Commit dfe2738d4a11da14b69e71564fc59eebca61a1c8

Authored by Luigi Tibaldo
1 parent 66f86404

tutorial to generate skymaps

doc/source/users/tutorials/hess_dr1/skymap.ipynb 0 → 100644
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Generating a sky map and finding sources"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/skymap.ipynb\" download=\"skymap.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn how to generate a sky map from the selected events and how to find potential sources in the data.**\n",
  22 + "\n",
  23 + "As next step you will make a sky map of the selected events to inspect what’s in the data. You do this using the ctskymap tool. \n",
  24 + "\n",
  25 + "We start with the usual gammalib, ctools, and cscripts imports."
  26 + ]
  27 + },
  28 + {
  29 + "cell_type": "code",
  30 + "execution_count": 2,
  31 + "metadata": {},
  32 + "outputs": [],
  33 + "source": [
  34 + "import gammalib\n",
  35 + "import ctools\n",
  36 + "import cscripts"
  37 + ]
  38 + },
  39 + {
  40 + "cell_type": "markdown",
  41 + "metadata": {},
  42 + "source": [
  43 + "We will use the matplotlib package to display the results."
  44 + ]
  45 + },
  46 + {
  47 + "cell_type": "code",
  48 + "execution_count": 3,
  49 + "metadata": {},
  50 + "outputs": [],
  51 + "source": [
  52 + "%matplotlib inline\n",
  53 + "import matplotlib.pyplot as plt"
  54 + ]
  55 + },
  56 + {
  57 + "cell_type": "markdown",
  58 + "metadata": {},
  59 + "source": [
  60 + "We will use the events selected in the previous step. Since the data correspond to observations of the Crab nebula we should centre the sky map on this source. The energy limits of the sky map should be set to the energy interval of the data (0.66 - 100 TeV). "
  61 + ]
  62 + },
  63 + {
  64 + "cell_type": "code",
  65 + "execution_count": 29,
  66 + "metadata": {},
  67 + "outputs": [],
  68 + "source": [
  69 + "obsfile = \"obs_crab_selected.xml\"\n",
  70 + "emin = 0.66\n",
  71 + "emax = 100.\n",
  72 + "\n",
  73 + "skymap = ctools.ctskymap()\n",
  74 + "skymap['inobs'] = obsfile\n",
  75 + "skymap['proj'] = 'CAR'\n",
  76 + "skymap['coordsys'] = 'CEL'\n",
  77 + "skymap['xref'] = 83.63\n",
  78 + "skymap['yref'] = 22.01\n",
  79 + "skymap['binsz'] = 0.02\n",
  80 + "skymap['nxpix'] = 200\n",
  81 + "skymap['nypix'] = 200\n",
  82 + "skymap['emin'] = emin\n",
  83 + "skymap['emax'] = emax\n",
  84 + "skymap['bkgsubtract'] = 'NONE'\n",
  85 + "\n",
  86 + "\n",
  87 + "skymap.run()"
  88 + ]
  89 + },
  90 + {
  91 + "cell_type": "markdown",
  92 + "metadata": {},
  93 + "source": [
  94 + "We can visualise the skymap."
  95 + ]
  96 + },
  97 + {
  98 + "cell_type": "code",
  99 + "execution_count": 30,
  100 + "metadata": {},
  101 + "outputs": [
  102 + {
  103 + "data": {
  104 + "image/png": 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\n",
  105 + "text/plain": [
  106 + "<matplotlib.figure.Figure at 0x11f6265f8>"
  107 + ]
  108 + },
  109 + "metadata": {},
  110 + "output_type": "display_data"
  111 + }
  112 + ],
  113 + "source": [
  114 + "#module used to stretch the color palette\n",
  115 + "import matplotlib.colors as colors\n",
  116 + "\n",
  117 + "ax = plt.subplot()\n",
  118 + "plt.imshow(skymap.skymap().array(),origin='lower',\n",
  119 + " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200],\n",
  120 + " # boundaries of the coord grid\n",
  121 + " norm=colors.PowerNorm(gamma=0.5),\n",
  122 + " cmap=plt.get_cmap('jet')) # square root scale \n",
  123 + "ax.set_xlabel('R.A. (deg)')\n",
  124 + "ax.set_ylabel('Dec (deg)')\n",
  125 + "cbar = plt.colorbar()\n",
  126 + "cbar.set_label('Counts')"
  127 + ]
  128 + },
  129 + {
  130 + "cell_type": "markdown",
  131 + "metadata": {},
  132 + "source": [
  133 + "Based on the sky map you can now extract a model for all potentially significant sources in the data. You do this using the cssrcdetect script, and to extract only significant sources, you should set the detection threshold for example to 5 sigma. We will make a copy of the skymap since the detection process involves smoothing and we do not want to modify the original map."
  134 + ]
  135 + },
  136 + {
  137 + "cell_type": "code",
  138 + "execution_count": 31,
  139 + "metadata": {},
  140 + "outputs": [],
  141 + "source": [
  142 + "srcmodel = 'crab.xml'\n",
  143 + "regfile = 'crab_sources.ds9'\n",
  144 + "\n",
  145 + "srcdetect = cscripts.cssrcdetect(skymap.skymap().copy())\n",
  146 + "srcdetect['srcmodel'] = 'POINT'\n",
  147 + "srcdetect['bkgmodel'] = 'NONE' # we will determine the background model in a later step\n",
  148 + "srcdetect['threshold'] = 5\n",
  149 + "srcdetect['corr_rad'] = 0.1\n",
  150 + "srcdetect['outmodel'] = srcmodel\n",
  151 + "srcdetect['outds9file'] = regfile\n",
  152 + "\n",
  153 + "srcdetect.execute()"
  154 + ]
  155 + },
  156 + {
  157 + "cell_type": "markdown",
  158 + "metadata": {},
  159 + "source": [
  160 + "Let's look at the resulting model. It contains a point source Src001 at the position of the Crab nebula."
  161 + ]
  162 + },
  163 + {
  164 + "cell_type": "code",
  165 + "execution_count": 32,
  166 + "metadata": {},
  167 + "outputs": [
  168 + {
  169 + "name": "stdout",
  170 + "output_type": "stream",
  171 + "text": [
  172 + "=== GModels ===\n",
  173 + " Number of models ..........: 1\n",
  174 + " Number of parameters ......: 6\n",
  175 + "=== GModelSky ===\n",
  176 + " Name ......................: Src001\n",
  177 + " Instruments ...............: all\n",
  178 + " Instrument scale factors ..: unity\n",
  179 + " Observation identifiers ...: all\n",
  180 + " Model type ................: PointSource\n",
  181 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  182 + " Number of parameters ......: 6\n",
  183 + " Number of spatial par's ...: 2\n",
  184 + " RA .......................: 83.6192131308071 +/- 0 deg (free,scale=1)\n",
  185 + " DEC ......................: 22.0199996472185 +/- 0 deg (free,scale=1)\n",
  186 + " Number of spectral par's ..: 3\n",
  187 + " Prefactor ................: 5.7e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  188 + " Index ....................: -2.48 +/- 0 [10,-10] (free,scale=-2.48,gradient)\n",
  189 + " PivotEnergy ..............: 300000 MeV (fixed,scale=300000,gradient)\n",
  190 + " Number of temporal par's ..: 1\n",
  191 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  192 + ]
  193 + }
  194 + ],
  195 + "source": [
  196 + "models = gammalib.GModels(srcmodel)\n",
  197 + "print(models)"
  198 + ]
  199 + },
  200 + {
  201 + "cell_type": "markdown",
  202 + "metadata": {},
  203 + "source": [
  204 + "We can overlay the position of the source we found to the smoothed skymap to make sure everything worked smoothly."
  205 + ]
  206 + },
  207 + {
  208 + "cell_type": "code",
  209 + "execution_count": 45,
  210 + "metadata": {},
  211 + "outputs": [
  212 + {
  213 + "name": "stderr",
  214 + "output_type": "stream",
  215 + "text": [
  216 + "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1238: UserWarning: Power-law scaling on negative values is ill-defined, clamping to 0.\n",
  217 + " warnings.warn(\"Power-law scaling on negative values is \"\n"
  218 + ]
  219 + },
  220 + {
  221 + "data": {
  222 + "text/plain": [
  223 + "<matplotlib.collections.PathCollection at 0x121307c88>"
  224 + ]
  225 + },
  226 + "execution_count": 45,
  227 + "metadata": {},
  228 + "output_type": "execute_result"
  229 + },
  230 + {
  231 + "name": "stderr",
  232 + "output_type": "stream",
  233 + "text": [
  234 + "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1199: RuntimeWarning: invalid value encountered in power\n",
  235 + " np.power(resdat, gamma, resdat)\n"
  236 + ]
  237 + },
  238 + {
  239 + "data": {
  240 + "image/png": 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\n",
  241 + "text/plain": [
  242 + "<matplotlib.figure.Figure at 0x120d52278>"
  243 + ]
  244 + },
  245 + "metadata": {},
  246 + "output_type": "display_data"
  247 + }
  248 + ],
  249 + "source": [
  250 + "ax1 = plt.subplot()\n",
  251 + "plt.imshow(srcdetect._map.array(),origin='lower',\n",
  252 + " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200],\n",
  253 + " # boundaries of the coord grid\n",
  254 + " norm=colors.PowerNorm(gamma=0.5),\n",
  255 + " cmap=plt.get_cmap('jet')) # square root scale \n",
  256 + "ax1.set_xlabel('R.A. (deg)')\n",
  257 + "ax1.set_ylabel('Dec (deg)')\n",
  258 + "cbar = plt.colorbar()\n",
  259 + "cbar.set_label('Counts')\n",
  260 + "\n",
  261 + "crab = models['Src001']\n",
  262 + "\n",
  263 + "ax1.scatter(crab['RA'].value(),crab['DEC'].value(),\n",
  264 + " marker='x',c='white')"
  265 + ]
  266 + }
  267 + ],
  268 + "metadata": {
  269 + "kernelspec": {
  270 + "display_name": "Python 3",
  271 + "language": "python",
  272 + "name": "python3"
  273 + },
  274 + "language_info": {
  275 + "codemirror_mode": {
  276 + "name": "ipython",
  277 + "version": 3
  278 + },
  279 + "file_extension": ".py",
  280 + "mimetype": "text/x-python",
  281 + "name": "python",
  282 + "nbconvert_exporter": "python",
  283 + "pygments_lexer": "ipython3",
  284 + "version": "3.5.4"
  285 + }
  286 + },
  287 + "nbformat": 4,
  288 + "nbformat_minor": 2
  289 +}
... ...
doc/source/users/tutorials/hess_dr1/skymap.rst deleted
1   -.. _hess_dr1_sky_map:
2   -
3   -Generating a sky map and finding sources
4   -----------------------------------------
5   -
6   - .. admonition:: What you will learn
7   -
8   - You will learn **how to generate a sky map from the selected events**
9   - and **how to find potential sources in the data**.
10   -
11   -As next step you will make a sky map of the selected events to inspect what's
12   -in the data.
13   -You do this using the :ref:`ctskymap` tool.
14   -Since the data correspond to observations of the Crab nebula you should centre
15   -the sky map on this source.
16   -The energy limits of the sky map should be set to the energy interval of the
17   -data (0.66 - 100 TeV).
18   -To create the sky map, type:
19   -
20   -.. code-block:: bash
21   -
22   - $ ctskymap
23   - Input event list or observation definition XML file [events.fits] obs_crab_selected.xml
24   - Coordinate system (CEL - celestial, GAL - galactic) (CEL|GAL) [CEL]
25   - Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
26   - First coordinate of image center in degrees (RA or galactic l) (0-360) [83.63]
27   - Second coordinate of image center in degrees (DEC or galactic b) (-90-90) [22.01]
28   - Image scale (in degrees/pixel) [0.02]
29   - Size of the X axis in pixels [200]
30   - Size of the Y axis in pixels [200]
31   - Lower energy limit (TeV) [0.1] 0.66
32   - Upper energy limit (TeV) [100.0]
33   - Background subtraction method (NONE|IRF|RING) [NONE]
34   - Output skymap file [skymap.fits]
35   -
36   -The resulting sky map, displayed using
37   -`ds9 <http://ds9.si.edu>`_ with a square-root color scaling, is shown below:
38   -
39   -.. figure:: skymap.png
40   - :width: 400px
41   - :align: center
42   -
43   - *Sky map of the selected events centred on the Crab nebula*
44   -
45   -Based on the sky map you can now extract a model for all potentially significant
46   -sources in the data.
47   -You do this using the :ref:`cssrcdetect` script, and to extract only significant
48   -sources, you should set the detection threshold for example to 5 sigma.
49   -For convenience, the :ref:`cssrcdetect` script can also add a background model
50   -component to the output
51   -:ref:`model definition XML file <glossary_moddef>`.
52   -Since the H.E.S.S. Instrument Response Function does not include a background
53   -template, the ``IRF`` option will not work.
54   -While sophisticated methods for the analysis of H.E.S.S. data still need to
55   -be developed, you may try the effective area model ``AEFF`` that uses the
56   -effective area as spatial template for the background component.
57   -So, now type:
58   -
59   -.. code-block:: bash
60   -
61   - $ cssrcdetect
62   - Input sky map file [skymap.fits]
63   - Source model type (POINT) [POINT]
64   - Background model type (NONE|IRF|AEFF|CUBE|RACC) [NONE] AEFF
65   - Detection threshold (Gaussian sigma) [5.0]
66   - Correlation kernel radius (deg) [0.1]
67   - Output model definition XML file [models.xml] crab_models.xml
68   - Output DS9 region file [ds9.reg] crab.reg
69   -
70   -Below an excerpt of the ``cssrcdetect.log`` log file.
71   -Only one significant source was found, and its position is consistent with the
72   -Crab nebula.
73   -This is also illustrated in the figure below where the region file
74   -``crab.reg`` that was produced by :ref:`cssrcdetect` was overlaid with
75   -`ds9 <http://ds9.si.edu>`_
76   -on the sky map.
77   -
78   -.. code-block:: none
79   -
80   - 2018-09-11T20:32:20: +==================+
81   - 2018-09-11T20:32:20: | Source detection |
82   - 2018-09-11T20:32:20: +==================+
83   - 2018-09-11T20:32:20: === Iteration 1 ===
84   - 2018-09-11T20:32:20: Map maximum ...............: 11.1003120131
85   - 2018-09-11T20:32:20: Src001 position ...........: (RA,Dec)=(83.6192131308071,22.0199996472185)
86   - 2018-09-11T20:32:20: === Iteration 2 ===
87   - 2018-09-11T20:32:20: Map maximum ...............: None above threshold
88   -
89   -.. figure:: skymap_crab.png
90   - :width: 400px
91   - :align: center
92   -
93   - *Sky map of the selected events centred on the Crab nebula*
94   -
95   -The
96   -:ref:`model definition file <glossary_moddef>`
97   -``crab_models.xml`` that was generated by :ref:`cssrcdetect` is shown below.
98   -It contains a point source ``Src001`` at the position of the Crab nebula
99   -and an effective area background model scaled by a spectral power law as
100   -background component.
101   -
102   -.. code-block:: xml
103   -
104   - <?xml version="1.0" encoding="UTF-8" standalone="no"?>
105   - <source_library title="source library">
106   - <source name="Src001" type="PointSource">
107   - <spectrum type="PowerLaw">
108   - <parameter name="Prefactor" value="1" error="0" scale="5.7e-18" min="0" free="1" />
109   - <parameter name="Index" value="1" error="-0" scale="-2.48" min="-4.03225806451613" max="4.03225806451613" free="1" />
110   - <parameter name="PivotEnergy" value="1" scale="300000" free="0" />
111   - </spectrum>
112   - <spatialModel type="PointSource">
113   - <parameter name="RA" value="83.6192131308071" error="0" scale="1" free="1" />
114   - <parameter name="DEC" value="22.0199996472185" error="0" scale="1" free="1" />
115   - </spatialModel>
116   - </source>
117   - <source name="Background" type="CTAAeffBackground">
118   - <spectrum type="PowerLaw">
119   - <parameter name="Prefactor" value="1" error="0" scale="1e-13" min="0" free="1" />
120   - <parameter name="Index" value="1" error="-0" scale="-2.5" min="-4" max="4" free="1" />
121   - <parameter name="PivotEnergy" value="1" scale="1000000" free="0" />
122   - </spectrum>
123   - </source>
124   - </source_library>
doc/source/users/tutorials/notebooks.rst
... ... @@ -20,6 +20,9 @@ this page.
20 20 * :download:`How to perform a stacked analysis? <howto/howto_stacked_analysis.ipynb>`
21 21 * :download:`How to do advanced model manipulation and fitting in Python? <howto/howto_advanced_python_fitting.ipynb>`
22 22 * :download:`Data preparation for 1DC How-tos <howto/howto_prepare_1dc.ipynb>`
23   -* :download:`Selecting events for H.E.S.S. public data <hess_dr1/selecting_events.ipynb>`
  23 +* :download:`Selecting events for H.E.S.S. public data
  24 + <hess_dr1/selecting_events.ipynb>`
  25 + * :download:`Generating a sky map and finding sources for
  26 + H.E.S.S. public data <hess_dr1/skymap.ipynb>`
24 27 * :download:`Classical analysis of H.E.S.S. public data <hess_dr1/classical.ipynb>`
25 28  
... ...