Coordinate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Stars List.xlsx', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Constellation</th>\n",
       "      <th>Common Name</th>\n",
       "      <th>Name</th>\n",
       "      <th>HD</th>\n",
       "      <th>Hipparcos Catalogue</th>\n",
       "      <th>RA</th>\n",
       "      <th>Dec</th>\n",
       "      <th>vis.\\nmag.</th>\n",
       "      <th>abs.\\nmag.</th>\n",
       "      <th>Dist. (ly)</th>\n",
       "      <th>Sp. class</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alkaid</td>\n",
       "      <td>η UMa</td>\n",
       "      <td>120315</td>\n",
       "      <td>67301</td>\n",
       "      <td>13h 47m 32.55s</td>\n",
       "      <td>+49° 18′ 47.9″</td>\n",
       "      <td>1.85</td>\n",
       "      <td>−0.60</td>\n",
       "      <td>101</td>\n",
       "      <td>B3V SB</td>\n",
       "      <td>Benetnasch, Alkaid, Elkeid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alcor</td>\n",
       "      <td>ζ UMa</td>\n",
       "      <td>116842</td>\n",
       "      <td>65477</td>\n",
       "      <td>13h 25m 13.54s</td>\n",
       "      <td>+54° 59′ 16.7″</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>A5Vn</td>\n",
       "      <td>Saidak, Suha, Arundhati; naked-eye double star...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Mizar</td>\n",
       "      <td>ζ1 UMa</td>\n",
       "      <td>116656</td>\n",
       "      <td>65378</td>\n",
       "      <td>13h 23m 55.54s</td>\n",
       "      <td>+54° 55′ 31.3″</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>83</td>\n",
       "      <td>A2V SB</td>\n",
       "      <td>Mizar A; spectroscopic binary, visual double s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alioth</td>\n",
       "      <td>ε UMa</td>\n",
       "      <td>112185</td>\n",
       "      <td>62956</td>\n",
       "      <td>12h 54m 01.63s</td>\n",
       "      <td>+55° 57′ 35.4″</td>\n",
       "      <td>1.76</td>\n",
       "      <td>−0.21</td>\n",
       "      <td>81</td>\n",
       "      <td>A0p</td>\n",
       "      <td>Alioth; brown dwarf companion?; α² CVn variable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Megrez</td>\n",
       "      <td>δ UMa</td>\n",
       "      <td>106591</td>\n",
       "      <td>59774</td>\n",
       "      <td>12h 15m 25.45s</td>\n",
       "      <td>+57° 01′ 57.4″</td>\n",
       "      <td>3.32</td>\n",
       "      <td>1.33</td>\n",
       "      <td>81</td>\n",
       "      <td>A3Vvar</td>\n",
       "      <td>Megrez, Kaffa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Dubhe</td>\n",
       "      <td>α UMa</td>\n",
       "      <td>95689</td>\n",
       "      <td>54061</td>\n",
       "      <td>11h 03m 43.84s</td>\n",
       "      <td>+61° 45′ 04.0″</td>\n",
       "      <td>1.81</td>\n",
       "      <td>−1.08</td>\n",
       "      <td>124</td>\n",
       "      <td>F7V comp</td>\n",
       "      <td>Dubhe, Dubh, Dubb, Thahr al Dub al Akbar, Ak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Merak</td>\n",
       "      <td>β UMa</td>\n",
       "      <td>95418</td>\n",
       "      <td>53910</td>\n",
       "      <td>11h 01m 50.39s</td>\n",
       "      <td>+56° 22′ 56.4″</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>79</td>\n",
       "      <td>A1V</td>\n",
       "      <td>Merak, Mirak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Phecda</td>\n",
       "      <td>γ UMa</td>\n",
       "      <td>103287</td>\n",
       "      <td>58001</td>\n",
       "      <td>11h 53m 49.74s</td>\n",
       "      <td>+53° 41′ 41.0″</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>84</td>\n",
       "      <td>A0V SB</td>\n",
       "      <td>Phad, Phecda, Phegda, Phekha, Phacd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Constellation Common Name    Name      HD  Hipparcos Catalogue  \\\n",
