{ "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/v0.9?urlpath=lab/tree/spectrum_simulation_cta.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", "[spectrum_simulation_cta.ipynb](../_static/notebooks/spectrum_simulation_cta.ipynb) |\n", "[spectrum_simulation_cta.py](../_static/notebooks/spectrum_simulation_cta.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectrum simulation for CTA\n", "\n", "A quick example how to simulate and fit a spectrum for the [Cherenkov Telescope Array (CTA)](https://www.cta-observatory.org).\n", "\n", "We will use the following classes:\n", "\n", "* [gammapy.spectrum.SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst)\n", "* [gammapy.spectrum.SpectrumSimulation](..\/api/gammapy.spectrum.SpectrumSimulation.rst)\n", "* [gammapy.spectrum.SpectrumFit](..\/api/gammapy.spectrum.SpectrumFit.rst)\n", "* [gammapy.irf.load_cta_irfs](..\/api/gammapy.irf.load_cta_irfs.rst)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 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": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from gammapy.irf import EnergyDispersion, EffectiveAreaTable\n", "from gammapy.spectrum import SpectrumSimulation, SpectrumFit\n", "from gammapy.spectrum.models import PowerLaw\n", "from gammapy.irf import load_cta_irfs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Define simulation parameters parameters\n", "livetime = 1 * u.h\n", "offset = 0.5 * u.deg\n", "# Energy from 0.1 to 100 TeV with 10 bins/decade\n", "energy = np.logspace(-1, 2, 31) * u.TeV" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Define spectral model\n", "model = PowerLaw(\n", " index=2.1,\n", " amplitude=2.5e-12 * u.Unit(\"cm-2 s-1 TeV-1\"),\n", " reference=1 * u.TeV,\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Load IRFs\n", "filename = (\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")\n", "cta_irf = load_cta_irfs(filename)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NDDataArray summary info\n", "energy : size = 42, min = 0.014 TeV, max = 177.828 TeV\n", "offset : size = 6, min = 0.500 deg, max = 5.500 deg\n", "Data : size = 252, min = 0.000 m2, max = 5371581.000 m2\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "aeff = cta_irf[\"aeff\"].to_effective_area_table(offset=offset, energy=energy)\n", "aeff.plot()\n", "plt.loglog()\n", "print(cta_irf[\"aeff\"].data)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NDDataArray summary info\n", "e_true : size = 30, min = 0.112 TeV, max = 89.125 TeV\n", "e_reco : size = 30, min = 0.112 TeV, max = 89.125 TeV\n", "Data : size = 900, min = 0.000, max = 0.926\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "edisp = cta_irf[\"edisp\"].to_energy_dispersion(\n", " offset=offset, e_true=energy, e_reco=energy\n", ")\n", "edisp.plot_matrix()\n", "print(edisp.data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Simulate data\n", "sim = SpectrumSimulation(\n", " aeff=aeff, edisp=edisp, source_model=model, livetime=livetime\n", ")\n", "sim.simulate_obs(seed=42, obs_id=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 0\n", "Livetime: 1.000 h\n", "On events: 411\n", "Off events: 0\n", "Alpha: 1.000\n", "Bkg events in On region: 0.00\n", "Excess: 411.00\n", "Excess / Background: inf\n", "Gamma rate: 411.00 1 / h\n", "Bkg rate: 0.00 1 / min\n", "Sigma: nan\n", "energy range: 0.10 TeV - 100.00 TeV\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sim.obs.peek()\n", "print(sim.obs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spectral analysis\n", "\n", "Now that we have some simulated CTA counts spectrum, let's analyse it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Fit data\n", "fit = SpectrumFit(obs_list=sim.obs, model=model, stat=\"cash\")\n", "fit.run()\n", "result = fit.result[0]" ] }, { "cell_type": "code", "execution_count": 11, "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 2.116e+00 3.446e-02 nan nan False\n", "\tamplitude 2.382e-12 1.205e-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 1.188e-03 -9.160e-16 0.000e+00\n", "\tamplitude -9.160e-16 1.451e-26 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: -1591.397 (cash)\n", "Fit Range: [ 0.1 100. ] TeV\n", "\n" ] } ], "source": [ "print(result)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy_range = [0.1, 100] * u.TeV\n", "model.plot(energy_range=energy_range, energy_power=2)\n", "result.model.plot(energy_range=energy_range, energy_power=2)\n", "result.model.plot_error(energy_range=energy_range, energy_power=2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "* Change the observation time to something longer or shorter. Does the observation and spectrum results change as you expected?\n", "* Change the spectral model, e.g. add a cutoff at 5 TeV, or put a steep-spectrum source with spectral index of 4.0" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Start the exercises here!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What next?\n", "\n", "In this tutorial we simulated and analysed the spectrum of source using CTA prod 2 IRFs.\n", "\n", "If you'd like to go further, please see the other tutorial notebooks." ] } ], "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": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }