problem_1_and_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Star Cluster Identifier Hackathon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"data/temp.txt\", \"w\")\n",
    "\n",
    "with open(\"data/Dataset.txt\") as file:\n",
    "    for line in file:\n",
    "        if line[0] != '#':\n",
    "            f.write(line)\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1756.4988</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
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       "      <td>1784.6356</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
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       "      <td>9.9999</td>\n",
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       "      <td>0</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>205.524928</td>\n",
       "      <td>28.341980</td>\n",
       "      <td>R0000003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6604.6484</td>\n",
       "      <td>1786.4238</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>205.528412</td>\n",
       "      <td>28.342000</td>\n",
       "      <td>R0000004</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7284.2275</td>\n",
       "      <td>1819.0413</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>99.9999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>99.9999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>205.519938</td>\n",
       "      <td>28.342358</td>\n",
       "      <td>R0000005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0          1        2        3    4       5   6   7        8   \\\n",
       "0  6954.4673  1738.9973 -99.9999  99.9999  0.0  9.9999   0   0 -99.9999   \n",
       "1  7111.1323  1756.4988 -99.9999  99.9999  0.0  9.9999   0   0 -99.9999   \n",
       "2  6884.0669  1784.6356 -99.9999  99.9999  0.0  9.9999   0   0 -99.9999   \n",
       "3  6604.6484  1786.4238 -99.9999  99.9999  0.0  9.9999   0   0 -99.9999   \n",
       "4  7284.2275  1819.0413 -99.9999  99.9999  0.0  9.9999   0   0 -99.9999   \n",
       "\n",
       "        9  ...   27      28      29  30  31   32          33         34  \\\n",
       "0  99.9999 ...  0.0  0.9999  0.0085   1   1 -1.0  205.524050  28.341480   \n",
       "1  99.9999 ...  0.0  0.9991 -0.0041   1   1 -1.0  205.522096  28.341672   \n",
       "2  99.9999 ...  0.0  0.9997 -0.0023   1   1 -1.0  205.524928  28.341980   \n",
       "3  99.9999 ...  0.0  1.0000  0.0086   1   1 -1.0  205.528412  28.342000   \n",
       "4  99.9999 ...  0.0  1.0000  0.0000  99  99 -1.0  205.519938  28.342358   \n",
       "\n",
       "         35  36  \n",
       "0  R0000001   1  \n",
       "1  R0000002   1  \n",
       "2  R0000003   1  \n",
       "3  R0000004   1  \n",
       "4  R0000005   1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = pd.read_csv('data/temp.txt', header=None, delimiter=r\"\\s+\")\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>f275w</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f438w</th>\n",
       "      <th>f606w</th>\n",
       "      <th>f814w</th>\n",
       "      <th>prob</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "      <td>199621.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5062.955907</td>\n",
       "      <td>4916.888151</td>\n",
       "      <td>-20.770262</td>\n",
       "      <td>-8.363505</td>\n",
       "      <td>-8.390557</td>\n",
       "      <td>22.361134</td>\n",
       "      <td>21.434464</td>\n",
       "      <td>35.983431</td>\n",
       "      <td>205.547639</td>\n",
       "      <td>28.376351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1301.778070</td>\n",
       "      <td>1289.670308</td>\n",
       "      <td>59.867925</td>\n",
       "      <td>54.505959</td>\n",
       "      <td>54.276369</td>\n",
       "      <td>8.054891</td>\n",
       "      <td>7.722576</td>\n",
       "      <td>47.294623</td>\n",
       "      <td>0.016237</td>\n",
       "      <td>0.014153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2074.261200</td>\n",
       "      <td>1693.539200</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>205.509822</td>\n",
       "      <td>28.340981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4101.594700</td>\n",
       "      <td>3990.908900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>-99.999900</td>\n",
       "      <td>21.159400</td>\n",
       "      <td>20.510800</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>205.535806</td>\n",
       "      <td>28.366189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5036.086400</td>\n",
       "      <td>4945.694800</td>\n",
       "      <td>21.428100</td>\n",
       "      <td>22.146700</td>\n",
       "      <td>22.281100</td>\n",
       "      <td>23.233600</td>\n",
       "      <td>22.206800</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>205.547975</td>\n",
       "      <td>28.376667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6011.716300</td>\n",
       "      <td>5868.073200</td>\n",
       "      <td>26.051500</td>\n",
       "      <td>26.057700</td>\n",
       "      <td>25.366100</td>\n",
       "      <td>24.601000</td>\n",
       "      <td>23.380000</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>205.559631</td>\n",
       "      <td>28.386789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8094.584500</td>\n",
       "      <td>7969.983400</td>\n",
       "      <td>28.958100</td>\n",
       "      <td>29.655600</td>\n",
       "      <td>28.873300</td>\n",
       "      <td>31.504600</td>\n",
       "      <td>30.860600</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>205.584904</td>\n",
       "      <td>28.409850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x              y          f275w          f336w  \\\n",
       "count  199621.000000  199621.000000  199621.000000  199621.000000   \n",
       "mean     5062.955907    4916.888151     -20.770262      -8.363505   \n",
       "std      1301.778070    1289.670308      59.867925      54.505959   \n",
       "min      2074.261200    1693.539200     -99.999900     -99.999900   \n",
       "25%      4101.594700    3990.908900     -99.999900     -99.999900   \n",
       "50%      5036.086400    4945.694800      21.428100      22.146700   \n",
       "75%      6011.716300    5868.073200      26.051500      26.057700   \n",
       "max      8094.584500    7969.983400      28.958100      29.655600   \n",
       "\n",
       "               f438w          f606w          f814w           prob  \\\n",
       "count  199621.000000  199621.000000  199621.000000  199621.000000   \n",
       "mean       -8.390557      22.361134      21.434464      35.983431   \n",
       "std        54.276369       8.054891       7.722576      47.294623   \n",
       "min       -99.999900     -99.999900     -99.999900      -1.000000   \n",
       "25%       -99.999900      21.159400      20.510800      -1.000000   \n",
       "50%        22.281100      23.233600      22.206800      -1.000000   \n",
       "75%        25.366100      24.601000      23.380000      96.800000   \n",
       "max        28.873300      31.504600      30.860600     100.000000   \n",
       "\n",
       "                  ra            dec  \n",
       "count  199621.000000  199621.000000  \n",
       "mean      205.547639      28.376351  \n",
       "std         0.016237       0.014153  \n",
       "min       205.509822      28.340981  \n",
       "25%       205.535806      28.366189  \n",
       "50%       205.547975      28.376667  \n",
       "75%       205.559631      28.386789  \n",
       "max       205.584904      28.409850  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [0,1,2,8,14,20,26,32,33,34,35]\n",
    "df = raw_df.iloc[:,cols]\n",
    "df.columns = ['x', 'y', 'f275w', 'f336w', 'f438w', 'f606w', 'f814w', 'prob', 'ra', 'dec', 'id']\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199621, 11)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df), len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatterPlot2D(df, title, xLabel, yLabel):\n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    for _, point in df.iterrows():\n",
    "        x.append(point[0])\n",
    "        y.append(point[1])\n",
    "\n",
    "    fig = plt.figure()\n",
    "\n",
    "    plot = fig.add_subplot(111)\n",
    "    plot.set_title(\"Subplot1\")\n",
    "    plot.set_xlabel(xLabel)\n",
    "    plot.set_ylabel(yLabel)\n",
    "    plot.scatter(x, y, s=30, c='r', label=\"feature1\")\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.legend(loc=2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ignoring fainter stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bol = df.loc[df['f606w'] >= (22-5*np.log10(1040))]\n",
    "df_bol = df_bol.loc[df_bol['f275w']>-99]\n",
    "df_bol = df_bol.loc[df_bol['f336w']>-99]\n",
    "df_bol = df_bol.loc[df_bol['f438w']>-99]\n",
    "df_bol = df_bol.loc[df_bol['f606w']>-99]\n",
    "df_bol = df_bol.loc[df_bol['f814w']>-99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94768 stars ignored\n"
     ]
    }
   ],
   "source": [
    "print(len(df)-len(df_bol), \"stars ignored\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.6925720884344875, 8.04648197486262)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = df1.loc[df1['ra'] == max(df1['ra'])]\n",
    "bottom = df1.loc[df1['ra'] == min(df1['ra'])]\n",
    "left = df1.loc[df1['dec'] == min(df1['dec'])]\n",
    "right = df1.loc[df1['dec'] == max(df1['dec'])]\n",
    "c1 = SkyCoord(ra=float(top['ra'])*u.degree, dec=float(top['dec'])*u.degree)\n",
    "c2 = SkyCoord(ra=float(bottom['ra'])*u.degree, dec=float(bottom['dec'])*u.degree)\n",
    "d1 = c1.separation(c2).to_value(u.radian)\n",
    "c3 = SkyCoord(ra=float(left['ra'])*u.degree, dec=float(left['dec'])*u.degree)\n",
    "c4 = SkyCoord(ra=float(right['ra'])*u.degree, dec=float(right['dec'])*u.degree)\n",
    "d2 = c3.separation(c4).to_value(u.radian)\n",
    "dist = d1+d2/2\n",
    "r_c = dist*10.4/2*1000\n",
    "r_e = r_c*np.power(1-(np.log(0.5)/7.67), -4)\n",
    "r_e, r_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective radius = 18.566686922406827 light years\n"
     ]
    }
   ],
   "source": [
    "print(\"Effective radius =\", (r_e*u.pc).to_value(u.lightyear), \"light years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting stars that fall under the error range of coordinates\n",
    "tmp1 = df1.loc[(df1['ra']>=205.5407-0.0001) & (df1['ra']<=205.5407+0.0001) & (df1['dec']>=28.3798-0.0001) & (df1['dec']<=28.3798+0.0001)]\n",
    "tmp2 = df1.loc[(df1['ra']>=205.5729-0.00044) & (df1['ra']<=205.5729+0.00044) & (df1['dec']>=28.359-0.00044) & (df1['dec']<=28.359+0.00044)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plot = fig.add_subplot(111)\n",
    "plot.set_title(\"Subplot1\")\n",
    "plot.set_xlabel(\"U-V\")\n",
    "plot.set_ylabel(\"U\")\n",
    "plot.scatter(tmp1['f336w']-tmp1['f606w'],tmp1['f336w'], s=5, c='r', label=\"feature\")\n",
    "\n",
    "plt.title(\"U-V, U plot\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plot = fig.add_subplot(111)\n",
    "plot.set_title(\"Subplot1\")\n",
    "plot.set_xlabel(\"U-V\")\n",
    "plot.set_ylabel(\"U\")\n",
    "plot.scatter(tmp2['f336w']-tmp2['f606w'],tmp2['f336w'], s=5, c='r', label=\"feature\")\n",
    "\n",
    "plt.title(\"U-V, U plot\")\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6566996844409398e-06\n",
      "1.8825979032794771e-06\n",
      "6.935796548504323e-07\n",
      "1.451877819668808e-06\n"
     ]
    }
   ],
   "source": [
    "c1 = SkyCoord(ra=float(205.5407)*u.degree, dec=float(28.3798)*u.degree)\n",
    "for _, row in tmp1.iterrows():\n",
    "    c2 = SkyCoord(ra=float(row['ra'])*u.degree, dec=float(row['dec'])*u.degree)\n",
    "    d = c1.separation(c2).to_value(u.radian)\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>f275w</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f438w</th>\n",
       "      <th>f606w</th>\n",
       "      <th>f814w</th>\n",
       "      <th>prob</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6413</th>\n",
       "      <td>618.1348</td>\n",
       "      <td>239.5454</td>\n",
       "      <td>17.8137</td>\n",
       "      <td>17.6522</td>\n",
       "      <td>18.6494</td>\n",
       "      <td>18.0839</td>\n",
       "      <td>17.3724</td>\n",
       "      <td>96.9</td>\n",
       "      <td>205.540715</td>\n",
       "      <td>28.379894</td>\n",
       "      <td>R0006439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121546</th>\n",
       "      <td>613.3892</td>\n",
       "      <td>238.7969</td>\n",
       "      <td>21.4064</td>\n",
       "      <td>20.6572</td>\n",
       "      <td>21.0700</td>\n",
       "      <td>20.3243</td>\n",
       "      <td>19.7642</td>\n",
       "      <td>99.3</td>\n",
       "      <td>205.540774</td>\n",
       "      <td>28.379886</td>\n",
       "      <td>R0121633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121551</th>\n",
       "      <td>622.1646</td>\n",
       "      <td>233.1406</td>\n",
       "      <td>21.6427</td>\n",
       "      <td>20.7703</td>\n",
       "      <td>21.0578</td>\n",
       "      <td>20.4809</td>\n",
       "      <td>19.8385</td>\n",
       "      <td>92.6</td>\n",
       "      <td>205.540664</td>\n",
       "      <td>28.379824</td>\n",
       "      <td>R0121638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121553</th>\n",
       "      <td>626.7573</td>\n",
       "      <td>229.5815</td>\n",
       "      <td>23.1155</td>\n",
       "      <td>21.9520</td>\n",
       "      <td>22.0853</td>\n",
       "      <td>21.1975</td>\n",
       "      <td>20.5604</td>\n",
       "      <td>97.7</td>\n",
       "      <td>205.540607</td>\n",
       "      <td>28.379785</td>\n",
       "      <td>R0121640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x         y    f275w    f336w    f438w    f606w    f814w  prob  \\\n",
       "6413    618.1348  239.5454  17.8137  17.6522  18.6494  18.0839  17.3724  96.9   \n",
       "121546  613.3892  238.7969  21.4064  20.6572  21.0700  20.3243  19.7642  99.3   \n",
       "121551  622.1646  233.1406  21.6427  20.7703  21.0578  20.4809  19.8385  92.6   \n",
       "121553  626.7573  229.5815  23.1155  21.9520  22.0853  21.1975  20.5604  97.7   \n",
       "\n",
       "                ra        dec        id  \n",
       "6413    205.540715  28.379894  R0006439  \n",
       "121546  205.540774  28.379886  R0121633  \n",
       "121551  205.540664  28.379824  R0121638  \n",
       "121553  205.540607  28.379785  R0121640  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.511707129159066e-06\n",
      "4.914784047373651e-07\n",
      "6.2923814253720515e-06\n",
      "7.696104307429348e-06\n",
      "6.513450341760507e-06\n"
     ]
    }
   ],
   "source": [
    "c1 = SkyCoord(ra=float(205.5729)*u.degree, dec=float(28.359)*u.degree)\n",
    "for _, row in tmp2.iterrows():\n",
    "    c2 = SkyCoord(ra=float(row['ra'])*u.degree, dec=float(row['dec'])*u.degree)\n",
    "    d = c1.separation(c2).to_value(u.radian)\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>f275w</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f438w</th>\n",
       "      <th>f606w</th>\n",
       "      <th>f814w</th>\n",
       "      <th>prob</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>-1995.6553</td>\n",
       "      <td>-1641.9351</td>\n",
       "      <td>19.3985</td>\n",
       "      <td>18.7910</td>\n",
       "      <td>19.0492</td>\n",
       "      <td>18.4969</td>\n",
       "      <td>17.9799</td>\n",
       "      <td>95.1</td>\n",
       "      <td>205.573309</td>\n",
       "      <td>28.359236</td>\n",
       "      <td>R0000821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33471</th>\n",
       "      <td>-1960.2625</td>\n",
       "      <td>-1663.4512</td>\n",
       "      <td>22.5010</td>\n",
       "      <td>21.9486</td>\n",
       "      <td>21.8407</td>\n",
       "      <td>21.2065</td>\n",
       "      <td>20.5149</td>\n",
       "      <td>97.4</td>\n",
       "      <td>205.572868</td>\n",
       "      <td>28.359000</td>\n",
       "      <td>R0033520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33472</th>\n",
       "      <td>-1948.2634</td>\n",
       "      <td>-1692.9072</td>\n",
       "      <td>20.2157</td>\n",
       "      <td>19.5943</td>\n",
       "      <td>20.0529</td>\n",
       "      <td>19.4233</td>\n",
       "      <td>18.9433</td>\n",
       "      <td>96.4</td>\n",
       "      <td>205.572718</td>\n",
       "      <td>28.358677</td>\n",
       "      <td>R0033521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33473</th>\n",
       "      <td>-1927.6006</td>\n",
       "      <td>-1644.2615</td>\n",
       "      <td>19.9340</td>\n",
       "      <td>19.3706</td>\n",
       "      <td>19.7683</td>\n",
       "      <td>19.2455</td>\n",
       "      <td>18.7627</td>\n",
       "      <td>96.7</td>\n",
       "      <td>205.572460</td>\n",
       "      <td>28.359211</td>\n",
       "      <td>R0033522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33539</th>\n",
       "      <td>-1995.8274</td>\n",
       "      <td>-1671.7722</td>\n",
       "      <td>-99.9999</td>\n",
       "      <td>22.8340</td>\n",
       "      <td>22.8769</td>\n",
       "      <td>21.7496</td>\n",
       "      <td>20.9065</td>\n",
       "      <td>97.6</td>\n",
       "      <td>205.573311</td>\n",
       "      <td>28.358908</td>\n",
       "      <td>R0033588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x          y    f275w    f336w    f438w    f606w    f814w  \\\n",
       "815   -1995.6553 -1641.9351  19.3985  18.7910  19.0492  18.4969  17.9799   \n",
       "33471 -1960.2625 -1663.4512  22.5010  21.9486  21.8407  21.2065  20.5149   \n",
       "33472 -1948.2634 -1692.9072  20.2157  19.5943  20.0529  19.4233  18.9433   \n",
       "33473 -1927.6006 -1644.2615  19.9340  19.3706  19.7683  19.2455  18.7627   \n",
       "33539 -1995.8274 -1671.7722 -99.9999  22.8340  22.8769  21.7496  20.9065   \n",
       "\n",
       "       prob          ra        dec        id  \n",
       "815    95.1  205.573309  28.359236  R0000821  \n",
       "33471  97.4  205.572868  28.359000  R0033520  \n",
       "33472  96.4  205.572718  28.358677  R0033521  \n",
       "33473  96.7  205.572460  28.359211  R0033522  \n",
       "33539  97.6  205.573311  28.358908  R0033588  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In the end, we chose the star closed to the corresponding X ray sources."
   ]
  }
 ],
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