{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy/v0.12?urlpath=lab/tree/pulsar_analysis.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[pulsar_analysis.ipynb](../_static/notebooks/pulsar_analysis.ipynb) |\n", "[pulsar_analysis.py](../_static/notebooks/pulsar_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pulsar analysis with Gammapy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to do a pulsar analysis with Gammapy. It's based on a Vela simulation file from the CTA DC1, which already contains a column of phases. We will produce a phasogram, a phase-resolved map and a phase-resolved spectrum of the Vela pulsar using the class PhaseBackgroundEstimator from gammapy.background.phase. \n", "\n", "The phasing in itself is not done here, and it requires specific packages like Tempo2 or PINT (https://nanograv-pint.readthedocs.io/en/latest/readme.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Opening the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first do the imports and load the only observation containing Vela in the CTA 1DC dataset shipped with Gammapy." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from regions import CircleSkyRegion\n", "from astropy.coordinates import SkyCoord\n", "import astropy.units as u\n", "\n", "from gammapy.maps import Map, WcsGeom\n", "from gammapy.cube import fill_map_counts\n", "from gammapy.data import DataStore\n", "from gammapy.background import PhaseBackgroundEstimator\n", "from gammapy.spectrum.models import PowerLaw\n", "from gammapy.utils.energy import EnergyBounds\n", "from gammapy.utils.fitting import Fit\n", "from gammapy.spectrum import (\n", " SpectrumExtraction,\n", " FluxPointsEstimator,\n", " FluxPointsDataset,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the data store (which is a subset of CTA-DC1 data):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define obsevation ID and print events:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EventList info:\n", "- Number of events: 101430\n", "- Median energy: 0.1 TeV\n", "- OBS_ID = 111630\n" ] } ], "source": [ "id_obs_vela = [111630]\n", "obs_list_vela = data_store.get_observations(id_obs_vela)\n", "print(obs_list_vela[0].events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our observation, let's select the events in 0.2° radius around the pulsar position." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EventList info:\n", "- Number of events: 843\n", "- Median energy: 0.107 TeV\n", "- OBS_ID = 111630\n" ] } ], "source": [ "pos_target = SkyCoord(ra=128.836 * u.deg, dec=-45.176 * u.deg, frame=\"icrs\")\n", "on_radius = 0.2 * u.deg\n", "\n", "# Apply angular selection\n", "events_vela = obs_list_vela[0].events.select_sky_cone(\n", " center=pos_target, radius=on_radius\n", ")\n", "print(events_vela)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's load the phases of the selected events in a dedicated array." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<Column name='PHASE' dtype='float32' length=10>\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
0.81847286
0.45646095
0.111507416
0.43416595
0.76837444
0.3639946
0.58693695
0.51095676
0.5606985
0.2505703
" ], "text/plain": [ "\n", " 0.81847286\n", " 0.45646095\n", "0.111507416\n", " 0.43416595\n", " 0.76837444\n", " 0.3639946\n", " 0.58693695\n", " 0.51095676\n", " 0.5606985\n", " 0.2505703" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phases = events_vela.table[\"PHASE\"]\n", "\n", "# Let's take a look at the first 10 phases\n", "phases[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phasogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the phases, we can make a phasogram. A phasogram is a histogram of phases and it works exactly like any other histogram (you can set the binning, evaluate the errors based on the counts in each bin, etc)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "nbins = 30\n", "phase_min, phase_max = (0, 1)\n", "values, bin_edges = np.histogram(\n", " phases, range=(phase_min, phase_max), bins=nbins\n", ")\n", "bin_width = (phase_max - phase_min) / nbins\n", "\n", "bin_center = (bin_edges[:-1] + bin_edges[1:]) / 2\n", "\n", "\n", "# Poissonian uncertainty on each bin\n", "values_err = np.sqrt(values)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(\n", " x=bin_center,\n", " height=values,\n", " width=bin_width,\n", " color=\"#d53d12\",\n", " alpha=0.8,\n", " edgecolor=\"black\",\n", " yerr=values_err,\n", ")\n", "plt.xlim(0, 1)\n", "plt.xlabel(\"Phase\")\n", "plt.ylabel(\"Counts\")\n", "plt.title(\"Phaseogram with angular cut of {}\".format(on_radius));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's add some fancy additions to our phasogram: a patch on the ON- and OFF-phase regions and one for the background level." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Off events: 234\n" ] } ], "source": [ "# Evaluate background level\n", "off_phase_range = (0.7, 1.0)\n", "on_phase_range = (0.5, 0.6)\n", "\n", "mask_off = (off_phase_range[0] < phases) & (phases < off_phase_range[1])\n", "\n", "count_bkg = mask_off.sum()\n", "print(\"Number of Off events: {}\".format(count_bkg))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# bkg level normalized by the size of the OFF zone (0.3)\n", "bkg = count_bkg / nbins / (off_phase_range[1] - off_phase_range[0])\n", "\n", "# error on the background estimation\n", "bkg_err = (\n", " np.sqrt(count_bkg) / nbins / (off_phase_range[1] - off_phase_range[0])\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's redo the same plot for the basis\n", "plt.bar(\n", " x=bin_center,\n", " height=values,\n", " width=bin_width,\n", " color=\"#d53d12\",\n", " alpha=0.8,\n", " edgecolor=\"black\",\n", " yerr=values_err,\n", ")\n", "\n", "# Plot background level\n", "x_bkg = np.linspace(0, 1, 50)\n", "\n", "kwargs = {\"color\": \"black\", \"alpha\": 0.5, \"ls\": \"--\", \"lw\": 2}\n", "\n", "plt.plot(x_bkg, (bkg - bkg_err) * np.ones_like(x_bkg), **kwargs)\n", "plt.plot(x_bkg, (bkg + bkg_err) * np.ones_like(x_bkg), **kwargs)\n", "\n", "plt.fill_between(\n", " x_bkg, bkg - bkg_err, bkg + bkg_err, facecolor=\"grey\", alpha=0.5\n", ") # grey area for the background level\n", "\n", "# Let's make patches for the on and off phase zones\n", "on_patch = plt.axvspan(\n", " on_phase_range[0], on_phase_range[1], alpha=0.3, color=\"gray\", ec=\"black\"\n", ")\n", "\n", "off_patch = plt.axvspan(\n", " off_phase_range[0],\n", " off_phase_range[1],\n", " alpha=0.4,\n", " color=\"white\",\n", " hatch=\"x\",\n", " ec=\"black\",\n", ")\n", "\n", "# Legends \"ON\" and \"OFF\"\n", "plt.text(0.55, 5, \"ON\", color=\"black\", fontsize=17, ha=\"center\")\n", "plt.text(0.895, 5, \"OFF\", color=\"black\", fontsize=17, ha=\"center\")\n", "plt.xlabel(\"Phase\")\n", "plt.ylabel(\"Counts\")\n", "plt.xlim(0, 1)\n", "plt.title(\"Phasogram with angular cut of {}\".format(on_radius));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phase-resolved map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the phases are computed, we want to do a phase-resolved sky map : a map of the ON-phase events minus alpha times the OFF-phase events. Alpha is the ratio between the size of the ON-phase zone (here 0.1) and the OFF-phase zone (0.3).\n", "It's a map of the excess events in phase, which are the pulsed events." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "geom = WcsGeom.create(binsz=0.02 * u.deg, skydir=pos_target, width=\"5 deg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Let's create an ON-map and an OFF-map:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "on_map = Map.from_geom(geom)\n", "off_map = Map.from_geom(geom)\n", "\n", "events_vela_on = events_vela.select_parameter(\"PHASE\", on_phase_range)\n", "events_vela_off = events_vela.select_parameter(\"PHASE\", off_phase_range)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fill_map_counts(on_map, events_vela_on)\n", "fill_map_counts(off_map, events_vela_off)\n", "\n", "# Defining alpha as the ratio of the ON and OFF phase zones\n", "alpha = (on_phase_range[1] - on_phase_range[0]) / (\n", " off_phase_range[1] - off_phase_range[0]\n", ")\n", "\n", "# Create and fill excess map\n", "# The pulsed events are the difference between the ON-phase count and alpha times the OFF-phase count\n", "excess_map = on_map - off_map * alpha\n", "\n", "# Plot excess map\n", "excess_map.smooth(kernel=\"gauss\", width=0.2 * u.deg).plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phase-resolved spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also do a phase-resolved spectrum. In order to do that, there is the class PhaseBackgroundEstimator. In a phase-resolved analysis, the background is estimated in the same sky region but in the OFF-phase zone.\n", "\n", "We start by estimating the background with the class PhaseBackgroundEstimator. It takes the observations, the ON-region, and an ON- and OFF-phase zones (the same we defined for the phasogram and the phase-resolved map). It results in a gammapy.background.phase.PhaseBackgroundEstimator that serves as an input for other spectral analysis classes in Gammapy." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Defining an on-region around the pulsar to pass it to the background estimator\n", "on_region = CircleSkyRegion(pos_target, on_radius)\n", "\n", "# The PhaseBackgroundEstimator uses the OFF-phase in the ON-region to estimate the background\n", "bkg_estimator = PhaseBackgroundEstimator(\n", " observations=obs_list_vela,\n", " on_region=on_region,\n", " on_phase=on_phase_range,\n", " off_phase=off_phase_range,\n", ")\n", "bkg_estimator.run()\n", "bkg_estimate = bkg_estimator.result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rest of the analysis is the same as for a standard spectral analysis with Gammapy. All the specificity of a phase-resolved analysis is contained in the PhaseBackgroundEstimator, where the background is estimated in the ON-region OFF-phase rather than in an OFF-region.\n", "\n", "We can now extract a spectrum with the SpectrumExtraction class. It takes the reconstructed and the true energy binning. Both are expected to be a Quantity with unit energy, i.e. an array with an energy unit. EnergyBounds is a dedicated class to do it." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/github/adonath/gammapy/gammapy/spectrum/extract.py:232: RuntimeWarning: invalid value encountered in true_divide\n", " self.containment = new_aeff.data.data.value / self._aeff.data.data.value\n", "No thresholds defined for obs Info for OBS_ID = 111630\n", "- Start time: 59300.83\n", "- Pointing pos: RA 130.89 deg / Dec -44.63 deg\n", "- Observation duration: 1800.0 s\n", "- Dead-time fraction: 2.000 %\n", "\n" ] } ], "source": [ "etrue = EnergyBounds.equal_log_spacing(0.005, 10.0, 100, unit=\"TeV\")\n", "ereco = EnergyBounds.equal_log_spacing(0.01, 10, 30, unit=\"TeV\")\n", "\n", "extraction = SpectrumExtraction(\n", " observations=obs_list_vela,\n", " bkg_estimate=bkg_estimate,\n", " containment_correction=True,\n", " e_true=etrue,\n", " e_reco=ereco,\n", ")\n", "\n", "extraction.run()\n", "extraction.compute_energy_threshold(\n", " method_lo=\"energy_bias\", bias_percent_lo=20\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's a look at the files we just created with spectrum_observation." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "extraction.spectrum_observations[0].peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll fit a model to the spectrum with the `Fit` class. First we load a power law model with an initial value for the index and the amplitude and then wo do a likelihood fit. The fit results are printed below." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 119\n", "\ttotal stat : 7.27\n", "\n" ] } ], "source": [ "model = PowerLaw(\n", " index=4, amplitude=\"1.3e-9 cm-2 s-1 TeV-1\", reference=\"0.02 TeV\"\n", ")\n", "\n", "fit_range = (0.04 * u.TeV, 0.4 * u.TeV)\n", "\n", "for obs in extraction.spectrum_observations:\n", " obs.model = model\n", " obs.set_fit_energy_range(fit_range[0], fit_range[1])\n", "\n", "joint_fit = Fit(extraction.spectrum_observations)\n", "joint_result = joint_fit.run()\n", "\n", "model.parameters.covariance = joint_result.parameters.covariance\n", "print(joint_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you might want to do the stacking here even if in our case there is only one observation which makes it superfluous.\n", "We can compute flux points by fitting the norm of the global model in energy bands." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "e_edges = EnergyBounds.equal_log_spacing(0.04, 0.4, 7, u.TeV)\n", "\n", "from gammapy.spectrum import SpectrumDatasetOnOffStacker\n", "\n", "stacker = SpectrumDatasetOnOffStacker(extraction.spectrum_observations)\n", "dataset = stacker.run()\n", "\n", "dataset.model = model\n", "\n", "fpe = FluxPointsEstimator(datasets=[dataset], e_edges=e_edges)\n", "\n", "flux_points = fpe.run()\n", "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 1\n", "\n", "amplitude_ref = 0.57 * 19.4e-14 * u.Unit(\"1 / (cm2 s MeV)\")\n", "spec_model_true = PowerLaw(\n", " index=4.5, amplitude=amplitude_ref, reference=\"20 GeV\"\n", ")\n", "\n", "flux_points_dataset = FluxPointsDataset(data=flux_points, model=model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can plot." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "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=(8, 6))\n", "ax_spectrum, ax_residual = flux_points_dataset.peek()\n", "\n", "ax_spectrum.set_ylim([1e-14, 3e-11])\n", "ax_residual.set_ylim([-1.7, 1.7])\n", "\n", "spec_model_true.plot(\n", " ax=ax_spectrum,\n", " energy_range=fit_range,\n", " label=\"Reference model\",\n", " c=\"black\",\n", " linestyle=\"dashed\",\n", " energy_power=2,\n", ")\n", "\n", "ax_spectrum.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial suffers a bit from the lack of statistics: there were 9 Vela observations in the CTA DC1 while there is only one here. When done on the 9 observations, the spectral analysis is much better agreement between the input model and the gammapy fit." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }