{ "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/v0.9?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](https://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](https://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": [ "3.0.5\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": [ "0.9\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": [ "3.0.2\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 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": 6, "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": 7, "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/deil/work/code/gammapy-docs/build/dev/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": 8, "metadata": {}, "outputs": [], "source": [ "path = hgps_data_path / \"hgps_catalog_v1.fits.gz\"\n", "hdu_list = fits.open(str(path))" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "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": 11, "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": 12, "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": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HESS J1026-582'" ] }, "execution_count": 14, "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": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'2-Gaussian', '3-Gaussian', 'Gaussian', 'Point-Like', 'Shell'}" ] }, "execution_count": 16, "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": 17, "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": 18, "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", "\n", "with open(str(path), \"w\") as f:\n", " f.write(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": 19, "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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is the problematic header key\n", "table.meta[\"comments\"]" ] }, { "cell_type": "code", "execution_count": 20, "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": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.4216965 0.9531619 2.260303 5.360023 12.710618 30.141624\n", " nan nan nan nan nan nan\n", " nan nan nan nan nan nan\n", " nan nan nan nan nan nan\n", " nan nan nan nan nan nan\n", " nan nan nan nan nan nan\n", " 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": 22, "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": 23, "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": 24, "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": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "astropy.io.fits.hdu.image.PrimaryHDU" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdu = hdu_list[0]\n", "type(hdu)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(hdu.data)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(500, 9400)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdu.data.shape" ] }, { "cell_type": "code", "execution_count": 28, "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": 28, "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": 29, "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": 29, "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": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "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": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array(3752.2868084), array(247.19237757))" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xp, yp = pos.to_pixel(wcs)\n", "xp, yp" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "(array(3752.2868084), array(247.19237757))\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": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(247, 3752)" ] }, "execution_count": 33, "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": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "89.49691" ] }, "execution_count": 34, "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": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "89.49691" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The pixel with the maximum significance\n", "hdu.data.max()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(247, 3752)" ] }, "execution_count": 36, "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": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "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": 38, "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": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HGPS_SOURCES'" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat.table.meta[\"EXTNAME\"]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'HGPS_GAUSS_COMPONENTS'" ] }, "execution_count": 40, "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": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SourceCatalogObjectHGPS('HESS J0835-455')" ] }, "execution_count": 41, "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": 42, "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 0.4737829\n", "HESS J0835-455 3FGL 3FGL J0835.3-4510 0.48186883\n", "HESS J0835-455 COMP G263.9-3.3 0.32130364\n", "HESS J0835-455 PSR B0833-45 0.48390508\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": 43, "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": 44, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$1.7432536 \\times 10^{-11} \\; \\mathrm{\\frac{1}{s\\,cm^{2}}}$$" ], "text/plain": [ "" ] }, "execution_count": 44, "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": 45, "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
0.42169650.287298470.61896586.5541766e-111.0499469e-111.037914e-118.636929e-11False
0.95316190.61896581.46779931.1986031e-111.2919824e-121.2886871e-121.4526381e-11False
2.2603031.46779933.48070053.52555e-122.3576906e-132.4082174e-134.0178963e-12False
5.3600233.48070058.2540428.324112e-135.495922e-145.6203943e-149.495746e-13False
12.7106188.25404219.5734181.5962396e-131.3232291e-141.3493189e-141.8696622e-13False
30.14162419.57341846.415891.11618885e-142.1889015e-152.3228553e-151.5994852e-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", "0.4216965 0.28729847 0.6189658 ... 1.037914e-11 8.636929e-11 False\n", "0.9531619 0.6189658 1.4677993 ... 1.2886871e-12 1.4526381e-11 False\n", " 2.260303 1.4677993 3.4807005 ... 2.4082174e-13 4.0178963e-12 False\n", " 5.360023 3.4807005 8.254042 ... 5.6203943e-14 9.495746e-13 False\n", "12.710618 8.254042 19.573418 ... 1.3493189e-14 1.8696622e-13 False\n", "30.141624 19.573418 46.41589 ... 2.3228553e-15 1.5994852e-14 False" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The flux points are available as a Table\n", "source.flux_points.table" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ExponentialCutoffPowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- --------- -------------- --- --- ------\n", "\t index 1.354e+00 7.733e-02 nan nan False\n", "\tamplitude 6.408e-12 3.260e-13 cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.697e+00 0.000e+00 TeV nan nan True\n", "\t lambda_ 8.152e-02 1.154e-02 TeV-1 nan nan False\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": 47, "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": 47, "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": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": 50, "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": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([89.49691], dtype=float32)" ] }, "execution_count": 54, "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": 55, "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": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "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 https://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": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }