\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/detect_ts.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",
"[detect_ts.ipynb](../_static/notebooks/detect_ts.ipynb) |\n",
"[detect_ts.py](../_static/notebooks/detect_ts.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source detection with Gammapy\n",
"\n",
"## Introduction\n",
"\n",
"This notebook show how to do source detection with Gammapy using one of the methods available in [gammapy.detect](..\/detect/index.rst).\n",
"\n",
"We will do this:\n",
"\n",
"* produce 2-dimensional test-statistics (TS) images using Fermi-LAT 2FHL high-energy Galactic plane survey dataset\n",
"* run a peak finder to make a source catalog\n",
"* do some simple measurements on each source\n",
"* compare to the 2FHL catalog\n",
"\n",
"Note that what we do here is a quick-look analysis, the production of real source catalogs use more elaborate procedures.\n",
"\n",
"We will work with the following functions and classes:\n",
"\n",
"* [gammapy.maps.WcsNDMap](..\/api/gammapy.maps.WcsNDMap.rst)\n",
"* [gammapy.detect.TSMapEstimator](..\/api/gammapy.detect.TSMapEstimator.rst)\n",
"* [gammapy.detect.find_peaks](..\/api/gammapy.detect.find_peaks.rst)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As always, let's get started with some setup ..."
]
},
{
"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": [],
"source": [
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.convolution import Gaussian2DKernel\n",
"from astropy.coordinates import SkyCoord\n",
"from gammapy.maps import Map\n",
"from gammapy.detect import TSMapEstimator, find_peaks\n",
"from gammapy.catalog import source_catalogs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute TS image"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Load data from files\n",
"filename = \"$GAMMAPY_DATA/fermi_survey/all.fits.gz\"\n",
"opts = {\n",
" \"position\": SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\"),\n",
" \"width\": (20, 8),\n",
"}\n",
"maps = {\n",
" \"counts\": Map.read(filename, hdu=\"COUNTS\").cutout(**opts),\n",
" \"background\": Map.read(filename, hdu=\"BACKGROUND\").cutout(**opts),\n",
" \"exposure\": Map.read(filename, hdu=\"EXPOSURE\").cutout(**opts),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 534 ms, sys: 57.7 ms, total: 591 ms\n",
"Wall time: 1.86 s\n"
]
}
],
"source": [
"%%time\n",
"# Compute a source kernel (source template) in oversample mode,\n",
"# PSF is not taken into account\n",
"kernel = Gaussian2DKernel(2.5, mode=\"oversample\")\n",
"estimator = TSMapEstimator()\n",
"images = estimator.run(maps, kernel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot images"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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