sensitivity_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "based on: [0.483662871, 1.0, 26.2359775, 0.00111820675, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]\n",
      "\n",
      "returned NPV for the number of reactors [10584234.36989537, 10619217.463052887, 10654200.556210406, 10689183.649367923, 10724166.742525442, 10759149.835682958, 10794132.928840477, 10829116.021997994, 10864099.115155512, 10899082.20831303]\n",
      "returned NPV for temperature [10585495.881161923, 10584486.935808586, 10583354.552073402, 10582012.507853094, 10580400.989515524, 33737122890.439342, 4816942951055.888, 15455859661515.197, 27164355727287.344, 37040107846983.59, 44332729279931.84, 48812340375649.22, 50168232725873.78, 47992264468476.95, 31029371560294.766]\n",
      "returned NPV for debt [10585674.555232147, 10587298.947765334, 10588926.408308163, 10590556.944393268, 10592190.563575603, 10593827.273432517, 10595467.081563842, 10597109.995591957, 10598756.02316187, 10600405.171941303, 10602057.44962076, 10603712.863913612, 10605371.422556177, 10607033.133307796, 10608698.003950909, 10610366.042291142, 10612037.256157387, 10613711.653401876, 10615389.241900267, 10617070.029551724, 10618754.024278998, 10620441.23402851, 10622131.666770432, 10623825.330498768, 10625522.233231438, 10627222.383010367, 10628925.787901554, 10630632.455995174, 10632342.395405645, 10634055.614271723, 10635772.120756585, 10637491.92304791, 10639215.02935797, 10640941.447923712, 10642671.187006844, 10644404.254893923, 10646140.65989644, 10647880.410350915, 10649623.514618967, 10651369.98108742, 10653119.818168383, 10654873.034299335, 10656629.637943221, 10658389.63758854, 10660153.041749429, 10661919.85896576, 10663690.097803222, 10665463.766853424, 10667240.87473397, 10669021.430088567, 10670805.441587102, 10672592.917925743, 10674383.867827032, 10676178.300039966, 10677976.223340113, 10679777.646529678, 10681582.578437617, 10683391.027919726, 10685203.00385873, 10687018.515164386, 10688837.570773574, 10690660.179650392, 10692486.350786258, 10694316.093199996, 10696149.415937947, 10697986.328074055, 10699826.838709967, 10701670.956975136, 10703518.692026915, 10705370.053050656, 10707225.049259814, 10709083.68989604, 10710945.984229282, 10712811.941557895, 10714681.57120873, 10716554.882537242, 10718431.884927588, 10720312.587792732, 10722197.00057455, 10724085.132743923]\n",
      "farm1 0\n",
      "[0. 1. 1. 1. 1. 1. 1.]\n",
      "farm1 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm1 [11695754.285616204, 10584234.36989537]\n",
      "farm2 0\n",
      "[1. 0. 1. 1. 1. 1. 1.]\n",
      "farm2 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm2 [12675124.24484399, 10584234.36989537]\n",
      "farm3 0\n",
      "[1. 1. 0. 1. 1. 1. 1.]\n",
      "farm3 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm3 [16421935.283120984, 10584234.36989537]\n",
      "farm4 0\n",
      "[1. 1. 1. 0. 1. 1. 1.]\n",
      "farm4 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm4 [16421935.283120984, 10584234.36989537]\n",
      "farm5 0\n",
      "[1. 1. 1. 1. 0. 1. 1.]\n",
      "farm5 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm5 [19391361.549874756, 10584234.36989537]\n",
      "farm6 0\n",
      "[1. 1. 1. 1. 1. 0. 1.]\n",
      "farm6 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm6 [14571295.00323239, 10584234.36989537]\n",
      "farm7 0\n",
      "[1. 1. 1. 1. 1. 1. 0.]\n",
      "farm7 1\n",
      "[1. 1. 1. 1. 1. 1. 1.]\n",
      "returned NPV for Farm7 [19124145.788342286, 10584234.36989537]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy.integrate import odeint\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sko.GA import GA\n",
    "\n",
    "from constants import *\n",
    "from sensitivity_cost_module import do_all_list_cp,system_npv,JtokWh ,farmer_npv\n",
    "from digesterModule import digester\n",
    "import sensitivity_cost_module as C2\n",
    "import Transport as T\n",
    "import biogas as B\n",
    "import pickle\n",
    "from math import inf\n",
    "\n",
    "# Variables we want to keep track in DOE\n",
    "farm=[]\n",
    "system=[]\n",
    "# with open('data_transport.p', 'rb') as fp:\n",
    "#     dict_T = pickle.load(fp)\n",
    "with open('full_transp.p', 'rb') as fp:\n",
    "    dict_T = pickle.load(fp)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import autograd.numpy as np\n",
    "\n",
    "vector = [4.83662871e-01, 1.00000000e+00, 2.62359775e+01, \n",
    "        1.11820675e-03, 1.00000000e+00, 0.00000000e+00,0.00000000e+00, \n",
    "        1.00000000e+00, 0.00000000e+00, 1.00000000e+00,0.00000000e+00]\n",
    "\n",
    "\n",
    "def biodigestor(dv_n,dv_in,printt=False,pen=True):\n",
    "    \n",
    "    global vector\n",
    "    \n",
    "    ##DESIGN VARIABLES##\n",
    "    n_g = 1 #number of reactor\n",
    "    Tdig = vector[2] #operating temperature\n",
    "    debt_level = vector[3] #Portion of total capex required funded with debt\n",
    "    active_farms = np.ones(7)\n",
    "    \n",
    "    #print('dv_in:',dv_in)\n",
    "    if dv_n==1: #Vgburn\n",
    "        temp = 1\n",
    "    elif dv_n==2: #generator\n",
    "        n_g = dv_in\n",
    "    elif dv_n==3: #temperature\n",
    "        Tdig = dv_in\n",
    "    elif dv_n==4: #debt\n",
    "        debt_level = dv_in\n",
    "    elif dv_n==5: #farm1\n",
    "        active_farms[0] = dv_in\n",
    "        print('farm1',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==6: #farm2\n",
    "        active_farms[1] = dv_in\n",
    "        print('farm2',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==7: #farm3\n",
    "        active_farms[2] = dv_in\n",
    "        print('farm3',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==8: #farm4\n",
    "        active_farms[3] = dv_in\n",
    "        print('farm4',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==9: #farm5\n",
    "        active_farms[4] = dv_in\n",
    "        print('farm5',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==10: #farm6\n",
    "        active_farms[5] = dv_in\n",
    "        print('farm6',dv_in)\n",
    "        print(active_farms)\n",
    "    elif dv_n==11: #farm7\n",
    "        active_farms[6] = dv_in\n",
    "        print('farm7',dv_in)\n",
    "        print(active_farms)\n",
    "        \n",
    "    \n",
    "\n",
    "#     active_farms= vector[4:11] \n",
    "#     active_farms = [0 if num<1 else 1 for num in active_farms ]\n",
    "\n",
    "    if printt:\n",
    "        [distance, wIn, total_solids_perc, wComp] = T.load_data(*active_farms,printt)\n",
    "    else:\n",
    "        [distance, wIn, total_solids_perc, wComp] = dict_T[tuple(active_farms)]\n",
    "    \n",
    "    [W_a, typ, V_d, G_in, G_comp, digOut, digOut_comp] = digester(wIn,wComp,Tdig)\n",
    "    # H_needed = JtokWh(H_needed*1000)\n",
    "    # print('----')\n",
    "    \n",
    "    #biogas module\n",
    "    V_g = B.biomethane(G_in, G_comp) #biomethane\n",
    "    f_p = B.biofertilizer(digOut) \n",
    "    ghg_r, ghg_c = B.ghg(W_a, wComp, G_in, G_comp) #ghg_r: released gas, ghg_c: captured gas\n",
    "    bgm_total = B.bgm_cost(G_comp, G_in, digOut)\n",
    "    \n",
    "    #COST Module\n",
    "    V_g =V_g*working_days\n",
    "    ghg = pd.DataFrame()\n",
    "    ghg['ghg_lf']=ghg_r\n",
    "    ghg['ghg_tech']=ghg_c\n",
    "    ghg['gas']= ['CH4','CO2','NOX','SOX']\n",
    "    list_ghg = []\n",
    "    for gas in ['CH4','CO2','NOX','SOX']:\n",
    "        list_ghg.append(ghg[ghg['gas']==gas].values.flatten().tolist())\n",
    "    list_ghg = do_all_list_cp(W_a,distance,list_ghg)\n",
    "    \n",
    "    #print(\"n_g\",n_g)\n",
    "    V_gburn = vector[0]*V_g\n",
    "\n",
    "    return -farmer_npv(n_g,V_gburn,V_d,typ,distance,f_p,V_g,debt_level,e_c,e_priceB,f_used,p_bf,printt,pen)\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "print('based on:',vector)\n",
    "print()\n",
    "\n",
    "#number of reactor\n",
    "ngs = np.linspace(1, 10,10)\n",
    "returned_dv1 = []\n",
    "for ng in ngs:\n",
    "    returned_dv1.append(biodigestor(2,ng))\n",
    "print('returned NPV for the number of reactors',returned_dv1)\n",
    "\n",
    "#temperature\n",
    "Tdigs = np.linspace(25, 40,15,dtype=int)\n",
    "returned_dv2 = []\n",
    "for dig in Tdigs:\n",
    "    returned_dv2.append(biodigestor(3,dig))\n",
    "print('returned NPV for temperature',returned_dv2)\n",
    "\n",
    "#debt\n",
    "debts = np.linspace(1,80,80,dtype=int)/100\n",
    "returned_dv3 = []\n",
    "for debt in debts:\n",
    "    returned_dv3.append(biodigestor(4,debt))\n",
    "print('returned NPV for debt',returned_dv3)\n",
    "\n",
    "#farm1\n",
    "farm1 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv4 = []\n",
    "for farm in farm1:\n",
    "    returned_dv4.append(biodigestor(5,farm))\n",
    "print('returned NPV for Farm1',returned_dv4)\n",
    "\n",
    "#farm2\n",
    "farm2 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv5 = []\n",
    "for farm in farm2:\n",
    "    returned_dv5.append(biodigestor(6,farm))\n",
    "print('returned NPV for Farm2',returned_dv5)\n",
    "\n",
    "#farm3\n",
    "farm3 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv6 = []\n",
    "for farm in farm1:\n",
    "    returned_dv6.append(biodigestor(7,farm))\n",
    "print('returned NPV for Farm3',returned_dv6)\n",
    "\n",
    "#farm4\n",
    "farm4 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv7 = []\n",
    "for farm in farm4:\n",
    "    returned_dv7.append(biodigestor(8,farm))\n",
    "print('returned NPV for Farm4',returned_dv6)\n",
    "\n",
    "#farm5\n",
    "farm5 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv8 = []\n",
    "for farm in farm5:\n",
    "    returned_dv8.append(biodigestor(9,farm))\n",
    "print('returned NPV for Farm5',returned_dv8)\n",
    "\n",
    "#farm6\n",
    "farm6 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv9 = []\n",
    "for farm in farm6:\n",
    "    returned_dv9.append(biodigestor(10,farm))\n",
    "print('returned NPV for Farm6',returned_dv9)\n",
    "\n",
    "#farm7\n",
    "farm7 = np.linspace(0,1,2,dtype=int)\n",
    "returned_dv10 = []\n",
    "for farm in farm7:\n",
    "    returned_dv10.append(biodigestor(11,farm))\n",
    "print('returned NPV for Farm7',returned_dv10)\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(5, 2,figsize=(10,15))\n",
    "axs[0, 0].plot(ngs, returned_dv1)\n",
    "axs[0, 0].set_title('Number of Reactors')\n",
    "axs[0, 1].plot(Tdigs, returned_dv2, 'tab:orange')\n",
    "axs[0, 1].set_title('Temperature')\n",
    "axs[1, 0].plot(debts, returned_dv3, 'tab:green')\n",
    "axs[1, 0].set_title('Debt Level')\n",
    "axs[1, 1].plot(farm1, returned_dv4, 'tab:red')\n",
    "axs[1, 1].set_title('N1_farm')\n",
    "axs[2, 0].plot(farm2, returned_dv5, 'tab:blue')\n",
    "axs[2, 0].set_title('N2_farm')\n",
    "axs[2, 1].plot(farm3, returned_dv6, 'tab:grey')\n",
    "axs[2, 1].set_title('N3_farm')\n",
    "axs[3, 0].plot(farm4, returned_dv7, 'tab:grey')\n",
    "axs[3, 0].set_title('N4_farm')\n",
    "axs[3, 1].plot(farm5, returned_dv8, 'tab:grey')\n",
    "axs[3, 1].set_title('N5_farm')\n",
    "axs[4, 0].plot(farm6, returned_dv9, 'tab:grey')\n",
    "axs[4, 0].set_title('N6_farm')\n",
    "axs[4, 1].plot(farm7, returned_dv10, 'tab:grey')\n",
    "axs[4, 1].set_title('N7_farm')\n",
    "fig.tight_layout()\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='Design variables', ylabel='NPV')\n",
    "\n",
    "#tornado graph (based on the change of design variables)\n",
    "#parameters\n",
    "#digester: volume of reactor ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradient [-2.27627252e+02 -2.65211556e+02 -3.17619258e+02 -2.97049627e+02\n",
      " -2.23134503e+02 -1.69247692e+02 -1.50335606e+02 -1.48340499e+02\n",
      " -2.42785866e+02  1.30942048e+02  1.77903281e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04 -1.39932373e+05\n",
      " -1.39932373e+05  3.49830932e+04  3.49830932e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04  3.49830932e+04\n",
      "  3.49830932e+04 -1.39932373e+05 -1.39932373e+05  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04 -1.39183465e+05\n",
      " -1.56788826e+05 -2.65211556e+02 -3.17619258e+02 -2.97049627e+02\n",
      " -2.23134503e+02 -1.69247692e+02 -1.50335606e+02 -1.48340499e+02\n",
      " -2.42785866e+02  1.30942048e+02  1.77903281e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04  3.49830932e+04\n",
      "  3.49830932e+04  3.49830932e+04  3.49830932e+04 -1.39183465e+05\n",
      " -1.56788826e+05 -2.65211556e+02 -3.17619258e+02 -2.97049627e+02\n",
      " -2.23134503e+02 -1.69247692e+02 -1.50335606e+02 -1.48340499e+02\n",
      " -2.42785866e+02  8.79849374e+02  9.33875233e+02 -2.65211556e+02\n",
      " -3.17619258e+02 -2.97049627e+02 -2.23134503e+02 -1.69247692e+02\n",
      " -1.50335606e+02 -1.48340499e+02 -2.42785866e+02  8.79849374e+02\n",
      "  9.33875233e+02 -2.65211556e+02 -3.17619258e+02 -2.97049627e+02\n",
      " -2.23134503e+02 -1.69247692e+02 -1.50335606e+02 -1.48340499e+02\n",
      " -2.42785866e+02 -3.35678971e+02]\n",
      "returned NPV for the number of reactors [10584234.36989537, 10619217.463052887, 10654200.556210406, 10689183.649367923, 10724166.742525442, 10759149.835682958, 10794132.928840477, 10829116.021997994, 10864099.115155512, 10899082.20831303]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f855da895f8>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## GRADIENT\n",
    "from autograd import grad  \n",
    "grad_bio = grad(biodigestor)\n",
    "\n",
    "\n",
    "\n",
    "print('gradient',np.gradient(np.array(returned_v, dtype=float), axis=0))\n",
    "\n",
    "\n",
    "#number of reactor\n",
    "ngs = np.linspace(1, 10,10)\n",
    "returned_dv1 = []\n",
    "for ng in ngs:\n",
    "    returned_dv1.append(biodigestor(2,ng))\n",
    "print('returned NPV for the number of reactors',returned_dv1)\n",
    "\n",
    "grad = np.gradient(np.array( returned_dv1, dtype=float), axis=0)\n",
    "\n",
    "from autograd import jacobian\n",
    "#dAdk1 = jacobian(biodigestor,3)\n",
    "#dAdk_1 = jacobian(biodigestor, 4)\n",
    "\n",
    "plt.plot(ngs, grad)\n",
    "#plt.plot(ngs, np.abs(dAdk_1(returned_v)))\n",
    "plt.xlim([-3.27627252e+02, -2.07627252e+02])\n",
    "plt.ylim([-3.27627252e+02, -2.07627252e+02])\n",
    "plt.xlabel('t')\n",
    "plt.legend(['$S_{k1}$', '$S_{k\\_1}$'])\n",
    "\n",
    "\n",
    "# plt.plot(ngs, grad(ngs))\n",
    "# plt.xlim([1, 10])\n",
    "# plt.ylim([46751289, 47751289])\n",
    "# plt.xlabel('Number of Reactor')\n",
    "# plt.ylabel('NPV')\n",
    "\n",
    "\n",
    "# import autograd.numpy as np\n",
    "# A0 = 1.0\n",
    "# def A(t, k1, k_1):\n",
    "#     return A0 / (k1 + k_1) * (k1 * np.exp(-(k1 + k_1) * t) + k_1)\n",
    "# %matplotlib inline\n",
    "# import matplotlib.pyplot as plt\n",
    "# t = np.linspace(0, 0.5)\n",
    "# k1 = 3.0\n",
    "# k_1 = 3.0\n",
    "# plt.plot(t, A(t, k1, k_1))\n",
    "# plt.xlim([0, 0.5])\n",
    "# plt.ylim([0, 1])\n",
    "# plt.xlabel('t')\n",
    "# plt.ylabel('A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Point out the design variables in the code\n",
    "* confirm x* and function output\n",
    "* which parameters to test with?\n",
    "* All the codes are finalized? Which one I can use?\n",
    "* If grad is not working, any possible workaround? e.g. gradienting the results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Mon Mar 22 11:34:16 2021\n",
    "\n",
    "@author: Ricardo Hopker, Niek Jansen van Rensburg\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "from scipy.integrate import odeint\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sko.GA import GA\n",
    "\n",
    "from constants import *\n",
    "from cost_module_funcs2 import do_all_list_cp,system_npv,JtokWh ,farmer_npv\n",
    "from digesterModule2 import digester\n",
    "import Transport as T\n",
    "import biogas as B\n",
    "import pickle\n",
    "from math import inf\n",
    "import scipy.optimize as op\n",
    "\n",
    "# Variables we want to keep track in DOE\n",
    "# farm=[]\n",
    "# system=[]\n",
    "# with open('data_transport.p', 'rb') as fp:\n",
    "#     dict_T = pickle.load(fp)\n",
    "with open('full_transp.p', 'rb') as fp:\n",
    "    dict_T = pickle.load(fp)\n",
    "\n",
    "\n",
    "# DOE = pd.read_csv('DOE.csv')\n",
    "#  #Variables below are which farms should be activated\n",
    "\n",
    "# DOE_vector=[]\n",
    "# for i in range(0,18):\n",
    "#     vector =  DOE.loc[i].values.flatten().tolist()\n",
    "#     DOE_vector.append(vector[1:])\n",
    "# DOE_n = 0\n",
    "def biodigestor(vector,lam = 1,multiJ =False,full=False,printt=False,pen=True):\n",
    "    #Use printt to print the text within your modules, when running the optimization it should be set to False\n",
    "    #Use pen to penalize the function contraints being violated, when running the optimization it should be set to True\n",
    "    # DOE_n = DOE_n+1\n",
    "    # print('Design of experiment #%.0f' % (DOE_n))\n",
    "    #Optimal latitude and longitude for Digestor\n",
    "    #Digest_location = T.digestor_loc\n",
    "\n",
    "    #This loads the respective farms - 1 is active, 0 is inactive. Total farms must be at least 3 active (required by annealing)\n",
    "    #TOTAL_SOLIDS PERCENTAGE IS NOT USED\n",
    "    active_farms= vector[5:12] \n",
    "    active_farms = [0 if num<1 or num==False  else 1 for num in active_farms]\n",
    "    # [distance, wIn, total_solids_perc, wComp] = T.load_data(1,1,1,1,1,1,1)\n",
    "    # [distance, wIn, total_solids_perc, wComp] = T.load_data(*active_farms,printt)\n",
    "    # if sum(active_farms)>2:\n",
    "    if printt:\n",
    "        [distance, wIn, total_solids_perc, wComp] = T.load_data(*active_farms,printt)\n",
    "    else:\n",
    "        [distance, wIn, total_solids_perc, wComp] = dict_T[tuple(active_farms)]\n",
    "\n",
    "\n",
    "    \n",
    "    #output from digester -- will return 9 values & print to console\n",
    "    Tdig = vector[2]\n",
    "    [W_a, typ, V_d, G_in, G_comp, digOut, digOut_comp] = digester(wIn,wComp,Tdig)\n",
    "    # H_needed = JtokWh(H_needed*1000)\n",
    "    # print('----')\n",
    "    \n",
    "    #biogas module\n",
    "    V_g = B.biomethane(G_in, G_comp) #biomethane\n",
    "    #bg = B.biomethane_validation(kilos, wComp)\n",
    "    f_p = B.biofertilizer(digOut) \n",
    "    ghg_r, ghg_c = B.ghg(W_a, wComp, G_in, G_comp) #ghg_r: released gas, ghg_c: captured gas\n",
    "    bgm_total = B.bgm_cost(G_comp, G_in, digOut)\n",
    "    \n",
    "    #print('Module biogas: ', G_in, 'Expected biogas: ', bg)\n",
    "    # print(\"Produced biomethane: \", V_g)\n",
    "    # print(\"Produced biofertilizer: \",f_p)\n",
    "    # print(\"Released gas (g/tonne): \", ghg_r)\n",
    "    # print(\"Captured gas (g/tonne): \", ghg_c)\n",
    "    \n",
    "    #issues for discussion\n",
    "    #1. released gas - amount for how many days? put per day for now. --> thats fine I just multiplied in the next line by working days\n",
    "    #2. G_in - is this already purified? methane's rate is already 0.9665, which meets the biomethane requirement\n",
    "    #          in general composition of biogas, methane is expected around 0.6\n",
    "    #3. digOut - digestate amount is 18.7. expected around 80%-90% of kilos (7963) --> how about 18.7 kg/day *330 days/year ~6200\n",
    "    \n",
    "    V_g =V_g*working_days\n",
    "    ghg = pd.DataFrame()\n",
    "    ghg['ghg_lf']=ghg_r\n",
    "    ghg['ghg_tech']=ghg_c\n",
    "    ghg['gas']= ['CH4','CO2','NOX','SOX']\n",
    "    list_ghg = []\n",
    "    gwpS =0\n",
    "    for gas in ['CH4','CO2','NOX','SOX']:\n",
    "        list_ghg.append(ghg[ghg['gas']==gas].values.flatten().tolist())\n",
    "        gwpS = gwpS + gwp(ghg[ghg['gas']==gas]['ghg_lf'].values,gas)\n",
    "    list_ghg = do_all_list_cp(W_a,distance,list_ghg)\n",
    "    \n",
    "    n_g = vector[1]\n",
    "    V_gburn = vector[0]*V_g\n",
    "    debt_level = vector[3]\n",
    "    # print('----')\n",
    "    # farm.append(farmer_npv(n_g,V_gburn,V_d,typ,distance,f_p,H_needed,W_out,V_g,debt_level,e_c,e_priceB,f_used,p_bf))\n",
    "    # print('----')\n",
    "    # system.append(system_npv(n_g,V_gburn,V_d,typ,distance,f_p,H_needed,W_out,V_g,debt_level,e_c,e_priceB,f_used,p_bf,list_ghg))\n",
    "    # print('----')\n",
    "    # return -system_npv(n_g,V_gburn,V_d,typ,distance,f_p,H_needed,W_out,V_g,debt_level,e_c,e_priceB,f_used,p_bf,list_ghg,printt,pen)\n",
    "    V_cng_p = vector[4]\n",
    "    farmerNPV = farmer_npv(n_g,V_gburn,V_cng_p,V_d,typ,distance,f_p,V_g,debt_level,e_c,e_priceB,e_priceS,f_used,p_bf,printt,pen)\n",
    "    if multiJ:\n",
    "        if full:\n",
    "            return [-farmerNPV*lam-(1-lam)*gwpS,-farmerNPV,-gwpS]\n",
    "        else: return -farmerNPV*lam-(1-lam)*gwpS\n",
    "    else: return -farmerNPV\n",
    "    # return -farmerNPV*lam-(1-lam)*gwpS\n",
    "# for vector in DOE_vector:\n",
    "#     vector.extend([0.7])\n",
    "#     system.append(biodigestor(vector))\n",
    "def gwp(x,gas): # https://www.epa.gov/ghgemissions/understanding-global-warming-potentials\n",
    "    if gas == 'CH4':\n",
    "        return x*32\n",
    "    elif gas == 'CO2':\n",
    "        return x\n",
    "    elif gas =='NOX':\n",
    "        return x*281.5\n",
    "    elif gas =='SOX':\n",
    "        return x*281.5\n",
    "    else:\n",
    "        raise NotImplementedError\n",
    "\n",
    "def cleanXopt(xopt_in,x1,x2,x3):\n",
    "#     s = 1/10**6 #scale [0,3,4]\n",
    "#     s1 = 1/10**4\n",
    "    xopt = xopt_in.copy()\n",
    "#     print(\"x1\",x1,\"x2\",x2,\"x3\",x3)\n",
    "#     print('After')\n",
    "#     print('V_burn',xopt[0])\n",
    "#     print('dept_level',xopt[3])\n",
    "#     print('V_cng',xopt[4])\n",
    "    print(float(x1))\n",
    "    xopt[0] = xopt[0]*float(x1)\n",
    "    xopt[3] = xopt[3]*x2\n",
    "    xopt[4] = xopt[4]*x3\n",
    "#     print('V_burn',xopt[0])\n",
    "#     print('dept_level',xopt[3])\n",
    "#     print('V_cng',xopt[4])\n",
    "    if xopt[0] >1: xopt[0]=1\n",
    "    elif xopt[0]<0: xopt[0]=0\n",
    "    xopt[1] = round(xopt[1],0)\n",
    "    if xopt[1]<1: xopt[1]=1\n",
    "    if xopt[3]>1: xopt[3]=1\n",
    "    elif xopt[3]<0: xopt[3]=0\n",
    "    if xopt[4]>1: xopt[4]=1\n",
    "    elif xopt[4]<0: xopt[4]=0\n",
    "    print(\"x1\",x1,\"x2\",x2,\"x3\",x3)\n",
    "    print('After')\n",
    "    print('V_burn',xopt[0])\n",
    "    print('dept_level',xopt[3])\n",
    "    print('V_cng',xopt[4])\n",
    "    for i in range(5,12):\n",
    "        if xopt[i]>1: xopt[i]=1\n",
    "        elif xopt[i]<1: xopt[i]=0\n",
    "    return xopt\n",
    "def cleanBiodigestor(x,x1,x2,x3,lam = 1,multiJ =False,full=False,printt=False,pen=True):\n",
    "    X = cleanXopt(x,x1,x2,x3)\n",
    "    return biodigestor(X,lam,multiJ,full,printt,pen)\n",
    "def fminClean(x0,args):\n",
    "    xopt = op.fmin(func=cleanBiodigestor,x0=x0,args=args)\n",
    "    xopt = cleanXopt(xopt)\n",
    "    return xopt\n",
    "\n",
    "sc = [9.84376823e-01, 1.00000000e+00, 3.27916570e+01, 1.17822503e-02,\n",
    " 5.03334049e-03, 1, 0, 0, 0, 0, 0, 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "x1 1 x2 1 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.0117822503\n",
      "V_cng 0.00503334049\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "97991.4073824175"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanBiodigestor(sc,1, 1, 1,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1 1 x2 1 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.0117822503\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 1 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.0117822503\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 0.1 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.00117822503\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 0.1 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.00117822503\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 0.01 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.00011782250300000001\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 0.01 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 0.00011782250300000001\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 0.001 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-05\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 0.001 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-05\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 0.0001 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-06\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 0.0001 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-06\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 1e-05 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-07\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 1e-05 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-07\n",
      "V_cng 0.0005033340489999999\n",
      "x1 1 x2 1e-06 x3 1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-08\n",
      "V_cng 0.00503334049\n",
      "x1 1 x2 1e-06 x3 0.1\n",
      "After\n",
      "V_burn 0.984376823\n",
      "dept_level 1.17822503e-08\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 1 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.0117822503\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 1 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.0117822503\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 0.1 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.00117822503\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 0.1 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.00117822503\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 0.01 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.00011782250300000001\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 0.01 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 0.00011782250300000001\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 0.001 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-05\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 0.001 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-05\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 0.0001 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-06\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 0.0001 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-06\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 1e-05 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-07\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 1e-05 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-07\n",
      "V_cng 0.0005033340489999999\n",
      "x1 0.1 x2 1e-06 x3 1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-08\n",
      "V_cng 0.00503334049\n",
      "x1 0.1 x2 1e-06 x3 0.1\n",
      "After\n",
      "V_burn 0.0984376823\n",
      "dept_level 1.17822503e-08\n",
      "V_cng 0.0005033340489999999\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#[V_gBurn,ng,Tdig,debt_level,V_cng_p,farm1,farm2,farm3,farm4,farm5,farm6,farm7]\n",
    "sc = [9.84376823e-01, 1.00000000e+00, 3.27916570e+01, 1.17822503e-02,\n",
    " 5.03334049e-03, 1, 0, 0, 0, 0, 0, 0]\n",
    "x1 = [1,1/10]\n",
    "x2 = [1,1/10,1/10**2,1/10**3,1/10**4,1/10**5,1/10**6]\n",
    "x3 = [1,1/10]\n",
    "\n",
    "data = []\n",
    "for i in x1:\n",
    "    for j in x2:\n",
    "        for k in x3:\n",
    "            npv = cleanBiodigestor(sc,i, j, k,True)\n",
    "            data.append([i,j,k,npv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     x1        x2   x3            npv\n",
      "0   1.0  1.000000  1.0   97991.407382\n",
      "1   1.0  1.000000  0.1   98008.848647\n",
      "2   1.0  0.100000  1.0   97916.025118\n",
      "3   1.0  0.100000  0.1   97933.429024\n",
      "4   1.0  0.010000  1.0   97908.500593\n",
      "5   1.0  0.010000  0.1   97925.900770\n",
      "6   1.0  0.001000  1.0   97907.748278\n",
      "7   1.0  0.001000  0.1   97925.148081\n",
      "8   1.0  0.000100  1.0   97907.673047\n",
      "9   1.0  0.000100  0.1   97925.072814\n",
      "10  1.0  0.000010  1.0   97907.665524\n",
      "11  1.0  0.000010  0.1   97925.065287\n",
      "12  1.0  0.000001  1.0   97907.664772\n",
      "13  1.0  0.000001  0.1   97925.064534\n",
      "14  0.1  1.000000  1.0  110540.821056\n",
      "15  0.1  1.000000  0.1  110558.262321\n",
      "16  0.1  0.100000  1.0  110438.558267\n",
      "17  0.1  0.100000  0.1  110455.962172\n",
      "18  0.1  0.010000  1.0  110428.350575\n",
      "19  0.1  0.010000  0.1  110445.750752\n",
      "20  0.1  0.001000  1.0  110427.329992\n",
      "21  0.1  0.001000  0.1  110444.729795\n",
      "22  0.1  0.000100  1.0  110427.227935\n",
      "23  0.1  0.000100  0.1  110444.627702\n",
      "24  0.1  0.000010  1.0  110427.217730\n",
      "25  0.1  0.000010  0.1  110444.617492\n",
      "26  0.1  0.000001  1.0  110427.216709\n",
      "27  0.1  0.000001  0.1  110444.616471\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame (data,columns=['x1','x2','x3','npv'])\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>npv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110558.262321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110540.821056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110455.962172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110445.750752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110444.729795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110444.627702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110444.617492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>110444.616471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110438.558267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110428.350575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110427.329992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110427.227935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110427.217730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110427.216709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98008.848647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97991.407382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97933.429024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97925.900770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97925.148081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97925.072814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97925.065287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97925.064534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97916.025118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97908.500593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97907.748278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97907.673047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97907.665524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97907.664772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1        x2   x3            npv\n",
       "15  0.1  1.000000  0.1  110558.262321\n",
       "14  0.1  1.000000  1.0  110540.821056\n",
       "17  0.1  0.100000  0.1  110455.962172\n",
       "19  0.1  0.010000  0.1  110445.750752\n",
       "21  0.1  0.001000  0.1  110444.729795\n",
       "23  0.1  0.000100  0.1  110444.627702\n",
       "25  0.1  0.000010  0.1  110444.617492\n",
       "27  0.1  0.000001  0.1  110444.616471\n",
       "16  0.1  0.100000  1.0  110438.558267\n",
       "18  0.1  0.010000  1.0  110428.350575\n",
       "20  0.1  0.001000  1.0  110427.329992\n",
       "22  0.1  0.000100  1.0  110427.227935\n",
       "24  0.1  0.000010  1.0  110427.217730\n",
       "26  0.1  0.000001  1.0  110427.216709\n",
       "1   1.0  1.000000  0.1   98008.848647\n",
       "0   1.0  1.000000  1.0   97991.407382\n",
       "3   1.0  0.100000  0.1   97933.429024\n",
       "5   1.0  0.010000  0.1   97925.900770\n",
       "7   1.0  0.001000  0.1   97925.148081\n",
       "9   1.0  0.000100  0.1   97925.072814\n",
       "11  1.0  0.000010  0.1   97925.065287\n",
       "13  1.0  0.000001  0.1   97925.064534\n",
       "2   1.0  0.100000  1.0   97916.025118\n",
       "4   1.0  0.010000  1.0   97908.500593\n",
       "6   1.0  0.001000  1.0   97907.748278\n",
       "8   1.0  0.000100  1.0   97907.673047\n",
       "10  1.0  0.000010  1.0   97907.665524\n",
       "12  1.0  0.000001  1.0   97907.664772"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='npv', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.010000\n",
       "1      0.010000\n",
       "2      0.010000\n",
       "3      0.010000\n",
       "4      0.010000\n",
       "         ...   \n",
       "120    0.000001\n",
       "121    0.000001\n",
       "122    0.000001\n",
       "123    0.000001\n",
       "124    0.000001\n",
       "Name: x1, Length: 125, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x3')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "# Data for a three-dimensional line\n",
    "x = df.x2\n",
    "y = df.x3\n",
    "z = df.npv\n",
    "ax.plot3D(xline, yline, zline, 'gray')\n",
    "\n",
    "# Data for three-dimensional scattered points\n",
    "ax.scatter3D(x, y, z, c=z, cmap='Greens');\n",
    "\n",
    "\n",
    "ax.set_xlabel('x1')\n",
    "ax.set_ylabel('x2')\n",
    "ax.set_zlabel('x3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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