process.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processes\n",
    "\n",
    "This code perform some processes to generate data and recover the results obtained after running the model with different datasets. The objective is to recreate Figures 3 and 5 of our report about the article *Centers for Disease Control and Prevention as a Strategic Agent in the Pediatric Vaccine Market: An Analytical Approach*. \n",
    "\n",
    "It uses the tailor-made package **functions**, which contains the modules:\n",
    "\n",
    "- **data** which generates the following datasets:\n",
    "> - Triangular distribution of the parameters capacity, target profit, demand and gamma.\n",
    "> - Different values of manufactures' capacity with the rest of the parameters constant.\n",
    "> - Different values of manufactures' target profit with the rest of the parameters constant.\n",
    "\n",
    "- **resultsprocessing** which takes the results obtained from NEOS and put them in a dataframe.\n",
    "\n",
    "The code should be used following these steps:\n",
    "- **Step 1:** Generate the data with triangular distribution, profit and capacity differences, using the function *get_data* and giving them an appropiate name. The files with the data will be stored in the folder *commands* of the current directory. These files will be used as **command** files when using NEOS Server.\n",
    "\n",
    "- **Step 2:** Upload model, data and command files to NEOS Server (https://neos-server.org/neos/solvers/minco:BARON/AMPL.html) to run BARON. Model and data files can be found in folder *Model* of current directory. You will need to provide an e-mail, in which you will receive a message when the results are ready. You will need to repeat this step for each command file created in previous step.\n",
    "\n",
    "- **Step 3:** Once the e-mail has been received, access the results by following the instructions. Copy and paste the results in a .txt file, name it appropiately and store it in the folder *results*.\n",
    "\n",
    "- **Step 4:** Use the code in this notebook to recreate Figures 3 and 5.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Import tailored packages for data generation and results processing\n",
    "import functions.resultsprocessing as results\n",
    "import functions.data as data\n",
    "\n",
    "#Import required packages\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get_data in module functions.data:\n",
      "\n",
      "get_data(triangular, profit, capacity, num_samples, file_name)\n",
      "    Generates data to perform trials with triangular distribution,\n",
      "    profit and capacity differences and store the results in txt file \n",
      "    with the required format to be input to NEOS.\n",
      "    \n",
      "    Input:\n",
      "            triangular (binary):      Takes value 1 if triangular distribution is required,\n",
      "                                                  0 otherwise\n",
      "                                           \n",
      "            profit (binary):          Takes value 1 if profit difference is required,\n",
      "                                                  0 otherwise   \n",
      "                                           \n",
      "            capacity (binary):        Takes value 1 if capacity difference is required,\n",
      "                                                  0 otherwise  \n",
      "                                           \n",
      "            num_samples (int):        Number of samples required\n",
      "            \n",
      "            file_name:                Name of the file with the data to be used as input to NEOS\n",
      "            \n",
      "                                      \n",
      "    Output:\n",
      "            file (txt):              File with the generated data in the format required to be input to NEOS.\n",
      "                                     File directory: <current directory>/Data/<file_name>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(data.get_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the capacity data, and solve the model with this data in NEOS\n",
    "#Store NEOS results in Folder Data\n",
    "data.get_data(0,0,1,10000,'capacity_commands.txt')\n",
    "\n",
    "#Get the profit data, and solve the model with this data in NEOS\n",
    "#Store NEOS results in Folder Data\n",
    "data.get_data(0,1,0,10000,'profit_commands.txt')\n",
    "\n",
    "#Get the triangular distribution data, and solve the model with this data in NEOS\n",
    "#Store NEOS results in Folder Data\n",
    "data.get_data(1,0,0,10000,'triangular_commands.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 and 3\n",
    "\n",
    "Steps 2 and 3 are performed outside this notebook. See the intructions at the beginning of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 3\n",
    "* Reproduces Figure 3 of the article.\n",
    "* Uses the results of the model obtained from NEOS with the triangular distribution data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#Recovers the results from txt format extracted from NEOS and get them in dataframe\n",
    "#Change the name of the file with the appropiate name\n",
    "sensitivity = results.generate_dataframe('results/NEOS_triangular_results.txt')\n",
    "\n",
    "#Computes demand fullfilment ratio and add it to dataframe\n",
    "sensitivity = sensitivity[sensitivity['value_obj_func']!='Not feasible']\n",
    "sensitivity['tot_d_fullfilment'] = (sensitivity['pub_q_infanrix'] + sensitivity['priv_q_infanrix'] +\\\n",
    "                                    sensitivity['pub_q_daptacel'] + sensitivity['priv_q_daptacel'])/\\\n",
    "                                    sensitivity['demand']\n",
    "\n",
    "sensitivity['pub_d_fullfilment'] = (sensitivity['pub_q_infanrix']+\\\n",
    "                                    sensitivity['pub_q_daptacel'])/(0.57*sensitivity['demand'])\n",
    "\n",
    "red_square = dict(markerfacecolor='None', marker='o', markeredgecolor='k',markeredgewidth=1,markersize = 1)\n",
    "\n",
    "#---------------------------------------------- Plot figure 3 a)---------------------------------------------\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.boxplot([sensitivity.pub_p_infanrix,sensitivity.pub_p_daptacel], vert = False, flierprops=red_square)\n",
    "\n",
    "#Add reference points\n",
    "plt.scatter(17.61,2.2, marker = 's', s=50,color = 'red', label = 'True Price\\nDaptacel $17.61\\nInfanrix $18.19')\n",
    "plt.scatter(18.19,0.8, marker = 's', s=50,color = 'red')\n",
    "plt.scatter(8.45,2.2, marker = 's', s=50,color = 'blue', label = 'Base case price\\nDaptacel $8.45\\nInfanrix $18.62')\n",
    "plt.scatter(18.62,0.8, marker = 's', s=50,color = 'blue')\n",
    "\n",
    "#Formatting\n",
    "plt.yticks([1,2], ['Infanrix (GSK)','Daptacel (SP)'])\n",
    "plt.xlabel('Price (USD)', fontsize = 'large')\n",
    "plt.xlim(0, 20)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#----------------------------------------------- Plot figure 3 b)---------------------------------------------\n",
    "\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.boxplot(x =sensitivity.PubCost/1000000, vert = False, flierprops=red_square)\n",
    "\n",
    "#Add reference points\n",
    "plt.scatter(34.4,0.89, marker = 's', s=50,color = 'red', label = 'Base case cost $34.4M')\n",
    "\n",
    "#Formatting\n",
    "plt.legend()\n",
    "plt.yticks([1], [''])\n",
    "plt.xlim(0, 50)\n",
    "plt.xlabel('Cost (million USD)', fontsize = 'large')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#---------------------------------------------- Plot figure 3 c)---------------------------------------------\n",
    "fig3, ax3 = plt.subplots()\n",
    "ax3.boxplot([sensitivity.pub_d_fullfilment,sensitivity.tot_d_fullfilment], \\\n",
    "            vert = False, flierprops=red_square)\n",
    "\n",
    "#Add reference points\n",
    "plt.scatter(1.589,2.2, marker = 's', s=50,color = 'red', \\\n",
    "            label = 'Base case demand fulfillment\\nTotal 1.589\\nPublic sector 1.326')\n",
    "plt.scatter(1.326,0.8, marker = 's', s=50,color = 'red')\n",
    "\n",
    "#Formatting\n",
    "plt.legend(loc='center right')\n",
    "plt.yticks([1,2], ['Public sector demand','Total demand'])\n",
    "plt.xlabel('Proportion', fontsize = 'large')\n",
    "plt.xlim(0.9, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 5\n",
    "* Reproduces Figure 5 of the article.\n",
    "* Uses the results of the model obtained from NEOS with the capacity and profit data for figures 5a) and 5b), respectively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K3gDs1bQv701WNOtN3vmzv1NRc8Re5zJrq9YGa5JxqwEWj6IgZmbj1NpgHREXpj/PGl1xntDEzLrK5APZ+q+izLjzu/bKhgeZPb2oGV5RnXaVbxF5y90M0Nqm6cG6zEBOl0raMvN6K0kX11oqM7MRa23TvYxnRsQDnRcRcb+kP6qvSIkmZdZF68tOPpDNtosGYqoy1VfREKtlu5ZX2b9X5p1X/523X5U6fbNRaHoPxjL/xeslbd95IWkHoNnfF8zMKpoMmfUngKskXZG+3geYV1+REk3KrMvW+VaZfCDvvEXZ7KB12nn75C0fdHCnojrzor+l67etCZqeWRcG64j4vqQXAy8FBBydTs9lZjZpNP0GY9lpvdYDq0m6me8iiYi4sr5ijf9TrsoQqXn1sFUmFyiaUDcv861Sp120vqgHZJGiLLpKlj3VM2sPXTs+rQ/Wkt4NHAXMBpaQZNhXA6+otWRmZiM07gSxSJmP8aOAPYA7I2I/YDfg17WWysxsxCbDDcbfR8TvJSHpaRFxi6Tn112wcX8lKRqoqWg86rwqj6Lu4r06pXSa/A16A7Ls+l7bVlG2a31R076pZCr+zk007phTpEywXpV2ivk2cKmk+4F76iyUmdmotT5YR8Qh6dPjJF0GzAK+X2upGH/9UdlhUbPPi5rWZWU7yBTdoMzLRqt0F++cq0oTuUEz66LMOa98Vbqzm9Wl9cEaIG26tzdJZ5j/FxGP1VoqM7MRa32wlvRJ4I3At9JFZ0j6ekT8nzoL1qTMuqjTR16ddV6W3SuzzRvIKWuQgZayBum0MtF61EE7xXS2nQr1uFPhd2yTpk8+UObd8hZgj4g4NiKOJWm697Z6i2VmNlrDbA0i6UBJt0paIeljOes/ImlJ+lgmab2kZ/Q7ZplqkDtIOsP8Pn39NOC2EvtNSJMy67xlVTq9FHUnLxpitajTStk6717qrh/Ouy5VBnoyG4VhveckTQdOAQ4AVgGLJF0QETdlznUScFK6/UEkPcN/2++4ZYL1o8BySZeS1FkfQDJWyOfTk9Y6Y4yZ2SgMMUGYC6yIiNsBJJ0DHAzc1GP7twD/VXTQMsH6vPTRcXmJfSZs3JlVUTvloskJyk5im922aHKCQYctLVK2Bccw9foWMtlbhkym32WyqRJzJM3jyQPaLYyIhenzbYG7MutWAXv2OM5mwIHAkUXnLNN0bywzxZiZjVKVYJ0G5oU9Vud9Ivc6+EEkLez6VoFAudYgOwEnALuQ1F0nZ454TtG+EzHuzDqraHD/vHbWefv3Wl/UGiSvTjuvzfUgEw7knWecJnuddROuseUb4jfLVcB2mdez6d2R8DBKVIFAudYgZwBfBNYB+wFnA18pc3Azs7YYYmuQRcBOkuZI2pgkIF/QvZGkWcBfAueXKV+ZYL1pRPwQUETcGRHH4RH3zGySGVawjoh1JHXQFwM3A+dGxHJJ8yXNz2x6CHBJRDxUpnylBnKSNA34b0lHAncDtc/B2KQG6nnVEHl6/REHGQ87b/9eywb5ap13rkFvUNbNM8nYKAzzPR8RFwEXdS1b0PX6TODMsscsk1l/ENgM+ADwEuDtwBFlT2Bm1gatHSJV0ibA0yNiUbpoLfBOSc8CHqy7YE3K7PJuMOZlxnldyLPLi7Lhov17bTtI07uiIVinT5+eW5a6dX6vyTSEqr8NtEOTYk6efu/8zwMvz1n+SuAz9RTHzGw8WptZA3tHxFNmMY+Ir0k6psYyAc38lOtVd5o3kFNRp5iy2XJWUbZdV6eYTpZd5ZhF80n2OlZR1/28a202DE1vLtovWPf7b2jfd1Ezsz7aHKxXS5obEddmF0ragxHMwdikzLqTxWVbqBTVWRcN9JQ1SKeYojdWUXf1Kq1Bis4/0b9V0XCxdQzhWjdn/u3T5mD9EeBcSWcC16XLdgcOJ2nkbWY2abQ2WEfEtZLmAu8D/iZdvBzYMyJW112wJl24onrSTsbdq865qH65+zzdqrQc6bf/oBMCFLV5rzIEaneZem1bZfLhJnJm3T5N+jafp2+nmDQoHzuispiZjU3TE4BSczCamU12DtYDatKFy5sXsKiDS/YrVdHs5J39e3VEmWinmLLN/Hp9dZ9op5tBbkZWqUZo+s1Ga4cmxZw8jQ3WZmaj1NpgLelCeg+YTUS8tpYSpZo0kFNHXrYM5Wc3zy7LZtEdRfMu9jp/XuZflNln1T1TTN61GLTpX9HY4uPmG4vt1eYbjCcP80SSXhsRTxnT1cysCZr0oZ+nX9O9KzrPJW0KbB8Rt5Y5qKRDuxcBp0jaKD32t3rs9/i8ZvPnz+dVr3pVmdONTNFMMVXqTvOy6KI646yizLko867SBbyKvMy3yrekvP2LBsVq0j+ZM+v2atL7KE+Zab0OIsmyNwbmSNoV+MeCapBzge8Dq3mi2/rmJPONBZAbrLPzmp133nnNvnJmNqm0PlgDx5FMrX45QEQskbRjwT4vA04kmd5mQUSEpH0j4p1lC9b0C5enSnf0QQZiKuquPmidddnzZ1UZznWQ4/bKnMu2XDGrqukxp8w7e11EVBq/Oh0D+wCSbPxHaU/IZl8JM5vS2jxEascySW8FpqcznX8A+EnRThGxAficpK8Dn61asKbfmc0qqnPOy3zz1hcN/t9rffd23ecqyrzzFG3bqzt40bZ5rUGG2Q7bdcY2qKbHnDKZ9fuBFwCPAv9JMkvMB8ueICLuiYg3RcRzBiqhmdkItD6zjoiHgU+kjwmTNC+9kVh03mGcbiSKMufu7cpsm63/LmqTXTbLLsq8e8mb0Leot2RRPXbePtlzFLUG6XX8cUxO4Gx+cmh6zCnMrCVdKmnLzOutJF08gXP6nW1mjbNhw4bSj3EoU2e9TUQ80HkREfdL+qOinTo3FSNikaRdgAOBWyLiSwOX1sysJk3PrMsE6w2Sto+IXwBI2oGClh2SjgVeDWwk6VJgT5Kmfx+TtFtEfKropE2/cHmKunP3ao6WV42R1any6LVd0fqiTjFFXbjLVk30OlfetlVuKg5yg3KUTfhcDTI5ND3mlAnWnwCuktTp0bgPaS/DPt4A7Ao8DbgXmB0RaySdBFwDFAZrM7NRanprkDI3GL8v6cXAS0nqm4+OiPsKdlsXEeuBhyXdFhFr0mM9IqnUFWn6p1yevGwVirO9vJlmhnVTsdf6PHU17avSXXyiAz3lnd+Zr5XR9JhTdojU9SRdxzcBdpFERFzZZ/vHJG2WtiR5SWehpFlAsz++zGxKan2wlvRu4ChgNrCEJMO+GnhFn932iYhH4fHOMR0zgCPKFKzpF24QdTfdK6ozzyrKZvO2LdORJa9pXV4Hn0Ga85UpS956Z9ZWRtNjTpm7MEcBewB3RsR+wG7Ar/vt0AnUOcvvi4illUtpZlaz1neKAX4fEb+XhKSnRcQtkp5fd8GaXtmfp0oLhEGy7CqtQfKuX9H6rCr110XHLZqCLC+LrtJCJK9+vO7u6M7WJ58mTniSVSZYr0o7xXwbuFTS/cA9dRbKzGzUmp4glmkNckj69DhJlwGzgO/VWiqaX3+UlVdPO0iWXbRP0TUpmnasVz14UT1wUTvsvPMW1WlXWT9Im+8q3d0H4cx68ml6zCnT3fwrnecRcUUkU3OdXmupzMxGbDJ0N39B9oWk6WSa41n+J/IgWXbRPlXaWWflZYFFZc5T1Csxe9yiHo5FrT0GPX7Zbwlm3VqbWUv6uKTfAS+UtEbS79LXq4HzR1ZCM7MRGGZrEEkHSrpV0gpJH+uxzb6Slkhanukh3lO/CXNPAE6QdEJEfLywdGZmLTas1iBp7cMpJLNlrQIWSbogIm7KbLMl8B/AgRHxizKD45WpBjkmna18b5IBnH4cEd+u/itU0/SvJFUMMrhQla7jRZ1eipr2TbTTTFF38qIbgFmd9Xk3QHuVJe9mY5Xu7mYw1NYgc4EVEXE7gKRzgIOBmzLbvBX4VmeAvIhYXXTQMtHjFGA+sBRYBsyXdEq1spuZNVuVahBJ8yQtzjyyg9ttC9yVeb0qXZb1PGArSZdLuk7S4UXlK5NZ/yXw55GmL5LOIgnctWpjZt3rplnHMJv2FQ0Lmnf+osGRqmSzg84+PtH1eecfpOmfM2vrViXmRDLbVa8Zr/LeXN0H34ikocb+wKbA1ZJ+GhE/73XOMsH6VmB74M709XbAjSX2MzNrjSFWg6wiiZMds3lqR8JVwH0R8RDwkKQrgRcBEwrWWwM3S7o2fb0HyafABQAR8dpy5Z/8ymSeHRNt2peXGVZZn5eFDzqQU962vQZyKurUkteppt95ej2v0nTQWbbBUL/NLwJ2kjQHuBs4jKSOOut84AuSNgI2Jpmg5TP9DlomWH+yelnNzNplWK1BImKdpCOBi4HpwOkRsVzS/HT9goi4WdL3SWopNgCnRcSyfsct0928sP1fHZreT79INnPNq0fNKttapChbz6pSZ112WNNeBsmcJ/otIWuQTjdVsihn3lPDMO+TRcRFwEVdyxZ0vT4JOKnsMct0N3+ppEWS1kp6TNJ6SWvKnsDMrA0mQ3fzL5DUuXwd2B04HNipzkK1VVE2WZSh9cpsBzl+lXrgugdyKsryB2nZUnSsKgNFOXM2aH4LtFLTekXECknTI5lX8QxJP6m5XGZmI9X0qtcywfphSRsDSyR9GvglsHm9xTIzG63JMPnAO0jqto8EjiZpP/j6OgsFzf9KUkUdNxDzjl/0db7XV/+iLtplu6OXWZ93LYpuJlbpbl52VL5e610lMnU1PeaUaQ1yp6Rnps+Pr79IZmaj19pgrSTFOJYkoxYwTdI64N8j4h9HVL7Wystiq3SEqbLtIE0Dq2TDeQa9QVj2vEU3CAdtxlh0fmfWU1fT66z7RYEPAnsBe0TE1hGxFUkvm70kHT2KwpmZjUqVgZzGoV81yOHAARFxX2dBRNwu6e3AJRR0jZzq8v6gVbK2ioPKAMVdvIvqaYu6aBcN9FRUvuzzYXZXLxpIqmi9s2mD5mfW/YL1jGyg7oiIX0uaUWOZzMxGrs3B+rEB1w1F0yv7i1TpAJNXJ52X2U60NUlRF+ysomy41+83yKBQVeZQnOj6fmXKPne2PfW0OVi/qEe3cgGb1FQeM7OxaHqC2G8Oxqf2d7axGrQ7emfbomy3ylRcE52oYJiZc7/fqdfzonbYzqynntYGazOzqcTB2oamylRgWXlvwqLBl6pkw3nn6rVt2Wm3Bu0hOazM3aaepv/tHazNzJgcY4OYmU16zqytNapUB1TptJJ3jmF2qhmk003RudwFfepxsDYzawEHa2u9iXbRLrrBmLdtXZ1e+h2z1/Ftamj639zB2swM32C0KaZsZjzo7OODdI0f5nrXX09ezqzNzFrAwdosxyjrnIfZ6caZ9eTlYG1m1gIO1mYTNGgWPNFz2dTS5iFSzcymDAdrswaZaJ21TV5N/zs7WJuZ4WBtZtYKDtZmDefxrg1cZ21m1gpN/yB2sDaroOn/0DY4Z9ZmZi3Q9A9iB2uzCoqGeLX2avrfdLAZWM3MJpkNGzaUfhSRdKCkWyWtkPSxnPX7SnpQ0pL08cmiY9aaWUt6JjAbWAesjIi1dZ7PzGxQw8qsJU0HTgEOAFYBiyRdEBE3dW3644h4Tdnj1pJZS9pF0g+Aq4FrgNOApZLOlDSrz37zJC2WtPjyyy+vo2hmZrmGmFnPBVZExO0R8RhwDnDwRMtXV2Z9OnBERNwqaS7wvojYU9J7gC8Db8jbKSIWAguzyyTNS5fbAHz9JsbXb+Lacg0jovT4t5LmAfMyixZmfsdtgbsy61YBe+Yc5mWSfgbcA3w4Ipb3O2ddddabRsStABFxLfAX6fNTgV0qHmte8SbWh6/fxPj6Tdyku4YRsTAids88sh9GeUG/u47lemCHiHgR8O/At4vOWVewvk3SP0j6H5JOBpYASJqBW6CY2eS2Ctgu83o2Sfb8uIhY07mHFxEXATMkbdPvoHUF63cBTweOAR4FjkqXbwYcXtM5zcyaYBGwk6Q5kjYGDgMuyG4g6dlK24GmVcXTgN/0O2gtWW5EPAB8NGf5g8BPKx6u8XVdDefrNzG+fhM3pa5hRKyTdCRwMTAdOD0ilkuan65fQHLf7r2S1gGPAIdFQXMUjboheFtuNpiZNck4OsV4xlEzs4pqC9aSdpa0v6QtulbdWdc5zcwmq7o6xXwAOB94P7BMUrZB+D93bbudpMsk3SxpuaSj6CJpK0nnSbpR0rWS/ryOcreVpE3S6/Kz9Boe32fbPSStl5Tb1n0qKnP9/B4sJmm6pBskfSdn3cHptVuSdnzbexxlbLO6mtG9B3hJRKyVtCPwDUk7RsTneGo1yDrg7yPieklPB66TdGlX18xjgCURcYiknUm6cu5fU9nb6FHgFen1ngFcJel7EfGkm7lpN9h/IbnxYU8oc/38Hix2FHAzMDNn3Q+BCyIiJL0QOBfYeZSFa7u6qkGmZ9oQ3gHsC7xa0r/RFawj4pcRcX36/Hckf+xtu463C8kfm4i4BdhR0rNqKnvrRKIz7sqM9JF35/j9wDeB1aMqWxuUvH5+D/YhaTbw1yRDSzxFRKzNtHbYnPz3p/VRV7C+V9KunRfpP8JrgG1IezPmSbPw3UjGE8n6GXBous1cYAeShuaWSr+CLiEJxJdGxDVd67cFDgEWjKF4jVd0/fB7sMhnSZrr9hw4Q9Ihkm4BvkvSF8MqqCtYHw7cm10QEesi4nBgn7wd0huR3wQ+GBFrulafCGyV/jO9H7iBpPrEUhGxPiJ2JQkgc3PqVD8L/O+IWD/qsrVBievn92APkl4DrI6I6/ptFxHnRcTOwOuAfxpF2SaTkbezzi1EUk/4HeDiiPi3gm0FrARemBPUDZB0LPBQRJycWbaSJ6qgtgEeBuZFxLdHX8Jmy7t+Xev9HsyQdALwDpIPr01I6qy/FRFv77PPSmCPiLhvNKVsv7FPPpC+8b8M3NwrUEvaMu22CfBu4Er/kzxB0jMlbZk+3xR4JXBLdpuImBMRO0bEjsA3gP/lQJ0oc/38HuwtIj4eEbPT99ZhwI+6A7WkP810r34xsDEF3avtyZowqNJeJJ/KS9OvmJDced8eHu+a+WfA2ZLWAzcBfzuGcjbZHwNnpa09pgHnRsR3urq3Wm9lrp/fgxV1Xb/XA4dL+gNJ9+o3F3WvtidrRDWImZn1N/ZqEDMzK+ZgbWbWAg7WZmYt4GBtZtYCDtZmZi3gYD1GktaW2Obl6UhwS9I2wBM953xJlaZWk/RhSbdIWpaOTDeSqdkknSZpl/T5MQPsv6mkK9ImeWX3efz6SDqzMzqhpMsl7Z4+v6jTLnvUql4TSedI2qn+klnd3HRvjCStjYju8b67t1kAXBMRZwzhfBtFRKUu0mlb2UOAN0bEGkmzgNdFxFkTLU/FchReq5x93gdslI72OMg5zwS+ExHfkHQ58OGIWDzIsepQ8v3zl8DbI+I9IyqW1cSZdQNI2jfN3L6RZrBfU+LdwJuAT6bLtpD0Q0nXS1qqdJxwSTsqGQ/81DQLv6SThafH/WdJVwBHSTouzZQ3krRI0r7pdidI+lRO8Y4h6e24BpJ5NDuBWtIn02Msk7Qw00PtckmflfSTdN3cdPncdNkN6c/np8unSzo5/Z1ulPT+zHF2l3QisGn67eJrkv5JmXHPJX1KyRjq3d5GMq565xpfIelcST+XdKKktykZm3qppOem2x0n6cMFf687lM5ELelD6e+4TNIHi/4eXcd5PHNPX6/t934ouCabS/qukm8+yyS9OT3sj4FXSmpCBzibiIjwY0wPYG36c1/gQZJBhKYBVwN7p+vOBN6QPt8ImJk+3wZYQTLex44k4zLsmq47lySbArgc+I/MOY8jyRABXkAyJO0BJAMTbdxVvqcD9/cp/zMyz78CHJQ556np832AZenzmSSZLiRdur+ZPn8vySBeG2WPmx5n9+y1Sp/vCFyfPp8G3AZs3VW2jYF7M6/3BR4g6a34NOBu4Ph03VHAZ3OuT/baZ8tyR3r9XwIsJRnycwtgOcmokT3/Hl1lfPz4Fd4Pva7J6zvXPH09K/P8UpLx5cf+nvdj8Icz6+a4NiJWRcQGYAnJP3w3Af8s6UbgByTjfnfGVF4ZEUvS59d17f9/804YEctJguyFwLsi4rGc8/WrJ9tP0jWSlgKvIAn+Hf+VnuNKYKaSOt5ZwNclLQM+k9n+lcCCSKtoIuK3fc5JJGOk/0bSbsCrgBsionuciW1IgnPWokjGT3+UJMBfki5fSv71LrI3cF5EPBTJMMDfAl6eruv39yijzPshaylJBv0vkl4eEQ9m1q0G/qTi+a1hHKyb49HM8/Xkj9vyNuCZJFnSrsCvSEY5K9r/oT7n/QuSoPaUgfQjqfp4SNJzutdJ2gT4D5LM8C+AUzNlgacG+SAZFvOyiPhz4KDM9kUfCnlOA/4GeCdwes76R7rKA0++Rhsyrzcw2Dg5/SZ/LvP3XEf6P5hWc2ycWVdm/8dFxM95ItM/QdInM6s3Ibke1mIO1u0yi2Tc4D9I2o9kAPyBSToU2JqkquLzym/hcAJwiqSZ6T4zJc3jiUB4n5KxyLvndHxzuv3ewINppjeLpPoBkkDbcQkwv1OvKukZOeX4g5KhdDvOAw4E9iBnmrKIuB+Ynn6o1OVK4HWSNpO0OcmN2B9X2P8OkgALcDDJDDVVPH5NJP0J8HBEfBU4GXhxZrvnkVTRWIv5pkO7fA24UNJikq/Gt/TfvLf0BtmJwP4RcZekLwCfA47o2vSLJPWxi5SMmPYH4F8j4gFJp5JkcncAi7r2u1/ST0jqqTuzgnyaZHS7DwE/ymx7GklAuTE9x6nAF7qOtzBdf31EvC0iHpN0GfBA9J5Q4RKSqoofFF2PQUQyb+iZwLXpotMi4gYlMx6VcSpwvqRrSaYM6/cNKM/j1wQ4GzhJ0gaSv9F7AZRMPfZIRPyy4rGtYdx0z4ZOI2jmJmkacD1Jk8L/7rHNbsCHIuIddZWj6SQdDayJiC+Puyw2Ma4GsdZR0ilkBfDDXoEaICJuAC5ThU4xk9ADwEjbxFs9nFmbmbWAM2szsxZwsDYzawEHazOzFnCwNjNrAQdrM7MW+P9/uT9B1NA/DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "colormap=sns.color_palette(\"Greys_r\", as_cmap=True)\n",
    "\n",
    "#---------------------------------------------- Plot figure 5 a)---------------------------------------------\n",
    "#Get capacity data\n",
    "#Change the name of the file with the appropiate name\n",
    "capacity_results = results.generate_dataframe('results/NEOS_capacity_results.txt')\n",
    "\n",
    "#R calculation\n",
    "C = capacity_results['P_infanrix'] - capacity_results['priv_p_infanrix']*capacity_results['priv_q_infanrix'] +\\\n",
    "capacity_results['P_daptacel'] - capacity_results['priv_p_daptacel']*capacity_results['priv_q_daptacel']\n",
    "\n",
    "R = C/(capacity_results['pub_p_infanrix']*capacity_results['pub_q_infanrix'] + capacity_results['pub_p_daptacel']*capacity_results['pub_q_daptacel'])\n",
    "capacity_results['R'] = R\n",
    "\n",
    "#Get pivot table of R with capacity values\n",
    "capacity = capacity_results.pivot(index='K_daptacel', columns = 'K_infanrix', values = 'R')\n",
    "\n",
    "#Plot figure\n",
    "ax = sns.heatmap(capacity, cmap = colormap, xticklabels=30, yticklabels=30,vmin=0.5, vmax=1, center= 0.75)\n",
    "\n",
    "#Formatting\n",
    "ax.invert_yaxis()\n",
    "xlabels = ['{:,.1f}'.format(x) for x in ax.get_xticks()*1.6/100+2.9];\n",
    "ylabels = ['{:,.1f}'.format(y) for y in ax.get_yticks()*1.6/100+2.9];\n",
    "ax.set_xticklabels(xlabels)\n",
    "ax.set_yticklabels(ylabels)\n",
    "ax.set_xlabel('Infanrix Capacity (million units)')\n",
    "ax.set_ylabel('Daptacel Capacity (million units)')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#---------------------------------------------- Plot figure 5 b)---------------------------------------------\n",
    "#Get profit data\n",
    "#Change the name of the file with the appropiate name\n",
    "profit_results = results.generate_dataframe('results/NEOS_profit_results.txt')\n",
    "\n",
    "#R calculation\n",
    "C = profit_results['P_infanrix'] - profit_results['priv_p_infanrix']*profit_results['priv_q_infanrix'] +\\\n",
    "    profit_results['P_daptacel'] - profit_results['priv_p_daptacel']*profit_results['priv_q_daptacel']\n",
    "\n",
    "R = C/(profit_results['pub_p_infanrix']*profit_results['pub_q_infanrix'] + \\\n",
    "       profit_results['pub_p_daptacel']*profit_results['pub_q_daptacel'])\n",
    "\n",
    "profit_results['R'] = R\n",
    "\n",
    "#Get pivot table of R with capacity values\n",
    "profit = profit_results.pivot(index='P_daptacel', columns = 'P_infanrix', values = 'R')\n",
    "\n",
    "#Replace infinity values of R by 2\n",
    "#This is a trick for plotting the graph properly\n",
    "for i in range(profit.shape[0]):\n",
    "    for j in range(profit.shape[1]):\n",
    "        if profit.iloc[i,j]==np.inf:\n",
    "            profit.iloc[i,j] = 2\n",
    "\n",
    "\n",
    "#Plot figure            \n",
    "colormap=sns.color_palette(\"Blues_r\", as_cmap=True)\n",
    "#This part of the plot ignores values over 1 (i.e. those which are originally infinity)\n",
    "ax = sns.heatmap(profit, cmap = colormap, mask = profit > 1, xticklabels=30, yticklabels=30 ,vmin=0.5, vmax=1)\n",
    "\n",
    "#This part of the plot considers  the values that were originally infinity\n",
    "ax = sns.heatmap(profit, mask=profit < 2., cmap=\"Greys_r\", cbar=False, xticklabels=30, yticklabels=30, center = 2,vmin=2, vmax=2)\n",
    "\n",
    "#Formatting\n",
    "ax.invert_yaxis()\n",
    "xlabels = ['{:,.0f}'.format(x) for x in ax.get_xticks()*24/100+28.45];\n",
    "ylabels = ['{:,.0f}'.format(y) for y in ax.get_yticks()*24/100+28.45];\n",
    "ax.set_xticklabels(xlabels)\n",
    "ax.set_yticklabels(ylabels)\n",
    "ax.set_xlabel('Infanrix Target Profit (million USD)')\n",
    "ax.set_ylabel('Daptacel Target Profit (million USD)')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}