\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_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/deil/work/code/gammapy-docs/build/dev/gammapy/gammapy \r\n",
"\tversion : 0.9 \r\n",
"\r\n",
"\r\n",
"Other packages:\r\n",
"\r\n",
"\tnumpy : 1.15.4 \r\n",
"\tscipy : 1.1.0 \r\n",
"\tmatplotlib : 3.0.2 \r\n",
"\tcython : 0.29 \r\n",
"\tastropy : 3.0.5 \r\n",
"\tastropy_healpix : 0.3.1 \r\n",
"\treproject : 0.4 \r\n",
"\tsherpa : 4.10.1 \r\n",
"\tpytest : 4.0.0 \r\n",
"\tsphinx : 1.7.9 \r\n",
"\thealpy : 1.12.5 \r\n",
"\tregions : 0.3 \r\n",
"\timinuit : 1.3.3 \r\n",
"\tnaima : 0.8.1 \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/deil/software/anaconda3/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 14.9 s, sys: 248 ms, total: 15.2 s\n",
"Wall time: 4.26 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 2.67 s, sys: 15.2 ms, total: 2.68 s\n",
"Wall time: 834 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 1.29 s, sys: 8.82 ms, total: 1.3 s\n",
"Wall time: 339 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: 1435.66\n",
"Excess: 941.34\n",
"Excess / Background: 0.66\n",
"Gamma rate: 0.18 1 / s\n",
"Bkg rate: 0.23 1 / min\n",
"Sigma: 22.14\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
103.91766798597166 .. 456.99639923973507
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
90.36639418669868 .. 325.9557965699136
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
30.815031634197425 .. 221.89791344848618
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
6.40018134993381 .. 87.99767787902442
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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