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Merge pull request #1 from astropy-learn/add_tutorial
Add tutorial
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‎FITS-tables.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Viewing and manipulating data from FITS tables\n",
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"\n",
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"## Authors\n",
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"Lia Corrales, Kris Stern\n",
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"\n",
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"## Learning Goals\n",
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"* Download a FITS table file from a URL \n",
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"* Open a FITS table file and view table contents\n",
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"* Make a 2D histogram with the table data\n",
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"* Close the FITS file after use\n",
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"\n",
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"## Keywords\n",
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"FITS, file input/output, table, numpy, matplotlib, histogram\n",
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"\n",
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"\n",
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"## Summary\n",
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"\n",
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"This tutorial demonstrates the use of `astropy.utils.data` to download a data file, then uses `astropy.io.fits` and `astropy.table` to open the file. Lastly, `matplotlib` is used to visualize the data as a histogram."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from astropy.io import fits\n",
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"from astropy.table import Table\n",
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"from matplotlib.colors import LogNorm\n",
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"\n",
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"# Set up matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The following line is needed to download the example FITS files used in this tutorial."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from astropy.utils.data import download_file"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"FITS files often contain large amounts of multi-dimensional data and tables. \n",
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"\n",
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"In this particular example, we'll open a FITS file from a Chandra observation of the Galactic Center. The file contains a list of events with x and y coordinates, energy, and various other pieces of information."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"event_filename = download_file(\n",
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" \"http://data.astropy.org/tutorials/FITS-tables/chandra_events.fits\", cache=True\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Opening the FITS file and viewing table contents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Since the file is big, let's open it with `memmap=True` to prevent RAM storage issues."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hdu_list = fits.open(event_filename, memmap=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hdu_list.info()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this case, we're interested in reading EVENTS, which contains information about each X-ray photon that hit the detector."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To find out what information the table contains, let's print the column names."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(hdu_list[1].columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we'll take this data and convert it into an [astropy table](http://docs.astropy.org/en/stable/table/). While it's possible to access FITS tables directly from the ``.data`` attribute, using [Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table) tends to make a variety of common tasks more convenient."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"evt_data = Table(hdu_list[1].data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For example, a preview of the table is easily viewed by simply running a cell with the table as the last line:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"evt_data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can extract data from the table by referencing the column name. Let's try making a histogram for the energy of each photon, which will give us a sense for the spectrum (folded with the detector's efficiency)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"energy_hist = plt.hist(evt_data[\"energy\"], bins=\"auto\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Making a 2D histogram with some table data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We'll make an image by binning the x and y coordinates of the events into a 2D histogram."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This particular observation spans five CCD chips. First, we determine the events that only fell on the main (ACIS-I) chips, which have number ids 0, 1, 2, and 3."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ii = np.in1d(evt_data[\"ccd_id\"], [0, 1, 2, 3])\n",
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"np.sum(ii)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Method 1: Use numpy to make a 2D histogram and imshow to display it"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This method allows us to create an image without stretching:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"NBINS = (100, 100)\n",
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"\n",
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"img_zero, yedges, xedges = np.histogram2d(evt_data[\"x\"][ii], evt_data[\"y\"][ii], NBINS)\n",
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"\n",
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"extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n",
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"\n",
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"plt.imshow(\n",
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" img_zero, extent=extent, interpolation=\"nearest\", cmap=\"gist_yarg\", origin=\"lower\"\n",
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")\n",
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"\n",
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"plt.xlabel(\"x\")\n",
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"plt.ylabel(\"y\")\n",
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"\n",
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"# To see more color maps\n",
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"# http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Method 2: Use hist2d with a log-normal color scheme"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"NBINS = (100, 100)\n",
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"img_zero_mpl = plt.hist2d(\n",
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" evt_data[\"x\"][ii], evt_data[\"y\"][ii], NBINS, cmap=\"viridis\", norm=LogNorm()\n",
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")\n",
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"\n",
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"cbar = plt.colorbar(ticks=[1.0, 3.0, 6.0])\n",
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"cbar.ax.set_yticklabels([\"1\", \"3\", \"6\"])\n",
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"\n",
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"plt.xlabel(\"x\")\n",
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"plt.ylabel(\"y\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Close the FITS file"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When you're done using a FITS file, it's often a good idea to close it. That way you can be sure it won't continue using up excess memory or file handles on your computer. (This happens automatically when you close Python, but you never know how long that might be...)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hdu_list.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercises"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Make a scatter plot of the same data you histogrammed above. The [plt.scatter](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter) function is your friend for this. What are the pros and cons of doing it this way?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Try the same with the [plt.hexbin](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hexbin) plotting function. Which do you think looks better for this kind of data?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Choose an energy range to make a slice of the FITS table, then plot it. How does the image change with different energy ranges?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"astropy-tutorials": {
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"author": "Lia R. Corrales <[email protected]>",
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"date": "January 2014",
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"description": "astropy.utils.data to download the file, astropy.io.fits to open and view the file, matplotlib for making both 1D and 2D histograms of the data.",
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"link_name": "Viewing and manipulating data from FITS tables",
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"name": "",
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"published": true
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython"
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎metadata.yml

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title: "Example Title"
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slug: "Example-Title"
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source: "tutorial--example-title"
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title: "FITS Tables"
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slug: "FITS-tables"
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source: "tutorial--FITS-tables"

‎requirements.txt

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astropy
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matplotlib
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numpy

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