{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e046c62",
   "metadata": {},
   "source": [
    "# Section 01 - Literature Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "726e49a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pylab import *\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1bb981c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = pd.read_excel('../../../bibliometric-data/Bibliometric Data - May 27, 2022.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "years  = DATA['Year'].values\n",
    "origin = DATA['Country of Origin'].values\n",
    "planet = DATA['Targets'].values\n",
    "tclass = DATA['Target Class'].values\n",
    "source = DATA['Journal / Conference / Report'].values\n",
    "author = DATA['Author Affiliation'].values\n",
    "sponsor= DATA['Funding Organization'].values\n",
    "\n",
    "publisher = DATA['Publisher'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bcefabd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 650x325 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 650x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years  = DATA['Year'].values\n",
    "\n",
    "years_ve = years\n",
    "years_ea = years\n",
    "years_ma = years\n",
    "years_ti = years\n",
    "years_ur = years\n",
    "years_ne = years\n",
    "years_ge = years\n",
    "years_ju = years\n",
    "years_sa = years\n",
    "\n",
    "ve_index_to_remove=np.array([0])\n",
    "ea_index_to_remove=np.array([0])\n",
    "ma_index_to_remove=np.array([0])\n",
    "ti_index_to_remove=np.array([0])\n",
    "ur_index_to_remove=np.array([0])\n",
    "ne_index_to_remove=np.array([0])\n",
    "ge_index_to_remove=np.array([0])\n",
    "ju_index_to_remove=np.array([0])\n",
    "sa_index_to_remove=np.array([0])\n",
    "\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Venus':\n",
    "        ve_index_to_remove=np.append(ve_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Earth':\n",
    "        ea_index_to_remove=np.append(ea_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Mars':\n",
    "        ma_index_to_remove=np.append(ma_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Titan':\n",
    "        ti_index_to_remove=np.append(ti_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Uranus':\n",
    "        ur_index_to_remove=np.append(ur_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Neptune':\n",
    "        ne_index_to_remove=np.append(ne_index_to_remove,i)\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Generic':\n",
    "        ge_index_to_remove=np.append(ge_index_to_remove,i)\n",
    "\n",
    "\n",
    "ve_index_to_remove=np.delete(ve_index_to_remove,0)\n",
    "ea_index_to_remove=np.delete(ea_index_to_remove,0)\n",
    "ma_index_to_remove=np.delete(ma_index_to_remove,0)\n",
    "ti_index_to_remove=np.delete(ti_index_to_remove,0)\n",
    "ur_index_to_remove=np.delete(ur_index_to_remove,0)\n",
    "ne_index_to_remove=np.delete(ne_index_to_remove,0)\n",
    "ge_index_to_remove=np.delete(ge_index_to_remove,0)\n",
    "\n",
    "years_ve = np.delete(years_ve, ve_index_to_remove)\n",
    "years_ea = np.delete(years_ea, ea_index_to_remove)\n",
    "years_ma = np.delete(years_ma, ma_index_to_remove)\n",
    "years_ti = np.delete(years_ti, ti_index_to_remove)\n",
    "years_ur = np.delete(years_ur, ur_index_to_remove)\n",
    "years_ne = np.delete(years_ne, ne_index_to_remove)\n",
    "years_ge = np.delete(years_ge, ge_index_to_remove)\n",
    "\n",
    "years_st    = [years_ve, years_ea, years_ma, years_ti, years_ur, years_ne, years_ge]\n",
    "\n",
    "fig=plt.figure(figsize=(6.5,3.25))\n",
    "from matplotlib import rcParams\n",
    "\n",
    "fig = plt.figure(figsize=(6.5, 3.5))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "\n",
    "colors = ['xkcd:light orange', 'xkcd:rust', 'xkcd:red orange', 'xkcd:sunshine yellow', 'xkcd:bright sea green', 'xkcd:cerulean blue', 'xkcd:grey']\n",
    "\n",
    "plt.hist(years_st, bins=np.arange(min(years), max(years)+2, 1), stacked=True, label=['Venus', 'Earth', 'Mars','Titan', 'Uranus', 'Neptune', 'Generic'],color=colors)\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.ylabel('Number of publications',fontsize=12);\n",
    "plt.xticks(np.arange(1955, max(years) + 10, 10))\n",
    "plt.yticks(np.arange(5, 25 + 1, 5))\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "\n",
    "\n",
    "plt.annotate(\"Florence \\n(1981)\"  , xy=(1981.5, 7), xytext=(1981.5, 11), va=\"center\", ha=\"center\", arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=12,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='none', ec='none', alpha=0.3))\n",
    "plt.annotate(\"Hall et al.\\n (2005)\"  , xy=(2006, 12.5), xytext=(1995,12.5), va=\"center\", ha=\"center\", arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=12,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='none', ec='none', alpha=0.3))\n",
    "plt.annotate(\"Ice Giant \\nPre-Decadal\"  , xy=(2016.5, 7.5), xytext=(2014,18), va=\"center\", ha=\"center\", arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=12,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='none', ec='none', alpha=0.3))\n",
    "plt.annotate(\"Girija et al.\\n (2022)\"  , xy=(2022.5, 17.5), xytext=(2014,23), va=\"center\", ha=\"center\", arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=12,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='none', ec='none', alpha=0.3))\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "plt.legend(loc='upper left', fontsize=10)\n",
    "\n",
    "ax.xaxis.set_tick_params(width=2)\n",
    "ax.yaxis.set_tick_params(width=2)\n",
    "ax.tick_params(length=6)\n",
    "\n",
    "\n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/hist-byPlanet-uranus-paper.png',bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/hist-byPlanet-uranus-paper.pdf', dpi=500,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/hist-byPlanet-uranus-paper.eps', dpi=500,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9019cfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-8732f3755516>:1: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  v_c = np.sum('Venus' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:2: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  e_c = np.sum('Earth' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:3: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  m_c = np.sum('Mars' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:4: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  t_c = np.sum('Titan' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:5: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  u_c = np.sum('Uranus' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:6: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  n_c = np.sum('Neptune' in s for s in planet)\n",
      "<ipython-input-6-8732f3755516>:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  g_c = np.sum('Generic' in s for s in planet)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 325x325 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_c = np.sum('Venus' in s for s in planet)\n",
    "e_c = np.sum('Earth' in s for s in planet)\n",
    "m_c = np.sum('Mars' in s for s in planet)\n",
    "t_c = np.sum('Titan' in s for s in planet)\n",
    "u_c = np.sum('Uranus' in s for s in planet)\n",
    "n_c = np.sum('Neptune' in s for s in planet)\n",
    "g_c = np.sum('Generic' in s for s in planet)\n",
    "\n",
    "sizes  = [v_c, e_c, m_c, t_c, u_c, n_c, g_c]\n",
    "labels = ['Venus', 'Earth', 'Mars', 'Titan', 'Uranus', 'Neptune', 'Generic']\n",
    "\n",
    "fig=plt.figure(figsize=(3.25,3.25))\n",
    "\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "colors = ['xkcd:light orange', 'xkcd:rust', 'xkcd:red orange', 'xkcd:sunshine yellow', 'xkcd:bright sea green', 'xkcd:cerulean blue', 'xkcd:grey']\n",
    "\n",
    "patches, texts, autotexts = plt.pie(sizes, labels=labels, colors = colors, autopct='%d%%', pctdistance = 0.75, shadow=False, startangle=90, textprops={'fontsize': 12})\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.setp(autotexts, fontsize=12)\n",
    "plt.setp(texts, fontsize=12)\n",
    "\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/pie-planets-uranus-paper.png',dpi=500, bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/pie-planets-uranus-paper.pdf', dpi=500,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/pie-planets-uranus-paper.eps', dpi=500,bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}