Data_analysis_stars.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# planetary-and-exoplanetary-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook contains data extracted from the following datasets:\n",
    "* Stellarium\n",
    "* Skyfield\n",
    "* HYG dataset\n",
    "* Exoplanets dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## download libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellarium\n",
    "The below mentioned files are taken from the Stellarium database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constellations names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "constellations_names_csv = pd.read_csv(\"data/constellation_names.csv\", \n",
    "                                       delimiter= ',', header = None)\n",
    "constellations_names_csv.columns = ['cons_name_abb', 'cons_name', 'cons_name_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "constellation_names = {}\n",
    "for rows, cols in constellations_names_csv.iterrows():\n",
    "    constellation_names[cols['cons_name_abb']] =  cols['cons_name']\n",
    "# print(constellation_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Aql', 'Aquila'), ('And', 'Andromeda'), ('Scl', 'Sculptor')]\n"
     ]
    }
   ],
   "source": [
    "# visualize\n",
    "print(list(constellation_names.items())[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constellations art"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 6: expected 11 fields, saw 12\\nSkipping line 74: expected 11 fields, saw 12\\n'\n"
     ]
    }
   ],
   "source": [
    "constellations_art_csv = pd.read_csv(\"data/constellations_art.csv\", delimiter=' ', header = None, error_bad_lines=False)\n",
    "constellations_art_csv.loc[-1] = [\"Ara\",\"ara.png\", \"98\", \"70\", \"83081\", \"191\", \"93\", \"85727\", \"107\", \"249\", \"88714\"] \n",
    "constellations_art_csv.loc[-2] = [\"Sge\", \"sagitta.png\", \"6\", \"12\", \"98920\", \"249\", \"213\", \"96757\", \"218\", \"243\", \"96837\"] \n",
    "\n",
    "constellations_art_csv.columns = ['cons_name_abb', 'image', \"star1_x\", \"star1_y\", \"star1_hip\", \"star2_x\", \"star2_y\", \"star2_hip\", \"star3_x\", \"star3_y\", \"star3_hip\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>cons_name_abb</th>\n",
       "      <th>image</th>\n",
       "      <th>star1_x</th>\n",
       "      <th>star1_y</th>\n",
       "      <th>star1_hip</th>\n",
       "      <th>star2_x</th>\n",
       "      <th>star2_y</th>\n",
       "      <th>star2_hip</th>\n",
       "      <th>star3_x</th>\n",
       "      <th>star3_y</th>\n",
       "      <th>star3_hip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>And</td>\n",
       "      <td>andromeda.png</td>\n",
       "      <td>198</td>\n",
       "      <td>215</td>\n",
       "      <td>3881</td>\n",
       "      <td>337</td>\n",
       "      <td>136</td>\n",
       "      <td>3092</td>\n",
       "      <td>224</td>\n",
       "      <td>428</td>\n",
       "      <td>9640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ant</td>\n",
       "      <td>antlia.png</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>51172</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>47758</td>\n",
       "      <td>42</td>\n",
       "      <td>120</td>\n",
       "      <td>48926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aps</td>\n",
       "      <td>apus.png</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>81852</td>\n",
       "      <td>76</td>\n",
       "      <td>87</td>\n",
       "      <td>81065</td>\n",
       "      <td>163</td>\n",
       "      <td>110</td>\n",
       "      <td>72370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cons_name_abb          image star1_x star1_y star1_hip star2_x star2_y  \\\n",
       "0           And  andromeda.png     198     215      3881     337     136   \n",
       "1           Ant     antlia.png       4      84     51172      72      69   \n",
       "2           Aps       apus.png      66      60     81852      76      87   \n",
       "\n",
       "  star2_hip star3_x star3_y star3_hip  \n",
       "0      3092     224     428      9640  \n",
       "1     47758      42     120     48926  \n",
       "2     81065     163     110     72370  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize\n",
    "constellations_art_csv.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the values of constellations_art_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "star1_x, star1_y, star2_x, star2_y, star3_x, star3_y = [], [], [], [], [], []\n",
    "star_x, star_y = [], []\n",
    "\n",
    "for i in range(len(constellations_art_csv)):\n",
    "    star1_x = list(constellations_art_csv['star1_x'])\n",
    "    star1_y = list(constellations_art_csv['star1_y'])\n",
    "    star2_x = list(constellations_art_csv['star2_x'])\n",
    "    star2_y = list(constellations_art_csv['star2_y'])\n",
    "    star3_x = list(constellations_art_csv['star3_x'])\n",
    "    star3_y = list(constellations_art_csv['star3_y'])\n",
    "    abb = list(constellations_art_csv['cons_name_abb'])\n",
    "\n",
    "plt.figure(figsize=(10,10), dpi=80)\n",
    "\n",
    "for i in range(83):\n",
    "    r = random.random()\n",
    "    b = random.random()\n",
    "    g = random.random()\n",
    "    color = (r, g, b)\n",
    "    star_x = ([star1_x[i], star2_x[i], star3_x[i]])\n",
    "    star_y = ([star1_y[i], star2_y[i], star3_y[i]])\n",
    "    plt.scatter(star_x, star_y, color = color, label = \"{} : {}\".format(abb[i], \n",
    "                                                                   constellation_names[abb[i]]))\n",
    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n",
    "          fancybox=True, shadow=True, ncol=5)\n",
    "plt.savefig(\"pictures/cons.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constellations hip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "column_names = list(range(1,60))\n",
    "constellations_hip_csv = pd.read_csv(\"data/constellations_hip.csv\", \n",
    "                                     delimiter=' ', header = None, names = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "constellations_lines = {}\n",
    "for i in range(88):\n",
    "    constellations_lines[constellations_hip_csv[1][i]] = constellations_hip_csv[2][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "constellation_lines_bw_hip = {}\n",
    "hip_values = []\n",
    "for i in range(88):\n",
    "    for j in range(3,59):\n",
    "        if not(math.isnan(float(constellations_hip_csv[j][i]))):\n",
    "            hip_values.append(constellations_hip_csv[j][i])\n",
    "    constellation_lines_bw_hip[constellations_hip_csv[1][i]] = hip_values\n",
    "    hip_values = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Aql', ['98036', 97649.0, 97649.0, 97278.0, 97649.0, 95501.0, 95501.0, 97804.0, 99473.0, 97804.0, 95501.0, 93747.0, 93747.0, 93244.0, 95501.0, 93805.0]), ('And', ['677', 3092.0, 3092.0, 5447.0, 9640.0, 5447.0, 5447.0, 4436.0, 4436.0, 3881.0]), ('Scl', ['116231', 4577.0, 4577.0, 115102.0, 115102.0, 116231.0])]\n"
     ]
    }
   ],
   "source": [
    "# visualize\n",
    "print(list(constellation_lines_bw_hip.items())[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ssystems major"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/ssystem_major.csv', newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    temp_data_ssystem_major = list(reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssystem_major = []\n",
    "\n",
    "for i in range(len(temp_data_ssystem_major)):\n",
    "    try:\n",
    "        if temp_data_ssystem_major[i][0][0] != \"#\":\n",
    "            data_ssystem_major.append(temp_data_ssystem_major[i])\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_major = []\n",
    "temp2_major = []\n",
    "for i in range(len(data_ssystem_major)):\n",
    "    if \"type=\" not in data_ssystem_major[i][0]:\n",
    "        temp_major.append(data_ssystem_major[i])\n",
    "    else:\n",
    "        temp_major.append(data_ssystem_major[i])\n",
    "        temp2_major.append(temp_major)\n",
    "        temp_major = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[['[adrastea]'], ['iau_moon_number=JXV'], ['absolute_magnitude=12.4'], ['albedo=0.1'], ['color=1.', ' 0.9', ' 0.75'], ['name=Adrastea'], ['orbit_AscendingNode=253.6924110934923'], ['orbit_Eccentricity=0.006543120249224196'], ['orbit_Epoch=2454619.50000'], ['orbit_Inclination=0.06166936906340893'], ['orbit_LongOfPericenter=249.4662007624690000'], ['orbit_MeanLongitude=252.4809048448870000'], ['orbit_Period=0.3023760117035458'], ['orbit_SemiMajorAxis=129866.6459712491'], ['parent=Jupiter'], ['radius=8.2'], ['rot_equator_ascending_node=213.7'], ['rot_obliquity=15.5'], ['rot_rotation_offset=309.52'], ['rot_pole_ra=268.05'], ['rot_pole_ra1=-0.009'], ['rot_pole_de=64.49'], ['rot_pole_de1=0.003'], ['rot_pole_w0=33.29'], ['rot_pole_w1=1206.9986602'], ['tex_map=lune.png'], ['type=moon']]]\n"
     ]
    }
   ],
   "source": [
    "# visualize\n",
    "print(temp2_major[0:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ssystems minor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/ssystem_minor.csv', newline='') as f:\n",
    "    reader1 = csv.reader(f)\n",
    "    temp_data_ssystem_minor = list(reader1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssystem_minor = []\n",
    "\n",
    "for i in range(len(temp_data_ssystem_minor)):\n",
    "    try:\n",
    "        if temp_data_ssystem_minor[i][0][0] != \"#\":\n",
    "            data_ssystem_minor.append(temp_data_ssystem_minor[i])\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_minor = [data_ssystem_minor[0][0]]\n",
    "temp2_minor = []\n",
    "for i in range(1,len(data_ssystem_minor)):\n",
    "    if \"=\" in data_ssystem_minor[i][0]:\n",
    "        temp_minor.append(data_ssystem_minor[i])\n",
    "    else:\n",
    "        temp2_minor.append(temp_minor)\n",
    "        temp_minor = [data_ssystem_minor[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['[TeslaRoadster]', ['type                        = artificial'], ['absolute_magnitude          = 25.256'], ['name                        = Tesla Roadster (Starman)'], ['albedo                      = 1'], ['color                       = 1.0', '0.0', '0.0'], ['orbit_Epoch                 = 2458167.500000000'], ['orbit_TimeAtPericenter      = 2458153.5979641485'], ['orbit_PericenterDistance    = 0.9860582813419811'], ['orbit_Eccentricity          = 0.2576842367146164'], ['orbit_ArgOfPericenter       = 177.3364041029062'], ['orbit_AscendingNode         = 317.3183428774018'], ['orbit_Inclination           = 1.085063111224933'], ['coord_func                  = comet_orbit']]]\n"
     ]
    }
   ],
   "source": [
    "# visualize\n",
    "print(temp2_minor[0:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/base_locations.txt', newline='') as f:\n",
    "    reader2 = csv.reader(f)\n",
    "    base_locations_csv = list(reader2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_locations_t = []\n",
    "\n",
    "for i in range(len(base_locations_csv)):\n",
    "    try:\n",
    "        if base_locations_csv[i][0][0] != \"#\":\n",
    "            base_locations_t.append(base_locations_csv[i])\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_base = []\n",
    "temp_base2 = []\n",
    "str = \"\"\n",
    "for i in base_locations_t:\n",
    "    i.append(\"\\t\")\n",
    "    for char in i[ : :1][0]:\n",
    "        if char != \"\\t\":\n",
    "            str = str+char\n",
    "        else:\n",
    "            temp_base.append(str)\n",
    "            str = \"\"\n",
    "    temp_base.append(str)\n",
    "    str = \"\"\n",
    "    temp_base2.append(temp_base)\n",
    "    temp_base = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"LocationName\", \"Provice/State\", \"Region\", \"Type code\", \"Population (thousand)\", \n",
    "        \"Latitude (deg[N/S]\", \"Longitude (deg[E/W])\", \"Altitude (meters)\", \n",
    "        \"Light Pollution (1-9 Bortle scale)\", \"TimeZone\", \"Planet\", \"LandscapeKey\"]\n",
    "base_locations = pd.DataFrame(temp_base2, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>LocationName</th>\n",
       "      <th>Provice/State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Type code</th>\n",
       "      <th>Population (thousand)</th>\n",
       "      <th>Latitude (deg[N/S]</th>\n",
       "      <th>Longitude (deg[E/W])</th>\n",
       "      <th>Altitude (meters)</th>\n",
