A9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fb4027f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1b58a84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# byte-by-byte delimiters\n",
    "colspecs = [(0, 4), (4, 14), (14, 25), (25, 31), (31, 37),\n",
    "(37, 41), (41, 42), (42, 43), (43, 44), (44, 49),\n",
    "(49, 51), (51, 60), (60, 62), (62, 64), (64, 68),\n",
    "(68, 69), (69, 71), (71, 73), (73, 75), (75, 77),\n",
    "(77, 79), (79, 83), (83, 84), (84, 86), (86, 88),\n",
    "(88, 90), (90, 96), (96, 102), (102, 107), (107, 108),\n",
    "(108, 109), (109, 114), (114, 115), (115, 120), (120, 121),\n",
    "(121, 126), (126, 127), (127, 147), (147, 148), (148, 154),\n",
    "(154, 160), (160, 161), (161, 166), (166, 170), (170, 174),\n",
    "(174, 176), (176, 179), (179, 180), (180, 184), (184, 190),\n",
    "(190, 194), (194, 196), (196, 197)]\n",
    "catalog = pd.read_fwf(\"bsc5.dat\", header = None, colspecs = colspecs,\n",
    "names = [\"HR\",\n",
    "\"Name\",\n",
    "\"DM\",\n",
    "\"HD\",\n",
    "\"SAO\",\n",
    "\"FK5\",\n",
    "\"IRflag\",\n",
    "\"r_IRflag\",\n",
    "\"Multiple\",\n",
    "\"ADS\",\n",
    "\"ADScomp\",\n",
    "\"VarID\",\n",
    "\"RAh1900\",\n",
    "\"RAm1900\",\n",
    "\"RAs1900\",\n",
    "\"DE-1900\",\n",
    "\"DEd1900\",\n",
    "\"DEm1900\",\n",
    "\"DEs1900\",\n",
    "\"RAh\",\n",
    "\"RAm\",\n",
    "\"RAs\",\n",
    "\"DE-\",\n",
    "\"DEd\",\n",
    "\"DEm\",\n",
    "\"DEs\",\n",
    "\"GLON\",\n",
    "\"GLAT\",\n",
    "\"Vmag\",\n",
    "\"n_Vmag\",\n",
    "\"u_Vmag\",\n",
    "\"B-V\",\n",
    "\"u_B-V\",\n",
    "\"U-B\",\n",
    "\"u_U-B\",\n",
    "\"R-I\",\n",
    "\"n_R-I\",\n",
    "\"SpType\",\n",
    "\"n_SpType\",\n",
    "\"pmRA\",\n",
    "\"pmDE\",\n",
    "\"n_Parallax\",\n",
    "\"Parallax\",\n",
    "\"RadVel\",\n",
    "\"n_RadVel\",\n",
    "\"l_RotVel\",\n",
    "\"RotVel\",\n",
    "\"u_RotVel\",\n",
    "\"Dmag\",\n",
    "\"Sep\",\n",
    "\"MultID\",\n",
    "\"MultCnt\",\n",
    "\"NoteFlag\"])\n",
    "catalog.to_csv(\"bsc5.csv\")\n",
    "df = pd.read_csv(\"bsc5.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dccc48c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis = 0, subset = ['Parallax'], inplace = True)\n",
    "df.dropna(axis = 0, subset = ['Vmag'], inplace = True)\n",
    "df.dropna(axis = 0, subset = ['B-V'], inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c06d6a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3207, 54)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7b790f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df[df['Parallax'] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "19a16545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3048, 54)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6da5f32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nn/n9ftdm353mn_z_w8794v5bzh0000gn/T/ipykernel_14096/3998535987.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_filtered['Distance'] = 1/df_filtered['Parallax']\n"
     ]
    }
   ],
   "source": [
    "df_filtered['Distance'] = 1/df_filtered['Parallax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9b93a6ab",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>HR</th>\n",
       "      <th>Name</th>\n",
       "      <th>DM</th>\n",
       "      <th>HD</th>\n",
       "      <th>SAO</th>\n",
       "      <th>FK5</th>\n",
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       "      <th>r_IRflag</th>\n",
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       "      <th>n_RadVel</th>\n",
       "      <th>l_RotVel</th>\n",
       "      <th>RotVel</th>\n",
       "      <th>u_RotVel</th>\n",
       "      <th>Dmag</th>\n",
       "      <th>Sep</th>\n",
       "      <th>MultID</th>\n",
       "      <th>MultCnt</th>\n",
       "      <th>NoteFlag</th>\n",
       "      <th>Distance</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
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       "      <td>&lt;</td>\n",
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       "      <td>2.5</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>V</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>SB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BD+28 4704</td>\n",
       "      <td>166.0</td>\n",
       "      <td>73743.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>V</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>158.6</td>\n",
       "      <td>AB</td>\n",
       "      <td>4.0</td>\n",
       "      <td>*</td>\n",
       "      <td>14.925373</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>21Alp And</td>\n",
       "      <td>BD+28    4</td>\n",
       "      <td>358.0</td>\n",
       "      <td>73765.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>SBO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>81.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
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       "<p>5 rows × 55 columns</p>\n",
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      ],
      "text/plain": [
       "    Unnamed: 0  HR       Name          DM     HD       SAO     FK5 IRflag  \\\n",
       "2            2   3  33    Psc  BD-06 6357   28.0  128572.0  1002.0      I   \n",
       "4            4   5        NaN  BD+57 2865  123.0   21085.0     NaN    NaN   \n",
       "5            5   6        NaN  CD-4914337  142.0  214963.0     NaN    NaN   \n",
       "7            7   8        NaN  BD+28 4704  166.0   73743.0     NaN    NaN   \n",
       "14          14  15  21Alp And  BD+28    4  358.0   73765.0     1.0      I   \n",
       "\n",
       "   r_IRflag Multiple  ...  n_RadVel l_RotVel RotVel  u_RotVel  Dmag    Sep  \\\n",
       "2       NaN      NaN  ...      SB1O        <   17.0       NaN   2.5    0.0   \n",
       "4       NaN      NaN  ...         V      NaN    NaN       NaN   0.8    1.4   \n",
       "5       NaN        W  ...        SB      NaN    NaN       NaN   5.7    5.4   \n",
       "7       NaN      NaN  ...         V      NaN    NaN       NaN   2.6  158.6   \n",
       "14      NaN      NaN  ...       SBO      NaN   56.0       NaN   8.5   81.5   \n",
       "\n",
       "   MultID  MultCnt  NoteFlag   Distance  \n",
       "2     NaN      3.0         *  71.428571  \n",
       "4     NaN      NaN         *  21.276596  \n",
       "5     NaN      NaN         *  20.000000  \n",
       "7      AB      4.0         *  14.925373  \n",
       "14    NaN      NaN         *  31.250000  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f376b7cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sashi_ayyalasomayajula/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/var/folders/nn/n9ftdm353mn_z_w8794v5bzh0000gn/T/ipykernel_14096/979074650.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_filtered['Abs V'] = df_filtered['Vmag'] + 5 - (5*np.log10(df_filtered['Distance']))\n"
     ]
    }
   ],
   "source": [
    "df_filtered['Abs V'] = df_filtered['Vmag'] + 5 - (5*np.log10(df_filtered['Distance']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "58ca0d39",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>HR</th>\n",
       "      <th>Name</th>\n",
       "      <th>DM</th>\n",
       "      <th>HD</th>\n",
       "      <th>SAO</th>\n",
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       "      <th>Abs V</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.194850</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BD+28 4704</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>158.6</td>\n",
       "      <td>AB</td>\n",
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       "      <td>*</td>\n",
       "      <td>14.925373</td>\n",
       "      <td>5.260374</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>21Alp And</td>\n",
       "      <td>BD+28    4</td>\n",
       "      <td>358.0</td>\n",
       "      <td>73765.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>81.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>-0.414250</td>\n",
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       "<p>5 rows × 56 columns</p>\n",
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      ],
      "text/plain": [
       "    Unnamed: 0  HR       Name          DM     HD       SAO     FK5 IRflag  \\\n",
       "2            2   3  33    Psc  BD-06 6357   28.0  128572.0  1002.0      I   \n",
       "4            4   5        NaN  BD+57 2865  123.0   21085.0     NaN    NaN   \n",
       "5            5   6        NaN  CD-4914337  142.0  214963.0     NaN    NaN   \n",
       "7            7   8        NaN  BD+28 4704  166.0   73743.0     NaN    NaN   \n",
       "14          14  15  21Alp And  BD+28    4  358.0   73765.0     1.0      I   \n",
       "\n",
       "   r_IRflag Multiple  ...  l_RotVel RotVel u_RotVel  Dmag    Sep  MultID  \\\n",
       "2       NaN      NaN  ...         <   17.0      NaN   2.5    0.0     NaN   \n",
       "4       NaN      NaN  ...       NaN    NaN      NaN   0.8    1.4     NaN   \n",
       "5       NaN        W  ...       NaN    NaN      NaN   5.7    5.4     NaN   \n",
       "7       NaN      NaN  ...       NaN    NaN      NaN   2.6  158.6      AB   \n",
       "14      NaN      NaN  ...       NaN   56.0      NaN   8.5   81.5     NaN   \n",
       "\n",
       "   MultCnt  NoteFlag   Distance     Abs V  \n",
       "2      3.0         *  71.428571  0.340640  \n",
       "4      NaN         *  21.276596  4.320489  \n",
       "5      NaN         *  20.000000  4.194850  \n",
       "7      4.0         *  14.925373  5.260374  \n",
       "14     NaN         *  31.250000 -0.414250  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "629e35a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_filtered['B-V'] \n",
    "y = df_filtered['Abs V']\n",
    "fig = figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel('B-V')\n",
    "ax.set_ylabel('Absolute Mag')\n",
    "ax.set_title('HR Diagram')\n",
    "scatter(x,y,s=1) # marker size\n",
    "gca().invert_yaxis()  # y-axis reversed\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a6fae11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From a visual analysis, I would say that about 63% of the stars are in the Main Sequence,\n",
    "# 35% of stars are Red Giants, and 2% of the stars are white dwarfs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}