{ "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-webpage/v0.13?urlpath=lab/tree/image_fitting_with_sherpa.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", "[image_fitting_with_sherpa.ipynb](../_static/notebooks/image_fitting_with_sherpa.ipynb) |\n", "[image_fitting_with_sherpa.py](../_static/notebooks/image_fitting_with_sherpa.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting 2D images with Sherpa\n", "\n", "### Introduction\n", "\n", "Sherpa is the X-ray satellite Chandra modeling and fitting application. It enables the user to construct complex models from simple definitions and fit those models to data, using a variety of statistics and optimization methods. \n", "The issues of constraining the source position and morphology are common in X- and Gamma-ray astronomy. \n", "This notebook will show you how to apply Sherpa to CTA data.\n", "\n", "Here we will set up Sherpa to fit the counts map and loading the ancillary images for subsequent use. A relevant test statistic for data with Poisson fluctuations is the one proposed by Cash (1979). The simplex (or Nelder-Mead) fitting algorithm is a good compromise between efficiency and robustness. The source fit is best performed in pixel coordinates.\n", "\n", "This tutorial has 2 important parts\n", "1. Generating the Maps\n", "2. The actual fitting with sherpa.\n", "\n", "Since sherpa deals only with 2-dim images, the first part of this tutorial shows how to prepare gammapy maps to make classical images." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Necessary imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import numpy as np\n", "from astropy.io import fits\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.data import DataStore\n", "from gammapy.irf import make_mean_psf\n", "from gammapy.maps import WcsGeom, MapAxis, Map\n", "from gammapy.cube import MapMaker, PSFKernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Generating the required Maps\n", "\n", "We first generate the required maps using 3 simulated runs on the Galactic center, exactly as in the [analysis_3d](analysis_3d.ipynb) tutorial.\n", "\n", "It is always advisable to make the maps on fine energy bins, and then sum them over to get an image." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define which data to use\n", "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps/\")\n", "obs_ids = [110380, 111140, 111159]\n", "observations = data_store.get_observations(obs_ids)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "energy_axis = MapAxis.from_edges(\n", " np.logspace(-1, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0),\n", " binsz=0.02,\n", " width=(10, 8),\n", " coordsys=\"GAL\",\n", " proj=\"CAR\",\n", " axes=[energy_axis],\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level [astropy.coordinates.builtin_frames.utils]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.3 s, sys: 1.94 s, total: 12.3 s\n", "Wall time: 12.3 s\n" ] } ], "source": [ "%%time\n", "maker = MapMaker(geom, offset_max=4.0 * u.deg)\n", "maps = maker.run(observations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making a PSF Map\n", "\n", "Make a PSF map and weigh it with the exposure at the source position to get a 2D PSF " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# mean PSF\n", "src_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "table_psf = make_mean_psf(observations, src_pos)\n", "\n", "# PSF kernel used for the model convolution\n", "psf_kernel = PSFKernel.from_table_psf(table_psf, geom, max_radius=\"0.3 deg\")\n", "\n", "# get the exposure at the source position\n", "exposure_at_pos = maps[\"exposure\"].get_by_coord(\n", " {\n", " \"lon\": src_pos.l.value,\n", " \"lat\": src_pos.b.value,\n", " \"energy\": energy_axis.center,\n", " }\n", ")\n", "\n", "# now compute the 2D PSF\n", "psf2D = psf_kernel.make_image(exposures=exposure_at_pos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make 2D images from 3D ones\n", "\n", "Since sherpa image fitting works only with 2-dim images,\n", "we convert the generated maps to 2D images using `run_images()` and save them as fits files. The exposure is weighed with the spectrum before averaging (assumed to be a power law by default).\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "maps = maker.run_images()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Path(\"analysis_3d\").mkdir(exist_ok=True)\n", "\n", "maps[\"counts\"].write(\"analysis_3d/counts_2D.fits\", overwrite=True)\n", "maps[\"background\"].write(\"analysis_3d/background_2D.fits\", overwrite=True)\n", "maps[\"exposure\"].write(\"analysis_3d/exposure_2D.fits\", overwrite=True)\n", "fits.writeto(\"analysis_3d/psf_2D.fits\", psf2D.data, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Analysis using sherpha\n", "\n", "### Read the maps and store them in a sherpa model\n", "\n", "We now have the prepared files which sherpa can read. \n", "This part of the notebook shows how to do image analysis using sherpa" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: imaging routines will not be available, \n", "failed to import sherpa.image.ds9_backend due to \n", "'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n", "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n" ] } ], "source": [ "import sherpa.astro.ui as sh\n", "\n", "sh.set_stat(\"cash\")\n", "sh.set_method(\"simplex\")\n", "\n", "sh.load_image(\"analysis_3d/counts_2D.fits\")\n", "sh.set_coord(\"logical\")\n", "\n", "sh.load_table_model(\"expo\", \"analysis_3d/exposure_2D.fits\")\n", "sh.load_table_model(\"bkg\", \"analysis_3d/background_2D.fits\")\n", "sh.load_psf(\"psf\", \"analysis_3d/psf_2D.fits\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To speed up this tutorial, we change the fit optimazation method to Levenberg-Marquardt and fix a required tolerance. This can make the fitting less robust, and in practise, you can skip this step unless you understand what is going on." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sh.set_method(\"levmar\")\n", "sh.set_method_opt(\"xtol\", 1e-5)\n", "sh.set_method_opt(\"ftol\", 1e-5)\n", "sh.set_method_opt(\"gtol\", 1e-5)\n", "sh.set_method_opt(\"epsfcn\", 1e-5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name = levmar\n", "ftol = 1e-05\n", "xtol = 1e-05\n", "gtol = 1e-05\n", "maxfev = None\n", "epsfcn = 1e-05\n", "factor = 100.0\n", "verbose = 0\n" ] } ], "source": [ "print(sh.get_method())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In principle one might first want to fit the background amplitude. However the background estimation method already yields the correct normalization, so we freeze the background amplitude to unity instead of adjusting it. The (smoothed) residuals from this background model are then computed and shown." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sh.set_full_model(bkg)\n", "bkg.ampl = 1\n", "sh.freeze(bkg)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid = Map.read(\"analysis_3d/counts_2D.fits\")\n", "resid.data = sh.get_data_image().y - sh.get_model_image().y\n", "resid_smooth = resid.smooth(width=4)\n", "resid_smooth.plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find and fit the brightest source\n", "We then find the position of the maximum in the (smoothed) residuals map, and fit a (symmetrical) Gaussian source with that initial position:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "yp, xp = np.unravel_index(\n", " np.nanargmax(resid_smooth.data), resid_smooth.data.shape\n", ")\n", "ampl = resid_smooth.get_by_pix((xp, yp))[0]\n", "\n", "# creates g0 as a gauss2d instance\n", "sh.set_full_model(bkg + psf(sh.gauss2d.g0) * expo)\n", "g0.xpos, g0.ypos = xp, yp\n", "sh.freeze(g0.xpos, g0.ypos) # fix the position in the initial fitting step\n", "\n", "# fix exposure amplitude so that typical exposure is of order unity\n", "expo.ampl = 1e-9\n", "sh.freeze(expo)\n", "sh.thaw(g0.fwhm, g0.ampl) # in case frozen in a previous iteration\n", "\n", "g0.fwhm = 10 # give some reasonable initial values\n", "g0.ampl = ampl" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 291433\n", "Final fit statistic = 289598 at function evaluation 43\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 1834.33\n", " g0.fwhm 112.773 +/- 2.20899 \n", " g0.ampl 0.441916 +/- 0.0126562 \n", "CPU times: user 1.94 s, sys: 31.1 ms, total: 1.97 s\n", "Wall time: 1.96 s\n" ] } ], "source": [ "%%time\n", "sh.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit all parameters of this Gaussian component, fix them and re-compute the residuals map." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 289598\n", "Final fit statistic = 289504 at function evaluation 26\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 94.5709\n", " g0.fwhm 107.444 +/- 2.10798 \n", " g0.xpos 235.835 +/- 1.23177 \n", " g0.ypos 195.201 +/- 1.28616 \n", " g0.ampl 0.472507 +/- 0.0131969 \n", "CPU times: user 1.23 s, sys: 15.5 ms, total: 1.24 s\n", "Wall time: 1.24 s\n" ] } ], "source": [ "%%time\n", "sh.thaw(g0.xpos, g0.ypos)\n", "sh.fit()\n", "sh.freeze(g0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid.data = sh.get_data_image().y - sh.get_model_image().y\n", "resid_smooth = resid.smooth(width=3)\n", "resid_smooth.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iteratively find and fit additional sources\n", "\n", "The residual map still shows the presence of additional components. Instantiate additional Gaussian components, and use them to iteratively fit sources, repeating the steps performed above for component g0. (The residuals map is shown after each additional source included in the model.) This would typically be done for many sources, but since this takes quite a bit of time, we demonstrate it for 3 iterations only here...." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# initialize components with fixed, zero amplitude\n", "for i in range(1, 3):\n", " model = sh.create_model_component(\"gauss2d\", \"g\" + str(i))\n", " model.ampl = 0\n", " sh.freeze(model)\n", "\n", "sources = [g0, g1, g2]\n", "sh.set_full_model(bkg + psf(g0 + g1 + g2) * expo)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 291453\n", "Final fit statistic = 289598 at function evaluation 43\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 1854.41\n", " g0.fwhm 112.768 +/- 2.20869 \n", " g0.ampl 0.441937 +/- 0.0126579 \n", "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 289598\n", "Final fit statistic = 289504 at function evaluation 26\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 94.5716\n", " g0.fwhm 107.444 +/- 2.10798 \n", " g0.xpos 235.835 +/- 1.23176 \n", " g0.ypos 195.201 +/- 1.28615 \n", " g0.ampl 0.472507 +/- 0.0131969 \n", "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 288955\n", "Final fit statistic = 288814 at function evaluation 20\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 140.418\n", " g1.fwhm 3.7736 +/- 0.479433 \n", " g1.ampl 16.6573 +/- 3.64526 \n", "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 288814\n", "Final fit statistic = 288736 at function evaluation 22\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 78.7651\n", " g1.fwhm 2.20896 +/- 0.565576 \n", " g1.xpos 253.097 +/- 0.182537 \n", " g1.ypos 197.91 +/- 0.183642 \n", " g1.ampl 46.2468 +/- 17.8069 \n", "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 288519\n", "Final fit statistic = 288448 at function evaluation 10\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 70.7811\n", " g2.fwhm 19.6011 +/- 1.2746 \n", " g2.ampl 0.958563 +/- 0.112208 \n", "Dataset = 1\n", "Method = levmar\n", "Statistic = cash\n", "Initial fit statistic = 288448\n", "Final fit statistic = 288330 at function evaluation 51\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 117.726\n", " g2.fwhm 35.4186 +/- 1.87188 \n", " g2.xpos 187.112 +/- 1.11498 \n", " g2.ypos 197.468 +/- 1.13358 \n", " g2.ampl 0.59605 +/- 0.0505517 \n", "CPU times: user 8.79 s, sys: 94.6 ms, total: 8.88 s\n", "Wall time: 8.87 s\n" ] } ], "source": [ "%%time\n", "for gs in sources:\n", " yp, xp = np.unravel_index(\n", " np.nanargmax(resid_smooth.data), resid_smooth.data.shape\n", " )\n", " ampl = resid_smooth.get_by_pix((xp, yp))[0]\n", " gs.xpos, gs.ypos = xp, yp\n", " gs.fwhm = 10\n", " gs.ampl = ampl\n", "\n", " sh.thaw(gs.fwhm)\n", " sh.thaw(gs.ampl)\n", " sh.fit()\n", "\n", " sh.thaw(gs.xpos)\n", " sh.thaw(gs.ypos)\n", " sh.fit()\n", " sh.freeze(gs)\n", "\n", " resid.data = sh.get_data_image().y - sh.get_model_image().y\n", " resid_smooth = resid.smooth(width=6)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid_smooth.plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating output table and Test Statistics estimation\n", "When adding a new source, one needs to check the significance of this new source. A frequently used method is the Test Statistics (TS). This is done by comparing the change of statistics when the source is included compared to the null hypothesis (no source ; in practice here we fix the amplitude to zero).\n", "\n", "$TS = Cstat(source) - Cstat(no source)$\n", "\n", "The criterion for a significant source detection is typically that it should improve the test statistic by at least 25 or 30. We have added only 3 sources to save time, but you should keep doing this till del(stat) is less than the required number." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "delstat glon glat sigma \n", "------- ------ --------- --------\n", " 1792.3 0.2933 -0.10598 0.91255\n", " 768.04 359.95 -0.051805 0.018761\n", " 405.36 1.2678 -0.06064 0.30082\n" ] } ], "source": [ "from astropy.stats import gaussian_fwhm_to_sigma\n", "from astropy.table import Table\n", "\n", "rows = []\n", "for g in sources:\n", " ampl = g.ampl.val\n", " g.ampl = 0\n", " stati = sh.get_stat_info()[0].statval\n", " g.ampl = ampl\n", " statf = sh.get_stat_info()[0].statval\n", " delstat = stati - statf\n", "\n", " geom = resid.geom\n", " # sherpa uses 1 based indexing\n", " coord = geom.pix_to_coord((g.xpos.val - 1, g.ypos.val - 1))\n", " pix_scale = geom.pixel_scales.mean().deg\n", " sigma = g.fwhm.val * pix_scale * gaussian_fwhm_to_sigma\n", " rows.append(\n", " dict(delstat=delstat, glon=coord[0], glat=coord[1], sigma=sigma)\n", " )\n", "\n", "table = Table(rows=rows, names=rows[0])\n", "for name in table.colnames:\n", " table[name].format = \".5g\"\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. Keep adding sources till there are no more significat ones in the field. How many Gaussians do you need?\n", "2. Use other morphologies for the sources (eg: disk, shell) rather than only Gaussian.\n", "3. Compare the TS between different models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More about sherpa\n", "\n", "These are good resources to learn more about Sherpa:\n", "\n", "* https://python4astronomers.github.io/fitting/fitting.html\n", "* https://github.com/DougBurke/sherpa-standalone-notebooks\n", "\n", "You could read over the examples there, and try to apply a similar analysis to this dataset here to practice.\n", "\n", "If you want a deeper understanding of how Sherpa works, then these proceedings are good introductions:\n", "\n", "* http://conference.scipy.org/proceedings/scipy2009/paper_8/full_text.pdf\n", "* http://conference.scipy.org/proceedings/scipy2011/pdfs/brefsdal.pdf" ] } ], "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 }