{ "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/v0.10?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", "import numpy as np\n", "from astropy.io import fits\n", "import astropy.units as u\n", "from astropy.wcs import WCS\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.extern.pathlib import Path\n", "from gammapy.data import DataStore\n", "from gammapy.irf import EnergyDispersion, make_mean_psf\n", "from gammapy.maps import WcsGeom, MapAxis, Map\n", "from gammapy.cube import MapMaker, PSFKernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate 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": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.1 s, sys: 1.89 s, total: 12 s\n", "Wall time: 12 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 `make_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.make_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": [ "### 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": [ "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": 9, "metadata": {}, "outputs": [], "source": [ "sh.set_full_model(bkg)\n", "bkg.ampl = 1\n", "sh.freeze(bkg)" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "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": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jer/anaconda/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 = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 291457\n", "Final fit statistic = 289635 at function evaluation 224\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 1822.4\n", " g0.fwhm 113.992 \n", " g0.ampl 0.436518 \n", "CPU times: user 10.3 s, sys: 112 ms, total: 10.5 s\n", "Wall time: 10.4 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": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 289635\n", "Final fit statistic = 289539 at function evaluation 362\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 95.6137\n", " g0.fwhm 106.655 \n", " g0.xpos 234.549 \n", " g0.ypos 195.511 \n", " g0.ampl 0.475583 \n" ] } ], "source": [ "sh.thaw(g0.xpos, g0.ypos)\n", "sh.fit()\n", "sh.freeze(g0)" ] }, { "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": [ "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", "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 takes some time..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# initialize components with fixed, zero amplitude\n", "for i in range(1, 10):\n", " model = sh.create_model_component(\"gauss2d\", \"g\" + str(i))\n", " model.ampl = 0\n", " sh.freeze(model)\n", "\n", "gs = [g0, g1, g2]\n", "sh.set_full_model(bkg + psf(g0 + g1 + g2) * expo)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jer/anaconda/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 = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 289100\n", "Final fit statistic = 288848 at function evaluation 223\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 252.422\n", " g1.fwhm 3.81745 \n", " g1.ampl 16.3083 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288848\n", "Final fit statistic = 288769 at function evaluation 488\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 79.225\n", " g1.fwhm 2.22087 \n", " g1.xpos 253.123 \n", " g1.ypos 197.983 \n", " g1.ampl 46.6338 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288558\n", "Final fit statistic = 288493 at function evaluation 223\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 65.0678\n", " g2.fwhm 20.1436 \n", " g2.ampl 0.91736 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288493\n", "Final fit statistic = 288381 at function evaluation 341\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 112.12\n", " g2.fwhm 35.1696 \n", " g2.xpos 185.717 \n", " g2.ypos 197.308 \n", " g2.ampl 0.588381 \n", "CPU times: user 1min 6s, sys: 706 ms, total: 1min 7s\n", "Wall time: 1min 8s\n" ] } ], "source": [ "%%time\n", "for i in range(1, len(gs)):\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[i].xpos, gs[i].ypos = xp, yp\n", " gs[i].fwhm = 10\n", " gs[i].ampl = ampl\n", "\n", " sh.thaw(gs[i].fwhm)\n", " sh.thaw(gs[i].ampl)\n", " sh.fit()\n", "\n", " sh.thaw(gs[i].xpos)\n", " sh.thaw(gs[i].ypos)\n", " sh.fit()\n", " sh.freeze(gs[i])\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": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jer/anaconda/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" ] }, { "data": { "text/html": [ "Table length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "
delstatglonglatsigma
float64float64float64float64
1813.60.29901-0.0797840.90585
770.68359.93-0.0303430.018862
387.461.2757-0.0438370.2987
" ], "text/plain": [ "\n", "delstat glon glat sigma \n", "float64 float64 float64 float64 \n", "------- ------- --------- --------\n", " 1813.6 0.29901 -0.079784 0.90585\n", " 770.68 359.93 -0.030343 0.018862\n", " 387.46 1.2757 -0.043837 0.2987" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from astropy.stats import gaussian_fwhm_to_sigma\n", "from astropy.table import Table\n", "\n", "rows = []\n", "for g in gs:\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", " coord = geom.pix_to_coord((g.xpos.val, g.ypos.val))\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", "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": "3.6.6" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }