{ "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", "- \"To store data from a Pandas `DataFrame`, a good option is an HDF file, which can contain multiple Datasets.\"\n", "\n", "---\n", "\n", "{% include links.md %}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Proper Motion\n", "\n", "In the previous 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": 55, "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" ] }, { "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 140,000 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 [download the file](https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_results.fits) or run the following cell." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "from os.path import basename, exists\n", "\n", "def download(url):\n", " filename = basename(url)\n", " if not exists(filename):\n", " from urllib.request import urlretrieve\n", " local, _ = urlretrieve(url, filename)\n", " print('Downloaded ' + local)\n", "\n", "download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +\n", " 'data/gd1_results.fits')" ] }, { "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": 57, "metadata": {}, "outputs": [], "source": [ "from astropy.table import Table\n", "\n", "filename = 'gd1_results.fits'\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": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\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" ] }, "execution_count": 58, "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": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['source_id', 'ra', 'dec', 'pmra', 'pmdec', 'parallax']" ] }, "execution_count": 59, "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": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140339>\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
142.48301935991023
142.25452941346344
142.64528557468074
142.57739430926034
142.58913564478618
141.81762228999614
143.18339801317677
142.9347319464589
142.26769745823267
142.89551292869012
142.2780935768316
142.06138786534987
...
143.05456487172972
144.0436496516182
144.06566578919313
144.13177563215973
143.77696341662764
142.945956347594
142.97282480557786
143.4166017695258
143.64484588686904
143.41554585481808
143.6908739159247
143.7702681295401
" ], "text/plain": [ "\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": 60, "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": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.table.column.Column" ] }, "execution_count": 61, "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": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Row index=0\n", "\n", "\n", "\n", "\n", "\n", "
source_idradecpmrapmdecparallax
degdegmas / yrmas / yrmas
int64float64float64float64float64float64
637987125186749568142.4830193599102321.75771616932985-2.51683846838757662.941813096629439-0.2573448962333354
" ], "text/plain": [ "\n", " source_id ra dec pmra pmdec parallax \n", " deg deg mas / yr mas / yr mas \n", " int64 float64 float64 float64 float64 float64 \n", "------------------ ------------------ ----------------- ------------------- ----------------- -------------------\n", "637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354" ] }, "execution_count": 62, "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": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.table.row.Row" ] }, "execution_count": 63, "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", "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": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "142.48301935991023" ] }, "execution_count": 64, "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": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "142.48301935991023" ] }, "execution_count": 65, "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", "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": 66, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pyplot is part of the Matplotlib library. It is conventional to import it using the shortened name `plt`.\n", "\n", "In recent versions of Jupyter, plots appear \"inline\"; that is, they are part of the notebook. In some older versions, plots appear in a new window.\n", "In that case, you might want to run the following Jupyter [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-matplotlib) in a notebook cell:\n", "\n", "```\n", "%matplotlib inline\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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", "The functions `xlabel` and `ylabel` put labels on the axes.\n", "\n", "Looking at this plot, we can see that the region we selected, which is a rectangle in GD-1 coordinates, is a non-rectanglar region in ICRS coordinates.\n", "\n", "However, 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": 68, "metadata": { "tags": [ "hide-cell" ] }, "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", "* By transforming the coordinates, 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", "* By transforming the proper motions, we can identify stars with non-zero proper motion along the $\\phi_1$ axis.\n", "\n", "To do the transformation, we'll put the results into a `SkyCoord` object. In a previous lesson we created a `SkyCoord` object like this:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "from astropy.coordinates import SkyCoord\n", "\n", "skycoord = SkyCoord(ra=results['ra'], dec=results['dec'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're going to do something similar, but in addition to `ra` and `dec`, we'll also include:\n", "\n", "* `pmra` and `pmdec`, which are proper motion in the ICRS frame, and\n", "\n", "* `distance` and `radial_velocity`, which we explain below." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "import astropy.units as u\n", "\n", "distance = 8 * u.kpc\n", "radial_velocity= 0 * u.km/u.s\n", "\n", "skycoord = SkyCoord(ra=results['ra'], \n", " dec=results['dec'],\n", " pm_ra_cosdec=results['pmra'],\n", " pm_dec=results['pmdec'], \n", " distance=distance, \n", " radial_velocity=radial_velocity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the first four arguments, we use columns from `results`.\n", "\n", "For `distance` and `radial_velocity` we use constants, 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": 71, "metadata": {}, "outputs": [], "source": [ "from gala.coordinates import GD1Koposov10\n", "\n", "gd1_frame = GD1Koposov10()\n", "transformed = skycoord.transform_to(gd1_frame)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is another `SkyCoord` object, now in the `GD1Koposov10` frame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reflex Correction\n", "\n", "The next step is to correct the proper motion measurements for the effect of the motion of our solar system around the Galactic center.\n", "\n", "When we created `skycoord`, we provided constant values for `distance` and `radial_velocity` rather than measurements from Gaia.\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, but we have to provide a value to avoid errors in the reflex correction calculation. So we provide `0` as an arbitrary place-keeper." ] }, { "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 the motion of the solar system." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "from gala.coordinates import reflex_correct\n", "\n", "skycoord_gd1 = reflex_correct(transformed)" ] }, { "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 and plot them like this:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = skycoord_gd1.phi1\n", "y = skycoord_gd1.phi2\n", "plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)\n", "\n", "plt.xlabel('phi1 (degree GD1)')\n", "plt.ylabel('phi2 (degree GD1)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that we started with a rectangle in the GD-1 frame. When transformed to the ICRS frame, it's a non-rectangular region. Now, transformed back to the GD-1 frame, 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": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.table.table.Table" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And `skycoord_gd1` is a `SkyCoord` object that contains the transformed coordinates and proper motions." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.coordinates.sky_coordinate.SkyCoord" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(skycoord_gd1)" ] }, { "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 choose a Pandas `DataFrame`, for two reasons:\n", "\n", "1. It provides capabilities that (almost) 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.\n", "\n", "It's easy to convert a `Table` to a Pandas `DataFrame`." ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "results_df = results.to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`DataFrame` provides `shape`, which shows the number of rows and columns." ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(140339, 6)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It also provides `head`, which displays the first few rows. `head` is useful for spot-checking large results as you go along." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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source_idradecpmrapmdecparallax
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1638285195917112960142.25452922.4761682.662702-12.1659840.422728
2638073505568978688142.64528622.16693218.306747-7.9506600.103640
3638086386175786752142.57739422.2279200.987786-2.584105-0.857327
4638049655615392384142.58913622.1107830.244439-4.9410790.099625
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" ], "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" ] }, "execution_count": 78, "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 `skycoord_gd1` and add them as columns in the `DataFrame`. `phi1` and `phi2` contain the transformed coordinates." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(140339, 8)" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_df['phi1'] = skycoord_gd1.phi1\n", "results_df['phi2'] = skycoord_gd1.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": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(140339, 10)" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_df['pm_phi1'] = skycoord_gd1.pm_phi1_cosphi2\n", "results_df['pm_phi2'] = skycoord_gd1.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", "One of the most useful of these functions is `describe`, which computes summary statistics for each column." ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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source_idradecpmrapmdecparallaxphi1phi2pm_phi1pm_phi2
count1.403390e+05140339.000000140339.000000140339.000000140339.000000140339.000000140339.000000140339.000000140339.000000140339.000000
mean6.792399e+17143.82312226.780285-2.484404-6.1007770.179492-50.091158-1.803301-0.8689631.409208
std3.792177e+163.6978503.0525925.9139397.2020470.7595902.8923443.4443986.6577146.518615
min6.214900e+17135.42569919.286617-106.755260-138.065163-15.287602-54.999989-8.029159-115.275637-161.150142
25%6.443517e+17140.96796624.592490-5.038789-8.341561-0.035981-52.602952-4.750426-2.948723-1.107128
50%6.888060e+17143.73440926.746261-1.834943-4.6895960.362708-50.147362-1.6715020.5850371.987149
75%6.976579e+17146.60735028.9905000.452893-1.9378090.657637-47.5932791.1605143.0017684.628965
max7.974418e+17152.77739334.285481104.31992320.9810700.999957-44.9999854.01460939.80247179.275199
\n", "
" ], "text/plain": [ " source_id ra dec pmra \\\n", "count 1.403390e+05 140339.000000 140339.000000 140339.000000 \n", "mean 6.792399e+17 143.823122 26.780285 -2.484404 \n", "std 3.792177e+16 3.697850 3.052592 5.913939 \n", "min 6.214900e+17 135.425699 19.286617 -106.755260 \n", "25% 6.443517e+17 140.967966 24.592490 -5.038789 \n", "50% 6.888060e+17 143.734409 26.746261 -1.834943 \n", "75% 6.976579e+17 146.607350 28.990500 0.452893 \n", "max 7.974418e+17 152.777393 34.285481 104.319923 \n", "\n", " pmdec parallax phi1 phi2 \\\n", "count 140339.000000 140339.000000 140339.000000 140339.000000 \n", "mean -6.100777 0.179492 -50.091158 -1.803301 \n", "std 7.202047 0.759590 2.892344 3.444398 \n", "min -138.065163 -15.287602 -54.999989 -8.029159 \n", "25% -8.341561 -0.035981 -52.602952 -4.750426 \n", "50% -4.689596 0.362708 -50.147362 -1.671502 \n", "75% -1.937809 0.657637 -47.593279 1.160514 \n", "max 20.981070 0.999957 -44.999985 4.014609 \n", "\n", " pm_phi1 pm_phi2 \n", "count 140339.000000 140339.000000 \n", "mean -0.868963 1.409208 \n", "std 6.657714 6.518615 \n", "min -115.275637 -161.150142 \n", "25% -2.948723 -1.107128 \n", "50% 0.585037 1.987149 \n", "75% 3.001768 4.628965 \n", "max 39.802471 79.275199 " ] }, "execution_count": 81, "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 make sense 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": 82, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "# Solution\n", "\n", "# The most noticeable issue is that some of the\n", "# parallax values are negative, which is non-physical.\n", "\n", "# The reason is that parallax measurements are less accurate\n", "# for stars that are far away.\n", "\n", "# Fortunately, we don't use the parallax measurements in\n", "# the analysis (one of the reasons we used constant distance\n", "# for reflex correction).\n" ] }, { "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 components of proper motion as a scatter plot:\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this figure, the shaded area identifies stars that are likely to be in GD-1 because:\n", "\n", "* Due to the nature of tidal streams, we expect the proper motion for stars in GD-1 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", "By plotting proper motion in the GD-1 frame, we hope to find this cluster.\n", "Then we will use the bounds of the cluster to select stars that are more likely to be in GD-1. \n", "\n", "The following figure is a scatter plot of proper motion, in the GD-1 frame, for the stars in `results_df`." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = results_df['pm_phi1']\n", "y = results_df['pm_phi2']\n", "plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)\n", " \n", "plt.xlabel('Proper motion phi1 (mas/yr GD1 frame)')\n", "plt.ylabel('Proper motion phi2 (mas/yr GD1 frame)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the proper motions are near the origin, but there are a few extreme values.\n", "Following the example in the paper, we'll use `xlim` and `ylim` to zoom in on the region near the origin." ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = results_df['pm_phi1']\n", "y = results_df['pm_phi2']\n", "plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)\n", " \n", "plt.xlabel('Proper motion phi1 (mas/yr GD1 frame)')\n", "plt.ylabel('Proper motion phi2 (mas/yr GD1 frame)')\n", "\n", "plt.xlim(-12, 8)\n", "plt.ylim(-10, 10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a hint of an overdense region near (-7.5, 0), but if you didn't know where to look, you would miss it.\n", "\n", "To see the cluster more clearly, we need a sample that contains a higher proportion of stars in GD-1.\n", "We'll do that by selecting stars close to the centerline." ] }, { "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", "\n", "\n", "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": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 85, "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": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi2_min = -1.0 * u.degree\n", "phi2_max = 1.0 * u.degree\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": 87, "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": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To select values that fall between `phi2_min` and `phi2_max`, we'll use the `&` operator, which computes \"logical AND\".\n", "The result is true where elements from both Boolean `Series` are true." ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "mask = (phi2 > phi2_min) & (phi2 < phi2_max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Python detail:** Python's logical operators (`and`, `or`, and `not`) don't work with NumPy or Pandas. Both libraries use the bitwise operators (`&`, `|`, and `~`) to do elementwise logical operations ([explanation here](https://stackoverflow.com/questions/21415661/logical-operators-for-boolean-indexing-in-pandas)).\n", "\n", "Also, 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": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25084" ] }, "execution_count": 89, "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": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 90, "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`.\n", "So it contains the stars near the centerline of GD-1.\n", "\n", "We can use `len` to see how many rows are in `centerline_df`:" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25084" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(centerline_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what fraction of the rows we've selected." ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.1787386257562046" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(centerline_df) / len(results_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are about 25,000 stars in this region, about 18% of the total." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting proper motion\n", "\n", "Since we've plotted proper motion several times, let's put that code in a function." ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "def plot_proper_motion(df):\n", " \"\"\"Plot proper motion.\n", " \n", " df: DataFrame with `pm_phi1` and `pm_phi2`\n", " \"\"\"\n", " x = df['pm_phi1']\n", " y = df['pm_phi2']\n", " plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", "\n", " plt.xlabel('Proper motion phi1 (mas/yr GD1 frame)')\n", " plt.ylabel('Proper motion phi2 (mas/yr GD1 frame)')\n", "\n", " plt.xlim(-12, 8)\n", " plt.ylim(-10, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can call it like this:" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_proper_motion(centerline_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see more clearly that there is a cluster near (-7.5, 0).\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", "\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": 95, "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 use `make_rectangle` to make two lists containing the coordinates of the corners of the rectangle." ] }, { "cell_type": "code", "execution_count": 96, "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": 97, "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": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_proper_motion(centerline_df)\n", "plt.plot(pm1_rect, pm2_rect, '-');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've identified the bounds of the cluster in proper motion, we'll use it to select rows from `results_df`.\n", "\n", "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": 99, "metadata": {}, "outputs": [], "source": [ "def between(series, low, high):\n", " \"\"\"Check whether values are between `low` and `high`.\"\"\"\n", " return (series > low) & (series < high)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following mask selects stars with proper motion in the region we chose." ] }, { "cell_type": "code", "execution_count": 100, "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": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1049" ] }, "execution_count": 101, "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": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1049" ] }, "execution_count": 102, "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": 103, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = selected_df['phi1']\n", "y = selected_df['phi2']\n", "plt.plot(x, y, 'ko', markersize=1, alpha=1)\n", "\n", "plt.xlabel('phi1 (degree GD1)')\n", "plt.ylabel('phi2 (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": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.table.table.Table" ] }, "execution_count": 104, "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. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": 105, "metadata": {}, "outputs": [], "source": [ "filename = 'gd1_data.hdf'\n", "\n", "selected_df.to_hdf(filename, 'selected_df', mode='w')" ] }, { "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 it will be convenient to give the Dataset in the file the same name as the `DataFrame`.\n", "\n", "The argument `mode='w'` means that if the file already exists, we should overwrite it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise \n", "\n", "We're going to need `centerline_df` later as well. Write a line of code to add it as a second Dataset in the HDF5 file.\n", "\n", "Hint: Since the file already exists, you should *not* use `mode='w'`." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "# Solution\n", "\n", "centerline_df.to_hdf(filename, 'centerline_df')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `getsize` to confirm that the file exists and check the size:" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.2084197998046875" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from os.path import getsize\n", "\n", "MB = 1024 * 1024\n", "getsize(filename) / MB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you forget what the names of the Datasets in the file are, you can read them back like this:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['/centerline_df', '/selected_df']\n" ] } ], "source": [ "with pd.HDFStore(filename) as hdf:\n", " print(hdf.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Python note:** We use a `with` statement here to open the file before the print statement and (automatically) close it after. Read more about [context managers](https://book.pythontips.com/en/latest/context_managers.html). \n", "\n", "The keys are the names of the Datasets. Notice that they start with `/`, which indicates that they are at the top level of the Dataset hierarchy, and not in a named \"group\".\n", "\n", "In future lessons we will add a few more Datasets to this file, but not so many that we need to organize them into groups." ] }, { "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.\n", "\n", "* To store data from a Pandas `DataFrame`, a good option is an HDF file, which can contain multiple Datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }