{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01 - Literature Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AthulGirija\\anaconda3\\envs\\env1\\lib\\site-packages\\openpyxl\\worksheet\\_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    }
   ],
   "source": [
    "DATA = pd.read_excel('../bibliometric-data/Bibliometric Data - Mar 12, 2020.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,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_histogramYears():\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.set_size_inches([6.25,3.25])\n",
    "    rcParams['font.family'] = 'sans-serif'\n",
    "    rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "    params = {'mathtext.default': 'regular' }          \n",
    "    plt.rcParams.update(params)\t\n",
    "\n",
    "    plt.hist(years, bins=np.arange(min(years), max(years)+2, 1), color = \"black\", ec=\"black\")\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",
    "    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",
    "    #ax.xaxis.set_ticks_position('both')\n",
    "\n",
    "    plt.annotate(\"London\"  , xy=(1962.5, 1), xytext=(1962.5, 5.5), 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(\"Repic et al.\"  , xy=(1968.5, 1), xytext=(1968.5, 9.5), 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(\"Cruz\"  , xy=(1979, 1), xytext=(1979,9.4), 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(\"Mars SR\"   , xy=(1981.5, 7), xytext=(1981.5, 13.5), 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(\"AFE\"   , xy=(1989.5, 7), xytext=(1989.5, 15.5), 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(\"Mars \\n2001\"   , xy=(1998.5, 6), xytext=(1998.5, 13.5), 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(\"ISPT \\nstudies\"  , xy=(2003, 20), xytext=(1996, 20), 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(\"Neptune \\nstudies\\n\\n Venus \\nSmallSats\"  , xy=(2018.5, 10), xytext=(2018.5, 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",
    "\n",
    "    plt.xlim([1957, 2025])\n",
    "    #plt.annotate(\"PSD assessment \\n\\nIce Giants\\n study \\n\\nVenus SmallSat \\nstudy\"  , xy=(2017, 10), xytext=(2017, 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",
    "    '''\n",
    "    plt.annotate(\"In-Space Propulsion (ISP) \\n funded sudies\"  , xy=(2003, 20), xytext=(1992, 20), va=\"center\", ha=\"center\", arrowprops=dict(width=1.0, facecolor='blue', edgecolor='blue', shrink=0.05), fontsize=10,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='yellow', alpha=0.3))\n",
    "    plt.annotate(\"PSD Aerocapture \\nAssessment \\n\\nIce Giants\\n Aerocapture Study \\n\\nVenus SmallSat \\nAerocapture Study\"  , xy=(2017, 10), xytext=(2017, 18), va=\"center\", ha=\"center\", arrowprops=dict(width=1.0, facecolor='blue', edgecolor='blue', shrink=0.05), fontsize=10,color='blue',bbox=dict(boxstyle='round,pad=0.2', fc='yellow', alpha=0.3))\n",
    "    '''\n",
    "\n",
    "    plt.savefig('../plots/publication-no-hist-new.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/publication-no-hist-new.pdf', dpi=300,bbox_inches='tight')\n",
    "    plt.savefig('../plots/publication-no-hist-new.eps', dpi=300,bbox_inches='tight')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x234 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_histogramYears()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pieTargetPlanet():\n",
    "\n",
    "    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:orange', 'xkcd:pastel blue', 'xkcd:light red', 'xkcd:goldenrod', 'xkcd:light turquoise', 'xkcd:cerulean blue', 'xkcd:neon green']\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.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-planets.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-planets.eps', dpi=500,bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py: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",
      "  v_c = sum('Venus' in s for s in planet)\n",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py: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",
      "  e_c = sum('Earth' in s for s in planet)\n",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py: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",
      "  m_c = sum('Mars' in s for s in planet)\n",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py: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",
      "  t_c = sum('Titan' in s for s in planet)\n",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py: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",
      "  u_c = sum('Uranus' in s for s in planet)\n",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py:8: 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",
      "C:\\Users\\ATHULG~1\\AppData\\Local\\Temp/ipykernel_82784/4008509295.py:9: 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 234x234 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_pieTargetPlanet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pieSource():\n",
    "    counter = Counter(source)\n",
    "\n",
    "    sizes  = counter.values()\n",
    "    labels = counter.keys()\n",
    "\n",
    "    tot = len(years)\n",
    "\n",
    "    JPC = counter['AIAA/ASME/SAE/ASEE Joint Propulsion Conference']\n",
    "    AFM = counter['Atmospheric Flight Mechanics Conference']\n",
    "    JSR = counter['Journal of Spacecraft and Rockets']\n",
    "    ASM = counter['Aerospace Sciences Meeting']\n",
    "    AAS = counter['AIAA/AAS Astrodynamics Conference']\n",
    "    NTM = counter['Technical Report'] + 6\n",
    "    DTC = counter['AIAA Aerodynamic Decelerator Systems Technology Conference']\n",
    "    TPC = counter['Thermophysics Conference']\n",
    "    AAJ = counter['Acta Astronautica']\n",
    "    JAS = counter['Journal of Astronautical Sciences']\n",
    "    SCI = counter['AIAA SciTech']\n",
    "    IPW = counter['International Planetary Probe Workshop']\n",
    "\n",
    "\n",
    "    MIS = len(source) - JSR - AFM - JPC - AAS - ASM - NTM - DTC - TPC - AAJ - JAS - SCI - IPW\n",
    "\n",
    "    sizes  = [JPC, AFM, JSR , ASM, AAS, SCI, NTM, DTC,  TPC, AAJ, JAS,  IPW,  MIS]\n",
    "    labels = ['AIAA Joint Propulsion\\n Conference', \n",
    "              'AIAA Atmospheric Flight\\n Mechanics Conference', \n",
    "              'AIAA Journal of Spacecraft \\n and Rockets', \n",
    "              'AIAA Aerospace Sciences Meeting', \n",
    "              'AIAA/AAS Astrodynamics Conference',\n",
    "              'AIAA SciTech',\n",
    "              'NASA / Other Technical Report',   \n",
    "              'AIAA Decelerator Systems Conference',  \n",
    "              'AIAA Thermophysics Conference', \n",
    "              'Acta Astronautica', \n",
    "              'Journal of Astronautical  Sciences',\n",
    "              'Intl. Planetary Probe Workshop',\n",
    "              'Others']\n",
    "\n",
    "    fig=plt.figure(figsize=(6.25, 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",
    "\n",
    "    colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\", \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\", 'xkcd:neon green', 'xkcd:sky blue', 'xkcd:salmon']\n",
    "\n",
    "    patches, texts, autotexts = plt.pie(sizes, labels=labels, colors = colors, labeldistance = 1.1 , pctdistance = 0.85, autopct='%d%%',shadow=False, startangle=130, textprops={'fontsize': 8})\n",
    "    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "    plt.setp(autotexts, fontsize=8)\n",
    "    plt.setp(texts, fontsize=8)\n",
    "\n",
    "    plt.savefig('../plots/pie-sources.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-sources.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-sources.eps', dpi=500,bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x234 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_pieSource()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pieInstitution():\n",
    "    counter = Counter(author)\n",
    "\n",
    "    sizes  = counter.values()\n",
    "    labels = counter.keys()\n",
    "\n",
    "    tot = len(years)\n",
    "\n",
    "    LANG = counter['NASA Langley Research Center']\n",
    "    AMES = counter['NASA Ames Research Center']\n",
    "    JPL  = counter['Jet Propulsion Lab']\n",
    "    PURD = counter['Purdue University']\n",
    "    GEOR = counter['Georgia Institute of Technology']\n",
    "    JOHN = counter['NASA Johnson Space Center']\n",
    "    MARS = counter['NASA Marshall Space Flight Center']\n",
    "    GLEN = counter['NASA Glenn Research Center']\n",
    "    GECO = counter['General Electric Co.']\n",
    "    JAXA = counter['Japanese Aerospace and Exploration Agency (JAXA)'] + 4\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    MIS = len(source) - LANG - AMES - JPL - PURD - GEOR - JOHN -  MARS - GLEN - GECO - JAXA\n",
    "\n",
    "    sizes  = [LANG, AMES, JPL, PURD, GEOR, JOHN, MARS, GLEN, GECO, JAXA, MIS ]\n",
    "    labels = ['NASA Langley \\n Research Center', \n",
    "              'NASA Ames \\nResearch Center', \n",
    "              'Jet Propulsion Laboratory', \n",
    "              'Purdue University', \n",
    "              'Georgia Institute of Technology',  \n",
    "              'NASA Johnson Space Center', \n",
    "              'NASA Marshall Space Flight Center',  \n",
    "              'NASA Glenn Research Center', \n",
    "              'General Electric Co.', \n",
    "              'JAXA / ISAS',\n",
    "              'Others']\n",
    "\n",
    "    fig=plt.figure(figsize=(6.25, 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",
    "\n",
    "    colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\", \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\", 'xkcd:salmon']\n",
    "\n",
    "    patches, texts, autotexts = plt.pie(sizes, labels=labels, colors = colors, labeldistance = 1.1 , pctdistance = 0.85, autopct='%d%%',shadow=False, startangle=130, textprops={'fontsize': 8})\n",
    "    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "    plt.setp(autotexts, fontsize=8)\n",
    "    plt.setp(texts, fontsize=8)\n",
    "\n",
    "    plt.savefig('../plots/pie-affiliation.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-affiliation.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-affiliation.eps', dpi=500,bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x234 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_pieInstitution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pieCountry():\n",
    "    counter = Counter(origin)\n",
    "\n",
    "    sizes  = counter.values()\n",
    "    labels = counter.keys()\n",
    "\n",
    "    tot = len(years)\n",
    "\n",
    "    US = counter['USA']\n",
    "    JP = counter['Japan']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    MIS = len(source) - US - JP \n",
    "\n",
    "    sizes  = [US, JP,  MIS ]\n",
    "    labels = ['USA', \n",
    "              'Japan', \n",
    "              'Others']\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",
    "\n",
    "    colors = ['xkcd:turquoise', 'xkcd:light red', 'xkcd:periwinkle']\n",
    "\n",
    "    patches, texts, autotexts = plt.pie(sizes, labels=labels, colors = colors, labeldistance = 1.1 , pctdistance = 0.75, autopct='%d%%',shadow=False, startangle=30, 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-country.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-country.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/pie-country.eps', dpi=500,bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 234x234 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_pieCountry()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_histogramYearsStackedByPlanetGroup():\n",
    "\n",
    "    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\t\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",
    "    plt.annotate(\"London\"  , xy=(1962.5, 1), xytext=(1962.5, 5.5), 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(\"Repic et al.\"  , xy=(1968.5, 1), xytext=(1968.5, 9.5), 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(\"Cruz\"  , xy=(1979, 1), xytext=(1979,9.4), 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(\"Mars SR\"   , xy=(1981.5, 7), xytext=(1981.5, 13.5), 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(\"AFE\"   , xy=(1989.5, 7), xytext=(1989.5, 15.5), 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(\"Mars \\n2001\"   , xy=(1998.5, 6), xytext=(1998.5, 13.5), 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(\"ISPT \\nstudies\"  , xy=(2003, 20), xytext=(1996, 20), 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(\"Neptune \\nstudies\\n\\n Venus \\nSmallSats\"  , xy=(2018.5, 14), xytext=(2018.5, 21), 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-new.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/hist-byPlanet-new.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/hist-byPlanet-new.eps', dpi=500,bbox_inches='tight')\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "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 468x234 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_histogramYearsStackedByPlanetGroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_histogramYearsStackedByCountry():\n",
    "\n",
    "    years  = DATA['Year'].values\n",
    "\n",
    "    years_us = years\n",
    "    years_jp = years\n",
    "    years_ru = years\n",
    "    years_de = years\n",
    "    years_fr = years\n",
    "    years_au = years\t\n",
    "    years_ca = years\n",
    "    years_in = years\n",
    "    years_it = years\n",
    "    years_nl = years\n",
    "    years_sk = years\n",
    "    years_sp = years\n",
    "    years_sv = years\n",
    "    years_cn = years\n",
    "\n",
    "    us_index_to_remove=np.array([0])\n",
    "    jp_index_to_remove=np.array([0])\n",
    "    ru_index_to_remove=np.array([0])\n",
    "    de_index_to_remove=np.array([0])\n",
    "    fr_index_to_remove=np.array([0])\n",
    "    au_index_to_remove=np.array([0])\n",
    "    ca_index_to_remove=np.array([0])\n",
    "    in_index_to_remove=np.array([0])\n",
    "    it_index_to_remove=np.array([0])\n",
    "    nl_index_to_remove=np.array([0])\n",
    "    sk_index_to_remove=np.array([0])\n",
    "    sp_index_to_remove=np.array([0])\n",
    "    sv_index_to_remove=np.array([0])\n",
    "    cn_index_to_remove=np.array([0])\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'USA':\n",
    "            us_index_to_remove=np.append(us_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Japan':\n",
    "            jp_index_to_remove=np.append(jp_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Russia':\n",
    "            ru_index_to_remove=np.append(ru_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Germany':\n",
    "            de_index_to_remove=np.append(de_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\t\t\n",
    "        if origin[i] != 'France':\n",
    "            fr_index_to_remove=np.append(fr_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Australia':\n",
    "            au_index_to_remove=np.append(au_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\t\t\n",
    "        if origin[i] != 'Canada':\n",
    "            ca_index_to_remove=np.append(ca_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'India':\n",
    "            in_index_to_remove=np.append(in_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\t\t\n",
    "        if origin[i] != 'Italy':\n",
    "            it_index_to_remove=np.append(it_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Netherlands':\n",
    "            nl_index_to_remove=np.append(nl_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'S. Korea':\n",
    "            sk_index_to_remove=np.append(sk_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Spain':\n",
    "            sp_index_to_remove=np.append(sp_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'Sweden':\n",
    "            sv_index_to_remove=np.append(sv_index_to_remove,i)\n",
    "\n",
    "    for i in range(0,len(years)):\n",
    "        if origin[i] != 'China':\n",
    "            cn_index_to_remove=np.append(cn_index_to_remove,i)\n",
    "\n",
    "\n",
    "\n",
    "    us_index_to_remove=np.delete(us_index_to_remove,0)\n",
    "    jp_index_to_remove=np.delete(jp_index_to_remove,0)\n",
    "    ru_index_to_remove=np.delete(ru_index_to_remove,0)\n",
    "    de_index_to_remove=np.delete(de_index_to_remove,0)\n",
    "    fr_index_to_remove=np.delete(fr_index_to_remove,0)\n",
    "    au_index_to_remove=np.delete(au_index_to_remove,0)\n",
    "    ca_index_to_remove=np.delete(ca_index_to_remove,0)\n",
    "    in_index_to_remove=np.delete(in_index_to_remove,0)\n",
    "    it_index_to_remove=np.delete(it_index_to_remove,0)\n",
    "    nl_index_to_remove=np.delete(nl_index_to_remove,0)\n",
    "    sk_index_to_remove=np.delete(sk_index_to_remove,0)\n",
    "    sp_index_to_remove=np.delete(sp_index_to_remove,0)\n",
    "    sv_index_to_remove=np.delete(sv_index_to_remove,0)\n",
    "    cn_index_to_remove=np.delete(cn_index_to_remove,0)\n",
    "\n",
    "    years_us = np.delete(years_us, us_index_to_remove)\n",
    "    years_jp = np.delete(years_jp, jp_index_to_remove)\n",
    "    years_ru = np.delete(years_ru, ru_index_to_remove)\n",
    "    years_de = np.delete(years_de, de_index_to_remove)\n",
    "    years_fr = np.delete(years_fr, fr_index_to_remove)\n",
    "    years_au = np.delete(years_au, au_index_to_remove)\n",
    "    years_ca = np.delete(years_ca, ca_index_to_remove)\n",
    "    years_in = np.delete(years_in, in_index_to_remove)\n",
    "    years_it = np.delete(years_it, it_index_to_remove)\n",
    "    years_nl = np.delete(years_nl, nl_index_to_remove)\n",
    "    years_sk = np.delete(years_sk, sk_index_to_remove)\n",
    "    years_sp = np.delete(years_sp, sp_index_to_remove)\n",
    "    years_sv = np.delete(years_sv, sv_index_to_remove)\n",
    "    years_cn = np.delete(years_cn, cn_index_to_remove)\n",
    "\n",
    "    years_st    = [years_us, years_jp,  years_de, years_fr, years_au, years_cn]\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",
    "    colors = ['xkcd:electric blue', 'xkcd:tangerine', 'xkcd:green yellow', 'xkcd:hot magenta', 'xkcd:cinnamon',  'xkcd:lightish red']\n",
    "\n",
    "\n",
    "\n",
    "    plt.hist(years_st, bins=np.arange(min(years), max(years)+2, 1), stacked=True, label=['USA', 'Japan', 'Germany', 'France', 'Australia',  'China'],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",
    "    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.legend(loc='upper left', fontsize=9)\n",
    "\n",
    "\n",
    "    plt.savefig('../plots/hist-byCountry-new.png',bbox_inches='tight')\n",
    "    plt.savefig('../plots/hist-byCountry-new.pdf', dpi=500,bbox_inches='tight')\n",
    "    plt.savefig('../plots/hist-byCountry-new.eps', dpi=500,bbox_inches='tight')\n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "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": [
    "create_histogramYearsStackedByCountry()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}