       "0           UMa      Alkaid   η UMa  120315                67301   \n",
       "1           UMa       Alcor   ζ UMa  116842                65477   \n",
       "2           UMa       Mizar  ζ1 UMa  116656                65378   \n",
       "3           UMa      Alioth   ε UMa  112185                62956   \n",
       "4           UMa      Megrez   δ UMa  106591                59774   \n",
       "5           UMa       Dubhe   α UMa   95689                54061   \n",
       "6           UMa       Merak   β UMa   95418                53910   \n",
       "7           UMa      Phecda   γ UMa  103287                58001   \n",
       "\n",
       "               RA             Dec  vis.\\nmag. abs.\\nmag.  Dist. (ly)  \\\n",
       "0  13h 47m 32.55s  +49° 18′ 47.9″        1.85      −0.60         101   \n",
       "1  13h 25m 13.54s  +54° 59′ 16.7″        3.99          2          82   \n",
       "2  13h 23m 55.54s  +54° 55′ 31.3″        2.23       0.32          83   \n",
       "3  12h 54m 01.63s  +55° 57′ 35.4″        1.76      −0.21          81   \n",
       "4  12h 15m 25.45s  +57° 01′ 57.4″        3.32       1.33          81   \n",
       "5  11h 03m 43.84s  +61° 45′ 04.0″        1.81      −1.08         124   \n",
       "6  11h 01m 50.39s  +56° 22′ 56.4″        2.34       0.41          79   \n",
       "7  11h 53m 49.74s  +53° 41′ 41.0″        2.41       0.36          84   \n",
       "\n",
       "  Sp. class                                              Notes  \n",
       "0    B3V SB                         Benetnasch, Alkaid, Elkeid  \n",
       "1      A5Vn  Saidak, Suha, Arundhati; naked-eye double star...  \n",
       "2    A2V SB  Mizar A; spectroscopic binary, visual double s...  \n",
       "3       A0p    Alioth; brown dwarf companion?; α² CVn variable  \n",
       "4    A3Vvar                                      Megrez, Kaffa  \n",
       "5  F7V comp       Dubhe, Dubh, Dubb, Thahr al Dub al Akbar, Ak  \n",
       "6       A1V                                       Merak, Mirak  \n",
       "7    A0V SB                Phad, Phecda, Phegda, Phekha, Phacd  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_RA_to_Num(value):\n",
    "    RA_Text = re.compile('\\+?(?P<hour>[0-9]{1,2})(?P<unit>[hH°]) ?((?P<minute>[0-9]{1,2}).)? ?((?P<second>[0-9]{1,2}\\.?[0-9]+).)?')\n",
    "    result = RA_Text.match(value)\n",
    "    if result:\n",
    "        coordonate = {}\n",
    "        for i in ['hour', 'minute', 'second']:\n",
    "            coordonate[i] = float(result.group(i)) if result.group(i) is not None else 0\n",
    "        res = coordonate['hour'] + (coordonate['minute'] / 60) + (coordonate['second'] / 3600)\n",
    "        if result.group('unit') in ['h', 'H']:\n",
    "            res = res * 360 / 24\n",
    "    else:\n",
    "        res = None\n",
    "    \n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184.69958333333338"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_RA_to_Num('12h 18m 47.9″')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RA_Num'] = df['RA'].apply(convert_RA_to_Num)\n",
    "df['Dec_Num'] = df['Dec'].apply(convert_RA_to_Num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Constellation</th>\n",
       "      <th>Common Name</th>\n",
       "      <th>Name</th>\n",
       "      <th>HD</th>\n",
       "      <th>Hipparcos Catalogue</th>\n",
       "      <th>RA</th>\n",
       "      <th>Dec</th>\n",
       "      <th>vis.\\nmag.</th>\n",
       "      <th>abs.\\nmag.</th>\n",
       "      <th>Dist. (ly)</th>\n",
       "      <th>Sp. class</th>\n",
       "      <th>Notes</th>\n",
       "      <th>RA_Num</th>\n",
       "      <th>Dec_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alkaid</td>\n",
       "      <td>η UMa</td>\n",
       "      <td>120315</td>\n",
       "      <td>67301</td>\n",
       "      <td>13h 47m 32.55s</td>\n",
       "      <td>+49° 18′ 47.9″</td>\n",
       "      <td>1.85</td>\n",
       "      <td>−0.60</td>\n",
       "      <td>101</td>\n",
       "      <td>B3V SB</td>\n",
       "      <td>Benetnasch, Alkaid, Elkeid</td>\n",
       "      <td>206.885625</td>\n",
       "      <td>49.313306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alcor</td>\n",
       "      <td>ζ UMa</td>\n",
       "      <td>116842</td>\n",
       "      <td>65477</td>\n",
       "      <td>13h 25m 13.54s</td>\n",
       "      <td>+54° 59′ 16.7″</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>A5Vn</td>\n",
       "      <td>Saidak, Suha, Arundhati; naked-eye double star...</td>\n",
       "      <td>201.306417</td>\n",
       "      <td>54.987972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Mizar</td>\n",
       "      <td>ζ1 UMa</td>\n",
       "      <td>116656</td>\n",
       "      <td>65378</td>\n",
       "      <td>13h 23m 55.54s</td>\n",
       "      <td>+54° 55′ 31.3″</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>83</td>\n",
       "      <td>A2V SB</td>\n",
       "      <td>Mizar A; spectroscopic binary, visual double s...</td>\n",
       "      <td>200.981417</td>\n",
       "      <td>54.925361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Alioth</td>\n",
       "      <td>ε UMa</td>\n",
       "      <td>112185</td>\n",
       "      <td>62956</td>\n",
       "      <td>12h 54m 01.63s</td>\n",
       "      <td>+55° 57′ 35.4″</td>\n",
       "      <td>1.76</td>\n",
       "      <td>−0.21</td>\n",
       "      <td>81</td>\n",
       "      <td>A0p</td>\n",
       "      <td>Alioth; brown dwarf companion?; α² CVn variable</td>\n",
       "      <td>193.506792</td>\n",
       "      <td>55.959833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Megrez</td>\n",
       "      <td>δ UMa</td>\n",
       "      <td>106591</td>\n",
       "      <td>59774</td>\n",
       "      <td>12h 15m 25.45s</td>\n",
       "      <td>+57° 01′ 57.4″</td>\n",
       "      <td>3.32</td>\n",
       "      <td>1.33</td>\n",
       "      <td>81</td>\n",
       "      <td>A3Vvar</td>\n",
       "      <td>Megrez, Kaffa</td>\n",
       "      <td>183.856042</td>\n",
       "      <td>57.032611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Dubhe</td>\n",
       "      <td>α UMa</td>\n",
       "      <td>95689</td>\n",
       "      <td>54061</td>\n",
       "      <td>11h 03m 43.84s</td>\n",
       "      <td>+61° 45′ 04.0″</td>\n",
       "      <td>1.81</td>\n",
       "      <td>−1.08</td>\n",
       "      <td>124</td>\n",
       "      <td>F7V comp</td>\n",
       "      <td>Dubhe, Dubh, Dubb, Thahr al Dub al Akbar, Ak</td>\n",
       "      <td>165.932667</td>\n",
       "      <td>61.751111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Merak</td>\n",
       "      <td>β UMa</td>\n",
       "      <td>95418</td>\n",
       "      <td>53910</td>\n",
       "      <td>11h 01m 50.39s</td>\n",
       "      <td>+56° 22′ 56.4″</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>79</td>\n",
       "      <td>A1V</td>\n",
       "      <td>Merak, Mirak</td>\n",
       "      <td>165.459958</td>\n",
       "      <td>56.382333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>UMa</td>\n",
       "      <td>Phecda</td>\n",
       "      <td>γ UMa</td>\n",
       "      <td>103287</td>\n",
       "      <td>58001</td>\n",
       "      <td>11h 53m 49.74s</td>\n",
       "      <td>+53° 41′ 41.0″</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>84</td>\n",
       "      <td>A0V SB</td>\n",
       "      <td>Phad, Phecda, Phegda, Phekha, Phacd</td>\n",
       "      <td>178.457250</td>\n",
       "      <td>53.694722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Constellation Common Name    Name      HD  Hipparcos Catalogue  \\\n",
       "0           UMa      Alkaid   η UMa  120315                67301   \n",
       "1           UMa       Alcor   ζ UMa  116842                65477   \n",
       "2           UMa       Mizar  ζ1 UMa  116656                65378   \n",
       "3           UMa      Alioth   ε UMa  112185                62956   \n",
       "4           UMa      Megrez   δ UMa  106591                59774   \n",
       "5           UMa       Dubhe   α UMa   95689                54061   \n",
       "6           UMa       Merak   β UMa   95418                53910   \n",
       "7           UMa      Phecda   γ UMa  103287                58001   \n",
       "\n",
       "               RA             Dec  vis.\\nmag. abs.\\nmag.  Dist. (ly)  \\\n",
       "0  13h 47m 32.55s  +49° 18′ 47.9″        1.85      −0.60         101   \n",
       "1  13h 25m 13.54s  +54° 59′ 16.7″        3.99          2          82   \n",
       "2  13h 23m 55.54s  +54° 55′ 31.3″        2.23       0.32          83   \n",
       "3  12h 54m 01.63s  +55° 57′ 35.4″        1.76      −0.21          81   \n",
       "4  12h 15m 25.45s  +57° 01′ 57.4″        3.32       1.33          81   \n",
       "5  11h 03m 43.84s  +61° 45′ 04.0″        1.81      −1.08         124   \n",
       "6  11h 01m 50.39s  +56° 22′ 56.4″        2.34       0.41          79   \n",
       "7  11h 53m 49.74s  +53° 41′ 41.0″        2.41       0.36          84   \n",
       "\n",
       "  Sp. class                                              Notes      RA_Num  \\\n",
       "0    B3V SB                         Benetnasch, Alkaid, Elkeid  206.885625   \n",
       "1      A5Vn  Saidak, Suha, Arundhati; naked-eye double star...  201.306417   \n",
       "2    A2V SB  Mizar A; spectroscopic binary, visual double s...  200.981417   \n",
       "3       A0p    Alioth; brown dwarf companion?; α² CVn variable  193.506792   \n",
       "4    A3Vvar                                      Megrez, Kaffa  183.856042   \n",
       "5  F7V comp       Dubhe, Dubh, Dubb, Thahr al Dub al Akbar, Ak  165.932667   \n",
       "6       A1V                                       Merak, Mirak  165.459958   \n",
       "7    A0V SB                Phad, Phecda, Phegda, Phekha, Phacd  178.457250   \n",
       "\n",
       "     Dec_Num  \n",
       "0  49.313306  \n",
       "1  54.987972  \n",
       "2  54.925361  \n",
       "3  55.959833  \n",
       "4  57.032611  \n",
       "5  61.751111  \n",
       "6  56.382333  \n",
       "7  53.694722  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dec: [206.885625, 49.31330555555555]\n",
      "Dec: [201.30641666666665, 54.987972222222226]\n",
      "Dec: [200.98141666666663, 54.92536111111111]\n",
      "Dec: [193.50679166666666, 55.959833333333336]\n",
      "Dec: [183.85604166666667, 57.03261111111111]\n",
      "Dec: [165.93266666666668, 61.75111111111111]\n",
      "Dec: [165.45995833333336, 56.382333333333335]\n",
      "Dec: [178.45725000000002, 53.69472222222222]\n"
     ]
    }
   ],
   "source": [
    "for i, txt in enumerate(df['Common Name']):\n",
    "    #print(i)\n",
    "    print('Dec: {}'.format(df.iloc[i][['RA_Num','Dec_Num']].values.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# Data for plotting\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['RA_Num'], df['Dec_Num'])\n",
    "\n",
    "ax.set(xlabel='RA', ylabel='Dec',\n",
    "       title='About as simple as it gets, folks')\n",
    "ax.grid()\n",
    "\n",
    "for i, txt in enumerate(df['Common Name']):\n",
    "    ax.annotate(txt, df.iloc[i][['RA_Num','Dec_Num']].values.tolist())\n",
    "\n",
    "#fig.savefig(\"test.png\")\n",
    "plt.xlim(210, 160)\n",
    "#plt.ylim(63, 45)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12*360/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def orthographic_Projection(point0, point):\n",
    "    R = 1\n",
    "    lat = point[0] * math.pi / 180\n",
    "    long = point[1] * math.pi / 180\n",
    "    lat0 = point0[0] * math.pi / 180\n",
    "    long0 = point0[1] * math.pi / 180\n",
    "    \n",
    "    x = R * math.cos(lat) * math.sin(long - long0)\n",
    "    y = R * (math.cos(lat0)*math.sin(lat) - math.sin(lat0) * math.cos(lat) * math.cos(long - long0))\n",
    "    c = math.sin(lat0) * math.sin(lat) + math.cos(lat0) * math.cos(lat) * math.cos(long - long0)\n",
    "    \n",
    "    print(f\"x: {x}, y: {y}, c:{c}\")\n",
    "    if c >= 0:\n",
    "        return (x,y)\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: 0.11771674622792336, y: 0.25881904510252046, c:0.9587259616541789\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.11771674622792336, 0.25881904510252046)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orthographic_Projection((180,56), (195,49))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: 0.29480641591099377, y: -0.05802144445938945, c:0.9537938399468515\n",
      "x: 0.20847467104782183, y: 0.014848734902363103, c:0.9779150405854804\n",
      "x: 0.20575964153964732, y: 0.01283209276924896, c:0.9785184246137817\n",
      "x: 0.13074115421410956, y: 0.01213419608174382, c:0.9913422778638125\n",
      "x: 0.03659477492355329, y: 0.019042771736647368, c:0.999148735320663\n",
      "x: -0.11504187680111432, y: 0.11197470620093158, c:0.987029397613512\n",
      "x: -0.13899703921905898, y: 0.02137343240533751, c:0.9900621189983737\n",
      "x: -0.015940659040656183, y: -0.04004590255915602, c:0.9990706787197651\n"
     ]
    }
   ],
   "source": [
    "points = []\n",
    "for i in df.iterrows():\n",
    "    points.append(orthographic_Projection((56,180), (i[1]['Dec_Num'], i[1]['RA_Num'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "for p in points:\n",
    "    x.append(p[0])\n",
    "    y.append(p[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.35, -0.2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Data for plotting\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x, y)\n",
    "\n",
    "ax.set(xlabel='RA', ylabel='Dec',\n",
    "       title='About as simple as it gets, folks')\n",
    "ax.grid()\n",
    "\n",
    "for i, txt in enumerate(df['Common Name']):\n",
    "    ax.annotate(txt, (x[i], y[i]))\n",
    "\n",
    "#fig.savefig(\"test.png\")\n",
    "plt.xlim(0.35, -0.20)\n",
    "#plt.ylim(63, 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate sphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_Meridians(start_angle=0, end_angle=360, step=10, resolution=5):\n",
    "    res = []\n",
    "    for i in range(start_angle, end_angle + step, step):\n",
    "        meridianX = []\n",
    "        meridianY = []\n",
    "        for j in range(0, 360 + resolution, resolution):\n",
    "            meridianX.append(j)\n",
    "            meridianY.append(i)\n",
    "        res.append([meridianX, meridianY])\n",
    "    return res\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "meridians = generate_Meridians()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0,\n",
       "  5,\n",
       "  10,\n",
       "  15,\n",
       "  20,\n",
       "  25,\n",
       "  30,\n",
       "  35,\n",
       "  40,\n",
       "  45,\n",
       "  50,\n",
       "  55,\n",
       "  60,\n",
       "  65,\n",
       "  70,\n",
       "  75,\n",
       "  80,\n",
       "  85,\n",
       "  90,\n",
       "  95,\n",
       "  100,\n",
       "  105,\n",
       "  110,\n",
       "  115,\n",
       "  120,\n",
       "  125,\n",
       "  130,\n",
       "  135,\n",
       "  140,\n",
       "  145,\n",
       "  150,\n",
       "  155,\n",
       "  160,\n",
       "  165,\n",
       "  170,\n",
       "  175,\n",
       "  180,\n",
       "  185,\n",
       "  190,\n",
       "  195,\n",
       "  200,\n",
       "  205,\n",
       "  210,\n",
       "  215,\n",
       "  220,\n",
       "  225,\n",
       "  230,\n",
       "  235,\n",
       "  240,\n",
       "  245,\n",
       "  250,\n",
       "  255,\n",
       "  260,\n",
       "  265,\n",
       "  270,\n",
       "  275,\n",
       "  280,\n",
       "  285,\n",
       "  290,\n",
       "  295,\n",
       "  300,\n",
       "  305,\n",
       "  310,\n",
       "  315,\n",
       "  320,\n",
       "  325,\n",
       "  330,\n",
       "  335,\n",
       "  340,\n",
       "  345,\n",
       "  350,\n",
       "  355,\n",
       "  360],\n",
       " [10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10,\n",
       "  10]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meridians[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "fig, ax = plt.subplots()\n",
    "for i in meridians:\n",
    "    ax.plot(i[0], i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: 0.0, y: -0.3420201433256687, c:0.9396926207859084\n",
      "x: 0.0, y: -0.25881904510252074, c:0.9659258262890684\n",
      "x: 0.0, y: -0.17364817766693033, c:0.984807753012208\n",
      "x: 0.0, y: -0.0871557427476582, c:0.9961946980917455\n",
      "x: 0.0, y: 0.0, c:1.0\n",
      "x: 0.0, y: 0.0871557427476582, c:0.9961946980917455\n",
      "x: 0.0, y: 0.1736481776669303, c:0.9848077530122081\n",
      "x: 0.0, y: 0.25881904510252074, c:0.9659258262890683\n",
      "x: 0.0, y: 0.3420201433256687, c:0.9396926207859084\n",
      "x: 0.0, y: 0.4226182617406995, c:0.90630778703665\n",
      "x: 0.0, y: 0.5, c:0.8660254037844386\n",
      "x: 0.0, y: 0.5735764363510462, c:0.8191520442889917\n",
      "x: 0.0, y: 0.6427876096865393, c:0.7660444431189781\n",
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      "x: 0.0, y: 0.7660444431189779, c:0.6427876096865394\n",
      "x: 0.0, y: 0.8191520442889919, c:0.573576436351046\n",
      "x: 0.0, y: 0.8660254037844386, c:0.5\n",
      "x: 0.0, y: 0.90630778703665, c:0.4226182617406994\n",
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      "x: -0.0, y: 0.9659258262890684, c:0.2588190451025207\n",
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      "x: -0.0, y: 0.9961946980917457, c:0.08715574274765808\n",
      "x: -0.0, y: 1.0, c:0.0\n",
      "x: -0.0, y: 0.9961946980917455, c:-0.08715574274765808\n",
      "x: -0.0, y: 0.9848077530122081, c:-0.17364817766693014\n",
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      "x: -6.339238302863539e-17, y: -0.9961946980917455, c:-0.08715574274765778\n",
      "x: -8.37707747535397e-17, y: -1.0, c:-5.551115123125783e-16\n",
      "x: -1.0351162030039322e-16, y: -0.9961946980917454, c:0.08715574274765842\n",
      "x: -1.2246467991473535e-16, y: -0.9848077530122081, c:0.17364817766693053\n",
      "x: -1.4048570936873082e-16, y: -0.9659258262890683, c:0.25881904510252074\n",
      "x: -1.574375577468397e-16, y: -0.9396926207859084, c:0.34202014332566866\n",
      "x: -1.731912112470986e-16, y: -0.90630778703665, c:0.4226182617406993\n",
      "x: -1.8762677505405457e-16, y: -0.8660254037844388, c:0.4999999999999997\n",
      "x: -2.006343858107049e-16, y: -0.8191520442889921, c:0.5735764363510458\n",
      "x: -2.121150477449813e-16, y: -0.7660444431189783, c:0.642787609686539\n",
      "x: -2.2198138608735082e-16, y: -0.7071067811865479, c:0.7071067811865471\n",
      "x: -2.3015831204557005e-16, y: -0.6427876096865393, c:0.7660444431189781\n",
      "x: -2.3658359427573397e-16, y: -0.573576436351046, c:0.8191520442889919\n",
      "x: -2.412083325003795e-16, y: -0.5000000000000008, c:0.8660254037844383\n",
      "x: -2.4399732966912404e-16, y: -0.42261826174069955, c:0.90630778703665\n",
      "x: -2.4492935982947064e-16, y: -0.34202014332566893, c:0.9396926207859083\n"
     ]
    }
   ],
   "source": [
    "merid = []\n",
    "for i in meridians:\n",
    "    points = []\n",
    "    for j in range(len(i[0])):\n",
    "        coord = orthographic_Projection((20,0), (i[0][j],i[1][j]))\n",
    "        if coord is not None:\n",
    "            points.append(coord)\n",
    "    merid.append(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "def rotate(l, n):\n",
    "    return l[n:] + l[:n]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for i in merid:\n",
    "    x = []\n",
    "    y = []\n",
    "    i = rotate(i, i.index(min(i, key=lambda k: k[0])) + 1 )\n",
    "    for p in i:\n",
    "        x.append(p[0])\n",
    "        y.append(p[1])\n",
    "    ax.plot(x, y)\n",
    "#plt.xlim(0.35, -0.20)\n",
    "#plt.ylim(-0.075, 0.125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3830222215594891, -0.9237208365458508)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(merid[5], key=lambda k: math.sqrt(k[0]**2 + k[1]**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merid[5].index(max(merid[5], key=lambda k: math.sqrt(k[0]**2 + k[1]**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merid[5]) - merid[5].index(min(merid[5], key=lambda k: k[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merid[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.5868240888334652, 0.43561072913832105),\n",
       " (0.5416752204197018, 0.5090082074909743),\n",
       " (0.49240387650610407, 0.5785318260370622),\n",
       " (0.43938504177070503, 0.6436524680599406),\n",
       " (0.3830222215594891, 0.7038745261528966),\n",
       " (0.3237443709670646, 0.7587396740907699),\n",
       " (0.262002630229385, 0.8078303549692712),\n",
       " (0.19826689127414615, 0.8507729590651519),\n",
       " (0.13302222155948906, 0.8872406672317885),\n",
       " (0.06676517241775068, 0.9169559381902291),\n",
       " (4.6906693763513654e-17, 0.9396926207859084),\n",
       " (-0.06676517241775076, 0.9552776751354893),\n",
       " (-0.13302222155948898, 0.9635924895648582),\n",
       " (-0.19826689127414623, 0.9645737833155855),\n",
       " (-0.2620026302293849, 0.9582140881497068),\n",
       " (-0.3237443709670645, 0.9445618051875232),\n",
       " (0.3830222215594891, -0.9237208365458508),\n",
       " (0.43938504177070503, -0.8958497945801739),\n",
       " (0.49240387650610395, -0.8611607947488463),\n",
       " (0.5416752204197017, -0.8199178412863753),\n",
       " (0.5868240888334649, -0.7724348179717865),\n",
       " (0.6275068715971329, -0.7190730992835372),\n",
       " (0.6634139481689382, -0.6602388001215318),\n",
       " (0.6942720440148836, -0.5963796850275142),\n",
       " (0.7198463103929542, -0.527981760426537),\n",
       " (0.739942111693848, -0.4555655758246112),\n",
       " (0.7544065067354888, -0.37968226211264533),\n",
       " (0.7631294127377697, -0.30090933712758267),\n",
       " (0.766044443118978, -0.2198463103929544),\n",
       " (0.766044443118978, -0.2198463103929542),\n",
       " (0.7631294127377697, -0.13711012048940385),\n",
       " (0.7544065067354889, -0.05333043977957483),\n",
       " (0.739942111693848, 0.03085511777877667),\n",
       " (0.7198463103929542, 0.11480584926000226),\n",
       " (0.6942720440148838, 0.1978828389066921),\n",
       " (0.6634139481689384, 0.27945382066437696),\n",
       " (0.6275068715971331, 0.3588979901079756)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rotate(l, n):\n",
    "    return l[n:] + l[:n]\n",
    "\n",
    "rotate(merid[5], -29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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 },
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}