{ "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/v0.15?urlpath=lab/tree/image_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", "[image_analysis.ipynb](../_static/notebooks/image_analysis.ipynb) |\n", "[image_analysis.py](../_static/notebooks/image_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting 2D images with Gammapy\n", "\n", "Gammapy does not have any special handling for 2D images, but treats them as a subset of maps. Thus, classical 2D image analysis can be done in 2 independent ways: \n", "\n", "1. Using the sherpa package\n", "\n", "2. Within gammapy, by assuming 2D analysis to be a sub-set of the generalised `maps`. Thus, analysis should proceed exactly as demonstrated in [analysis_3d.ipynb](analysis_3d.ipynb), taking care of a few things that we mention in this tutorial\n", "\n", "We consider 2D `images` to be a special case of 3D `maps`, ie, maps with only one energy bin. This is a major difference while analysing in `sherpa`, where the `maps` must not contain any energy axis. In this tutorial, we do a classical image analysis using three example observations of the Galactic center region with CTA - i.e., study the source flux and morphology.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.stats import norm\n", "\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.convolution import Tophat2DKernel\n", "from regions import CircleSkyRegion\n", "\n", "from gammapy.detect import compute_lima_on_off_image\n", "from gammapy.data import DataStore\n", "from gammapy.irf import make_mean_psf\n", "from gammapy.maps import Map, MapAxis, WcsGeom\n", "from gammapy.cube import (\n", " SafeMaskMaker,\n", " PSFKernel,\n", " MapDataset,\n", " MapDatasetMaker,\n", " MapDatasetOnOff,\n", " RingBackgroundMaker,\n", ")\n", "from gammapy.modeling.models import (\n", " SkyModel,\n", " BackgroundModel,\n", " PowerLaw2SpectralModel,\n", " PointSpatialModel,\n", ")\n", "from gammapy.modeling import Fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare modeling input data\n", "\n", "### The counts, exposure and the background maps\n", "This is the same drill - use `DataStore` object to access the CTA observations and retrieve a list of observations by passing the observations IDs to the `.get_observations()` method, then use `MapMaker` to make the maps." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data store:\n", "HDU index table:\n", "BASE_DIR: /Users/adonath/data/gammapy-data/cta-1dc/index/gps\n", "Rows: 24\n", "OBS_ID: 110380 -- 111630\n", "HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']\n", "HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_3gauss']\n", "\n", "\n", "Observation table:\n", "Observatory name: 'N/A'\n", "Number of observations: 4\n", "\n" ] } ], "source": [ "# Define which data to use and print some information\n", "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps/\")\n", "data_store.info()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$2 \\; \\mathrm{h}$$" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_store.obs_table[\"ONTIME\"].quantity.sum().to(\"hour\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Select some observations from these dataset by hand\n", "obs_ids = [110380, 111140, 111159]\n", "observations = data_store.get_observations(obs_ids)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "emin, emax = [0.1, 10] * u.TeV\n", "energy_axis = MapAxis.from_bounds(\n", " emin.value, emax.value, 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": "markdown", "metadata": {}, "source": [ "Note that even when doing a 2D analysis, it is better to use fine energy bins in the beginning and then sum them over. This is to ensure that the background shape can be approximated by a power law function in each energy bin. The `run_images()` can be used to compute maps in fine bins and then squash them to have one bin. This can be done by specifying `keep_dims = True`. This will compute a summed counts and background maps, and a spectral weighted exposure map." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "stacked = MapDataset.create(geom)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.59 s, sys: 934 ms, total: 4.53 s\n", "Wall time: 4.75 s\n" ] } ], "source": [ "%%time\n", "maker = MapDatasetMaker(selection=[\"counts\", \"exposure\", \"background\"])\n", "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=4.0 * u.deg)\n", "\n", "datasets = []\n", "\n", "for obs in observations:\n", " cutout = stacked.cutout(obs.pointing_radec, width=\"8 deg\")\n", " dataset = maker.run(cutout, obs)\n", " dataset = maker_safe_mask.run(dataset, obs)\n", " datasets.append(dataset)\n", " stacked.stack(dataset)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "spectrum = PowerLaw2SpectralModel(index=2)\n", "dataset_2d = stacked.to_image(spectrum=spectrum)\n", "\n", "maps2D = {\n", " \"counts\": dataset_2d.counts,\n", " \"exposure\": dataset_2d.exposure,\n", " \"background\": dataset_2d.background_model.map,\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a typical 2D analysis, using an energy dispersion usually does not make sense. A PSF map can be made as in the regular 3D case, taking care to weight it properly with the spectrum." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# mean PSF\n", "geom2d = maps2D[\"exposure\"].geom\n", "src_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "table_psf = make_mean_psf(observations, src_pos)\n", "\n", "table_psf_2d = table_psf.table_psf_in_energy_band(\n", " (emin, emax), spectrum=spectrum\n", ")\n", "\n", "# PSF kernel used for the model convolution\n", "psf_kernel = PSFKernel.from_table_psf(\n", " table_psf_2d, geom2d, max_radius=\"0.3 deg\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the analysis proceeds as usual. Just take care to use the proper geometry in this case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define a mask" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "region = CircleSkyRegion(center=src_pos, radius=0.6 * u.deg)\n", "mask = Map.from_geom(geom2d, data=geom2d.region_mask([region]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling the source\n", "\n", "This is the important thing to note in this analysis. Since modeling and fitting in `~gammapy.maps` needs to have a combination of spectral models, we have to use a dummy Powerlaw as for the spectral model and fix its index to 2. Since we are interested only in the integral flux, we will use the `PowerLaw2SpectralModel` model which directly fits an integral flux." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "spatial_model = PointSpatialModel(\n", " lon_0=\"0.01 deg\", lat_0=\"0.01 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLaw2SpectralModel(\n", " emin=emin, emax=emax, index=2.0, amplitude=\"3e-12 cm-2 s-1\"\n", ")\n", "model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)\n", "model.parameters[\"index\"].frozen = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling the background\n", "\n", "Gammapy fitting framework assumes the background to be an integrated model.\n", "Thus, we will define the background as a model, and freeze its parameters for now." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "background_model = BackgroundModel(maps2D[\"background\"])\n", "background_model.parameters[\"norm\"].frozen = True\n", "background_model.parameters[\"tilt\"].frozen = True" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dataset = MapDataset(\n", " models=model,\n", " counts=maps2D[\"counts\"],\n", " exposure=maps2D[\"exposure\"],\n", " background_model=background_model,\n", " mask_fit=mask,\n", " psf=psf_kernel,\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.02 s, sys: 15 ms, total: 1.04 s\n", "Wall time: 1.04 s\n" ] } ], "source": [ "%%time\n", "fit = Fit([dataset])\n", "result = fit.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see the actual best-fit parameters, do a print on the result" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", " name value error unit min max frozen\n", "--------- ---------- ----- -------- ---------- --------- ------\n", " lon_0 -5.363e-02 nan deg nan nan False\n", " lat_0 -5.057e-02 nan deg -9.000e+01 9.000e+01 False\n", "amplitude 4.294e-11 nan cm-2 s-1 nan nan False\n", " index 2.000e+00 nan nan nan True\n", " emin 1.000e-01 nan TeV nan nan True\n", " emax 1.000e+01 nan TeV nan nan True\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=9\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namevalueerrorunitminmaxfrozen
str9float64float64str8float64float64bool
lon_0-5.363e-023.261e-03degnannanFalse
lat_0-5.057e-023.308e-03deg-9.000e+019.000e+01False
amplitude4.294e-111.747e-12cm-2 s-1nannanFalse
index2.000e+000.000e+00nannanTrue
emin1.000e-010.000e+00TeVnannanTrue
emax1.000e+010.000e+00TeVnannanTrue
norm1.000e+000.000e+000.000e+00nanTrue
tilt0.000e+000.000e+00nannanTrue
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str8 float64 float64 bool \n", "--------- ---------- --------- -------- ---------- --------- ------\n", " lon_0 -5.363e-02 3.261e-03 deg nan nan False\n", " lat_0 -5.057e-02 3.308e-03 deg -9.000e+01 9.000e+01 False\n", "amplitude 4.294e-11 1.747e-12 cm-2 s-1 nan nan False\n", " index 2.000e+00 0.000e+00 nan nan True\n", " emin 1.000e-01 0.000e+00 TeV nan nan True\n", " emax 1.000e+01 0.000e+00 TeV nan nan True\n", " norm 1.000e+00 0.000e+00 0.000e+00 nan True\n", " tilt 0.000e+00 0.000e+00 nan nan True\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/github/adonath/gammapy/gammapy/modeling/parameter.py:528: RuntimeWarning: invalid value encountered in true_divide\n", " return self.covariance / np.outer(err, err)\n" ] }, { "data": { "text/plain": [ "array([[ 1. , 0.07391212, -0.02417381, nan],\n", " [ 0.07391212, 1. , -0.03242375, nan],\n", " [-0.02417381, -0.03242375, 1. , nan],\n", " [ nan, nan, nan, nan]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.correlation[:4, :4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classical Ring Background Analysis\n", "\n", "No we repeat the same analysis but using a classical ring background estimation. We define an exclusion mask and then use the `~gammapy.cube.RingBackgroundMaker`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geom_image = geom.to_image().to_cube([energy_axis.squash()])\n", "\n", "regions = CircleSkyRegion(center=spatial_model.position, radius=0.3 * u.deg)\n", "\n", "exclusion_mask = Map.from_geom(geom_image)\n", "exclusion_mask.data = geom_image.region_mask([regions], inside=False)\n", "exclusion_mask.sum_over_axes().plot();" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "ring_maker = RingBackgroundMaker(\n", " r_in=\"0.3 deg\", width=\"0.3 deg\", exclusion_mask=exclusion_mask\n", ")" ] }, { "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/astropy/units/quantity.py:464: RuntimeWarning: invalid value encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 290 ms, sys: 100 ms, total: 390 ms\n", "Wall time: 396 ms\n" ] } ], "source": [ "%%time\n", "stacked_on_off = MapDatasetOnOff.create(geom=geom_image)\n", "\n", "for dataset in datasets:\n", " dataset_image = dataset.to_image()\n", " dataset_on_off = ring_maker.run(dataset_image)\n", " stacked_on_off.stack(dataset_on_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the estimate of the ring background we compute a Li&Ma significance image: " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "scale = geom.pixel_scales[0].to(\"deg\")\n", "# Using a convolution radius of 0.05 degrees\n", "theta = 0.15 * u.deg / scale\n", "tophat = Tophat2DKernel(theta)\n", "tophat.normalize(\"peak\")\n", "\n", "lima_maps = compute_lima_on_off_image(\n", " stacked_on_off.counts,\n", " stacked_on_off.counts_off,\n", " stacked_on_off.acceptance,\n", " stacked_on_off.acceptance_off,\n", " tophat,\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "significance_map = lima_maps[\"significance\"]\n", "excess_map = lima_maps[\"excess\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is what the excess and significance look like:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " ,\n", " )" ] }, "execution_count": 23, "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=(10, 10))\n", "ax1 = plt.subplot(221, projection=significance_map.geom.wcs)\n", "ax2 = plt.subplot(222, projection=excess_map.geom.wcs)\n", "\n", "ax1.set_title(\"Significance map\")\n", "significance_map.get_image_by_idx((0,)).plot(ax=ax1, add_cbar=True)\n", "\n", "ax2.set_title(\"Excess map\")\n", "excess_map.get_image_by_idx((0,)).plot(ax=ax2, add_cbar=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we take a look at the signficance distribution outside the exclusion region:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# create a 2D mask for the images\n", "significance_map_off = significance_map * exclusion_mask" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit results: mu = -0.04, std = 1.27\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "significance_all = significance_map.data[np.isfinite(significance_map.data)]\n", "significance_off = significance_map_off.data[\n", " np.isfinite(significance_map_off.data)\n", "]\n", "\n", "plt.hist(\n", " significance_all,\n", " density=True,\n", " alpha=0.5,\n", " color=\"red\",\n", " label=\"all bins\",\n", " bins=21,\n", ")\n", "\n", "plt.hist(\n", " significance_off,\n", " density=True,\n", " alpha=0.5,\n", " color=\"blue\",\n", " label=\"off bins\",\n", " bins=21,\n", ")\n", "\n", "# Now, fit the off distribution with a Gaussian\n", "mu, std = norm.fit(significance_off)\n", "x = np.linspace(-8, 8, 50)\n", "p = norm.pdf(x, mu, std)\n", "plt.plot(x, p, lw=2, color=\"black\")\n", "plt.legend()\n", "plt.xlabel(\"Significance\")\n", "plt.yscale(\"log\")\n", "plt.ylim(1e-5, 1)\n", "xmin, xmax = np.min(significance_all), np.max(significance_all)\n", "plt.xlim(xmin, xmax)\n", "\n", "print(f\"Fit results: mu = {mu:.2f}, std = {std:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "1. Update the exclusion mask in the ring background example by thresholding the significance map and re-run the background estimator \n", "1. Plot residual maps as done in the [analysis_3d](analysis_3d.ipynb) notebook\n", "1. Iteratively add and fit sources" ] } ], "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": 4 }