{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\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/v0.13?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 Angle\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.maps import WcsGeom, MapAxis, WcsNDMap\n", "from gammapy.spectrum.models import PowerLaw\n", "from gammapy.image.models import SkyGaussian\n", "from gammapy.cube.models import SkyModel, BackgroundModel\n", "from gammapy.cube import MapDataset, PSFKernel\n", "from gammapy.cube import make_map_exposure_true_energy, make_map_background_irf\n", "from gammapy.utils.fitting import Fit\n", "from gammapy.data import FixedPointingInfo" ] }, { "cell_type": "code", "execution_count": 3, "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 : 0.13 \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", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")\n", "irfs = load_cta_irfs(filename)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to simulate the data\n", "spatial_model = SkyGaussian(lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\")\n", "spectral_model = PowerLaw(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "sky_model = SkyModel(\n", " spatial_model=spatial_model, spectral_model=spectral_model\n", ")\n", "print(sky_model)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define map geometry\n", "axis = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0), binsz=0.02, width=(5, 4), coordsys=\"GAL\", axes=[axis]\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define some observation parameters\n", "# We read in the pointing info from one of the 1dc event list files as an example\n", "pointing = FixedPointingInfo.read(\n", " \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n", ")\n", "\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": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exposure = make_map_exposure_true_energy(\n", " pointing=pointing.radec, livetime=livetime, aeff=irfs[\"aeff\"], geom=geom\n", ")\n", "exposure.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 9, "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" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "background = make_map_background_irf(\n", " pointing=pointing, ontime=livetime, bkg=irfs[\"bkg\"], geom=geom\n", ")\n", "background.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "psf = irfs[\"psf\"].to_energy_dependent_table_psf(theta=offset)\n", "psf_kernel = PSFKernel.from_table_psf(psf, geom, max_radius=0.3 * u.deg)\n", "psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\");" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy = axis.edges\n", "edisp = irfs[\"edisp\"].to_energy_dispersion(\n", " offset, e_reco=energy, e_true=energy\n", ")\n", "edisp.plot_matrix();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to compute `npred` maps, i.e. \"predicted counts per pixel\" given the model and the observation infos: exposure, background, PSF and EDISP. For this we use the `MapDataset` object:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "background_model = BackgroundModel(background)\n", "dataset = MapDataset(\n", " model=sky_model,\n", " exposure=exposure,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "npred = dataset.npred()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "npred.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.data)\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 1.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 5.000e-01 nan deg 0.000e+00 nan False\n", "\t index 2.000e+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 fit the data\n", "spatial_model = SkyGaussian(lon_0=\"0.1 deg\", lat_0=\"0.1 deg\", sigma=\"0.5 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", "print(model)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BackgroundModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- ---- --------- --- ------\n", "\t norm 1.000e+00 nan 0.000e+00 nan True\n", "\t tilt 0.000e+00 nan nan nan True\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "background_model.parameters[\"norm\"].value = 1.0\n", "background_model.parameters[\"norm\"].frozen = True\n", "background_model.parameters[\"tilt\"].frozen = True\n", "\n", "print(background_model)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "dataset = MapDataset(\n", " model=model,\n", " exposure=exposure,\n", " counts=counts_map,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 2.34E+05 | Ncalls=212 (212 total) |\n", "| EDM = 4.42E-06 (Goal: 1E-05) | up = 1.0 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", "| True | True | False | False |\n", "------------------------------------------------------------------\n", "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", "CPU times: user 10.5 s, sys: 286 ms, total: 10.8 s\n", "Wall time: 10.8 s\n" ] } ], "source": [ "%%time\n", "fit = Fit(dataset)\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "True model:" ] }, { "cell_type": "code", "execution_count": 21, "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 -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 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": [ "print(sky_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Best-fit model:" ] }, { "cell_type": "code", "execution_count": 22, "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 1.859e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 8.768e-02 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 2.963e-01 nan deg 0.000e+00 nan False\n", "\t index 3.056e+00 nan nan nan False\n", "\tamplitude 9.060e-12 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the errors on the model, we can check the covariance table:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=9\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namelon_0lat_0sigmaindexamplitudereferencenormtilt
str9float64float64float64float64float64float64float64float64
lon_07.777e-053.528e-07-9.542e-08-4.065e-064.963e-170.000e+000.000e+000.000e+00
lat_03.528e-077.652e-051.827e-063.151e-064.934e-170.000e+000.000e+000.000e+00
sigma-9.542e-081.827e-063.720e-05-5.745e-077.922e-160.000e+000.000e+000.000e+00
index-4.065e-063.151e-06-5.745e-079.350e-04-1.197e-140.000e+000.000e+000.000e+00
amplitude4.963e-174.934e-177.922e-16-1.197e-142.130e-250.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
norm0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
tilt0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
" ], "text/plain": [ "\n", " name lon_0 lat_0 sigma ... reference norm tilt \n", " str9 float64 float64 float64 ... float64 float64 float64 \n", "--------- ---------- --------- ---------- ... --------- --------- ---------\n", " lon_0 7.777e-05 3.528e-07 -9.542e-08 ... 0.000e+00 0.000e+00 0.000e+00\n", " lat_0 3.528e-07 7.652e-05 1.827e-06 ... 0.000e+00 0.000e+00 0.000e+00\n", " sigma -9.542e-08 1.827e-06 3.720e-05 ... 0.000e+00 0.000e+00 0.000e+00\n", " index -4.065e-06 3.151e-06 -5.745e-07 ... 0.000e+00 0.000e+00 0.000e+00\n", "amplitude 4.963e-17 4.934e-17 7.922e-16 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " norm 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " tilt 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.covariance_to_table()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.055577317541667 0.030577480211105224\n" ] } ], "source": [ "# Or, to see the value of and error on an individual parameter, say index:\n", "print(result.parameters[\"index\"].value, result.parameters.error(\"index\"))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# TODO: show e.g. how to make a residual image" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }