\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/v0.10?urlpath=lab/tree/cta_data_analysis.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_data_analysis.ipynb](../_static/notebooks/cta_data_analysis.ipynb) |\n",
"[cta_data_analysis.py](../_static/notebooks/cta_data_analysis.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![CTA first data challenge logo](images/cta-1dc.png)\n",
"\n",
"# CTA data analysis with Gammapy\n",
"\n",
"## Introduction\n",
"\n",
"**This notebook shows an example how to make a sky image and spectrum for simulated CTA data with Gammapy.**\n",
"\n",
"The dataset we will use is three observation runs on the Galactic center. This is a tiny (and thus quick to process and play with and learn) subset of the simulated CTA dataset that was produced for the first data challenge in August 2017.\n",
"\n",
"**This notebook can be considered part 2 of the introduction to CTA 1DC analysis. Part one is here: [cta_1dc_introduction.ipynb](cta_1dc_introduction.ipynb)**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As usual, we'll start 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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"Gammapy package:\r\n",
"\r\n",
"\tpath : /Users/jer/git/gammapy/gammapy/gammapy \r\n",
"\tversion : 0.10 \r\n",
"\tgithash : c6bfb5371a5a1682cea9aaf56ebedb3c42010a43 \r\n",
"\r\n",
"\r\n",
"Other packages:\r\n",
"\r\n",
"\tnumpy : 1.16.0 \r\n",
"\tscipy : 1.2.0 \r\n",
"\tmatplotlib : 3.0.2 \r\n",
"\tcython : 0.29.3 \r\n",
"\tastropy : 3.1.1 \r\n",
"\tastropy_healpix : 0.4 \r\n",
"\treproject : 0.4 \r\n",
"\tsherpa : 4.10.2 \r\n",
"\tpytest : 4.1.1 \r\n",
"\tsphinx : 1.8.3 \r\n",
"\thealpy : 1.12.8 \r\n",
"\tregions : 0.3 \r\n",
"\timinuit : 1.3.3 \r\n",
"\tnaima : 0.8.3 \r\n",
"\tuncertainties : 3.0.3 \r\n",
"\r\n"
]
}
],
"source": [
"!gammapy info --no-envvar --no-system"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import astropy.units as u\n",
"from astropy.coordinates import SkyCoord, Angle\n",
"from astropy.convolution import Gaussian2DKernel\n",
"from regions import CircleSkyRegion\n",
"from gammapy.utils.energy import EnergyBounds\n",
"from gammapy.data import DataStore\n",
"from gammapy.spectrum import (\n",
" SpectrumExtraction,\n",
" SpectrumFit,\n",
" SpectrumResult,\n",
" models,\n",
" SpectrumEnergyGroupMaker,\n",
" FluxPointEstimator,\n",
")\n",
"from gammapy.maps import Map, MapAxis, WcsNDMap, WcsGeom\n",
"from gammapy.cube import MapMaker\n",
"from gammapy.background import ReflectedRegionsBackgroundEstimator\n",
"from gammapy.detect import TSMapEstimator, find_peaks"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Configure the logger, so that the spectral analysis\n",
"# isn't so chatty about what it's doing.\n",
"import logging\n",
"\n",
"logging.basicConfig()\n",
"log = logging.getLogger(\"gammapy.spectrum\")\n",
"log.setLevel(logging.ERROR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select observations\n",
"\n",
"Like explained in [cta_1dc_introduction.ipynb](cta_1dc_introduction.ipynb), a Gammapy analysis usually starts by creating a `DataStore` and selecting observations.\n",
"\n",
"This is shown in detail in the other notebook, here we just pick three observations near the galactic center."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Just as a reminder: this is how to select observations\n",
"# from astropy.coordinates import SkyCoord\n",
"# table = data_store.obs_table\n",
"# pos_obs = SkyCoord(table['GLON_PNT'], table['GLAT_PNT'], frame='galactic', unit='deg')\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",
"# table.show_in_browser(jsviewer=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"obs_id = [110380, 111140, 111159]\n",
"observations = data_store.get_observations(obs_id)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"ObservationTable length=3\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot sources on top of significance sky image\n",
"images_ts[\"sqrt_ts\"].plot(add_cbar=True)\n",
"\n",
"plt.gca().scatter(\n",
" source_pos.ra.deg,\n",
" source_pos.dec.deg,\n",
" transform=plt.gca().get_transform(\"icrs\"),\n",
" color=\"none\",\n",
" edgecolor=\"white\",\n",
" marker=\"o\",\n",
" s=200,\n",
" lw=1.5,\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spatial analysis\n",
"\n",
"See other notebooks for how to run a 3D cube or 2D image based analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spectrum\n",
"\n",
"We'll run a spectral analysis using the classical reflected regions background estimation method,\n",
"and using the on-off (often called WSTAT) likelihood function.\n",
"\n",
"### Extraction\n",
"\n",
"The first step is to \"extract\" the spectrum, i.e. 1-dimensional counts and exposure and background vectors, as well as an energy dispersion matrix from the data and IRFs."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jer/anaconda/envs/gammapy-dev/lib/python3.7/site-packages/matplotlib/patches.py:75: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties.\n",
" warnings.warn(\"Setting the 'color' property will override\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.44 s, sys: 144 ms, total: 3.58 s\n",
"Wall time: 3.49 s\n"
]
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%%time\n",
"bkg_estimator = ReflectedRegionsBackgroundEstimator(\n",
" observations=observations,\n",
" on_region=on_region,\n",
" exclusion_mask=exclusion_mask,\n",
")\n",
"bkg_estimator.run()\n",
"bkg_estimate = bkg_estimator.result\n",
"bkg_estimator.plot();"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 917 ms, sys: 17.9 ms, total: 935 ms\n",
"Wall time: 988 ms\n"
]
}
],
"source": [
"%%time\n",
"extract = SpectrumExtraction(\n",
" observations=observations, bkg_estimate=bkg_estimate\n",
")\n",
"extract.run()\n",
"observations = extract.spectrum_observations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model fit\n",
"\n",
"The next step is to fit a spectral model, using all data (i.e. a \"global\" fit, using all energies)."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Fit result info \n",
"--------------- \n",
"Model: PowerLaw\n",
"\n",
"Parameters: \n",
"\n",
"\t name value error unit min max frozen\n",
"\t--------- --------- --------- -------------- --- --- ------\n",
"\t index 2.225e+00 2.616e-02 nan nan False\n",
"\tamplitude 3.013e-12 1.396e-13 cm-2 s-1 TeV-1 nan nan False\n",
"\treference 1.000e+00 0.000e+00 TeV nan nan True\n",
"\n",
"Covariance: \n",
"\n",
"\t name index amplitude reference\n",
"\t--------- ---------- ---------- ---------\n",
"\t index 6.842e-04 -9.885e-16 0.000e+00\n",
"\tamplitude -9.885e-16 1.950e-26 0.000e+00\n",
"\treference 0.000e+00 0.000e+00 0.000e+00 \n",
"\n",
"Statistic: 91.148 (wstat)\n",
"Fit Range: [1.e-02 1.e+02] TeV\n",
"\n",
"CPU times: user 304 ms, sys: 7.01 ms, total: 311 ms\n",
"Wall time: 314 ms\n"
]
}
],
"source": [
"%%time\n",
"model = models.PowerLaw(\n",
" index=2, amplitude=1e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n",
")\n",
"fit = SpectrumFit(observations, model)\n",
"fit.run()\n",
"print(fit.result[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spectral points\n",
"\n",
"Finally, let's compute spectral points. The method used is to first choose an energy binning, and then to do a 1-dim likelihood fit / profile to compute the flux and flux error."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*** Observation summary report ***\n",
"Observation Id: [110380-111159]\n",
"Livetime: 1.470 h\n",
"On events: 2377\n",
"Off events: 34876\n",
"Alpha: 0.041\n",
"Bkg events in On region: 1434.36\n",
"Excess: 942.64\n",
"Excess / Background: 0.66\n",
"Gamma rate: 10.69 1 / min\n",
"Bkg rate: 0.23 1 / min\n",
"Sigma: 22.18\n",
"energy range: 0.01 TeV - 100.00 TeV\n"
]
}
],
"source": [
"# Flux points are computed on stacked observation\n",
"stacked_obs = extract.spectrum_observations.stack()\n",
"print(stacked_obs)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=4\n",
"
\n",
"
e_ref
e_min
e_max
ref_dnde
ref_flux
ref_eflux
ref_e2dnde
norm
loglike
norm_err
norm_errp
norm_errn
norm_ul
sqrt_ts
ts
norm_scan [11]
dloglike_scan [11]
dnde
dnde_ul
dnde_err
dnde_errp
dnde_errn
\n",
"
TeV
TeV
TeV
1 / (cm2 s TeV)
1 / (cm2 s)
TeV / (cm2 s)
TeV / (cm2 s)
1 / (cm2 s TeV)
1 / (cm2 s TeV)
1 / (cm2 s TeV)
1 / (cm2 s TeV)
1 / (cm2 s TeV)
\n",
"
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
\n",
"
1.565
1.000
2.448
1.112e-12
1.638e-12
2.443e-12
2.724e-12
0.986
11.661
0.111
0.114
0.108
1.221
13.858
192.058
0.200 .. 5.000
103.9176679854109 .. 456.99639924600837
1.096e-12
1.358e-12
1.234e-13
1.273e-13
1.196e-13
\n",
"
3.831
2.448
5.995
1.517e-13
5.469e-13
1.997e-12
2.226e-12
1.036
3.755
0.128
0.134
0.123
1.312
13.709
187.941
0.200 .. 5.000
90.36639418623737 .. 325.9557965738492
1.571e-13
1.989e-13
1.942e-14
2.026e-14
1.863e-14
\n",
"
10.000
5.995
16.681
1.794e-14
1.958e-13
1.840e-12
1.794e-12
0.663
13.520
0.146
0.156
0.137
0.994
7.466
55.736
0.200 .. 5.000
30.8150316340727 .. 221.89791345026072
1.190e-14
1.783e-14
2.628e-15
2.795e-15
2.466e-15
\n",
"
26.102
16.681
40.842
2.121e-15
5.211e-14
1.296e-12
1.445e-12
0.352
5.607
0.195
0.225
0.168
0.865
2.756
7.594
0.200 .. 5.000
6.400181349922619 .. 87.99767787949281
7.476e-16
1.836e-15
4.145e-16
4.768e-16
3.569e-16
\n",
"
"
],
"text/plain": [
"
\n",
" e_ref e_min e_max ... dnde_err dnde_errp dnde_errn \n",
" TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n",
"float64 float64 float64 ... float64 float64 float64 \n",
"------- ------- ------- ... --------------- --------------- ---------------\n",
" 1.565 1.000 2.448 ... 1.234e-13 1.273e-13 1.196e-13\n",
" 3.831 2.448 5.995 ... 1.942e-14 2.026e-14 1.863e-14\n",
" 10.000 5.995 16.681 ... 2.628e-15 2.795e-15 2.466e-15\n",
" 26.102 16.681 40.842 ... 4.145e-16 4.768e-16 3.569e-16"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebounds = EnergyBounds.equal_log_spacing(1, 40, 4, unit=u.TeV)\n",
"\n",
"seg = SpectrumEnergyGroupMaker(obs=stacked_obs)\n",
"seg.compute_groups_fixed(ebounds=ebounds)\n",
"\n",
"fpe = FluxPointEstimator(\n",
" obs=stacked_obs, groups=seg.groups, model=fit.result[0].model\n",
")\n",
"flux_points = fpe.run()\n",
"flux_points.table_formatted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot\n",
"\n",
"Let's plot the spectral model and points. You could do it directly, but there is a helper class.\n",
"Note that a spectral uncertainty band, a \"butterfly\" is drawn, but it is very thin, i.e. barely visible."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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