Extracting Gaia data for beginners.ipynb

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     "text": [
      "C:\\Users\\sophi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\astroquery\\ipac\\irsa\\sha\\__init__.py:14: UserWarning: Experimental: SHA has not yet been refactored to have its API match the rest of astroquery.\n",
      "  warnings.warn(\"Experimental: SHA has not yet been refactored to have its \"\n"
     ]
    }
   ],
   "source": [
    "# Library imports! I basically copy/paste this into the first cell of all my codes, as these libraries are all heavily used\n",
    "# in astronomy. Many of them might go unused, but I don't mind importing a couple extra things :)\n",
    "\n",
    "# If you don't have any of these installed (i.e. you get an error when you try to run this cell), you can go to your command \n",
    "# line and type in \"pip install _____\" as long as you have pip installed (comes automatically with a lot of places you download \n",
    "# Python)--let me know if you need help installing pip\n",
    "\n",
    "import pandas as pd # this library is used for working with DataFrames--very useful in astronomy\n",
    "import csv # for working with csv files, like you'd see in Microsoft Excel\n",
    "from matplotlib import pyplot as plt # used for plotting graphs\n",
    "import numpy as np # all sorts of mathematical applications\n",
    "import glob # used for grabbing a bunch of files at once\n",
    "import statistics # the name says it all\n",
    "import scipy # again, all sorts of mathematical applications\n",
    "from mpl_toolkits import mplot3d # used for three-dimensional plots, which we'll revisit at a later time\n",
    "from astroquery import gaia # Gaia!\n",
    "from astroquery.gaia import Gaia\n",
    "import astropy.units as u # very handy for keeping track of units, which are very easy to get mixed up\n",
    "from astropy.units import Quantity\n",
    "from astropy.coordinates.sky_coordinate import SkyCoord # used for interpreting RA and Dec coordinates\n",
    "from astropy import table # sometimes astronomy data downloads in tables, which I find incredibly annoying to work with\n",
    "from astropy.io.votable import parse # used to parse through votable files (again, often seen in astronomy)\n",
    "from scipy.stats import norm # some extra stats stuff I've used for fitting\n",
    "from scipy.stats import powerlaw\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.optimize import least_squares\n",
    "import math\n",
    "import seaborn as sns # super cool library that allows us to make contour plots and cool data visualizations\n",
    "from astroquery.ipac.irsa import sha # some extra imports for downloading data\n",
    "from astropy import coordinates as coord\n",
    "from astroquery.ipac.irsa import Irsa\n",
    "import numpy.ma as ma\n",
    "Irsa.ROW_LIMIT = 40000 # limits the number of rows that a query returns so that the query doesn't run forever--just make sure\n",
    "# to check that the number of rows in your data hasn't hit this number afterwards so you know you aren't missing any sources"
   ]
  },
  {
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   "id": "62d08b0c",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Query finished. [astroquery.utils.tap.core]\n"
     ]
    }
   ],
   "source": [
    "Gaia.ROW_LIMIT = 100000\n",
    "\n",
    "# this has you specify the center of the area that you would like to look at in right ascension (ra) and Declination (dec)\n",
    "# for the purposes of this tutorial, I'm going to have you look at the Orion Nebula Cluster\n",
    "# I got these coordinates from SIMBAD (which you can Google)\n",
    "onc_ra = 83.82084 #degrees\n",
    "onc_dec = -5.391111 #degrees\n",
    "coord = SkyCoord(ra = onc_ra, dec = onc_dec, unit=(u.degree, u.degree), frame='icrs') \n",
    "#ICRS = International Celestial Reference System\n",
    "\n",
    "#then, you define the size of the area you'd like to look at. Here, we'll look at a cone with a radius of half a degree \n",
    "job = Gaia.cone_search_async(coord, radius=u.Quantity(0.5, u.deg))\n",
    "\n",
    "results = job.get_results()\n",
    "\n",
    "#This cell may take a second to run...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62032caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results) #tells you what format the results are in -- here we have an astropy Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "871f3768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4846"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results) #make sure this number is smaller than the Gaia row limit you assigned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0aa897cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I like working with pandas DataFrames, so here I am converting the astropy table to a DataFrame\n",
    "dictionary = {}\n",
    "for name in results.colnames:\n",
    "    dictionary[str(name)] = results[str(name)]\n",
    "gaia = pd.DataFrame(data=dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f1839cd",
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   "outputs": [
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       "              solution_id                   DESIGNATION            source_id  \\\n",
       "0     1635721458409799680  Gaia DR2 3017364063331140224  3017364063331140224   \n",
       "1     1635721458409799680  Gaia DR2 3017364059023817600  3017364059023817600   \n",
       "2     1635721458409799680  Gaia DR2 3017364063325263360  3017364063325263360   \n",
       "3     1635721458409799680  Gaia DR2 3017364063330718080  3017364063330718080   \n",
       "4     1635721458409799680  Gaia DR2 3017363960246048384  3017363960246048384   \n",
       "...                   ...                           ...                  ...   \n",
       "4841  1635721458409799680  Gaia DR2 3017400828249372928  3017400828249372928   \n",
       "4842  1635721458409799680  Gaia DR2 3017395158892542976  3017395158892542976   \n",
       "4843  1635721458409799680  Gaia DR2 3017241497837015424  3017241497837015424   \n",
       "4844  1635721458409799680  Gaia DR2 3017253871638555520  3017253871638555520   \n",
       "4845  1635721458409799680  Gaia DR2 3017290460463981440  3017290460463981440   \n",
       "\n",
       "      random_index  ref_epoch         ra  ra_error       dec  dec_error  \\\n",
       "0        102240017     2015.5  83.819719  0.053257 -5.390340   0.059865   \n",
       "1       1051452587     2015.5  83.821116  0.076893 -5.392786   0.085720   \n",
       "2       1291616513     2015.5  83.820770  0.254868 -5.393625   0.263055   \n",
       "3       1673371952     2015.5  83.818619  0.266350 -5.389696   0.253225   \n",
       "4        299916628     2015.5  83.821084  0.282569 -5.394377   0.354643   \n",
       "...            ...        ...        ...       ...       ...        ...   \n",
       "4841     101448698     2015.5  84.192894  0.121078 -5.055855   0.122449   \n",
       "4842     164077697     2015.5  84.253623  0.079504 -5.138012   0.080706   \n",
       "4843    1573114537     2015.5  83.573922  0.793104 -5.826437   0.800807   \n",
       "4844     601463814     2015.5  83.496841  0.112240 -5.773118   0.113491   \n",
       "4845     627386269     2015.5  84.209046  0.196142 -5.708458   0.206489   \n",
       "\n",
       "      parallax  ...  flame_flags  radius_val  radius_percentile_lower  \\\n",
       "0     2.472314  ...     200111.0    2.385153                 1.416072   \n",
       "1     1.536199  ...          NaN         NaN                      NaN   \n",
       "2     2.056557  ...          NaN         NaN                      NaN   \n",
       "3     2.706629  ...          NaN         NaN                      NaN   \n",
       "4     1.538196  ...          NaN         NaN                      NaN   \n",
       "...        ...  ...          ...         ...                      ...   \n",
       "4841  0.721482  ...          NaN         NaN                      NaN   \n",
       "4842  0.496347  ...          NaN         NaN                      NaN   \n",
       "4843  1.796852  ...          NaN         NaN                      NaN   \n",
       "4844  2.512176  ...          NaN         NaN                      NaN   \n",
       "4845  0.090289  ...          NaN         NaN                      NaN   \n",
       "\n",
       "      radius_percentile_upper    lum_val  lum_percentile_lower  \\\n",
       "0                    2.690297  12.891893             12.359015   \n",
       "1                         NaN        NaN                   NaN   \n",
       "2                         NaN        NaN                   NaN   \n",
       "3                         NaN        NaN                   NaN   \n",
       "4                         NaN        NaN                   NaN   \n",
       "...                       ...        ...                   ...   \n",
       "4841                      NaN        NaN                   NaN   \n",
       "4842                      NaN        NaN                   NaN   \n",
       "4843                      NaN        NaN                   NaN   \n",
       "4844                      NaN        NaN                   NaN   \n",
       "4845                      NaN        NaN                   NaN   \n",
       "\n",
       "      lum_percentile_upper                                       datalink_url  \\\n",
       "0                13.424771  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "1                      NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "2                      NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "3                      NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "4                      NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "...                    ...                                                ...   \n",
       "4841                   NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "4842                   NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "4843                   NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "4844                   NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "4845                   NaN  https://gea.esac.esa.int/data-server/datalink/...   \n",
       "\n",
       "      epoch_photometry_url      dist  \n",
       "0                           0.001364  \n",
       "1                           0.001691  \n",
       "2                           0.002510  \n",
       "3                           0.002633  \n",
       "4                           0.003269  \n",
       "...                    ...       ...  \n",
       "4841                        0.499671  \n",
       "4842                        0.499780  \n",
       "4843                        0.499893  \n",
       "4844                        0.499911  \n",
       "4845                        0.499995  \n",
       "\n",
       "[4846 rows x 97 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's a preview of what the DataFrame looks like!\n",
    "gaia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "048f5579",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now you have a DataFrame with all the information you need. You can start playing around with this and seeing what\n",
    "# you can do! To get you started, here's me just plotting the sources on ra and dec (basically just x and y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cdd16c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10)) # making sure it's the same size on both axes so we don't get a weird image\n",
    "plt.scatter(gaia['ra'], gaia['dec'], s=1)\n",
    "plt.show()\n",
    "# here you can see the cluster in the center of the image!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7680a42f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this cell, try plotting a histogram of all the ra values--what do you see?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e2c30dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this cell, do the same with the dec values and make some observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afbe56a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this cell, try using the parallax values to calculate the distances of all the sources (use a 3018 formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d645aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this cell, plot the distances on a histogram and make some observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05832390",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}