       "      <th>Light Pollution (1-9 Bortle scale)</th>\n",
       "      <th>TimeZone</th>\n",
       "      <th>Planet</th>\n",
       "      <th>LandscapeKey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>les Escaldes</td>\n",
       "      <td>Escaldes-Engordany</td>\n",
       "      <td>19</td>\n",
       "      <td>R</td>\n",
       "      <td>15.853</td>\n",
       "      <td>42.50729N</td>\n",
       "      <td>1.53414E</td>\n",
       "      <td>1033</td>\n",
       "      <td>5</td>\n",
       "      <td>Europe/Andorra</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LocationName       Provice/State Region Type code Population (thousand)  \\\n",
       "0  les Escaldes  Escaldes-Engordany     19         R                15.853   \n",
       "\n",
       "  Latitude (deg[N/S] Longitude (deg[E/W]) Altitude (meters)  \\\n",
       "0          42.50729N             1.53414E              1033   \n",
       "\n",
       "  Light Pollution (1-9 Bortle scale)        TimeZone Planet LandscapeKey  \n",
       "0                                  5  Europe/Andorra   None         None  "
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize\n",
    "base_locations_df = pd.DataFrame(base_locations[0:1])\n",
    "base_locations_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYG dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyg_visualization = pd.read_csv(\"data/hygdata_v3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracted the star distance according to the hip number\n",
    "collect_hyg_hip = list(hyg_visualization['hip'])\n",
    "collect_hyg_dist = list(hyg_visualization['dist'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Skyfield (hip_main.dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RA and Dec ( RAhms and DEdms)\n",
    "* **RA** is the position of a star measured in the east–west direction on the sky (like longitude, the position of a place on Earth measured east or west of the prime meridian at Greenwich, England).\n",
    "* **Dec** is the position of the star measured in the north–south direction, like the latitude of a city, which is measured north or south of the equator.\n",
    "* RA in **units of hours, minutes, and seconds**, like time. We list Dec in **degrees, minutes, and seconds of arc**. Ninety degrees make up a right angle, 60 minutes of arc make up a degree, and 60 seconds of arc equal a minute of arc. A minute or second of arc is also often called an “arc minute” or an “arc second,” respectively.\n",
    "* The process will be to convert the equitorial coordinates to the galactic coordinates so that they can be plotted using matplotlib.\n",
    " * **ra, dec = equatorial coordinates**\n",
    " * **l,b = galatic coordinates**\n",
    "* For this, first convert the ra and dec values into degrees and then convert these values into the galatic coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/skyfielders/python-skyfield/blob/aa59e2d4711c3a95804170889f138402edbf4237/skyfield/data/hipparcos.py\n",
    "#https://github.com/skyfielders/python-skyfield/blob/aa59e2d4711c3a95804170889f138402edbf4237/skyfield/documentation/stars.rst\n",
    "\n",
    "COLUMN_NAMES = ('Catalog', 'hip', 'Proxy', 'RAhms', 'DEdms', 'Vmag',\n",
    "    'VarFlag', 'r_Vmag', 'RAdeg', 'DEdeg', 'AstroRef', 'Plx', 'pmRA',\n",
    "    'pmDE', 'e_RAdeg', 'e_DEdeg', 'e_Plx', 'e_pmRA', 'e_pmDE', 'DE:RA',\n",
    "    'Plx:RA', 'Plx:DE', 'pmRA:RA', 'pmRA:DE', 'pmRA:Plx', 'pmDE:RA',\n",
    "    'pmDE:DE', 'pmDE:Plx', 'pmDE:pmRA', 'F1', 'F2', '---', 'BTmag',\n",
    "    'e_BTmag', 'VTmag', 'e_VTmag', 'm_BTmag', 'B-V', 'e_B-V', 'r_B-V',\n",
    "    'V-I', 'e_V-I', 'r_V-I', 'CombMag', 'Hpmag', 'e_Hpmag', 'Hpscat',\n",
    "    'o_Hpmag', 'm_Hpmag', 'Hpmax', 'HPmin', 'Period', 'HvarType',\n",
    "    'moreVar', 'morePhoto', 'CCDM', 'n_CCDM', 'Nsys', 'Ncomp',\n",
    "    'MultFlag', 'Source', 'Qual', 'm_HIP', 'theta', 'rho', 'e_rho',\n",
    "    'dHp', 'e_dHp', 'Survey', 'Chart', 'Notes', 'HD', 'BD', 'CoD',\n",
    "    'CPD', '(V-I)red', 'SpType', 'r_SpType',)\n",
    "\n",
    "hip_main_txt = pd.read_csv(\"data/hip_main.txt\", delimiter = \"|\", names=COLUMN_NAMES, \n",
    "                           compression=None)\n",
    "hip_main_txt = hip_main_txt.set_index('hip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating general purpose functions from the hip dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get RA and Dec values from hip values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RA : 17h, 57m, 48.97s\n",
      "Dec : +04deg, 40m, 05.8s\n"
     ]
    }
   ],
   "source": [
    "def get_RA_dec_by_hip(hip):\n",
    "    RA_list = []\n",
    "    Dec_list = []\n",
    "    for rows, cols in hip_main_txt.iterrows():\n",
    "        if rows == hip:\n",
    "            RA_list = cols['RAhms'].split()\n",
    "            Dec_list = cols['DEdms'].split()\n",
    "            print(\"RA : {}h, {}m, {}s\".format(RA_list[0], RA_list[1], RA_list[2]))\n",
    "            print(\"Dec : {}deg, {}m, {}s\".format(Dec_list[0], Dec_list[1], Dec_list[2]))\n",
    "            return RA_list, Dec_list\n",
    "\n",
    "RA, Dec = get_RA_dec_by_hip(87937)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get RA and Dec values (in degrees) from hip values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RA: 000.00091185 and Dec: +01.08901332\n"
     ]
    }
   ],
   "source": [
    "def get_RA_dec_by_hip_degree(hip):\n",
    "    for rows, cols in hip_main_txt.iterrows():\n",
    "        if rows == hip:\n",
    "            return (cols['RAdeg'], cols['DEdeg'])\n",
    "            \n",
    "\n",
    "RA_deg, Dec_deg = get_RA_dec_by_hip_degree(1)\n",
    "print(\"RA: {} and Dec: {}\".format(RA_deg, Dec_deg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert equatorial coordinates (ra, dec) and galactic coordinates (𝑙,𝑏)\n",
    "####  𝑏=𝑠𝑖𝑛−1(𝑐𝑜𝑠(dec)∗𝑐𝑜𝑠(27.4)∗𝑐𝑜𝑠(ra−192.25)+𝑠𝑖𝑛(dec)∗𝑠𝑖𝑛(27.4))\n",
    "#### 𝑙=𝑡𝑎𝑛−1(𝑠𝑖𝑛(dec)−𝑠𝑖𝑛(𝑏)∗𝑠𝑖𝑛(27.4)𝑐𝑜𝑠(dec)∗𝑐𝑜𝑠(27.4)∗𝑠𝑖𝑛(ra−192.25))+33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#where (𝛼,𝛿) is the (ra, dec) values\n",
    "\n",
    "def equ2gal(ra, dec):\n",
    "    \n",
    "    ra = np.deg2rad(float(ra))\n",
    "    dec = np.deg2rad(float(dec))\n",
    "    b = np.arcsin(np.cos(dec)*np.cos(27.4)*np.cos(ra-192.25)+\n",
    "         np.sin(dec)*np.sin(27.4))\n",
    "    l = np.arctan((np.sin(dec)-np.sin(b))*np.sin(27.4)*\n",
    "                  np.cos(dec)*np.cos(27.4)*np.sin(ra-192.25))+33\n",
    "    return l, b\n",
    "\n",
    "x_pos = []\n",
    "y_pos = []\n",
    "\n",
    "for rows, cols in hip_main_txt.iterrows():\n",
    "    try:\n",
    "        ra = float(cols['RAdeg'])\n",
    "    except:\n",
    "        ra = 0\n",
    "    try:\n",
    "        dec = float(cols['DEdeg'])\n",
    "    except:\n",
    "        dec = 0\n",
    "    l, b = equ2gal(ra, dec)\n",
    "    x_pos.append(l)\n",
    "    y_pos.append(b)\n",
    "\n",
    "hip_main_txt[\"x_position\"] = x_pos\n",
    "hip_main_txt[\"y_position\"] = y_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33.146182060432565, 0.5695493882865139)\n"
     ]
    }
   ],
   "source": [
    "print(equ2gal(000.00091185, +01.08901332))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### display the constellations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_constellations_by_abb(name):\n",
    "    x = []\n",
    "    y = []\n",
    "    # get all the hip numbers of a constellation\n",
    "    for i in constellation_lines_bw_hip[name]:\n",
    "        # get the ra and dec values in degree from hip number\n",
    "        \n",
    "        r, d = get_RA_dec_by_hip_degree(float(i))\n",
    "        # convert r, d to galatic coordinates\n",
    "        l, b = equ2gal(r, d)\n",
    "        x.append(l)\n",
    "        y.append(b)\n",
    "    # connect successive points to form a constellation\n",
    "    for i in range(0, len(x), 2):\n",
    "        plt.plot(np.round(x[i:i+2],3), np.round(y[i:i+2],3), 'ro-')\n",
    "    plt.title(\"{} constellation\".format(constellation_names[name]))\n",
    "    plt.xlabel('l values')\n",
    "    plt.ylabel('b values')\n",
    "    plt.grid()\n",
    "    plt.savefig(\"pictures/display_cons.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "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": [
    "display_constellations_by_abb('Cru')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## extracting the data from hip_main.txt\n",
    "* hip values\n",
    "* ra (hms) and dec (dms) values\n",
    "* ra and dec values in degrees\n",
    "* magnitude of stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLUMN_NAMES = ('Catalog', 'hip', 'Proxy', 'RAhms', 'DEdms', 'Vmag',\n",
    "    'VarFlag', 'r_Vmag', 'RAdeg', 'DEdeg', 'AstroRef', 'Plx', 'pmRA',\n",
    "    'pmDE', 'e_RAdeg', 'e_DEdeg', 'e_Plx', 'e_pmRA', 'e_pmDE', 'DE:RA',\n",
    "    'Plx:RA', 'Plx:DE', 'pmRA:RA', 'pmRA:DE', 'pmRA:Plx', 'pmDE:RA',\n",
    "    'pmDE:DE', 'pmDE:Plx', 'pmDE:pmRA', 'F1', 'F2', '---', 'BTmag',\n",
    "    'e_BTmag', 'VTmag', 'e_VTmag', 'm_BTmag', 'B-V', 'e_B-V', 'r_B-V',\n",
    "    'V-I', 'e_V-I', 'r_V-I', 'CombMag', 'Hpmag', 'e_Hpmag', 'Hpscat',\n",
    "    'o_Hpmag', 'm_Hpmag', 'Hpmax', 'HPmin', 'Period', 'HvarType',\n",
    "    'moreVar', 'morePhoto', 'CCDM', 'n_CCDM', 'Nsys', 'Ncomp',\n",
    "    'MultFlag', 'Source', 'Qual', 'm_HIP', 'theta', 'rho', 'e_rho',\n",
    "    'dHp', 'e_dHp', 'Survey', 'Chart', 'Notes', 'HD', 'BD', 'CoD',\n",
    "    'CPD', '(V-I)red', 'SpType', 'r_SpType',)\n",
    "\n",
    "hip_file = pd.read_csv(\"data/hip_main.txt\", delimiter = \"|\", \n",
    "                            names=COLUMN_NAMES, compression=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "collect_hip = list(hip_file['hip']) #118218\n",
    "collect_ra = list(hip_file['RAhms']) #118218\n",
    "collect_dec = list(hip_file['DEdms']) #118218\n",
    "collect_mag = list(hip_file['Vmag']) #118218\n",
    "collect_r = list(hip_file['RAdeg']) #118218\n",
    "collect_d = list(hip_file['DEdeg']) #118218\n",
    "collect_distance = [0]*118218"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating the xy positions from ra and dec values\n",
    "collect_xy = []\n",
    "collect_temp = []\n",
    "for i in range(len(collect_hip)):\n",
    "    try:\n",
    "        x, y =equ2gal(float(collect_r[i]), float(collect_d[i]))\n",
    "    except:\n",
    "        x = 0\n",
    "        y = 0\n",
    "    collect_temp.append(x)\n",
    "    collect_temp.append(y)\n",
    "    collect_xy.append(collect_temp)\n",
    "    collect_temp = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "getting the constellations names based on the hip values from the file \n",
    "constellations_lines_bw_hip'''\n",
    "collect_dict_hip_cons = {}\n",
    "for i in constellation_lines_bw_hip.keys():\n",
    "    for j in constellation_lines_bw_hip[i]:\n",
    "        collect_dict_hip_cons[j] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "collect_consnames = [''] * 118218\n",
    "collect_keys = collect_dict_hip_cons.keys()\n",
    "for i in collect_keys:\n",
    "    if float(i) in collect_hip:\n",
    "        pos = collect_hip.index(float(i))\n",
    "        collect_consnames[pos] = constellation_names[collect_dict_hip_cons[i]]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "collect the distance value for the hip value from the hyg dataset and then \n",
    "assign those values to the new dataset based on teh hip values '''\n",
    "\n",
    "collect_common_hip = list(set(collect_hyg_hip).intersection(collect_hip))\n",
    "for i in collect_common_hip:\n",
    "    pos = collect_hyg_hip.index(i)\n",
    "    value_d1 = collect_hyg_dist[pos]\n",
    "    pos2 = collect_hip.index(i)\n",
    "    collect_distance[pos2] = value_d1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exoplanet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "exo = pd.read_csv(\"data/exoplanet.eu_catalog.dat\", delimiter = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "collect_name = exo['# name']\n",
    "collect_mass = exo['mass']\n",
    "collect_distance2 = exo['star_distance']\n",
    "collect_radius = exo['radius']\n",
    "collect_r2 = exo['ra']\n",
    "collect_d2 = exo['dec']\n",
    "collect_mag2 = exo['mag_v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting cons names from star names\n",
    "collect_temp_cons_name = [\" \"]* 4870\n",
    "collect_name = list(collect_name)\n",
    "for i in range(len(collect_name)):\n",
    "    try:\n",
    "        s = collect_name[i].split()[1]\n",
    "        collect_temp_cons_name[i] = constellation_names[s]\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting hip numbers from star names\n",
    "collect_temp_hip = [\" \"]* 4870\n",
    "collect_name = list(collect_name)\n",
    "for i in range(len(collect_name)):\n",
    "    if collect_name[i].split()[0] == 'HIP':\n",
    "        collect_temp_hip[i] = collect_name[i].split()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to calculate the xy positions of the stars\n",
    "collect_xy2 = []\n",
    "collect_temp2 = []\n",
    "c2 = 0\n",
    "for i2 in range(len(collect_temp_hip)):\n",
    "    try:\n",
    "        x2, y2 =equ2gal(float(collect_r2[i2]), float(collect_d2[i2]))\n",
    "    except:\n",
    "        x2 = 0\n",
    "        y2 = 0\n",
    "        c2 = c2+1\n",
    "    collect_temp2.append(x2)\n",
    "    collect_temp2.append(y2)\n",
    "    collect_xy2.append(collect_temp2)\n",
    "    collect_temp2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert ra and dec from degree to hms and dms\n",
    "\n",
    "def deg2original(ra, dec):\n",
    "    rs = \"+\"\n",
    "    if ra <0:\n",
    "        rs = \"-\"\n",
    "    raH = int(abs(ra)/15)\n",
    "    raM = int(((abs(ra)/15)-raH)*60)\n",
    "    raS = ((((abs(ra)/15)-raH)*60)-raM)*60\n",
    "    RA = '{0}{1} {2} {3}'.format(rs, raH, raM, raS)\n",
    "    \n",
    "    ds = \"+\"\n",
    "    if dec <0:\n",
    "        ds = \"-\"\n",
    "    d = int(abs(dec))\n",
    "    m = int((abs(dec) - d) * 60)\n",
    "    s = (abs(dec) - d - m/60) * 3600.00\n",
    "    DEC = '{0}{1} {2} {3}'.format(ds, d, m, s)\n",
    "    \n",
    "    return RA, DEC\n",
    "    \n",
    "collect_ra2 = []\n",
    "collect_dec2 = []\n",
    "for i in range(len(collect_r2)):\n",
    "    ra2, dec2 = deg2original(collect_r2[i], collect_d2[i])\n",
    "    collect_ra2.append(ra2)\n",
    "    collect_dec2.append(dec2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20578/1018129695.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  collect_mag2[collect_temp_hip.index(i)] = collect_mag[pos]\n"
     ]
    }
   ],
   "source": [
    "# exporting the magnitude values from hip_main and adding them to the exoplanets list\n",
    "\n",
    "for i in collect_temp_hip:\n",
    "    try:\n",
    "        if float(i) in collect_hip:\n",
    "            pos = collect_hip.index(float(i))\n",
    "            collect_mag2[collect_temp_hip.index(i)] = collect_mag[pos]\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert the collected data into dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far I have collected :\n",
    "* hip\n",
    "* mag\n",
    "* ra and dec (units)\n",
    "* ra and dec (deg)\n",
    "* x and y\n",
    "* constellation names\n",
    "* distance\n",
    "* mass\n",
    "* radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from hip_main dataset\n",
    "collect_df1= pd.DataFrame()\n",
    "collect_df1['hip'] = collect_hip\n",
    "collect_df1['ra(hms)'] = collect_ra\n",
    "collect_df1['dec(dms)'] = collect_dec\n",
    "collect_df1['ra(deg)'] = collect_r\n",
    "collect_df1['dec(deg)'] = collect_d\n",
    "collect_df1['magnitude'] = collect_mag\n",
    "collect_df1['xy position'] = collect_xy\n",
    "collect_df1['constellation names'] = collect_consnames\n",
    "collect_df1['distance(pc)'] = collect_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting the rows whose hip values are common to the hip values of the exoplanet database\n",
    "\n",
    "for i in collect_temp_hip:\n",
    "    try:\n",
    "        collect_df1 = collect_df1[collect_df1.hip != float(i)]\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from exoplanet dataset\n",
    "collect_df2 = pd.DataFrame()\n",
    "# collect_df2['star_name'] = collect_name\n",
    "collect_df2['mass(ju)'] = collect_mass\n",
    "collect_df2['distance(pc)'] = collect_distance2\n",
    "collect_df2['constellation names'] = collect_temp_cons_name\n",
    "collect_df2['hip'] = collect_temp_hip\n",
    "collect_df2['radius(ju)'] = collect_radius\n",
    "collect_df2['ra(deg)'] = collect_r2\n",
    "collect_df2['dec(deg)'] = collect_d2\n",
    "collect_df2['magnitude'] = collect_mag2\n",
    "collect_df2['xy position'] = collect_xy2\n",
    "collect_df2['ra(hms)'] = collect_ra2\n",
    "collect_df2['dec(dms)'] = collect_dec2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging the two dataset\n",
    "collect_df3 = [collect_df1, collect_df2]\n",
    "collect_df4 = pd.concat(collect_df3, ignore_index=True)\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>hip</th>\n",
       "      <th>ra(hms)</th>\n",
       "      <th>dec(dms)</th>\n",
       "      <th>ra(deg)</th>\n",
       "      <th>dec(deg)</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>xy position</th>\n",
       "      <th>constellation names</th>\n",
       "      <th>distance(pc)</th>\n",
       "      <th>mass(ju)</th>\n",
       "      <th>radius(ju)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>00 00 00.22</td>\n",
       "      <td>+01 05 20.4</td>\n",
       "      <td>000.00091185</td>\n",
       "      <td>+01.08901332</td>\n",
       "      <td>9.1</td>\n",
       "      <td>[33.146182060432565, 0.5695493882865139]</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>00 00 00.91</td>\n",
       "      <td>-19 29 55.8</td>\n",
       "      <td>000.00379737</td>\n",
       "      <td>-19.49883745</td>\n",
       "      <td>9.27</td>\n",
       "      <td>[33.15155299282359, 0.24095994926580427]</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>00 00 01.20</td>\n",
       "      <td>+38 51 33.4</td>\n",
       "      <td>000.00500795</td>\n",
       "      <td>+38.85928608</td>\n",
       "      <td>6.61</td>\n",
       "      <td>[33.05779634019187, 1.0972309975855212]</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>00 00 02.01</td>\n",
       "      <td>-51 53 36.8</td>\n",
       "      <td>000.00838170</td>\n",
       "      <td>-51.89354612</td>\n",
       "      <td>8.06</td>\n",
       "      <td>[33.08833798337465, -0.28352395291685617]</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>00 00 02.39</td>\n",
       "      <td>-40 35 28.4</td>\n",
       "      <td>000.00996534</td>\n",
       "      <td>-40.59122440</td>\n",
       "      <td>8.55</td>\n",
       "      <td>[33.11766086823969, -0.10076304160735447]</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  hip      ra(hms)     dec(dms)       ra(deg)      dec(deg) magnitude  \\\n",
       "0   1  00 00 00.22  +01 05 20.4  000.00091185  +01.08901332       9.1   \n",
       "1   2  00 00 00.91  -19 29 55.8  000.00379737  -19.49883745      9.27   \n",
       "2   3  00 00 01.20  +38 51 33.4  000.00500795  +38.85928608      6.61   \n",
       "3   4  00 00 02.01  -51 53 36.8  000.00838170  -51.89354612      8.06   \n",
       "4   5  00 00 02.39  -40 35 28.4  000.00996534  -40.59122440      8.55   \n",
       "\n",
       "                                 xy position constellation names  \\\n",
       "0   [33.146182060432565, 0.5695493882865139]                       \n",
       "1   [33.15155299282359, 0.24095994926580427]                       \n",
       "2    [33.05779634019187, 1.0972309975855212]                       \n",
       "3  [33.08833798337465, -0.28352395291685617]                       \n",
       "4  [33.11766086823969, -0.10076304160735447]                       \n",
       "\n",
       "   distance(pc)  mass(ju)  radius(ju)  \n",
       "0           0.0       NaN         NaN  \n",
       "1           0.0       NaN         NaN  \n",
       "2           0.0       NaN         NaN  \n",
       "3           0.0       NaN         NaN  \n",
       "4           0.0       NaN         NaN  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize\n",
    "collect_df4.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write the dataframe to a json file\n",
    "### to a folder names extracted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "collect_json = collect_df4.to_json(orient=\"index\", \n",
    "                                   path_or_buf=\"extracted_data/combined_data.json\")"
   ]
  }
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