{ "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/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": [ "### 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": "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 16.9 s, sys: 1.84 s, total: 18.7 s\n", "Wall time: 9.84 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": [ "### 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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: Unable to load the ciao_version module to determine version number- defaulting 'group' version to 0.0.0\n", " return f(*args, **kwds)\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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" ] }, "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/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 = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 291433\n", "Final fit statistic = 289598 at function evaluation 230\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 1834.36\n", " g0.fwhm 113.287 \n", " g0.ampl 0.439958 \n", "CPU times: user 11.1 s, sys: 83.2 ms, total: 11.2 s\n", "Wall time: 11.1 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 = 289598\n", "Final fit statistic = 289503 at function evaluation 383\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 95.2248\n", " g0.fwhm 106.247 \n", " g0.xpos 234.649 \n", " g0.ypos 195.635 \n", " g0.ampl 0.478146 \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/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 = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 289067\n", "Final fit statistic = 288814 at function evaluation 214\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 253.351\n", " g1.fwhm 3.76114 \n", " g1.ampl 16.7742 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288814\n", "Final fit statistic = 288735 at function evaluation 428\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 79.3409\n", " g1.fwhm 2.13994 \n", " g1.xpos 253.123 \n", " g1.ypos 197.983 \n", " g1.ampl 50.184 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288525\n", "Final fit statistic = 288460 at function evaluation 223\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 64.6649\n", " g2.fwhm 20.0902 \n", " g2.ampl 0.919413 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288460\n", "Final fit statistic = 288348 at function evaluation 333\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 112.159\n", " g2.fwhm 35.2131 \n", " g2.xpos 185.688 \n", " g2.ypos 197.289 \n", " g2.ampl 0.587145 \n", "CPU times: user 1min 1s, sys: 322 ms, total: 1min 1s\n", "Wall time: 1min 1s\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/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" ] }, { "data": { "text/html": [ "Table length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "
delstatglonglatsigma
float64float64float64float64
1825.10.31702-0.0973090.90238
768.39359.95-0.0503390.018175
386.671.2962-0.064230.29907
" ], "text/plain": [ "\n", "delstat glon glat sigma \n", "float64 float64 float64 float64 \n", "------- ------- --------- --------\n", " 1825.1 0.31702 -0.097309 0.90238\n", " 768.39 359.95 -0.050339 0.018175\n", " 386.67 1.2962 -0.06423 0.29907" ] }, "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", " # 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", "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" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [] } ], "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 }