{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulations\n", "\n", "So maybe you're not satisfied with looking at real data; you want to make your own. The ``gbm.simulate`` module contains some classes and functions that allow you to simulate GBM data. Let's take a look at how you would do this.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate PHA Data\n", "\n", "Consider a single spectrum. We don't really care about the lightcurve, just the spectrum of some particular source. What do you need to simulate what the signal looks like in GBM? For starters, you need a model of what the source spectrum looks like, and you need a detector response. Remember that the response is angular-dependent, so if you try responses with different source-detector angles, the signal will look different. You also want a realistic background model. For the source spectrum, we have an actual functional model, for the background (currently) we do not. Hmmm.\n", "\n", "Our solution here is to phenomenologically model some *real* background data and use that background model for our simulations. Let's use the example from the [PHA Export notebook](./PhaExport.ipynb):" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from gbm import test_data_dir\n", "from gbm.data import TTE, RSP\n", "from gbm.binning.unbinned import bin_by_time\n", "\n", "# open a TTE file and response\n", "tte = TTE.open(test_data_dir+'/glg_tte_n9_bn090131090_v00.fit')\n", "rsp = RSP.open(test_data_dir+'/glg_cspec_n9_bn090131090_v00.rsp2')\n", "\n", "# bin to 1.024 s resolution, reference time is trigger time\n", "phaii = tte.to_phaii(bin_by_time, 1.024, time_ref=0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the background:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gbm.background import BackgroundFitter\n", "from gbm.background.binned import Polynomial\n", "\n", "bkgd_times = [(-20.0, -5.0), (75.0, 200.0)]\n", "backfitter = BackgroundFitter.from_phaii(phaii, Polynomial, time_ranges=bkgd_times)\n", "\n", "backfitter.fit(order=1)\n", "bkgd = backfitter.interpolate_bins(phaii.data.tstart, phaii.data.tstop)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In principle, you can select any part of the background model that you think accurately reflects the background data. Let's try a selection." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from gbm.plot import Lightcurve, Spectrum\n", "\n", "select_time = (150.0, 152.0)\n", "\n", "# the background model integrated over the source selection time\n", "spec_bkgd = bkgd.integrate_time(*select_time) # <-- This is what we will use for our simulations\n", "\n", "# and the data\n", "spec_data = phaii.to_spectrum(time_range=select_time)\n", "\n", "# plot the comparison between the background model and the data\n", "specplot = Spectrum(data=spec_data, background=spec_bkgd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the background model (red) closely follows the background data (blue), so we can consider this an acceptable background model to use for our simulation. Next, we want to define our source spectrum and exposure:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Band function with amplitude=0.1, Epeak=300 keV, alpha=-1.0, beta=-2.5\n", "from gbm.spectra.functions import Band\n", "band_params = (0.1, 300.0, -1.0, -2.5)\n", "\n", "# 2 s exposure\n", "exposure = 2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, we need to decide if our background is to be Poisson or Gaussian generated. For GBM, it is usually most appropriate to assume it is Gaussian, so that is what we use here. Now we can create our PHA simulator:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from gbm.simulate import PhaSimulator\n", "pha_sims = PhaSimulator(rsp, Band(), band_params, exposure, spec_bkgd, 'Gaussian')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alright, the simulator has been initialized. What now? Well first, let's look to see how well the simulated background matches our background model:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# make 20 simulations of the background\n", "bkgd_devs = pha_sims.simulate_background(20)\n", "\n", "\n", "# And plot the background model and 20 simulations\n", "# SpectrumBackground is the plotting class for spectral background models\n", "from gbm.plot.gbmplot import SpectrumBackground\n", "\n", "specplot = Spectrum()\n", "# background from each simulation\n", "sim_bkgds = [SpectrumBackground(bkgd_dev, specplot.ax, color='gray', alpha=0.1) for bkgd_dev in bkgd_devs]\n", "# our original background model\n", "specplot.set_background(spec_bkgd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good. Now let's simulate the background+source, which is essentially what we observe in reality, and compare to just the simulated background:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 20 source+background simulations\n", "summed_devs = pha_sims.simulate_sum(20)\n", "\n", "# Histo is the plotting class for observed lightcurves or count spectra\n", "from gbm.plot.gbmplot import Histo\n", "\n", "specplot = Spectrum()\n", "# background from each simulation\n", "sim_bkgds = [SpectrumBackground(bkgd_dev, specplot.ax, color='gray', alpha=0.1) for bkgd_dev in bkgd_devs]\n", "# the summed \"observed\" data from each simulation\n", "sim_signals = [Histo(summed_dev, specplot.ax, color='midnightblue', alpha=0.1) for summed_dev in summed_devs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fantastic! Another cool thing you can do is generate fully formed ``BAK`` and ``PHA`` objects from the simulations:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[, , , , ]\n", "[, , , , ]\n" ] } ], "source": [ "# create 5 simulated BAK and PHA objects\n", "baks = pha_sims.to_bak(5)\n", "phas = pha_sims.to_pha(5)\n", "print(baks)\n", "print(phas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those objects can then be written to disk if desired by the corresponding ``.write()`` methods. You can also create (and write) a ``PHAII`` object by concatenating all of your simulations into a single time-series:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# concatenate 10 simulations into a single PHAII object\n", "pha_sims.to_phaii(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Simulate TTE Data\n", "\n", "Simulating PHA data isn't interesting enough, you say? Well, good news: you can also simulate TTE data. Specifically, you can simulate a lightcurve with the full spectral resolution of the GBM detector response. To do that, we'll use the TTE simulators and some time profile functions:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# source and background simulators\n", "from gbm.simulate import TteSourceSimulator, TteBackgroundSimulator\n", "\n", "# A tophat (rectangular) pulse and a constant background\n", "from gbm.simulate.profiles import tophat, constant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we simulate the source spectrum and time profile:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# amplitude, start and stop times of the tophat pulse\n", "tophat_params = (0.05, 0.0, 2.0)\n", "\n", "# initialize simulator with the spectral model and time profile\n", "tte_sim = TteSourceSimulator(rsp, Band(), band_params, tophat, tophat_params)\n", "\n", "# time range over which to simulate source\n", "tte_src = tte_sim.to_tte(-5.0, 10.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we simulate the background:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# use our background model spectrum and scale to 200% of the background amplitude\n", "constant_params = (2.0,)\n", "tte_sim = TteBackgroundSimulator(spec_bkgd, 'Gaussian', constant, constant_params)\n", "\n", "# time range over which to simulate background\n", "tte_bkgd = tte_sim.to_tte(-10.0, 10.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now we have simulated data for the source and the background. We want source+background:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# merge the background and source\n", "tte_total = TTE.merge([tte_bkgd, tte_src])\n", "\n", "# bin to 64 ms resolution so we can make a lightcurve plot\n", "phaii = tte_total.to_phaii(bin_by_time, 0.064)\n", "lcplot = Lightcurve(data=phaii.to_lightcurve(energy_range=(8.0, 900.0)))\n", "lcplot.errorbars.hide()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting...but not exactly a realistic looking signal. Let's try something that looks quite a bit more realistic:" ] }, { "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": [ "# a Norris pulse shape and a quadratic background\n", "from gbm.simulate.profiles import norris, quadratic\n", "\n", "norris_params = (0.05, 0.0, 0.1, 0.5)\n", "quadratic_params = (1.0, 0.05, 0.003)\n", "\n", "# source simulation\n", "tte_sim = TteSourceSimulator(rsp, Band(), band_params, norris, norris_params)\n", "tte_src = tte_sim.to_tte(-5.0, 10.0)\n", " \n", "# background simulation\n", "tte_sim = TteBackgroundSimulator(spec_bkgd, 'Gaussian', quadratic, quadratic_params)\n", "tte_bkgd = tte_sim.to_tte(-10.0, 10.0)\n", " \n", "# merge the background and source\n", "tte_total = TTE.merge([tte_bkgd, tte_src])\n", "\n", "# bin to 64 ms resolution so we can make a lightcurve plot\n", "phaii = tte_total.to_phaii(bin_by_time, 0.064)\n", "lcplot = Lightcurve(data=phaii.to_lightcurve(energy_range=(8.0, 900.0)))\n", "lcplot.errorbars.hide()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voila!\n", "\n", "There are lots of things you can do once you create the simulations; you can essentially treat them like data.\n", "If you're interested in a variety of other aspects of the GBM Data Tools, and a deeper dive under the hood, please [continue on!](./DataPrimitives.ipynb)" ] } ], "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }