{ "cells": [ { "cell_type": "markdown", "id": "114322ae-f7f9-4696-8865-6cbaa487bbbb", "metadata": {}, "source": [ "# Interfacing nDspec with Xspec models\n", "\n", "This tutorial shows how to use call Xspec models in Python using nDspec. This functionality allows users to call *any* Fortran or C model in Python, as long as the function and format are compatible with Xspec models." ] }, { "cell_type": "code", "execution_count": 1, "id": "b8422f0c-66fb-461d-ae75-46ab3ade99a4", "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pylab as pl\n", "from matplotlib import cm\n", "from matplotlib.colors import TwoSlopeNorm\n", "import matplotlib.gridspec as gridspec\n", "\n", "from matplotlib import rc, rcParams\n", "rc('text',usetex=True)\n", "rc('font',**{'family':'serif','serif':['Computer Modern']})\n", "fi = 22\n", "plt.rcParams.update({'font.size': fi-5})\n", "\n", "sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath('__file__'))))\n", "from ndspec.Response import ResponseMatrix\n", "import ndspec.XspecInterface as XSModels" ] }, { "cell_type": "markdown", "id": "907a0555-fbf4-41af-a2d5-10c3733d92bb", "metadata": {}, "source": [ "## Loading the Xspec library and initializing models\n", "\n", "The Xspec models are all compiled in a library file contained at ```path\\_to\\_heasoft\\_installation/Xspec/platform\\_name/lib/``` called ```libXSFunctions.so``` (on Linux systems) and ```lib.XSFunctions.dylib``` (on MacOS). The class ```FortranInterface``` automatically finds this file and runs the necessary HEASOFT-related setup when one of its object is initialized. After a couple more initialization steps, we will be able to use this class to run the Fortran wrappers which call any model compiled in our library file." ] }, { "cell_type": "code", "execution_count": 2, "id": "8de90fad-5e91-40b2-991e-96d4d62918d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 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": [ "model_library = XSModels.FortranInterface()" ] }, { "cell_type": "markdown", "id": "b6f7aa09-559f-464d-83ed-dd6b68c6a091", "metadata": {}, "source": [ "We can now define a set of dummy functions, named identically to any Xspec model we might want to use. We will then use these functions to tell ```model_liberary``` which models to make available for use. We can either initialize one model at a time with the ```add_model``` method, or pass a dictionary of models we might want with the ```load_models``` method. In this case, we will use a set of models one might use to run a simple fit of an accreting black hole, including a disk (```diskbb```), a Comptonized component (```nthcomp```), line of sight absorption (```tbabs```) and a simple reflection model (```reflect```):" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc1a6166-8fac-4baf-b9e9-51e2931938ca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def tbabs(ear, params):\n", " pass\n", "\n", "def diskbb(ear, params):\n", " pass\n", "\n", "def nthcomp(ear, params):\n", " pass\n", "\n", "def reflect(ear,params,seed):\n", " pass\n", "\n", "model_library.load_models({\n", " \"diskbb\": diskbb,\n", " \"tbabs\": tbabs,\n", " \"nthcomp\": nthcomp\n", "})\n", "\n", "model_library.add_model(reflect)" ] }, { "cell_type": "markdown", "id": "2f34d5cb-7aa4-4508-9fb8-d25f6fefd53d", "metadata": {}, "source": [ "We can now print the models we initialized, together with their parameters and the model type, using the ```print_model_info``` method:" ] }, { "cell_type": "code", "execution_count": 4, "id": "adb11904-3cc7-4e36-94f3-aeba5faf094f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Initialized Xspec models:\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", "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", "nthcomp:\n", " type: add\n", " function called: C_nthcomp\n", " parameters:\n", " Gamma: value: 1.7, min: 1.001, max: 10.0, unit: n/a\n", " kT_e: value: 100.0, min: 1.0, max: 1000.0, unit: keV\n", " kT_bb: value: 0.1, min: 0.001, max: 10.0, unit: keV\n", " inp_type: value: 0.0, min: 0.0, max: 1.0, unit: 0/1\n", " Redshift: value: 0.0, min: -0.999, max: 10.0, unit: n/a\n", " norm: value: 1, min: 0, max: 1e+20, unit: n/a\n", "\n", "reflect:\n", " type: con\n", " function called: C_reflct\n", " parameters:\n", " rel_refl: value: 0.0, min: -1.0, max: 1000000.0, unit: n/a\n", " Redshift: value: 0.0, min: -0.999, max: 10.0, unit: n/a\n", " abund: value: 1.0, min: 0.0, max: 1000000.0, unit: n/a\n", " Fe_abund: value: 1.0, min: 0.0, max: 1000000.0, unit: n/a\n", " cosIncl: value: 0.45, min: 0.05, max: 0.95, unit: n/a\n", "\n" ] } ], "source": [ "model_library.print_model_info()" ] }, { "cell_type": "markdown", "id": "ca383e73-ba11-4e80-b1e4-9d48c13576b2", "metadata": {}, "source": [ "## Evaluating and plotting additive and multiplicative models\n", "\n", "In order to evaluate models, we now need to define a grid of photon energies. Let us define our energy grid from the NICER instrument response. \n", "\n", "IMPORTANT: because our models come from Xspec and follow its standard formatting, what we need to pass is a grid of all *energy bin edges*, rather than mid points of each energy bin. We can do this simply by appending the last element of the ```energ_hi``` array (which contains the upper bound of each energy bin in the matrix) to the entire ```energ_lo``` array (which contains all the lower bounds of the same grid)." ] }, { "cell_type": "code", "execution_count": 5, "id": "7bc5eeff-7b17-4184-98e5-2086cd6be6c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Arf missing, please load it\n" ] } ], "source": [ "rmfpath = os.getcwd()+\"/data/nicer-rmf6s-teamonly-array50.rmf\"\n", "nicer_matrix = ResponseMatrix(rmfpath)\n", "\n", "energs = np.append(nicer_matrix.energ_lo,nicer_matrix.energ_hi[-1])" ] }, { "cell_type": "markdown", "id": "6ffe015c-09aa-4c07-9e8e-4444ecf245f7", "metadata": {}, "source": [ "Now all that we need to do to evaluate one of our models is to define the appropriate arrays of parameters. Once these are defined, we can call our model functions directly from the library, which now contains a method for each of the models initialized above, with the appropriate name:" ] }, { "cell_type": "code", "execution_count": 6, "id": "c837aa65-5b38-447d-b3c8-7d79fe7ac8b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Counts/s/keV')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#define the parameters\n", "Tin = 0.5\n", "disknorm = 50\n", "diskpars = np.array([Tin,disknorm])\n", "\n", "#now call one of the models and plot it\n", "midpoint = 0.5*(energs[1:]+energs[:-1])\n", "\n", "disk = model_library.diskbb(energs,diskpars)\n", "\n", "plt.loglog(midpoint,disk)\n", "plt.title(\"Model in nDspec\")\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"Counts/s/keV\")" ] }, { "cell_type": "markdown", "id": "228becdc-aa0b-4748-9a16-9736700f92ef", "metadata": {}, "source": [ "It is extremely important to be mindful of the units models are computed in. By default, Xspec functions calculate the flux in units of counts/s, and therefore renormalize by the width of the energy bin. The equivalent in nDspec is:" ] }, { "cell_type": "code", "execution_count": 7, "id": "a16c4bbc-7c32-42a9-af40-58c0d2453227", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Counts/s')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "binwidth = nicer_matrix.energ_hi - nicer_matrix.energ_lo\n", "\n", "plt.loglog(midpoint,disk*binwidth)\n", "plt.title(\"Model in Xspec units\")\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"Counts/s\")" ] }, { "cell_type": "markdown", "id": "c462b86c-4fe5-4253-8e76-3375b46a8b48", "metadata": {}, "source": [ "These unit conversions are handled automatically under the hood by nDspec, and the computation of folding instrument responses etc is identical. The choice of returning models in units of counts/s/keV is to expose arrays that are easily interpretable (as they lack the narrow features of models renormalized by bin width - compare the first plot in this tutorial with the second).\n", "\n", "Now that we have corrected the units of our models, let us include two more components:" ] }, { "cell_type": "code", "execution_count": 8, "id": "e3cf137f-84c2-4a7c-8f97-643b8ea66fc2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0015, 0.05)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nH = 1\n", "abspars = np.array([nH])\n", "\n", "Gamma = 2.1\n", "kT_e = 10.\n", "kT_bb = Tin\n", "inp_type = 1\n", "Redshift = 0\n", "compnorm = 0.01\n", "nthcomppars = np.array([Gamma,kT_e,kT_bb,inp_type,Redshift,compnorm])\n", "\n", "absorption = model_library.tbabs(energs,abspars)\n", "compton = model_library.nthcomp(energs,nthcomppars)\n", "\n", "model = absorption*(compton+disk)\n", "\n", "#let us also convert the y axis to flux\n", "plt.loglog(midpoint,disk*midpoint**2,label=\"diskbb\",lw=2.5)\n", "plt.loglog(midpoint,compton*midpoint**2,label=\"nthcomp\",lw=2.5)\n", "plt.loglog(midpoint,model*midpoint**2,label=\"model\",lw=3)\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"keV$^{2}\\\\times$counts/s/keV\")\n", "plt.legend(loc=\"upper left\")\n", "plt.ylim([1.5e-3,0.05])" ] }, { "cell_type": "markdown", "id": "e469e8c6-1768-48e1-90a6-be23ddb097d8", "metadata": {}, "source": [ "## Handling convolution models\n", "\n", "Finally, we can try to include a convolution model like ```reflect```. This case is more complex, for two reasons. First,we also need to provide the starting array to be convolved with the model. Second, in order to avoid numerical issues (particularly near the bondary of the energy grid), we actually need to use a wider energy grid, and then interpolate back to the NICER grid after performing the convolution. We will use ```scipy``` for this. " ] }, { "cell_type": "code", "execution_count": 9, "id": "3355108d-871b-4266-8121-86ce9942e200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tbvabs Version 2.3\n", "Cosmic absorption with grains and H2, modified from\n", "Wilms, Allen, & McCray, 2000, ApJ 542, 914-924\n", "Questions: Joern Wilms\n", "joern.wilms@sternwarte.uni-erlangen.de\n", "joern.wilms@fau.de\n", "\n", "http://pulsar.sternwarte.uni-erlangen.de/wilms/research/tbabs/\n", "\n", "PLEASE NOTICE:\n", "To get the model described by the above paper\n", "you will also have to set the abundances:\n", " abund wilm\n", "\n", "Note that this routine ignores the current cross section setting\n", "as it always HAS to use the Verner cross sections as a baseline.\n", "Compton reflection. See help for details.\n", "If you use results of this model in a paper,\n", "please refer to Magdziarz & Zdziarski 1995 MNRAS, 273, 837\n" ] }, { "data": { "text/plain": [ "(0.0015, 0.05)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.interpolate import interp1d\n", "\n", "energs_extend = np.logspace(-2.,3,1000)\n", "energs_extend = np.array(energs_extend)\n", "binwidth_extend = np.diff(energs_extend)\n", "compton_extend = model_library.nthcomp(energs_extend,nthcomppars)\n", "\n", "rel_refl = 1\n", "Redshift = 0\n", "abund = 1\n", "Fe_abund = 1\n", "cosIncl = 1\n", "\n", "refpars = np.array([rel_refl,Redshift,abund,Fe_abund,cosIncl])\n", "\n", "reflection_extend = model_library.reflect(energs_extend,refpars,compton_extend)\n", "\n", "#now define an interpolation object and go back to the NICER energy grid\n", "energs_extend_center = 0.5*(energs_extend[1:]+energs_extend[:-1])\n", "array_interp = interp1d(energs_extend_center,reflection_extend,fill_value='extrapolate') \n", "reflection = array_interp(midpoint)\n", "\n", "#finally, put all the model components together\n", "model = absorption*(reflection+disk)\n", "\n", "plt.loglog(midpoint,disk*midpoint**2,label=\"diskbb\",lw=2)\n", "plt.loglog(midpoint,compton*midpoint**2,label=\"nthcomp\",lw=2)\n", "plt.loglog(midpoint,reflection*midpoint**2,label=\"reflect\",lw=2.5)\n", "plt.loglog(midpoint,model*midpoint**2,label=\"model\",lw=2.5)\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"keV$^{2}\\\\times$counts/s/keV\")\n", "plt.legend(loc=\"upper left\")\n", "plt.ylim([1.5e-3,0.05])" ] }, { "cell_type": "markdown", "id": "03ef707c-2eb8-45ec-9893-6822f3699f3b", "metadata": {}, "source": [ "## User-defined C models\n", "\n", "Users can also load user-defined libraries and models other than those built by a HEASOFT installation, provided that they use the same input and output format. In this example, we will use the ```CInterface``` class to initialize and run the [Relxill](https://www.sternwarte.uni-erlangen.de/~dauser/research/relxill) reflection model. In this case, users always need to specify both the path to the library they want to load, as well as that to the corresponding ```lmodel.dat``` file containing the model definitions and parameters." ] }, { "cell_type": "code", "execution_count": 10, "id": "b6a500c0-1d04-4281-bb32-f86faae37b38", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Initialized Xspec models:\n", "relxill:\n", " type: add\n", " function called: c_lmodrelxill\n", " parameters:\n", " Index1: value: 3.0, min: -10.0, max: 10.0, unit: \n", " Index2: value: 3.0, min: -10.0, max: 10.0, unit: \n", " Rbr: value: 15.0, min: 1.0, max: 1000.0, unit: \n", " a: value: 0.998, min: -0.998, max: 0.998, unit: \n", " Incl: value: 30.0, min: 3.0, max: 87.0, unit: deg\n", " Rin: value: -1.0, min: -100.0, max: -1.0, unit: \n", " Rout: value: 400.0, min: 1.0, max: 1000.0, unit: \n", " z: value: 0.0, min: 0.0, max: 10.0, unit: \n", " gamma: value: 2.0, min: 1.0, max: 3.4, unit: \n", " logxi: value: 3.1, min: 0.0, max: 4.7, unit: \n", " Afe: value: 1.0, min: 0.5, max: 10.0, unit: \n", " Ecut: value: 300.0, min: 5.0, max: 1000.0, unit: keV\n", " refl_frac: value: 3.0, min: -1000.0, max: 1000.0, unit: \n", " norm: value: 1, min: 0, max: 1e+20, unit: n/a\n", "\n" ] } ], "source": [ "#define the path to the libraries and lmodel file to be loaded\n", "relxillpath = '/home/matteo/Software/Relxill/librelxill.so'\n", "relxillfile = '/home/matteo/Software/Relxill/lmodel_relxill.dat'\n", "\n", "custom_library = XSModels.CInterface(relxillpath,relxillfile)\n", "\n", "def relxill(ear, params):\n", " pass\n", " \n", "custom_library.add_model(relxill)\n", "custom_library.print_model_info()" ] }, { "cell_type": "markdown", "id": "d3a85b45-e015-4640-9ed3-c8634877d5f6", "metadata": {}, "source": [ "We can now define a set of parameters and evaluate the model identically to before:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a1af10a7-1307-42e5-bedc-b5012956160d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Index1 = 3\n", "Index2 = 3\n", "Rbr = 15\n", "a = 0.998\n", "incl = 30\n", "Rin = -5\n", "Rout = 400\n", "z = 0\n", "gamma = 2\n", "logxi = 3\n", "Afe = 1\n", "Ecut = 50\n", "refl_frac = 1\n", "norm = 1\n", "\n", "params_relxill = np.array([Index1,Index2,Rbr,a,incl,Rin,Rout,z,gamma,logxi,Afe,Ecut,refl_frac,norm])\n", "\n", "energs = np.logspace(-1.,2,1000)\n", "binwidth = np.diff(energs)\n", "midpoint = 0.5*(energs[1:]+energs[:-1])\n", "\n", "relxill_flux = custom_library.relxill(energs,params_relxill)\n", "\n", "plt.figure(1)\n", "plt.loglog(midpoint,relxill_flux*midpoint**2,lw=2.5,label=\"Relxill\")\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"keV$^{2}\\\\times$counts/s/keV\")\n", "plt.legend(loc=\"lower left\")" ] }, { "cell_type": "markdown", "id": "695e14a5-62e0-4f98-a893-b20ef3ff8f5f", "metadata": {}, "source": [ "## Setting up XspecInterface calls for a nDspec fitter object\n", "\n", "Finally, we would like to prepare a function that can be passed to an nDspec fitter object for our inference. Doing this is as simple as wrapping the function call in a LMFit Model object, taking care to use a naming convention consistent with nDspec (ie, the input energy array needs to be called \"ear\"):" ] }, { "cell_type": "code", "execution_count": 12, "id": "f1c5f4d5-3872-45d2-b468-a616fdb7bf5c", "metadata": {}, "outputs": [], "source": [ "from lmfit import Model as LM_Model\n", "\n", "def ndspec_relxill(ear,Index1,Index2,Rbr,a,incl,Rin,Rout,z,gamma,logxi,Afe,Ecut,refl_frac,norm):\n", " par_array = np.array([Index1,Index2,Rbr,a,incl,Rin,Rout,z,gamma,logxi,Afe,Ecut,refl_frac,norm])\n", " model = custom_library.relxill(ear,par_array)\n", " return model\n", "\n", "reflection_model = LM_Model(ndspec_relxill)" ] } ], "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 }