{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `photutils` Aperture Photometry\n", "\n", "If you've followed the lecture materials, there are two major ways to do aperture photometry:\n", "\n", "1. Find stars using `DAOStarFinder`. Generate sky map by SExtractor. Subtract it from the image. Do the usual aperture sum to the star.\n", "2. Find stars using `DAOStarFinder`. Get aperture sum from aperture, get sky from annulus. Subtract sky from the aperture sum.\n", "\n", "I will demonstrate both here using the M13 image we were using." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Common code\n", "\n", "For brevity, I want to import all necessary packages first, load the image, and do `DAOStarFinder` and make apertures first, because these will be used for both methods.\n", "\n", "The image has already been converted to \"electrons per second\" unit, so the process of error estimation is somewhat tricky. So, let me just multiply 10^4 and integerize the pixels to mimic the original ADU image. The total read noise is about 5 electrons and gain is 7 e/ADU.\n" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from astropy.io import fits\n", "from astropy.stats import sigma_clip, sigma_clipped_stats\n", "from photutils import DAOStarFinder, Background2D, SigmaClip, SExtractorBackground\n", "from photutils import CircularAperture as CircAp\n", "from photutils import CircularAnnulus as CircAn\n", "from photutils import aperture_photometry as APPHOT\n", "\n", "#%%\n", "hdu = fits.open('HST_Tutorial/M13.fits')\n", "img = hdu[0].data[900:1200, 900:1200]\n", "# if image value < 10^(-6), replace the pixel as 10^(-6)\n", "img[img < 1.e-6] = 1.e-6\n", "\n", "# multiply 10**4 and integrize to mimic ADU\n", "img *= 1.e4\n", "img = img.astype(int)\n", "ronoise = 5 # electrons\n", "gain = 7 # e/ADU\n", "\n", "FWHM = 2.5\n", "sky_th = 500 # sky_th * sky_sigma will be used for detection lower limit\n", "sky_a, sky_m, sky_s = sigma_clipped_stats(img) # 3-sigma, 5 iters\n", "thresh = sky_th * sky_s\n", "\n", "find = DAOStarFinder(fwhm=FWHM, threshold=thresh,\n", " sharplo=0.2, sharphi=1.0, # default values \n", " roundlo=-1.0, roundhi=1.0, # default values\n", " sigma_radius=1.5, # default values\n", " ratio=1.0, # 1.0: circular gaussian\n", " exclude_border=True) # To exclude sources near edges\n", "found = find(img)\n", "\n", "N_star = len(found)\n", "# save XY coordinates and aperture:\n", "coord = (found['xcentroid'], found['ycentroid']) \n", "apert = CircAp(positions=coord, r=max(3, FWHM))\n", "annul = CircAn(positions=coord, r_in=4*FWHM, r_out=6*FWHM)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **TIP**: It's best to use aperture of `max(3, 1.0 * FWHM)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We must also transform the aperture sum (in ADU) to instrument magnitude. To do so, let me define a function, as well as `sky_fit`:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mag_inst(flux, ferr):\n", " ''' Returns magnitude from flux.\n", " '''\n", " m_inst = -2.5 * np.log10(flux)\n", " merr = 2.5/np.log(10) * ferr / flux\n", " return m_inst, merr\n", "\n", "def sky_fit(all_sky, method='mode', sky_nsigma=3, sky_iter=5, \\\n", " mode_option='sex', med_factor=2.5, mean_factor=1.5):\n", " '''\n", " Estimate sky from given sky values.\n", "\n", " Parameters\n", " ----------\n", " all_sky : ~numpy.ndarray\n", " The sky values as numpy ndarray format. It MUST be 1-d for proper use.\n", " method : {\"mean\", \"median\", \"mode\"}, optional\n", " The method to estimate sky value. You can give options to \"mode\"\n", " case; see mode_option.\n", " \"mode\" is analogous to Mode Estimator Background of photutils.\n", " sky_nsigma : float, optinal\n", " The input parameter for sky sigma clipping.\n", " sky_iter : float, optinal\n", " The input parameter for sky sigma clipping.\n", " mode_option : {\"sex\", \"IRAF\", \"MMM\"}, optional.\n", " sex == (med_factor, mean_factor) = (2.5, 1.5)\n", " IRAF == (med_factor, mean_factor) = (3, 2)\n", " MMM == (med_factor, mean_factor) = (3, 2)\n", "\n", " Returns\n", " -------\n", " sky : float\n", " The estimated sky value within the all_sky data, after sigma clipping.\n", " std : float\n", " The sample standard deviation of sky value within the all_sky data,\n", " after sigma clipping.\n", " nsky : int\n", " The number of pixels which were used for sky estimation after the\n", " sigma clipping.\n", " nrej : int\n", " The number of pixels which are rejected after sigma clipping.\n", " -------\n", "\n", " '''\n", " sky = all_sky.copy()\n", " if method == 'mean':\n", " return np.mean(sky), np.std(sky, ddof=1)\n", "\n", " elif method == 'median':\n", " return np.median(sky), np.std(sky, ddof=1)\n", "\n", " elif method == 'mode':\n", " sky_clip = sigma_clip(sky, sigma=sky_nsigma, iters=sky_iter)\n", " sky_clipped= sky[np.invert(sky_clip.mask)]\n", " nsky = np.count_nonzero(sky_clipped)\n", " mean = np.mean(sky_clipped)\n", " med = np.median(sky_clipped)\n", " std = np.std(sky_clipped, ddof=1)\n", " nrej = len(all_sky) - len(sky_clipped)\n", "\n", " if nrej < 0:\n", " raise ValueError('nrej < 0: check the code')\n", "\n", " if nrej > nsky: # rejected > survived\n", " raise Warning('More than half of the pixels rejected.')\n", "\n", " if mode_option == 'IRAF':\n", " if (mean < med):\n", " sky = mean\n", " else:\n", " sky = 3 * med - 2 * mean\n", "\n", " elif mode_option == 'MMM':\n", " sky = 3 * med - 2 * mean\n", "\n", " elif mode_option == 'sex':\n", " if (mean - med) / std > 0.3:\n", " sky = med\n", " else:\n", " sky = (2.5 * med) - (1.5 * mean)\n", " else:\n", " raise ValueError('mode_option not understood')\n", "\n", " return sky, std, nsky, nrej\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Using SExtractor\n" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "image/png": 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WG9YI7mMEvZscxk+gCYy520ZoV+HnKiSpIabpYSvh+SM7ZTFYI6ECXiLxqosS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mCodwD4OeRBjnuZ+az++xyAvQNHjSk7JFuq0+QGoyUIFFctR7+bAQRU/C3XFQoY/0UtyHC5mdJc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\ndMzGvouavYZxe+NGI8lkjtocsm55FqfnwXn814LpS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f014754a400>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " id xcenter ycenter aperture_sum aperture_sum_err\n", " pix pix \n", "--- ------------------ ------------------ ------------- ----------------\n", " 1 133.22403833586998 2.083764297528783 39422.682536 94.6782836987\n", " 2 20.29308459255889 3.379328460375729 350919.980755 236.188874518\n", " 3 139.10054870433788 18.133488862654865 55865.4832182 106.480792646\n", " 4 21.738099399931475 35.7746203491017 78191.6797728 128.326530097\n", " 5 32.67023094901688 55.29096540534828 87838.1883383 132.710939968\n", " 6 179.9388019681347 60.297706747190695 54721.8098395 105.033413503\n", " 7 201.64762230868146 62.64675355972699 144267.244622 153.779404664\n", " 8 87.36978548680972 66.59588726615048 116346.552834 143.032418684\n", " 9 154.0115559009857 74.94082376649413 126753.034967 146.618926077\n", " 10 99.52406103805424 84.88554016621657 117199.641601 141.647778147\n", "... ... ... ... ...\n", " 25 10.672580858177014 230.95616327234617 166372.530172 166.173266553\n", " 26 19.89493803495595 239.29070943288687 99378.7189224 134.304790778\n", " 27 40.675842245545134 244.70772268105486 89127.2174576 128.945467771\n", " 28 108.0482498707155 257.4517408016522 145333.205551 157.113856973\n", " 29 134.25960211203284 262.775608432513 86866.4496005 128.820104864\n", " 30 283.3751827356947 268.6906175695615 51753.3619609 108.266451106\n", " 31 214.7089032474912 278.1125426710156 127367.018763 149.658273435\n", " 32 186.32335334333374 288.86130589787365 344317.218432 231.411648314\n", " 33 126.06473259706331 291.5315137037525 291752.399039 214.56820529\n", " 34 105.38571145988207 295.63078716857996 93702.0935619 132.96460656\n", "Length = 34 rows\n" ] }, { "data": { "image/png": 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ADgPYxsw3E9H/A/B/AXyQtvlNzHyIiJYCeJyZNxFRAMDzAAYBaAJwBzO35Duu3IciqI4W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\no6q7eLuLqt5GeBIoRDSRiHY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f01451d1780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "box = np.array((30, 30)).astype(int) # 30 by 30 box, so 10 by 10 mesh.\n", "filt = np.array((2*FWHM, 2*FWHM)).astype(int)\n", "sigma_clip = SigmaClip(sigma=3., iters=10)\n", "\n", "# The SExtractor method\n", "bkg_sex = Background2D(img, \n", " box_size=box, \n", " filter_size=filt,\n", " sigma_clip=sigma_clip, \n", " bkg_estimator=SExtractorBackground())\n", "\n", "# sky_subtracted\n", "img_ss = img - bkg_sex.background\n", "\n", "# pixel-wise errors\n", "img_em = np.sqrt(img * gain + ronoise**2 \n", " + (bkg_sex.background_rms * gain)**2) / gain\n", "# This is used to make errormap in ADU scale. \n", "\n", "plt.imshow(img_ss/img_em, vmin=0, vmax=100, origin='lower')\n", "plt.title('pixel-wise signal to noise ratio')\n", "plt.colorbar()\n", "plt.show()\n", "\n", "# do photometry\n", "phot_sex = APPHOT(img_ss, apert, error=img_em)\n", "phot_sex.pprint(max_width=200)\n", "mag_sex, merr_sex = mag_inst(phot_sex['aperture_sum'], phot_sex['aperture_sum_err'])\n", "for i in range(0, N_star):\n", " plt.errorbar(i, mag_sex[i], yerr=merr_sex[i], marker='x', ms=10, capsize=3)\n", "plt.xlabel('Star ID')\n", "plt.ylabel('Instrumental mag')\n", "plt.grid(ls=':')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Using Circular Annulus\n" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f01454faba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mag_ann = np.zeros(N_star)\n", "merr_ann = np.zeros(N_star)\n", "\n", "\n", "# aperture sum\n", "apert_sum = APPHOT(img, apert, method='exact')['aperture_sum']\n", "ap_area = apert.area()\n", "\n", "for i in range(0, N_star):\n", " # sky estimation\n", " mask_annul = (annul.to_mask(method='center'))[i]\n", " sky_apply = mask_annul.apply(img)\n", " sky_non0 = np.nonzero(sky_apply)\n", " sky_pixel = sky_apply[sky_non0]\n", " msky, sky_std, nsky, nrej = sky_fit(sky_pixel, method='mode', mode_option='sex')\n", " \n", " flux_star = apert_sum[i] - msky * ap_area # total - sky\n", " flux_err = np.sqrt(apert_sum[i] * gain # Poissonian (star + sky)\n", " + ap_area * ronoise**2 # Gaussian\n", " + (ap_area * (gain * sky_std))**2 / nsky ) \n", " mag_ann[i], merr_ann[i] = mag_inst(flux_star, flux_err)\n", " plt.errorbar(i, mag_ann[i], yerr=merr_ann[i], marker='x', ms=10, capsize=3)\n", "plt.xlabel('Star ID')\n", "plt.ylabel('Instrumental mag')\n", "plt.grid(ls=':')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Comparison (SExtractor VS Annulus)\n", "\n" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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A6uuTuzBdewXQqqo6X5nUr+501xhCdSfSCSejxhjLaKV0ZPhYSWb6TvbM+lDt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\nVO2m6o6GdTA+KSgoSLcEX1jdqcXqTj2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VdzSsg/FJ9+7dmTy4gLkXj2bdF9up2rYjYc7l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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0148bea0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mag_diff = mag_ann - mag_sex\n", "merr_diff = np.sqrt(merr_ann**2 + merr_sex**2)\n", "med, mean = np.median(mag_diff), np.mean(mag_diff)\n", "\n", "plt.errorbar(np.arange(N_star), mag_diff, yerr=merr_diff, marker='x', ms=10,capsize=3, ls='')\n", "plt.xlabel('Star ID')\n", "plt.ylabel('$\\Delta m = m_{ann}-m_{sex}$')\n", "plt.title('Comparison (SExtractor VS Annulus)')\n", "plt.grid(ls=':')\n", "plt.ylim(-0.03, 0.03)\n", "\n", "plt.axhline(med , color='b', ls=':', label='med ({:.5f})'.format(med))\n", "plt.axhline(mean, color='r', ls=':', label='mean ({:.5f})'.format(mean))\n", "plt.legend()\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the graph, you can see that both method gives similar result, with median of ~ 0.003 magnitude (0.3%) difference, that is, annulus method regards the target fainter (higher magnitude).\n", "\n", "* **Question**: Check whether this holds true for even fainter stars." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }