{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PSF normalization\n", "\n", "Let us assume that we have reduced an observation, for which we have determined the PSF by stacking the flux of point-like sources. The PSF we obtain will not be as high S/N as the instrumental PSF that has been determined by the instrument team. Moreover, it is likely to be fattened due to the some small pointing errors. We need to find out what fraction of a point-like flux the PSF we have determined represent. In order to do this, we use the growth curve of the theoretical PSF that has been determine by the instrument team, and compare it to the growth curve we determine from our PSF.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import what we will need. \n", "%matplotlib inline\n", "import numpy as np\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "from astropy.io import ascii as asciiread\n", "from matplotlib import pyplot as plt\n", "from scipy import interpolate \n", "from scipy import special\n", "from scipy import signal\n", "from scipy import fftpack" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2) Real data: MIPS observations\n", "\n", "We will look at a real stack of point sources in the MIPS ELAIS-N2 observations, and try to find its normalization factor. \n", "\n", "Let's load the stacked PSF:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stackhd_im = fits.open('./data/70101880.70101880-0.MIPS.1.help.fits')\n", "stackhd = fits.open('./data/70101880.70101880-0.MIPS.1.help.psf.fits')\n", "psf = stackhd[1].data\n", "hd = stackhd[1].header\n", "cpix=np.int((hd['NAXIS1']+1)/2.0)\n", "rad=40\n", "plt.imshow(psf[cpix-rad-1:cpix+rad,cpix-rad-1:cpix+rad])\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "resol= np.abs(stackhd[1].header['CD1_1'])/np.abs(stackhd_im[1].header['CD1_1'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resol" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "hdp = stackhd[2].header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in MIPS 24 $\\mathrm{\\mu m}$ Instrumental PSF\n", "We take the instrumental PSF from [Gonzalo J.Aniano's webpage](http://www.astro.princeton.edu/~ganiano/Kernels/Ker_2017/PSF_FITS_Files/)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "insthd = fits.open('../data/PSF_Original_MIPS_24.fits.gz')\n", "psf_inst_full = insthd[0].data\n", "hdinst = insthd[0].header\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#hdinst\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rad=1000\n", "cpix=np.int((hdinst['NAXIS1']+1)/2.0)\n", "\n", "plt.imshow(psf_inst_full[cpix-rad-1:cpix+rad,cpix-rad-1:cpix+rad])\n", "#psf_inst=psf_inst_full[hdinst['CRPIX1']-rad-1:hdinst['CRPIX1']+rad,hdinst['CRPIX2']-rad-1:hdinst['CRPIX2']+rad]\n", "psf_inst=psf_inst_full[cpix-rad-1:cpix+rad,cpix-rad-1:cpix+rad]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalise instrumental PSF such that integral=1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "psf_inst=psf_inst/(np.sum(psf_inst))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's build the growthcurve for our PSFs." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find the brightest pixel, it will be our center.\n", "jmax, imax = np.unravel_index(np.argmax(psf), psf.shape)\n", "jmax_inst, imax_inst = np.unravel_index(np.argmax(psf_inst), psf_inst.shape)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# build the array of coordinates\n", "x = np.arange(hd['NAXIS1'])\n", "y = np.arange(hd['NAXIS2'])\n", "xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')\n", "xp = (xv-imax)*np.abs(hd['CD1_1'])*3600.\n", "yp = (yv-jmax)*np.abs(hd['CD2_2'])*3600.\n", "r = np.sqrt(xp**2 + yp**2)\n", "\n", "x_inst = np.arange(1+rad*2)\n", "y_inst = np.arange(1+rad*2)\n", "xv_inst, yv_inst = np.meshgrid(x_inst, y_inst, sparse=False, indexing='xy')\n", "xp_inst = (xv_inst-imax_inst)*np.abs(hdinst['CD1_1']*3600.0)\n", "yp_inst = (yv_inst-jmax_inst)*np.abs(hdinst['CD1_1']*3600.0)\n", "r_inst = np.sqrt(xp_inst**2 + yp_inst**2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# build the growth curve\n", "radii = np.unique(r)\n", "encircled_flux = np.zeros(radii.shape)\n", "nbpix = np.zeros(radii.shape)\n", "for i, radius in enumerate(radii):\n", " idj, idi = np.where(r <= radius)\n", " nbpix[i] =len(idi)\n", " #encircled_flux[i] = np.sum(psf[idj, idi])*resol**2\n", " #multiply by ((np.abs(hd['CDELT1'])*3600.)**2)/4.25E10 as map is in units of MJy/sr\n", " encircled_flux[i] = np.sum(psf[idj, idi])*((np.abs(hd['CD1_1'])*3600.)**2)/4.25E10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Normally we run the code below for the instrumental PSF. This is slow and remains the same for each field so I just load up the result instead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "radii_inst = np.unique(r_inst)\n", "encircled_flux_inst = np.zeros(radii_inst.shape)\n", "nbpix_inst = np.zeros(radii_inst.shape)\n", "for i, radius in enumerate(radii_inst):\n", " if i % 1000 == 0:\n", " print(i,len(radii_inst))\n", " idj, idi = np.where(r_inst <= radius)\n", " nbpix_inst[i] =len(idi)\n", " encircled_flux_inst[i] = np.sum(psf_inst[idj, idi])\n", "``` " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "inst_data=np.load('../data/MIPS_encircled_flux_inst.npz')\n", "encircled_flux_inst=inst_data['encircled_flux_inst']\n", "radii_inst=inst_data['radii_inst']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Encircled flux')" ] }, "execution_count": 14, "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.plot(radii, encircled_flux)\n", "\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the shape of the encircled flux, it looks like the background level of our PSF is not zero. Let's check" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.003720775\n" ] } ], "source": [ "# This is clearly. \n", "print(np.median(psf[0:5,:]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Encircled flux')" ] }, "execution_count": 16, "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.plot(nbpix[10:], encircled_flux[10:])\n", "plt.xlabel('Number of pixels')\n", "plt.ylabel('Encircled flux')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len: 777\n", "29.0\n" ] } ], "source": [ "print('len:' , len(nbpix))\n", "print(nbpix[6])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Lets do a linear fit to the outer part of the curve to determine the backgound\n", "p = np.polyfit(nbpix[50:], encircled_flux[50:], 1)\n", "bkg=p[0]/(((np.abs(hd['CD1_1'])*3600.)**2)/4.25E10)\n", "#bkg = p[0]/resol**2" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0036798443196937457\n" ] } ], "source": [ "print(bkg)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#print(nbpix[100:])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Lets correct the psf and encircled flux\n", "psf = psf - bkg\n", "encircled_flux = encircled_flux - p[0]* nbpix" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Encircled flux')" ] }, "execution_count": 22, "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.plot(radii, encircled_flux)\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our PSF does now behaves correctly.\n", "\n", "Now let us compare our growth curve with the encircled energy curve from the instrumental PSF. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yEJoUlFIuVVZdx4JVe5n/9W6KK2pJSYjghlP7kRgVzJTBPegZFuDuEJUDTQpKKZew2QwPLk3n9ZWZNNgMJ/aP5rapA5mQHNVlJqzxRpoUlFLt7qvtBfz+vQ3kllYzc3Qsl0xIYFJytA4ReQFNCkqpdmGMIX1/Kd/uKOTR/20lPjKQp+ekMHN0rCYDL6JJQSl1XD5Pz+Pvy3eyp6Cc0up6AGJC/HnhqlSG9A5r5dXK02hSUEq1SWF5DT/tPchfPt5CaVU9M1NiGdI7jLOG9aJHqL+eN/BSmhSUUsfkvTXZPPvFDvYWVwIQGuDLYxeMYvrIPm6OTLUHTQpKKafsO1jFg0s288nmPOIiArn77CGMTYxkZFy41iTqRDQpKKVa1GAzvLcmiz9+sBmD4ZeTk7j5tAFEh/i7OzTVkvpaOJAB1SVQV+n0yzQpKKWalF9WzcI1+3jzx0yyiqsYlxjJUxen0Dday1Z3uIZ6qCmF2grrAF9bbi3XOi5XQEU+FGyHwu1wYA/Y6o/5rTQpKKUardxdxJrMA2QWVbB8WwEFZTWMig/n7rOHMm14b720tCnGWAfq+hrrINxQB7Y660Buq7cv1zk8V+/wfJ31uqoDUFkMVcVNPB6AmhLnYunmC1H9oecQGDYTYgZBUDT4BcGfTnKqCU0KSim+2l7A6z9k8PmWfMC6pHRI71BeuCqVlIQI9wbnLsZYB+vyPCjbD2V5UJ7b9GNdRfu8p38YBEZCUBQERkF0f+sxKAoCwq2De/dg6B5ifzzyXyj4HN9hXZOCUl1YSVUd//lmN89+uZOo4O5cMakv805MZkDPEHeH1nFqymDTQsjdCGW59iSQZz021By9f/dQCO0FIb0hdoz1GNIDfAOtA3I3P/Dxsx67+fy87ONrfZNvfN7XevTpbiWCwEhr3c00KSjVBRlj+GFXEb96LY3K2gZGxoXz0rzx9AjtQieP96+HtJdh47vWuHxAOIT2gZBekHiC9Rja5+cEENrb2ubfuRNmq0lBRIYZY9KP2DbFGLPCZVEppdqdMYaN+0r4ZHMuS9bvZ29xJTEh/tx7zjAum9jX3eG5ls0GlYVQkgW5m2Dta7Avzfp2P+ICSL0a4saC3nDnVE/hHRF5HfgrEGB/TAVOcGVgSqnjZ4zh1e8z+HJbAek5pRSW19BNYPKAGG4+fQDTRvQmLMD9QxbHrboUSvdBSfbP/xzXS/dBQ+3P+8cMhmmPweg51rCNauRMUpgIPAZ8D4QCC4DJrgxKKXV86htsbM0tY8Gqvfz3x70M7hXKKQNjmDwghtOH9CTSm2Y4q6+1DurNHvT3HX11jnSD0FgIj7N6AEPPhfAEaz0iEXoN115BM5xJCnVAFRCI1VPYY4yxuTQqpVSbZBRW8OjHW/lmRwEVtQ0AzE6J5ak5KZ5Zi8hmg4oC+0E+++eDfEnWzwf98nzAHP66oGgIi4PIZEg6CcLjrfVDB/6Q3sd9FU5X5cynthr4EBgPRAP/FpELjTEXujQypZTTMosqWL41n7dWZ7E1t4xLJ/RlUr8oxiVGEhcR6FkJoboUdnwKW5bAzs+tk7yO/IJ+PsgPPMtadjzoh8VCd72BzlWcSQrXGGPS7Mu5wCwRudKFMSmlWlFZW8+XW/PZW1zJJ5vzWJ91EIDwQD9+dVIy984Y5uYIj1BRBNs/hvTFsHu5Nb4f3BNGXgi9Rjgc9OOtMX5PSmJdjDNJIV9Ejrw04StXBKOUal5JZR1f7ShgxdZ8vtyWz8HKOgB6hwVw19lDOGdkHxKiPOgbdGkObP0ItiyGjO/ANEB4Xxh/rTXGnzDBuo5feRRnksJHWAN6gnVOIRnYBgx3YVxKKbvckmr++r+tfLg+hwabITLIjymDejArJY4JyVEE+3vQ2Hnxbtiy1Boayv7R2hYzGE663UoEfUZrL8DDtfrbZIwZ6bguImOB610WkVJdnM1m2F1YTkZhJeU19Ty0NJ0DlbVcOSmRmSlxpCRE4ONJNYiKdsHmRZD+gXVXMFgH/9P/aCWCHoPdG586Jsf8FcMYs1ZExrsiGKW6opr6BrbuL2ND9kFWbCvgx4xiyqoPr2750OwRXDkp0U0RNqFwJ6Qvgs0fQp49EcRPgLP+bCWCSA+KVR0TZ+5o/j+H1W7AWKDAmcZFZBrwDOADvGiMebSJfS4GHsAaolpvjLnMmbaV8na7Csp5+vMdfJ6eR1WddfloXEQg546OZUxCBIN6hRIW6EdEoJ/n3FeQvQaW/gZyN1jrCRPhF3+xKnKGx7s3NtUunOkphDos12OdY1jY2otExAd4DjgTyAZWi8hix5IZIjIQuBuYbIw5ICI9jyV4pbxFdV0Dewor+HBdDrX1NnYWlPP1duu71WUT+3LygBhGJUQQGx7gWZePHmIMrH4R/ne3VQNIE0Gn5cw5hT+1se0JwE5jzG4AEXkLmAU41lG6FnjOGHPA/l75bXwvpTzK2r0H2LK/lNySanYXVvDFljyq636+5zMuIpB5JyZx4bh4RsSFuzFSJ9SUW72Dje9a9w2c92+rlLPqlJpNCiKyhKNuI/yZMWZmK23HAVkO69lYJTMcDbK/13dYQ0wPGGP+10q7Snmc/NJqVmwvYNXuYjbnlLA1twwAn25Cr1B/zhsTz8TkKJJjghkVH+6ZvYGmFGyDd66yZvI6/V446Q7o1s3dUSkXaqmn8MRxtt3Ub/2RScYXGAhMAeKBb0RkhDHm4GENiVwHXAfQt28nr+aoPFpxRS078srIK6shr6SafQer+Gp7AXsKrUlWYkL8GR4bxkWpCUwf2ZseIf74+njpQXTTQvjwFvALhCsXQb8p7o5IdYCWksJ9xpipIvKYMeb3bWg7G0hwWI8HcprYZ6Uxpg7YIyLbsJLEasedjDHzgfkAqampzfZelGpvJZV1/LC7iJW7i/hhVxHb8soOez7ArxvjEiO5YlIiKQkRjO0b4T29gEMOzTB2MBMO7oUDmZDzE2x+3zqRfNErVmkJ1SW0lBT6iMipwEz7+YDDftONMWtbaXs1MFBEkoF9wCXAkVcWfQBcCrwiIjFYw0m7jyF+pdpFdV0Dq/YUk32gktKqeg5W1bI28wDrs0qobbAR4NeN8UlRzEyJZWRcOH3CA+gZFkBYgK93JIHqkp8P+Af32v85JIHaw5MdAeFw4i0w9X6PmA1MdZwWewrAXVjf8P/G4UnBAKe31LAxpl5EbgY+wTpf8JIxZrOIPAikGWMW2587S0TSgQbgt8aYojb/NEo5obbexo78MrKKK8k5WM2Pe4r5ekcBlfaqogB+PkL/HiFcPqkv00f2YXR8BN19PXgYqLbiiIN+pvXv0Hr1wcP37x5ilZCO6GtVGY3o+/N6RF8I7KLzMivEmJZHY0Tkj8aYhzoonlalpqaatLS01ndUykFNfQM78spZnVHMU59tp9Th5rBeYf6cMbQXZwzrxbA+YYQH+uHv281zewBVByA7DbJ+tEpJ5G6yZhVz5Bv48wE+MvHwg35kkhad64JEZI0xJrW1/Zy5JNVjEoJSzqhrsLGroJwt+0vZsr+MLftLWZt5oHF+gaToIB6aPYL+PUKIjQgkMsjPcxOAzQaF235OAFk/WlcCgTWRTM/hMPhsiEq2H/TtB/6QnnrQV23iQZW0lGq7mvoGPvhpH69+n8mO/DLqGqwecHefbgzsFcKsMXFM7h/D0D6hJEYHe1btIEdVB625g7NWW0kge83Ps4oFRlqlJEZdbD3GjQX/0JbbU+oYaVJQXm/fwSounb+SvcWVDO0TxjUn9WNon1CG9gkjOSYYP0+9JNRmg6IdDr2A1VCwlcaixD2HwYjzrRLT8RMgur9++1cu19LNay3esmiMKW7/cJRqnjGGoopatueV8cbKTPYUVlJWXUdxhTUh+0vzUpkyqCfdPLUXUF0K+9ZA9mrIWmWdFzh0AjggAuLHW0kgfjzEjYOAMPfGq7qklnoKa/h5HoW+wAH7cgSwF2teBaU6xMcb93P3oo2NE8t09+nG5AHRDOkdSmiAL9NG9ObE/jFujrIJu5ZbJaWzVkN+Oo1/Uj2GwLBZDr2AAXqnsPIIzSYFY0wygIj8C1hsjFlmXz8bOKNjwlNdWVVtA89+uYPvdxayPtsaV//dtMEM7RPGiNhweoT6uznCFlQUwSd3w4a3wT8c4lOtAnLx463lAA+vd6S6LGfOKYw3xtxwaMUY87GI6BVJyqX2FFZw7Wtp7Mwv54R+0Vx/aj+unJRIfKQHTTfZFGOsCWeW/dYaGjr193DyHeDrwQlMKQfOJIVCEbkXeAOr73sFoDeYqXZX32Dj4Y+28P2uQrbnlRMV3J03rpnISQM9cFioKaX74aM7YNtHEDsGZn4IvUe4OyqljokzSeFS4H5gEVZS+Nq+Tal2kVtSzceb9vPmqr3syC9nyuAenDqoBxenJjCwlxdccmkMrH0NPv0jNNTAmQ/BpF+Dj17cp7yPMzevFQO3iUiIMaa8A2JSXUB9g42PN+Xy6vcZpGUeAGBUfDjPXJLCrJQ4N0d3DIr3wJJbYc/XkHgSzHzWunRUKS/lzHScJwIvAiFAXxEZDVxvjPm1q4NTndOW/aX85q11bMsro19MMHeeNYhfDO/tHb0CsHoG+9ZYVxX9+CJ084UZT8PYuXoFkfJ6zvRvnwJ+ASwGMMasF5FTXBqV6pTySqt5ZNkWlqzPISrYn+cvH8u04b09974CR7YG2LsStiyGLUugdJ+VDIacY01NGe5FvRulWuDUoKcxJuuI2jANze2rVFPqG2zctGAtm3JKuPbkflx/an+iPGUy+uY01FnDQlsWw9aPoKIAfPxhwFQ4/Y8weJpVekKpTsSZpJBlH0IyItIduBXY4tqwVGfz8EdbSMs8wFNzRnPeGA+Z7N0Ya56B8jwoy4XyfCjPtZZLsmH3CuuyUr9gGHimdZ/BwLO03pDq1JxJCjcAz2DNuZwNfArc5MqglPerrbexKaeEtIxivtlRyDc7CvnVSckdkxBsDVBRaD/A5zk85h2+rTwf6quPfr1vAIT0gkG/gKEzrZ6BX6Dr41bKAzhz9VEhcHkHxKI6iYeXpvP6ykxq6m0AJEYHcfsZg7j59AHH13Bd9REH+EPf8O0H+LJca1tFARjb0a8PiIDQ3lZZ6YRJENoLQnr/vC2kt7XNP0wLz6kuq6WCeH/Hui+hScaYW10SkfJa5TX1vPZDBi9+u4dpw3szKyWWcUmR9AwNOLaGSnNg1b+tk7mHDvTledZQz5GkGwT3tA7moX0gNsX6lh/Sy36wP3TA7wV+xxiHUl1QSz0Fnd5MOe3l7/bw5GfbKauu5+SBMTw5ZzRB3dtw81ZFEbw6Ew7sgfB462DeYwj0m3LEwd7+GBQN3Xza+8dRqstqqSDeqx0ZiPJe/9uUy5+WpHPKoB7cceYgRie0cX7fmnJYcCGUZMHcJZB4YvsGqpRqVat32ojIZyIS4bAeKSKfuDYs5S32FFbw23fXMyo+nBeuGtf2hFBfC29fAfvXw0WvaEJQyk2cuf2yhzHm4KEVY8wBoKfrQlLe5L4PN9Gtm/D85WPx923jMI7NBouuh93LYebfrTmHlVJu4UxSaBCRvodWRCSRFk5Aq66huq6B13/I4Jsdhdx0Wv+2l7Q2Bv73e9j8Ppz5IIzRC92UcidnzgTeA3wrIl/Z108BrnNdSMpT1TXY+HZHIUvW5/Bpeh7lNfVMTI7iqhOS2t7o10/Aj/PhxFtg8m3tFqtSqm1aTApi1bbYDIwFJmFNx3m7/d4F1cX89t31fLAuh7AAX84e0ZtzR8dyYv9ofH3aWAQu7SVY/jCMvhTOeLB9g1VKtUmLScEYY0TkA2PMOGBpB8WkPFBmUQUfrs/hqhMSufecYXT3Pc5qoJs/gKX/BwN/YZ1H0OqiSnkEZ/4SV4rIeJdHojzay99l4NtNuOm0AcefEHZ/Be9fa01af9Er4OPXLjEqpY6fM+cUTgOuF5FMoAJrCMkYY0a5NDLldrsLynk7LYuvthWwNbeM88fE0SvsOO8KzvkJ3roMogfAZW9Ddw+fc1mpLsaZpKDXB3ZByzbu585311Nbb2N8UhR3nT2Eyyf2bf2FLSnaBW9cCIFRcMVCLTutlAdqqfZRmDGmFCjrwHiUB3hz1V7+sGialQ0pAAAY6klEQVQjY/pG8M/Lx9E7vB1qBpXuh9dnAwauXARhscffplKq3bXUU3gTmAGswbovwbFspAH6uTAu5UZfbMkjMTqIt66b1PYb0hxVHYQ3LoDKYqt8RcxxVktVSrlMS7WPZtgfkzsuHOUJqusbCAvwa5+EULwHPrgRinbA5e9C3Njjb1Mp5TLO1D46T0TCHdYjRGS2M42LyDQR2SYiO0Xkrhb2u1BEjIikOhe2cpW80mpW7S5mYnJU2xsp3GHdlPavk+HZFMj6Ec6fb1U6VUp5NGdONN9vjFl0aMUYc1BE7gc+aOlFIuIDPAeciTVj22oRWWyMST9iv1CsKT5XHWvwqv3kllTz3PKdvL82G4BLj+WksjGQv8Wayzj9Q8i3/xfHT4CzHrZmL4tMdEHUSqn25kxSaKo34czrJgA7jTG7AUTkLWAWkH7Efg8BfwXudKJN5QIbs0u45tXVHKysY8aoPvxycjL9e4S0/CJjIHeDlQTSF1vDQ4hV3fTsv8KQGRAe1yHxK6XajzMH9zQReRLrW78BbsE6+dyaOCDLYT0bmOi4g4iMARKMMUtFpNmkICLXYa+31LfvcV4WqRrZbIYP1u3jnkWbiAruztJbT2JQrxYmpTcG9q2xEsGWxXAgA8QHkk6CSTdaiSC0V4fFr5Rqf84khVuAPwJvY12B9ClwkxOva2qS28bqqiLSDXgKmNdaQ8aY+cB8gNTUVK3QepyMMazYVsDjn2wjfX8pY/pG8O8rxzU9babNBlmr7ENDi6E0G7r5Qb9T4eQ7YPA5EBzd8T+EUsolWk0KxpgKoNmTxC3IBhIc1uOBHIf1UGAEsMKqu0dvYLGIzDTG6FSgLrLvYBW/eesnVmccIDE6iGcuSeHcUbF06+aQwxvqYe/39h7BUijPBR9/GDAVTr8XBk/TG8+U6qRaTQoiMghrvD/JcX9jzOmtvHQ1MFBEkoF9wCXAZQ6vLwFiHN5nBXCnJgTXem75TjZkl/Dw7BHMGZ+A36EKpw11sOcrqzewdSlUFoFvIAw8E4bNgkG/AP8WhpaUUp2CM8NH7wL/Al4EGpxt2BhTLyI3A58APsBLxpjNIvIgkGaMWdyWgFXb1dbbWLZxP9NG9OaKSYlQXwPblltDQ1s/guqD0D3ESgDDZsGAM6B7sLvDVkp1IGeSQr0x5p9tadwYswxYdsS2+5rZd0pb3kM5719f7aK6spzrexbAwudg+ydQUwr+4TBkunXpaP/Twa8dyloopbySM0lhiYj8GlgE1BzaaIwpdllUqn3VlLPnh/fpv+IN1geux//raqso3bBZMGw2JJ8Cvt3dHaVSygM4kxTm2h9/67BNax95i43vwYc3k1xfRZhPBJJyCYw8DxJPAh9n/vuVUl2JM1cfae0jb5W+GN6/Dlv8eK7YcxajT5zG76cPd3dUSikP1mztIxH5ncPyRUc894grg1LtYPun8N7VEDeONSfN5/v6IYxLimn9dUqpLq2lgniXOCzffcRz01wQi2ovu1fA21dgeg1j9Unz+ft3eQCMS9R7C5RSLWtp+EiaWW5qXXmKzO8x/72U4oAELi/6P7a+kk5Qdx9uOq0/kcF6Mlkp1bKWkoJpZrmpdeUJstdgW3ARObZoZhfdwdCBfbhxejxnDutFUHc9qayUal1LR4rRIlKK1SsItC9jX9cL2T3N/vXUvTqb3LpgrrL9gXvmnMJ5Y+LdHZVSysu0NPNaO0y7pTpE/hbqX51Nfq0f90X+hZeumEZyjN6JrJQ6djqm4O2KdsFrs6hq6Mbc+ntZeP1MwgP93B2VUspLaVLwZgcy4NVzMbYGrpf7SBw4TBOCUuq4tDpHs/JQJfvg1ZmY2gqe6vNXvi+N4byxOtOZUur4aE/BG5XlwWszMVUH+EfcEzy7OYDbpg5kxqhYd0emlPJy2lPwNhVF8NosKN3P6/0e52/pIdx6+gB+c8ZAd0emlOoENCl4k6oD8PpsOLCHmove5C+bIpidEsvtZw7CPnudUkodF00K3qKmDN64EPK3wJwFLK8dQlVdAxelJmhCUEq1G00K3qC2AhZcDDk/wUWvUJZwKi99t4eIID8mJEe5OzqlVCeiJ5o9XV01vHUZZK2EC15kV8wUrn3uOzKLKnl49oif51hWSql2oEnBk9XXwjtXWVVPZ/+TmiGzueHZbymprGPBryYyqV+0uyNUSnUy+jXTUzXUw8JrYMcnMOMpSLmM55fvYkd+OU9cPFoTglLKJTQpeCJbA3xwA2xZDL/4C6RezYbsgzy/YiezU2I5bXBPd0eolOqkdPjI09hssORW2PguTL2PrMHzePbd9Sxcm01UsD9/nDHM3REqpToxTQqexBj4+Hfw0xtwyu94wZzHX/+2AhHhl5OTueHU/kSH+Ls7SqVUJ6ZJwVMYA5/9EVa/ACfewou+l/DnZVuYNrw3D8wcTu9wncJCKeV6mhQ8gc0GXz4E3/8dxl/LayHX8PDidM4Z2YdnLknBVy87VUp1EE0K7pa/FRbfAtk/wtireK/Xrdz33kbOHNaLpzUhKKU6mCYFd6mvhW+fgm+egO7BcN6/sY24mL8++iWpiZH847IxemOaUqrDaVJwh+w0q3eQnw4jLoBpj0FID37KPEB+WQ33nDMUf1+dDVUp1fE0KXSk2gr48mFY+U8Ii4VL34bB0xqf/mRzLn4+wmlD9D4EpZR7aFLoKDu/gKW/gYN7IfUaOOMBCAhrfHpbbhkL12RzYv8YwgJ0Sk2llHu4dNBaRKaJyDYR2SkidzXx/P+JSLqIbBCRL0Qk0ZXxuEVlMSy6Ed44H3y6wy8/hhlPHpYQVu0u4qJ/fY+vj3DPOUPdGKxSqqtzWU9BRHyA54AzgWxgtYgsNsakO+z2E5BqjKkUkRuBvwJzXBVThzIGNi+ybkarOgAn3wmn/Bb8Dr/f4H+b9nPrW+tIiAzk1asnEB8Z5KaAlVLKtcNHE4CdxpjdACLyFjALaEwKxpjlDvuvBK5wYTwdp2QffHQHbP8YYsfAlYug98ijdnv9hwzuW7yZMQkR/GfueCKDu3d8rEop5cCVSSEOyHJYzwYmtrD/NcDHLozH9Ww2WPMyfP4ANNTBWQ/DxBvB5/CP2RjD3z7dzj+W7+SMob34+6VjCOyuVxsppdzPlUmhqTkiTZM7ilwBpAKnNvP8dcB1AH379m2v+NpX4U6rkF3md5B8Cpz7DET1a3LXB5em8/J3GVw6IYGHZo3QG9SUUh7DlUkhG0hwWI8Hco7cSUTOAO4BTjXG1DTVkDFmPjAfIDU1tcnE4jYNdfD9s7DiMet8wcx/wJgroJl5k5dvzefl7zKYd2IS9587TOdXVkp5FFcmhdXAQBFJBvYBlwCXOe4gImOAfwPTjDH5LozFNXJ+sm5Cy90IQ2fC9MchtHezu5dU1nHX+xsY1CuEu6cP0YSglPI4LksKxph6EbkZ+ATwAV4yxmwWkQeBNGPMYuBxIAR4136A3GuMmemqmNpNbSWs+Av88A8I7glz3oCh57b6sgeXplNYXsuLV43XO5aVUh7JpTevGWOWAcuO2Hafw/IZrnx/l9j9FSy5DQ7sgbFz4cwHITCi1Zd9uTWPhWuzueX0AYyMD++AQJVS6tjpHc3OqjoIn94LP71unUCeu8Q6oeyEkso67lq4kSG9Q7nl9IEuDlQppdpOk4Iz0hfDsjuhohAm3wZT7ga/QKdf/rfPtlFcUctL88bT3VevNFJKeS5NCi0py7WSwZYl1s1nl70DsSnH1ERtvY0PftrHuaNjGRGnw0ZKKc+mSaEpxljDRJ/eC3XVMPV+OPEW8Dn2QnXf7iygtLqec0f3cUGgSinVvjQpHKl4t3Uiec/XkDgZzn0WYga0ubml6/cTHujHSQN6tGOQSinlGpoUDmmoh5XPw/JHrB7BjKetq4u6te0cQFl1HW+u2suyTfuZOTpWzyUopbyCJgWA/Rusm9D2r4PB58A5T1iT4LRBflk1L3+XwRs/ZFJWU8/kAdF6xZFSymt07aRQVw1fPQbfPQNBUXDRKzBsdrMlKlrzynd7eOTjrdQ12Jg+og/Xn9qPUfGt38OglFKeousmhYzvrAJ2RTsh5XKromlQVJuaarAZHv7IKnJ3xtCe3HPOMJJjgts5YKWUcr2ulxSqS+Hz+yHtJYjoa8110P/0NjdXVdvAbW/9xKfpeVw9OZl7zhmKTzetaaSU8k5dKyls+xiW/h+U58Kkm+D0e6B727/RF5bXcM2raWzIPsj95w7jl5OT2zFYpZTqeF0jKZQXWNNibn4feg6zCtjFjzuuJnMOVjFn/g8UlNXwryvG8YvhzVdHVUopb9G5k4IxsP4t+ORuqK2A0+61ylT4Hv+0l//6ahd5pTW8c/0JpCToyWSlVOfQeZPCgUxY+hvY9SUkTISZf4ceg9ul6craehat3cc5I/toQlBKdSqdLynYGmDVv+HLh0C6wfQnIPWaNt+E1pSlG/ZTVlPPpRM8dGpQpZRqo86VFPLSrZvQ9qXBwLPgnCchIqH11x2jN1ftZUDPEMYnRbZ720op5U6dIynU18A3f4NvnoSAMDj/RRh5YZtvQmvJ8q35rMs6yB9n6PzKSqnOx/uTQtaP8OHNULgNRl4M0x6F4GiXvNXO/DJu/e9PDO0TxqUT2r8HopRS7ua9SaGmDL54CH6cD2FxcPl7MPBMl7xVcUUtH23cz79W7MLfz4cX56YS1N17PzqllGqOdx7ZdnwGS2+HkmyYcB1M/SP4h7brW1TW1vNZeh4frsvh6+0F1NsMA3uG8NzlY4mLcH7WNaWU8ibelxQOZsKCCyFmMFz9CfSd2K7Nb9lfyr+/2sWn6XlU1jbQJzyAa05KZlZKHEP7hOp5BKVUp+Z9SaHqAJz6IJx8B/j6t2vT1XUN/PLl1VTW1jMrJY5ZKbFMSIqim9YyUkp1Ed6XFGIGw2l/cEnTr36fQW5pNf+9dhIn9HfNyWqllPJk3jcdmJ9rxvNLKut4fsUupgzuoQlBKdVleV9ScJF/frWL0uo6fveLIe4ORSml3Mb7ho/amTGGVXuKefm7PcxOiWNYbJi7Q1JKKbfpskkht6SahWuzeW9NNnsKK4gM8uP/zhzk7rCUUsqtulRSqKlv4Ist+byTlsXX2wuwGZiQFMWvp/Rn+sg+BPt3qY9DKaWO0iWOgg02w8K12fzt023kldbQOyyAG6f058JxCTqXslJKOej0SeGbHQX8+aMtbM0tIyUhgkcvGMUpA3voPMpKKdWETpkUDlTUsibzAG+symTFtgLiIwP5+6VjmDGqj96RrJRSLXBpUhCRacAzgA/wojHm0SOe9wdeA8YBRcAcY0zGsbyHMYbsA1WszihmdcYB0jKK2ZFfDkBogC9/mD6EuScm4e/r0w4/kVJKdW4uSwoi4gM8B5wJZAOrRWSxMSbdYbdrgAPGmAEicgnwGDCntbY37SshLaOY1ZlWEsgrrQGsJDAuMZLZY+JITYxkdEIEAX6aDJRSylmu7ClMAHYaY3YDiMhbwCzAMSnMAh6wL78H/ENExBhjmmt0c04pM/7+LQCx4QFMTI5mfFIkqUlRDOoVqucKlFLqOLgyKcQBWQ7r2cCRJU0b9zHG1ItICRANFDbXaESQH89ckkJqUpSWsFZKqXbmyjIXTX1lP7IH4Mw+iMh1IpImImnd68qZlRKnCUEppVzAlUkhG3CcszIeyGluHxHxBcKB4iMbMsbMN8akGmNSe/To4aJwlVJKuTIprAYGikiyiHQHLgEWH7HPYmCufflC4MuWzicopZRyLZedU7CfI7gZ+ATrktSXjDGbReRBIM0Ysxj4D/C6iOzE6iFc4qp4lFJKtc6l9ykYY5YBy47Ydp/DcjVwkStjUEop5TydT0EppVQjTQpKKaUaaVJQSinVSJOCUkqpRuJtV4CKSBmwzd1xHKMYWrhL2wN5W7ygMXcEb4sXNGZHicaYVm/08sbS2duMManuDuJYiEiaN8XsbfGCxtwRvC1e0JjbQoePlFJKNdKkoJRSqpE3JoX57g6gDbwtZm+LFzTmjuBt8YLGfMy87kSzUkop1/HGnoJSSikX8aqkICLTRGSbiOwUkbvcHU9LRCRBRJaLyBYR2Swit7k7JmeJiI+I/CQiS90dizNEJEJE3hORrfbP+wR3x9QSEbnd/juxSUT+KyIB7o7pSCLykojki8gmh21RIvKZiOywP0a6M8YjNRPz4/bfiw0iskhEItwZo6Om4nV47k4RMSIS09FxeU1ScJjz+WxgGHCpiAxzb1QtqgfuMMYMBSYBN3l4vI5uA7a4O4hj8AzwP2PMEGA0Hhy7iMQBtwKpxpgRWBWEPbE68CvAtCO23QV8YYwZCHxhX/ckr3B0zJ8BI4wxo4DtwN0dHVQLXuHoeBGRBKy57fd2dEDgRUkBhzmfjTG1wKE5nz2SMWa/MWatfbkM60AV596oWici8cA5wIvujsUZIhIGnIJVhh1jTK0x5qB7o2qVLxBon1gqiKMnn3I7Y8zXHD3h1SzgVfvyq8DsDg2qFU3FbIz51BhTb19diTXZl0do5jMGeAr4HU3MQtkRvCkpNDXns8cfZAFEJAkYA6xybyROeRrrF9Lm7kCc1A8oAF62D3m9KCLB7g6qOcaYfcATWN8C9wMlxphP3RuV03oZY/aD9aUH6OnmeI7V1cDH7g6iJSIyE9hnjFnvrhi8KSk4NZ+zpxGREGAh8BtjTKm742mJiMwA8o0xa9wdyzHwBcYC/zTGjAEq8LxhjUb2cfhZQDIQCwSLyBXujarzE5F7sIZ0F7g7luaISBBwD3Bfa/u6kjclBWfmfPYoIuKHlRAWGGPed3c8TpgMzBSRDKzhudNF5A33htSqbCDbGHOoF/YeVpLwVGcAe4wxBcaYOuB94EQ3x+SsPBHpA2B/zHdzPE4RkbnADOByD5/utz/Wl4X19r/BeGCtiPTuyCC8KSk4M+ezxxARwRrn3mKMedLd8TjDGHO3MSbeGJOE9fl+aYzx6G+xxphcIEtEBts3TQXS3RhSa/YCk0QkyP47MhUPPjF+BMc51ecCH7oxFqeIyDTg98BMY0ylu+NpiTFmozGmpzEmyf43mA2Mtf+OdxivSQr2k0WH5nzeArxjjNns3qhaNBm4Euvb9jr7v+nuDqqTugVYICIbgBTgETfH0yx7j+Y9YC2wEetv0OPuuhWR/wI/AINFJFtErgEeBc4UkR1YV8c86s4Yj9RMzP8AQoHP7H+D/3JrkA6aidft9I5mpZRSjbymp6CUUsr1NCkopZRqpElBKaVUI00KSimlGmlSUEop1UiTglJKqUaaFJRXEpEG+3Xnm0RkybGWRBaRB0TkTvvygyJyxnHGkyQiVSKy7njaaU8iMsdeZt4rSqArz6BJQXmrKmNMir38dDFwU1sbMsbcZ4z5vB1i2mWMSTmWF9hLwruEMeZt4Feual91TpoUVGfwA/aKuSISIiJfiMhaEdkoIo3l1UXkHvskTZ8Dgx22vyIiF9qXMw5NbCIiqSKywr58qsOd6T+JSGhrQYnIByKyxj6hznUO28vtvZNVwAkiMl5EvheR9SLyo4iEishw+/I6+wQxA+2vvcJh+78PJRWxJqBaa2/ji+P/SFVX5evuAJQ6HvaD4lTs8ykA1cB5xphS+8F9pYgsxiqSdwlWCXNfrDITx1IN9k7gJmPMd/bKt9VOvOZqY0yxiAQCq0VkoTGmCAgGNhlj7rPX8doKzDHGrLbPD1EF3AA8Y4xZYN/HR0SGAnOAycaYOhF5HrhcRD4GXgBOMcbsEZGoY/i5lDqMJgXlrQLt4/dJWAf3z+zbBXhERE7BmhMiDugFnAwsOlQUzZ4ojsV3wJMisgB43xiT7cRrbhWR8+zLCcBAoAhowKqeC1aPZb8xZjXAofLqIvIDcI990qP3jTE7RGQqMA4rwQAEYlUqnQR8bYzZY2+jqYlblHKKDh8pb1VlH79PBLrz8zmFy4EewDj783nAoTmQnSn0Vc/PfxeNcycbYx7FGp8PxOp9DGmpERGZglUm+wRjzGjgJ4f2qo0xDYd2bSouY8ybwEysXsMnInK6fd9X7edSUowxg40xDzTXhlJtoUlBeTVjTAnWnMd32uevCMeaKKhORE7DShoAXwPniUig/XzAuc00mYH1bRzggkMbRaS/vbTxY0Aa0GJSsMdxwBhTaU8gk5rZbysQKyLj7e8TKiK+ItIP2G2MeRarZPUorHmRLxSRnvZ9o0QkEeucyqkiknxoeyuxKdUsHT5SXs8Y85OIrMc6Z7AAWCIiacA6rIMuxpi1IvK2fVsm8E0zzf0J+I+I/IHDp0/9jT3JNGDN19DatI7/A26wl/PehjU/cFOx14rIHODv9nMPVVg9jDnAFSJSB+QCD9rPT9wLfCoi3YA6rPMcK+0nst+3b8/HKm2t1DHT0tlKtQOx5uFear9E1mPYh7HuNMbMcHcsyjvo8JFS7aMBCPe0m9eA54ED7o5FeQ/tKSillGqkPQWllFKNNCkopZRqpElBKaVUI00KSimlGmlSUEop1ej/AcW2Gq3pyVkKAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(radii_inst, encircled_flux_inst, label='Calibration')\n", "plt.plot(radii, encircled_flux/np.max(encircled_flux), label='Our PSF')\n", "plt.xlim([0, 15])\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will work below 30\" where our PSF is well behaved" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "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.plot(radii_inst, encircled_flux_inst, label='Calibration')\n", "plt.plot(radii, encircled_flux/np.max(encircled_flux), label='Our PSF')\n", "plt.xlim([0, 20])\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that while the calibration curve still rises beyond 30\", our PSF has reached a plateau. Let's note the calibration $C(r)$. Our PSF encirled energy is of the form:\n", "\n", "$E(r) = \\alpha C(r \\times \\beta)$\n", "\n", "Where $\\beta$ is the fattening of the PSF.\n", "\n", "We could take the derivative, but this too noisy. Instead we do a brute force approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seb's suggestion.. look at derivative!! Also see how correction parameters change as a function of where I do correction" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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.plot(radii_inst, encircled_flux_inst, label='Calibration')\n", "plt.plot(radii, encircled_flux/np.max(encircled_flux), label='Our PSF')\n", "plt.xlim([0, 100])\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rf = 0.700, ff = 0.620, residual = 0.000\n", "751426000.0\n", "rf = 0.590, ff = 0.640, residual = 0.002\n", "727943940.0\n", "rf = 0.610, ff = 0.660, residual = 0.002\n", "705885000.0\n", "rf = 0.720, ff = 0.750, residual = 0.004\n", "621178800.0\n", "rf = 0.690, ff = 0.730, residual = 0.004\n", "638197440.0\n", "rf = 0.740, ff = 0.760, residual = 0.004\n", "613005440.0\n", "rf = 0.760, ff = 0.770, residual = 0.004\n", "605044350.0\n", "rf = 0.720, ff = 0.750, residual = 0.004\n", "621178800.0\n", "rf = 0.860, ff = 0.820, residual = 0.004\n", "568151360.0\n", "rf = 0.860, ff = 0.820, residual = 0.004\n", "568151360.0\n", "rf = 0.880, ff = 0.830, residual = 0.004\n", "561306200.0\n", "rf = 0.860, ff = 0.820, residual = 0.004\n", "568151360.0\n", "rf = 0.840, ff = 0.810, residual = 0.005\n", "575165600.0\n", "rf = 0.810, ff = 0.800, residual = 0.005\n", "582355140.0\n", "rf = 0.810, ff = 0.800, residual = 0.005\n", "582355140.0\n", "rf = 0.790, ff = 0.790, residual = 0.005\n", "589726700.0\n", "rf = 0.790, ff = 0.790, residual = 0.005\n", "589726700.0\n" ] } ], "source": [ "rfactor = np.arange(0.5,3., 1e-2)\n", "ffactor = np.arange(0.5,3., 1e-2)\n", "# work with the data points between 3 and 25\"\n", "for r in np.arange(3,20):\n", " idx, = np.where((radii > 2) & (radii < r))\n", " xv = radii[idx]\n", " yv = encircled_flux[idx]/np.max(encircled_flux)\n", " resid = np.zeros((len(rfactor), len(ffactor)))\n", " for i, rf in enumerate(rfactor):\n", " tck = interpolate.splrep(radii_inst*rf,encircled_flux_inst , s=1)#changed s=0 to 1 as I was getting NaNs\n", " yfit = interpolate.splev(xv, tck, der=0)\n", "\n", " for j, ff in enumerate(ffactor):\n", " resid[i, j] = np.sum((yv-yfit*ff)**2)\n", " imin = np.argmin(resid)\n", " rmin, fmin = np.unravel_index(imin, resid.shape)\n", " print(\"rf = {:.3f}, ff = {:.3f}, residual = {:.3f}\".format(rfactor[rmin], ffactor[fmin], resid[rmin, fmin]))\n", " print(np.max((psf/np.max(encircled_flux)/ffactor[fmin])))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows a minimum, with some degeneracy. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "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.imshow(np.log(resid))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rf = 0.790, ff = 0.790, residual = 0.005\n" ] } ], "source": [ "imin = np.argmin(resid)\n", "rmin, fmin = np.unravel_index(imin, resid.shape)\n", "print(\"rf = {:.3f}, ff = {:.3f}, residual = {:.3f}\".format(rfactor[rmin], ffactor[fmin], resid[rmin, fmin]))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "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.plot(radii_inst*rfactor[rmin],encircled_flux_inst, label='Calibration')\n", "plt.plot(radii, encircled_flux/np.max(encircled_flux)/ffactor[fmin], label='Our PSF')\n", "plt.xlim([0, 20])\n", "plt.xlabel('Radius [arcsec]')\n", "plt.ylabel('Encircled flux')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.2247631205398632" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The two curve overlap\n", "rad=20\n", "psfok = (psf/np.max(encircled_flux)/ffactor[fmin])\n", "cpix=np.int((hd['NAXIS1']+1)/2.0)\n", "np.sum(psfok[cpix-rad-1:cpix+rad,cpix-rad-1:cpix+rad])*((np.abs(hd['CD1_1'])*3600.)**2)/4.25E10\n", "#np.sum(psfok[cpix-rad-1:cpix+rad,cpix-rad-1:cpix+rad])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "psfok is the PSF that a source of flux 1 Jy has in our data, and is to be used for source extraction.\n", "## As units of map in MJy/sr, divide by 1E6" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "589726700.0" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.max(psfok)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(psfok.flatten(),bins=np.arange(0,500000000,500));\n", "plt.yscale('log')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "psfok2=psfok/1.0E6" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(psfok2.flatten(),bins=np.arange(-50,200.0,1));\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Validation\n", "To check PSF is reasonable, lets look at a 24 micron source (Spitzer index 201233 in spitzer-cat-full-master-mips24.fits) that has a flux of 812.65 microJy. Maximum value in our normalised PSF gives a peak below. Since PSF is double resolution of map, it could also be off centre. Map has value of 0.5315 MJy/sr but does appear to be off centre. " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max PSF = 0.47886 Jy/sr, off pixel Max PSF = 0.21144 Jy/sr\n" ] } ], "source": [ "print(\"Max PSF = {:.5f} Jy/sr, off pixel Max PSF = {:.5f} Jy/sr\".format(psfok2[cpix-1,cpix-1]*8.12E-4,psfok2[cpix-2,cpix-2]*8.12E-4))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: hdu=0 does not contain any data, using hdu=1 instead [aplpy.core]\n", "WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]\n", "WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import aplpy\n", "import seaborn as sns\n", "sns.set_style(\"white\")\n", "cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)\n", "fig=aplpy.FITSFigure('./data/70101880.70101880-0.MIPS.1.help.fits')\n", "fig.recenter(214.39,52.5, radius=0.004)\n", "fig.show_colorscale(vmin=0.0,vmax=10.0,cmap=cmap)\n", "fig.add_colorbar()\n", "fig.colorbar.set_location('top')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In summary, the PSF is within 10% of this source, and given noise and shape of source will add additional uncertianty this seems reasonable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create PSF fits file" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stackhd[1].data=psfok2\n", "stackhd.writeto('./data/output_data/dmu17_MIPS_EGS_20100204_sr.fits',overwrite=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }