{ "cells": [ { "cell_type": "markdown", "id": "b1913ad1-049c-4f50-9593-9b84f54d11a9", "metadata": {}, "source": [ "# Simulating Observations\n", "\n", "nDspec allows users to simulate three spectral-timing products for proposals, checking results, etc: lightcurves, time-averaged spectra, and lag energy spectra, starting from user-defined models and observation details." ] }, { "cell_type": "code", "execution_count": 1, "id": "9524afd0-74a7-4f82-87eb-0105dfa8564d", "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "import gc\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from lmfit import Model as LM_Model\n", "from lmfit import Parameters as LM_Parameters\n", "\n", "sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath('__file__'))))\n", "\n", "from ndspec.Response import ResponseMatrix\n", "from ndspec.Timing import PowerSpectrum, CrossSpectrum\n", "import ndspec.Models as models" ] }, { "cell_type": "markdown", "id": "2d158f2e-0d64-44fe-9bc1-5599783bdb50", "metadata": {}, "source": [ "## How to simulate a lightcurve\n", "\n", "Simulating a lightcurve from an assumed model power spectrum is extremely simple, and relies on the implementation of the Timmer and Konig algorithm in Stingray. We begin by defining a model power spectrum, starting from a time grid which will inform the Fourier frequencies our model will probe:" ] }, { "cell_type": "code", "execution_count": 2, "id": "9eb2f4ee-988c-46ff-9e2c-341c4131b3fc", "metadata": {}, "outputs": [], "source": [ "#Define our time grid and power spectrum object\n", "obs_time = 5e2\n", "dt = 0.05\n", "N = int(obs_time/dt)\n", "times = np.linspace(dt,obs_time,N)\n", "\n", "psd_model = PowerSpectrum(times)" ] }, { "cell_type": "markdown", "id": "1ffe1b92-a2b4-47cf-a8fb-14c5b47f8d5d", "metadata": {}, "source": [ "Having initialized our power spectrum object, we can decide what kind of model it will contain. For this simulation, we will use three Lorentzians to make up broadband noise similar to that observed in black hole X-ray binaries:" ] }, { "cell_type": "code", "execution_count": 3, "id": "79428c9b-7e2c-4e85-b72d-3145be5fff61", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "15479" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def lorentz(freq,peak_f,q,rms):\n", " par_array = np.array([peak_f,q,rms])\n", " model = models.lorentz(freq,par_array)\n", " return model\n", "\n", "#Define our model for the power spectrum directly in Fourier space, as a sum of\n", "#two Lorentzians\n", "Lorentz_model = LM_Model(lorentz, prefix=\"l1_\") + \\\n", " LM_Model(lorentz, prefix=\"l2_\") + \\\n", " LM_Model(lorentz, prefix=\"l3_\")\n", "\n", "Lorentz_model_parameters = LM_Parameters()\n", "Lorentz_model_parameters.add_many(('l1_peak_f', 0.06, True, 1e-3, 0.3),\n", " ('l1_q', 0.5, True, 0, 2),\n", " ('l1_rms', 0.20, True),\n", " ('l2_peak_f', 0.9, True, 0.3, 1.25),\n", " ('l2_q', 0.3, True, 0, 2),\n", " ('l2_rms', 0.15, True),\n", " ('l3_peak_f', 4.0, True, 1.25, 10),\n", " ('l3_q', 0.2, True, 0, 2),\n", " ('l3_rms', 0.12, True),\n", " )\n", "\n", "#Assign the model array to power_spec and plot the result\n", "psd_model.set_psd_model(Lorentz_model,Lorentz_model_parameters)\n", "psd_model.compute_psd() \n", "psd_model.plot_psd()\n", "gc.collect()" ] }, { "cell_type": "markdown", "id": "34f0b74b-b417-4de4-8d00-7d37049af127", "metadata": {}, "source": [ "Once the observation details and model are specified, we can then assume some expected source count rate use the ``simulate_lightcurve`` function to create a stingray Lightcurve object, which contains the counts per bin over the simulated observation. As a sanity check, we can ensure that the mean count rate is indeed identical to our input:" ] }, { "cell_type": "code", "execution_count": 4, "id": "1ae3c091-9d89-4778-8868-3d5bdc702679", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ndspec.Simulator import simulate_lightcurve\n", "\n", "countrate = 1000\n", "\n", "simulated_observation = simulate_lightcurve(psd_model,obs_time,dt,countrate)\n", "\n", "simulated_observation.plot()" ] }, { "cell_type": "code", "execution_count": 5, "id": "fa9724d9-cb68-4edd-aab7-9c3a7da417ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000.198\n" ] } ], "source": [ "print(np.mean(simulated_observation.counts)/dt)" ] }, { "cell_type": "markdown", "id": "fbd784c2-0674-426f-b869-cc139d8cc718", "metadata": {}, "source": [ "## How to simulate a time-averaged spectrum\n", "\n", "Simulating a time-averaged spectrum is similar, but we also need to specify an instrument response matrix to be used in the simulation. In this case, we will use the FPMA detector on NuSTAR:" ] }, { "cell_type": "code", "execution_count": 6, "id": "aac21464-c2d1-42e9-b1ba-f15cbedcc1bc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Arf missing, please load it\n", "Arf loaded\n" ] } ], "source": [ "nustar = os.getcwd() + \"/data/\"\n", "\n", "rmfpath = nustar + \"/nu90401309004A01_sr.rmf\"\n", "fpma = ResponseMatrix(rmfpath)\n", "arfpath = nustar + \"/nu90401309004A01_sr.arf\"\n", "fpma.load_arf(arfpath)" ] }, { "cell_type": "markdown", "id": "07710127-779d-4639-8fb9-abfa97d7f7f5", "metadata": {}, "source": [ "For our model we will use the Xspec model interface provided by nDspec. We will simulate a standard combination of accretion disk emission (``diskbb``) and coronal power-law emission (``powerlaw``), absorbed by neutral gas (``tbabs``). For the model parameters we will pick some sensible value resembling a hard state X-ray binary:" ] }, { "cell_type": "code", "execution_count": 7, "id": "e86484ab-3246-44a4-80ab-1bc2155b69bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Initialized Xspec models:\n", "tbabs:\n", " type: mul\n", " function called: C_tbabs\n", " parameters:\n", " nH: value: 1.0, min: 0.0, max: 1000000.0, unit: 10^22\n", "\n", "powerlaw:\n", " type: add\n", " function called: C_powerLaw\n", " parameters:\n", " PhoIndex: value: 1.0, min: -3.0, max: 10.0, unit: n/a\n", " norm: value: 1, min: 0, max: 1e+20, unit: n/a\n", "\n", "diskbb:\n", " type: add\n", " function called: xsdskb\n", " parameters:\n", " Tin: value: 1.0, min: 0.0, max: 1000.0, unit: keV\n", " norm: value: 1, min: 0, max: 1e+20, unit: n/a\n", "\n", " Solar Abundance Vector set to angr: Anders E. & Grevesse N. Geochimica et Cosmochimica Acta 53, 197 (1989)\n", " Cross Section Table set to vern: Verner, Ferland, Korista, and Yakovlev 1996\n" ] } ], "source": [ "import ndspec.XspecInterface as xsmodels\n", "\n", "def tbabs(ear, params):\n", " pass\n", "\n", "def powerlaw(ear, params):\n", " pass\n", "\n", "def diskbb(ear, params):\n", " pass\n", "\n", "xspec_library = xsmodels.FortranInterface()\n", "\n", "xspec_library.load_models({\n", " \"tbabs\": tbabs,\n", " \"powerlaw\": powerlaw,\n", " \"diskbb\": diskbb,\n", "})\n", "\n", "def ndspec_tbabs(ear,nH):\n", " par_array = np.array([nH])\n", " model = xspec_library.tbabs(ear,par_array)\n", " return model\n", "\n", "def ndspec_powerlaw(ear,gamma,norm_pl):\n", " par_array = np.array([gamma,norm_pl])\n", " model = xspec_library.powerlaw(ear,par_array)\n", " return model\n", "\n", "def ndspec_diskbb(ear,Tin,norm_dbb):\n", " norm_dbb = 10**norm_dbb\n", " par_array = np.array([Tin,norm_dbb])\n", " model = xspec_library.diskbb(ear,par_array)\n", " return model\n", "\n", "sim_model = LM_Model(ndspec_tbabs)*(LM_Model(ndspec_diskbb)+LM_Model(ndspec_powerlaw))\n", "\n", "#make sure that these normalizations make sense for xspec models as well\n", "sim_params = sim_model.make_params(nH=dict(value=0.1,min=0.05,max=100,vary=True),\n", " Tin=dict(value=0.25,min=0.15,max=5.,vary=True),\n", " norm_dbb=dict(value=5.7,min=-2.,max=8.,vary=True),\n", " gamma=dict(value=1.6,min=1.3,max=3.5,vary=True), \n", " norm_pl=dict(value=6,min=1e-1,max=1e6,vary=True)\n", " )\n", "\n", "\n", "xspec_library.print_model_info()" ] }, { "cell_type": "markdown", "id": "00633dad-7bd5-44a6-a45e-148af1afc007", "metadata": {}, "source": [ "Once again, having defined the instrument response, model, model parameters, and observation time (in seconds), we can use the ``simulate_time_averaged`` function to create a Poisson realization of the assumed model, as observed through the detector we specified." ] }, { "cell_type": "code", "execution_count": 8, "id": "05dffa48-18ed-4f09-b5e1-65008300ca45", "metadata": {}, "outputs": [], "source": [ "from ndspec.Simulator import simulate_time_averaged\n", "\n", "exposure = 5e4\n", "\n", "simulated_spectrum = simulate_time_averaged(fpma,sim_model,sim_params,exposure_time=exposure)" ] }, { "cell_type": "markdown", "id": "4ebdcff7-b718-44a7-ac01-736a2f8e891f", "metadata": {}, "source": [ "We can now plot the ``simulated_spectrum`` array, which is returned in units of counts/channel, as well as unfold it to retrieve the original model in units of physical flux (here, kev per unit time and area):" ] }, { "cell_type": "code", "execution_count": 9, "id": "cd80d313-5e56-4356-8d1b-c72c94e72e56", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/matteo/Software/nDspec/src/ndspec/Response.py:653: RuntimeWarning: invalid value encountered in divide\n", " unfold_model = array/unfold_array\n" ] }, { "data": { "text/plain": [ "20" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "midpoint = 0.5*(fpma.emax+fpma.emin)\n", "\n", "plt.figure(1)\n", "plt.loglog(midpoint,simulated_spectrum)\n", "plt.xlabel(\"Channel energy (keV)\")\n", "plt.ylabel(\"Counts/channel\")\n", "\n", "plt.figure(2)\n", "plt.loglog(midpoint,fpma.unfold_response(simulated_spectrum,units_in=\"channel\")*midpoint**2/5e4)\n", "plt.xlabel(\"Channel energy (keV)\")\n", "plt.ylabel(\"$\\\\rm{\\\\nu}$F$\\\\rm{\\\\nu}$ (keV$^2$/cm$^2$/s/keV)\")\n", "gc.collect()" ] }, { "cell_type": "markdown", "id": "feda1ae6-a025-4fd9-a3e9-528bc8d43867", "metadata": {}, "source": [ "## How to simulate a lag energy spectrum\n", "\n", "Simulating a lag-energy spectrum is more complex, as it requires the user make more assumptions. First, we must set up an instrument response that has been binned to a sufficiently coarse number of channels of interest - here we will use 20 channels, spaced between 3 and 78 keV. The methodology described here is identical to that of [Ingram et al. 2022 (section 3)](https://ui.adsabs.harvard.edu/abs/2022MNRAS.509..619I/abstract), which we encourage users to read. " ] }, { "cell_type": "code", "execution_count": 10, "id": "8404e1a0-540b-49d2-98cd-2a9f9354ece3", "metadata": {}, "outputs": [], "source": [ "#turn the stuff above into a function\n", "#first set up the response we want\n", "rebin_grid = np.geomspace(3,78,21)\n", "#include one channel between the start of the fpma response and 3keV, and one between 78 keV and the end of the response\n", "rebin_grid = np.append(rebin_grid,fpma.emin[0])\n", "rebin_grid = np.append(rebin_grid,fpma.emax[-1])\n", "rebin_grid = np.unique(np.sort(rebin_grid))\n", "rebin_fpma = fpma.rebin_channels(rebin_grid[:-1],rebin_grid[1:])" ] }, { "cell_type": "markdown", "id": "bea954a7-2a6b-4d64-ab24-959a39cd4ec8", "metadata": {}, "source": [ "Having defined the instrument response we will use, we must make two more assumptions on the time-averaged spectrum of the source, as well as the instrument background, as both are needed to calculate the error bars for the lags. Here we will re-use the time-averaged spectrum from earlier, and very optimistically assume the background to be 0" ] }, { "cell_type": "code", "execution_count": 11, "id": "1cbfac77-560c-449d-8ff5-0bf445f8e155", "metadata": {}, "outputs": [], "source": [ "#from the response, we take the energy grid and calculate the models \n", "energ_grid = 0.5*(rebin_fpma.energ_lo+rebin_fpma.energ_hi)\n", "\n", "#now we prepare the grid of energy bounds to calculate our spectral model, and calculate it\n", "rebin_ear = np.append(rebin_fpma.energ_lo,rebin_fpma.energ_hi[-1])\n", "spectrum_model = sim_model.eval(sim_params,ear=rebin_ear)\n", "\n", "#next we set up the background - optimi\n", "bkg_rate = np.zeros(rebin_fpma.n_chans-1)" ] }, { "cell_type": "markdown", "id": "15d49d43-b018-4fa3-a755-6ff795769a21", "metadata": {}, "source": [ "Finally, we must build our model for the full cross spectrum, including both real and imaginary parts, as these are also required in the uncertainty calculation. \n", "\n", "Here we will use a combination of (variable) corona emission, in the form of a pivoting power-law, and reverberation in the form of a Gaussian flash at 6.4 keV. The model parameters are similar as in the rest of the tutorials, except for the normalizations which have been adjusted to produce an absolute rms typical of a hard state X-ray binary:" ] }, { "cell_type": "code", "execution_count": 12, "id": "a34a8179-e7ef-4cb9-9e30-ac6c697c4396", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "67320" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#define the reference band to use - we will take the full NuSTAR band here\n", "ref_emin = 3\n", "ref_emax = 78\n", "\n", "#first the pivoting powerlaw\n", "pl_norm = 5.\n", "pl_slope = -1.6\n", "gamma_0 = 0.3\n", "gamma_nu = -0.1\n", "phi_0 = -0.3\n", "phi_nu = -0.1\n", "nu_0 = psd_model.freqs[0]\n", "piv_transfer = models.pivoting_pl(psd_model.freqs,energ_grid,np.array([pl_norm,pl_slope,gamma_0,gamma_nu,phi_0,phi_nu,nu_0]))\n", "\n", "continuum = CrossSpectrum(psd_model.times,energ=energ_grid)\n", "continuum.set_transfer(piv_transfer)\n", "continuum.set_psd_weights(psd_model.power_spec)\n", "continuum.set_reference_energ([ref_emin,ref_emax])\n", "continuum.cross_from_transfer()\n", "\n", "#now the reverberation\n", "gauss_norm = 2.5\n", "gauss_sigma = 1.5\n", "gauss_center = 6.5\n", "rise_slope = 3\n", "decay_slope = -2 \n", "peak_time = 15\n", "width_slope = -0.2\n", "\n", "gauss_flash = models.gauss_bkn(psd_model.times,energ_grid,\n", " np.array([gauss_norm,gauss_sigma,gauss_center,rise_slope,\n", " decay_slope,peak_time,width_slope]),\n", " )\n", "\n", "reverb = CrossSpectrum(psd_model.times,energ=energ_grid)\n", "reverb.set_psd_weights(psd_model.power_spec)\n", "reverb.set_impulse(gauss_flash)\n", "reverb.set_reference_energ([ref_emin,ref_emax])\n", "reverb.cross_from_irf()\n", "\n", "#finally we combine both\n", "full_model = CrossSpectrum(psd_model.times,energ=energ_grid)\n", "full_model.set_psd_weights(psd_model.power_spec)\n", "full_model.set_transfer(continuum.trans_func + reverb.trans_func)\n", "full_model.set_reference_energ([ref_emin,ref_emax])\n", "full_model.cross_from_transfer()\n", "full_model.plot_cross_2d(energy_limits=[3,10])\n", "gc.collect()\n", "#we are now ready to simulate our observation" ] }, { "cell_type": "markdown", "id": "224d412b-d8c4-471d-abf8-96846886e51a", "metadata": {}, "source": [ "Having defined all of our models, all that remains is to specify the interval of Fourier frequency over which to simulate the lag spectrum, the coherence over these frequencies, the power (in units of fractional rms), and the assumed exposure time.\n", "\n", "Once these are set,we can call the ``simulate_lag_energy`` function to re-derive our simulated lags" ] }, { "cell_type": "code", "execution_count": 13, "id": "ff2f631c-1e0c-4978-bbd4-80f4b462f6cd", "metadata": {}, "outputs": [], "source": [ "from ndspec.Simulator import simulate_lag_energy\n", "\n", "fmin = 5e-3\n", "fmax = 5e-2\n", "coherence = 1\n", "power = 5e-3\n", "exposure = 5e4\n", "\n", "lag_sim, lag_err, emin , emax = simulate_lag_energy(rebin_fpma,spectrum_model,full_model,\n", " bkg_rate,[fmin,fmax],[ref_emin,ref_emax],\n", " coherence,power,exposure)" ] }, { "cell_type": "markdown", "id": "29a8f45e-5f3a-4b6d-8cb5-6ae020dd9901", "metadata": {}, "source": [ "Finally, we can compare our simulated data with the original input model to reassure ourselves that the output makes sense:" ] }, { "cell_type": "code", "execution_count": 14, "id": "78e24038-0504-40a8-ad44-71791141abc9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3, 78)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ebounds = 0.5*(emin+emax)\n", "\n", "plt.figure(1)\n", "plt.step(ebounds,lag_sim,where='mid',color=\"C0\")\n", "plt.errorbar(ebounds,lag_sim,yerr=lag_err,ls=\"\",color=\"C0\")\n", "plt.plot(full_model.energ,full_model.lag_energy([fmin,fmax]),color=\"C1\")\n", "plt.xscale(\"log\",base=10)\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"Lag (s)\")\n", "plt.ylim([-5,5])\n", "plt.xlim([3,78])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }