\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/simulate_3d.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",
"[simulate_3d.ipynb](../_static/notebooks/simulate_3d.ipynb) |\n",
"[simulate_3d.py](../_static/notebooks/simulate_3d.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D simulation and fitting\n",
"\n",
"This tutorial shows how to do a 3D map-based simulation and fit.\n",
"\n",
"For a tutorial on how to do a 3D map analyse of existing data, see the `analysis_3d` tutorial.\n",
"\n",
"This can be useful to do a performance / sensitivity study, or to evaluate the capabilities of Gammapy or a given analysis method. Note that is is a binned simulation as is e.g. done also in Sherpa for Chandra, not an event sampling and anbinned analysis as is done e.g. in the Fermi ST or ctools."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports and versions"
]
},
{
"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",
"import astropy.units as u\n",
"from astropy.coordinates import SkyCoord, Angle\n",
"from gammapy.irf import load_cta_irfs\n",
"from gammapy.maps import WcsGeom, MapAxis, WcsNDMap, Map\n",
"from gammapy.spectrum.models import PowerLaw\n",
"from gammapy.image.models import SkyGaussian\n",
"from gammapy.cube.models import SkyModel, SkyModels\n",
"from gammapy.cube import MapFit, MapEvaluator, PSFKernel\n",
"from gammapy.cube import make_map_exposure_true_energy, make_map_background_irf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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"
]
}
],
"source": [
"!gammapy info --no-envvar --no-dependencies --no-system"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"filename = (\n",
" \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n",
")\n",
"irfs = load_cta_irfs(filename)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SkyModel\n",
"\n",
"Parameters: \n",
"\n",
"\t name value error unit min max frozen\n",
"\t--------- --------- ----- -------------- --------- --- ------\n",
"\t lon_0 2.000e-01 nan deg nan nan False\n",
"\t lat_0 1.000e-01 nan deg nan nan False\n",
"\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n",
"\t index 3.000e+00 nan nan nan False\n",
"\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n",
"\treference 1.000e+00 nan TeV nan nan True\n"
]
}
],
"source": [
"# Define sky model to simulate the data\n",
"spatial_model = SkyGaussian(lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\")\n",
"spectral_model = PowerLaw(\n",
" index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n",
")\n",
"sky_model = SkyModel(\n",
" spatial_model=spatial_model, spectral_model=spectral_model\n",
")\n",
"print(sky_model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Define map geometry\n",
"axis = MapAxis.from_edges(\n",
" np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n",
")\n",
"geom = WcsGeom.create(\n",
" skydir=(0, 0), binsz=0.02, width=(5, 4), coordsys=\"GAL\", axes=[axis]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Define some observation parameters\n",
"# Here we just have a single observation,\n",
"# we are not simulating many pointings / observations\n",
"pointing = SkyCoord(1, 0.5, unit=\"deg\", frame=\"galactic\")\n",
"livetime = 1 * u.hour\n",
"offset_max = 2 * u.deg\n",
"offset = Angle(\"2 deg\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = axis.edges * axis.unit\n",
"edisp = irfs[\"edisp\"].to_energy_dispersion(\n",
" offset, e_reco=energy, e_true=energy\n",
")\n",
"edisp.plot_matrix();"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 14 µs, sys: 0 ns, total: 14 µs\n",
"Wall time: 18.1 µs\n"
]
}
],
"source": [
"%%time\n",
"# The idea is that we have this class that can compute `npred`\n",
"# maps, i.e. \"predicted counts per pixel\" given the model and\n",
"# the observation infos: exposure, background, PSF and EDISP\n",
"evaluator = MapEvaluator(\n",
" model=sky_model,\n",
" exposure=exposure,\n",
" background=background,\n",
" psf=psf_kernel,\n",
" edisp=edisp,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Accessing and saving a lot of the following maps is for debugging.\n",
"# Just for a simulation one doesn't need to store all these things.\n",
"# dnde = evaluator.compute_dnde()\n",
"# flux = evaluator.compute_flux()\n",
"npred = evaluator.compute_npred()\n",
"npred_map = WcsNDMap(geom, npred)"
]
},
{
"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": [
"npred_map.sum_over_axes().plot(add_cbar=True);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# This one line is the core of how to simulate data when\n",
"# using binned simulation / analysis: you Poisson fluctuate\n",
"# npred to obtain simulated observed counts.\n",
"# Compute counts as a Poisson fluctuation\n",
"rng = np.random.RandomState(seed=42)\n",
"counts = rng.poisson(npred)\n",
"counts_map = WcsNDMap(geom, counts)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"counts_map.sum_over_axes().plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit\n",
"\n",
"Now let's analyse the simulated data.\n",
"Here we just fit it again with the same model we had before, but you could do any analysis you like here, e.g. fit a different model, or do a region-based analysis, ..."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SkyModel\n",
"\n",
"Parameters: \n",
"\n",
"\t name value error unit min max frozen\n",
"\t--------- --------- ----- -------------- --------- --------- ------\n",
"\t lon_0 0.000e+00 nan deg nan nan False\n",
"\t lat_0 0.000e+00 nan deg nan nan False\n",
"\t sigma 1.000e+00 nan deg 0.000e+00 nan False\n",
"\t index 2.000e+00 nan 0.000e+00 1.000e+01 False\n",
"\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n",
"\treference 1.000e+00 nan TeV nan nan True\n"
]
}
],
"source": [
"# Define sky model to fit the data\n",
"spatial_model = SkyGaussian(lon_0=\"0 deg\", lat_0=\"0 deg\", sigma=\"1 deg\")\n",
"spectral_model = PowerLaw(\n",
" index=2, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n",
")\n",
"model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)\n",
"\n",
"# Impose that specrtal index remains within limits\n",
"spectral_model.parameters[\"index\"].min = 0.0\n",
"spectral_model.parameters[\"index\"].max = 10.0\n",
"\n",
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"