{ "cells": [ { "cell_type": "markdown", "id": "be20519c-55a5-49ec-ba5e-bde08d2f9b80", "metadata": {}, "source": [ "# Fitting a one-dimensional cross spectrum\n", "\n", "This notebook covers how to fit in nDspec one of the most common spectral timing products: a one dimensional cross spectrum, as a function of Fourier frequency, between two energy bands. For least-chi squares fitting, nDspec makes use of the [lmfit library](https://lmfit.github.io/lmfit-py/). For error estimation, we encourage users to use Bayesian sampling, as detailed in the tutorial on [fitting power spectra](https://ndspec.readthedocs.io/en/latest/fit_psd.html). Users should never use the error estimates from `lmfit` in scientific publications, as they are highly unreliable for the complex parameter spaces that are common in X-ray spectral and spectral-timing models." ] }, { "cell_type": "code", "execution_count": 1, "id": "7f6d5b5d-11b4-4fb0-b724-f38072365afa", "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import numpy as np\n", "\n", "sys.path.append('/home/matteo/Software/nDspec/src/')\n", "from ndspec.Response import ResponseMatrix\n", "import ndspec.FitCrossSpectrum as fitcross\n", "import ndspec.Models as models\n", "from ndspec.SimpleFit import load_lc" ] }, { "cell_type": "markdown", "id": "2ff87ea2-7c88-4c40-af1e-b020d5c3332d", "metadata": {}, "source": [ "## Setting up the data with Stingray\n", "\n", "There are two methods of loading a frequency-dependent cross spectrum in nDspec. Users can either provide a Stingray `EventList` object, containing a level-2 event file, or they can provide their own cross spectrum and its errors, along with the corresponding Fourier frequency grid, in the form of a Numpy array. In this notebook, we will use the former method. Because the cross-spectrum is inherently an energy-dependent product, users must also supply an appropriate `ResponseMatrix` object. \n", "\n", "We begin by loading an event file and removing bad time intervals from it (for more details, see the [Stingray documentation page](https://docs.stingray.science/en/stable/notebooks/Spectral%20Timing/Spectral%20Timing%20Exploration.html#Data-loading-and-cleanup.)). We then loand and store the NICER response for this observation." ] }, { "cell_type": "code", "execution_count": 2, "id": "a44bf383-5cf9-4861-b0af-797433c0eb3a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Arf missing, please load it\n", "Arf loaded\n" ] } ], "source": [ "from stingray import EventList\n", "from stingray.gti import get_gti_lengths, get_btis, get_total_gti_length\n", "\n", "obsid = str(106)\n", "path = \"/home/matteo/Data/J1820/EventFiles/\"\n", "fname = path + \"ni1200120\" + obsid+ \"_0mpu7_cl.evt\"\n", "events = EventList.read(fname, \"hea\", additional_columns=[\"DET_ID\"])\n", "\n", "gti_lengths = get_gti_lengths(events.gti)\n", "btis = get_btis(events.gti)\n", "bti_lengths = get_gti_lengths(btis)\n", "\n", "ev_filled = events.fill_bad_time_intervals(max_length=1, buffer_size=4)\n", "\n", "rmfpath = path+\"1200120106_rmf.pha\"\n", "nicer_matrix = ResponseMatrix(rmfpath)\n", "arfpath = path+\"1200120106_arf.pha\"\n", "nicer_matrix.load_arf(arfpath)" ] }, { "cell_type": "markdown", "id": "3271d478-901a-4898-a428-c7debae476c8", "metadata": {}, "source": [ "After loading the data and response, we can set up our fitter object. Because a cross spectrum is more complicated than a time-averaged or power spectrum, we need to specify several more quantities.\n", "\n", "First, after initializing our fitter object `FitCrossSpectrum`, we need to set the coordinates that we will use for the complex cross spectra both in the model and data. Users can do this with the `set_coordinates` method, which selects between polar coordinates (modulus and phase), cartesian coordinates (real and imaginary parts), or lags (phase alone, converted into a time lag). Additionally, we need to specify whether we will use frequency or energy dependent products - for example, lag-frequency or lag-energy spectra. In this notebook we will focus on frequency dependent products. Users can do this with the `set_product_dependence` method.\n", "\n", "Having set up the fitter object, we then need to define the information required to convert our loaded event file into cross spectra. We need to define reference and subject bands (here, 0.5-1.5 and 2-5 keV respectively), the size of the segments to average over (which will set the lowest Fourier frequency bin), the time resolution to use (which will set the highest Fourier frequency bin), and the normalization to use for the data. The `set_data` then converts the provided `EventList` object internally into a Poisson-noise subtracted cross spectrum, based on these inputs. For more information on calculating a cross spectrum, we refer users to [Uttley et al. 2014](https://ui.adsabs.harvard.edu/abs/2014A%26ARv..22...72U/abstract). \n", "\n", "After loading the data, we can plot it with the `plot_data_1d` method. " ] }, { "cell_type": "code", "execution_count": 3, "id": "9e792c63-3233-4b39-a615-be262bb75d24", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/matteo/.local/lib/python3.10/site-packages/stingray/fourier.py:1139: RuntimeWarning: invalid value encountered in sqrt\n", " dRe = dIm = dG = np.sqrt(power_over_2n * (seg_power - frac))\n", "/home/matteo/.local/lib/python3.10/site-packages/stingray/fourier.py:1141: RuntimeWarning: invalid value encountered in sqrt\n", " dphi = np.sqrt(\n", "/home/matteo/.local/lib/python3.10/site-packages/stingray/crossspectrum.py:2991: UserWarning: Some error bars in the Averaged Crossspectrum are invalid.Defaulting to sqrt(2 / M) in Leahy norm, rescaled to the appropriate norm.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lorentz_fit = fitcross.FitCrossSpectrum()\n", "lorentz_fit.set_coordinates(\"polar\")\n", "lorentz_fit.set_product_dependence(\"frequency\")\n", "\n", "ref_band = [0.5,1.5]\n", "sub_band = [2.,5.]\n", "segment_size = 8\n", "dt = 0.05\n", "norm_data = \"frac\"\n", "\n", "lorentz_fit.set_data(nicer_matrix,ref_band,sub_band,events,\n", " time_res=dt,seg_size=segment_size,norm=norm_data) \n", "\n", "lorentz_fit.plot_data_1d()" ] }, { "cell_type": "markdown", "id": "35ea1813-a991-40e8-9373-a77d591c0d35", "metadata": {}, "source": [ "Note that we don't have a one-dimensional cross spectrum yet - in fact, the data is binned in four or five channels: the reference band (0.5-1.5 keV), the subject band (2-5 keV), the channels between reference and subject bands (1.5-2 keV), and finally the channels from whichever is lowest/highest among the subject/reference band bounds and the start/end of the response matrix (0-0.5 keV and 5-15 keV respectively). \n", "\n", "For our one-dimensional analysis we are clearly not interested in modelling these additional channels (including the reference band channels), so we must set them to be ignored identically to how one would ignore energy bins in a time-averaged spectrum. Therefore, we ignore all the data except that contained in the subject band channels. Once we plot the data again, we see that the only channel left is the subject band we are interested in." ] }, { "cell_type": "code", "execution_count": 4, "id": "e69dafb5-50ef-4e8e-9625-e9989074ab9d", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lorentz_fit.ignore_energies(0,2.0)\n", "lorentz_fit.ignore_energies(5.0,20.0)\n", "lorentz_fit.plot_data_1d()" ] }, { "cell_type": "markdown", "id": "99132b28-220f-4476-b839-5c775d6c8f22", "metadata": {}, "source": [ "An important note is that in any cross spectrum fit, nDspec automatically rebins the instrument response to match the channel grid specified when loading the data. In this case, the response is rebinned down to just the five channels discussed above. This greatly speeds up folding the response matrix with our chosen model." ] }, { "cell_type": "code", "execution_count": 5, "id": "73c56c6a-a9ed-4e85-aeaf-7c982bdf7d75", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/matteo/Software/nDspec/src/ndspec/Response.py:534: RuntimeWarning: divide by zero encountered in log10\n", " p = plt.pcolormesh(x_axis,energy_array,np.log10(self.resp_matrix),\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lorentz_fit.response.plot_response()" ] }, { "cell_type": "markdown", "id": "253426f8-851a-4195-a1dc-79889dae1283", "metadata": {}, "source": [ "Finally, loading data in cartesian rather than polar coordinates is identical. We simply specify the coordinates differently when setting up the fitter object; everything else (including model calcluations to return the correct products) is handled internally by the fitter class:" ] }, { "cell_type": "code", "execution_count": 6, "id": "65a0bde6-a439-4c40-9684-0a4f9416bce6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/matteo/.local/lib/python3.10/site-packages/stingray/fourier.py:1139: RuntimeWarning: invalid value encountered in sqrt\n", " dRe = dIm = dG = np.sqrt(power_over_2n * (seg_power - frac))\n", "/home/matteo/.local/lib/python3.10/site-packages/stingray/fourier.py:1141: RuntimeWarning: invalid value encountered in sqrt\n", " dphi = np.sqrt(\n", "/home/matteo/.local/lib/python3.10/site-packages/stingray/crossspectrum.py:2991: UserWarning: Some error bars in the Averaged Crossspectrum are invalid.Defaulting to sqrt(2 / M) in Leahy norm, rescaled to the appropriate norm.\n", " warnings.warn(\n", "/home/matteo/Software/nDspec/src/ndspec/FitCrossSpectrum.py:543: RuntimeWarning: invalid value encountered in sqrt\n", " error_second_dim = np.sqrt((ps_sub.power*ps_ref.power- \\\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cartesian_coords = fitcross.FitCrossSpectrum()\n", "cartesian_coords.set_coordinates(\"cartesian\")\n", "cartesian_coords.set_product_dependence(\"frequency\")\n", "\n", "ref_band = [0.5,1.5]\n", "sub_band = [2.,5.]\n", "\n", "cartesian_coords.set_data(nicer_matrix,ref_band,sub_band,events,\n", " time_res=dt,seg_size=segment_size,norm=\"frac\") \n", "\n", "cartesian_coords.ignore_energies(0,2.0)\n", "cartesian_coords.ignore_energies(5.0,20.0)\n", "cartesian_coords.plot_data_1d()" ] }, { "cell_type": "markdown", "id": "c4b6ba81-2bef-455e-82d0-5c6eea0e88c3", "metadata": {}, "source": [ "## Defining a model for the cross spectrum and preparing a fit\n", "\n", "Now that the data is loaded, we can define a model for the complex cross spectrum itself. nDspec then handles the conversion from a complex cross spectrum, to any (real) spectral timing products (such as phase or modulus vs frequency) internally, depending on the data coordinates and dependence specified by the user.\n", "\n", "In this example, we will use the model model of [Mendez et al. 2023](https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9405M/abstract): a sum of multiple Lorentzians, similar to standard fits of a powerspectrum, multiplying each Lorentzian by a phase to introduce an imaginary component. This model is included in the nDspec model library, but we will re-define it here for clarity. \n", "\n", "For this particular observation, we will use a sum of six Lorentzian components. We instatiate an lmfit `Model` object, and assign the sum of Lorentzians to it. Then, we create an lmfit `Parameters` object to store the parameters of each Lorentzian. In this particular problem, we also have to ensure that the bounds of the Fourier frequency covered by each Lorentzian do not overlap, in order to simplify the parameter space and speed up the fit. \n", "\n", "Once both the `Model` and `Paramters` obejct are defined, we can pass them to our `FitCrossSpectrum` object. When passing the model, we also must specify what kind of model we are using - in this case, one that returns a complex cross spectrum. This is necessary to ensure that the conversion from complex cross spectrum to modulus/phase as a function of frequency is handled correctly. nDspec also supports models for impulse response and transfer functions, but these are covered [here](https://ndspec.readthedocs.io/en/latest/fit_cross_2d.html)." ] }, { "cell_type": "code", "execution_count": 7, "id": "80f352f3-9157-4f5c-8403-0adc0b55847c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Adding parameter \"l1_freqs\"\n", " - Adding parameter \"l1_rms\"\n", " - Adding parameter \"l1_peak_f\"\n", " - Adding parameter \"l1_q\"\n", " - Adding parameter \"l1_phase\"\n", " - Adding parameter \"l2_freqs\"\n", " - Adding parameter \"l2_rms\"\n", " - Adding parameter \"l2_peak_f\"\n", " - Adding parameter \"l2_q\"\n", " - Adding parameter \"l2_phase\"\n", " - Adding parameter \"l3_freqs\"\n", " - Adding parameter \"l3_rms\"\n", " - Adding parameter \"l3_peak_f\"\n", " - Adding parameter \"l3_q\"\n", " - Adding parameter \"l3_phase\"\n", " - Adding parameter \"l4_freqs\"\n", " - Adding parameter \"l4_rms\"\n", " - Adding parameter \"l4_peak_f\"\n", " - Adding parameter \"l4_q\"\n", " - Adding parameter \"l4_phase\"\n", " - Adding parameter \"l5_freqs\"\n", " - Adding parameter \"l5_rms\"\n", " - Adding parameter \"l5_peak_f\"\n", " - Adding parameter \"l5_q\"\n", " - Adding parameter \"l5_phase\"\n", " - Adding parameter \"l6_freqs\"\n", " - Adding parameter \"l6_rms\"\n", " - Adding parameter \"l6_peak_f\"\n", " - Adding parameter \"l6_q\"\n", " - Adding parameter \"l6_phase\"\n" ] } ], "source": [ "from lmfit import Model as LM_Model\n", "from lmfit import Parameters as LM_Parameters\n", "\n", "def cross_lorentz(energs,freqs,rms,peak_f,q,phase):\n", " n_energs = len(energs)\n", " n_freqs = len(freqs)\n", " lorentz_params = np.array([peak_f,q,rms])\n", " #calculate a standard Lorentzian, and include the phase term\n", " lorentz_arr = models.lorentz(freqs,lorentz_params)*np.exp(1j*phase)\n", " #reshape the model into an array of Fourier frequency times energy channels\n", " twod_lorentz = np.tile(lorentz_arr,n_energs).reshape((n_energs,n_freqs))\n", " twod_lorentz = np.transpose(twod_lorentz)\n", " return twod_lorentz\n", "\n", "CrossLor_model = LM_Model(cross_lorentz, prefix=\"l1_\") + \\\n", " LM_Model(cross_lorentz, prefix=\"l2_\") + \\\n", " LM_Model(cross_lorentz, prefix=\"l3_\") + \\\n", " LM_Model(cross_lorentz, prefix=\"l4_\") + \\\n", " LM_Model(cross_lorentz, prefix=\"l5_\")+ \\\n", " LM_Model(cross_lorentz, prefix=\"l6_\")\n", "\n", "Cross_model_parameters = LM_Parameters()\n", "Cross_model_parameters.add_many(('l1_peak_f', 0.05, True, 0, 0.07),\n", " ('l1_q', 0.3, True, 0, 2),\n", " ('l1_rms', 0.008, True),\n", " ('l1_phase', 0.1, True,-np.pi,+np.pi),\n", " \n", " ('l2_peak_f', 0.3, True, 0.07, 0.5),\n", " ('l2_q', 0.5, True, 0, 2),\n", " ('l2_rms', 0.003, True),\n", " ('l2_phase', 0.3, True,-np.pi,+np.pi),\n", " \n", " ('l3_peak_f', 1., True, 0.5, 1.5),\n", " ('l3_q', 0.7, True, 0, 2),\n", " ('l3_rms', 0.002, True),\n", " ('l3_phase', 0.1, True,-np.pi,+np.pi),\n", "\n", " ('l4_peak_f', 2, True, 1.5, 3),\n", " ('l4_q', 0.2, True, 0, 2),\n", " ('l4_rms', 0.005, True),\n", " ('l4_phase', 0, True,-np.pi,+np.pi),\n", " \n", " ('l5_peak_f', 5, True, 3, 10),\n", " ('l5_q', 1, True, 0, 2),\n", " ('l5_rms', 0.003, True),\n", " ('l5_phase', -0.13, True,-np.pi,+np.pi),\n", "\n", " ('l6_peak_f', 15, True, 10, 30),\n", " ('l6_q', 1.0, True, 0, 8),\n", " ('l6_rms', 0.001, True),\n", " ('l6_phase', -0.5, True,-np.pi,+np.pi) )\n", "\n", "lorentz_fit.set_model(CrossLor_model,model_type=\"cross\")\n", "lorentz_fit.set_params(Cross_model_parameters)" ] }, { "cell_type": "markdown", "id": "691fafa5-3018-4fb2-8467-125e316b32fe", "metadata": {}, "source": [ "Having set up our model, initialized the parameters, and passd it in our fitter obejct, we can evaluate the model and print the parameter values and intervals, and fit statistic, by calling the methods `model_params.pretty_print` and `print_fit_stat`. Finally, we can directly plot model and residuals against the data with the `plot_model_1d` method." ] }, { "cell_type": "code", "execution_count": 8, "id": "071ed5b5-71fa-4eb4-b496-abcd5df7e2ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr Brute_Step\n", "l1_peak_f 0.05 0 0.07 None True None None\n", "l1_phase 0.1 -3.142 3.142 None True None None\n", "l1_q 0.3 0 2 None True None None\n", "l1_rms 0.008 -inf inf None True None None\n", "l2_peak_f 0.3 0.07 0.5 None True None None\n", "l2_phase 0.3 -3.142 3.142 None True None None\n", "l2_q 0.5 0 2 None True None None\n", "l2_rms 0.003 -inf inf None True None None\n", "l3_peak_f 1 0.5 1.5 None True None None\n", "l3_phase 0.1 -3.142 3.142 None True None None\n", "l3_q 0.7 0 2 None True None None\n", "l3_rms 0.002 -inf inf None True None None\n", "l4_peak_f 2 1.5 3 None True None None\n", "l4_phase 0 -3.142 3.142 None True None None\n", "l4_q 0.2 0 2 None True None None\n", "l4_rms 0.005 -inf inf None True None None\n", "l5_peak_f 5 3 10 None True None None\n", "l5_phase -0.13 -3.142 3.142 None True None None\n", "l5_q 1 0 2 None True None None\n", "l5_rms 0.003 -inf inf None True None None\n", "l6_peak_f 15 10 30 None True None None\n", "l6_phase -0.5 -3.142 3.142 None True None None\n", "l6_q 1 0 8 None True None None\n", "l6_rms 0.001 -inf inf None True None None\n", "Goodness of fit metrics:\n", "Fit statistic: chisq\n", "Fit statistic 43572.570894401215\n", "Reduced fit stat 325.16843951045684\n", "Data bins: 158\n", "Free parameters: 24\n", "Degrees of freedom: 134\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lorentz_fit.model_params.pretty_print()\n", "lorentz_fit.print_fit_stat()\n", "lorentz_fit.plot_model_1d()" ] }, { "cell_type": "markdown", "id": "dd0a05c3-3ff1-41a9-8973-d960b4b785ef", "metadata": {}, "source": [ "## Running a fit\n", "\n", "Having st up the data and model, we can now fit the data by minimizing the least-chi squared statistic with the `fit_data` method. Once the fit is complete, nDspec outputs the best-fit statistics, as well as the values of each parameters. \n", "\n", "We caution users that the uncertainty ranges reported this way are highly unreliable, and should never be used for publication in peer-reviewed journals. Instead, they should be seen as a rough indication of how constrained each parameter may or may not be. For calculating the credible intervals of each parameter, we encourage users to carefully perform Bayesian inference of each posterior distribution, which is discussed in the [nDspec notebook on fitting power spectra](https://ndspec.readthedocs.io/en/latest/fit_psd.html)." ] }, { "cell_type": "code", "execution_count": null, "id": "31092df6-29ea-49e9-87ae-944f4e07f17e", "metadata": {}, "outputs": [], "source": [ "lorentz_fit.fit_data()\n", "lorentz_fit.plot_model_1d()" ] } ], "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 }