{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", "**This is a fixed-text formatted version of a Jupyter notebook.**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.8?urlpath=lab/tree/hgps.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[hgps.ipynb](../_static/notebooks/hgps.ipynb) |\n", "[hgps.py](../_static/notebooks/hgps.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# HGPS\n", "\n", "\n", "## Introduction\n", "\n", "The **H.E.S.S. Galactic Plane Survey (HGPS)** is the first deep and wide survey of the Milky Way in TeV gamma-rays.\n", "\n", "In April 2018, a paper on the HGPS was published, and survey maps and a source catalog on the HGPS in FITS format released.\n", "\n", "More information, and the HGPS data for download in FITS format is available here:\n", "https://www.mpi-hd.mpg.de/hfm/HESS/hgps\n", "\n", "**Please read the Appendix A of the paper to learn about the caveats to using the HGPS data. Especially note that the HGPS survey maps are correlated and thus no detailed source morphology analysis is possible, and also note the caveats concerning spectral models and spectral flux points.**\n", "\n", "## Notebook Overview\n", "\n", "This is a Jupyter notebook that illustrates how to work with the HGPS data from Python.\n", "\n", "You will learn how to access the HGPS images as well as the HGPS catalog and other tabular data using Astropy and Gammapy.\n", "\n", "* In the first part we will only use Astropy to do some basic things.\n", "* Then in the second part we'll use Gammapy to do some things that are a little more advanced.\n", "\n", "The notebook is pretty long: feel free to skip ahead to a section of your choice after executing the cells in the \"Setup\" and \"Download data\" sections below.\n", "\n", "Note that there are other tools to work with FITS data that we don't explain here. Specifically [DS9](http://ds9.si.edu/) and [Aladin](http://aladin.u-strasbg.fr/) are good FITS image viewers, and [TOPCAT](http://www.star.bris.ac.uk/~mbt/topcat/) is great for FITS tables. Astropy and Gammapy are just one way to work with the HGPS data; any tool that can access FITS data can be used.\n", "\n", "## Packages\n", "\n", "We will be using the following Python packages\n", "\n", "* [astropy](http://docs.astropy.org/)\n", "* [gammapy](http://docs.gammapy.org/)\n", "* [matplotlib](https://matplotlib.org/) for plotting\n", "\n", "Under the hood all of those packages use Numpy arrays to store and work with data.\n", "\n", "More specifically, we will use the following functions and classes:\n", "\n", "* From [astropy](http://docs.astropy.org/), we will use [astropy.io.fits](http://docs.astropy.org/en/stable/io/fits/index.html) to read the FITS data, [astropy.table.Table](http://docs.astropy.org/en/stable/table/index.html) to work with the tables, but also [astropy.coordinates.SkyCoord](http://docs.astropy.org/en/stable/coordinates/index.html) and [astropy.wcs.WCS](http://docs.astropy.org/en/stable/wcs/index.html) to work with sky and pixel coordinates and [astropy.units.Quantity](http://docs.astropy.org/en/stable/units/index.html) to work with quantities.\n", "\n", "* From [gammapy](http://docs.gammapy.org/), we will use [gammapy.maps.WcsNDMap](..\/api/gammapy.maps.WcsNDMap.rst) to work with the HGPS sky maps, and [gammapy.catalog.SourceCatalogHGPS](..\/api/gammapy.catalog.SourceCatalogHGPS.rst) and [gammapy.catalog.SourceCatalogObjectHGPS](..\/api/gammapy.catalog.SourceCatalogObjectHGPS.rst) to work with the HGPS catalog data, especially the HGPS spectral data using [gammapy.spectrum.models.SpectralModel](..\/api/gammapy.spectrum.models.SpectralModel.rst) and [gammapy.spectrum.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst) objects.\n", "\n", "* [matplotlib](https://matplotlib.org/) for all plotting. For sky image plotting, we will use matplotlib via [astropy.visualization](http://docs.astropy.org/en/stable/visualization/index.html) and [gammapy.maps.WcsNDMap.plot](..\/api/gammapy.maps.WcsNDMap.rst#gammapy.maps.WcsNDMap.plot).\n", "\n", "If you're not familiar with Python, Numpy, Astropy, Gammapy or matplotlib yet, use the tutorial introductions as explained [here](..\/tutorials.rst), as well as the links to the documentation that we just mentioned." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "We start by importing everything we will use in this notebook, and configuring the notebook to show plots inline.\n", "\n", "If you get an error here, you probably have to install the missing package and re-start the notebook.\n", "\n", "If you don't get an error, just go ahead, no nead to read the import code and text in this section." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0.4\n" ] } ], "source": [ "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "from astropy.wcs import WCS\n", "\n", "import astropy\n", "\n", "print(astropy.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8\n" ] } ], "source": [ "from gammapy.maps import Map\n", "from gammapy.catalog import SourceCatalogHGPS\n", "\n", "import gammapy\n", "\n", "print(gammapy.__version__)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.2.3\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import matplotlib\n", "\n", "print(matplotlib.__version__)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# This will work on Python 3, but fail on Python 2\n", "from pathlib import Path\n", "from urllib.request import urlretrieve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're still using Python 2, please update to Python 3!\n", "\n", "Also, it's time to start using [JupyterLab](http://jupyterlab.readthedocs.io/)\n", "(which is Python 3 only, Jupyter dropped Python 2 support in 2017).\n", "\n", "But OK, to be honest, actually everything here will work on Python 2,\n", "we avoided the use of Python 3 only features for now.\n", "You just have to import `Path` and `urlretrieve` from here instead:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# This will work on Python 2 as well as Python 3\n", "# from gammapy.extern.six.moves.urllib.request import urlretrieve\n", "# from gammapy.extern.pathlib import Path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download Data\n", "\n", "First, you need to download the HGPS FITS data from https://www.mpi-hd.mpg.de/hfm/HESS/hgps .\n", "\n", "If you haven't already, you can use the following commands to download the files to your local working directory.\n", "\n", "You don't have to read the code in the next cell; that's just how to downlaod files from Python.\n", "You could also download the files with your web browser, or from the command line e.g. with curl:\n", "\n", " mkdir hgps_data\n", " cd hgps_data\n", " curl -O https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/hgps_catalog_v1.fits.gz\n", " curl -O https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/hgps_map_significance_0.1deg_v1.fits.gz\n", "\n", "**The rest of this notebook assumes that you have the data files at ``hgps_data_path``.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Download HGPS data used in this tutorial to a folder of your choice\n", "# The default `hgps_data` used here is a sub-folder in your current\n", "# working directory (where you started the notebook)\n", "hgps_data_path = Path(\"hgps_data\")\n", "\n", "# In this notebook we will only be working with the following files\n", "# so we only download what is needed.\n", "hgps_filenames = [\n", " \"hgps_catalog_v1.fits.gz\",\n", " \"hgps_map_significance_0.1deg_v1.fits.gz\",\n", "]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/hgps_catalog_v1.fits.gz to hgps_data/hgps_catalog_v1.fits.gz\n", "Downloading https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/hgps_map_significance_0.1deg_v1.fits.gz to hgps_data/hgps_map_significance_0.1deg_v1.fits.gz\n", "\n", "\n", "Files at /Users/jer/git/gammapy/temp/hgps_data :\n", "\n", "hgps_data/hgps_map_significance_0.1deg_v1.fits.gz\n", "hgps_data/hgps_catalog_v1.fits.gz\n" ] } ], "source": [ "def hgps_data_download():\n", " base_url = \"https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/\"\n", " for filename in hgps_filenames:\n", " url = base_url + filename\n", " path = hgps_data_path / filename\n", " if path.exists():\n", " print(\"Already downloaded: {}\".format(path))\n", " else:\n", " print(\"Downloading {} to {}\".format(url, path))\n", " urlretrieve(url, str(path))\n", "\n", "\n", "hgps_data_path.mkdir(parents=True, exist_ok=True)\n", "hgps_data_download()\n", "\n", "print(\"\\n\\nFiles at {} :\\n\".format(hgps_data_path.absolute()))\n", "for path in hgps_data_path.iterdir():\n", " print(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Catalog with Astropy\n", "\n", "### FITS file content\n", "\n", "Let's start by just opening up `hgps_catalog_v1.fits.gz` and looking at the content.\n", "\n", "Note that ``astropy.io.fits.open`` doesn't work with `Path` objects yet,\n", "so you have to call `str(path)` and pass a string." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "path = hgps_data_path / \"hgps_catalog_v1.fits.gz\"\n", "hdu_list = fits.open(str(path))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: hgps_data/hgps_catalog_v1.fits.gz\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 15 () \n", " 1 HGPS_SOURCES 1 BinTableHDU 327 78R x 78C [16A, 6A, 10A, 20A, 7A, E, E, E, E, E, E, E, E, K, 18A, 103A, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, 4A, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, K, 40E, 40E, 40E, 40E, 40E, 40E, 40E, 40B] \n", " 2 HGPS_GAUSS_COMPONENTS 1 BinTableHDU 87 98R x 13C [9A, 16A, 15A, E, E, E, E, E, E, E, E, E, E] \n", " 3 HGPS_ASSOCIATIONS 1 BinTableHDU 39 223R x 4C [16A, 5A, 21A, E] \n", " 4 HGPS_IDENTIFICATIONS 1 BinTableHDU 53 31R x 9C [16A, 17A, 9A, 11A, 21A, 21A, E, E, E] \n", " 5 HGPS_LARGE_SCALE_COMPONENT 1 BinTableHDU 55 50R x 7C [E, E, E, E, E, E, E] \n", " 6 SNRCAT 1 BinTableHDU 56 282R x 7C [11A, E, E, 29A, E, E, E] \n" ] } ], "source": [ "hdu_list.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are six tables. Each table and column is described in detail in the HGPS paper.\n", "\n", "### Access table data\n", "\n", "We could work with the `astropy.io.fits.HDUList` and `astropy.io.fits.BinTable` objects.\n", "However, in Astropy a nicer class to work with tables has been developed: `astropy.table.Table`.\n", "\n", "We will only be using `Table`, so let's convert the FITS tabular data into a `Table` object:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "table = Table.read(hdu_list[\"HGPS_SOURCES\"])\n", "\n", "# Alternatively, reading from file directly would work like this:\n", "# table = Table.read(str(path), hdu='HGPS_SOURCES')\n", "# Usually you have to look first what HDUs are in a FITS file\n", "# like we did above; `Table` is just for one table" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " name dtype shape unit n_bad\n", "------------------------------ ------- ----- --------------- -----\n", " Source_Name str16 0\n", " Analysis_Reference str6 0\n", " Source_Class str10 0\n", " Identified_Object str20 0\n", " Gamma_Cat_Source_ID str7 0\n", " RAJ2000 float32 deg 0\n", " DEJ2000 float32 deg 0\n", " GLON float32 deg 0\n", " GLON_Err float32 deg 14\n", " GLAT float32 deg 0\n", " GLAT_Err float32 deg 14\n", " Pos_Err_68 float32 deg 3\n", " Pos_Err_95 float32 deg 14\n", " ROI_Number int64 0\n", " Spatial_Model str18 0\n", " Components str103 0\n", " Sqrt_TS float32 14\n", " Size float32 deg 23\n", " Size_Err float32 deg 25\n", " Size_UL float32 deg 61\n", " R70 float32 deg 14\n", " RSpec float32 deg 0\n", " Excess_Model_Total float32 14\n", " Excess_RSpec float32 14\n", " Excess_RSpec_Model float32 14\n", " Background_RSpec float32 14\n", " Livetime float32 h 0\n", " Energy_Threshold float32 TeV 14\n", " Flux_Map float32 1 / (cm2 s) 0\n", " Flux_Map_Err float32 1 / (cm2 s) 0\n", " Flux_Map_RSpec_Data float32 1 / (cm2 s) 14\n", " Flux_Map_RSpec_Source float32 1 / (cm2 s) 14\n", " Flux_Map_RSpec_Other float32 1 / (cm2 s) 14\n", " Flux_Map_RSpec_LS float32 1 / (cm2 s) 14\n", " Flux_Map_RSpec_Total float32 1 / (cm2 s) 14\n", " Containment_RSpec float32 14\n", " Contamination_RSpec float32 14\n", "Flux_Correction_RSpec_To_Total float32 14\n", " Livetime_Spec float32 h 0\n", " Energy_Range_Spec_Min float32 TeV 1\n", " Energy_Range_Spec_Max float32 TeV 5\n", " Background_Spec float32 14\n", " Excess_Spec float32 14\n", " Spectral_Model str4 0\n", " TS_ECPL_over_PL float32 70\n", " Flux_Spec_Int_1TeV float32 1 / (cm2 s) 0\n", " Flux_Spec_Int_1TeV_Err float32 1 / (cm2 s) 0\n", " Flux_Spec_Energy_1_10_TeV float32 erg / (cm2 s) 0\n", " Flux_Spec_Energy_1_10_TeV_Err float32 erg / (cm2 s) 0\n", " Energy_Spec_PL_Pivot float32 TeV 4\n", " Flux_Spec_PL_Diff_Pivot float32 1 / (cm2 s TeV) 4\n", " Flux_Spec_PL_Diff_Pivot_Err float32 1 / (cm2 s TeV) 4\n", " Flux_Spec_PL_Diff_1TeV float32 1 / (cm2 s TeV) 4\n", " Flux_Spec_PL_Diff_1TeV_Err float32 1 / (cm2 s TeV) 4\n", " Index_Spec_PL float32 4\n", " Index_Spec_PL_Err float32 4\n", " Energy_Spec_ECPL_Pivot float32 TeV 66\n", " Flux_Spec_ECPL_Diff_Pivot float32 1 / (cm2 s TeV) 66\n", " Flux_Spec_ECPL_Diff_Pivot_Err float32 1 / (cm2 s TeV) 66\n", " Flux_Spec_ECPL_Diff_1TeV float32 1 / (cm2 s TeV) 66\n", " Flux_Spec_ECPL_Diff_1TeV_Err float32 1 / (cm2 s TeV) 66\n", " Index_Spec_ECPL float32 66\n", " Index_Spec_ECPL_Err float32 66\n", " Lambda_Spec_ECPL float32 1 / TeV 66\n", " Lambda_Spec_ECPL_Err float32 1 / TeV 66\n", " Flux_Spec_PL_Int_1TeV float32 1 / (cm2 s) 4\n", " Flux_Spec_PL_Int_1TeV_Err float32 1 / (cm2 s) 4\n", " Flux_Spec_ECPL_Int_1TeV float32 1 / (cm2 s) 66\n", " Flux_Spec_ECPL_Int_1TeV_Err float32 1 / (cm2 s) 66\n", " N_Flux_Points int64 0\n", " Flux_Points_Energy float32 (40,) TeV 2546\n", " Flux_Points_Energy_Min float32 (40,) TeV 2647\n", " Flux_Points_Energy_Max float32 (40,) TeV 2647\n", " Flux_Points_Flux float32 (40,) 1 / (cm2 s TeV) 2558\n", " Flux_Points_Flux_Err_Lo float32 (40,) 1 / (cm2 s TeV) 2558\n", " Flux_Points_Flux_Err_Hi float32 (40,) 1 / (cm2 s TeV) 2558\n", " Flux_Points_Flux_UL float32 (40,) 1 / (cm2 s TeV) 2724\n", " Flux_Points_Flux_Is_UL uint8 (40,) 0\n" ] } ], "source": [ "# List available columns\n", "table.info()\n", "# To get shorter output here, we just list a few\n", "# table.info(out=None)['name', 'dtype', 'shape', 'unit'][:10]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Row index=0\n", "
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" ], "text/plain": [ "\n", " HESS J0835-455\n", " HESS J0852-463\n", "HESS J1018-589 A\n", "HESS J1018-589 B\n", " HESS J1023-575" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Columns are accessed by indexing into the table\n", "# with a column name string\n", "# Then you can slice the column to get the rows you want\n", "table[\"Source_Name\"][:5]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HESS J1026-582'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accessing a given element of the table like this\n", "table[\"Source_Name\"][5]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# If you know some Python and Numpy, you can now start\n", "# to ask questions about the HGPS data.\n", "# Just to give one example: \"What spatial models are used?\"" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'2-Gaussian', '3-Gaussian', 'Gaussian', 'Point-Like', 'Shell'}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(table[\"Spatial_Model\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert formats\n", "\n", "The HGPS catalog is only released in FITS format.\n", "\n", "We didn't provide multiple formats (DS9, CSV, XML, VOTABLE, ...) because everyone needs something a little different (in terms of format or content). Instead, here we show how you can convert to any format / content you like wiht a few lines of Python.\n", "\n", "#### DS9 region format\n", "\n", "At [Fermi-LAT 3FGL webpage](https://fermi.gsfc.nasa.gov/ssc/data/access/lat/4yr_catalog/),\n", "we see that they also provide [DS9 region files](http://ds9.si.edu/doc/ref/region.html) that look like this:\n", "\n", " global color=red\n", " fk5;point( 0.0377, 65.7517)# point=cross text={3FGL J0000.1+6545}\n", " fk5;point( 0.0612,-37.6484)# point=cross text={3FGL J0000.2-3738}\n", " fk5;point( 0.2535, 63.2440)# point=cross text={3FGL J0001.0+6314}\n", " ... more lines, one for each source ...\n", "\n", "because many people use [DS9](http://ds9.si.edu/) to view astronomical images, and to overplot catalog data.\n", "\n", "Let's convert the HGPS catalog into this format.\n", "(to avoid very long text output, we use `table[:3]` to just use the first three rows)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "lines = [\"global color=red\"]\n", "for row in table[:3]:\n", " fmt = \"fk5;point({:.5f},{:.5f})# point=cross text={{{}}}\"\n", " vals = row[\"RAJ2000\"], row[\"DEJ2000\"], row[\"Source_Name\"]\n", " lines.append(fmt.format(*vals))\n", "txt = \"\\n\".join(lines)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "global color=red\n", "fk5;point(128.88670,-45.65894)# point=cross text={HESS J0835-455}\n", "fk5;point(133.00000,-46.37000)# point=cross text={HESS J0852-463}\n", "fk5;point(154.74777,-58.93176)# point=cross text={HESS J1018-589 A}\n" ] } ], "source": [ "# To save it to a DS9 region text file\n", "path = hgps_data_path / \"hgps_my_format.reg\"\n", "path.write_text(txt)\n", "\n", "# Print content of the file to check\n", "print(path.read_text())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is an extra package [astropy-regions](https://astropy-regions.readthedocs.io/) that has classes to represent region objects and supports the DS9 format. We could have used that to generate the DS9 region strings, or we could use it to read the DS9 region file via:\n", "\n", " import regions\n", " region_list = regions.ds9.read_ds9(str(path))\n", "\n", "We will not show examples how to work with regions or how to do region-based analyses here, but if you want to do something like e.g. measure the total flux in a given region in the HGPS maps, the region package would be useful." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### CSV format\n", "\n", "Let's do one more example, converting part of the HGPS catalog table information to CSV, i.e. comma-separated format.\n", "This is a format that can be read by any tool for tabular data, e.g. if you are using Excel or ROOT for your gamma-ray data analysis, this section is for you!\n", "\n", "Usually you can just use the CSV writer in Astropy like this:\n", "\n", " path = hgps_data_path / 'hgps_my_format.csv'\n", " table.write(str(path), format='ascii.csv')\n", " \n", "However, this doesn't work here for two reasons:\n", "\n", "1. this table contains metadata that trips up the Astropy CSV writer (I filed an [issue](https://github.com/astropy/astropy/issues/7357) with Astropy)\n", "1. this table contains array-valued columns for the spectral points and CSV can only have scalar values." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['This is part of the HESS Galactic plane survey (HGPS) data.',\n", " '',\n", " ' Paper: https://doi.org/10.1051/0004-6361/201732098',\n", " ' Webpage: https://www.mpi-hd.mpg.de/hfm/HESS/hgps',\n", " ' Contact: contact@hess-experiment.eu',\n", " '',\n", " 'The HGPS observations were taken from 2004 to 2013.',\n", " 'The paper was published and this data was released in April 2018.',\n", " '',\n", " 'A detailed description is available in the paper.']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is the problematic header key\n", "table.meta[\"comments\"]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flux_Points_Energy (78, 40)\n", "Flux_Points_Energy_Min (78, 40)\n", "Flux_Points_Energy_Max (78, 40)\n", "Flux_Points_Flux (78, 40)\n", "Flux_Points_Flux_Err_Lo (78, 40)\n", "Flux_Points_Flux_Err_Hi (78, 40)\n", "Flux_Points_Flux_UL (78, 40)\n", "Flux_Points_Flux_Is_UL (78, 40)\n" ] } ], "source": [ "# These are the array-valued columns\n", "array_colnames = tuple(\n", " name for name in table.colnames if len(table[name].shape) > 1\n", ")\n", "for name in array_colnames:\n", " print(name, table[name].shape)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.42169651 0.9531619 2.26030302 5.36002302 12.71061802\n", " 30.14162445 nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan]\n", "[ 6.55417665e-11 1.19860311e-11 3.52555008e-12 8.32411180e-13\n", " 1.59623955e-13 1.11618885e-14 nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan\n", " nan nan nan nan]\n" ] } ], "source": [ "# So each source has an array of spectral flux points,\n", "# and FITS allows storing such arrays in table cells\n", "# Knowing this, you could work with the spectral points directly.\n", "# We won't give an example here; but instead show how to work\n", "# with HGPS spectra in the Gammapy section below, because it's easier.\n", "print(table[\"Flux_Points_Energy\"][0])\n", "print(table[\"Flux_Points_Flux\"][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get back to our actual goal of converting part of the HGPS catalog table data to CSV format.\n", "The following code makes a copy of the table that contains just the scalar columns, then removes the `meta` information (most CSV variants / readers don't support metadata anyways) and writes a CSV file." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "scalar_colnames = tuple(\n", " name for name in table.colnames if len(table[name].shape) <= 1\n", ")\n", "table2 = table[scalar_colnames]\n", "table2.meta = {}\n", "path = hgps_data_path / \"hgps_my_format.csv\"\n", "table2.write(str(path), format=\"ascii.csv\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Astropy ASCII writer and reader supports many variants.\n", "Let's do one more example, using the ``ascii.fixed_width`` format which is a bit easier to read for humans.\n", "We will just select a few columns and rows to print here." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Source_Name | GLON | GLAT |\n", "| HESS J0835-455 | 263.960 | -3.048 |\n", "| HESS J0852-463 | 266.287 | -1.243 |\n", "| HESS J1018-589 A | 284.351 | -1.674 |\n", "\n" ] } ], "source": [ "table2 = table[\"Source_Name\", \"GLON\", \"GLAT\"][:3]\n", "table2.meta = {}\n", "table2[\"Source_Name\"].format = \"<20s\"\n", "table2[\"GLON\"].format = \">8.3f\"\n", "table2[\"GLAT\"].format = \">8.3f\"\n", "path = hgps_data_path / \"hgps_my_format.csv\"\n", "table2.write(str(path), format=\"ascii.fixed_width\", overwrite=True)\n", "\n", "# Print the CSV file contents to check what we have\n", "print(path.read_text())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you know how to work with the HGPS catalog with Python and Astropy. For the other tables (e.g. `HGPS_GAUSS_COMPONENTS`) it's the same: you should read the description in the HGPS paper, then access the information you need or convert it to the format you want as shows for `HGPS_SOURCES` here). Let's move on and have a look at the HGPS maps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maps with Astropy\n", "\n", "This section shows how to load an HGPS survey map with Astropy, and give examples how to work with sky and pixel coordinates to read off map values at given positions.\n", "\n", "We will keep it short, for further examples see the \"Maps with Gammapy\" section below.\n", "\n", "### Read\n", "\n", "To read the map, use `fits.open` to get an `HDUList` object, then access `[0]` to get the first and only image HDU in the FITS file, and finally use the `hdu.data` Numpy array, `hdu.header` header object or `wcs = WCS(hdu.header)` WCS object to work with the data." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: hgps_data/hgps_map_significance_0.1deg_v1.fits.gz\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 Significance 1 PrimaryHDU 30 (9400, 500) float32 \n" ] } ], "source": [ "path = hgps_data_path / \"hgps_map_significance_0.1deg_v1.fits.gz\"\n", "hdu_list = fits.open(str(path))\n", "hdu_list.info()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.io.fits.hdu.image.PrimaryHDU" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdu = hdu_list[0]\n", "type(hdu)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(hdu.data)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(500, 9400)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdu.data.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / conforms to FITS standard \n", "BITPIX = -32 / array data type \n", "NAXIS = 2 / number of array dimensions \n", "NAXIS1 = 9400 \n", "NAXIS2 = 500 \n", "EXTNAME = 'Significance' \n", "HDUNAME = 'Significance' \n", "CTYPE1 = 'GLON-CAR' / Type of co-ordinate on axis 1 \n", "CTYPE2 = 'GLAT-CAR' / Type of co-ordinate on axis 2 \n", "EQUINOX = 2000. / [yr] Epoch of reference equinox \n", "CRPIX1 = 4700.5 / Reference pixel on axis 1 \n", "CRPIX2 = 250.5 / Reference pixel on axis 2 \n", "CRVAL1 = 341 / Value at ref. pixel on axis 1 \n", "CRVAL2 = 0 / Value at ref. pixel on axis 2 \n", "CDELT1 = -0.02 / Pixel size on axis 1 \n", "CDELT2 = 0.02 / Pixel size on axis 2 \n", "CUNIT1 = 'deg ' / units of CRVAL1 and CDELT1 \n", "CUNIT2 = 'deg ' / units of CRVAL2 and CDELT2 \n", "TELESCOP= 'H.E.S.S.' / name of telescope \n", "ORIGIN = 'The H.E.S.S. Collaboration' / organization responsible for the data \n", "COMMENT This is part of the HESS Galactic plane survey (HGPS) data. \n", "COMMENT \n", "COMMENT Paper: https://doi.org/10.1051/0004-6361/201732098 \n", "COMMENT Webpage: https://www.mpi-hd.mpg.de/hfm/HESS/hgps \n", "COMMENT Contact: contact@hess-experiment.eu \n", "COMMENT \n", "COMMENT The HGPS observations were taken from 2004 to 2013. \n", "COMMENT The paper was published and this data was released in April 2018. \n", "COMMENT \n", "COMMENT A detailed description is available in the paper. " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The FITS header contains the information about the\n", "# WCS projection, i.e. the pixel to sky coordinate transform\n", "hdu.header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WCS and coordinates\n", "\n", "To actually do pixel to sky coordinate transformations,\n", "you have to create a \"Word coordinate system transform (WCS)\"\n", "object from the FITS header." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WCS Keywords\n", "\n", "Number of WCS axes: 2\n", "CTYPE : 'GLON-CAR' 'GLAT-CAR' \n", "CRVAL : 341.0 0.0 \n", "CRPIX : 4700.5 250.5 \n", "PC1_1 PC1_2 : 1.0 0.0 \n", "PC2_1 PC2_2 : 0.0 1.0 \n", "CDELT : -0.02 0.02 \n", "NAXIS : 9400 500" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wcs = WCS(hdu.header)\n", "wcs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's find the (arguably) most interesting pixel in the HGPS map and look up it's value in the significance image." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pos = SkyCoord.from_name('Sgr A*')\n", "pos = SkyCoord(266.416826, -29.007797, unit=\"deg\")\n", "pos" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array(3752.286808396431), array(247.19237757373006))" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xp, yp = pos.to_pixel(wcs)\n", "xp, yp" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "(array(3752.286808396431), array(247.19237757373006))\n" ] } ], "source": [ "# Note that SkyCoord makes it easy to transform\n", "# to other frames, and it knows about the frame\n", "# of the WCS object, so for HGPS we have `frame=\"galactic\"`\n", "# and in the call above the transformation from ICRS\n", "# to Galactic and then to pixel all happened in the\n", "# `pos.to_pixel` call. This gives the same pixel postion:\n", "print(pos.galactic)\n", "print(pos.galactic.to_pixel(wcs))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(247, 3752)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# FITS WCS and Numpy have opposite array axis order\n", "# So to look up a pixel in the `hdu.data` Numpy array,\n", "# we need to switch to (y, x), and we also need to\n", "# round correctly to the nearest int, thus the `+ 0.5`\n", "idx = int(yp + 0.5), int(xp + 0.5)\n", "idx" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "89.49691" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now, finally the value of this pixel\n", "hdu.data[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this is a significance map with correlation radius 0.1 deg.\n", "That means that within a circle of radius 0.1 deg around the pixel center,\n", "the signal has a significance of 74.2 sigma.\n", "\n", "This does not directly correspond to the significance of a gamma-ray source,\n", "because this circle contains emission from multiple sources and underlying diffuse emission,\n", "and for the sources in this circle their emission isn't fully contained because of the size of the HESS PSF.\n", "\n", "We remind you of the caveat the HGPS paper (see Appendix A) that it is not possible to do a detailed quantitative measurement on the released HGPS maps; the measurements in the HGPS paper were done using likelihood fits on uncorrelated counts images taking the PSF shape into account." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do one more exercise: find the sky position for the pixel with the highest significance:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "89.49691" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The pixel with the maximum significance\n", "hdu.data.max()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(247, 3752)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# So it is the pixel in the HGPS map that contains Sgr A*\n", "# and we already roughly know it's position\n", "# Still, let's find the exact pixel center sky position\n", "\n", "# We use `np.nanargmax` to find the index, but it's an index\n", "# into the flattened array, so we have to \"unravel\" it to get a 2D index\n", "yp, xp = np.unravel_index(np.nanargmax(hdu.data), hdu.data.shape)\n", "yp, xp" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pos = SkyCoord.from_pixel(xp, yp, wcs)\n", "pos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, working with FITS images and sky / pixel coordinates directly requires that you learn how to use the WCS object and Numpy arrays and to know that the axis order is `(x, y)` in FITS and WCS, but `(row, column)`, i.e. `(y, x)` in Numpy. It seems quite complex at first, but most astronomers get used to it and manage after a while. `gammapy.maps.WcsNDMap` is a wrapper class that is a bit simpler to use (see below), but under the hood it just calls these Numpy and Astropy methods for you, so it's good to know what is going on in any case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Catalog with Gammapy\n", "\n", "As you have seen above, working with the HGPS FITS catalog data using Astropy is pretty nice.\n", "But still, there are some common tasks that aren't trivial to do and require reading the\n", "FITS table description in detail and writing quite a bit of Python code.\n", "\n", "So that you don't have to, we have done this for HGPS in [gammapy.catalog.SourceCatalogHGPS](..\/api/gammapy.catalog.SourceCatalogHGPS.rst) and also for a few other catalogs that are commonly used in gamma-ray astronomy in [gammapy.catalog](..\/catalog/index.rst).\n", "\n", "### Read\n", "\n", "Let's start by reading the HGPS catalog via the `SourceCatalogHGPS` class (which is just a wrapper class for `astropy.table.Table`) and access some information about a given source. Feel free to choose any source you like here: we have chosen simply the first one on the table: the pulsar-wind nebula Vela X, a large and bright TeV source around the [Vela pulsar](https://en.wikipedia.org/wiki/Vela_Pulsar)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "path = hgps_data_path / \"hgps_catalog_v1.fits.gz\"\n", "cat = SourceCatalogHGPS(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tables\n", "\n", "Now all tables from the FITS file were loaded\n", "and stored on the ``cat`` object. See the [SourceCatalogHGPS](..\/api/gammapy.catalog.SourceCatalogHGPS.rst) docs, or just try accessing one:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HGPS_SOURCES'" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat.table.meta[\"EXTNAME\"]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HGPS_GAUSS_COMPONENTS'" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat.table_components.meta[\"EXTNAME\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Source\n", "\n", "You can access a given source by row index (starting at zero) or by source name.\n", "This creates [SourceCatalogObjectHGPS](..\/api/gammapy.catalog.SourceCatalogObjectHGPS.rst) objects that have a copy of all the data for a given source. See the class docs for a full overview." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SourceCatalogObjectHGPS('HESS J0835-455')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# These all give the same source object\n", "source = cat[0]\n", "# When accessing by name, the value has to match exactly\n", "# the content from one of these columns:\n", "# `Source_Name` or `Identified_Object`\n", "# which in this case are \"HESS J0835-455\" and \"Vela X\"\n", "source = cat[\"HESS J0835-455\"]\n", "source = cat[\"Vela X\"]\n", "source" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "*** Basic info ***\n", "\n", "Catalog row index (zero-based) : 0\n", "Source name : HESS J0835-455\n", "Analysis reference : HGPS\n", "Source class : PWN\n", "Identified object : Vela X\n", "Gamma-Cat id : 37\n", "\n", "\n", "*** Info from map analysis ***\n", "\n", "RA : 128.887 deg = 8h35m33s\n", "DEC : -45.659 deg = -45d39m32s\n", "GLON : 263.960 +/- 0.025 deg\n", "GLAT : -3.048 +/- 0.027 deg\n", "Position Error (68%) : 0.057 deg\n", "Position Error (95%) : 0.093 deg\n", "ROI number : 18\n", "Spatial model : 3-Gaussian\n", "Spatial components : HGPSC 001, HGPSC 002, HGPSC 003\n", "TS : 1553.2\n", "sqrt(TS) : 39.4\n", "Size : 0.585 +/- 0.052 (UL: nan) deg\n", "R70 : 0.887 deg\n", "RSpec : 0.500 deg\n", "Total model excess : 8781.6\n", "Excess in RSpec : 3580.1\n", "Model Excess in RSpec : 3637.4\n", "Background in RSpec : 5936.9\n", "Livetime : 64.9 hours\n", "Energy threshold : 0.47 TeV\n", "Source flux (>1 TeV) : (15.362 +/- 0.534) x 10^-12 cm^-2 s^-1 = (67.97 +/- 2.36) % Crab\n", "\n", "Fluxes in RSpec (> 1 TeV):\n", "Map measurement : 5.571 x 10^-12 cm^-2 s^-1 = 24.65 % Crab\n", "Source model : 5.659 x 10^-12 cm^-2 s^-1 = 25.04 % Crab\n", "Other component model : 0.000 x 10^-12 cm^-2 s^-1 = 0.00 % Crab\n", "Large scale component model : 0.000 x 10^-12 cm^-2 s^-1 = 0.00 % Crab\n", "Total model : 5.659 x 10^-12 cm^-2 s^-1 = 25.04 % Crab\n", "Containment in RSpec : 36.8 %\n", "Contamination in RSpec : 0.0 %\n", "Flux correction (RSpec -> Total) : 271.4 %\n", "Flux correction (Total -> RSpec) : 36.8 %\n", "\n", "*** Info from spectral analysis ***\n", "\n", "Livetime : 18.8 hours\n", "Energy range: : 0.29 to 61.90 TeV\n", "Background : 9993.6\n", "Excess : 2584.4\n", "Spectral model : ECPL\n", "TS ECPL over PL : 43.0\n", "Best-fit model flux(> 1 TeV) : (17.433 +/- 1.404) x 10^-12 cm^-2 s^-1 = (77.14 +/- 6.21) % Crab\n", "Best-fit model energy flux(1 to 10 TeV) : (76.057 +/- 7.100) x 10^-12 erg cm^-2 s^-1\n", "Pivot energy : 3.02 TeV\n", "Flux at pivot energy : (1.833 +/- 0.070) x 10^-12 cm^-2 s^-1 TeV^-1 = (8.11 +/- 0.20) % Crab\n", "PL Flux(> 1 TeV) : (16.574 +/- 0.632) x 10^-12 cm^-2 s^-1 = (73.34 +/- 2.80) % Crab\n", "PL Flux(@ 1 TeV) : (14.805 +/- 0.737) x 10^-12 cm^-2 s^-1 TeV^-1 = (65.51 +/- 2.10) % Crab\n", "PL Index : 1.89 +/- 0.03\n", "ECPL Flux(@ 1 TeV) : (12.089 +/- 0.869) x 10^-12 cm^-2 s^-1 TeV^-1 = (53.49 +/- 2.48) % Crab\n", "ECPL Flux(> 1 TeV) : (17.433 +/- 1.404) x 10^-12 cm^-2 s^-1 = (77.14 +/- 6.21) % Crab\n", "ECPL Index : 1.35 +/- 0.08\n", "ECPL Lambda : 0.082 +/- 0.012 TeV^-1\n", "ECPL E_cut : 12.27 +/- 1.74 TeV\n", "\n", "*** Flux points info ***\n", "\n", "Number of flux points: 6\n", "Flux points table: \n", "\n", "e_ref e_min e_max dnde dnde_errn dnde_errp dnde_ul is_ul\n", " TeV TeV TeV 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV) \n", "------ ------ ------ --------------- --------------- --------------- --------------- -----\n", " 0.422 0.287 0.619 6.554e-11 1.050e-11 1.038e-11 8.637e-11 False\n", " 0.953 0.619 1.468 1.199e-11 1.292e-12 1.289e-12 1.453e-11 False\n", " 2.260 1.468 3.481 3.526e-12 2.358e-13 2.408e-13 4.018e-12 False\n", " 5.360 3.481 8.254 8.324e-13 5.496e-14 5.620e-14 9.496e-13 False\n", "12.711 8.254 19.573 1.596e-13 1.323e-14 1.349e-14 1.870e-13 False\n", "30.142 19.573 46.416 1.116e-14 2.189e-15 2.323e-15 1.599e-14 False\n", "\n", "*** Gaussian component info ***\n", "\n", "Number of components: 3\n", "Spatial components : HGPSC 001, HGPSC 002, HGPSC 003\n", "\n", "Component HGPSC 001:\n", "GLON : 263.969 +/- 0.023 deg\n", "GLAT : -3.344 +/- 0.027 deg\n", "Size : 0.237 +/- 0.023 deg\n", "Flux (>1 TeV) : (2.76 +/- 0.52) x 10^-12 cm^-2 s^-1 = (12.2 +/- 2.3) % Crab\n", "\n", "Component HGPSC 002:\n", "GLON : 263.699 +/- 0.021 deg\n", "GLAT : -2.827 +/- 0.037 deg\n", "Size : 0.156 +/- 0.018 deg\n", "Flux (>1 TeV) : (1.08 +/- 0.21) x 10^-12 cm^-2 s^-1 = (4.8 +/- 0.9) % Crab\n", "\n", "Component HGPSC 003:\n", "GLON : 263.983 +/- 0.033 deg\n", "GLAT : -2.997 +/- 0.046 deg\n", "Size : 0.650 +/- 0.027 deg\n", "Flux (>1 TeV) : (11.52 +/- 0.73) x 10^-12 cm^-2 s^-1 = (51.0 +/- 3.2) % Crab\n", "\n", "\n", "*** Source associations info ***\n", "\n", " Source_Name Association_Catalog Association_Name Separation\n", " deg \n", "-------------- ------------------- ----------------- ----------\n", "HESS J0835-455 2FHL 2FHL J0835.3-4511 0.473783\n", "HESS J0835-455 3FGL 3FGL J0835.3-4510 0.481869\n", "HESS J0835-455 COMP G263.9-3.3 0.321304\n", "HESS J0835-455 PSR B0833-45 0.483905\n", "\n", "*** Source identification info ***\n", "\n", "Source_Name: HESS J0835-455\n", "Identified_Object: Vela X\n", "Class: PWN\n", "Evidence: Morphology\n", "Reference: 2006A%26A...448L..43A\n", "Distance_Reference: SNRCat\n", "Distance: 0.2750000059604645 kpc\n", "Distance_Min: 0.25 kpc\n", "Distance_Max: 0.30000001192092896 kpc\n", "\n" ] } ], "source": [ "# To see a pretty-printed text version of all\n", "# HGPS data on a given source:\n", "print(source)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "*** Info from map analysis ***\n", "\n", "RA : 128.887 deg = 8h35m33s\n", "DEC : -45.659 deg = -45d39m32s\n", "GLON : 263.960 +/- 0.025 deg\n", "GLAT : -3.048 +/- 0.027 deg\n", "Position Error (68%) : 0.057 deg\n", "Position Error (95%) : 0.093 deg\n", "ROI number : 18\n", "Spatial model : 3-Gaussian\n", "Spatial components : HGPSC 001, HGPSC 002, HGPSC 003\n", "TS : 1553.2\n", "sqrt(TS) : 39.4\n", "Size : 0.585 +/- 0.052 (UL: nan) deg\n", "R70 : 0.887 deg\n", "RSpec : 0.500 deg\n", "Total model excess : 8781.6\n", "Excess in RSpec : 3580.1\n", "Model Excess in RSpec : 3637.4\n", "Background in RSpec : 5936.9\n", "Livetime : 64.9 hours\n", "Energy threshold : 0.47 TeV\n", "Source flux (>1 TeV) : (15.362 +/- 0.534) x 10^-12 cm^-2 s^-1 = (67.97 +/- 2.36) % Crab\n", "\n", "Fluxes in RSpec (> 1 TeV):\n", "Map measurement : 5.571 x 10^-12 cm^-2 s^-1 = 24.65 % Crab\n", "Source model : 5.659 x 10^-12 cm^-2 s^-1 = 25.04 % Crab\n", "Other component model : 0.000 x 10^-12 cm^-2 s^-1 = 0.00 % Crab\n", "Large scale component model : 0.000 x 10^-12 cm^-2 s^-1 = 0.00 % Crab\n", "Total model : 5.659 x 10^-12 cm^-2 s^-1 = 25.04 % Crab\n", "Containment in RSpec : 36.8 %\n", "Contamination in RSpec : 0.0 %\n", "Flux correction (RSpec -> Total) : 271.4 %\n", "Flux correction (Total -> RSpec) : 36.8 %\n", "\n" ] } ], "source": [ "# You can also more selectively print subsets of info:\n", "print(source.info(\"map\"))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$1.7432536 \\times 10^{-11} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$" ], "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# All of the data for this source is available\n", "# via the `source.data` dictionary if you want\n", "# to do some computations\n", "source.data[\"Flux_Spec_Int_1TeV\"]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=6\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
e_refe_mine_maxdndednde_errndnde_errpdnde_ulis_ul
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)
float32float32float32float32float32float32float32bool
0.4216970.2872980.6189666.55418e-111.04995e-111.03791e-118.63693e-11False
0.9531620.6189661.46781.1986e-111.29198e-121.28869e-121.45264e-11False
2.26031.46783.48073.52555e-122.35769e-132.40822e-134.0179e-12False
5.360023.48078.254048.32411e-135.49592e-145.62039e-149.49575e-13False
12.71068.2540419.57341.59624e-131.32323e-141.34932e-141.86966e-13False
30.141619.573446.41591.11619e-142.1889e-152.32286e-151.59949e-14False
" ], "text/plain": [ "\n", " e_ref e_min e_max ... dnde_errp dnde_ul is_ul\n", " TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) \n", "float32 float32 float32 ... float32 float32 bool\n", "-------- -------- -------- ... --------------- --------------- -----\n", "0.421697 0.287298 0.618966 ... 1.03791e-11 8.63693e-11 False\n", "0.953162 0.618966 1.4678 ... 1.28869e-12 1.45264e-11 False\n", " 2.2603 1.4678 3.4807 ... 2.40822e-13 4.0179e-12 False\n", " 5.36002 3.4807 8.25404 ... 5.62039e-14 9.49575e-13 False\n", " 12.7106 8.25404 19.5734 ... 1.34932e-14 1.86966e-13 False\n", " 30.1416 19.5734 46.4159 ... 2.32286e-15 1.59949e-14 False" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The flux points are available as a Table\n", "source.flux_points.table" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ExponentialCutoffPowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max\n", "\t--------- --------- --------- --------------- --- ---\n", "\t index 1.354e+00 7.733e-02 nan nan\n", "\tamplitude 6.408e-12 3.260e-13 1 / (cm2 s TeV) nan nan\n", "\treference 1.697e+00 0.000e+00 TeV nan nan\n", "\t lambda_ 8.152e-02 1.154e-02 1 / TeV nan nan\n", "\n", "Covariance: \n", "\n", "\t name index amplitude reference lambda_ \n", "\t--------- --------- --------- --------- ---------\n", "\t index 5.980e-03 0.000e+00 0.000e+00 0.000e+00\n", "\tamplitude 0.000e+00 1.063e-25 0.000e+00 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n", "\t lambda_ 0.000e+00 0.000e+00 0.000e+00 1.331e-04\n" ] } ], "source": [ "# The spectral model is available as a\n", "# Gammapy spectral model object:\n", "spectral_model = source.spectral_model()\n", "print(spectral_model)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$[1.5950556 \\times 10^{-11},~1.0744153 \\times 10^{-12}] \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$" ], "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# One common task is to compute integral fluxes\n", "# The error is computed using the covariance matrix\n", "# (off-diagonal info not given in HGPS, i.e. this is an approximation)\n", "spectral_model.integral_error(emin=1 * u.TeV, emax=10 * u.TeV)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's plot the spectrum\n", "source.spectral_model().plot(source.energy_range)\n", "source.spectral_model().plot_error(source.energy_range)\n", "source.flux_points.plot();" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Or let's make the same plot in the common\n", "# format with y = E^2 * dnde\n", "opts = dict(energy_power=2, flux_unit=\"erg-1 cm-2 s-1\")\n", "source.spectral_model().plot(source.energy_range, **opts)\n", "source.spectral_model().plot_error(source.energy_range, **opts)\n", "source.flux_points.plot(**opts)\n", "plt.ylabel(\"E^2 dN/dE (erg cm-2 s-1)\")\n", "plt.title(\"Vela X HGPS spectrum\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next section we will see how to work with the HGPS survey maps from Gammapy, as well as work with other data from the catalog (position and morphology information)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maps with Gammapy\n", "\n", "Let's use the [gammapy.maps.Map.read](..\/api/gammapy.maps.Map.rst#gammapy.maps.Map.read) method to load up the HGPS significance survey map." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "path = hgps_data_path / \"hgps_map_significance_0.1deg_v1.fits.gz\"\n", "survey_map = Map.read(path)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Map has a quick-look plot method, but it's not\n", "# very useful for a survey map that wide with default settings\n", "survey_map.plot();" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This is a little better\n", "fig = plt.figure(figsize=(15, 3))\n", "_ = survey_map.plot(stretch=\"sqrt\")\n", "# Note that we also assign the return value (a tuple)\n", "# from the plot method call to a variable called `_`\n", "# This is to avoid Jupyter printing it like in the last cell,\n", "# and generally `_` is a variable name used in Python\n", "# for things you don't want to name or care about at all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at a cutout of the Galactic center:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = survey_map.cutout(pos, width=(3.8, 2.5) * u.deg)\n", "fig, ax, _ = image.plot(stretch=\"sqrt\", cmap=\"inferno\")\n", "ax.coords[0].set_major_formatter(\"dd\")\n", "ax.coords[1].set_major_formatter(\"dd\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Side comment: If you like, you can format stuff to make it a bit more pretty. With a few lines you can get nice plots, with a few dozen publication-quality images. This is using [matplotlib](https://matplotlib.org/) and [astropy.visualization](http://docs.astropy.org/en/stable/visualization/index.html). Both are pretty complex, but there's many examples available and there's not really another good alternative anyways for astronomical sky images at the moment, so you should just go ahead and learn those.\n", "\n", "There's also a convenience method to look up the map value at a given sky position.\n", "Let's repeat this for the same position we did above with Numpy and Astropy:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 89.4969101], dtype=float32)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pos = SkyCoord(266.416826, -29.007797, unit=\"deg\")\n", "survey_map.get_by_coord(pos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vela X\n", "\n", "To finish up this tutorial, let's do something a bit more advanced, than involves the survey map, the HGPS source catalog, the multi-Gauss morphology component model. Let's show the Vela X pulsar wind nebula and the Vela Junior supernova remnant, and overplot some HGPS catalog data." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HESS J0835-455\n", "Component HGPSC 001:\n", "GLON : 263.969 +/- 0.023 deg\n", "GLAT : -3.344 +/- 0.027 deg\n", "Size : 0.237 +/- 0.023 deg\n", "Flux (>1 TeV) : (2.76 +/- 0.52) x 10^-12 cm^-2 s^-1 = (12.2 +/- 2.3) % Crab\n", "Component HGPSC 002:\n", "GLON : 263.699 +/- 0.021 deg\n", "GLAT : -2.827 +/- 0.037 deg\n", "Size : 0.156 +/- 0.018 deg\n", "Flux (>1 TeV) : (1.08 +/- 0.21) x 10^-12 cm^-2 s^-1 = (4.8 +/- 0.9) % Crab\n", "Component HGPSC 003:\n", "GLON : 263.983 +/- 0.033 deg\n", "GLAT : -2.997 +/- 0.046 deg\n", "Size : 0.650 +/- 0.027 deg\n", "Flux (>1 TeV) : (11.52 +/- 0.73) x 10^-12 cm^-2 s^-1 = (51.0 +/- 3.2) % Crab\n" ] } ], "source": [ "# The spatial model for Vela X in HGPS was three Gaussians\n", "print(source.name)\n", "for component in source.components:\n", " print(component)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from astropy.visualization import simple_norm\n", "from matplotlib.patches import Circle\n", "\n", "# Cutout and plot a nice image\n", "pos = SkyCoord(264.5, -2.5, unit=\"deg\", frame=\"galactic\")\n", "image = survey_map.cutout(pos, width=(\"6 deg\", \"4 deg\"))\n", "norm = simple_norm(image.data, stretch=\"sqrt\", min_cut=0, max_cut=20)\n", "fig = plt.figure(figsize=(12, 8))\n", "fig, ax, _ = image.plot(fig=fig, norm=norm, cmap=\"inferno\")\n", "transform = ax.get_transform(\"galactic\")\n", "\n", "# Overplot the pulsar\n", "# print(SkyCoord.from_name('Vela pulsar').galactic)\n", "ax.scatter(263.551, -2.787, transform=transform, s=500, color=\"cyan\")\n", "\n", "# Overplot the circle that was used for the HGPS spectral measurement of Vela X\n", "# It is centered on the centroid of the emission and has a radius of 0.5 deg\n", "x = source.data[\"GLON\"].value\n", "y = source.data[\"GLAT\"].value\n", "r = source.data[\"RSpec\"].value\n", "c = Circle(\n", " (x, y), r, transform=transform, edgecolor=\"white\", facecolor=\"none\", lw=4\n", ")\n", "ax.add_patch(c)\n", "\n", "# Overplot circles that represent the components\n", "for c in source.components:\n", " x = c.data[\"GLON\"].value\n", " y = c.data[\"GLAT\"].value\n", " r = c.data[\"Size\"].value\n", " c = Circle(\n", " (x, y), r, transform=transform, edgecolor=\"0.7\", facecolor=\"none\", lw=3\n", " )\n", " ax.add_patch(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note that for HGPS there are already spatial models available:\n", "\n", " print(source.spatial_model())\n", " source.components[0].spatial_model\n", "\n", "With some effort, you can use those to make HGPS model flux images.\n", "\n", "There are two reasons we're not showing this here: First, the spatial model code in Gammapy is work in progress, it will change soon. Secondly, doing morphology measurements on the public HGPS maps is discouraged; we note again that the maps are correlated and no detailed PSF information is published. So please be careful / conservative when extracting quantitative mesurements from the HGPS maps e.g. for a source of interest for you." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "This concludes this tutorial how to access and work with the HGPS data from Python, using Astropy and Gammapy.\n", "\n", "\n", "* If you have any questions about the HGPS data, please use the contact given at https://www.mpi-hd.mpg.de/hfm/HESS/hgps/ .\n", "* If you have any questions or issues about Astropy or Gammapy, please use the Gammapy mailing list (see http://gammapy.org/contact.html).\n", "\n", "**Please read the Appendix A of the paper to learn about the caveats to using the HGPS data. Especially note that the HGPS survey maps are correlated and thus no detailed source morphology analysis is possible, and also note the caveats concerning spectral models and spectral flux points.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "* Re-run this notebook, but change the HGPS source that was used for examples. If you have any questions about the data, try to access and print or plot it. Just to give a few ideas: How many identified pulsar wind nebulae are in HGPS? Which are the 5 brightest HGPS sources in the 1-10 TeV energy band? Which is the second-highest significance source in the HPGS image after the Galactic center?\n", "* Try to reproduce some of the figures in the HGPS paper. Don't try to reproduce them exactly, but just try to write ~ 10 lines of code to access the relevant HGPS data and make a quick plot that shows the same or similar information.\n", "* Fit a spectral model to the spectral points of Vela X and compare with the HGPS model fit. (they should be similar, but not identical, in HGPS a likelihood fit to counts data was done). For this task, the [spectrum_models](..\/notebooks/spectrum_models.rst) and [sed_fitting_gammacat_fermi](..\/notebooks/sed_fitting_gammacat_fermi.rst) tutorials will be useful.\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }