{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35165025",
   "metadata": {},
   "source": [
    "# Example - 75 - Literature Survey Update - May 28, 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7cae0e0",
   "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": "8b8481b7",
   "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": "25b91ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 650x325 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",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Jupiter':\n",
    "        ju_index_to_remove=np.append(ju_index_to_remove,i)\n",
    "\n",
    "\n",
    "for i in range(0,len(years)):\n",
    "    if tclass[i] != 'Saturn':\n",
    "        sa_index_to_remove=np.append(sa_index_to_remove,i)\n",
    "\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",
    "#ju_index_to_remove=np.delete(ju_index_to_remove,0)\n",
    "#sa_index_to_remove=np.delete(sa_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",
    "#years_ju = np.delete(years_ju, ju_index_to_remove)\n",
    "#years_sa = np.delete(years_sa, sa_index_to_remove)\n",
    "\n",
    "\n",
    "#years_st    = [years_ve, years_ea, years_ma, years_ju, years_sa, years_ti, years_ur, years_ne, years_ge]\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",
    "\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.style.use('dark_background')\n",
    "plt.rcParams.update(plt.rcParamsDefault)\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=10)\n",
    "plt.ylabel('Number of publications',fontsize=10);\n",
    "plt.xticks(np.arange(1955, max(years) + 10, 10))\n",
    "plt.yticks(np.arange(5, 25 + 1, 5))\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "\n",
    "\n",
    "plt.annotate(\"GE Generic\\nAerocapture Study\"  , xy=(1980, 4), xytext=(1980, 11), va=\"center\", ha=\"center\", arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=10,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=10,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=10,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=10,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='none', ec='none', alpha=0.3))\n",
    "\n",
    "\n",
    "#plt.xlim([1958,2027])\n",
    "\n",
    "#plt.annotate(\"Neptune aerocapture studies \\n (Lockwood et al., 2006)\"  , xy=(2004.5, 15.0), xytext=(1988, 15.0), va=\"center\", ha=\"center\", arrowprops=dict(width=0.3, facecolor='black', edgecolor='black', shrink=0.05, headwidth=4.0), fontsize=9,color='black',bbox=dict(boxstyle='round,pad=0.2', fc='none',ec='none', alpha=0.3))\n",
    "#plt.annotate(\"PSD assessment  \\n (Spilker et al., 2016) \\n \\n Aerocapture \\n assessment \\n (Saikia et al., 2016)\"  , xy=(2017.5, 8.6),  xytext=(2017.5, 18.0), va=\"center\", ha=\"center\", arrowprops=dict(width=0.3, facecolor='black', edgecolor='black', shrink=0.05, headwidth=4.0), fontsize=9,color='black',bbox=dict(boxstyle='round,pad=0.2', fc='none',ec='none', alpha=0.3))\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "plt.legend(loc='upper left', fontsize=9)\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('../plots/hist-byPlanet-may-28.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/hist-byPlanet-may-28.pdf', dpi=500,bbox_inches='tight')\n",
    "plt.savefig('../plots/hist-byPlanet-may-28.eps', dpi=500,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5653aa56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-09d32554365e>: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 = sum('Venus' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = sum('Earth' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = sum('Mars' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = sum('Titan' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = sum('Uranus' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = sum('Neptune' in s for s in planet)\n",
      "<ipython-input-4-09d32554365e>: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 = 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 = sum('Venus' in s for s in planet)\n",
    "e_c = sum('Earth' in s for s in planet)\n",
    "m_c = sum('Mars' in s for s in planet)\n",
    "t_c = sum('Titan' in s for s in planet)\n",
    "u_c = sum('Uranus' in s for s in planet)\n",
    "n_c = sum('Neptune' in s for s in planet)\n",
    "g_c = 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",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\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': 10})\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.setp(autotexts, fontsize=10)\n",
    "plt.setp(texts, fontsize=10)\n",
    "\n",
    "plt.savefig('../plots/pie-planets-may-28.png',dpi=500, bbox_inches='tight')\n",
    "plt.savefig('../plots/pie-planets-may-28.pdf', dpi=500,bbox_inches='tight')\n",
    "plt.savefig('../plots/pie-planets-may-28.eps', dpi=500,bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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