{ "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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p4tDJN4Gf7uUJEXFeRHwxIvZFxG0RcUlEPLxln5mIuDgivhcRP4iID0fEUS37HBsRH42If28c508j4h4t+zw5Iv45IvZHxO6IeGG/b1SSJEnjqZ8FZYe33NZExCOANwDX9ni4nwMuBn4GOINiisMnI+LepX3eDPwS8KzG/g+klB2OiEOBjwKrgMcDLwBeCLy+tM9xjX0+DZwMbAPeGRFP63G8kiRJGmP9zLndw8ELyoKiLe9zezlQZj590UGKbOptwCnAZyJiDfAbwPMy8x8a+7wI+HpE/Exmfh74eeBRwOmZeStwZUS8BtgSEa/LzAPAWcD1mdlsj/H1iHgC8Arg0l7GLEmSpPHVT3D7lJb7Pwa+C+zOzB+ucDxrGl+/3/h6CkU297LmDpn5jYj4F+BU4PONr1c1AtumS4HtwInAVxr7XMZil1JkcA8SEauBckuNw/p5M5JUB0kuLDibmVlFRIx4RJLUWT8Lyv7PIAYSEYdQBJv/lJlXNzYfDRzIzD0tu9/aeKy5z61tHqeLfQ6PiNnMnGt57DzgD3t/F5JUP5vO3sK1190EwIb169i+bbMBrqSx1deCsog4PiLeGhGXNW5viYjjVziWi4H19Di1YUDOp8giN28PHu1wJGl0moEtwK6rdy/UxpXUn2bjhLm5/WRW0zxhEMecVD1nbhuLsP4WuBL4p8bmnwWuiYhfysy/7+OYFwEbgSdl5k2lh74DrIqItS3Z26MajzX3eWzLIY8qPdb8elSbffa2ydqSmfuBhaKPZigkSVIVyo0ToJpPQwZxzEnWT+b2TcCbM/NxmfnKxu1xFFMKtvRyoChcBDwTeGpmXt+yy5eBu4DTSs95OHAscEVj0xXASRFxZOl5ZwB7ga+V9jmNxc4oHUOSJGngyo0ToJpPQwZxzEnWT3D7SOBdbba/m6JqQS8uBn4NeB6wLyKObtxmATLzzsZrXRgRT4mIU4C/AK5oVEoA+CRFEPv+iHh0I7P8BuDiRgYW4G3AwyLigoh4RET8FvBsijJjkiRJqol+gtvvUtSKbXUyRRmvXmyimNN6OXBL6fac0j6vAHYCHwY+QzHF4FeaD2bmjyimNPyIIhP7AeB9wGtL+1wP/CJFtvarwLnASzLTMmCSJEk10k8psHcAb4+IhwGfa2z7WeBVwIW9HCgzl50MkpnzwEsbt0773AD8wjLHuRx4TC/jk6RpMTOzig3r17Hr6t0AnLDumFr1mpeqkpkLH/lbGm889RPc/jGwjyL7eX5j283A64C3VDMsSdIwRQTbt21e+E87SU7f+PIRj0oaLy7cmgz91LlNirmqb46Iwxrb9lU9MEnScEUEs7NF/5pm0wZJd+u0cKv572ZalbPZeVAT2+HrpxTYccA9MvPaclAbEScAd2XmtyscnyRJksZUazb7hHXHjHhE/S0oew/w+DbbH9d4TJIkSVOgNZs9DnP1+wluH8PdzRvKPk/7KgqSJEnSUPQT3CZwWJvta4BDVzYcSZIkjVJ5akH5+2QyWvz2Uy3hM8B5EfGrjRqzRMShwHnAP1Y5OEmSJA3X9m2bCYoKEOXKKZvO3sK1190E3F0pYhz1E9y+iiLA/WZEfLax7YnA4cBTqxqYJEmShi9oXzmlGdjCeLf47XlaQmZ+DdgA/C/gSIopCu8DHpGZV1c7PEmSpHobxMf9i445BuW5hqmfzC2ZeTPwexWPRZIkaeq0+7h/pY0hyscch/Jcw9RV5jYiju3loBHxoP6GI0mSViJzMhb96G6D+Li/fMxxKM81TN1mbr8YEZcA78zML7bbISLWAM8Gzgbejq14JUkaKtvDSt0Ht48Cfh/4+4iYB74M3AzMA0c0Hj8R+Gdgc2Z+bABjlSRJS7A9bG/KbWNnZlat6I+AKo81KZIcy0VlXQW3mfk94JUR8fvALwJPAB4CzAK3Ax8ELnVBmSRJmgRVZrmnNWNentc7TnpaUJaZc8COxk2SJGkitcty37FnH0esPaznoLTbjHlmMje/v/XpXWtmh4eZGZ6ZWcWG9evYdfVuoFic1pzDWw5sy9tHra9qCZIkSXWz8cxzB5Z1bc3uruT5w8wMRwTbt21emH5QburQtHPHVmZmVh20fVT6ab8rSZJUG+VSWYNqTtCa3e21PFf5+cNuoBBRNHWYnV290LmsbHam/fZRMXMrSerJ3Pz+qVkwo+nQzExuPPPcobzeuGU668bMrSSpJxvPPJdN51xgDVXVRhDMzgyvosS4ZTrrxuBWktSVYXx0K1VpUhtadNOOd5rb6y6nr2kJEfFfgLOA44BTM/OGiDgHuD4z/6bKAUqSxsOwP7qVVmKSy3O1a8e71D7T1l53OT1nbiNiE3Ah8DFgLXBo46E9wDnVDU2SNE5aP7odt2zRpGbpNBidynMNW/O67OXfSzfteKe5ve5y+sncvhz4r5l5SUS8urT9S8B/r2ZYkqRxt+mcC3jP214zFpmwSc7Sqb7K16XZ1eHpZ87tccBX2mzfD9x7ZcORJK3ESovEL2dmZtXCf9LX7r5xbObdjkuWblgmLUs9aeOtSvm6NLs6PP1kbq8HTgZuaNn+dODrKx6RJKkvKy0S341mQfd+Sxg1OywBlhPr06RlqSdtvJp8/QS3FwIXR8QMEMBjI+JXgfOAl1Q5OElS91qzlxvWr2NmZlVfx2ptuVk+Vr8ljAxyqtFtq9dxMWnjnTTltrfj1AJ3lHoObjPznRExB7wBuBfwl8DNwNmZ+aGKxydJ6sPOHVs5Yu1hfQeOrS03q8iyGuTU16gy8n4SUFQxaf7B2a417jTqqxRYZn4Q+GBE3Au4T2beVu2wJEkrMTuzesX/0TdbbkpLGVVG3k8CCsHd/07n5gY3336S9FMK7B8iYi1AZv57M7CNiMMj4h+qHqAkSRpfw17M11ycdseefVO1iFDd6ydz+2Sg3SSuGeCJKxqNJElSB8NYNKnJ13VwGxEbSncfFRFHl+4fSlEt4V+rGpgkafw1W4DC9M551PC0ZoknUXNxplnmweklc3slkI1bu+kHcxQNHiRJU6Jdm1ADXOlgO3dsZXZmtX8EDkEvwe1xFKW/vgU8Fvhu6bEDwG2Z+aMKxyZJGnPt2oT2sgitmfn1P/zJUq5SMG5tmMfV7MzqvhdoJjm0TO9yJQQ7lQkcp0x018FtZjabNvTT1UySpIM0M79mfSdH67xX28oOXvkTkkHpNrM8iDKBVeurFBhARDwKOJaWxWWZ+bcrHZQkaTo0/8O25u3kaJ33atOAwRt0YAu9ZZbHvUxgz8FtRDwM+N/ASRTzb5vhevNziUOrGZokSZpWNmg42M6/3sp5r/sfi2r79tuFsM76ydz+GXA9cFrj62OB+wFbgd+pbmiSJGkadWrQMO1mZ1fztj97lUH/MvoJbk8FnpqZt0fEj4EfZ+Y/RsR5wFuAx1Q6QkmStKTmwry6LO4admOISTLuUwLGQT/B7aHAvsb3twMPBL4J3AA8vKJxSZKkLjUXHLm4S+qv8sHVwKMb338B2BwRPwu8lqJMmCRJGqLmgqNeFnc129jOze0nc3QZ33EZxzgo/3HiHyr96ydz+wbg3o3vXwvsBD4LfA94bkXjkiRJA9JpTuuw5286t3ax7ds2E411+kly+kZ7Y/Wj58xtZl6amR9pfL87Mx8B3B84MjM/VfUAJUmqq2FmLcuvNTe/v5I5rSvNNPYyt3Zufrhzirv92STJ3Pz+Sl4zKObTzs6uZnZmNRvWr1t4bNwrIzSbOzSNcrx917kty8zvR8QDIuL3M/NlVRxTkqQ6G2b2dFCNF4aZadx45rlD+6g+6T6jPKgGC5PQLKFsnMbbU+Y2Ik6MiJdFxG9GxNrGtvtHxDaK+bZPGcQgJUmqm2FWBBhU44VypjEYTCBTDmiH1TCil59NObCtOvhuVkaYnV091oFt07iMt+vMbUT8MrCj9JzNEfFfgf8FfBl4ZmZ+ovohSpKkadXMBm4889zKjpnZ+1SCJJf842Pnjq3MzKyamHmyy72fSdbLtIQ/AC4GXgO8BLiQoq7tL2TmFwcwNkmSNOWCYHamurqurVM0urXc9IMqxzgMg5pOMQ56mZbwcODizPwB8Fbgx8ArDGwlSdI4aDaz6NTQIknu2LNvUWDbduFTm/VjvU4/GPYCuE46Lforv59xX6zWq14yt4cBewEy80cRMYd1bSVJ0pgoZyPbBaCt2cqdO7ZyxNrDDpofuumcLR1fo9vpB8NcALeU5Rb9dToHk6zXUmBPi4hfbsy/PQQ4rXm/tF2SVGOtJX/G4T9wCRZnI9stPmvNVpaDupmZVQvX8o3/elvH15id6bx4rvXfxrAWwC1luUV/szOTsVitF72WAntvy/0/b7mfFO15JUk11Vryx2LzmjTtspXN63ol13LzGHfs2VfpAjj1puvMbWYe0sXNwFaSpsCikj8DKgGl/plNX1qnbGUV13JEtQvg1LtKmjhIkqTR27ljK7Mzq82ma6r13H5XkiSNp9kZs+mSmVtJksZEs5QVjH+7VWlcGdxKkjQmyqWqNqxfV5RxMsCVeuK0BEmSxkS5VNWuq3dX0h51XJoJSMPSc+Y2Ir4F/HRmfq9l+1rgnzPzYVUNTpIkrcy4NBOQhqWfzO1DaV/LdjXwoBWNRpI00ebm95NplnDUxrGZgAav9edet7a63eo6c9vSfexpEXFn6f6hwGnAtysalyRpAm0881znio4BmwlMp9YGK9O6KLGXaQmXNL4mB3cqu4sisPVfkCRNmWa2aNfVu4G754rOzq6skH1mTv1/0iux0mYCzfM/iHPvHODBaTZYmWY9dygD/gU4sqUz2erMfHhm7uzlxSPiSRHxdxFxc0RkRDyj5fH3NLaXb59o2ee+EfHBiNgbEXsi4l0RcZ+WfTZExGcjYj4iboyIzb2MU5LUWTNbtHPH1sqOmZmcdfYWTtv4Mk7b+DI2nXOB0x2GqHz+B3Hu/XlqkHqec5uZx2Xm7RW9/r2BrwIvXWKfTwAPKN1+teXxDwInAmcAG4EnAW9vPhgRhwOfBG4ATgF+F3hdRPxmNW9BklR1y9H5+QNcdc11C/erqhyg7pTPf5Xn/pgHHQkUc4Dr/PPsZu6r82MHp686txFxGsUc2yNpCZAz88XdHiczPw58vHHMTrvtz8zvdBjHI4GnU1Rv+FJj28uBj0XE72TmzcDzgVXAizPzAHBNRJwMvJJSECxJGpzMZG5+/6iHoRHbvu1VbHxW/WcwdjP31fmxg9Nz5jYi/pAiE3oacH/giJZb1Z4cEbdFxDcjYntE3K/02KnAnmZg23AZ8GPgcaV9PtMIbJsuBR4eEYMYrySppPkRtwubNE1dgZtzX2dnV3cMWrvZR73rpxTYWcALM/NxmfmMzHxm+Vbx+D4B/DpFIP0q4OeAj0dEsxTZ0cBt5Sdk5g+B7zcea+5za8txby09dpCIWB0RhzdvwGErfSOSNE2abWTn5vYzN79/0RQDa65Wp3wup/lj7fJ5aHd9dXueWqcK1O1anZapEP1MS1gFfK7qgbSTmR8q3b0qInYB1wFPBj41wJc+D/jDAR5fkmqt3Ea2HCDs3LGVmZlVnL7x5aMaWq1s37aZaKRDp/lj7fJ5SPKg66vb89Q6VaDdsSbZtEyF6Cdz+07geVUPpBuZ+S3gdqD5Z8d3KOb9LoiIewD3bTzW3OeolkMdVXqsnfOBNaXbg1c0cEmaMuU2suUGArMzqxeCjE6cn9u9YHI/1q6yLfCi89Dm+urlPJWnCszOrK5dpnMapkL0k7mdAX4zIk4HdlHUuF2Qma+sYmDtRMSDgfsBtzQ2XQGsjYhTMvPLjW1PpQjav1Da508i4p6Z2RzrGcA3M/OOdq+TmfuBhd+sdf3hS9K4ac7PLU9jUD1NQlvgacl01k0/mdsNwJUUi7bWA48p3U7u5UARcZ+IOLlRvQDguMb9YxuP/WlE/ExEPLRRoeFvgN0UC8LIzK9TzMt9R0Q8NiJ+FrgI+FCjUgLAXwIHgHdFxIkR8RzgbODCPt67JI2VzLvnttahMH5rCbBxD36mTbdZ9U5tmLtpC9wuozvKubDTkOmsm54zt5n5lApf/6eAT5fuNwPO9wKbKALpFwBrgZspqjS8ppFZbXo+RUD7KYqA+8PAb5fGe7CQq00AACAASURBVGdE/DxwMfBlimkNr89My4BJmmitWc66BYLOzx0vvWTVy22Yy4Ll2wK3y+jWfS6sqtVXnduqZOblLF0Y5GldHOP7LDMHODN3AU/saXCSNOZas5ztsmCTJMlFhf2rbAqhlWu93lrnn3Zqw9yqU8OPE9Yds3ANt7uWy21l5+ZGOye79b3WYS5unfQc3EbEp6HzZ1+Z+dQVjUiSNJXKFRY0Xubm9y8K3nbu2MoRaw9b9DF9M7u6VFZ2Kc3M7CTUQ3Yu7njrJ3N7Zcv9e1LMtV1PMZ1AkqSelQPbZiaszi1aJ0nrVIHZmfbzT1fShjmotoXzoJUzyRov/cy5fUW77RHxOuA+Kx2QJGm6tcsKTqLMrFVmb9KnvWh69FMtoZMPAC+u8HiSpCnUKSs4SZqLr07b+DJO2/gyNp1zQdvqAZNgxwfeOOohSD2pMrg9FZiv8HiSVKlm2axJDTK02LDLoJVfb+F121xLmckde/YtWnzVaXHVJJgZw6kC5dJgLuZSq34WlH2kdRPwAIqyXn9cxaAkqWrlMkbNEkWTnh2cZsMug9apDFbrtWQTiuEoL+iqw5QPVaufzO2dLbfvA5cDv5CZf1Td0CSpOuUyRpOcRVNh2GXQWl+vqfVasglFd1qbMvSTfW0u6DKwVat+FpS9aBADkSRpEuzcsRVg2ZJVNqHozFJaGqS+mzhExCnAIxt3r8nMr1QzJEmSxle35aomqazVUgbVsMBSWhqUfubcHgl8CHgysKexeW2jucNzM/O71Q1PkiSNUrNlrllWTYp+5ty+FTgMODEz75uZ96Vo4HA48JYqBydJkkavmWV1jqsmQT/TEp4OnJ6ZX29uyMyvRcRLgU9WNjJJkjR0mcnc/P5RD0PqWz/B7SHAXW2230W1dXMlSdIQWcpMddBPMPoPwJ9FxAObGyLiQcCbgU9VNTBJkgZtUSOIPpt7zM0Xzx1ExrN57CqVy5OVv08Obj5hgwRNon4yty8D/hb4dkQ0CwseA1wN/FpVA5MkaZBas5T9NvfYeOa5nHTi8QCVZzw3nnlu5U1Htm/bTNBoOkEulCrbdPYWrr3upoX9du7YyhFrD3OOrSZOP3Vub4yInwROBx7R2Pz1zLys0pFJkjRArQ0Xmg0ZuilP1Voeq13nspmZVYsaPMzN71/Igs7PH1iyZfAJ645ZaEzRy7i6Edxdgmtu7u5Mczmw3bB+XSWBbfk9S8PSV53bLD4j+fvGTZKkqdJsQnDHnn0HNXPolPFszfAu1b2sWXpruUYRg1BlxnbjmefapU1D1/Wc24h4akR8LSIOb/PYmoi4JiKeWO3wJEmTorWlatVBzdz8/rvnxy6R9exGFfNjI6Jto4bZmbvLZbWek6uuuW4hy7tUy+Cg/bGHoTz+frS+50G3Rl7u9Z03PH16ydyeA7wjM/e2PpCZd0bEnwOvBD5b1eAkSZOjtaVqeT5nFcpZzJUEzsOsCLBUhreuRv2ebe2rXqolPBr4xBKPfxI4ZWXDkSRNskXF/ll5QNGahWtaSTawda7toD8275ThrbNRv2ebTky3XjK3R9G+vm3TD4GfWNlwJKk+MrPv7NFKnlsnrVm4ufn9lWYDd+7YyszMqkozzJJGq5fg9l8p2uzu7vD4BuCWFY9IkmpgJWWmqipRVRfNLNwgTFtGVZoGvUxL+BjwxxEx0/pARMwCfwTsrGpgkjQJmk0AWgvtdyoztdQxmsfp5bnTZtCL1lot/GzGpB2trXGl5fWSuX0D8CvA/4uIi4BvNrY/AngpcCjwJ9UOT5LGVznD2m92tV2W9sI3nT2I4dbCoBetlY1bK9pxG480rroObjPz1oh4PLAdOB8WVgokcCnw0sy8tfohStJ4KmdY+y20b5a2d+VpCuUmBK2aGc5+5yy3/mwGqdy0oVMDiCRtjSt1oacmDpl5A/ALEXEEsI4iwL02M+8YxOAkSepXc+HZJMxZLrfEbReMtzZDsDWu1Fkvc24XZOYdmfnFzPy/BraS1JtFc2xX2IxAyxtkNrzbhgGdSpo1NVvilktXLdUMYaWNFoahn2YKNmBQFfpqvytJ6k/rvElbk062bhsGlPfrtpzZqJshrFQ/zRRswKAq9JW5lST1p3Ue57Bbk6p63TYMaO53xNrDus5ORkRP+4+bfpop2IBBK2XmVpKkIeo1O2k2U+qNwa0kSUPWa2OKQTaykOrG4FbSSNheVpI0CAa3kobO9rKSpEFxQZmkobNxgaoy7Ha8dWPpLdWRmVtJ0sQaZjveOnKxmurIzK0kVSSxOUPZsLKCi0pH0T4w82fTmaW3VDdmbiWpIpvO3sK1190E+PE4jFdW0J+NND3M3EpSRZrBE9icoWlcsoLT+rNxTq2mkZlbVcryTpImWXP6QvP7STdO2XNpWAxuVRnLO0madHWcvmADCE0bpyXUUGZp4UQOL/MwivJOo3qvgzKu72dcxzVNFi2IqvHPoOqPzcsBajfB6rROX5DqxMxtzUxT9rRu73Vc38+4jmvalDOKzZ9BnezcsZXZmdWVf2y+fdvmhQoKlgmTpoOZ25qZpuL4dXuv4/p+xnVc06acUazjz2B2ZjCLzoLly4TZCEKqFzO3kqSpZiMIqV4MbiWpD5nJ3Pz+UQ9DFSkvumpWS5A0mQxuJalHrfOQJUnjwzm3ktSj1nnIkqTxYXArqXaqLF223LF2fOCNKzq+JKlaTkuQVCudSpcN6lgzMxbHl6RxYuZWqplpb7hQZemyqsugzc3vH1hL13L5qqobIUySclmvaT4P0jQzcyvViA0XxtvGM88dWA3VcrOCqhshTJJyWa9pPg/SNDNzq5Gb9kxjlerecGFSr5VyQDuolq6LmhVMeUDXLOs17edBmlZmbjVSZhrVrUm+VpqZxI1nnjvqoUhS7Zm51UjVPdOo/jWztM0MbTfXSqfGCsnB25PFWeBBZoWDYLbihWeDnL+rgzWz787jlVZmGM1vzNxKGjvlLG231Q6Waqyw6ewtXHvdTR23nXTi8QCVVFgYlkHO39XBmnOanccrrcyzf/33B/4aZm4ldTSqFrPlLG232fzWzO6iea4tgW3rtquuua7STxCqrFxQPtZJJx6/EIjD4Obv1lW5kgL09rNpzmk2sJV61/pvb9DM3EpjrPnROfS/An5ufn/Xz83MhaBu9ep7sumcCyayE9fOHVuZmVnF6Rtfvmj7jg+8kTN/7fcG/vpVVi5oPRbAHXv2OX+3D+VKCjDdVSWkYSr/29u7dy9HH/2+gb6ewa00xsofnfe7gGrjmed29dzWj/VPWHfMosxg6/1x1ml+67AaLjSzfIM6VtXzd6dJs5KCpOFq/tu7667B//szuNXQlLOCdc6YNN9nu/dYPgcHPa/N4qDyR+e7rt7NHXv2LQpsljqP5WC0+TH77Ozqjj+H1o/1y4Fsaya0l2zwIJUz21Uvrmq+x1GYH8FUEEmqC4NbDUWVLVHHWbuFUM0AcKkFT0BXi4NaP4peKiPbrvxUv+W0WjOF3WaDB62c2a56cdUoF2wNY+qEJNXVSBeURcSTIuLvIuLmiMiIeEbL4xERr4+IWyJiLiIui4gTWva5b0R8MCL2RsSeiHhXRNynZZ8NEfHZiJiPiBsjon5RVR+GWRB/3Et+tZad6tdSC6Faz0Grfj7yX+o8tis/tZKfQ+uCgGYmueprp5dFbOXMdlVTJgbVcKH1/HUTOFt2SpJ6N+rM7b2BrwLvBj7S5vHNwG8DLwCuB/4YuDQiHpWZ8419Pgg8ADgDuCfwF8DbgecBRMThwCeBy4CzgJOAd0fEnsx8+4De19ib5IL4VVsq2zooO3dsXQg85+b397Q4aOeOrcDBWdxBay4IKC9mqjqDu1x2exgG1XChdTFTkgcteCvbuWMrR6w9bCr/TUrSSow0c5uZH8/MP8jM/936WBS/0c8B3pCZf5OZu4BfBx4IPKOxzyOBpwMvycwvZOY/Ai8HnhsRD2wc6vnAKuDFmXlNZn4IeAvwykG/v3HWbQavU3a326zv3PxgssJVlqjqtuzUcu+5lzHNzqxeaJXa6+Kg2Znen1OViOCItYcdlMGtKgu/XHa7qZz1rHrqwHINF1bSPKG5oGJ2dvVCBYROZmc6l51aSUkrLc/zK022UWdul3IccDRFxhWAzLwzIr4AnAp8qPF1T2Z+qfS8y4AfA48D/ndjn89kZvl/30uBV0XEEZl5x2DfxuRaap5st/Nnm5m9C9909sDGNQzLZbrHIeM4LO0yuIOwVNmucnms5TKgVRuH5gmWtBosz6802ca5icPRja+3tmy/tfTY0cBt5Qcz84fA91v2aXeM8mssEhGrI+Lw5g04rPfhj49us6et+3XK7naT9S3/51/1/NqlivUPynLvuVPGsarM9bi1/oyovp1sq6XKdjXLY3WTAa3KoObitnuNbn7Oi7LAExZ4TUJmdJLPrzTtxjlzO0rnAX846kFUpdt5kVXOnxzUvMVWnYr1j1I541g+pyuxVOvPcSnLVXfDuKanpcWrmVFJgzTOmdvvNL4e1bL9qNJj3wGOLD8YEfcA7tuyT7tjlF+j1fnAmtLtwb0MfFx0mz0dRJZ1uXmLVZmdGV7mrltrBzAndanWnxvPPJdN51ww8IoX067TNV1lFnKaWryaGZU0KOOcub2eIvg8DbgSFiofPA7Y3tjnCmBtRJySmV9ubHsqRdD+hdI+fxIR98zMuxrbzgC+2Wm+bWbuBxZWBi33i3dcmxN0m2kaVpa1V4M4r/0cs9fFa8Hg56Q2A6pdV+8Gxq+02jRp/rzH8XeAJE2jkQa3jXq060qbjouIk4HvZ+a/RMQ24A8i4lruLgV2M3AJQGZ+PSI+AbwjIs6iKAV2EfChzLy5ccy/pJhi8K6I2AKsB84GXlHFexjnklrdZk+HlWXtxSDOaz+NJPpdKDboOanDWtSl7tjSVZLGx6inJfwU8JXGDeDCxvevb9y/AHgrRd3aLwL3AZ5eqnELRamvbwCfAj4G/CPwm80HM/NO4Ocpqi98GdgKvL6qGrfj3pxgnC1VUmmp89pvGaauy59xd8mvufn9Q1+81q1hLOqq0qJSaj3+DPspvzWIRUuTsBBKkqbdSDO3mXk5dJ4wmcUkwtc2bp32+T6Nhg1L7LMLeGJ/o9Sg9FNSKTPZdM4FAxpRoVNL13FcvDYpWjPgvf7c+7lW2i1aWvHcZxdCSdLYG3XmttaG2d52kqykpNL8/IGF55yw7piDsmZVlN7q1NJ1HBevVaHfTHjrMZa6zluz5u1+7sniuc2tWdJ+ym8NYtFSVcc0CyxJgzHOC8om2jjPxR21qhawtTufVbeDnQabzrmA97ztNSs7RiPbvZJzX86Yw3AW5o2SWWBJGgwztwOQJHfs2TdVc3F7mRNZ1QK2Zha1NQNW93NdlWMeVFTRu3b3jSs+X82gdCXnvhzYNrOY7dr91inDaTksSaqemdsBaM1ATYNRtiS1ckB/tm97FRufNX7na+eOrRyx9rCFYM8MpySpF2ZuB6Ac2I7T6vpBGEZL0m5MWuWAsTCm8eHszMFZTDOckqRuGdwO0M4dW1fcdnU5zUVrVS5Y62VR1vZtm9m5Y2tlr11X/ZSyGqbWxVwrUcWiPkmS+uW0hAEadCaxvGitm4YE3WouyrrwTWcvu+84NoAYR6OcttGNKqfSuKhPkjRKZm7HRGvZsOXKiGUuXrS26+rd3LFn35LZwaUyapOwKGuY2c9eAtFO7Xm7LWXVut8oguCqp9KM4/UjSZoOZm7HQGvZsJNOPB6gYxmxTi1hl8sOljNqrSZhUdYwsp87d2xldmY1SXbVrGGp9rzdntPWBVPdvvYgrLRRxY4PvJEzf+33Kh6VJEndM3Pbg7ZZ1R4yieVC9+VjtLZ4veqa65YsI9ZaEL+sNTvYS0Z2XBdlDXLRWutc09mZYtHS7MzqrspPtf4sWvfr9pwuWjA1wpVevTSqaNeEYO3aww7ar1Nme1yUr6/mz88GC5I0uczc9uCXnv07PObRjwToq41oc15ja2Z2JdnInTu2snbNfdhz5w/aZgcnISO7nKqaPrTTaa5pP+WnWktY1V037W2XymyPi+3bNi+qmdz8+Vl+TJImk5nbHrVmVXvJJDaDqKWOUQ50uwl6Z2dWc8ghhyyZHRzXjGy3BrlorV3jgIXX7bH8VLsSVnW31Dmamz/4U4l213S7zOkwBe3fg+XHJGkymbkdM+Us0ijnXk6bacu6DkPrHOlO83k7ZU4lSeqHmdsxsyiLNK5V9lvUYX7iNGZdB2GpChGd5vN2ypxKktQPM7cTorkYrfn9SszN7680+LQ9qprqMMdbkjTZDG4nRHnh00rLYQ2ipFZzfqI06XO8JUmTzWkJE6K88KmfcljdNhSoC1vATo86TIuRJFXHzO2UmLaPi20BOz2cFiNJKjNzWxPdZK8igiPWHtZ1lmt+RIX3WxsrdKtdw4o79uybmgxur+2J65TxtGyXJKnJzG1NdJu96iXLNao2qp0aKyynXXZ6qZbDddPrXOrAjKckqX7M3NZIt9mrpfZrzebByhew9WqpxgrLaZedXsjgrrDKRKteM6WDsNK51GY8JUl1Y+ZWi7RmdmF0zST6bazQKYNbdZA+iGP2atrmUkuStBwztyvQa6vcSVHO5g2zmURrG9aVdAxrl8GtokLEMKpO9DoXttvSW93OZR7kXNw6zfOVJI0nM7crYKvcalXdhnUQWc1hZEoHtfq/27nMg6w+YGUDSdKgmbldgVG0ym3NbtYp6zWINqyDaCgwjCYFvc6F7SYj2stc5kHOxXWeryRpkMzcVqQZXOy6ejdwd/BQnrtahaqzm6qHXjKi/c5lliRpEhjcVmRYH7c2s5tSq25bIM/OmDGVJNWX0xIq5MetS2ipmDVNC4tG+V6n6Tx3w/MhSfVn5lZDsemcLYvuT9PColG+12k6z93wfEhS/Zm57cH6Rz5s4ftuMz7tmiJMsl5a487MrFpYAHfjv9520OOjzHSvdGFeX+W6RvRe/URhMc+HJNWbmdsevHnLOaxaNQN0n/EpZ4rm5vdPfKH9XlrjNt/7OJZIW+nCPDOAkiSNJ4PbHnS7YKeq542jXlvjDqtEWq+qWJhXp5+rJEl1YXCrvlhOSpIkjSPn3Kovwy4n5Sp3SZLUDTO3mgjOcZUkSd0wuNXEcI6rJElajtMSxti4fBS/knGMy3uQJEnTwcztGBuXj+JXMo5xeQ+SJGk6mLkdc+NScH4l4xiX96DhMWMvSRoVM7eSKmfGXpI0KmZuh6g1m1VuASvVjRl7SdIomLkdotZsVpJj2ZpWkiRpUhncDlm5nNXc3P4Rj0aSJKlenJYgSZKk2jC4lSRJUm0Y3I6Q5ZIkSZKq5ZzbEbJckiRJUrUMbkesvMBMkiRJK+O0BEmSJNWGwa0kSZJqw+BWkiRJtWFwq9qzKoUkSdMjMnPUYxh7EXE4cOedd97J4YcfPurhqA+ZaVUKSZJGbO/evaxZswZgTWbuHcRrWC1BU8GqFJIkTQenJUiSJKk2DG4lSZJUGwa3kiRJqg2DW0mSJNWGwa0kSZJqw2oJPdi7dyAVKyRJkqbCMGIp69x2ISIeBNw06nFIkiTVxIMz818HcWCD2y5EUfH/gcC+UY9lAhxG8YfAg/F8LcXz1D3PVXc8T93zXHXH89Q9z1V3mufpEcD/ywEFoU5L6ELj5A/kr4u6KXX+2jeoziN14HnqnueqO56n7nmuuuN56p7nqjul83TLoAJbcEGZJEmSasTgVpIkSbVhcKuq7Qf+qPFVnXmeuue56o7nqXueq+54nrrnuerOUM6TC8okSZJUG2ZuJUmSVBsGt5IkSaoNg1tJkiTVhsGtJEmSasPgVisSEU+OiOxw++klnnd5m/3fNsyxD1tEfLvNe371Ms+ZiYiLI+J7EfGDiPhwRBw1rDGPQkQ8NCLeFRHXR8RcRFwXEX8UEauWeV7tr6mIeGnjOpqPiC9ExGOX2f9ZEfGNxv5XRcQvDGusoxIR50XEFyNiX0TcFhGXRMTDl3nOC9tcO/PDGvMoRMTr2rznbyzznKm7nqDj7+6MiIs77D8V11NEPCki/i4ibm68x2e0PB4R8fqIuKXxu/yyiDihi+P29HuuHYNbrdTngAe03N4JXA98aZnnvqPleZsHN8yx8VoWv+e3LrP/m4FfAp4F/BxFG+iPDHKAY+ARFL+b/htwIvAK4CzgjV08t7bXVEQ8B7iQoozOTwJfBS6NiCM77P944H8C7wIeA1wCXBIR64cz4pH5OeBi4GeAM4B7Ap+MiHsv87y9LL52HjLIQY6Ja1j8np/Qaccpvp4AfprF5+mMxva/XuI503A93Zvi99BLOzy+Gfhtit/fjwP+jeJ31kynA/b6e66jzPTmrbIbxX8ktwGvWWa/y4Ftox7vkM/Nt4Fzeth/DXAAOLO07RFAAj8z6vcz5HP3u8C3ltmn1tcU8AXgotL9Qyjagr+6w/5/Bexs2fZ54G2jfi9DPm8/0fg386Ql9nkhsGfUYx3yeXkdcGUP+3s93f2+twG7aZRTbfP4NF5PCTyjdD+AW4DfKW1bA8wDz13iOD39nut0M3Orqv0ycD/gL7rY9/kRcXtEXB0R50fEvQY8tnHw6sYUg69ExO9GxD2W2PcUij8WLmtuyMxvAP8CnDrgcY6bNcD3u9ivltdUY0rGKSy+Fn7cuN/pWji1vH/DpUvsX1drGl+Xu37uExE3RMSNEfE3EXHioAc2Bk5ofKT8rYj4YEQcu8S+Xk8s/Fv8NeDd2Yi+OpjG66nsOOBoFv/OupMieG17zfT5e66tpf5jlfrxG8ClmXnTMvv9JXADcDOwAdgCPBz4lcEOb6TeAvwzxX+yjwfOp/i46pUd9j8aOJCZe1q239p4bCpExDrg5cDvLLNrna+p+wOHUvzsy26lyOa3c3SH/afp2jmEIsv2T5l59RK7fhN4MbCLIhj+HeBzEXFiF7/LJtUXKDKM36T4PfSHwGcjYn1m7muz/9RfTw3PANYC71lin2m8nlo1r4terpl+fs+1ZXCrtiLiTcCrltntkY1MYvM5DwaeBjx7ueNn5ttLd6+KiFuAT0XE8Zl5XT9jHoVezlNmXljatisiDgB/HhHnZWbtWzb2eU09CPgE8NeZ+Y6lnliXa0qVuhhYzxJzSQEy8wrgiub9iPgc8HWKed+vGeQARyUzP166uysivkDxx+GzKebVqr3fAD6emTd32mEar6dxY3CrTray9F+mAN9quf8i4HvA3/bxel9ofF0HTFIg0s95avoCxb/Bh1L8pd/qO8CqiFjbkr09qvHYpOnpXEXEA4FPUyxa/M0+Xm9Sr6l2bgd+RPGzL1vqWvhOj/vXSkRcBGykmGvbU7YsM++KiK9QXDtTITP3RMT/o/N7nurrCSAiHgKcTo+fBk3j9cTd18VRFHNvKd2/ssNz+vk915bBrdrKzO8C3+12/4gIiuD2fZl5Vx8veXLj6y1L7jVmej1PLU4GfkyxAK+dLwN3AacBHwZolDQ6llJWYFL0cq4aGdtPU5yDFzXmXfVqIq+pdjLzQER8meJauAQWPnI/Dbiow9OuaDy+rbTtDCbw2ulF43fRW4FnAk/OzOv7OMahwEnAxyoe3tiKiPsAxwPv77DLVF5PLV5E8fv6o708aRqvJ4qKSd+huGauBIiIwymqJmxv94Q+f8+1N+oVdt7qcWtcfAk8os1jDwK+ATy2cf94io9mTqHIWv4yRWbt/4z6fQzw/JwKnAM8GngY8HyKX5Lv7XSeGtu2U3xU+JTG+foc8LlRv58Bn6sHAddSLCJ4EMX8rKOBo6f5mgKeQ7HS+AXAI4E/B+4Ajmo8/j7g/NL+j6f44+hcivlqr6OovrF+1O9lwOfpfwB7KEqCHV26zZb2aT1XrwV+vvFv8ycpSl7NAY8a9fsZ4Hn6741z9NDGtfL3FH98/oTXU9vzdUjjd/Gb2jw2ldcTcB+KJMLJFP//v6Lx/bGNx1/V+B31yxTB/SUUn87NlI7xKeBlpftL/p7r9mbmVlX5DYqgq10R8HtSLOxprlw/QPHRzjkUdfJupMhMvmEI4xyV/cBzKf5DWE3xV+2bKer5NbWeJyh+WfyY4vysplid/FuDH+5InUHx8d06oPXj5Gh8nbprKjP/KiJ+Ang9RbB2JfD0zGwuvjiW4lpp7v+5iHgexTl4I8UfDM/IpRdW1cGmxtfLW7a/iLunxSw6V8ARFDWSj6b4j/TLwOMz82sDG+XoPZgi6LofRVD7jxQlBpufrng9LXY6xTl5d5vHpvV6+imKT9iamv+fvZdiseIFFL+P306xCO8fKX5nlRtaHE+xkAzo6vdcV6IRKUuSJEkTzzq3kiRJqg2DW0mSJNWGwa0kSZJqw+BWkiRJtWFwK0mSpNowuJUkSVJtGNxKkiSpNgxuJUmSVBsGt5I0YhHxnoi4ZISv//6I+L0u9/1QRJw76DFJUr/sUCZJAxQRy/2S/SOKVsyRmXuGMKRFIuLRwD8AD8nMH3Sx/3rgM8BxmXnnoMcnSb0yuJWkAYqIo0t3n0PRM/3hpW0/6CaoHJSIeCfww8w8q4fnfBF4T2ZePLiRSVJ/nJYgSQOUmd9p3oA7i013b8vMH7ROS4iIyyPirRGxLSLuiIhbI+K/RsS9I+IvImJfROyOiP9Yfq2IWB8RH4+IHzSe8/6IuH+nsUXEocCZwN+1bP+tiLg2IuYbx9nR8tS/A5670nMjSYNgcCtJ4+kFwO3AY4G3AtuBvwY+B/wk8Eng/RFxL4CIWEsxveArwE8BTweOAv7XEq+xAVgDfKm5ISJ+CngL8FqKDPPTKaYhlP1f4LERsXpF71CSBsDgVpLG01cz8w2ZeS1wPjAP3J6Z72hsez1wP4oAFeBlwFcy8/cy8xuZ+RXgxcBTIuI/dHiNhwA/Am4rbTsW+DdgZ2bekJlfycy3tDzvZmAVcDSSNGYMbiVpxfzt2QAAAbJJREFUPO1qfpOZPwK+B1xVevzWxtcjG18fTRHI/qB5A77ReOz4Dq8xC+zPxYsv/h64AfhWY1rD85vZ4ZK5xtfW7ZI0cga3kjSe7mq5n+VtpYC0+Xv8PhRzYU9uuZ3AwdMKmm4H7hURq0rH3Ucx7eFXgVsoMsRfbUx7aLpv4+t3e3tLkjR4BreSVA//DJwIfDszd7fc/q3Dc65sfH1UeWNm/jAzL8vMzRTTHh4KPLW0y3rgpsy8vdq3IEkrZ3ArSfVwMUVG9X9GxE9HxPER8bRGdYVD2z0hM79LERQ/obktIjZGxG9HxMkR8RDg1yn+r/hm6alPpFjQJkljx+BWkmogM28GfhY4lCLwvArYBuwBfrzEU98JPL90fw/wKxSVF74OnAX8amZeAxARM8AzgHdU/BYkqRI2cZCkKRYRsxRZ2edk5hVd7L8JeGZm/vzABydJfTBzK0lTLDPnKKYedGz20OIu4OWDG5EkrYyZW0mSJNWGmVtJkiTVhsGtJEmSasPgVpIkSbVhcCtJkqTaMLiVJElSbRjcSpIkqTYMbiVJklQbBreSJEmqDYNbSZIk1cb/B5ZUhBtTG+LfAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "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 }