{
"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/cta_1dc_introduction.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",
"[cta_1dc_introduction.ipynb](../_static/notebooks/cta_1dc_introduction.ipynb) |\n",
"[cta_1dc_introduction.py](../_static/notebooks/cta_1dc_introduction.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![CTA first data challenge logo](images/cta-1dc.png)\n",
"\n",
"# CTA first data challenge (1DC) with Gammapy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In 2017 and 2018, the [CTA observatory](https://www.cta-observatory.org/) did a first data challenge, called CTA DC-1, where CTA high-level data was simulated assuming a sky model and using CTA IRFs.\n",
"\n",
"The main page for CTA 1DC is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA internal)\n",
"\n",
"There you will find information on 1DC sky models, data access, data organisation, simulation and analysis tools, simulated observations, as well as contact information if you have any questions or comments.\n",
"\n",
"### This tutorial notebook\n",
"\n",
"This notebook shows you how to get started with CTA 1DC data and what it contains.\n",
"\n",
"You will learn how to use Astropy and Gammapy to:\n",
"\n",
"* access event data\n",
"* access instrument response functions (IRFs)\n",
"* use index files and the ``gammapy.data.DataStore`` to access all data\n",
"* use the observation index file to select the observations you're interested in\n",
"* read model XML files from Python (not integrated in Gammapy yet)\n",
"\n",
"This is to familiarise ourselves with the data files and to get an overview.\n",
"\n",
"### Further information\n",
"\n",
"How to analyse the CTA 1DC data with Gammapy (make an image and spectrum) is shown in a second notebook [cta_data_analysis.ipynb](cta_data_analysis.ipynb). If you prefer, you can of course just skim or skip this notebook and go straight to second one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook and Gammapy Setup\n",
"\n",
"Before we get started, please execcute the following code cells.\n",
"\n",
"The first one configures the notebooks so that plots are shown inline (if you don't do this, separate windows will pop up). The second cell imports and checks the version of the packages we will use below. If you're missing some packages, install them and then select \"Kernel -> Restart\" above to restart this notebook.\n",
"\n",
"In case you're new to Jupyter notebooks: to execute a cell, select it, then type \"SHIFT\" + \"ENTER\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy: 1.15.4\n",
"astropy: 3.0.5\n",
"gammapy: 0.9\n"
]
}
],
"source": [
"import numpy as np\n",
"import astropy\n",
"import gammapy\n",
"\n",
"print(\"numpy:\", np.__version__)\n",
"print(\"astropy:\", astropy.__version__)\n",
"print(\"gammapy:\", gammapy.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DC-1 data\n",
"\n",
"In this and other Gammapy tutorials we will only access a few data files from CTA DC-1 from `$GAMMAPY_DATA/cta-1dc` that you will have if you followed the \"Getting started with Gammapy\" instructions and executed `gammapy download tutorials`.\n",
"\n",
"Information how to download more or all of the DC-1 data (in total 20 GB) is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access\n",
"\n",
"Working with that data with Gammapy is identical to what we show here, except that the recommended way to do it is to point `DataStore.read` at `$CTADATA/index/gps` or whatever dataset or files you'd like to access there."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/deil/work/gammapy-tutorials/datasets/cta-1dc\r\n"
]
}
],
"source": [
"!echo $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README.md \u001b[34mcaldb\u001b[m\u001b[m \u001b[34mdata\u001b[m\u001b[m \u001b[34mindex\u001b[m\u001b[m make.py\r\n"
]
}
],
"source": [
"!ls $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Let's have a look around at the directories and files in `$CTADATA`.\n",
"\n",
"We will look at the `data` folder with events, the `caldb` folder with the IRFs and the `index` folder with the index files. At the end, we will also mention what the `model` and `obs` folder contains, but they aren't used with Gammapy, at least not at the moment.\n",
"\n",
"## EVENT data\n",
"\n",
"First, the EVENT data (RA, DEC, ENERGY, TIME of each photon or hadronic background event) is in the `data` folder, with one observation per file. The \"baseline\" refers to the assumed CTA array that was used to simulate the observations. The number in the filename is the observation identifier `OBS_ID` of the observation. Observations are ~ 30 minutes, pointing at a fixed location on the sky."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gps_baseline_110380.fits\r\n",
"gps_baseline_111140.fits\r\n",
"gps_baseline_111159.fits\r\n",
"gps_baseline_111630.fits\r\n"
]
}
],
"source": [
"!ls -1 $GAMMAPY_DATA/cta-1dc/data/baseline/gps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open up the first event list using the Gammapy `EventList` class, which contains the ``EVENTS`` table data via the ``table`` attribute as an Astropy `Table` object."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import EventList\n",
"\n",
"path = \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n",
"events = EventList.read(path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.data.event_list.EventList"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.table.table.Table"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events.table)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Row index=0 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID\n",
" s deg deg TeV deg deg \n",
" uint32 float64 float32 float32 float32 float32 float32 int32\n",
"-------- ----------------- --------- ---------- ---------- ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First event (using [] for \"indexing\")\n",
"events.table[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=2 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First few events (using [] for \"slicing\")\n",
"events.table[:2]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.time.core.Time"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event times can be accessed as Astropy Time objects\n",
"type(events.time)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.time[:2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2021-01-21T12:00:03.045(TT)' '2021-01-21T12:00:05.258(TT)']\n"
]
}
],
"source": [
"# Convert event time to more human-readable format\n",
"print(events.time[:2].fits)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event positions can be accessed as Astropy SkyCoord objects\n",
"print(type(events.radec))\n",
"events.radec[:2]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.galactic[:2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# The event header information is stored\n",
"# in the `events.table.meta` dictionary\n",
"print(type(events.table.meta))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(267.68121338, -29.6075)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# E.g. to get the observation pointing position in degrees:\n",
"events.table.meta[\"RA_PNT\"], events.table.meta[\"DEC_PNT\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EVENT analysis example\n",
"\n",
"As an example how to work with EVENT data, let's look at the spatial and energy distribution of the events for a single run.\n",
"\n",
"Note that EVENT data in Gammapy is just stored in an Astropy Table, which is basically a dictionary mapping column names to column data, where the column data is a Numpy array. This means you can efficienly process it from Python using any of the scientific Python tools you like (e.g. Numpy, Scipy, scikit-image, scikit-learn, ...)\n",
"\n",
"### EVENT spatial distribution\n",
"\n",
"To illustrate a bit how to work with EVENT table an header data,\n",
"let's plot the event positions as well as the pointing position."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Event positions\n",
"pos = events.galactic[::300] # sub-sample every 100th event\n",
"plt.scatter(pos.l.wrap_at(\"180 deg\").deg, pos.b.deg, s=10)\n",
"# Pointing position\n",
"pos_pnt = events.pointing_radec.galactic\n",
"plt.scatter(\n",
" pos_pnt.l.wrap_at(\"180 deg\").deg, pos_pnt.b.deg, marker=\"*\", s=400, c=\"red\"\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")\n",
"pos_pnt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EVENT energy distribution\n",
"\n",
"Let's have a look at the event energy distribution."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"plt.hist(energy, bins=energy_bins)\n",
"plt.semilogx()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A double-peak, at ~ 30 GeV and ~ 100 GeV? .... let's try to find out what's going on ...\n",
"\n",
"### EVENT MC_ID\n",
"\n",
"One idea could be to split the data into gamma-ray and hadronic background events.\n",
"Now from looking at the FITS header, one can see that ``MC_ID == 1`` is the hadronic background, and the other values are for different gamma-ray emission components."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 106217\n",
"Number of gammas: 8239\n",
"Number of hadrons: 97978\n"
]
}
],
"source": [
"is_gamma = events.table[\"MC_ID\"] != 1\n",
"print(\"Number of events: \", len(events.table))\n",
"print(\"Number of gammas: \", is_gamma.sum())\n",
"print(\"Number of hadrons: \", len(events.table) - is_gamma.sum())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"opts = dict(bins=energy_bins, density=True, histtype=\"step\")\n",
"plt.hist(energy[~is_gamma], label=\"hadron\", **opts)\n",
"plt.hist(energy[is_gamma], label=\"gamma\", **opts)\n",
"plt.loglog()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\")\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the energy spectra are roughly power-laws. \n",
"When plotting in log-log, one can see that the spectra are roughly power-laws (as expected), and the \"double peak\" we saw before looks more like a \"double energy threshold\" pattern.\n",
"It's there for gammas and background, and below 100 GeV the signal to background ratio is lower.\n",
"\n",
"What we're seeing here is the result of a mixed-array in CTA with LSTs, MSTs and SSTs, which have different energy thresholds:\n",
"\n",
"* ~ 30 GeV is the energy threshold of the LSTs\n",
"* ~ 100 GeV is the energy threshold of the MSTs\n",
"* the energy threshold of the SSTs is at a few TeV and doesn't show up as a clear feature in the gamma and background energy distribution (probably the rise in slope in gamma in the 1-10 TeV range is due to the SSTs).\n",
"\n",
"Let's look a little deeper and also check the event offset distribution in the field of view ...\n",
"\n",
"### EVENT FOV offset\n",
"\n",
"The event position and offset in the field of view (FOV) can be computed from the event RA, DEC position and the observation pointing RA, DEC position.\n",
"\n",
"But actually, the field of view position is stored as extra columns in the EVENT list: ``DETX`` and ``DETY`` (angles in degree, I think RA / DEC aligned field of view system).\n",
"\n",
"I presume (hope) this unnecessary information will be dropped from the CTA event lists in the future to save some space (make the CTA DL3 data ~25% smaller), but for now, let's use those columns to compute the field of view offset and look at the offset-energy distribution of the events."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Offset (deg)')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy_bins = 10 ** np.linspace(-2, 2, 100)\n",
"offset_bins = np.arange(0, 5.5, 0.1)\n",
"\n",
"t = events.table\n",
"offset = np.sqrt(t[\"DETX\"] ** 2 + t[\"DETY\"] ** 2)\n",
"hist = np.histogram2d(\n",
" x=t[\"ENERGY\"], y=offset, bins=(energy_bins, offset_bins)\n",
")[0].T\n",
"\n",
"from matplotlib.colors import LogNorm\n",
"\n",
"plt.pcolormesh(energy_bins, offset_bins, hist, norm=LogNorm())\n",
"plt.semilogx()\n",
"plt.colorbar()\n",
"plt.xlabel(\"Energy (TeV)\")\n",
"plt.ylabel(\"Offset (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the CTA field of view increases with energy in steps. The energy distribution we saw before was the combination of the energy distribution at all offsets. Even at a single offset, the double energy-threshold at ~ 30 GeV and ~ 100 GeV is present.\n",
"\n",
"There is also a quick-look peek method you can use to get a peek at the contents of an event list:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"events.peek()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stacking EVENTS tables\n",
"\n",
"As a final example of how to work with event lists, here's now to apply arbitrary event selections and how to stack events tables from several observations into a single event list. \n",
"\n",
"We will just use `astropy.table.Table` directly, not go via the `gammapy.data.EventList` class. Note that you can always make an `EventList` object from a `Table` object via `event_list = EventList(table)`. One point to keep in mind is that `Table.read` doesn't resolve environment variables in filenames, so we'll use the Python standard library `os` package to construct the filenames."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from astropy.table import Table\n",
"from astropy.table import vstack as table_vstack\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_110380.fits\",\n",
")\n",
"t1 = Table.read(filename, hdu=\"EVENTS\")\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_111140.fits\",\n",
")\n",
"t2 = Table.read(filename, hdu=\"EVENTS\")\n",
"tables = [t1, t2]\n",
"table = table_vstack(tables, metadata_conflicts=\"silent\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 212603\n"
]
}
],
"source": [
"print(\"Number of events: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events after selection: 215\n"
]
}
],
"source": [
"# Let's select gamma rays with energy above 10 TeV\n",
"mask_mc_id = table[\"MC_ID\"] != 1\n",
"mask_energy = table[\"ENERGY\"] > 10\n",
"mask = mask_mc_id & mask_energy\n",
"table2 = table[mask]\n",
"print(\"Number of events after selection:\", len(table2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When processing a lot or all of the 1DC events, you would write a for loop, and apply the event selection before putting the table in the list of tables, or you might run out of memory. An example is [here](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py).\n",
"\n",
"That's all for ``EVENTS``. You now know what every column in the event table contains, and how to work with event list tables using ``gammapy.data.EventList`` and ``astropy.table.Table``. \n",
"\n",
"Just in case that there is some observation parameter in the FITS EVENTS header that you're interested in, you can find the full description of the keys you can access via the ``events.table.meta`` dictionary [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IRFs\n",
"\n",
"The CTA instrument reponse functions (IRFs) are given as FITS files in the `caldb` folder.\n",
"\n",
"Note that this is not realistic. Real CTA data at the DL3 level (what we have here, what users get) will mostly likely have per-observation or per-time interval IRFs, and the IRFs will not be stored in a separate CALDB folder, but distributed with the EVENTS (probably in the same file, or at least in the same folder, so that it's together).\n",
"\n",
"For now, the EVENT to IRF association (i.e. which IRF is the right one for given EVENTS) is done by index files. We will discuss those in the next section, but before we do, let's look at the CTA IRFs for one given configuration: `South_z20_50h`."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"caldb\r\n",
"└── data\r\n",
" └── cta\r\n",
" └── 1dc\r\n",
" └── bcf\r\n",
" └── South_z20_50h\r\n",
" └── irf_file.fits\r\n",
"\r\n",
"5 directories, 1 file\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree caldb)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: /Users/deil/work/gammapy-tutorials/datasets/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\n",
"No. Name Ver Type Cards Dimensions Format\n",
" 0 PRIMARY 1 PrimaryHDU 8 () \n",
" 1 EFFECTIVE AREA 1 BinTableHDU 53 1R x 5C [42E, 42E, 6E, 6E, 252E] \n",
" 2 POINT SPREAD FUNCTION 1 BinTableHDU 70 1R x 10C [25E, 25E, 6E, 6E, 150E, 150E, 150E, 150E, 150E, 150E] \n",
" 3 ENERGY DISPERSION 1 BinTableHDU 56 1R x 7C [500E, 500E, 300E, 300E, 6E, 6E, 900000E] \n",
" 4 BACKGROUND 1 BinTableHDU 59 1R x 7C [36E, 36E, 36E, 36E, 21E, 21E, 27216E] \n"
]
}
],
"source": [
"# Let's look at the content of one of the IRF FITS files.\n",
"# IRFs are stored in `BinTable` HDUs in a weird format\n",
"# that you don't need to care about because it's implemented in Gammapy\n",
"irf_filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\",\n",
")\n",
"from astropy.io import fits\n",
"\n",
"hdu_list = fits.open(irf_filename)\n",
"hdu_list.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effective area\n",
"\n",
"The effective area is given as a 2-dim array with energy and field of view offset axes."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.irf import EffectiveAreaTable2D\n",
"\n",
"aeff = EffectiveAreaTable2D.read(irf_filename, hdu=\"EFFECTIVE AREA\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.irf.effective_area.EffectiveAreaTable2D"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.utils.nddata.NDDataArray"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"energy : size = 42, min = 0.014 TeV, max = 177.828 TeV\n",
"offset : size = 6, min = 0.500 deg, max = 5.500 deg\n",
"Data : size = 252, min = 0.000 m2, max = 5371581.000 m2\n",
"\n"
]
}
],
"source": [
"print(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"aeff.peek()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[3.7835869] \\; \\mathrm{km^{2}}$$"
],
"text/plain": [
""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What is the on-axis effective area at 10 TeV?\n",
"aeff.data.evaluate(energy=\"10 TeV\", offset=\"0 deg\").to(\"km2\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how you slice out an `EffectiveAreaTable` object\n",
"# at a given field of view offset for analysis\n",
"aeff.to_effective_area_table(offset=\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy dispersion\n",
"\n",
"Let's have a look at the CTA energy dispersion with three axes: true energy, fov offset and migra = e_reco / e_true and has dP / dmigra as value.\n",
"\n",
"Similar to the event energy distribution above, we can see the mixed-telescope array reflected in the EDISP.\n",
"At low energies the events are only detected and reconstructed by the LSTs.\n",
"At ~100 GeV, the MSTs take over and EDISP is chaotic in the ~ 50 GeV to 100 GeV energy range.\n",
"So it can be useful to have quick access to IRFs like with Gammapy (e.g. for spectral line searches in this case), even if for 95% of science analyses users later won't have to look at the IRFs and just trust that everything is working."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDispersion2D\n",
"\n",
"edisp = EnergyDispersion2D.read(irf_filename, hdu=\"ENERGY DISPERSION\")\n",
"print(type(edisp))\n",
"print(type(edisp.data))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"e_true : size = 500, min = 0.005 TeV, max = 495.450 TeV\n",
"migra : size = 300, min = 0.005, max = 2.995\n",
"offset : size = 6, min = 0.500 deg, max = 5.500 deg\n",
"Data : size = 900000, min = 0.000, max = 10595.855\n",
"\n"
]
}
],
"source": [
"print(edisp.data)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"edisp.peek()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out an `EnergyDispersion`\n",
"# object at a given offset:\n",
"edisp.to_energy_dispersion(offset=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Point spread function\n",
"\n",
"The point spread function (PSF) in this case is given as an analytical Gaussian model."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary PSF info\n",
"----------------\n",
"Theta : size = 6, min = 0.000 deg, max = 5.000 deg\n",
"Energy hi : size = 25, min = 0.020 TeV, max = 1258.925 TeV\n",
"Energy lo : size = 25, min = 0.013 TeV, max = 794.328 TeV\n",
"Safe energy threshold lo: 0.100 TeV\n",
"Safe energy threshold hi: 100.000 TeV\n",
"68% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.05099999 deg\n",
"68% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.03700000 deg\n",
"95% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.08269457 deg\n",
"95% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.05999410 deg\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDependentMultiGaussPSF\n",
"\n",
"psf = EnergyDependentMultiGaussPSF.read(\n",
" irf_filename, hdu=\"POINT SPREAD FUNCTION\"\n",
")\n",
"print(psf.info())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf.peek()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out the PSF\n",
"# at a given field of view offset\n",
"psf.to_energy_dependent_table_psf(\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Background\n",
"\n",
"The hadronic background for CTA DC-1 is given as a template model with an absolute rate that depends on `energy`, `detx` and `dety`. The coordinates `detx` and `dety` are angles in the \"field of view\" coordinate frame.\n",
"\n",
"Note that really the background model for DC-1 and most CTA IRFs produced so far are radially symmetric, i.e. only depend on the FOV offset. The background model here was rotated to fill the FOV in a rotationally symmetric way, for no good reason."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background3D\n",
"NDDataArray summary info\n",
"energy : size = 21, min = 0.016 TeV, max = 158.489 TeV\n",
"fov_lon : size = 36, min = -5.833 deg, max = 5.833 deg\n",
"fov_lat : size = 36, min = -5.833 deg, max = 5.833 deg\n",
"Data : size = 27216, min = 0.000 1 / (MeV s sr), max = 0.421 1 / (MeV s sr)\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import Background3D\n",
"\n",
"bkg = Background3D.read(irf_filename, hdu=\"BACKGROUND\")\n",
"print(bkg)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# TODO: implement a peek method for Background3D\n",
"# bkg.peek()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[1.2053316 \\times 10^{-5}] \\; \\mathrm{\\frac{1}{MeV\\,s\\,sr}}$$"
],
"text/plain": [
""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bkg.data.evaluate(energy=\"3 TeV\", fov_lon=\"1 deg\", fov_lat=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Index files and DataStore\n",
"\n",
"As we saw, you can access all of the CTA data using Astropy and Gammapy.\n",
"\n",
"But wouldn't it be nice if there were a better, simpler way?\n",
"\n",
"Imagine what life could be like if you had a butler that knows where all the files and HDUs are, and hands you the 1DC data on a silver platter, you just have to ask for it.\n",
"\n",
"![gammapy.data.DataStore - your butler for CTA data](images/gammapy_datastore_butler.png)\n",
"\n",
"Well, the butler exists. He's called `gammapy.data.DataStore` and he keeps track of the data using index files.\n",
"\n",
"### Index files\n",
"\n",
"The files with in the `index` folder with names `obs-index.fits.gz` and `hdu-index.fits.gz` are so called \"observation index files\" and \"HDU index files\".\n",
"\n",
"* The purpose of observation index files is to get a table of available observations, with the most relevant parameters commonly used for observation selection (e.g. pointing position or observation time). Their format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/obs_index/index.html).\n",
"* The purpose of HDU index files is to locate all FITS header data units (HDUs) for a given observation. At the moment, for each observation, there are six relevant HDUs: EVENTS, GTI, AEFF, EDISP, PSF and BKG. The format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/hdu_index/index.html).\n",
"\n",
"For 1DC there is one set of index files per simulated dataset, as well as a set of index files listing all available data in the all directory."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"index\r\n",
"└── gps\r\n",
" ├── hdu-index.fits.gz\r\n",
" └── obs-index.fits.gz\r\n",
"\r\n",
"1 directory, 2 files\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gammapy DataStore\n",
"\n",
"If you want to access data and IRFs from the CTA 1DC GPS dataset, just create a `DataStore` by pointing at a folder where the index files are located."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import DataStore\n",
"\n",
"data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")\n",
"\n",
"# If you want to access all CTA DC-1 data,\n",
"# set the CTADATA env var and use this:\n",
"# data_store = DataStore.from_dir(\"$CTADATA/index/gps\")\n",
"# Or point at the directly with the index files directly"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data store:\n",
"HDU index table:\n",
"BASE_DIR: /Users/deil/work/gammapy-tutorials/datasets/cta-1dc/index/gps\n",
"Rows: 24\n",
"OBS_ID: 110380 -- 111630\n",
"HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']\n",
"HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_3gauss']\n",
"\n",
"Observation table:\n",
"Observatory name: 'N/A'\n",
"Number of observations: 4\n"
]
}
],
"source": [
"# Print out some basic information about the available data:\n",
"data_store.info()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of observations: 4\n",
"['OBS_ID', 'RA_PNT', 'DEC_PNT', 'GLON_PNT', 'GLAT_PNT', 'ZEN_PNT', 'ALT_PNT', 'AZ_PNT', 'ONTIME', 'LIVETIME', 'DEADC', 'TSTART', 'TSTOP', 'DATE-OBS', 'TIME-OBS', 'DATE-END', 'TIME-END', 'N_TELS', 'OBJECT', 'CALDB', 'IRF', 'EVENTS_FILENAME', 'EVENT_COUNT']\n",
"2.0\n"
]
}
],
"source": [
"# The observation index is loaded as a table\n",
"print(\"Number of observations: \", len(data_store.obs_table))\n",
"print(data_store.obs_table.colnames)\n",
"# Compute and print total obs time in hours\n",
"print(data_store.obs_table[\"ONTIME\"].sum() / 3600)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24\n",
"['OBS_ID', 'HDU_TYPE', 'HDU_CLASS', 'FILE_DIR', 'FILE_NAME', 'HDU_NAME']\n"
]
}
],
"source": [
"# The HDU index is loaded as a table\n",
"print(len(data_store.hdu_table))\n",
"print(data_store.hdu_table.colnames)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# Of course, you can look at the tables if you like\n",
"# data_store.obs_table[:10].show_in_browser(jsviewer=True)\n",
"# data_store.hdu_table[:10].show_in_browser(jsviewer=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select observations\n",
"\n",
"With ``data_store.obs_table`` you have a table with the most common per-observation parameters that are used for observation selection. Using Python / Table methods it's easy to apply any selection you like, always with the goal of making a list or array of `OBS_ID`, which is then the input to analysis.\n",
"\n",
"For the current 1DC dataset it's pretty simple, because the only quantities useful for selection are:\n",
"* pointing position\n",
"* which irf (i.e. array / zenith angle)\n",
"\n",
"With real data, there will be more parameters of interest, such as data quality, observation duration, zenith angle, time of observation, ...\n",
"\n",
"Let's look at one example: select observations that are at offset 1 to 2 deg from the Galactic center."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of selected observations: 3\n"
]
}
],
"source": [
"from astropy.coordinates import SkyCoord\n",
"\n",
"table = data_store.obs_table\n",
"pos_obs = SkyCoord(\n",
" table[\"GLON_PNT\"], table[\"GLAT_PNT\"], frame=\"galactic\", unit=\"deg\"\n",
")\n",
"pos_target = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n",
"offset = pos_target.separation(pos_obs).deg\n",
"mask = (1 < offset) & (offset < 2)\n",
"table = table[mask]\n",
"print(\"Number of selected observations: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"ObservationTable length=3 \n",
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
"deg deg \n",
"int64 float64 float64 bytes13 \n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h \n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h \n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h \n",
"
"
],
"text/plain": [
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
" deg deg \n",
"int64 float64 float64 bytes13 \n",
"------ ------------------ ------------------ -------------\n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h\n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h\n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at the first few\n",
"table[[\"OBS_ID\", \"GLON_PNT\", \"GLAT_PNT\", \"IRF\"]][:5]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'South_z20_50h'}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check which IRFs were used ... it's all south and 20 deg zenith angle\n",
"set(table[\"IRF\"])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Galactic latitude (deg)')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Check the pointing positions\n",
"# The grid pointing positions at GLAT = +- 1.2 deg are visible\n",
"from astropy.coordinates import Angle\n",
"\n",
"plt.scatter(\n",
" Angle(table[\"GLON_PNT\"], unit=\"deg\").wrap_at(\"180 deg\"), table[\"GLAT_PNT\"]\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data\n",
"\n",
"Once you have selected the observations of interest, use the `DataStore` to load the data and IRF for those observations. Let's say we're interested in `OBS_ID=110380`."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"obs = data_store.obs(obs_id=110380)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Info for OBS_ID = 110380\n",
"- Start time: 59235.50\n",
"- Pointing pos: RA 267.68 deg / Dec -29.61 deg\n",
"- Observation duration: 1800.0 s\n",
"- Dead-time fraction: 2.000 %\n",
"\n"
]
}
],
"source": [
"print(obs)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=3 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"3 664502408.8205513 -93.20372 -28.599625 0.04009629 1.0049409 -0.7769775 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2\n",
" 3 664502408.8205513 -93.20372 -28.599625 ... 1.0049409 -0.7769775 2"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events.table[:3]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.gti"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.aeff"
]
},
{
"cell_type": "code",
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""
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"source": [
"obs.edisp"
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"source": [
"Here's an example how to loop over many observations and analyse them: [cta_1dc_make_allsky_images.py](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py)"
]
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"source": [
"## Model XML files\n",
"\n",
"The 1DC sky model is distributed as a set of XML files, which in turn link to a ton of other FITS and text files."
]
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"source": [
"# TODO: copy an example XML file for tutorials\n",
"# This one is no good: $CTADATA/models/models_gps.xml\n",
"# Too large, private in CTA, loads large diffuse model\n",
"# We need to prepare something custom.\n",
"# !ls $CTADATA/models/*.xml | xargs -n 1 basename"
]
},
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"cell_type": "code",
"execution_count": 66,
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"source": [
"# This is what the XML file looks like\n",
"# !tail -n 20 $CTADATA/models/models_gps.xml"
]
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"At the moment, you cannot read and write these sky model XML files with Gammapy.\n",
"\n",
"There are multiple reasons why this XML serialisation format isn't implemented in Gammapy yet (all variants of \"it sucks\"):\n",
"\n",
"* XML is tedious to read and write for humans.\n",
"* The format is too strict in places: there are many use cases where \"min\", \"max\", \"free\" and \"error\" aren't needed, so it should be possible to omit them (see e.g. dummy values above)\n",
"* The parameter \"scale\" is an implementation detail that very few optimisers need. There's no reason to bother all gamma-ray astronomers with separating value and scale in model input and result files.\n",
"* The \"unit\" is missing. Pretty important piece of information, no?\n",
" All people working on IACTs use \"TeV\", why should CTA be forced to use \"MeV\"?\n",
"* Ad-hoc extensions that keep being added and many models can't be put in one file (see extra ASCII and FITS files with tables or other info, e.g. pulsar models added for CTA 1DC). Admittedly complex model serialisation is an intrinsically hard problem, there is not simple and flexible solution.\n",
"\n",
"Also, to be honest, I also want to say / admit:\n",
"\n",
"* A model serialisation format is useful, even if it will never cover all use cases (e.g. energy-dependent morphology or an AGN with a variable spectrum, or complex FOV background models).\n",
"* The Gammapy model package isn't well-implemented or well-developed yet.\n",
"* So far users were happy to write a few lines of Python to define a model instead of XML.\n",
"* Clearly with CTA 1DC now using the XML format there's an important reason to support it, there is the legacy of Fermi-LAT using it and ctools using / extending it for CTA.\n",
"\n",
"So what now?\n",
"\n",
"* In Gammapy, we have started to implement a modeling package in `gammapy.utils.modeling`, with the goal of supporting this XML format as one of the serialisation formats. It's not very far along, will be a main focus for us, help is welcome.\n",
"* In addition we would like to support a second more human-friendly model format that looks something like [this](https://github.com/gammapy/gamma-cat/blob/b651de8d1d793e924764ffb13c8ec189bce9ea7d/input/data/2006/2006A%2526A...457..899A/tev-000025.yaml#L11)\n",
"* For now, you could use Gammalib to read the XML files, or you could read them directly with Python. The Python standard library contains [ElementTree](https://docs.python.org/3/library/xml.etree.elementtree.html) and there's [xmltodict](https://github.com/martinblech/xmltodict) which simply hands you back the XML file contents as a Python dictionary (containing a very nested hierarchical structure of Python dict and list objects and strings and numbers.\n",
"\n",
"As an example, here's how you can read an XML sky model and access the spectral parameters of one source (the last, \"Arp200\" visible above in the XML printout) and create a [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst) object."
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"source": [
"# Read XML file and access spectrum parameters\n",
"# from gammapy.extern import xmltodict\n",
"\n",
"# filename = os.path.join(os.environ[\"CTADATA\"], \"models/models_gps.xml\")\n",
"# data = xmltodict.parse(open(filename).read())\n",
"# data = data[\"source_library\"][\"source\"][-1]\n",
"# data = data[\"spectrum\"][\"parameter\"]\n",
"# data"
]
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"execution_count": 68,
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"source": [
"# Create a spectral model the the right units\n",
"# from astropy import units as u\n",
"# from gammapy.spectrum.models import PowerLaw\n",
"\n",
"# par_to_val = lambda par: float(par[\"@value\"]) * float(par[\"@scale\"])\n",
"# spec = PowerLaw(\n",
"# amplitude=par_to_val(data[0]) * u.Unit(\"cm-2 s-1 MeV-1\"),\n",
"# index=par_to_val(data[1]),\n",
"# reference=par_to_val(data[2]) * u.Unit(\"MeV\"),\n",
"# )\n",
"# print(spec)"
]
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"source": [
"## Exercises\n",
"\n",
"* Easy: Go over this notebook again, and change the data / code a little bit:\n",
" * Pick another run (any run you like)\n",
" * Plot the energy-offset histogram of the events separately for gammas and background\n",
"\n",
"* Medium difficulty: Find all runs within 1 deg of the Crab nebula.\n",
" * Load the \"all\" index file via the `DataStore`, then access the ``.obs_table``, then get an array-valued ``SkyCoord`` with all the observation pointing positions.\n",
" * You can get the Crab nebula position with `astropy.coordinates.SkyCoord` via ``SkyCoord.from_name('crab')`` \n",
" * Note that to compute the angular separation between two ``SkyCoord`` objects can be computed via ``separation = coord1.separation(coord2)``.\n",
"\n",
"* Hard: Find the PeVatrons in the 1DC data, i.e. the ~ 10 sources that are brightest at high energies (e.g. above 10 TeV).\n",
" * This is difficult, because it's note clear how to best do this, and also because it's time-consuming to crunch through all relevant data for any given method.\n",
" * One idea could be to go brute-force through **all** events, select the ones above 10 TeV and stack them all into one table. Then make an all-sky image and run a peak finder, or use an event cluster-finding method e.g. from scikit-learn.\n",
" * Another idea could be to first make a list of targets of interest, either from the CTA 1DC sky model or from gamma-cat, compute the model integral flux above 10 TeV and pick candidates that way, then run analyses."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# start typing here ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"* This notebook gave you an overview of the 1DC files and showed you have to access and work with them using Gammapy.\n",
"* To see how to do analysis, i.e. make a sky image and spectrum, see [cta_data_analysis.ipynb](cta_data_analysis.ipynb) next."
]
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