\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/v0.12?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/adonath/github/adonath/gammapy/gammapy \r\n",
"\tversion : 0.12 \r\n",
"\r\n",
"\r\n",
"Other packages:\r\n",
"\r\n",
"\tnumpy : 1.16.2 \r\n",
"\tscipy : 1.2.1 \r\n",
"\tmatplotlib : 3.0.2 \r\n",
"\tcython : 0.29.4 \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 : 4.2.0 \r\n",
"\tsphinx : 1.8.4 \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\n",
"from astropy.convolution import Gaussian2DKernel\n",
"from regions import CircleSkyRegion\n",
"from gammapy.utils.energy import EnergyBounds\n",
"from gammapy.utils.fitting import Fit\n",
"from gammapy.data import DataStore\n",
"from gammapy.spectrum import (\n",
" SpectrumExtraction,\n",
" models,\n",
" FluxPointsEstimator,\n",
" FluxPointsDataset,\n",
")\n",
"from gammapy.maps import 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": [
"exclusion_mask = geom.to_image().region_mask([on_region], inside=False)\n",
"exclusion_mask = WcsNDMap(geom.to_image(), exclusion_mask)\n",
"exclusion_mask.plot();"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level [astropy.coordinates.builtin_frames.utils]\n",
"WARNING:astropy:Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['counts', 'exposure', 'background'])\n",
"CPU times: user 6.4 s, sys: 479 ms, total: 6.88 s\n",
"Wall time: 3.85 s\n"
]
}
],
"source": [
"%%time\n",
"maker = MapMaker(geom, offset_max=\"2 deg\")\n",
"maps = maker.run(observations)\n",
"print(maps.keys())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# The maps are cubes, with an energy axis.\n",
"# Let's also make some images:\n",
"images = maker.run_images()\n",
"\n",
"excess = images[\"counts\"].copy()\n",
"excess.data -= images[\"background\"].data\n",
"images[\"excess\"] = excess"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show images\n",
"\n",
"Let's have a quick look at the images we computed ..."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"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": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.6 s, sys: 206 ms, total: 5.81 s\n",
"Wall time: 2.94 s\n"
]
},
{
"data": {
"image/png": 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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 1.33 s, sys: 10.3 ms, total: 1.34 s\n",
"Wall time: 773 ms\n"
]
}
],
"source": [
"%%time\n",
"extract = SpectrumExtraction(\n",
" observations=observations, bkg_estimate=bkg_estimate\n",
")\n",
"extract.run()"
]
},
{
"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": [
"OptimizeResult\n",
"\n",
"\tbackend : minuit\n",
"\tmethod : minuit\n",
"\tsuccess : True\n",
"\tmessage : Optimization terminated successfully.\n",
"\tnfev : 75\n",
"\ttotal stat : 224.78\n",
"\n",
"CPU times: user 785 ms, sys: 4.12 ms, total: 789 ms\n",
"Wall time: 399 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",
"\n",
"for dataset in extract.spectrum_observations:\n",
" dataset.model = model\n",
"\n",
"fit = Fit(extract.spectrum_observations)\n",
"result = fit.run()\n",
"print(result)"
]
},
{
"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": [
"\n"
]
}
],
"source": [
"# Flux points are computed on stacked observation\n",
"from gammapy.spectrum import SpectrumDatasetOnOffStacker\n",
"\n",
"stacker = SpectrumDatasetOnOffStacker(extract.spectrum_observations)\n",
"stacked_obs = stacker.run()\n",
"\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
counts [1]
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
int64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
\n",
"
1.565
1.000
2.448
1.112e-12
1.637e-12
2.442e-12
2.722e-12
0.924
2.408
0.191
37
0.202
0.180
1.349
7.494
56.164
0.200 .. 5.000
28.183813660262814 .. 151.1674243036436
1.027e-12
1.499e-12
2.122e-13
2.243e-13
2.006e-13
\n",
"
3.831
2.448
5.995
1.516e-13
5.467e-13
1.996e-12
2.226e-12
0.846
2.096
0.205
23
0.219
0.191
1.314
6.956
48.385
0.200 .. 5.000
20.95673034885535 .. 120.80943116300818
1.283e-13
1.992e-13
3.105e-14
3.322e-14
2.895e-14
\n",
"
10.000
5.995
16.681
1.794e-14
1.958e-13
1.840e-12
1.794e-12
0.554
12.855
0.239
8
0.267
0.214
1.148
3.529
12.454
0.200 .. 5.000
16.23702428487214 .. 86.25317890134858
9.938e-15
2.059e-14
4.293e-15
4.781e-15
3.830e-15
\n",
"
26.102
16.681
40.842
2.122e-15
5.211e-14
1.297e-12
1.445e-12
0.474
4.859
0.374
2
0.468
0.289
1.612
2.265
5.128
0.200 .. 5.000
5.72235477846527 .. 29.95230420166929
1.006e-15
3.421e-15
7.931e-16
9.923e-16
6.142e-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 ... 2.122e-13 2.243e-13 2.006e-13\n",
" 3.831 2.448 5.995 ... 3.105e-14 3.322e-14 2.895e-14\n",
" 10.000 5.995 16.681 ... 4.293e-15 4.781e-15 3.830e-15\n",
" 26.102 16.681 40.842 ... 7.931e-16 9.923e-16 6.142e-16"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebounds = EnergyBounds.equal_log_spacing(1, 40, 4, unit=u.TeV)\n",
"\n",
"stacked_obs.model = model\n",
"\n",
"fpe = FluxPointsEstimator(datasets=[dataset], e_edges=ebounds)\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": [],
"source": [
"model.parameters.covariance = result.parameters.covariance\n",
"flux_points_dataset = FluxPointsDataset(data=flux_points, model=model)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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