( p*finalTree[j , i] + (1-p)*finalTree[j+1 , i] )           \n",
    "            finalTree[j , i-1] = case1\n",
    "\n",
    "\n",
    "    plotarr.append(finalTree[0,0])\n",
    "    #pd.DataFrame(finalTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "722383c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4b691c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "3ae3082a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Option value £(Mill)')"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s9tu5c2fD19fX2Lt3r9MxJiUlGYCRlJSUw6MTERG5RFycYbRqZRhjxxoGGIavr2H8+qu7oyq0cvL97bFTe7GxsRQrVoyHHnrIrr1bt24EBAQwb948AM6cOcOKFSto2bIl9evXt/ULDg5mwIAB7Ny5k7i4OFt7TEwMgYGBDLReCfGfYcOGcf78eT744IO8OygRERFHFiyA2Fh49VXz/dixcNNNbg1JTDme2jt//jyxsbGsWbOGbdu2cfToUSwWC6VKlaJOnTq0aNGCVq1a4evrmxfx2qSlpREQEIDFYrFr9/LyolixYvz9998kJiaya9cu0tLSaNq0aZZ9NGnSBIC4uDiioqLIzMzkl19+oUGDBgRctnAvKioKi8Vil3Q5iiktLc32Pjk5+VoOUUREirJ9+8wpPIsFrP+JT0+H2rXhjjvM7RUrujdGcT6ROnLkCG+99Rbz588nMTERwzDw8fGhZMmSGIbB5s2b+fzzz/nf//5HeHg4ffv2Zfjw4Vx//fV5Enjt2rXZsWMHW7dutRtp2rp1KydPngTg33//JSEhAYCIiIgs+7C2HTx4EICTJ09y9uxZh339/f0JDw+39XVkwoQJjBs3zuVjEhERsalUyXH7tm3w30AAugjK7Zya2nv55ZepXr06M2bM4K677iImJoa9e/eSnp7O4cOHOXLkCOnp6fzzzz/ExMTQvn17pk+fTvXq1Xklj2pbDBs2DC8vL7p27crKlSv5999/WbVqFd26dbONhqWmppKamgqYidDlrKNO1j5X6mvtb+3jyKhRo0hKSrI99u/f7/oBiohI0bZoEfhkM97h42NuF7dzakRq5syZjB8/nv79+9uuhHOkYsWKVKxYkW7dupGamsqsWbN47bXXeP7553MtYKvmzZuzdOlShgwZwj333AOAt7c3AwYMoHbt2ixfvpzQ0FBbvJdOuVmdO3cOwNbnSn2t/a90/P7+/tkmYSIiIjnSowf4+UHXrlm3bdoEDRrkf0yShVOJ1J49e7KsGbqawMBAhg4dyqOPPupSYM7o0qULnTt35vfffyclJYUbbriB0qVLExUVhY+PD9WqVePMmTMADqfkrG3WqbwSJUpQrFgxh33T0tJITEykRYsWeXY8IiIiNseOwdCh9m1eXpCZ6Z54xCGnEqmcJlG59VlneHt7262ROnz4MFu2bKFFixYEBgZSt25d/P392bBhQ5bPbty4EYBGjRoB5kL1Bg0asGXLFtLS0uxGl37++WcMw7D1FRERyTPnzkHHjnDokFm9vG5ds37UnDmwfz+ULu3uCOU/Hlv+wJHMzEyGDBlCRkYGo0ePBswyBx06dGDNmjX8+uuvtr6nT59m9uzZVK9enaioKFt79+7dSU1N5b333rPb99tvv42Pjw/dunXLn4MREZGiyTCgf39Yvx6KF4dffjEfjz5qTunt3QuRke6OUv7j1IhUv379crxji8XCnDlzcvw5Z50+fZqoqCg6depE5cqVSUpKYsmSJcTHxzN+/HhatWpl6zthwgS+++472rVrx/DhwwkNDWXWrFkcPHiQL7/80q6EwsCBA5k3bx4jRoxg79691KpVi5UrV7J8+XKef/55KmV3FYWIiEhuePlliIkxF5R/9JF9vSiLBbQWt0CxGMbVr5308sr5wJXFYiEjI8OloJyRnp5Onz592LhxI4cOHSIwMJDGjRszYsQI2rdvn6X/X3/9xbPPPssPP/xAeno6DRo0YOzYsXa3h7E6deoUzz//PJ988gnHjx+natWqPPHEEwwePDhL3aorSU5OJiwsjKSkJEJDQ6/peEVEpAhYuhS6dzdfv/ceXFYcWvJHTr6/nUqkxDVKpERExGkbN0LLlpCWBiNGwMSJ7o6oyMrJ93ehWiMlIiLikfbuhfvvN5Oo++6D1193d0TiJCVSIiIi7pScDB06wNGjUL8+LF5sXqknHsGpxeYvvfRSjndssVh44YUXcvw5ERGRIuPCBXjoIfjjDyhbFj7/HIKD3R2V5IDHLjb3BFojJSIiVzRkCEydCsWKwdq1oFqFBUJOvr+dGpH6559/ciUwERER+c/06WYSBeZ985REeSSnEqmKFSvmdRwiIiJFx1dfmaNRABMmQOfO7o1HXKbF5iIiIvnpjz/MGxFnZsIjj8D//Z+7I5Jr4NSI1IIFCwDo1asXFovF9v5qevfu7XpkIiIihc3Ro+YVeikpcPvt8O67ZrVy8VhOLza3WCycPXsWPz8/2/srfVSLzbXYXERELnHuHLRuDRs2QLVqZgHO665zd1TiQK4vNo+NjQXAz8/P7r2IiIhcxebNMHIk+PqaSVTx4vDFF0qiCgmnEqkWLVpc8b2IiIhkY8ECsA5A+PjAxx/DDTe4NybJNU4lUiIiIpID+/ZBYqK5/un99y+2P/sshIWZ23VFfKHg9E2L165dm+Od33777Tn+TGGiNVIiIkWUMwvInfv6FTfI9TVSAC1btsTi5JUFhmFosbmIiBRdixaZpQ0uXMi6zccH5s/P74gkj+Roai8gIIDOnTtTv379PApHRESkEOjRAw4cMKfyLrdpEzRokP8xSZ5wOpGKjo4mJiaGmJgYtm3bRr9+/ejRowclSpTIy/hEREQ8z6lT8MYb9m1eXmYRTilUnK5sPnnyZBISEli6dClly5Zl+PDhlCtXju7du/P111/nZYwiIiKe5dln4fhx8PaGhg1h5kzzuUwZKF3a3dFJLnJ6sfnlEhISmD9/Pu+//z67du0iMjKSRx55hEGDBhEZGZnbcXokLTYXESmCfvoJmjc3X3/9NbRpYy4+NwxITwd/f/fGJ1eVk+9vl++1V65cOZ577jl27NjB2rVrueGGGxg/fjxz5851dZciIiKeLS0NBg0yXw8YAG3bXryCz2JRElUIXVMdqbS0ND755BPmzZvH999/T0BAAFWqVMmt2ERERDzLa6/BX3+Z03evv+7uaCQfuDQiFRcXx+OPP07ZsmXp0aMHSUlJvPPOOxw6dIiePXvmdowiIiIF3/btMH68+XryZNDFWEWC0yNSR48eZeHChcybN48///yT0qVL069fP/r168eNN96YlzGKiIgUbJmZ5pReejrcdRd06+buiCSfOJ1IRUZGYrFYuOuuuxg/fjz33nsv3t7eeRmbiIiIZ5g7F378EQID4Z13nKtsLoWC01fteXl54evri7+TC+UsFgtJSUnXFJyn01V7IiJFwJEjULOmWTtq4kQYMcLdEck1ypNbxNx+++1O3yJGRESkyBg2zEyiGjSAIUPcHY3kM6cTqTVr1uRhGCIiIh5o5UpYutSsWj5rlnkfPSlSXK4jJSIiUqSdPg1PPGG+HjZM988ropRIiYiIuGLMGNi3DypWhHHj3B2NuIlTiVTz5s1Zu3Ztjnf+/fffc9ttt+X4cyIiIgVafDy8/bb5+p13IDjYreGI+ziVSJUrV46WLVvSsGFDpkyZwq5du7Lt++eff/Lmm29Sr1492rZtS4UKFXItWBEREbe7cMGsGZWZCQ89BHff7e6IxI2cLn+wbt06XnrpJb799lsAihcvTuXKlSlZsiSGYXDixAn27NlDSkoKFouF9u3b88ILL9CkSZM8PYCCTOUPREQKobfegqeeguLFzWrm11/v7ogkl+Xk+9vpRMpqz549LFu2jLVr1/Lnn39y7NgxLBYLpUqVok6dOrRs2ZIHHniASpUqXcsxFApKpERECpm9e6F2bUhNNa/SGzDA3RFJHsjTREqcp0RKRKQQMQxzGu+rr+D22yE21ix7IIVOTr6/9RsgIiLijA8+MJMoPz94910lUQIokRIREbm6Eydg6FDz9ejR5i1hRMhBIjV79myesBYeu4rPPvuMKlWquByUiIhIgTJyJBw9CrVqwf/9n7ujkQLE6URqxowZWW5YnJ6ezoIFCzhy5Ihd++nTp9m3b1/uRCgiIuIumzebFcvnzDHfv/ceXPZdKEWb04nUzp07qV27tl1bSkoKffv2Zdu2bbkemIiIiNvNnQtbtpivBw0CFZmWyzh9d0WLxcKJEyeytOuiPxERKVT27YPERLBY4P33zTYvL3j4YbOieXi4eVsYEXIwIlWvXj2mTZvGL7/8kmWbxWLJ1aCcdfr0aV599VXq1q1LSEgI4eHhNGvWjPnz52dJ8DZt2kSbNm0ICQkhNDSUO++8k61btzrcb0JCAr1796ZUqVIUK1aMRo0asWzZsnw4IhERcbtKlaBRI2jY0KwXBWYV85YtzXbVSZRLOF1H6ptvvuGee+4hIyMDb29vAgICADOZKVasGN7e3ra+Fy5cIC0tjYyMjLyJGsjMzKRFixasX7+ePn360KRJE1JTU1myZAk///wzI0eO5LXXXgNg48aNtGzZkoiICJ588kkApk2bxtGjR1m/fj1169a17ffEiRM0atSIo0ePMmLECCIjI4mJieGHH35g7ty59O3b1+kYVUdKRMQDLV4MffqAo+8wHx+YPx969Mj3sCT/5FlBzl9//ZU5c+awa9cuzp07x/nz59mwYQN169alRIkStn5Hjhxhx44deZpIbdiwgWbNmjFs2DAmTZpka09PT6dmzZqcOHGCU6dOARAVFcX27dv566+/iIiIAODgwYPUqlWLJk2a8PXXX9s+P3LkSN544w1WrFhBhw4dAMjIyKBp06bs2bOHffv2EezkzSmVSImIeKCvv4YOHSA9Peu2+Hhz8bkUajn5/nZ6jRSY03tTpkyxvU9MTKR06dK89dZbtG7d2ta+aNEi+vTpk8OwcyY5ORkwb6h8KT8/P8LDw0lLSwNg9+7dxMXF0a9fP1sSBRAREUGXLl2YN28ehw8fpkyZMgDExMRQtWpVWxIF4O3tTXR0NL1792blypV07do1T49NRETc5KuvoGPHi0mUl5c5rWd9FrnMNRXkLFasGH369MmSzISEhFChQoVrCuxqoqKiKF68OK+//jrLli3j33//Zfv27YwaNYr4+HjGjh0LQFxcHABNmzbNso8mTZpgGAbx8fEAHDp0iIMHDzq80bK1zbo/R9LS0khOTrZ7iIiIh/jyS7j/fkhLg3btzJsRN2wIM2eaz2XKQOnS7o5SCpgcjUhdLigoiHnz5mVpv//++7n//vuvZddXVaJECVasWMGAAQPsRohCQkL4+OOP6dixI2AuHAfsRqOsLp3my2lfRyZMmMC4ceNcOBoREXGrzz+HBx6A8+ehc2dYssS8t56fn3n13qBB5iiVakjJZTz6FjHBwcHUqVOHp59+mk8++YTZs2dTrVo1Hn74Yb755hsAUv+74uLyYqKAbcG8tU9O+joyatQokpKSbI/9+/dfw9GJiEi++Oyzi0lUly6wdKmZQPn7m0kUmM9KosSBaxqRcqfff/+dZs2aMWnSJB577DFbe/fu3alTpw4DBw5kz549BAYGAtjWTF3q3LlzALY+OenriL+/v8MkTERECqhPPoFu3eDCBfN50SLzyjwRJ3nsiNSkSZM4d+4cXbp0sWsPDAzknnvuYd++fezdu9e2fsvRlJy1zTptl5O+IiLi4ZYtg65dzSTq4YeVRIlLPDaRsiY2jkosXLhwwfbcuHFjwCyXcLmNGzdisVho2LAhAGXLliUiIoKNGzc67AvQqFGj3DkAERFxnw8+gO7dzVpRvXrBggVKosQlHptI3XjjjQDMnz/frv3UqVN89tlnlChRgmrVqlGtWjVbZXLrYnIwF5YvW7aM1q1b20ofgDk1uGfPHj7//HNbW0ZGBlOnTqV48eLcfffdeXtgIiKSt2JizBGojAx45BGYNw8uKSotkhM5KshZkOzbt48GDRpw8uRJevTowa233sqJEyeYNWsWe/fuZfr06TzxxBMArF+/nlatWhEZGUl0dDQAU6dO5ciRI6xbt4569erZ9nv8+HEaNmzI8ePHGTFiBBERESxZsoQ1a9Ywe/Zs+vfv73SMKsgpIlLALFxoJk+ZmdCvH8yaZdaIErlEjr6/jWu0a9cu46effjJOnTp1rbvKsd27dxu9e/c2IiIiDB8fHyMkJMRo3ry58fHHH2fpu379eqN169ZGUFCQERwcbLRr186Ij493uN8DBw4YPXv2NK677jrD39/fuPnmm42lS5fmOL6kpCQDMJKSknL8WRERyWXz5hmGxWIYYBgDBxpGRoa7I5ICKiff3y6PSH3xxRcMHTqUvXv3Aua9+Fq3bs3Ro0dp1qwZ//vf/3jwwQdd2XWhoREpEZECYu5cGDDArA31+OMwbZpGoiRbOfn+dum3aM2aNXTq1ImSJUsyZswYLs3FSpcuTdWqVVm6dKkruxYREbl2mzdD69bm83vvQf/+ZhI1eDBMn64kSnKNS79JL730EvXq1WPTpk0MHjw4y/amTZvyyy+/XHNwIiIiLlmwAGJj4Zln4NFHzbYhQ2Dq1ItFNkVygUvXesbFxfHSSy/hlU1GHxkZyeHDh68pMBERkRzZtw8SE81E6YMPzLY1a8znhx+G4cOVREmucymRyszMvGIF78TERPz8/FwOSkREJMcqVcp+W0yM+fDMC9WlAHNpaq9WrVr8+OOP2W7/4osv7EoKiIiI5LkrVSb38TG3i+QylxKp/v3789FHHzFnzhwyMzMBsFgspKamMmTIEDZs2MCgQYNyNVAREZEr6tEDPv3U8bZNm8ztIrnM5fIHPXv2JCYmhtDQUFJSUihVqhTHjx8nIyODvn37MmfOnNyO1eOo/IGISD46cwYaNICdOy+2eXmZxTfj481tIk7I8/IHAIsWLeLjjz/mjjvuoGbNmpQsWZK7776bZcuWKYkSEZH8ZRjQt6+ZRFkscNNNMHMmNGwIZcpA6dLujlAKKY+9RYwn0IiUiEg+efVVGD0afH3hq6+gVSszoTIMSE+HK1wgJXK5nHx/61bXIiLi2T7/HJ5/3nw9fbpZiNPKYlESJXnKpUTqpZdeumofi8XCCy+84MruRUREnPPnn+YicsOAJ56AgQPdHZEUMS5N7WVXiBPMBMowDCwWCxkZGdcUnKfT1J6ISB46cQKiomDPHmjZEr7+2pzaE7lGeT61988//2Rpu3DhAnv27GHSpEkkJSXx/vvvu7JrERGRq7twAR56yEyiKlaEZcuURIlb5Ppic8MwuP3222nevDmvvvpqbu7a42hESkQkj4wYAZMmQWAgrF8PKgItuShfyh9kx2Kx8OCDD7JgwYLc3rWIiAi8/76ZRIF5c2IlUeJGuZ5IAaSnp3P8+PG82LWIiBRlmzbBo4+ar198ER54wL3xSJGX64nU5s2bmTx5MrVq1crtXYuISFGWkACdOkFaGnTsCGPGuDsiEdcWm1epUsVh+4kTJ0hJScHHx4fZs2dfU2AiIiI2586ZSdShQ1C7tjmld4UryEXyi0uJVIUKFbBYLHZtFouFBg0aUKNGDQYNGkSlSpVyIz4RESnqDMOczvv5ZyhZElasgJAQd0clAriYSK1ZsyaXwxAREcnGpEnmCJS3N3z4IWQzKyLiDhoXFRGRguvrr+GZZ8zXb70Fd9zh3nhELqNESkRECpbNm8375X3yCXTrBpmZ0K8fREe7OzKRLJya2vPy8sqyJupqLBYLFy5ccCkoEREpwhYsgNhY2LoVTp2Cpk3hnXfMGxCLFDBOJVK9e/fOcSIlIiLitH37IDHRTJY++MBsO3kSSpUyyxwcPmzeCkakgMn1W8TIRbpFjIiIk5z5z7q+riSfuPUWMSIiIjm2aBH4ZDNJ4uNjbhcpgFwqfyAiIpKrevSA66+Htm2zbtu0CRo0yP+YRJzg8ojUunXruPfeeylVqhQ+Pj54e3vbPXyy+5+FiIjI5QwDxo2zb1PlcvEALv2Wrl27llatWrFp0yZuueUWMjMzadWqFY0bN8YwDOrUqUOvXr1yO1YRESmspk6Fn34yX9euDTNnQsOGUKYMlC7t3thErsClxebt27dn+/btbN68GYvFQunSpfn2229p3bo1X3/9NQ8++CCrVq3i1ltvzYuYPYYWm4uIOOG336BxY0hPN4tuDhtmLj43DLPN39/dEUoRk+eLzX/++WcGDBhAqVKl8Ppv6DUzMxOAdu3a0atXL1544QVXdi0iIkVJaio89JCZMHXocDGJAvNZSZQUcC4lUmlpaURERADg/98veUpKim17/fr1iY+Pz4XwRESkUBsxAv76C8qWhblzVXRTPI5LiVTZsmU5cOAAAEFBQRQvXpw//vjDtv3AgQNabC4iIle2fDm8+66ZPC1YAOHh7o5IJMdcynYaN27MunXrbO/btWvHpEmTqFixIpmZmUybNo1bbrkl14IUEZFC5sABGDDAfP3MM9CmjXvjEXGRSyNS/fv3Jzw8nLNnzwLw6quvUqxYMR555BH69euHv78/r7/+eq4GKiIihURGBvTsCSdOQKNG8PLL7o5IxGW5douYM2fO8N133+Ht7c1tt91GWFhYbuzWo+mqPRERB159FUaPhqAg88bE1aq5OyIROzn5/s61hUxBQUHcd999ubU7EREpjDZuhBdfNF9Pn64kSjyeS1N7DRo0YMqUKSQmJuZ2PCIiUlglJcHDD5tTe927Q+/e7o5I5Jq5lEgdPXqUYcOGERERQceOHfnkk084f/58bscmIiKFyeDB8M8/UKkSzJihUgdSKLiUSO3fv5/Vq1fTtWtXvvvuO7p06ULZsmV58skniYuLy+0YHRo7diwWiyXbh6+vr13/HTt20LFjR0qUKEFQUBDNmzfn+++/d7jvpKQkoqOjiYiIICAggNq1azNjxgxyaTmZiEjRs3AhLF4M3t4QEwNaRyuFxDUvNj9z5gwfffQRCxYs4IcffsAwDGrUqEGfPn149tlncyvOLH777Td+++03h+1vvPEGnTp14pNPPgFgz549REVF4ePjw7BhwwgLC2PWrFn88ccfrFq1ijaXXHabnp7ObbfdxpYtW4iOjqZWrVqsWrWK5cuXM2bMGMaOHet0jFpsLiIC7N4NN98Mp0+bV+g9/7y7IxK5ohx9fxu5aP/+/carr75qhIWFGd7e3rm5a6cNGjTIAIwvvvjC1talSxfDy8vL2LJli60tJSXFqFChglGjRg0jMzPT1j59+nQDMKZMmWK3386dOxu+vr7G3r17nY4lKSnJAIykpCTXD0hExJOlpRlG48aGAYbRooVhXLjg7ohErion398uTe058vfffzN79mzmzJlDcnIy3t7eubVrp505c4alS5cSGRnJnXfeaWtbsWIFLVu2pH79+ra+wcHBDBgwgJ07d9pNR8bExBAYGMjAgQPt9j1s2DDOnz/PBx98kC/HIiJSKIwZA3FxUKKEOb3nhu8Gkbx0TYlUUlIS7733HrfddhvVq1fnpZdeIjg4mIkTJ7J///7citFpy5YtIzk5mUceecSWyP3222+kpaXRtGnTLP2bNGkCYEukMjMz+eWXX7j55psJCAiw6xsVFYXFYrniGrC0tDSSk5PtHiIiRdb338Nrr5mvZ8+G8uXdG49IHnCpjtQXX3zBggUL+OKLLzh37hylS5dm6NCh9OnTh3r16uV2jE6bM2cOFouFfv362doSEhIAbDdZvpS17eDBgwCcPHmSs2fPOuzr7+9PeHi4ra8jEyZMYNy4cdd0DCIihUJiolm93DBg0CDo3NndEYnkCZcSqfvuuw9/f386dOhAnz59uPPOO90ylXepHTt28NNPP3HHHXdQuXJlW3tqaipgJkKXs446Wftcqa+1v7WPI6NGjWLEiBG298nJyZTX/8BEpCjZvBlGjjRrRR06BLVqwaRJ7o5KJM+4lEi98847PPTQQxQvXjyXw3HdnDlzABhgvQnmfwIDAwFz2u1y586ds+tzpb7W/tY+jvj7+2ebhImIFAkLFkBsrPnazw+WLIEr/Lsp4ulcSqQee+yx3I7jmly4cIEFCxZw3XXX0alTJ7tt5cqVA3A4JWdts07llShRgmLFijnsm5aWRmJiIi1atMjt8EVEPNu+feZUnsVi1oqyio6GCxfM7RUrui8+kTyUa/fac6fPP/+cI0eOMHTo0CwjQnXr1sXf358NGzZk+dzGjRsBaNSoEQBeXl40aNCALVu2kJaWZrevn3/+GcMwbH1FROQ/lSo5bp840XyAuVZKpBDKtfIH7mSd1uvfv3+WbcHBwXTo0IE1a9bw66+/2tpPnz7N7NmzqV69OlFRUbb27t27k5qaynvvvWe3n7fffhsfHx+6deuWR0chIuKhFi0Cn2z+X+7jY24XKaSuubK5uyUkJFChQgUaNmzIpk2bHPbZvXs3UVFR+Pr6Mnz4cEJDQ5k1axa///47X375Je3bt7f1TU9Pp1mzZvz6668MGTKEWrVqsXLlSpYvX87zzz/Pyy+/7HRsqmwuIkVGdDRMm5a1PT4eGjTI/3hErkFOvr89fmpv/vz5ZGRkZFlkfqlq1aqxbt06nn32Wf73v/+Rnp5OgwYN+Oqrr+xuDwPg5+fHt99+y/PPP8+SJUs4fvw4VatWZerUqQwePDivD0dExPN8/vnFJMpiMafxvLwgM9O9cYnkA48fkSrINCIlIoXetm3QpIl5H73AQKhdG/r3hzlzYP9+s6p5ZKS7oxTJkSI1IiUiIm5y/Djcd5+ZRLVsaY5MBQWZo1KDBkF6OqgkjBRyLidSZ86cISYmhl27dnH8+HEuH9iyWCy2ReAiIlLInD8PXbvC339D5cqwbBkEB1/cbrEoiZIiwaVE6ueff+bee+8lMTEx2z5KpERECrGnnjLvpRccDCtWQHi4uyMScQuXyh+MGDGC9PR0PvzwQxITE8nMzMzyyMjIyO1YRUSkIJg1C6ZONV8vWgR16rg3HhE3cmlEKj4+nueee44HH3wwt+MREZGC7KefwHoF8yuvwP33uzceETdzaUQqNDSU6667LrdjERGRguzff6FzZ3N9VLdu8Nxz7o5IxO1cSqQ6d+7M6tWrczsWEREpqM6cMUefjh2Dm2+GuXPNBeUiRZxLidRrr73G0aNHiY6OZs+ePVmu2BMRkULEMOCRR2DrVihdGj77zKwZJSKuFeT08vLCcpX/iVgsFi5cuOByYIWBCnKKSKHw8svw4ovg6wuxsXDrre6OSCRP5XlBzt69e181kRIRkUJg+XIziQKYOVNJlMhlXEqk5s+fn8thiIhIgfPbb9Crl/l66FDo18+98YgUQC6tkRIRkULu2DHz9i9nzkCbNvDmm+6OSKRAuqZ77cXGxrJ8+XL+/vtvAKpUqUKnTp1o1apVrgQnIiJukJ4ODz4I+/ZBtWrwwQfgo1uzijji0t+MzMxM+vTpQ0xMDIZh4OXlZWufPn06PXr04P3339c6KhERTzR0KKxdCyEh5u1fSpZ0d0QiBZZLU3sTJ05k8eLFPPjgg2zdupWzZ89y9uxZtm7dSteuXVm8eDFvvfVWbscqIiJ5ZfNmaN0ann3WXFRuscCSJVCrlrsjEynQXCp/ULt2bcqXL89XX33lcPudd97J/v372bZt2zUH6MlU/kBEPMaQIeb98ywWs27Ua6/ByJHujkrELXLy/e3SiNTff/9Nhw4dst3eoUMH27opEREpoPbtg/h4+OUXiIkx2wwD7rrLHJ3at8+98Yl4AJfWSAUFBXHkyJFstx8+fJigoCCXgxIRkXxQqZLj9lWrzAeYiZWIZMulEanmzZszbdo0h1N3f/75J9OnT+f222+/5uBERCQPLVqU/dV4Pj7mdhG5IpfWSP3+++80adKE8+fPc//993PjjTcCsG3bNj7//HP8/PxYv349devWzfWAPYnWSIlIgffII/D++1nb4+OhQYN8D0ekIMjzW8TUrVuXH374gaFDh/Lxxx/z8ccf27Y1a9aMyZMnF/kkSkSkwFu48GISZV1k7uUFmZnujUvEg7hcYa1Ro0asW7eOY8eO8c8//wBQuXJlSpUqlWvBiYhIHtmwAQYMMF8HBcGNN0L//jBnDuzfD6VLuzc+EQ9xzaVqS5UqpeRJRMST/PsvdOxoVjDv1AkWL4aAAHNUatAgs93f391RingE1fwXESlKzpyB+++Ho0ehXj1YsACKFbu43WJREiWSA04lUl5eXnh5eZGamoqfnx9eXl5Xvf2LxWLhwoULuRKkiIjkgsxM6NMHtm41p+4++wyCg90dlYhHcyqR6t27NxaLBW9vb7v3IiLiQcaNg48/Bj8/WL4cKlZ0d0QiHs+l8gfiHJU/EJEC44MP4KGHzNfz5pllD0TEoTy/RczatWs5duxYttsTExNZu3atK7sWEZHctnnzxcTp6aeVRInkIpcSqVatWvHNN99ku/27776jVatWLgclIiK5JCHBXFx+7hzcfTf873/ujkikUHEpkbrabGBGRgZeXi7tWkREcsvZs2aZg4QEs07UkiXw31pXEckdLmc7V1psvn79esLDw13dtYiIXCvDMAtsxsVByZKwYgVoraZIrnO6jtTkyZOZPHmy7f2wYcMYPXp0ln4nT54kOTmZfv365U6EIiKScxMmmCNQPj7w0UdQtaq7IxIplJxOpIoXL07F/y6V3bt3L9dddx3XX3+9XR+LxUKdOnVo0qQJw4cPz91IRUTEOcuXg/U/utOmgdasiuQZl8ofVK5cmcmTJ3PfffflRUyFhsofiEi++/VXuPVWs4J5dDRMmeLuiEQ8Tp6XP/jnn3+URImIFBSbN0Pr1vD113DffWYS1aYNvPWWuyMTKfSu6V57CQkJfP755/z9998AVKlShXvvvZeIiIhcCU5ERJywYAHExsL27XDoEFSvDh9+aK6PEpE85fLfspdffplXXnmFCxcu2JVDiI6OZvTo0YwZMyZXAhQREQf27YPERPMmwx98YLYdOmTeO2/CBEhOhhIl3BujSBHgUiI1bdo0xowZQ+PGjRk+fDg33ngjANu2bWPSpEm89NJLXHfddTz55JO5GqyIiPynUiXH7adPw4MPmq91BzCRPOfSYvMbbriBEiVK8NNPP+Fz2dDx+fPnufXWW0lKSmLHjh25Fqgn0mJzEckzixebt3q5cCHrNh8fmD8fevTI76hECoU8X2z+77//0r179yxJFICvry89evTg33//dWXXOXbixAmefvppqlWrRkBAAKVKlaJVq1b8+OOPdv02bdpEmzZtCAkJITQ0lDvvvJOtW7c63GdCQgK9e/emVKlSFCtWjEaNGrFs2bJ8OBoRESf16AGrVzvetmmTkiiRfOLS1F6FChVISUnJdntKSgoVKlRwOShn7du3j5YtW3L69Gn69+9PjRo1SEpK4rfffuPgwYO2fhs3bqRly5ZERETw0ksvAeb0ZPPmzVm/fj1169a19T1x4gS33XYbR48eZcSIEURGRhITE0PXrl2ZO3cuffv2zfPjEhG5KsOAcePs27y8IDPTPfGIFFWGC6ZMmWJERkYaCQkJWbYdOHDAiIiIMKZOnerKrnPktttuyzaOSzVu3NgICQkxDhw4YGs7cOCAERISYrRt29au7zPPPGMAxooVK2xtFy5cMBo3bmyULFnSSElJcTq+pKQkAzCSkpKc/oyIiFMmTzYMM50yjBtvNIyZMw2jcWPDKFPGMPbvd3d0Ih4tJ9/fLo1IhYWFcf3111OzZk169uxJzZo1Afjrr79YvHgxNWrUIDQ0lAULFth9rnfv3teY9l20du1afvrpJ6ZMmULZsmU5f/4858+fJzAw0K7f7t27iYuLo1+/fnZlGSIiIujSpQvz5s3j8OHDlClTBoCYmBiqVq1Khw4dbH29vb2Jjo6md+/erFy5kq5du+bacYiI5Ngvv8Azz5ivJ06E4cPNq/cGDYL0dPD3d298IkWIS4nUI488Yns9Y8aMLNvj4+Pt+oB5+5jcTKRWrlwJmNOMHTp0YNWqVWRkZFC9enVefPFFevbsCUBcXBwATZs2zbKPJk2aMHfuXOLj47nnnns4dOgQBw8epIeDtQVNmjSx7S+7RCotLY20tDTb++Tk5Gs7SBGRy6WkQLduZsLUsePFJArMZyVRIvnKpUQqNjY2t+PIMesVgQMHDqR69eq8//77pKenM3HiRHr16sX58+fp27cvCQkJAA6LhFrbrOupctLXkQkTJjDu8jULIiK5xTDg8cdh924oXx7mzLmYRImIW7iUSLVo0SK348gx62L3kJAQYmNj8fPzA6Bjx45UqVKF5557jj59+pCamgqAv4P/pQUEBADY+uSkryOjRo1ixIgRtvfJycmUL18+x8cmIuLQ/Plm2QNvb1iyBEqWdHdEIkWeS+UPrFJTUzl06NAVk4u8UqxYMQC6d+9uS6IASpQowX333cfhw4fZsWOHbc3UpVNuVufOnQOw9clJX0f8/f0JDQ21e4iI5Iq//gJrkeOXXjJvTCwibpfjROrYsWOMHDmSqlWrEhISQmRkJCEhIVStWpWRI0dy9OjRvIgzi8jISADbIvFLlS1bFoCTJ09Srlw5wPGUnLXNOm2Xk74iIvnm7FlzXVRqqnkz4mefdXdEIvKfHCVSGzdupG7durz55pscOHCAOnXq0KxZM+rUqcOBAwd48803qVevHps2bcqreG2ioqIAOHDgQJZt1rbSpUvTuHFjADZs2JCl38aNG7FYLDRs2BAwE7CIiAg2btzosC9Ao0aNcucAREScNWIE/P47lC4NCxea9aJEpGBwtqbCkSNHjPDwcKN48eLGO++8Y6SmptptT01NNWbMmGGUKFHCKFWqlHHkyJGcF27IgRMnThghISFGRESEXW2nhIQEIygoyKhRo4atrVGjRkZISIhx8OBBW9vBgweNkJAQ44477rDb79NPP51tHanixYsbycnJTseoOlIics0+/PBivajVq90djUiRkJPvb6cTqWeeecbw8/Mz4uPjr9gvPj7e8Pf3N0aOHOnsrl327rvvGoBRu3ZtY+LEicaECROMChUqGL6+vsbqS/7BWbduneHn52dUqVLFmDRpkjFp0iSjSpUqRlBQkLF161a7fSYmJhoVK1Y0goODjRdffNF49913jZYtWxqAMXv27BzFp0RKRK7J338bRliYmUQ9+6y7oxEpMvIkkbrxxhuN/v37O9W3f//+Rq1atZzd9TX5+OOPjVtuucUIDAw0goODjbZt2xo//fRTln7r1683WrdubQQFBRnBwcFGu3btsk0KDxw4YPTs2dO47rrrDH9/f+Pmm282li5dmuPYlEiJiMvS0w3jllvMJKppU/O9iOSLnHx/WwzDMJyZAgwKCmLy5MkMGDDgqn1nzZrFsGHDOHPmjKszjoVCTu4eLSJiZ+RIeOMNKF4ctm6FihXdHZFIkZGT72+nVyx6e3tz/vx5p/peuHABb29vZ3ctIiKX+uorM4kCs+imkiiRAsvpRKp69epOVzRfs2YN1apVczkoEZEiKyEBrLfTGjwYOnd2bzwickVOJ1IdO3bkk08+4auvvrpiv9WrV/PJJ5/QWX/5RURyJiMDevaEY8egXj148013RyQiV+F0IjVs2DDKly9Px44dGTVqFH///bfd9r///pvnnnuO+++/n8jISIYOHZrrwYqIFGoTJkBsLAQFwQcfwH+3phKRgsvpxeYAO3fupEOHDuzatQuLxUJISAhhYWEkJyeTnJyMYRhUq1aNFStWULNmzbyM2yNosbmIOO3HH6FlS8jMhPffvzi9JyL5Lk8WmwPUqFGDrVu3MnnyZG677TZ8fHw4dOgQ3t7eNG/enLfffputW7cqiRIRcdbmzdC8OTz4oJlE9eqlJErEg+RoREpyRiNSInJV0dEwbZr5ukYNiI+H4GD3xiRSxOXk+9snn2ISERGrffsgMREsFpg//2L72LGwYweEh6vkgYiHUCIlIpLfKlVy3P7wwxdfa7JAxCPoFuIiIvlt0SLwyeb/sT4+5nYR8QhKpERE8lu3blC/vuNtmzZBjx75Go6IuE6JlIhIfnvuOfNqPTDXSQF46Z9jEU+kv7kiIvlp2bKL99ErXhwaNYKZM6FhQyhTBkqXdmt4IpIzWmwuIpJftm2Dvn3N1888Ay+/DH5+5qjUoEGQng7+/u6NUURyRImUiEh+SEoyb0B85gy0bg2vvmq/4NxiURIl4oE0tSciktcyM6FPH9i5E8qXh6VLs79qT0Q8ihIpEZG8NmECfPaZOY338cdQqpS7IxKRXKJESkQkL61eDS+8YL5+5x1o3Ni98YhIrlIiJSKSV/75B7p3N6uUDxoE/fu7OyIRyWVKpERE8kJqqrm4/ORJiIqCKVPcHZGI5AElUiIiuc0w4LHHYOtWcz3Uxx/rijyRQkqJlIhIbnvnHVi4ELy94YMPIDLS3RGJSB5RIiUikpvWrYNhw8zXr78OrVq5NRwRyVtKpEREcsuhQ9ClC1y4YN6YePhwd0ckInlMiZSISG5ITzeTqEOHoHZtmD374g2JRaTQUiIlIpIbnn7anNYLDYXlyyE42N0RiUg+UCIlInKtFi2CqVMvvq5e3b3xiEi+USIlIuKKzZvNmw8vXmwW2wSzgnmHDu6NS0Tyle6aKSLiigULIDYW4uPh7Fm46y4YM8bdUYlIPlMiJSLirH37IDHRXES+dKnZlpwMERHmGqkDB6BiRffGKCL5SomUiIizKlVy3H7wINxxh/naMPItHBFxP62REhFx1qJF4JPN/z99fMztIlKkaERKRMRZPXqAlxc8/HDWbZs2QYMG+R+TiLiVRqRERJx17BiMGGHf5qV/RkWKMv0LICLijPPn4cEH4fBh82bEN98MM2dCw4ZQpgyULu3uCEXEDTS1JyLijKFDYe1aCAmBH3+Em24yr94bNMi8PYy/v7sjFBE3UCIlInI1774LM2aYiVNMDNSrd3GbxaIkSqQI09SeiMiV/PgjPPmk+Xr8eLj3XvfGIyIFihIpEZHs/PsvPPAAXLgA3brBs8+6OyIRKWA8OpGyWCwOH8EO7rq+Y8cOOnbsSIkSJQgKCqJ58+Z8//33DveblJREdHQ0ERERBAQEULt2bWbMmIGhQnsiRUdqKnTsaF6pd/PNMHeuOY0nInIJj18j1bx5cwZZbxj6H19fX7v3e/bsoVmzZvj4+DBy5EjCwsKYNWsW7du3Z9WqVbRp08bWNz09nbZt27Jlyxaio6OpVasWq1at4oknnuDIkSOMHTs2Pw5LRNzJMKBfP9iyBUqVgk8/hcBAd0clIgWQxfDgYRaLxUKfPn2YP3/+Fft17dqVjz/+mPj4eOrXrw/A6dOnqV27NgEBAWzfvh3Lf//TfOeddxg8eDBTpkwhOjrato8HHniAzz//nF27dlHRyXtpJScnExYWRlJSEqGhoS4do4i4wYQJ8NxzZrXy77+H5s3dHZGI5KOcfH979NSeVXp6OqdPn3a47cyZM6xYsYKWLVvakiiA4OBgBgwYwM6dO4mLi7O1x8TEEBgYyMCBA+32M2zYMM6fP88HH3yQJ8cgIgXEF1/A6NHm62nTlESJyBV5fCL10UcfERgYSEhICKVLlyY6OpqkpCTb9t9++420tDSaNm2a5bNNmjQBsCVSmZmZ/PLLL9x8880EBATY9Y2KisJisdglXZdLS0sjOTnZ7iEiHuSvv8zbvxgGPPYYPPqouyMSkQLOoxOpqKgoxo4dy0cffcT7779P69atmTZtGs2bN7eNUCUkJAAQERGR5fPWtoMHDwJw8uRJzp4967Cvv78/4eHhtr6OTJgwgbCwMNujfPny13yM2dq8GVq3Np9F5NqdPAn33w8pKXD77TB5srsjEhEP4NGLzTdt2mT3vnfv3tx0002MHj2ayZMnM3r0aFJTUwEzEbqcddTJ2udKfa39rX0cGTVqFCMuuQ9XcnJy3iVTCxZAbCwsXAiNGuXNzxApKjIyoHt32LULKlSAZcvAz8/dUYmIB/DoESlHnnnmGfz8/Pjyyy8BCPzvSpu0tLQsfc+dO2fX50p9rf0Dr3Dljr+/P6GhoXaPXLVvH8THwy+/mIkUwNKl5vv4eHO7iOTcs8/C6tVQrBh89pnumyciTvPoESlHfH19KVeuHImJiQCUK1cOwOGUnLXNOpVXokQJihUr5rBvWloaiYmJtGjRIq9Cv7pKlbK2HT1q3jTV6uRJKF48vyIS8XwLF8Kbb5qv58+HSy5KERG5mkI3InXu3DkOHDjA9ddfD0DdunXx9/dnw4YNWfpu3LgRgEb/TY15eXnRoEEDtmzZkmVU6ueff8YwDFtft1i0yLwc+0pKlICaNaF3b/OKo7g4yGaETeuspMiy/u6//z5Yr9AdPRq6dnVvXCLicTw2kTp+/LjD9hdeeIELFy7QoUMHwCxz0KFDB9asWcOvv/5q63f69Glmz55N9erViYqKsrV3796d1NRU3nvvPbv9vv322/j4+NCtW7c8OBon9egBl60Ls2nfHqpUMV/v2GH+Lzs6GqKiIDQUbrnFfL9oEezcCZmZ9uusRIoS6+/+4MHmfzQ6dICXXnJ3VCLigTy2IOfw4cPZuHEjrVq1okKFCpw+fZqVK1cSGxvLLbfcQmxsLMWKFQNg9+7dREVF4evry/DhwwkNDWXWrFn8/vvvfPnll7Rv39623/T0dJo1a8avv/7KkCFDqFWrFitXrmT58uU8//zzvPzyy07HmCcFOX/5xZzK8/IykyHrc3w8NGgAiYnmKNTPP5tJ188/g6OkMzgY0tPNR1gYfPKJ+RweDk4WHBXxKPv2mX8/LBa4807z1i9gTpnPn28+63dfRMjh97fhoT799FOjXbt2Rrly5Qx/f38jMDDQqFevnjF+/Hjj7NmzWfr/+eefxn333WeEhYUZxYoVM2699Vbjm2++cbjvkydPGoMHDzbKli1r+Pn5GbVq1TKmTp1qZGZm5ijGpKQkAzCSkpJcOkaH9u83jDJlDKNxY8OYOdN8LlPGbHckM9Mw9uwxjCVLDGPYMMMwK+Rc+TFzpmH8+qthXLjgXExxcYbRqpX5LFJQOfO7LyJi5Oz722NHpDxBnt0iJi3NvDTbYjH/+U9Ph2xKNmSxeDE88oh5N/urCQkxpwabNjUfTZpAyZJZ+w0ZAlOnms+qvSMF1ZV+9318zFGpHj3yOyoRKYBy8v2tRCoPFdh77VmnBy83ebI53bFhgzkt6Oi2OzfcYCZUNWqYa7KqVoV77zWvHixdGlatMpM7TRFKQbR4MfTsmbXdOjUuIkLOvr8LXfkDyYHL11nddtvFL5OMDNi2zUyqrI+dO82F7Dt2ON7f5aUYcpKjb94MI0fC66+rwKjkjZMnzd+xS1l/90VEXKREqigqXRrKlIHy5aF/f5gzB/bvty9C6O0NN91kPqz3Gzt+HDZuNJOq5cvhzz+z/xlhYXDHHXDjjVCrlvl8441QqpQ5JXk5VWqXvGQY5rReQoKZPNWrZ/5eO/rdFxHJAU3t5aECO7UH17bOyiouzlxDlRMlS15MrsqUMb/AqlSBvn01PSh55403zNEoPz9Ys8acnr6W330RKdQ0tSdXd+kXh8Xi2heJt7f5fPkU4Q8/QECAOWL155/w11/m8z//wIkT8NNP5sORa5keFHFk7VoYNcp8PWWKeeGElau/+yIi/1EiJa7LboqwShWIjMw6WnX2rLm+yppcffXVlauq33QTTJxoThHedJOZqInkxOHD0K2bueavZ08YNMjdEYlIIaOpvTxUoKf2csu1ThFmdwXh5cLDzVt63HGH+ahSJetaKy1Yl0tduABt25pTebVrm1eiBgW5OyoR8QCa2pP8kxtThJB1ejAmxlwY/N135tRMYiJ8+KH5AHPtlDWpuuMOuP56LVgXey++aCZRwcHw8cdKokQkT2hEKg8ViRGpa3XgADRunHV6MC7OnB4Ec5Tr55/NpOrbb80rBy8vqli1qpl4nT1rjl6tXq0F60XZ55/DffeZr5cuNaf3REScpIKcBYQSKSfldHrw9Gn48UczsZo48er7X78ebr7ZXADvDE0RerZ//jHroZ06Zd6oe8oUd0ckIh4mJ9/fWr0r7ufvf3G9kzPTg8HBcNdd8OabsGiReXuPK2nWDEJDzcXv0dHmZ3btyv6KwEunCMWznDsHDz5oJlG33GL+joiI5CGNSOUhjUjlk+wWrD/+uDlNuGmTeeuby5UsaX7Z3nILVKpkTi8WL24maapp5ZkefxxmzoTrrjN/LypUcHdEIuKBtNhciqbLF6wPGGBO8RgG7N1rJlSbNplrrLZsMWtarVplPhw5dkw1rTzJokVmEmWxmK+VRIlIPlAiJZ7vare8sVigcmXz8dBDZlt6Ovz668Xk6ptv4MgR+/1aEycvL3jqKThzRld+FVTbtl28ldELL8Cdd7o3HhEpMjS1l4c0tZePcuOWN99/b5ZSyI6fn3lj53btzEe9etkXCdWC9fyTkmJe+bljh1k3atWqi1X3RURcoMXmUvTkdMG6I8WLm8/W5Mi6v86dzfVR6elmsvXss+aUYdmyZrXsBQvg0CH7fWnBev4wDHMKd8cOiIiAxYuVRIlIvtKIVB7SiJSHuVJNq4gI80q/r782H7GxZhmGS91wg/n5Jk1g3DhzjZUWrOetadPMKzF9fMzCrZfeR09ExEWqI1VAKJHyQM5OEaanm4vWV682E6v4eOcWox89aiZUl9/exhFND17Zxo1w++1w/jy8/TYMHeruiESkkNBVeyKucvaWN35+5pf47bfD+PHmLWzGjYPp06+cUJUuDSEhZiV2R4/y5S9OTemWN45t3gzDh5sjhOfPm3Wjhgxxd1QiUkRpRCoPaUSqCMquplWDBuZo1IEDV/68j4+59ioyErZuNW95U6IEfPWVmWBpetCcyps2zXxdvbqZWOnvl4jkIo1Iibjb5TWtZs0yk6lz58xbmOzZY//4+2/zkZ5ursvav//ivk6eNIuGWn33nbkOKzAw/4/LXfbtM0f9LBaYN+9i+8svmyNTSjBFxE00IpWHNCJVBDlzE+bsZGaaIy0jRkBGxpX7+viYI1/Nm5uPW281q3k7UhjWWjmzpkz/lIlILtFi8wJCiVQRda01rbKbHnz2WXNk5scfHU8R3njjxcSqefOLlb2HDIGpU83nyZNdOyZ3unABBg2yH4m6lI8PzJ8PPXrka1giUngpkSoglEiJS6yJ1OXTg/HxF295Y02orI/t27Pup0wZqF8f1q0zi1Z6YimG77+HYcPg99+z72M9LyIiuURrpEQ8mTO3vKlUyXz06mW2HTtmJkzWxCouDg4fNhepWx09aj/SdfLkxSKkBc2ePfD00/Dpp+b7EiVg4EBzevLyBFNExI00IpWHNCIlLrvW6cE5c8x7z11prZXFYk4HNmtmFrJs2tQsKupoPVJ+rbNKSTHLSUyaZB6ztzc8/jiMHWtewejq+jMRkRzQ1F4BoURK3Cq7tVZ33WXeUuXvv7NuK1nSvCLQmlxFRUFwcN6vs8rMNNc5PffcxZtHt2sHb70FtWtf7Jcb91QUEbkKJVIFhBIpcaurrbU6csSsDr5+PWzYYI7snDtnvw+LxazVtH+/OSJUsuTFmwLn1jqrn34y10HFx5vvq1c3E6h77nHuaj0RkVymmxaLyMW1Vg0bwsyZ5nOZMhfXWl1/Pdx/P7z2mnmfuqQk+Pln83Yr3bqZfQwDdu40kyiAEyfMmlaNGplrtGbMMJOx1NSrx7N5M7RubT4D/PsvPPSQeYVhfLxZVHPiRPjjD7j3XiVRIuIRNCKVhzQiJW53LVNhixfDI4+Y5QeuxssLatSAm2+2f1xa28o6Pfj441CqlLne6tw5M7aBA83imtYkT0TEjTS1V0AokRKPl906q9deM0ewtmwxH4cPO/582bLmVF3NmvDBB+ZnLr3arkkTc7SsXr28OwYRkRxS+QMRyV2Xr7Nq08a+dtPhwxeTKutjzx44dMh8rF17se+lJQs2blQSJSIeTYmUiGTvajWtrMqUMa8GvOuui21JSfDmm/Dqq47rPVkrkouIeDBN7eUhTe1JoZBXt7xRRXIRKaB01Z6I5B5//4tX0Fksrtdt8vKyfxYRKQT0L5qI5K2rlWEQEfFgWiMlInkrMhL27r04PThokCqSi0ihoURKRPLepUnTtUwPiogUMJraExEREXFRoUqkUlNTqVKlChaLhSeffDLL9h07dtCxY0dKlChBUFAQzZs35/vvv3e4r6SkJKKjo4mIiCAgIIDatWszY8YMdJGjiIiIWBWqqb0XX3yRY8eOOdy2Z88emjVrho+PDyNHjiQsLIxZs2bRvn17Vq1aRZs2bWx909PTadu2LVu2bCE6OppatWqxatUqnnjiCY4cOcLYsWPz6YhERESkQDMKifj4eMPb29uYOHGiARiDBw+2296lSxfDy8vL2LJli60tJSXFqFChglGjRg0jMzPT1j59+nQDMKZMmWK3j86dOxu+vr7G3r17nYopKSnJAIykpCTXD0xERETyVU6+vwvF1F5GRgYDBw7kzjvvpHPnzlm2nzlzhhUrVtCyZUvq169vaw8ODmbAgAHs3LmTuLg4W3tMTAyBgYEMHDjQbj/Dhg3j/PnzfPDBB3l2LCIiIuI5CsXU3qRJk9i+fTsff/yxw+2//fYbaWlpNG3aNMu2Jk2aABAXF0dUVBSZmZn88ssvNGjQgICAALu+UVFRWCwWu6TrUmlpaaSlpdneJycnu3pIIiIi4gE8fkTqn3/+YcyYMbz44otUqlTJYZ+EhAQAIiIismyzth08eBCAkydPcvbsWYd9/f39CQ8Pt/W93IQJEwgLC7M9ypcv78ohiYiIiIfw+ETqscceo0qVKowYMSLbPqmpqYCZCF3OOupk7XOlvtb+1j6XGzVqFElJSbbH/v37nT8QERER8TgePbW3aNEivvnmG9auXYuvr2+2/QIDAwHspt2szp07Z9fnSn2t/a19Lufv759tAiYiIiKFj8eOSKWlpTFixAjuvvtuypQpw+7du9m9ezf79u0DzDpQu3fv5tSpU5QrVw7A4ZSctc06lVeiRAmKFSvmsG9aWhqJiYkOp/1ERESk6PHYEamzZ89y7NgxvvzyS7788sss2xctWsSiRYt44403eOyxx/D392fDhg1Z+m3cuBGARo0aAeDl5UWDBg3YsmULaWlpdiNMP//8M4Zh2PpejfFf8U4tOhcREfEc1u9tw4ki3B6bSAUFBbFs2bIs7ceOHeOJJ57gzjvvpH///tx0000EBwfToUMHPvnkE3799Vfq1asHwOnTp5k9ezbVq1cnKirKto/u3buzbt063nvvPaKjo23tb7/9Nj4+PnTr1s2pGFNSUgC06FxERMQDpaSkEBYWdsU+FsOZdMuD7N27l8qVKzN48GCmTZtma9+9ezdRUVH4+voyfPhwQkNDmTVrFr///jtffvkl7du3t/VNT0+nWbNm/PrrrwwZMoRatWqxcuVKli9fzvPPP8/LL7/sVCyZmZkkJCQQEhKCxWKxtScnJ1O+fHn2799PaGho7h18Eafzmnd0bvOOzm3e0HnNO0Xh3BqGQUpKCuXKlcPL68qroDx2RCqnqlWrxrp163j22Wf53//+R3p6Og0aNOCrr76yuz0MgJ+fH99++y3PP/88S5Ys4fjx41StWpWpU6cyePBgp3+ml5cXkZGR2W4PDQ0ttL+E7qTzmnd0bvOOzm3e0HnNO4X93F5tJMqq0I1IeYLk5GTCwsJISkoq1L+E+U3nNe/o3OYdndu8ofOad3Ru7XnsVXsiIiIi7qZEyg38/f0ZM2aMak7lMp3XvKNzm3d0bvOGzmve0bm1p6k9ERERERdpREpERETERUqkRERERFykREpERETERUqkRERERFykRCqfZGZmMmnSJGrWrElAQADly5fnqaee4syZM+4OzWNMmDCBLl26UKVKFSwWC5UqVbpi/02bNtGmTRtCQkIIDQ3lzjvvZOvWrfkSqyfZuXMnL774Ik2aNKFUqVKEhIRQv359xo8f7/D3c8eOHXTs2JESJUoQFBRE8+bN+f77790QecG3Y8cOevToQa1atQgLCyMwMJCaNWsyYsQIDh065LC/zq1rUlNTbf82PPnkk1m269w6z2KxOHwEBwdn6avzWoQqm7vb8OHDmTJlCp06deKpp57ir7/+YsqUKWzZsoVvv/32qiXoBZ577jlKlixJgwYNOHXq1BX7bty4kZYtWxIREcFLL70EwLRp02jevDnr16+nbt26+RCxZ5g7dy7Tp0/nvvvuo0ePHvj6+hIbG8vzzz/Phx9+yMaNGylWrBgAe/bsoVmzZvj4+DBy5EjCwsKYNWsW7du3Z9WqVVnuElDUHThwgEOHDtGpUyciIyPx8fHh999/57333mPp0qVs3bqV0qVLAzq31+rFF1/k2LFjDrfp3OZc8+bNGTRokF2br6+v3Xud1/8Ykuf++OMPw2KxGJ07d7ZrnzJligEYixcvdlNknmXPnj2217Vr1zYqVqyYbd/GjRsbISEhxoEDB2xtBw4cMEJCQoy2bdvmZZgeJy4uzjh16lSW9tGjRxuAMXXqVFtbly5dDC8vL2PLli22tpSUFKNChQpGjRo1jMzMzPwI2eN9+OGHBmC89tprtjadW9fFx8cb3t7exsSJEw3AGDx4sN12nducAYw+ffpctZ/Oq0nDIPlgyZIlGIbBsGHD7NoHDhxIYGAgixYtck9gHqZKlSpO9du9ezdxcXF06dKFiIgIW3tERARdunTh22+/5fDhw3kVpsdp1KiRw3tKdevWDYA//vgDgDNnzrBixQpatmxJ/fr1bf2Cg4MZMGAAO3fuJC4uLl9i9nQVK1YE4OTJk4DO7bXIyMhg4MCB3HnnnXTu3DnLdp1b16Wnp3P69GmH23ReL1IilQ/i4uLw8vIiKirKrj0gIID69esXmV+2/GI9n02bNs2yrUmTJhiGQXx8fH6H5XEOHDgAwPXXXw/Ab7/9RlpaWrbnFdDvcjbOnTtHYmIiBw4c4Ouvv+bRRx8F4O677wZ0bq/FpEmT2L59O9OmTXO4XefWNR999BGBgYGEhIRQunRpoqOjSUpKsm3Xeb1Ia6TyQUJCAuHh4Q7L6UdERLB+/XrS09Px8/NzQ3SFT0JCAoDdaJSVte3gwYP5GpOnycjI4OWXX8bHx4eHH34Y0Hm9FrNnzyY6Otr2vlKlSixatIjmzZsDOreu+ueffxgzZgwvvvgilSpVYu/evVn66NzmXFRUFF26dKFatWokJyezcuVKpk2bxg8//MD69esJDg7Web2EEql8kJqamu09iQICAmx9lEjljtTUVACH5/zS8y3ZGzZsGBs2bODVV1/lhhtuAHRer0XHjh2pWbMmp0+fZsuWLaxYsYLExETbdp1b1zz22GNUqVKFESNGZNtH5zbnNm3aZPe+d+/e3HTTTYwePZrJkyczevRonddLKJHKB4GBgRw9etThtnPnztn6SO6wnsu0tLQs23S+r+6FF15g2rRpDBo0iFGjRtnadV5dFxkZSWRkJGAmVQ888ACNGzcmNTWVUaNG6dy6YNGiRXzzzTesXbs2y9Vkl9K5zR3PPPMM48aN48svv2T06NE6r5fQGql8UK5cORITEx3+wh08eJDw8HCNRuWicuXKAY6Hla1tjoajBcaOHcsrr7xC3759mTlzpt02ndfcc9NNN3HzzTfzzjvvADq3OZWWlsaIESO4++67KVOmDLt372b37t3s27cPgKSkJHbv3s2pU6d0bnOJr6+v7bsM9Dt7KSVS+aBx48ZkZmby888/27WfO3eOrVu30qhRIzdFVjg1btwYgA0bNmTZtnHjRiwWCw0bNszvsAq8sWPHMm7cOPr06cPs2bOxWCx22+vWrYu/v3+25xXQ73IOnD17lhMnTgA6tzl19uxZjh07xpdffkn16tVtj5YtWwLmaFX16tWZPXu2zm0uOXfuHAcOHLBdfKLzegl3118oCn777bcr1pFauHChmyLzXFerI9WoUSMjJCTEOHjwoK3t4MGDRkhIiHHHHXfkQ4SeZdy4cQZg9OrVy8jIyMi234MPPmh4eXkZW7dutbVZ68ZUr169yNSNcdahQ4cctn///feGl5eX0bp1a1ubzq3z0tPTjWXLlmV5vPPOOwZg3HnnncayZcuMHTt2GIahc5sTiYmJDtuffvrpLLXPdF5NFsMwDPemckVDdHQ006ZNo1OnTtx99922yua33nor33//vSqbO2HhwoW2ofupU6eSnp7OU089BZh1eXr16mXru379elq1akVkZKTtaqmpU6dy5MgR1q1bR7169fL/AAqo6dOn8+STT1KhQgVefvnlLL+L119/PW3btgXMGl1RUVH4+voyfPhwQkNDmTVrFr///jtffvkl7du3d8chFFidOnXi0KFDtG7dmooVK3Lu3Dni4+NZunQpgYGBrFmzxlaDR+f22u3du5fKlSszePBgu3IIOrfOGz58OBs3bqRVq1ZUqFCB06dPs3LlSmJjY7nllluIjY213elA5/U/7s7kiooLFy4Yb775plGjRg3Dz8/PKFeunDF8+HAjJSXF3aF5jBYtWhiAw0eLFi2y9F+/fr3RunVrIygoyAgODjbatWtnxMfH53/gBVyfPn2yPa+Ozu2ff/5p3HfffUZYWJhRrFgx49ZbbzW++eYb9wRfwH3wwQfGPffcY0RGRhr+/v5GQECAccMNNxhPPvmksW/fviz9dW6vzT///OOwsrlh6Nw669NPPzXatWtnlCtXzvD39zcCAwONevXqGePHjzfOnj2bpb/Oq0akRERERFym+SQRERERFymREhEREXGREikRERERFymREhEREXGREikRERERFymREhEREXGREikRERERFymREhEREXGREikRERERFymREimELBYLjzzyiLvDyNb8+fOxWCysWbMmXz/rbpUqVaJly5buDsPjrVmzBovFwvz5890diogSKRFPYP3iyO7h4+PjlrjGjh3Lp59+6paffamtW7cyduxY9u7dmyf7r1Spkt359vPzo1KlSgwYMID9+/fnyc/MS7t37+aBBx4gPDwcf39/qlevbru5tyN16tShe/fuwMVE1mKx8Oabbzrsv2XLFlufSxP6smXLUr58eYefqVmzJhaLhdmzZ2fZtnTpUiwWCxMnTszBUYrkD/f86ysiLunevTt33313lnYvL/f8n2jcuHH06dOHjh075uhzvXr14qGHHsLPzy/HP9PRZ7du3cq4ceNo2bIllSpVyvE+nREZGcmECRMASElJYc2aNcydO5eVK1fy22+/ER4eftV97NixA4vFkifxOevs2bO0bt2aY8eOMXjwYG644QYOHDjAt99+67D/rl272LZtGy+++KJde0BAAPPmzePpp5/O8pm5c+cSEBDAuXPn7NpbtmzJ0qVL2b17N9WqVbO1Hzp0iB07duDj48OaNWsYMGCA3ediY2MBaNWqlUvHLJKXlEiJeJAGDRrQs2dPd4fhspSUFEJCQvD29sbb29ulfVzLZ69FWFiY3bl//PHHKV26NNOmTWPevHk888wzDj93/vx5MjIyCAgIwN/fP7/CzdaqVavYv38/r7/+ul3M48aNc9h/+fLl+Pv7c9ddd9m1d+rUiSVLlvDzzz8TFRVla09LSyMmJobOnTsTExNj95lWrVqxdOlS1qxZY5dIWadpe/Xqxddff50lhjVr1lC8eHHq16+f08MVyXOa2hMpQr799lvatWtH8eLFCQgI4KabbmLmzJkO+27ZsoUuXbpw/fXX4+/vT/ny5enevTt79uxh7969tpGV999/327ay8o6rfPdd99x2223ERwcTIcOHYDs1zmlp6fz+uuvU79+fQIDAwkLC6NRo0ZMmzbN1ufyz44dO5a+ffsC5hf1pVNKy5cvx2KxMGvWLIfHWLt2bapVq4ZhGC6dz/bt2wPmVJk1FovFwrZt2xgxYgSRkZEEBASwceNGIPs1Ulc615fKyZ9fdnbu3AnALbfc4lT/5cuXc8cddxASEmLX3qFDB8LDw5k3b55d+2effcaJEydsfyaXso4oWUeYrNasWUPNmjXp2rUrBw8eZNeuXbZthw4dYufOndx+++0OR17nzZtH7dq18ff3p2LFirz++utOHZdIbtGIlIgHSU1NJTExMUu7n58foaGhV/zse++9x2OPPUaTJk0YPXo0QUFBfPPNNzz++OPs2bOHN954w9b3iy++4IEHHiAoKIgBAwZQrVo1Dh8+zOrVq/njjz9o06YNCxcupFevXjRv3pxBgwY5/JmbN2/m448/ZuDAgfTp0+eK8aWnp9O+fXvWrFlDu3bt6NmzJwEBAfz+++988sknPPnkkw4/17lzZw4dOsR7773Hc889R61atQCoWrUqjRs3pkyZMsydO5eBAwfafW7jxo38+eefjB8/3uXpNusX/uXTej169KBYsWI89dRTWCwWypYtm+0+rnauq1atCuTsz+9KrNNtgYGBV+2bkJDApk2bePfdd7Ns8/X1pWfPnsyfP59JkyYREBAAmNN6N998s8PRo+rVqxMREZElgV6zZg2tWrXi1ltvtU3vVa9e3bYNHE/rzZw5kyNHjtC/f3+KFy/OokWL+L//+z8iIyN5+OGHr3p8IrnCEJECLzY21gCyfdxzzz12/QGjT58+tvcJCQmGv7+/0b179yz7HjJkiOHl5WXs2bPHMAzDOHPmjBEeHm6UKlXKOHDgQJb+GRkZ2f6cy2MAjG+++SbLtnnz5hmAERsba2t77bXXDMAYNWrUFX+mo886arMaNWqUARjbtm2zax8wYIDh7e1tHDx40GH8l6pYsaJRs2ZN49ixY8axY8eMv//+25g7d64RFhZm+Pj4GL///rthGIYxZswYAzBatGhhnD9/3uF+WrRoYXvv7LnOyZ9fds6dO2ccO3bMeOaZZwzA+Pbbb23Hk5KS4vAz77zzjuHl5WUcOXLE1mY918uWLTN+++03AzAWL15sGIZh7N+/3/Dy8jKmTp1qHDt2zOHvR8+ePQ3A2LFjh+3YAGPJkiWGYRhGVFSU3XEOGjTIAIwtW7bY2qx/H8qWLWucOnXK1m49n02aNLniuRDJTZraE/EggwYN4ptvvsnyGD9+/BU/99FHH5GWlkb//v1JTEy0e3To0IHMzEzbYuPVq1eTmJjIU089RURERJZ95WRhe7169WjTpo1TfRcvXkyJEiWyLGrO6c+83MCBA7FYLMyZM8fWdubMGT744APuuusuypUr59R+tm/fTqlSpShVqhRVqlShX79+hIeH89lnn1GnTh27vsOGDXPqSkpnz3VO/vyys2TJEkqVKmUbuWrTpo3teLIb7fv0009p2rQppUuXdri9bt26NGrUyDa99/777+Pr60uPHj2yjcM6smQdabI+t2jRwvZ86YjVmjVrKFmyJDfddFOWffXt25ewsDDb+8DAQJo0aWI3NSiS1zS1J+JBqlev7nRicqm//voL4IqfPXLkCHBxuurmm292IUJ7NWrUcLrvrl27qF+/vm2KKLdUrlzZNhX5v//9D19fXz788ENSUlKyXB12JZUqVbKttfLz86NcuXJ2C6Yv5exxO3uuc/Lnl5327dvzzTffsGDBAhYuXMj06dNtcTpKJk+dOkVsbKztSsXs9O3bl+joaPbt28f8+fO5//77KVGihMMpaLBfJzVo0CDbNJ51+rNFixa88cYb7Nixg9DQUHbu3EnHjh0dJtNVqlTJ0nbddddx/PjxK8YskpuUSIkUAcZ/i6kXLFiQ7XodR19K18qZdTj5YdCgQXTp0oUVK1bwwAMPMGfOHMqUKcM999zj9D6CgoKcTmJz+7hz48+vbNmylC1blp9++gmAqKgoGjVqlG3/L7/8kvPnz1+1tMXDDz/MU089xcCBA9m9e7fdhQGOVK5cmYoVK/LDDz8A5oiTdTQK4LbbbsPLy4s1a9bY1v1lV/bAHVdvilxOiZRIEWBduBseHn7VZMA6SrF161batWuX57Fd+nO3b99OWlpajssEXG2x+P3330/p0qWZM2cOderUYd26dfzf//2f2wqZWjl7rnPy55dbli9fTt26dW2L3bNTvHhxWymE8uXL07Zt26vuu1WrVsyfP5/Y2Fh27tzJCy+8YNsWFhZG/fr1iY2NtU3bqX6UFGRaIyVSBHTt2hV/f3/GjBnD2bNns2xPSkoiLS0NgHbt2hEeHs7EiRM5dOhQlr7GJaUCgoODOXHiRK7E2KNHD06ePMkrr7xyxZ/pSHBwMEC2sfj6+vLII4+wevVqW72k/v37X2PE187Zc52TP7/ccO7cOb766is6derkVP9nn32WMWPGMG3aNKfWs1kTo7FjxwLYjUhZ3//www+sWbOG8PDwLGvQRAoSjUiJeJBffvmFRYsWOdzWsWNHW0JxucjISGbMmMGAAQOoVasWvXr1omLFihw7dozff/+dTz/9lD///JNKlSoRGBjInDlzePDBB6lTp47tkvxjx46xevVqRowYwf333w9AkyZN+Pbbb3nttdeoUKECFouFhx56yKVjGzp0KJ9//jmvvPIKcXFxtGvXjoCAALZt28aOHTuuuJi6cePGeHl5MX78eE6ePElQUBCVK1e2q5U0cOBA3njjDZYsWUKLFi1sozzu5Oy5zsmfX3Y2bNjAnj17+O233wCzMOf27dvt+nTq1ImgoCC+/vprzpw543TF+ptuusnhYvDsWBOptWvXUrly5Sy3jWnRogWTJk3i8OHDPPDAA26vBi9yRW69ZlBEnHK18geAsWvXLlt/silL8NNPPxkdO3Y0SpUqZfj6+hply5Y1WrZsabz55pvG2bNn7fpu2rTJuP/++43rrrvO8PPzM8qXL288/PDDdpfZ79y502jbtq0REhJii+NqMRhG9uUKzp49a7zyyivGjTfeaPj7+xthYWFGo0aNjOnTp1/1s/Pnzzdq1apl+Pr6ZvuzW7dubQDGggULHMaVnYoVKxq1a9e+aj9r+YN//vkn2/1cWv7AyplzbRg5+/O7XJ8+fa76O2SNu2/fvkbFihUd7ufS8gdXkl35A6uqVasagPHII49k2Xb8+HHDYrEYgDF16tQs261/H+bNm5ftcYrkF4thuFjSV0TEw9x9991s2LCBhIQEihUr5u5wCqSMjAzKlClDjx49ePvtt90djkiBp6k9ESkSdu/ezerVq3niiSeURF3B8ePHGTx4MF27dnV3KCIeQSNSIlKobdq0ib/++ospU6bw119/8ddff11xLZGISE7oqj0RKdRmzJhBv379SE5OZvHixUqiRCRXaURKRERExEUakRIRERFxkRIpERERERcpkRIRERFxkRIpERERERcpkRIRERFxkRIpERERERcpkRIRERFxkRIpERERERf9PwXrYP0xRXhQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 13})\n",
    "\n",
    "#matplotlib.rc('font', **font)\n",
    "plt.plot([i*2 for i in range(1,28)],plotarr,'-*r')\n",
    "plt.xlabel(\"Electricity Price £/MWh\")\n",
    "plt.ylabel(\"Option value £(Mill)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5614a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2fb29e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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