{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# The Stingray Modeling API Explained\n", "\n", "Some more in-depth explanations of how the Stingray modeling API works.\n", "\n", "Who should be using this API?\n", "Basically, anyone who wants to model power spectral products with parametric functions. The purpose of this API is two-fold:\n", "(1) provide convenient methods and classes in order to model a large range of typical data representations implemented in Stingray\n", "(2) provide a more general framework for users to build their own models\n", "\n", "A note on terminology: in this tutorial, we largely use _model_ to denote both the parametric model describing the underlying process that generated the data, and the statistical model used to account for uncertainties in the measurement process. \n", "\n", "The `modeling` subpackage defines a wider range of classes for typical statistical models than most standard modelling packages in X-ray astronomy, including likelihoods for Gaussian-distributed uncertainties (what astronomers call the $\\chi^2$ likelihood), Poisson-distributed data (e.g. light curves) and $\\chi^2$-distributed data (confusingly, *not* what astronomers call the $\\chi^2$ likelihood, but the likelihood of data with $\\chi^2$-distributed uncertainties appropriate for power spectra). It also defines a superclass `LogLikelihood` that make extending the framework to other types of data uncertainties straightforward. It supports Bayesian modelling via the `Posterior` class and its subclasses (for different types of data, equivalent to the likelihood classes) and provides support for defining priors. \n", "\n", "The class `ParameterEstimation` and its data type-specific subclasses implement a range of operations usually done with power spectra and other products, including optimization (fitting), sampling (via Markov-Chain Monte Carlo), calibrating models comparison metrics (particularly likelihood ratio tests) and outlier statistics (for finding periodic signal candidates).\n", "\n", "Overall, it is designed to be as modular as possible and extensible to new data types and problems in many places, though we do explicitly *not* aim to provide a fully general modelling framework (for example, at the moment, we have given no thought to modeling multi-variate data, though this may change in the future).\n", "\n", "\n", "## Some background\n", "\n", "Modeling power spectra and light curves with parametric models is a fairly standard task. Stingray aims to make solving these problems as easy as possible. \n", "\n", "We aim to integrate our existing code with `astropy.modeling` for for maximum compatibility. Please note, however, that we are only using the models, not the fitting interface, which is too constrained for our purposes. \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "# ignore warnings to make notebook easier to see online\n", "# COMMENT OUT THESE LINES FOR ACTUAL ANALYSIS\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "try:\n", " import seaborn as sns\n", " sns.set_palette(\"colorblind\")\n", "except ImportError:\n", " print(\"Install seaborn. It help you make prettier figures!\")\n", "\n", "import numpy as np\n", "\n", "from astropy.modeling import models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The models and API of `astropy.modeling.models` is explained in the [astropy documentation](http://docs.astropy.org/en/stable/modeling/) in more detail.\n", "\n", "Here's how you instantiate a simple 1-D Gaussian:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "g = models.Gaussian1D()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate fake data\n", "np.random.seed(0)\n", "x = np.linspace(-5., 5., 200)\n", "y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)\n", "y += np.random.normal(0., 0.2, x.shape)\n", "yerr = 0.2\n", "\n", "plt.figure(figsize=(8,5))\n", "plt.errorbar(x, y, yerr=yerr, fmt='ko')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Likelihoods and Posteriors\n", "\n", "In general, model fitting will happen either in a frequentist (Maximum Likelihood) or Bayesian framework. Stingray's strategy is to let the user define a posterior in both cases, but ignore the prior in the former case. \n", "\n", "Let's first make some fake data:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# define power law component\n", "pl = models.PowerLaw1D()\n", "\n", "# fix x_0 of power law component\n", "pl.x_0.fixed = True\n", "\n", "# define constant\n", "c = models.Const1D()\n", "\n", "# make compound model\n", "plc = pl + c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to pick some fairly standard parameters for our data:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# parameters for fake data.\n", "alpha = 2.0\n", "amplitude = 5.0\n", "white_noise = 2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now a frequency array:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "freq = np.linspace(0.01, 10.0, int(10.0/0.01))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can set the parameters in the model:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from astropy.modeling.fitting import _fitter_to_model_params\n", "\n", "_fitter_to_model_params(plc, [amplitude, alpha, white_noise])\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "psd_shape = plc(freq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a last step, we need to add noise by picking from a chi-square distribution with 2 degrees of freedom:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "powers = psd_shape*np.random.chisquare(2, size=psd_shape.shape[0])/2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the result:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,7))\n", "plt.loglog(freq, powers, ds=\"steps-mid\", label=\"periodogram realization\")\n", "plt.loglog(freq, psd_shape, label=\"power spectrum\")\n", "\n", "\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maximum Likelihood Fitting\n", "\n", "Let's assume we've observed this periodogram from our source. We would now like to estimate the parameters. \n", "This requires the definition of *likelihood*, which describes the probability of observing the data plotted above given some underlying model with a specific set of parameters. To say it differently, the likelihood encodes what we know about the underlying model (here a power law and a constant) and the statistical properties of the data (power spectra generally follow a chi-square distribution) and then allows us to compare data and model for various parameters under the assumption of the statistical uncertainties.\n", "\n", "In order to find the best parameter set, one generally maximizes the likelihood function using an optimization algorithm. Because optimization algorithms generally *minimize* functions, they effectively minimize the log-likelihood, which comes out to be the same as maximizing the likelihood itself.\n", "\n", "Below is an implementation of the $\\chi^2$ likelihood as appropriate for power spectral analysis, with comments for easier understanding. The same is also implemented in `posterior.py` in Stingray:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [], "source": [ "logmin = -1e16\n", "class PSDLogLikelihood(object):\n", "\n", " def __init__(self, freq, power, model, m=1):\n", " \"\"\"\n", " A Chi-square likelihood as appropriate for power spectral analysis.\n", "\n", " Parameters\n", " ----------\n", " freq : iterable\n", " x-coordinate of the data\n", "\n", " power : iterable\n", " y-coordinte of the data\n", "\n", " model: an Astropy Model instance\n", " The model to use in the likelihood.\n", "\n", " m : int\n", " 1/2 of the degrees of freedom, i.e. the number of powers \n", " that were averaged to obtain the power spectrum input into \n", " this routine.\n", "\n", " \"\"\"\n", " \n", " self.x = ps.freq # the x-coordinate of the data (frequency array)\n", " self.y = ps.power # the y-coordinate of the data (powers)\n", " self.model = model # an astropy.models instance\n", " self.m = m\n", " \n", " self.params = [k for k,l in self.model.fixed.items() if not l]\n", " self.npar = len(self.params) # number of free parameters\n", "\n", " def evaluate(self, pars, neg=False):\n", " \"\"\"\n", " Evaluate the log-likelihood.\n", " \n", " Parameters\n", " ----------\n", " pars : iterable\n", " The list of parameters for which to evaluate the model.\n", " \n", " neg : bool, default False\n", " If True, compute the *negative* log-likelihood, otherwise \n", " compute the *positive* log-likelihood.\n", " \n", " Returns\n", " -------\n", " loglike : float\n", " The log-likelihood of the model\n", " \n", " \"\"\"\n", " # raise an error if the length of the parameter array input into \n", " # this method doesn't match the number of free parameters in the model\n", " if np.size(pars) != self.npar:\n", " raise Exception(\"Input parameters must\" +\n", " \" match model parameters!\")\n", "\n", " # set parameters in self.model to the parameter set to be used for \n", " # evaluation\n", " _fitter_to_model_params(self.model, pars)\n", "\n", " # compute the values of the model at the positions self.x\n", " mean_model = self.model(self.x)\n", "\n", " # if the power spectrum isn't averaged, compute simple exponential \n", " # likelihood (chi-square likelihood for 2 degrees of freedom)\n", " if self.m == 1:\n", " loglike = -np.sum(np.log(mean_model)) - \\\n", " np.sum(self.y/mean_model)\n", " # otherwise use chi-square distribution to compute likelihood\n", " else:\n", " loglike = -2.0*self.m*(np.sum(np.log(mean_model)) +\n", " np.sum(self.y/mean_model) +\n", " np.sum((2.0 / (2. * self.m) - 1.0) *\n", " np.log(self.y)))\n", "\n", " if not np.isfinite(loglike):\n", " loglike = logmin\n", "\n", " if neg:\n", " return -loglike\n", " else:\n", " return loglike\n", " \n", " def __call__(self, parameters, neg=False):\n", " return self.evaluate(parameters, neg)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make an object and see what it calculates if we put in different parameter sets. First, we have to make our sample PSD into an actual `Powerspectrum` object:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from stingray import Powerspectrum\n", "\n", "ps = Powerspectrum()\n", "ps.freq = freq\n", "ps.power = powers\n", "ps.df = ps.freq[1] - ps.freq[0]\n", "ps.m = 1" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "loglike = PSDLogLikelihood(ps.freq, ps.power, plc, m=ps.m)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-4835.88214847462" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_pars = [1, 5, 100]\n", "loglike(test_pars)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2869.5582486265116" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_pars = [4.0, 10, 2.5]\n", "loglike(test_pars)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2375.704120812954" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_pars = [2.0, 5.0, 2.0]\n", "loglike(test_pars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Something close to the parameters we put in should yield the largest log-likelihood. Feel free to play around with the test parameters to verify that this is true.\n", "\n", "You can similarly import the `PSDLogLikelihood` class from `stingray.modeling` and do the same:\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2375.704120812954" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from stingray.modeling import PSDLogLikelihood\n", "\n", "loglike = PSDLogLikelihood(ps.freq, ps.power, plc, m=ps.m)\n", "loglike(test_pars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To estimate the parameters, we can use an optimization routine, such as those implemented in `scipy.optimize.minimize`.\n", "We have wrapped some code around that, to make your lives easier. We will not reproduce the full code here, just demonstrate its functionality.\n", "\n", "Now we can instantiate the `PSDParEst` (for PSD Parameter Estimation) object. This can do more than simply optimize a single model, but we'll get to that later.\n", "\n", "The `PSDParEst` object allows one to specify the fit method to use (however, this must be one of the optimizers in `scipy.optimize`). The parameter `max_post` allows for doing maximum-a-posteriori fits on the Bayesian posterior rather than maximum likelihood fits (see below for more details). We'll set it to `False` for now, since we haven't defined any priors:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import PSDParEst\n", "\n", "parest = PSDParEst(ps, fitmethod=\"L-BFGS-B\", max_post=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to fit a model, make an instance of the appropriate `LogLikelihood` or `Posterior` subclass, andsimply call the `fit` method with that instance and starting parameters you would like to fit." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "loglike = PSDLogLikelihood(ps.freq, ps.power, plc, m=ps.m)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2., 1., 5., 2.])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loglike.model.parameters" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loglike.npar" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "starting_pars = [3.0, 1.0, 2.4]\n", "res = parest.fit(loglike, starting_pars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is an `OptimizationResults` object, which computes various summaries and useful quantities.\n", "\n", "For example, here's the value of the likelihood function at the maximum the optimizer found:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2183.789677035487" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: Optimizers routinely get stuck in *local* minima (corresponding to local maxima of the likelihood function). It is usually useful to run an optimizer several times with different starting parameters in order to get close to the global maximum.\n", "\n", "Most useful are the estimates of the parameters at the maximum likelihood and their uncertainties:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4.72916493 2.09193061 2.10372265]\n", "[3.78311696 0.7300253 0.55312843]\n" ] } ], "source": [ "print(res.p_opt)\n", "print(res.err)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: uncertainties are estimated here via the covariance matrix between parameters, i.e. the inverse of the Hessian at the maximum. This only represents the true uncertainties for specific assumptions about the likelihood function (Gaussianity), so use with care!\n", "\n", "It also computes Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) for model comparison purposes:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AIC: 2189.789677035487\n", "BIC: 2204.512942872433\n" ] } ], "source": [ "print(\"AIC: \" + str(res.aic))\n", "print(\"BIC: \" + str(res.bic))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, it also produces the values of the mean function for the parameters at the maximum. Let's plot that and compare with the power spectrum we put in:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "plt.loglog(ps.freq, psd_shape, label=\"true power spectrum\",lw=3)\n", "plt.loglog(ps.freq, ps.power, label=\"simulated data\")\n", "plt.loglog(ps.freq, res.mfit, label=\"best fit\", lw=3)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks pretty good!\n", "\n", "You can print a summary of the fitting results by calling `print_summary`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The best-fit model parameters plus errors are:\n", " 0) Parameter amplitude_0 : \n", "4.72916 +/- 3.78312 \n", "[ None None]\n", " 1) Parameter x_0_0 : \n", "1.00000 (Fixed) \n", " 2) Parameter alpha_0 : \n", "2.09193 +/- 0.73003 \n", "[ None None]\n", " 3) Parameter amplitude_1 : \n", "2.10372 +/- 0.55313 \n", "[ None None]\n", "\n", "\n", "Fitting statistics: \n", " -- number of data points: 1000\n", " -- Deviance [-2 log L] D = 4367.579354.3\n", " -- The Akaike Information Criterion of the model is: 2189.789677035487.\n", " -- The Bayesian Information Criterion of the model is: 2204.512942872433.\n", " -- The figure-of-merit function for this model is: 1079.682849.5f and the fit for 997 dof is 1.082932.3f\n", " -- Summed Residuals S = 69267.121618.5f\n", " -- Expected S ~ 6000.000000.5 +/- 109.544512.5\n" ] } ], "source": [ "res.print_summary(loglike)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Likelihood Ratios\n", "\n", "The parameter estimation code has more functionality than act as a simple wrapper around `scipy.optimize`. For example, it allows for easy computation of likelihood ratios. Likelihood ratios are a standard way to perform comparisons between two models (though they are not always statistically meaningful, and should be used with caution!).\n", "\n", "To demonstrate that, let's make a broken power law model" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# broken power law model\n", "bpl = models.BrokenPowerLaw1D()\n", "\n", "# add constant\n", "bplc = bpl + c" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('amplitude_0', 'x_break_0', 'alpha_1_0', 'alpha_2_0', 'amplitude_1')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bplc.param_names" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# define starting parameters\n", "bplc_start_pars = [2.0, 1.0, 3.0, 1.0, 2.5]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "loglike_bplc = PSDLogLikelihood(ps.freq, ps.power, bplc, m=ps.m)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "pval, plc_opt, bplc_opt = parest.compute_lrt(loglike, starting_pars, loglike_bplc, bplc_start_pars)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Likelihood Ratio: 2.2374827070098036\n" ] } ], "source": [ "print(\"Likelihood Ratio: \" + str(pval))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Bayesian Parameter Estimation\n", "\n", "For Bayesian parameter estimation, we require a prior along with the likelihood defined above. Together, they form the *posterior*, the probability of the parameters given the data, which is what we generally want to compute in science.\n", "\n", "Since there are no universally accepted priors for a model (they depend on the problem at hand and your physical knowledge about the system), they cannot be easily hard-coded in stingray. Consequently, setting priors is slightly more complex. \n", "\n", "Analogously to the `LogLikelihood` above, we can also define a `Posterior` object. Each posterior object has three methods: `logprior`, `loglikelihood` and `logposterior`. \n", "\n", "We have pre-defined some `Posterior` objects in `posterior.py` for common problems, including power spectral analysis. We start by making a `PSDPosterior` object:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import PSDPosterior" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "lpost = PSDPosterior(ps.freq, ps.power, plc, m=ps.m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The priors are set as a dictionary of functions:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "import scipy.stats\n", "\n", "# flat prior for the power law index\n", "p_alpha = lambda alpha: ((-1. <= alpha) & (alpha <= 5.))\n", "\n", "# flat prior for the power law amplitude\n", "p_amplitude = lambda amplitude: ((0.01 <= amplitude) & (amplitude <= 10.0))\n", "\n", "# normal prior for the white noise parameter\n", "p_whitenoise = lambda white_noise: scipy.stats.norm(2.0, 0.1).pdf(white_noise)\n", "\n", "priors = {}\n", "priors[\"alpha_0\"] = p_alpha\n", "priors[\"amplitude_0\"] = p_amplitude\n", "priors[\"amplitude_1\"] = p_whitenoise\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a function `set_logprior` in `stingray.modeling` that sets the prior correctly:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import set_logprior" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "lpost.logprior = set_logprior(lpost, priors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also set the priors when you instantiate the posterior object:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "lpost = PSDPosterior(ps.freq, ps.power, plc, priors=priors, m=ps.m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much like before with the log-likelihood, we can now also compute the log-posterior for various test parameter sets:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "log-prior: -198.61635344021062\n", "log-likelihood: -2412.2493594640564\n", "log-posterior: -2610.865712904267\n" ] } ], "source": [ "test_pars = [1.0, 2.0, 4.0]\n", "print(\"log-prior: \" + str(lpost.logprior(test_pars)))\n", "print(\"log-likelihood: \" + str(lpost.loglikelihood(test_pars)))\n", "print(\"log-posterior: \" + str(lpost(test_pars)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the prior is zero (so the log-prior is -infinity), it automatically gets set to a very small value in order to avoid problems when doing the optimization:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "log-prior: -1e+16\n", "log-likelihood: -2534.0567826161864\n", "log-posterior: -1e+16\n" ] } ], "source": [ "test_pars = [6, 6, 3.0]\n", "print(\"log-prior: \" + str(lpost.logprior(test_pars)))\n", "print(\"log-likelihood: \" + str(lpost.loglikelihood(test_pars)))\n", "print(\"log-posterior: \" + str(lpost(test_pars)))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "log-prior: 1.383646559789373\n", "log-likelihood: -2184.6739536386162\n", "log-posterior: -2183.290307078827\n" ] } ], "source": [ "test_pars = [5.0, 2.0, 2.0]\n", "print(\"log-prior: \" + str(lpost.logprior(test_pars)))\n", "print(\"log-likelihood: \" + str(lpost.loglikelihood(test_pars)))\n", "print(\"log-posterior: \" + str(lpost(test_pars)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same parameter estimation as above, except now it's called maximum-a-posteriori instead of maximum likelihood and includes the prior (notice we set `max_post=True`):" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "parest = PSDParEst(ps, fitmethod='BFGS', max_post=True)\n", "res = parest.fit(lpost, starting_pars)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best-fit parameters:\n", "4.8949 +/- 0.0762\n", "2.0690 +/- 0.0636\n", "2.0547 +/- 0.0149\n" ] } ], "source": [ "print(\"best-fit parameters:\")\n", "for p,e in zip(res.p_opt, res.err):\n", " print(\"%.4f +/- %.4f\"%(p,e))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same outputs exist as for the Maximum Likelihood case:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The best-fit model parameters plus errors are:\n", " 0) Parameter amplitude_0 : \n", "4.89491 +/- 0.07623 \n", "[ None None]\n", " 1) Parameter x_0_0 : \n", "1.00000 (Fixed) \n", " 2) Parameter alpha_0 : \n", "2.06898 +/- 0.06363 \n", "[ None None]\n", " 3) Parameter amplitude_1 : \n", "2.05471 +/- 0.01489 \n", "[ None None]\n", "\n", "\n", "Fitting statistics: \n", " -- number of data points: 1000\n", " -- Deviance [-2 log L] D = 4367.845867.3\n", " -- The Akaike Information Criterion of the model is: 2188.688941098666.\n", " -- The Bayesian Information Criterion of the model is: 2203.412206935612.\n", " -- The figure-of-merit function for this model is: 1104.686605.5f and the fit for 997 dof is 1.108011.3f\n", " -- Summed Residuals S = 75870.935552.5f\n", " -- Expected S ~ 6000.000000.5 +/- 109.544512.5\n" ] } ], "source": [ "res.print_summary(lpost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike in the maximum likelihood case, we can also *sample* from the posterior probability distribution. The method `sample` uses the [emcee](http://dfm.io/emcee/current/) package to do MCMC. \n", "\n", "**Important**: Do *not* sample from the likelihood function. This is formally incorrect and can lead to incorrect inferences about the problem, because there is no guarantee that a posterior with improper (flat, infinite) priors will be bounded!\n", "\n", "**Important**: emcee has had a major upgrade to version 3, which came with a number of API changes. To ensure compatibility with stingray, please update emcee to the latest version, if you haven't already.\n", "\n", "Much like the optimizer, the sampling method requires a model and a set of starting parameters `t0`. Optionally, it can be useful to also input a covariance matrix, for example from the output of the optimizer.\n", "\n", "Finally, the user should specify the number of walkers as well as the number of steps to use for both burn-in and sampling:\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Chains too short to compute autocorrelation lengths.