astropy/astropy_hands_on.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands On Astropy Tutorial \n",
    "**Tutors:** Axel Donath and Christoph Deil\n",
    "\n",
    "This hands-on tutorial gives an introduction and overview of the [Astropy](http://www.astropy.org/) Python package for Astronomy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preface\n",
    "We recommend to follow this tutorial by **executing the code cells on your local machine**, along with the tutor. Every sub-topic we will cover in this tutorial will be concluded by a few **exercises with different levels of difficulty** (*easy*, *advanced* and *hard*). We will give you **~15-20 min to solve the exercises** and present a possible solution afterwards. In case we don't have time to show all the solutions or in the likely case that you don't manage to solve all of them: we provide a **sample solution** for all exercises in the [solutions notebook](astropy_hands_on_solutions.ipynb) you can look at **after the course**.   \n",
    "\n",
    "The estimated time for this tutorial is ~3 hours. Feel free to **interrupt at any time** to ask questions or **talk to the assisting tutors** when you encounter errors or the code doesn't work as expected. We have marked some of the sections that deal with more advanced topics as \"optional\". \n",
    "\n",
    "We're happy to receive any **feedback or questions** on the tutorial via mail to *axel.donath@mpi-hd.mpg.de* or using the \n",
    "repository's [issue tracker](https://github.com/Asterics2020-Obelics/School2019/issues). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Astropy?\n",
    "\n",
    "![](astropy_banner_96.png)\n",
    "\n",
    "    \"The Astropy Project is a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages.\"\n",
    "\n",
    "\n",
    "The concept and structure of the package is decribed in more detail in the first [Astropy paper 2013](http://adsabs.harvard.edu/abs/2013A%26A...558A..33A). The development infrastructure\n",
    "and status of the v2.0 core package is described in the second [Astropy paper 2018](http://adsabs.harvard.edu/abs/2018AJ....156..123A).\n",
    "\n",
    "The **Astropy package is structured into several submodules** and we will cover (what we consider) the most important of them in the following order:\n",
    "\n",
    "1. [astropy.units](http://docs.astropy.org/en/stable/units/index.html) and in particular [astropy.units.Quantity](http://docs.astropy.org/en/stable/api/astropy.units.Quantity.html) to do astronomical calculations with units.\n",
    "\n",
    "2. [astropy.coordinates](http://docs.astropy.org/en/stable/coordinates/) and in particular the classes [SkyCoord](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html) and [Angle](http://docs.astropy.org/en/stable/coordinates/angles.html) to handle astronomical sky positions, coordinate systems and coordinate transformations.\n",
    "\n",
    "3. [astropy.tables](http://docs.astropy.org/en/stable/table/index.html) and the [Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) class to handle astronomical data tables.\n",
    "\n",
    "4. [astropy.io.fits](http://docs.astropy.org/en/stable/io/fits/index.html) to open and write data files in [FITS format](https://fits.gsfc.nasa.gov/fits_documentation.html).\n",
    "\n",
    "5. (optional) Plotting of astronomical sky images with [astropy.visualization.wcsaxes](http://docs.astropy.org/en/stable/visualization/wcsaxes/)\n",
    "\n",
    "In addition to the Astropy core package there is an infrastructure of  [Astropy affiliated packages](http://www.astropy.org/affiliated/) dedicated to specific fields of Astronomy or analysis tasks, such as:\n",
    "\n",
    "- [Astroplan](https://astroplan.readthedocs.io/en/latest/): observation planning\n",
    "- [Reproject](http://reproject.readthedocs.io/en/stable/): reprojection of sky images\n",
    "- [Regions](http://astropy-regions.readthedocs.io/en/latest/): handling of sky regions \n",
    "- [Photutils](https://photutils.readthedocs.io/en/stable/): source detection and photometry\n",
    "- [Gammapy](docs.gammapy.org): gamma-ray Astronomy data analysis\n",
    "- ...and many others, or maybe your package?\n",
    "\n",
    "\n",
    "## Other Ressources\n",
    "\n",
    "There are other ressources with Astropy tutorials, we can recommend:\n",
    "\n",
    "- [Learn.Astropy](http://learn.astropy.org) webpage with a lot of tutorial material.\n",
    "- [Astropy documentation](http://docs.astropy.org) webpage, with lots of small usage examples.\n",
    "- [Astropy Tutorials](https://github.com/astropy/astropy-tutorials) repository (same as linked on Learn.Astropy)\n",
    "- [Astropy workshop](https://github.com/astropy/astropy-workshop) held the AAS meeting 2019. \n",
    "- [STAK project](https://stak-notebooks.readthedocs.io/en/latest/) provided by STSci, with tutorial notebooks for typical IRAF analysis tasks. \n",
    "- [Webinar on Youtube](https://www.youtube.com/watch?v=YP42k3J08_o&list=PL7kL5D8ITGyV7zeT-oADweFKHsZNh3tKV) provided by Astronomy Data and Computing Services (ADACS). \n",
    "\n",
    "As the content that can be covered in this ~3 hour tutorial is very limited, we recommend to also **check-out the ressources** listed above after the school.\n",
    "\n",
    "See https://www.astropy.org/help.html for list of references how to **get help on Astropy**.\n",
    "\n",
    "If you would like to contribute to Astropy, please start by reading the [contribute page](http://www.astropy.org/contribute.html)\n",
    "on the website.\n",
    "\n",
    "Let's start with the setup and check of the notebook:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Setup\n",
    "\n",
    "Check package versions. All examples should work with Astropy > 2.0 and Numpy > 1.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy: 1.16.2\n",
      "astropy: 3.1.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import astropy\n",
    "print('numpy:', np.__version__)\n",
    "print('astropy:', astropy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Units and Quantities\n",
    "\n",
    "The [astropy.units]() subpackage provides functions and classes to handle physical quantities with units. \n",
    "\n",
    "### 1.1. Basics\n",
    "The recommended way to import the `astropy.units` submodule is: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy import units as u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Quantities` are created by multiplying any number with a unit object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 lyr\n"
     ]
    }
   ],
   "source": [
    "distance = 1. * u.lightyear\n",
    "print(distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by passing a string to the general `Quantity` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = u.Quantity('1 lyr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the availabe units with tab completion on the units module, `u.<TAB>`.\n",
    "\n",
    "Quantities can be also created using lists and arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  3. 10.] lyr\n",
      "[ 1.  3. 10.] lyr\n"
     ]
    }
   ],
   "source": [
    "distances = [1, 3, 10] * u.lightyear\n",
    "print(distances)\n",
    "\n",
    "distances = np.array([1, 3, 10]) * u.lightyear\n",
    "print(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3., 10.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The quantity object has a value attribute, which is a plain `numpy.ndarray`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(distances.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a unit, which is represented by a `astropy.units.core.Unit` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\mathrm{lyr}$"
      ],
      "text/plain": [
       "Unit(\"lyr\")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances.unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.units.core.Unit"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(distances.unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quantity behaves in many ways just like a `numpy.ndarray` with an attached unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[10,~30,~100] \\; \\mathrm{lyr}$"
      ],
      "text/plain": [
       "<Quantity [ 10.,  30., 100.] lyr>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many numpy functions will work as expected and return again a `Quantity` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$10 \\; \\mathrm{lyr}$"
      ],
      "text/plain": [
       "<Quantity 10. lyr>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$4.6666667 \\; \\mathrm{lyr}$"
      ],
      "text/plain": [
       "<Quantity 4.66666667 lyr>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(distances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there are exceptions, where the unit handling is not well defined, e.g. in `np.log` arguments have to be dimensionless, such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1.0986123 \\; \\mathrm{}$"
      ],
      "text/plain": [
       "<Quantity 1.09861229>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.log(30 * u.MeV) # Will raise an UnitConversionError\n",
    "np.log(30 * u.MeV / (10 * u.MeV))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probably the most useful method on the `Quantity` object is the `.to()` method which allows to convert a quantity to different units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$9.4607305 \\times 10^{15} \\; \\mathrm{m}$"
      ],
      "text/plain": [
       "<Quantity 9.46073047e+15 m>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance.to('meter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$0.30660139 \\; \\mathrm{pc}$"
      ],
      "text/plain": [
       "<Quantity 0.30660139 pc>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance.to(u.parsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantities can be combined with any arithmetical expression to derive other quantities, `astropy.units` will propagate\n",
    "the units correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "299792.458 km / s\n"
     ]
    }
   ],
   "source": [
    "speed_of_light = distance / u.year\n",
    "print(speed_of_light.to('km/s'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5902068371199996e+23 Angstrom / d\n"
     ]
    }
   ],
   "source": [
    "print(speed_of_light.to('angstrom/day'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For standardized unit systems such as `'si'` or `'cgs'` there are convenience attributes on the quantity object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$2.9979246 \\times 10^{8} \\; \\mathrm{\\frac{m}{s}}$"
      ],
      "text/plain": [
       "<Quantity 2.99792458e+08 m / s>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speed_of_light.si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$2.9979246 \\times 10^{10} \\; \\mathrm{\\frac{cm}{s}}$"
      ],
      "text/plain": [
       "<Quantity 2.99792458e+10 cm / s>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speed_of_light.cgs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 1.2. Equivalencies\n",
    "\n",
    "In Astronomy and other fields of physics quantities are often measured in more practical units, which are equivalent to the actual physical unit. In `astropy.units` this is handled with the concept of \"equivalencies\".  \n",
    "\n",
    "For example consider units to measure spectral quantities such as wavelength, frequency and energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1.2407003 \\; \\mathrm{MeV}$"
      ],
      "text/plain": [
       "<Quantity 1.2407003 MeV>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency = 3e20 * u.hertz\n",
    "frequency.to('MeV', equivalencies=u.spectral())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$0.99930819 \\; \\mathrm{pm}$"
      ],
      "text/plain": [
       "<Quantity 0.99930819 pm>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency.to('picometer', equivalencies=u.spectral())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or for converting temperatures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = 25 * u.Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$298.15 \\; \\mathrm{K}$"
      ],
      "text/plain": [
       "<Quantity 298.15 K>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature.to(\"Kelvin\", equivalencies=u.temperature())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.0 deg_F\n"
     ]
    }
   ],
   "source": [
    "with u.imperial.enable():\n",
    "    print(temperature.to(\"deg_F\", equivalencies=u.temperature()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Miscellaneous\n",
    "\n",
    "Astropy provides a lot of builtin physical and astronomical constants quantitites in the [astropy.constants]() submodule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "299792.458 km / s\n"
     ]
    }
   ],
   "source": [
    "from astropy import constants as const\n",
    "\n",
    "print(const.c.to('km / s'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a [list of available constants](http://docs.astropy.org/en/stable/constants/#module-astropy.constants).\n",
    "\n",
    "If you write a function you can make sure the input is given in the right units using the [astropy.units.quantity_input](http://docs.astropy.org/en/stable/api/astropy.units.quantity_input.html#astropy.units.quantity_input) decorator: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "@u.quantity_input(frequency=u.hertz, temperature=u.K)\n",
    "def blackbody(frequency, temperature): \n",
    "    pre_factor = 2 * (const.h * frequency ** 3) / const.c ** 2\n",
    "    exponential_factor = 1. / (np.exp((const.h * frequency) / (const.k_B * temperature)) - 1)\n",
    "    return pre_factor * exponential_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# blackbody(300 * u.nm, 500 * u.K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Interfacing quantities with third-party code (optional) \n",
    "\n",
    "When writing code that uses quantities we are typically bound to use it everywhere in the code. But often we'd like to interface with functions (e.g. from [SciPy](https://www.scipy.org/) or [Matplotlib](https://matplotlib.org/)) or other third-party code that doesn't know how to handle quantities.\n",
    "\n",
    "For Matplotlib, Astropy has a builtin support using the [quantity_support()](http://docs.astropy.org/en/stable/api/astropy.visualization.quantity_support.html) context manager:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/astropy/units/quantity.py:463: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import quantity_support\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "temperature = 1000 * u.K\n",
    "frequencies = np.linspace(1E-5, 0.5e15, 1000) * u.hertz\n",
    "\n",
    "radiance = blackbody(frequency=frequencies, temperature=temperature)\n",
    "\n",
    "with quantity_support():   \n",
    "    plt.plot(frequencies, radiance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But in other cases there is only the option to convert the quantity to well defined units, strip off the unit and re-attach it after the computation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          1.29154967  1.66810054  2.15443469  2.7825594   3.59381366\n",
      "  4.64158883  5.9948425   7.74263683 10.        ] TeV\n"
     ]
    }
   ],
   "source": [
    "emin, emax = [1, 10] * u.TeV\n",
    "energies = np.logspace(np.log10(emin.value), np.log10(emax.value), 10) * emin.unit\n",
    "print(energies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Performance Tips (optional)\n",
    "\n",
    "As the main data structure of the Quantity is a standard numpy array, they are suitable for computations with large arrays. Still you have to take care of a few pitfalls.\n",
    "\n",
    "When creating a Quantity with `data * u.km` or `data * u.Unit()` by default a copy of the data is made:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.ones((500, 500, 500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "311 ms ± 5.22 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "quantity = values * u.kpc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to avoid the copy you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.92 µs ± 114 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "quantity = u.Quantity(values, unit=\"kpc\", copy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Astropy > 3.1 a new operator was introduced to achieve the same behaviour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.6 µs ± 147 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "quantity = values << u.Unit(\"kpc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid copies when converting to different units and getting the values, you can use: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantity = u.Quantity(values, unit=\"kpc\", copy=False)\n",
    "values = quantity.to_value(\"m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using`quantity.to(\"m\").value`.  More details can be found on http://docs.astropy.org/en/stable/units/index.html#performance-tips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Exercises\n",
    "\n",
    "- (*easy*) How long does the light travel from the sun to the earth in minutes? How long does the light travel from the Galactic center (assume a distance of 8 kpc) in years? \n",
    "- (*advanced*) Define a new unit called `\"baro-meter\"`, which is eqivalent to 25 cm and use it to measure the height of the empire state building (assume a height of 381 meters). Please read the [Astropy documentation on combining and defining units](http://docs.astropy.org/en/stable/units/combining_and_defining.html) for an example how to do this (For other ways to measure the height of a building using a barometer see [barometer question on Wikipedia](https://en.wikipedia.org/wiki/Barometer_question)...)\n",
    "- (*expert*) Find the frequency with the maximum photon rate of the black body spectrum $\\mathcal{B}(\\nu, T)$ with temperature $T=5000~\\mathrm{K}$. For this you can use a numerical optimization routine such as [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html).\n",
    "As the frequency as well as the radiance can cover many orders of magnitude, the stability of the algorithm can be much improved by minimizing the (negative) logarithm of the radiance $-\\log{\\mathcal{B}}$ as well as minimizing with respect to the logarithm of the frequency by introducing the variable $x = \\log{\\nu}$. Finally compare the result against the analytical solution from [Wien's displacement law](https://en.wikipedia.org/wiki/Wien%27s_displacement_law). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Coordinates\n",
    "\n",
    "With the submodule [astropy.coordinates](http://docs.astropy.org/en/stable/coordinates/) Astropy provides a framework to handle sky positions in various coordinate systems and transformations between them.\n",
    "\n",
    "\n",
    "### 2.1 Basics\n",
    "The basic class to handle sky coordinates is [SkyCoord](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be created by passing a position angle for longitude and latitude and a keyword specifying a coordinate frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SkyCoord (ICRS): (ra, dec) in deg\n",
      "    (83.63, 22.01)>\n"
     ]
    }
   ],
   "source": [
    "position_crab = SkyCoord(83.63 * u.deg,  22.01 * u.deg, frame='icrs')\n",
    "print(position_crab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for `Quantities` the instanciation with `lists`, `arrays` or even `Quantities` also works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = SkyCoord([345., 234.3] * u.deg,  [-0.1, 0.2] * u.deg, frame='galactic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively the angles can be specified as string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_crab = SkyCoord('5h34m31.97s', '22d0m52.10s', frame='icrs')\n",
    "\n",
    "# or\n",
    "\n",
    "position_crab = SkyCoord('5:34:31.97', '22:0:52.10',\n",
    "                         unit=(u.hour, u.deg), frame='icrs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where in the first case the unit doesn't have to specified because it is encoded in the string via `'hms'` and `'dms'`.\n",
    "\n",
    "A very convenient way to get the coordinates of an individual object is qerying the [Sesame](http://cds.u-strasbg.fr/cgi-bin/Sesame) database with `SkyCoord.from_name()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SkyCoord.from_name('Crab')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform the coordinates to a different coordinate system we can use `SkyCoord.transform_to()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_gal = position_crab.transform_to('galactic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience we can also directly use the `.galactic` or `.icrs` attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    (184.55754381, -5.78427369)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_crab.galactic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (83.63320833, 22.01447222)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_crab.icrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the `longitude` and `latitude` angles individually: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$5^\\mathrm{h}34^\\mathrm{m}31.97^\\mathrm{s}$"
      ],
      "text/plain": [
       "<Longitude 5.57554722 hourangle>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_crab.data.lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$22^\\circ00{}^\\prime52.1{}^{\\prime\\prime}$"
      ],
      "text/plain": [
       "<Latitude 22.01447222 deg>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_crab.data.lat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Measuring distances between positions in the sky\n",
    "The angular distance between two [SkyCoord](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html) objects, can be found using the [SkyCoord.separation()](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord.separation) method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$172^\\circ38{}^\\prime26.7431{}^{\\prime\\prime}$"
      ],
      "text/plain": [
       "<Angle 172.64076197 deg>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#position_saga = SkyCoord.from_name('Sag A*')\n",
    "position_saga = SkyCoord(0 * u.deg, 0 * u.deg, frame='galactic')\n",
    "\n",
    "position_crab.separation(position_saga)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (83.63320833, 22.01447222)>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_crab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 ALT - AZ coordinates (optional)\n",
    "\n",
    "In various cirumstances, e.g. for planning observations, it can be usefull to transform a sky coordinate into a position in the horizontal coordinate system given a location on earth and a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.coordinates import EarthLocation, AltAz\n",
    "from astropy.time import Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a location using [EarthLocation](http://docs.astropy.org/en/stable/api/astropy.coordinates.EarthLocation.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeodeticLocation(lon=<Longitude 2.3508 deg>, lat=<Latitude 48.8567 deg>, height=<Quantity 7.2605446e-10 m>)\n"
     ]
    }
   ],
   "source": [
    "paris = EarthLocation(lat=48.8567 * u.deg, lon=2.3508 * u.deg)\n",
    "print(paris.geodetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a time using the [Time](http://docs.astropy.org/en/stable/api/astropy.time.Time.html) object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-04-15 09:03:27.757036\n"
     ]
    }
   ],
   "source": [
    "now = Time.now()\n",
    "print(now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define a horizontal coordinate system using the [AltAz]([docs.astropy.org/en/stable/api/astropy.coordinates.AltAz.html) class and use it to convert from the sky coordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SkyCoord (AltAz: obstime=2019-04-15 09:03:27.757036, location=(4200910.64325784, 172456.78503911, 4780088.65877593) m, pressure=0.0 hPa, temperature=0.0 deg_C, relative_humidity=0.0, obswl=1.0 micron): (az, alt) in deg\n",
      "    (66.29666875, 8.67150744)>\n"
     ]
    }
   ],
   "source": [
    "altaz = AltAz(obstime=now, location=paris)\n",
    "crab_altaz = position_crab.transform_to(altaz)\n",
    "print(crab_altaz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Exercises\n",
    "\n",
    "- (*easy*) Define the sky coordinate for your favorite astronomical object and find the angular distance to the Crab Nebula as well as the Galactic center.\n",
    "- (*expert*) Make a plot of the height above horizon vs.time for the crab position at the location of Annecy. Mark the time range where it is visible. Would the Crab Nebula be visible tonight?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tables\n",
    "\n",
    "Astropy provides the [Table](http://docs.astropy.org/en/stable/api/astropy.io.votable.tree.Table.html) class in order to handle data tables.\n",
    "\n",
    "### 3.1 Basics\n",
    "\n",
    "Table objects can be created as shown in the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = Table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add columns to the table like we would add entries to a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "table['Source_Name'] = ['Crab', 'Sag A*', 'Cas A']\n",
    "table['GLON'] = [184.55754381, 0, 111.74169477] * u.deg\n",
    "table['GLAT'] = [-5.78427369, 0, -2.13544151] * u.deg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By executing the following cell, we get a nicely formatted version of the table printed in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table6657584432\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th><th>GLAT</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>str6</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>Crab</td><td>184.55754381</td><td>-5.78427369</td></tr>\n",
       "<tr><td>Sag A*</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>Cas A</td><td>111.74169477</td><td>-2.13544151</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "Source_Name     GLON         GLAT   \n",
       "                deg          deg    \n",
       "    str6      float64      float64  \n",
       "----------- ------------ -----------\n",
       "       Crab 184.55754381 -5.78427369\n",
       "     Sag A*          0.0         0.0\n",
       "      Cas A 111.74169477 -2.13544151"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Accessing rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have access to the defined columns. To check which ones are availbe you can use `Table.colnames`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Source_Name', 'GLON', 'GLAT']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And access individual columns just by their name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;GLON&apos; dtype=&apos;float64&apos; unit=&apos;deg&apos; length=3&gt;\n",
       "<table>\n",
       "<tr><td>184.55754381</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>111.74169477</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='GLON' dtype='float64' unit='deg' length=3>\n",
       "184.55754381\n",
       "         0.0\n",
       "111.74169477"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table['GLON']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And also a subset of columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table6720386664\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>str6</th><th>float64</th></tr></thead>\n",
       "<tr><td>Crab</td><td>184.55754381</td></tr>\n",
       "<tr><td>Sag A*</td><td>0.0</td></tr>\n",
       "<tr><td>Cas A</td><td>111.74169477</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "Source_Name     GLON    \n",
       "                deg     \n",
       "    str6      float64   \n",
       "----------- ------------\n",
       "       Crab 184.55754381\n",
       "     Sag A*          0.0\n",
       "      Cas A 111.74169477"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[['Source_Name', 'GLON']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, it is handy to get the column data as [astropy.units.Quantity](http://docs.astropy.org/en/stable/api/astropy.units.Quantity.html#astropy.units.Quantity) using the `.quantity` property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[184.55754,~0,~111.74169] \\; \\mathrm{{}^{\\circ}}$"
      ],
      "text/plain": [
       "<Quantity [184.55754381,   0.        , 111.74169477] deg>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table['GLON'].quantity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows can be accessed using numpy indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table6720387616\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th><th>GLAT</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>str6</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>Crab</td><td>184.55754381</td><td>-5.78427369</td></tr>\n",
       "<tr><td>Sag A*</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "Source_Name     GLON         GLAT   \n",
       "                deg          deg    \n",
       "    str6      float64      float64  \n",
       "----------- ------------ -----------\n",
       "       Crab 184.55754381 -5.78427369\n",
       "     Sag A*          0.0         0.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by using a boolean numpy array for indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=1</i>\n",
       "<table id=\"table6695183192\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th><th>GLAT</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>str6</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>Crab</td><td>184.55754381</td><td>-5.78427369</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "Source_Name     GLON         GLAT   \n",
       "                deg          deg    \n",
       "    str6      float64      float64  \n",
       "----------- ------------ -----------\n",
       "       Crab 184.55754381 -5.78427369"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = table['Source_Name'] == 'Crab'\n",
    "table[selection]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a more sophisticated indexing scheme, which is explained [here](http://docs.astropy.org/en/stable/table/indexing.html), but not covered in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Reading / Writing tables to disk\n",
    "Astropy tables can be serialized into many formats. For an overview see [here](http://docs.astropy.org/en/latest/io/unified.html#built-in-table-readers-writers). To write the table in FITS format we can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.write('data/example.fits', overwrite=True, format='fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.write('data/example.ecsv', overwrite=True, format='ascii.ecsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table6709325720\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th><th>GLAT</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>bytes6</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>Crab</td><td>184.55754381</td><td>-5.78427369</td></tr>\n",
       "<tr><td>Sag A*</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>Cas A</td><td>111.74169477</td><td>-2.13544151</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "Source_Name     GLON         GLAT   \n",
       "                deg          deg    \n",
       "   bytes6     float64      float64  \n",
       "----------- ------------ -----------\n",
       "       Crab 184.55754381 -5.78427369\n",
       "     Sag A*          0.0         0.0\n",
       "      Cas A 111.74169477 -2.13544151"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table.read('data/example.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Miscellaneous\n",
    "\n",
    "These are a few other useful operations when working with Astropy tables.\n",
    "\n",
    "Sort by key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.sort('GLON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table6657584432\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>GLON</th><th>GLAT</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>str6</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>Sag A*</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>Cas A</td><td>111.74169477</td><td>-2.13544151</td></tr>\n",
       "<tr><td>Crab</td><td>184.55754381</td><td>-5.78427369</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "Source_Name     GLON         GLAT   \n",
       "                deg          deg    \n",
       "    str6      float64      float64  \n",
       "----------- ------------ -----------\n",
       "     Sag A*          0.0         0.0\n",
       "      Cas A 111.74169477 -2.13544151\n",
       "       Crab 184.55754381 -5.78427369"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `.sort()` is an in place operation on the table, i.e. changes the actual table.\n",
    "\n",
    "To remove a specific row by index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.remove_row(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Astropy tables also support row-wise iteration in Python loops:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cas A\n",
      "Crab\n"
     ]
    }
   ],
   "source": [
    "for row in table:\n",
    "    print(row['Source_Name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful feature for quickly inspecting the data contained in the table is the `.show_in_browser()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Exercises\n",
    "\n",
    "- (*easy*) Add columns with the `RA` and `DEC` coordinates of the objects to the example table.\n",
    "- (*advanced *) Load the Fermi 2FHL catalog table (`'data/fermi_2fhl_catalog.fits'`) and find the brighest source (the brightness value is stored in the `'Flux50'` column). What is the common name of the source (column `'ASSOC'`)?\n",
    "Find all sources within 10 deg from the position of this source.\n",
    "- (*expert*) Make an allsky plot (using an 'Aitoff' projection) of all the sources in the Fermi 2FHL catalog grouped by source class (i.e. choose a different marker color for every source class). The class of the sources is stored in the `'CLASS'` column. There are a few possible ways to identify the groups of source class. One of them is to use [Table.group_by()](http://docs.astropy.org/en/stable/api/astropy.table.Table.html#astropy.table.Table.group_by) followed by a loop over the groups (check Astropy documentation!) another way is to use a regular Python `set` to idenfity the unique entries in the `'CLASS'` column and loop over those. You can use `plt.subplot(projection='aitoff')` to instanciate a matplotlib axes with `'Aitoff'` projection. Note that the `'Aitoff'` projection requires angular positions measured in radians."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. FITS Images and WCS\n",
    "\n",
    "The [flexible image transport system](https://fits.gsfc.nasa.gov/fits_documentation.html) format (FITS) is widely used data format for astronomical images and tables. As example we will use image data of the supernova remnant [Cassiopeia A](https://en.wikipedia.org/wiki/Cassiopeia_A), taken by the [Chandra X-ray observatory](http://chandra.harvard.edu/).\n",
    "\n",
    "### 4.1 Basics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To open the fits file we use `fits.open()` and just specify the filename as an argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdulist = fits.open('data/casa_0.5-1.5keV.fits.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can retrieve some basic information on the  header data unit (HDU) by calling `.info()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: data/casa_0.5-1.5keV.fits.gz\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      26   (1024, 1024)   float32   \n"
     ]
    }
   ],
   "source": [
    "hdulist.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains only one `PrimaryHDU` with data dimensions `(1024, 1024)` of format `float32`. To access the hdu we use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_hdu = hdulist['PRIMARY'] \n",
    "\n",
    "#or\n",
    "\n",
    "image_hdu = hdulist[0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the data with the `.data` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_hdu.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a plain 2d numpy array. We use `plt.imshow()` to visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1909284e0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_hdu.data, origin='lower', cmap='afmhot')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional meta information is stored in the `.header` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / Fits standard                                  \n",
       "BITPIX  =                  -32 / Bits per pixel                                 \n",
       "NAXIS   =                    2 / Number of axes                                 \n",
       "NAXIS1  =                 1024 / Axis Length                                    \n",
       "NAXIS2  =                 1024 / Axis Length                                    \n",
       "OBJECT  = 'Cassiopeia A'                                                        \n",
       "DATE-OBS= '2007-12-05T22:01:59'                                                 \n",
       "DATE-END= '2007-12-08T20:07:24'                                                 \n",
       "LTM1_1  =                    1                                                  \n",
       "LTM1_2  =                    0                                                  \n",
       "LTM2_1  =                    0                                                  \n",
       "LTM2_2  =                    1                                                  \n",
       "LTV1    =                -3651                                                  \n",
       "LTV2    =                -3807                                                  \n",
       "CTYPE1  = 'RA---TAN'                                                            \n",
       "CTYPE2  = 'DEC--TAN'                                                            \n",
       "CUNIT1  = 'deg'                                                                 \n",
       "CUNIT2  = 'deg'                                                                 \n",
       "CRPIX1  =                445.5                                                  \n",
       "CRPIX2  =                289.5                                                  \n",
       "CRVAL1  =          350.8841248                                                  \n",
       "CRVAL2  =          58.78133011                                                  \n",
       "CDELT1  =     -0.0001366666693                                                  \n",
       "CDELT2  =      0.0001366666693                                                  \n",
       "EQUINOX =                 2000                                                  \n",
       "RADESYS = 'ICRS'                                                                "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_hdu.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now use the header information to create a world coordinate to pixel coordinate transformation, using the [astropy.wcs.WCS](http://docs.astropy.org/en/stable/api/astropy.wcs.WCS.html#astropy.wcs.WCS) class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.wcs import WCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCS Keywords\n",
      "\n",
      "Number of WCS axes: 2\n",
      "CTYPE : 'RA---TAN'  'DEC--TAN'  \n",
      "CRVAL : 350.8841248  58.78133011  \n",
      "CRPIX : 445.5  289.5  \n",
      "PC1_1 PC1_2  : 1.0  0.0  \n",
      "PC2_1 PC2_2  : 0.0  1.0  \n",
      "CDELT : -0.0001366666693  0.0001366666693  \n",
      "NAXIS : 1024  1024\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(image_hdu.header)\n",
    "print(wcs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the helper methods [SkyCoord.to_pixel()](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord.to_pixel) and [SkyCood.from_pixel()](http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord.from_pixel) we can now convert every position in the image to the corresponding sky coordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (351.00119731, 58.74184873)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SkyCoord.from_pixel(0, 0, wcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(511.59886806), array(511.29670003))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# position_casa = SkyCoord.from_name('Cas A')\n",
    "\n",
    "# or\n",
    "\n",
    "position_casa = SkyCoord('23h23m27.94s', '+58d48m42.4s', frame='icrs')\n",
    "\n",
    "position_casa.to_pixel(wcs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Exercises\n",
    "\n",
    "- (*easy*) Read in the data from the other energy bands (`'data/casa_1.5-3.0keV.fits.gz'` and `'data/casa_4.0-6.0keV.fits.gz'`) as well, sum up all the data from the three energy bands. Plot the summed data using [plt.imshow()](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.axes.Axes.imshow.html) again.\n",
    "\n",
    "- (*advanced*) Cutout the central region of the image using [astropy.nddata.Cutout2D](http://docs.astropy.org/en/stable/api/astropy.nddata.Cutout2D.html) (size of 0.05 deg x 0.05 deg around the position of `'Cas A'`). Find the sky position of the brightest pixel in the cutout data. For this [np.argmax()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html) and [np.unravel_index()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.unravel_index.html) might be helpful. Make sure you use the updated `.data` and `.wcs` attributes of the `Cutout2D` object to achieve the latter.\n",
    "   \n",
    "- (*expert, very hard*) Calculate a radial profile of the image data to estimate the size of the shell of the supernova remnant. For this sum up all the data that is contained in the rings (specified  by `radii_min` and `radii_max`) around the center position of *Cas A*. Choose a total minimum radius of `0 arcmin` and total maximum radius of `5 arcmin` divided in 50 steps. You can use [np.indices()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.indices.html) to get arrays of x and y pixel positions, that can be transformed to sky positions. Make sure you use clever numpy masking and broadcasting to avoid a Python loop over the `radii_min` and `radii_max` arrays (**hint**: [ndarray.reshape(-1, ...)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html#numpy.reshape) might be useful to expand the data and radius arrays to the third dimension). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plotting of sky images (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The [Matplotlib](https://matplotlib.org/) package is a very popular plotting package for Python. Astropy provides a helper module [astropy.visualization.wcsaxes](http://docs.astropy.org/en/stable/visualization/wcsaxes/) to simplify plotting of sky images with Matplotlib.\n",
    "\n",
    "To use it we just pass `projection=wcs` to the `plt.subplot()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "ax = plt.subplot(projection=wcs)\n",
    "ax.imshow(image_hdu.data, cmap='afmhot', origin='lower')\n",
    "\n",
    "ax.set_xlabel('RA (deg)')\n",
    "ax.set_ylabel('DEC (deg)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we get the plot with the x-axis and y-axis in sky coordinates. \n",
    "\n",
    "We can add a coordinate grid with `ax.grid()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.grid(linewidth=0.3, linestyle='dashed', color='white')\n",
    "ax.figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add a white cross to mark the center position of `'Cas A'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra = position_casa.icrs.ra.deg\n",
    "dec = position_casa.icrs.dec.deg\n",
    "ax.scatter(ra, dec, transform=ax.get_transform('icrs'), color='white', marker='x')\n",
    "ax.figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Exercises\n",
    "\n",
    "- (*easy*) Make a combined plot of the Chandra data for all energy bands side by side (one row, three columns)\n",
    "and put the energy information in the title of the subplot. You can use e.g. `plt.subplot()` (check the matplotlib documentation).  \n",
    "- (*advanced*) Make a combined RGB image of the Chandra data in different energy bands. Therefore you have to\n",
    "renormalize the data per energy band to the maximum value of respective energy band and combine the data into a 3d array with a shape that is compatible with `plt.imshow()` (check the matplotlib documentation!). You can also try to experiment with the Astropy function [make_lupton_rgb](http://docs.astropy.org/en/stable/api/astropy.visualization.make_lupton_rgb.html#astropy.visualization.make_lupton_rgb). For this you can find a [tutorial in the Astropy documentation](http://docs.astropy.org/en/stable/visualization/lupton_rgb.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "![](asterics_log.png)\n",
    "\n",
    "This tutorial was supported by the H2020-Astronomy ESFRI and Research Infrastructure Cluster (Grant Agreement number: 653477)."
   ]
  }
 ],
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