AstronomicalData/_sources/03_motion.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "---\n",
    "title: \"Plotting and Pandas\"\n",
    "teaching: 3000\n",
    "exercises: 0\n",
    "\n",
    "questions:\n",
    "\n",
    "- \"How do we make scatter plots in Matplotlib? How do we store data in a Pandas `DataFrame`?\"\n",
    "\n",
    "objectives:\n",
    "\n",
    "- \"Select rows and columns from an Astropy `Table`.\"\n",
    "\n",
    "- \"Use Matplotlib to make a scatter plot.\"\n",
    "\n",
    "- \"Use Gala to transform coordinates.\"\n",
    "\n",
    "- \"Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\"\n",
    "\n",
    "- \"Save a `DataFrame` in an HDF5 file.\"\n",
    "\n",
    "keypoints:\n",
    "\n",
    "- \"When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\"\n",
    "\n",
    "- \"For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\"\n",
    "\n",
    "- \"An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice.\"\n",
    "\n",
    "---\n",
    "\n",
    "{% include links.md %}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proper Motion\n",
    "\n",
    "This is the third in a series of notebooks related to astronomy data.\n",
    "\n",
    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
    "\n",
    "In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
    "\n",
    "In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.\n",
    "\n",
    "Now we'll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "Here are the steps in this lesson:\n",
    "\n",
    "1. We'll read back the results from the previous lesson, which we saved in a FITS file.\n",
    "\n",
    "2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.\n",
    "\n",
    "3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.\n",
    "\n",
    "4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.\n",
    "\n",
    "5. Finally, we'll select and plot the stars whose proper motion is in that region.\n",
    "\n",
    "After completing this lesson, you should be able to\n",
    "\n",
    "* Select rows and columns from an Astropy `Table`.\n",
    "\n",
    "* Use Matplotlib to make a scatter plot.\n",
    "\n",
    "* Use Gala to transform coordinates.\n",
    "\n",
    "* Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\n",
    "\n",
    "* Save a `DataFrame` in an HDF5 file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "## Installing libraries\n",
    "\n",
    "If you are running this notebook on Colab, you can run the following cell to install the libraries we'll use.\n",
    "\n",
    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  See the instructions in the preface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# If we're running on Colab, install libraries\n",
    "\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install astroquery astro-gala wget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload the data\n",
    "\n",
    "In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.\n",
    "\n",
    "If you ran the previous lesson successfully, you should already have a file called `gd1_results.fits` that contains the data we downloaded.\n",
    "\n",
    "If not, you can run the following cell, which downloads the data from our repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from wget import download\n",
    "\n",
    "filename = 'gd1_results.fits'\n",
    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
    "\n",
    "if not os.path.exists(filename):\n",
    "    print(download(path+filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now here's how we can read the data from the file back into an Astropy `Table`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "results = Table.read(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is an Astropy `Table`.\n",
    "\n",
    "We can use `info` to refresh our memory of the contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Table length=140340>\n",
       "      name       dtype    unit                              description                            \n",
       "--------------- ------- -------- ------------------------------------------------------------------\n",
       "      source_id   int64          Unique source identifier (unique within a particular Data Release)\n",
       "             ra float64      deg                                                    Right ascension\n",
       "            dec float64      deg                                                        Declination\n",
       "           pmra float64 mas / yr                         Proper motion in right ascension direction\n",
       "          pmdec float64 mas / yr                             Proper motion in declination direction\n",
       "       parallax float64      mas                                                           Parallax\n",
       " parallax_error float64      mas                                         Standard error of parallax\n",
       "radial_velocity float64   km / s                                                    Radial velocity"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting rows and columns\n",
    "\n",
    "In this section we'll see operations for selecting columns and rows from an Astropy `Table`.  You can find more information about these operations in the [Astropy documentation](https://docs.astropy.org/en/stable/table/access_table.html).\n",
    "\n",
    "We can get the names of the columns like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['source_id',\n",
       " 'ra',\n",
       " 'dec',\n",
       " 'pmra',\n",
       " 'pmdec',\n",
       " 'parallax',\n",
       " 'parallax_error',\n",
       " 'radial_velocity']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And select an individual column like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;ra&apos; dtype=&apos;float64&apos; unit=&apos;deg&apos; description=&apos;Right ascension&apos; length=140340&gt;\n",
       "<table>\n",
       "<tr><td>142.48301935991023</td></tr>\n",
       "<tr><td>142.25452941346344</td></tr>\n",
       "<tr><td>142.64528557468074</td></tr>\n",
       "<tr><td>142.57739430926034</td></tr>\n",
       "<tr><td>142.58913564478618</td></tr>\n",
       "<tr><td>141.81762228999614</td></tr>\n",
       "<tr><td>143.18339801317677</td></tr>\n",
       "<tr><td>142.9347319464589</td></tr>\n",
       "<tr><td>142.26769745823267</td></tr>\n",
       "<tr><td>142.89551292869012</td></tr>\n",
       "<tr><td>142.2780935768316</td></tr>\n",
       "<tr><td>142.06138786534987</td></tr>\n",
       "<tr><td>...</td></tr>\n",
       "<tr><td>143.05456487172972</td></tr>\n",
       "<tr><td>144.0436496516182</td></tr>\n",
       "<tr><td>144.06566578919313</td></tr>\n",
       "<tr><td>144.13177563215973</td></tr>\n",
       "<tr><td>143.77696341662764</td></tr>\n",
       "<tr><td>142.945956347594</td></tr>\n",
       "<tr><td>142.97282480557786</td></tr>\n",
       "<tr><td>143.4166017695258</td></tr>\n",
       "<tr><td>143.64484588686904</td></tr>\n",
       "<tr><td>143.41554585481808</td></tr>\n",
       "<tr><td>143.6908739159247</td></tr>\n",
       "<tr><td>143.7702681295401</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140340>\n",
       "142.48301935991023\n",
       "142.25452941346344\n",
       "142.64528557468074\n",
       "142.57739430926034\n",
       "142.58913564478618\n",
       "141.81762228999614\n",
       "143.18339801317677\n",
       " 142.9347319464589\n",
       "142.26769745823267\n",
       "142.89551292869012\n",
       " 142.2780935768316\n",
       "142.06138786534987\n",
       "               ...\n",
       "143.05456487172972\n",
       " 144.0436496516182\n",
       "144.06566578919313\n",
       "144.13177563215973\n",
       "143.77696341662764\n",
       "  142.945956347594\n",
       "142.97282480557786\n",
       " 143.4166017695258\n",
       "143.64484588686904\n",
       "143.41554585481808\n",
       " 143.6908739159247\n",
       " 143.7702681295401"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['ra']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `Column` object that contains the data, and also the data type, units, and name of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.column.Column"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results['ra'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rows in the `Table` are numbered from 0 to `n-1`, where `n` is the number of rows.  We can select the first row like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table140569154410096\">\n",
       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>637987125186749568</td><td>142.48301935991023</td><td>21.75771616932985</td><td>-2.5168384683875766</td><td>2.941813096629439</td><td>-0.2573448962333354</td><td>0.823720794509811</td><td>1e+20</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Row index=0>\n",
       "    source_id              ra                dec                pmra              pmdec             parallax        parallax_error  radial_velocity\n",
       "                          deg                deg              mas / yr           mas / yr             mas                mas             km / s    \n",
       "      int64             float64            float64            float64            float64            float64            float64          float64    \n",
       "------------------ ------------------ ----------------- ------------------- ----------------- ------------------- ----------------- ---------------\n",
       "637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354 0.823720794509811           1e+20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you might have guessed, the result is a `Row` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.row.Row"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.\n",
    "\n",
    "If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.\n",
    "\n",
    "If you apply the bracket operator twice, you can select a column and then an element from the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.48301935991023"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['ra'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can select a row and then an element from the row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.48301935991023"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[0]['ra']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You get the same result either way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot\n",
    "\n",
    "To see what the results look like, we'll use a scatter plot.  The library we'll use is [Matplotlib](https://matplotlib.org/), which is the most widely-used plotting library for Python.\n",
    "\n",
    "The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.\n",
    "\n",
    "We'll import like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name `plt`.\n",
    "\n",
    "Pyplot provides two functions that can make scatterplots, [plt.scatter](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html) and [plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
    "\n",
    "* `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.\n",
    "\n",
    "* `plot` is more limited, but for simple cases, it can be substantially faster.  \n",
    "\n",
    "Jake Vanderplas explains these differences in [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)\n",
    "\n",
    "Since we are plotting more than 100,000 points and they are all the same size and color, we'll use `plot`.\n",
    "\n",
    "Here's a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "x = results['ra']\n",
    "y = results['dec']\n",
    "plt.plot(x, y, 'ko')\n",
    "\n",
    "plt.xlabel('ra (degree ICRS)')\n",
    "plt.ylabel('dec (degree ICRS)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style.  In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue).\n",
    "\n",
    "The functions `xlabel` and `ylabel` put labels on the axes.\n",
    "\n",
    "This scatter plot has a problem.  It is \"[overplotted](https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/)\", which means that there are so many overlapping points, we can't distinguish between high and low density areas.\n",
    "\n",
    "To fix this, we can provide optional arguments to control the size and transparency of the points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "In the call to `plt.plot`, use the keyword argument `markersize` to make the markers smaller.\n",
    "\n",
    "Then add the keyword argument `alpha` to make the markers partly transparent.\n",
    "\n",
    "Adjust these arguments until you think the figure shows the data most clearly.\n",
    "\n",
    "Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which [you can read about here](https://www.cosmos.esa.int/web/gaia/scanning-law).  The dataset we are using, [Gaia Data Release 2](https://www.cosmos.esa.int/web/gaia/dr2), covers 22 months of observations; during this time, some parts of the sky were scanned more than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# x = results['ra']\n",
    "# y = results['dec']\n",
    "# plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)\n",
    "\n",
    "# plt.xlabel('ra (degree ICRS)')\n",
    "# plt.ylabel('dec (degree ICRS)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform back\n",
    "\n",
    "Remember that we selected data from a rectangle of coordinates in the `GD1Koposov10` frame, then transformed them to ICRS when we constructed the query.\n",
    "The coordinates in `results` are in ICRS.\n",
    "\n",
    "To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the orbit of GD-1, which is useful for two reasons:\n",
    "\n",
    "* We can identify stars that are likely to be in GD-1 by selecting stars near the centerline of the stream, where $\\phi_2$ is close to 0.\n",
    "\n",
    "* We expect stars in GD-1 to have similar proper motion along the $\\phi_1$ axis.\n",
    "\n",
    "To do the transformation, we'll put the results into a `SkyCoord` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "import astropy.units as u\n",
    "\n",
    "skycoord = SkyCoord(\n",
    "               ra=results['ra'], \n",
    "               dec=results['dec'],\n",
    "               pm_ra_cosdec=results['pmra'],\n",
    "               pm_dec=results['pmdec'], \n",
    "               distance=8*u.kpc, \n",
    "               radial_velocity=0*u.km/u.s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the arguments we send to `SkyCoord` come directly from `results`.\n",
    "\n",
    "We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below.\n",
    "\n",
    "The result is an Astropy `SkyCoord` object, which we can transform to the GD-1 frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.coordinates.sky_coordinate.SkyCoord"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gala.coordinates import GD1Koposov10\n",
    "\n",
    "gd1_frame = GD1Koposov10()\n",
    "transformed = skycoord.transform_to(gd1_frame)\n",
    "type(transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is another `SkyCoord` object, now in the `GD1Koposov10` frame.\n",
    "\n",
    "The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.\n",
    "\n",
    "When we created `skycoord`, we provided `distance` and `radial_velocity` as arguments, but we did not use the measurements provided by Gaia.  Instead, we use fixed values for these parameters.\n",
    "\n",
    "That might seem like a strange thing to do, but here's the motivation:\n",
    "\n",
    "* Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc.  See [Koposov, Rix, and Hogg, 2010](https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract).\n",
    "\n",
    "* For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.\n",
    "\n",
    "* The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.\n",
    "\n",
    "We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this preparation, we can use `reflex_correct` from Gala ([documentation here](https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html)) to correct for solar reflex motion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.coordinates.sky_coordinate.SkyCoord"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gala.coordinates import reflex_correct\n",
    "\n",
    "gd1_coord = reflex_correct(transformed)\n",
    "type(gd1_coord)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `SkyCoord` object that contains \n",
    "\n",
    "* `phi1` and `phi2`, which represent the transformed coordinates in the `GD1Koposov10` frame.\n",
    "\n",
    "* `pm_phi1_cosphi2` and `pm_phi2`, which represent the transformed and corrected proper motions.\n",
    "\n",
    "We can select the coordinates like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "phi1 = gd1_coord.phi1\n",
    "phi2 = gd1_coord.phi2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot them like this:"
   ]
  },
  {
   "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": [
    "plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)\n",
    "\n",
    "plt.xlabel('ra (degree GD1)')\n",
    "plt.ylabel('dec (degree GD1)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that we started with a rectangle in GD-1 coordinates.  When transformed to ICRS, it's a non-rectangular polygon.  Now that we have transformed back to GD-1 coordinates, it's a rectangle again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas DataFrame\n",
    "\n",
    "At this point we have two objects containing different subsets of the data.  `results` is the Astropy `Table` we downloaded from Gaia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And `gd1_coord` is a `SkyCoord` object that contains the transformed coordinates and proper motions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.coordinates.sky_coordinate.SkyCoord"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gd1_coord)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On one hand, this division of labor makes sense because each object provides different capabilities.  But working with multiple object types can be awkward.\n",
    "\n",
    "It will be more convenient to choose one object and get all of the data into it.  We'll use a Pandas DataFrame, for two reasons:\n",
    "\n",
    "1. It provides capabilities that are pretty much a superset of the other data structures, so it's the all-in-one solution.\n",
    "\n",
    "2. Pandas is a general-purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.\n",
    "\n",
    "However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy to convert a `Table` to a Pandas `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140340, 8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results_df = results.to_pandas()\n",
    "results_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame` provides `shape`, which shows the number of rows and columns.\n",
    "\n",
    "It also provides `head`, which displays the first few rows.  It is useful for spot-checking large results as you go along."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>source_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>radial_velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>637987125186749568</td>\n",
       "      <td>142.483019</td>\n",
       "      <td>21.757716</td>\n",
       "      <td>-2.516838</td>\n",
       "      <td>2.941813</td>\n",
       "      <td>-0.257345</td>\n",
       "      <td>0.823721</td>\n",
       "      <td>1.000000e+20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>638285195917112960</td>\n",
       "      <td>142.254529</td>\n",
       "      <td>22.476168</td>\n",
       "      <td>2.662702</td>\n",
       "      <td>-12.165984</td>\n",
       "      <td>0.422728</td>\n",
       "      <td>0.297472</td>\n",
       "      <td>1.000000e+20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>638073505568978688</td>\n",
       "      <td>142.645286</td>\n",
       "      <td>22.166932</td>\n",
       "      <td>18.306747</td>\n",
       "      <td>-7.950660</td>\n",
       "      <td>0.103640</td>\n",
       "      <td>0.544584</td>\n",
       "      <td>1.000000e+20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>638086386175786752</td>\n",
       "      <td>142.577394</td>\n",
       "      <td>22.227920</td>\n",
       "      <td>0.987786</td>\n",
       "      <td>-2.584105</td>\n",
       "      <td>-0.857327</td>\n",
       "      <td>1.059607</td>\n",
       "      <td>1.000000e+20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>638049655615392384</td>\n",
       "      <td>142.589136</td>\n",
       "      <td>22.110783</td>\n",
       "      <td>0.244439</td>\n",
       "      <td>-4.941079</td>\n",
       "      <td>0.099625</td>\n",
       "      <td>0.486224</td>\n",
       "      <td>1.000000e+20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            source_id          ra        dec       pmra      pmdec  parallax  \\\n",
       "0  637987125186749568  142.483019  21.757716  -2.516838   2.941813 -0.257345   \n",
       "1  638285195917112960  142.254529  22.476168   2.662702 -12.165984  0.422728   \n",
       "2  638073505568978688  142.645286  22.166932  18.306747  -7.950660  0.103640   \n",
       "3  638086386175786752  142.577394  22.227920   0.987786  -2.584105 -0.857327   \n",
       "4  638049655615392384  142.589136  22.110783   0.244439  -4.941079  0.099625   \n",
       "\n",
       "   parallax_error  radial_velocity  \n",
       "0        0.823721     1.000000e+20  \n",
       "1        0.297472     1.000000e+20  \n",
       "2        0.544584     1.000000e+20  \n",
       "3        1.059607     1.000000e+20  \n",
       "4        0.486224     1.000000e+20  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python detail: `shape` is an attribute, so we display its value without calling it as a function; `head` is a function, so we need the parentheses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can extract the columns we want from `gd1_coord` and add them as columns in the `DataFrame`.  `phi1` and `phi2` contain the transformed coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140340, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df['phi1'] = gd1_coord.phi1\n",
    "results_df['phi2'] = gd1_coord.phi2\n",
    "results_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140340, 12)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n",
    "results_df['pm_phi2'] = gd1_coord.pm_phi2\n",
    "results_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detail:** If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.\n",
    "\n",
    "We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring data\n",
    "\n",
    "One benefit of using Pandas is that it provides functions for exploring the data and checking for problems.\n",
    "\n",
    "One of the most useful of these functions is `describe`, which computes summary statistics for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>source_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>radial_velocity</th>\n",
       "      <th>phi1</th>\n",
       "      <th>phi2</th>\n",
       "      <th>pm_phi1</th>\n",
       "      <th>pm_phi2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.403400e+05</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>1.403400e+05</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "      <td>140340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.792378e+17</td>\n",
       "      <td>143.822971</td>\n",
       "      <td>26.780161</td>\n",
       "      <td>-2.484410</td>\n",
       "      <td>-6.100784</td>\n",
       "      <td>0.179474</td>\n",
       "      <td>0.518068</td>\n",
       "      <td>9.931167e+19</td>\n",
       "      <td>-50.091337</td>\n",
       "      <td>-1.803264</td>\n",
       "      <td>-0.868980</td>\n",
       "      <td>1.409215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.792015e+16</td>\n",
       "      <td>3.697824</td>\n",
       "      <td>3.052639</td>\n",
       "      <td>5.913923</td>\n",
       "      <td>7.202013</td>\n",
       "      <td>0.759622</td>\n",
       "      <td>0.505558</td>\n",
       "      <td>8.267982e+18</td>\n",
       "      <td>2.892321</td>\n",
       "      <td>3.444439</td>\n",
       "      <td>6.657700</td>\n",
       "      <td>6.518573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.214900e+17</td>\n",
       "      <td>135.425699</td>\n",
       "      <td>19.286617</td>\n",
       "      <td>-106.755260</td>\n",
       "      <td>-138.065163</td>\n",
       "      <td>-15.287602</td>\n",
       "      <td>0.020824</td>\n",
       "      <td>-1.792684e+02</td>\n",
       "      <td>-54.999989</td>\n",
       "      <td>-8.029159</td>\n",
       "      <td>-115.275637</td>\n",
       "      <td>-161.150142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.443515e+17</td>\n",
       "      <td>140.967807</td>\n",
       "      <td>24.592348</td>\n",
       "      <td>-5.038746</td>\n",
       "      <td>-8.341641</td>\n",
       "      <td>-0.035983</td>\n",
       "      <td>0.141108</td>\n",
       "      <td>1.000000e+20</td>\n",
       "      <td>-52.603097</td>\n",
       "      <td>-4.750410</td>\n",
       "      <td>-2.948851</td>\n",
       "      <td>-1.107074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.888056e+17</td>\n",
       "      <td>143.734183</td>\n",
       "      <td>26.746169</td>\n",
       "      <td>-1.834971</td>\n",
       "      <td>-4.689570</td>\n",
       "      <td>0.362705</td>\n",
       "      <td>0.336103</td>\n",
       "      <td>1.000000e+20</td>\n",
       "      <td>-50.147567</td>\n",
       "      <td>-1.671497</td>\n",
       "      <td>0.585038</td>\n",
       "      <td>1.987196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.976578e+17</td>\n",
       "      <td>146.607180</td>\n",
       "      <td>28.990490</td>\n",
       "      <td>0.452995</td>\n",
       "      <td>-1.937833</td>\n",
       "      <td>0.657636</td>\n",
       "      <td>0.751171</td>\n",
       "      <td>1.000000e+20</td>\n",
       "      <td>-47.593466</td>\n",
       "      <td>1.160632</td>\n",
       "      <td>3.001761</td>\n",
       "      <td>4.628859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.974418e+17</td>\n",
       "      <td>152.777393</td>\n",
       "      <td>34.285481</td>\n",
       "      <td>104.319923</td>\n",
       "      <td>20.981070</td>\n",
       "      <td>0.999957</td>\n",
       "      <td>4.171221</td>\n",
       "      <td>1.000000e+20</td>\n",
       "      <td>-45.000086</td>\n",
       "      <td>4.014794</td>\n",
       "      <td>39.802471</td>\n",
       "      <td>79.275199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          source_id             ra            dec           pmra  \\\n",
       "count  1.403400e+05  140340.000000  140340.000000  140340.000000   \n",
       "mean   6.792378e+17     143.822971      26.780161      -2.484410   \n",
       "std    3.792015e+16       3.697824       3.052639       5.913923   \n",
       "min    6.214900e+17     135.425699      19.286617    -106.755260   \n",
       "25%    6.443515e+17     140.967807      24.592348      -5.038746   \n",
       "50%    6.888056e+17     143.734183      26.746169      -1.834971   \n",
       "75%    6.976578e+17     146.607180      28.990490       0.452995   \n",
       "max    7.974418e+17     152.777393      34.285481     104.319923   \n",
       "\n",
       "               pmdec       parallax  parallax_error  radial_velocity  \\\n",
       "count  140340.000000  140340.000000   140340.000000     1.403400e+05   \n",
       "mean       -6.100784       0.179474        0.518068     9.931167e+19   \n",
       "std         7.202013       0.759622        0.505558     8.267982e+18   \n",
       "min      -138.065163     -15.287602        0.020824    -1.792684e+02   \n",
       "25%        -8.341641      -0.035983        0.141108     1.000000e+20   \n",
       "50%        -4.689570       0.362705        0.336103     1.000000e+20   \n",
       "75%        -1.937833       0.657636        0.751171     1.000000e+20   \n",
       "max        20.981070       0.999957        4.171221     1.000000e+20   \n",
       "\n",
       "                phi1           phi2        pm_phi1        pm_phi2  \n",
       "count  140340.000000  140340.000000  140340.000000  140340.000000  \n",
       "mean      -50.091337      -1.803264      -0.868980       1.409215  \n",
       "std         2.892321       3.444439       6.657700       6.518573  \n",
       "min       -54.999989      -8.029159    -115.275637    -161.150142  \n",
       "25%       -52.603097      -4.750410      -2.948851      -1.107074  \n",
       "50%       -50.147567      -1.671497       0.585038       1.987196  \n",
       "75%       -47.593466       1.160632       3.001761       4.628859  \n",
       "max       -45.000086       4.014794      39.802471      79.275199  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Review the summary statistics in this table.\n",
    "\n",
    "* Do the values makes senses based on what you know about the context?\n",
    "\n",
    "* Do you see any values that seem problematic, or evidence of other data issues?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# A few issues that are likely to come up:\n",
    "\n",
    "# 1. Why are some of the parallax values negative?\n",
    "#    Some parallax measurements are inaccurate, especially\n",
    "#    stars that are far away.\n",
    "\n",
    "# 2. Why are some of the radial velocities 1e20?\n",
    "#    It seems like this value is used to indicate invalid data.\n",
    "#    Notice that the 25th percentile is 1e20, which indicates\n",
    "#    that at least 75% of these values are invalid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot proper motion\n",
    "\n",
    "Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:\n",
    "\n",
    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
    "\n",
    "In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.  \n",
    "\n",
    "* Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of `phi2` to be near 0.\n",
    "\n",
    "* In the direction of `phi1`, we don't have a prior expectation for proper motion, except that it should form a cluster at a non-zero value. \n",
    "\n",
    "To locate this cluster, we will select stars near the centerline_df of GD-1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the centerline\n",
    "\n",
    "As we can see in the following figure, many stars in GD-1 are less than 1 degree from the line `phi2=0`.\n",
    "\n",
    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">\n",
    "\n",
    "So stars near this line have the highest probability of being in GD-1.\n",
    "\n",
    "To select them, we will use a \"Boolean mask\".  We'll start by selecting the `phi2` column from the `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phi2 = results_df['phi2']\n",
    "type(phi2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `Series`, which is the structure Pandas uses to represent columns.\n",
    "\n",
    "We can use a comparison operator, `>`, to compare the values in a `Series` to a constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phi2_min = -1.0 * u.deg\n",
    "phi2_max = 1.0 * u.deg\n",
    "\n",
    "mask = (phi2 > phi2_min)\n",
    "type(mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `Series` of Boolean values, that is, `True` and `False`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: phi2, dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (phi2 > phi2_min) & (phi2 < phi2_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python note: We need the parentheses around the conditions; otherwise the order of operations is incorrect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25084"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerline_df = results_df[mask]\n",
    "type(centerline_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`centerline_df` is a `DataFrame` that contains only the rows from `results_df` that correspond to `True` values in `mask`; that is, in contains the stars near the centerline of GD-1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting proper motion\n",
    "\n",
    "Here's a scatter plot of proper motion for the selected stars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "pm1 = centerline_df['pm_phi1']\n",
    "pm2 = centerline_df['pm_phi2']\n",
    "\n",
    "plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)\n",
    "    \n",
    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
    "plt.ylabel('Proper motion phi2 (GD1 frame)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).\n",
    "\n",
    "We can use `xlim` and `ylim` to set the limits on the axes and zoom in on the region near (0, 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "pm1 = centerline_df['pm_phi1']\n",
    "pm2 = centerline_df['pm_phi2']\n",
    "\n",
    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
    "    \n",
    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
    "\n",
    "plt.xlim(-12, 8)\n",
    "plt.ylim(-10, 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the smaller cluster more clearly.\n",
    "\n",
    "You might notice that our figure is less dense than the one in the paper.  That's because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.\n",
    "\n",
    "In the next lesson we'll go back and select stars from a larger region.  But first we'll use the proper motion data to identify stars likely to be in GD-1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering based on proper motion\n",
    "\n",
    "The next step is to select stars in the \"overdense\" region of proper motion, which are candidates to be in GD-1.\n",
    "\n",
    "In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.\n",
    "\n",
    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
    "\n",
    "We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.\n",
    "\n",
    "Here are bounds on proper motion we chose by eye:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm1_min = -8.9\n",
    "pm1_max = -6.9\n",
    "pm2_min = -2.2\n",
    "pm2_max =  1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To draw these bounds, we'll make two lists containing the coordinates of the corners of the rectangle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_rectangle(x1, x2, y1, y2):\n",
    "    \"\"\"Return the corners of a rectangle.\"\"\"\n",
    "    xs = [x1, x1, x2, x2, x1]\n",
    "    ys = [y1, y2, y2, y1, y1]\n",
    "    return xs, ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm1_rect, pm2_rect = make_rectangle(\n",
    "    pm1_min, pm1_max, pm2_min, pm2_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the plot looks like with the bounds we chose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
    "plt.plot(pm1_rect, pm2_rect, '-')\n",
    "    \n",
    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
    "\n",
    "plt.xlim(-12, 8)\n",
    "plt.ylim(-10, 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select rows that fall within these bounds, we'll use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def between(series, low, high):\n",
    "    \"\"\"Make a Boolean Series.\n",
    "    \n",
    "    series: Pandas Series\n",
    "    low: lower bound\n",
    "    high: upper bound\n",
    "    \n",
    "    returns: Boolean Series\n",
    "    \"\"\"\n",
    "    return (series > low) & (series < high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following mask select stars with proper motion in the region we chose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm1 = results_df['pm_phi1']\n",
    "pm2 = results_df['pm_phi2']\n",
    "\n",
    "pm_mask = (between(pm1, pm1_min, pm1_max) & \n",
    "           between(pm2, pm2_min, pm2_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the sum of a Boolean series is the number of `True` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1049"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm_mask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this mask to select rows from `results_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1049"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df = results_df[pm_mask]\n",
    "len(selected_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the stars we think are likely to be in GD-1.  Let's see what they look like, plotting their coordinates (not their proper motion)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "phi1 = selected_df['phi1']\n",
    "phi2 = selected_df['phi2']\n",
    "\n",
    "plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)\n",
    "\n",
    "plt.xlabel(r'$\\phi_1$ (degree GD1)')\n",
    "plt.ylabel(r'$\\phi_2$ (degree GD1)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that's starting to look like a tidal stream!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the DataFrame\n",
    "\n",
    "At this point we have run a successful query and cleaned up the results; this is a good time to save the data.\n",
    "\n",
    "To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_table = Table.from_pandas(selected_df)\n",
    "type(selected_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we could write the `Table` to a FITS file, as we did in the previous lesson.  \n",
    "\n",
    "But Pandas provides functions to write DataFrames in other formats; to see what they are [find the functions here that begin with `to_`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).\n",
    "\n",
    "One of the best options is HDF5, which is Version 5 of [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format).\n",
    "\n",
    "HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).\n",
    "\n",
    "An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  ([Multi-extension FITS files](https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html) can also contain more than one table.)\n",
    "\n",
    "And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).\n",
    "\n",
    "Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can write a Pandas `DataFrame` to an HDF5 file like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_dataframe.hdf5'\n",
    "\n",
    "results_df.to_hdf(filename, 'results_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because an HDF5 file can contain more than one Dataset, we have to provide a name, or \"key\", that identifies the Dataset in the file.\n",
    "\n",
    "We could use any string as the key, but in this example I use the variable name `results_df`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise \n",
    "\n",
    "We're going to need `centerline_df` and `selected_df` later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "centerline_df.to_hdf(filename, 'centerline_df')\n",
    "selected_df.to_hdf(filename, 'selected_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detail:** Reading and writing HDF5 tables requires a library called `PyTables` that is not always installed with Pandas.  You can install it with pip like this:\n",
    "\n",
    "```\n",
    "pip install tables\n",
    "```\n",
    "\n",
    "If you install it using Conda, the name of the package is `pytables`.\n",
    "\n",
    "```\n",
    "conda install pytables\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `ls` to confirm that the file exists and check the size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 downey downey 20M Dec 29 11:48 gd1_dataframe.hdf5\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh gd1_dataframe.hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using Windows, `ls` might not work; in that case, try:\n",
    "\n",
    "```\n",
    "!dir gd1_dataframe.hdf5\n",
    "```\n",
    "\n",
    "We can read the file back like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140340, 12)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_back_df = pd.read_hdf(filename, 'results_df')\n",
    "read_back_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this lesson, we re-loaded the Gaia data we saved from a previous query.\n",
    "\n",
    "We transformed the coordinates and proper motion from ICRS to a frame aligned with the orbit of GD-1, and stored the results in a Pandas `DataFrame`.\n",
    "\n",
    "Then we replicated the selection process from the Price-Whelan and Bonaca paper:\n",
    "\n",
    "* We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.\n",
    "\n",
    "* We identified a region of proper motion that contains stars likely to be in GD-1.\n",
    "\n",
    "* We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.\n",
    "\n",
    "So far, we have used data from a relatively small region of the sky.  In the next lesson, we'll write a query that selects stars based on proper motion, which will allow us to explore a larger region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best practices\n",
    "\n",
    "* When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\n",
    "\n",
    "* For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\n",
    "\n",
    "* An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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