\n", "-- The acceptance fraction is: 0.640200.5\n", "R_hat for the parameters is: [0.33858822 0.00779588 0.00477259]\n", "-- Posterior Summary of Parameters: \n", "\n", "parameter \t mean \t\t sd \t\t 5% \t\t 95% \n", "\n", "---------------------------------------------\n", "\n", "theta[0] \t 4.92699673203164\t0.5826084748010877\t4.001167475075788\t5.916405947428704\n", "\n", "theta[1] \t 2.0850162824299567\t0.08840420643721274\t1.945198565812\t2.236054242762929\n", "\n", "theta[2] \t 2.059927524015745\t0.06916995745141118\t1.944976347964247\t2.172179088048585\n", "\n" ] } ], "source": [ "sample = parest.sample(lpost, res.p_opt, cov=res.cov, nwalkers=400,\n", " niter=100, burnin=300, namestr=\"psd_modeling_test\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sampling method returns an object with various attributes that are useful for further analysis, for example the acceptance fraction:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6402000000000001" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.acceptance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or the mean and confidence intervals of the parameters:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4.92699673, 2.08501628, 2.05992752])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.mean" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4.00116748, 1.94519857, 1.94497635],\n", " [5.91640595, 2.23605424, 2.17217909]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.ci" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The method `print_results` prints the results:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-- The acceptance fraction is: 0.640200.5\n", "R_hat for the parameters is: [0.33858822 0.00779588 0.00477259]\n", "-- Posterior Summary of Parameters: \n", "\n", "parameter \t mean \t\t sd \t\t 5% \t\t 95% \n", "\n", "---------------------------------------------\n", "\n", "theta[0] \t 4.92699673203164\t0.5826084748010877\t4.001167475075788\t5.916405947428704\n", "\n", "theta[1] \t 2.0850162824299567\t0.08840420643721274\t1.945198565812\t2.236054242762929\n", "\n", "theta[2] \t 2.059927524015745\t0.06916995745141118\t1.944976347964247\t2.172179088048585\n", "\n" ] } ], "source": [ "sample.print_results()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, the method `plot_results` produces a bunch of plots:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = sample.plot_results(nsamples=1000, fig=None, save_plot=True,\n", " filename=\"modeling_tutorial_mcmc_corner.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calibrating Likelihood Ratio Tests\n", "\n", "In order to use likelihood ratio tests for model comparison, one must compute the p-value of obtaining a likelihood ratio at least as high as that observed given that the null hypothesis (the simpler model) is true. The distribution of likelihood ratios under that assumption will only follow an analytical distribution if\n", "* the models are nested, i.e. the simpler model is a special case of the more complex model *and*\n", "* the parameter values that transform the complex model into the simple one do not lie on the boundary of parameter space. \n", "\n", "Imagine e.g. a simple model without a QPO, and a complex model with a QPO, where in order to make the simpler model out of the more complex one you would set the QPO amplitude to zero. However, the amplitude cannot go below zero, thus the critical parameter value transforming the complex into the simple model lie on the boundary of parameter space.\n", "\n", "If these two conditions are not given, the observed likelihood ratio must be calibrated via simulations of the simpler model. In general, one should *not* simulate from the best-fit model alone: this ignores the uncertainty in the model parameters, and thus may artificially inflate the significance of the result.\n", "\n", "In the purely frequentist (maximum likelihood case), one does not know the shape of the probability distribution for the parameters. A rough approximation can be obtained by assuming the likelihood surface to be a multi-variate Gaussian, with covariances given by the inverse Fisher information. One may sample from that distribution and then simulate fake data sets using the sampled parameters. Each simulated data set will be fit with both models to compute a likelihood ratio, which is then used to build a distribution of likelihood ratios from the simpler model to compare the observed likelihood ratio to.\n", "\n", "In the Bayesian case, one may sample from the posterior for the parameters directly and then use these samples as above to create fake data sets in order to derive a posterior probability distribution for the likelihood ratios and thus a posterior predictive p-value.\n", "\n", "For the statistical background of much of this, see [Protassov et al, 2002](http://adsabs.harvard.edu/abs/2002ApJ...571..545P).\n", "\n", "Below, we set up code that will do exactly that, for both the frequentist and Bayesian case.\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "import copy\n", "\n", "def _generate_model(lpost, pars):\n", " \"\"\"\n", " Helper function that generates a fake PSD similar to the \n", " one in the data, but with different parameters.\n", " \n", " Parameters\n", " ----------\n", " lpost : instance of a Posterior or LogLikelihood subclass\n", " The object containing the relevant information about the\n", " data and the model\n", " \n", " pars : iterable\n", " A list of parameters to be passed to lpost.model in oder \n", " to generate a model data set.\n", " \n", " Returns:\n", " --------\n", " model_data : numpy.ndarray\n", " An array of model values for each bin in lpost.x\n", " \n", " \"\"\"\n", " # get the model\n", " m = lpost.model\n", "\n", " # reset the parameters\n", " _fitter_to_model_params(m, pars)\n", " \n", " # make a model spectrum\n", " model_data = lpost.model(lpost.x)\n", " \n", " return model_data\n", "\n", "def _generate_psd(ps, lpost, pars):\n", " \"\"\"\n", " Generate a fake power spectrum from a model.\n", " \n", " Parameters:\n", " ----------\n", " lpost : instance of a Posterior or LogLikelihood subclass\n", " The object containing the relevant information about the\n", " data and the model\n", " \n", " pars : iterable\n", " A list of parameters to be passed to lpost.model in oder \n", " to generate a model data set.\n", " \n", " Returns:\n", " --------\n", " sim_ps : stingray.Powerspectrum object\n", " The simulated Powerspectrum object\n", " \n", " \"\"\"\n", " \n", " model_spectrum = _generate_model(lpost, pars)\n", " \n", " # use chi-square distribution to get fake data\n", " model_powers = model_spectrum*np.random.chisquare(2*ps.m, \n", " size=model_spectrum.shape[0])/(2.*ps.m)\n", "\n", " sim_ps = copy.copy(ps)\n", "\n", " sim_ps.powers = model_powers\n", " \n", "\n", " return sim_ps\n", " \n", "def _compute_pvalue(obs_val, sim):\n", " \"\"\"\n", " Compute the p-value given an observed value of a test statistic \n", " and some simulations of that same test statistic.\n", " \n", " Parameters\n", " ----------\n", " obs_value : float\n", " The observed value of the test statistic in question\n", " \n", " sim: iterable\n", " A list or array of simulated values for the test statistic\n", " \n", " Returns\n", " -------\n", " pval : float [0, 1]\n", " The p-value for the test statistic given the simulations.\n", " \n", " \"\"\"\n", " \n", " # cast the simulations as a numpy array\n", " sim = np.array(sim)\n", " \n", " # find all simulations that are larger than \n", " # the observed value\n", " ntail = sim[sim > obs_val].shape[0]\n", " \n", " # divide by the total number of simulations\n", " pval = ntail/sim.shape[0]\n", "\n", " return pval\n", "\n", "def calibrate_lrt(ps, lpost1, t1, lpost2, t2, sample=None, neg=True, max_post=False, \n", " nsim=1000, niter=200, nwalker=500, burnin=200, namestr=\"test\"):\n", " \n", " \n", " # set up the ParameterEstimation object\n", " parest = PSDParEst(ps, fitmethod=\"L-BFGS-B\", max_post=False)\n", "\n", " # compute the observed likelihood ratio\n", " lrt_obs, res1, res2 = parest.compute_lrt(lpost1, t1, \n", " lpost2, t2,\n", " neg=neg, \n", " max_post=max_post)\n", " \n", " # simulate parameter sets from the simpler model\n", " if not max_post:\n", " # using Maximum Likelihood, so I'm going to simulate parameters \n", " # from a multivariate Gaussian\n", " \n", " # set up the distribution\n", " mvn = scipy.stats.multivariate_normal(mean=res1.p_opt, cov=res1.cov)\n", " \n", " # sample parameters\n", " s_all = mvn.rvs(size=nsim)\n", " \n", " else:\n", " if sample is None:\n", " # sample the posterior using MCMC\n", " sample = parest.sample(lpost, res1.p_opt, cov=res1.cov, \n", " nwalkers=nwalker, niter=niter, \n", " burnin=burnin, namestr=namestr)\n", " \n", " \n", " # pick nsim samples out of the posterior sample\n", " s_all = sample[np.random.choice(sample.shape[0], nsim, replace=False)]\n", " \n", " lrt_sim = np.zeros(nsim)\n", " \n", " # now I can loop over all simulated parameter sets to generate a PSD\n", " for i,s in enumerate(s_all):\n", " \n", " # generate fake PSD\n", " sim_ps = _generate_psd(ps, lpost1, s)\n", "\n", " # make LogLikelihood objects for both:\n", " if not max_post:\n", " sim_lpost1 = PSDLogLikelihood(sim_ps.freq, sim_ps.power,\n", " model=lpost1.model, m=sim_ps.m)\n", " sim_lpost2 = PSDLogLikelihood(sim_ps.freq, sim_ps.power, \n", " model=lpost2.model, m=sim_ps.m)\n", " else:\n", " # make a Posterior object\n", " sim_lpost1 = PSDPosterior(sim_ps.freq, sim_ps.power, \n", " lpost1.model, m=sim_ps.m)\n", " sim_lpost1.logprior = lpost1.logprior\n", " \n", " sim_lpost2 = PSDPosterior(sim_ps.freq, sim_ps.power, \n", " lpost2.model, m=sim_ps.m)\n", " sim_lpost2.logprior = lpost2.logprior\n", "\n", " \n", " parest_sim = PSDParEst(sim_ps, max_post=max_post)\n", " \n", " lrt_sim[i], _, _ = parest_sim.compute_lrt(sim_lpost1, t1, \n", " sim_lpost2, t2, \n", " neg=neg, \n", " max_post=max_post)\n", "\n", " # now I can compute the p-value:\n", " pval = _compute_pvalue(lrt_obs, lrt_sim)\n", " return pval" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pval = calibrate_lrt(ps, loglike, starting_pars, \n", " loglike_bplc, bplc_start_pars, \n", " max_post=False, nsim=100)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The p-value for rejecting the simpler model is: 0.97\n" ] } ], "source": [ "print(\"The p-value for rejecting the simpler model is: \" + str(pval))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the p-value for rejecting the powerlaw model is fairly large: since we simulated from that model, we would be surprised if it generated a small p-value, causing us to reject this model (note, however, that if the null hypothesis is true, the p-value will be uniformely distributed between 0 and 1. By definition, then, you will get a p-value smaller or equal to 0.01 in approximately one out of a hundred cases)\n", "\n", "We can do the same with the Bayesian model, in which case the result is called a *posterior predictive p-value*, which, in turn, is often used in posterior model checking (not yet implemented!).\n", "\n", "We have not yet defined a `PSDPosterior` object for the bent power law model, so let's do that. First, let's define some priors:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "import scipy.stats\n", "\n", "# flat prior for the power law indices\n", "p_alpha1 = lambda alpha: ((-1. <= alpha) & (alpha <= 5.))\n", "p_alpha2 = lambda alpha: ((-1. <= alpha) & (alpha <= 5.))\n", "\n", "# flat prior for the break frequency\n", "p_x_break = lambda xbreak: ((0.01 <= xbreak) & (10.0 >= xbreak))\n", "\n", "# flat prior for the power law amplitude\n", "p_amplitude = lambda amplitude: ((0.01 <= amplitude) & (amplitude <= 10.0))\n", "\n", "# normal prior for the white noise parameter\n", "p_whitenoise = lambda white_noise: scipy.stats.norm(2.0, 0.1).pdf(white_noise)\n", "\n", "priors = {}\n", "priors[\"alpha_1_0\"] = p_alpha\n", "priors[\"alpha_2_0\"] = p_alpha\n", "\n", "priors[\"amplitude_0\"] = p_amplitude\n", "priors[\"amplitude_1\"] = p_whitenoise\n", "priors[\"x_break_0\"] = p_x_break\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can set up the `PSDPosterior` object:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "lpost_bplc = PSDPosterior(ps.freq, ps.power, bplc, priors=priors, m=ps.m)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2230.14039643262" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lpost_bplc(bplc_start_pars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And do the posterior predictive p-value. Since we've already sampled from the simple model, we can pass that sample to the `calibrate_lrt` function, in order to cut down on computation time (if the keyword `sample` is not given, it will automatically run MCMC:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pval = calibrate_lrt(ps, lpost, starting_pars, \n", " lpost_bplc, bplc_start_pars, \n", " sample=sample.samples,\n", " max_post=True, nsim=100)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The posterior predictive p-value is: p = 1.0\n" ] } ], "source": [ "print(\"The posterior predictive p-value is: p = \" + str(pval))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we find that the p-value does not suggest rejecting the powerlaw model.\n", "\n", "Of course, a slightly modified version is implemented in `stingray` as a subclass of the `PSDParEst` class:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import PSDParEst" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "parest = PSDParEst(ps, fitmethod=\"BFGS\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pval = parest.calibrate_lrt(lpost, starting_pars, lpost_bplc, bplc_start_pars, \n", " sample=sample.samples, nsim=100, max_post=True, seed=200)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2\n" ] } ], "source": [ "print(pval)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian-ish QPO Searches\n", "\n", "When searching for quasi-periodic oscillations (QPOs) in light curves that are not constant (for example because they are bursts or have other types of variability), one must take care that the variable background is accurately modelled (most standard tools assume that the light curve is constant). \n", "\n", "In [Vaughan et al, 2010](http://adsabs.harvard.edu/abs/2010MNRAS.402..307V), a method was introduced to search for QPOs in the presence of red noise (stochastic variability), and in [Huppenkothen et al, 2013](http://adsabs.harvard.edu/abs/2013ApJ...768...87H) it was extended to magnetar bursts, and in [Inglis et al, 2015](http://adsabs.harvard.edu/abs/2015ApJ...798..108I) and [Inglis et al, 2016](http://adsabs.harvard.edu/abs/2016ApJ...833..284I) a similar approach was used to find QPOs in solar flares.\n", "\n", "Based on a model for the broadband spectral noise, the algorithm finds the highest outlier in a test statistic based on the data-model residuals (under the assumption that if the broadband model is correct, the test statistic $T_R = \\max_j(2 D_j/m_j)$ for $j$ power spectral bins with powers $D_j$ and model powers $m_j$ will be distributed following a $\\chi^2$ distribution with two degrees of freedom). The observed test statistic $T_R$ is then compared to a theoretical distribution based on simulated power spectra without an outlier in order to compute a posterior predictive p-value as above for the likelihood ratio.\n", "\n", "Since the concept is very similar to that above, we do not show the full code here. Instead, the p-value can be calculated using the method `calibrate_highest_outlier`, which belongs to the `PSDParEst` class:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# compute highest outlier in the data, and the frequency and index\n", "# where that power occurs\n", "max_power, max_freq, max_ind = parest._compute_highest_outlier(lpost, res)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([16.79715722])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max_power" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pval = parest.calibrate_highest_outlier(lpost, starting_pars, sample=sample,\n", " max_post=True,\n", " nsim=100, niter=200, nwalkers=500,\n", " burnin=200, namestr=\"test\")" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.15" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pval" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convenience Functions\n", "\n", "For convenience, we have implemented some simple functions to reduce overhead with having to instantiate objects of the various classes.\n", "\n", "Note that these convenience function use similar approaches and guesses in all cases; this might work for some simple quicklook analysis, but when preparing publication-ready results, one should approach the analysis with more care and make sure the options chosen are appropriate for the problem at hand.\n", "\n", "### Fitting a power spectrum with some model\n", "\n", "The code above allows for a lot of freedom in building an appropriate model for your application. However, in everyday life, one might occasionally want to do a quick fit for various applications, without having to go too much into details. Below is a convenience function written for exactly that purpose.\n", "\n", "Please note that while this aims to use reasonable defaults, this is unlikely to produce publication-ready results!\n", "\n", "So let's fit a power law and a constant to some data, which we'll create below:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "from stingray import Powerspectrum\n", "\n", "m = 1\n", "nfreq = 100000\n", "freq = np.linspace(1, 1000, nfreq)\n", "\n", "np.random.seed(100) # set the seed for the random number generator\n", "noise = np.random.exponential(size=nfreq)\n", "\n", "model = models.PowerLaw1D() + models.Const1D()\n", "model.x_0_0.fixed = True\n", "\n", "alpha_0 = 2.0\n", "amplitude_0 = 100.0\n", "amplitude_1 = 2.0\n", "\n", "model.alpha_0 = alpha_0\n", "model.amplitude_0 = amplitude_0\n", "model.amplitude_1 = amplitude_1\n", "\n", "p = model(freq)\n", "power = noise * p\n", "\n", "ps = Powerspectrum()\n", "ps.freq = freq\n", "ps.power = power\n", "ps.m = m\n", "ps.df = freq[1] - freq[0]\n", "ps.norm = \"leahy\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What does this data set look like?" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.loglog(ps.freq, ps.power, ds=\"steps-mid\", lw=2, color=\"black\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to fit this, we'll write a convenience function that can take the power spectrum, a model, some starting parameters and just run with it:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import PSDLogLikelihood, PSDPosterior, PSDParEst\n", "\n", "def fit_powerspectrum(ps, model, starting_pars, max_post=False, priors=None,\n", " fitmethod=\"L-BFGS-B\"):\n", " \n", " if priors:\n", " lpost = PSDPosterior(ps, model, priors=priors)\n", " else:\n", " lpost = PSDLogLikelihood(ps.freq, ps.power, model, m=ps.m)\n", "\n", " parest = PSDParEst(ps, fitmethod=fitmethod, max_post=max_post)\n", " res = parest.fit(lpost, starting_pars, neg=True)\n", "\n", " return parest, res\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if it works. We've already defined our model above, but to be explicit, let's define it again:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "model_to_test = models.PowerLaw1D() + models.Const1D()\n", "model_to_test.x_0_0.fixed = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we just need some starting parameters:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "t0 = [80, 1.5, 2.5]" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "parest, res = fit_powerspectrum(ps, model_to_test, t0)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([109.14539343, 2.07102572, 2.00200532])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.p_opt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like it worked! Let's plot the result, too:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.figure()\n", "plt.loglog(ps.freq, ps.power, ds=\"steps-mid\", lw=2, color=\"black\")\n", "plt.plot(ps.freq, res.mfit, lw=3, color=\"red\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can find the function in the `scripts` sub-module:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling.scripts import fit_powerspectrum" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([108.96093418, 2.0699128 , 2.00198643])" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parest, res = fit_powerspectrum(ps, model_to_test, t0)\n", "res.p_opt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fitting Lorentzians\n", "\n", "Fitting Lorentzians to power spectra is a routine task for most astronomers working with power spectra, hence there is a function that can produce either Maximum Likelihood or Maximum-A-Posteriori fits of the data." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "l = models.Lorentz1D" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('amplitude', 'x_0', 'fwhm')" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l.param_names" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "def fit_lorentzians(ps, nlor, starting_pars, fit_whitenoise=True, max_post=False, priors=None,\n", " fitmethod=\"L-BFGS-B\"):\n", " \n", " model = models.Lorentz1D()\n", " \n", " if nlor > 1:\n", " for i in range(nlor-1):\n", " model += models.Lorentz1D()\n", " \n", " if fit_whitenoise:\n", " model += models.Const1D()\n", " \n", " parest = PSDParEst(ps, fitmethod=fitmethod, max_post=max_post)\n", " lpost = PSDPosterior(ps.freq, ps.power, model, priors=priors, m=ps.m)\n", " res = parest.fit(lpost, starting_pars, neg=True)\n", " \n", " return parest, res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a dataset so we can test it!" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "np.random.seed(400)\n", "nlor = 3\n", "\n", "x_0_0 = 0.5\n", "x_0_1 = 2.0\n", "x_0_2 = 7.5\n", "\n", "amplitude_0 = 150.0\n", "amplitude_1 = 50.0\n", "amplitude_2 = 15.0\n", "\n", "fwhm_0 = 0.1\n", "fwhm_1 = 1.0\n", "fwhm_2 = 0.5\n", "\n", "whitenoise = 2.0\n", "\n", "model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \\\n", " models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \\\n", " models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \\\n", " models.Const1D(whitenoise)\n", " \n", "p = model(ps.freq)\n", "noise = np.random.exponential(size=len(ps.freq))\n", "\n", "power = p*noise\n", "\n", "plt.figure()\n", "plt.loglog(ps.freq, power, lw=1, ds=\"steps-mid\", c=\"black\")\n", "plt.loglog(ps.freq, p, lw=3, color=\"red\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make this into a `Powerspectrum` object:" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "import copy" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "ps_new = copy.copy(ps)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "ps_new.power = power" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now we can fit this model with our new function, but first, we need to define the starting parameters for our fit. The starting parameters will be `[amplitude, x_0, fwhm]` for each component plus the white noise component at the end:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "t0 = [150, 0.4, 0.2, 50, 2.3, 0.6, 20, 8.0, 0.4, 2.1]\n", "parest, res = fit_lorentzians(ps_new, nlor, t0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the output:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.49011854e+02, 1.06004236e+00, -4.00733295e-05, 4.54780918e+01,\n", " 1.89830161e+00, 1.10287737e+00, 1.01732386e+01, 7.49528676e+00,\n", " 6.72319819e-01, 1.99444430e+00])" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.p_opt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool, that seems to work! For convenience `PSDParEst` also has a plotting function:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "parest.plotfits(res, save_plot=False, namestr=\"lorentzian_test\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function exists in the library as well for ease of use:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "from stingray.modeling import fit_lorentzians" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "parest, res = fit_lorentzians(ps_new, nlor, t0)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.47811631e+02, 3.65200027e-02, 1.35036166e-03, 4.03665876e+01,\n", " 1.89162600e+00, 1.20693953e+00, 1.05461311e+01, 7.49865621e+00,\n", " 6.36152472e-01, 1.99437422e+00])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.p_opt" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }