From abfad68fdca8e6b112795e318ea2092bc578033f Mon Sep 17 00:00:00 2001 From: Melanie Kae Olaes Date: Wed, 5 Dec 2018 15:02:37 -0500 Subject: [PATCH] Added final example to saturation trail notebook. FINAL DRAFTgit add acs_saturation_trails/acs_saturation_trails.ipynb git add acs_saturation_trails/acs_saturation_trails.ipynb git add acs_saturation_trails/acs_saturation_trails.ipynb git add acs_saturation_trails/acs_saturation_trails.ipynb --- .../acs_saturation_trails.ipynb | 1354 +++++++++-------- 1 file changed, 755 insertions(+), 599 deletions(-) diff --git a/acs_saturation_trails/acs_saturation_trails.ipynb b/acs_saturation_trails/acs_saturation_trails.ipynb index 8a7307b..716b25e 100644 --- a/acs_saturation_trails/acs_saturation_trails.ipynb +++ b/acs_saturation_trails/acs_saturation_trails.ipynb @@ -26,38 +26,44 @@ "\n", "The ACS/WFC CCD becomes saturated around 80000 counts. When this occurs, excess charge from the source spills out lengthwise along the columns of the CCD. This can lead to issues with photometry when using very bright stars, since a significant portion of the star's flux may fall outside of a reasonable extraction radius.\n", "\n", - "However, accurate relative photometry can be obtained as long as a large enough aperture is selected to contain the spilled flux (ACS ISR 2004-01). While one could simply use a larger circular aperture, that may introduce error when working with a crowded field (as bright stars often are).\n", + "However, accurate relative photometry can be obtained as long as a large enough aperture is selected to contain the spilled flux ([ACS ISR 2004-01](http://www.stsci.edu/hst/acs/documents/isrs/isr0401.pdf)). While one could simply use a larger circular aperture, that may introduce error when working with a crowded field (where bright stars are often located).\n", "\n", - "Here we present a method to identify and perform photometry on saturated sources by defining a custom aperture that is a combination of a standard 0.5\" arcsecond circular aperture and the pixels affected by saturation trails.\n", + "Here we present a method to identify and perform photometry on saturated sources by defining a custom aperture that is a combination of a standard 0.5\" arcsecond circular aperture and the pixels affected by saturation trails. This method has been tested on ACS/WFC observations of 47 Tuc in the F660W band. The plot below shows the results of using this alternative method to recover flux.\n", + "\n", + "![title](photometry_plot.png)\n", "\n", "### This tutorial will show you how to...\n", "\n", - "#### [Prepare Images](#_prep) \n", + "#### 1. [Prepare Images](#_prep) \n", "\n", "* Apply Pixel Area Map\n", "* Separate by long and short exposure\n", "* Make sure you have images of the same field\n", "\n", - "#### [Identify Saturated Stars](#_identify)\n", + "#### 2. [Identify Saturated Stars](#_identify)\n", "\n", "* Identify the saturated pixels using the data quality (DQ) array\n", "* Determine whether or not the saturation trails extend significantly away from the target\n", "\n", - "#### [Bleed the Saturation Mask](#_bleed)\n", + "#### 3. [Bleed the Saturation Mask](#_bleed)\n", "\n", "* Construct a convolution kernel\n", "* Bleed the saturation mask with the convolution kernel\n", "\n", - "#### [Define a Custom Aperture](#_define)\n", + "#### 4. [Define a Custom Aperture](#_define)\n", "\n", "* Isolate central clump from your saturation mask\n", "* Obtain circular aperture as a boolean mask\n", "* Combine circular aperture with saturation mask\n", "\n", - "#### [Photometry with a Custom Aperture](#_phot)\n", + "#### 5. [Photometry with a Custom Aperture](#_phot)\n", "\n", "* Extract counts with the custom aperture\n", - "* Estimate background to be subtracted" + "* Estimate background to be subtracted\n", + "\n", + "#### 5. [Additional Results](#_results)\n", + "\n", + "* A worked example with several stars" ] }, { @@ -73,11 +79,12 @@ "| Package Name | module | docs | used for |\n", "|------------------|:-----------------|:-------------:|:------------|\n", "| `os` | `system` | link|command line input|\n", - "|`glob` | `glob` | link| search for files based on Unix shell rules |\n", + "|`shutil` | `rmtree` | link| remove directory tree |\n", "|`numpy` | `_s` | link| construct array slice object |\n", "|`matplotlib` |`pyplot` | link| plotting |\n", "|`astroquery.mast` |`Observations` | link| download data from MAST |\n", "|`astropy.io` | `fits` | link| access and update fits files |\n", + "|`astropy.table` | `Table` | link| constructing and editing in a tabular format |\n", "|`astropy.stats` |`sigma_clip`| link| sigma clipping image for background estimation |\n", "|`scipy.signal` |`convolve2d`| link| convolve saturation mask with kernel |\n", "|`stsci.skypac` |`pamutils`| link|obtain pixel area maps (PAM) |\n", @@ -87,21 +94,12 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The following task in the stsci.skypac package can be run with TEAL:\n", - " skymatch \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import os\n", - "import glob\n", + "import shutil\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -109,6 +107,7 @@ "from astroquery.mast import Observations\n", "\n", "from astropy.io import fits\n", + "from astropy.table import Table\n", "from astropy.stats import sigma_clip\n", "\n", "from scipy.signal import convolve2d\n", @@ -123,12 +122,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Setting environment variables for later use with the Calibration Reference Data System (CRDS)." + "Here we set environment variables for later use with the Calibration Reference Data System (CRDS)." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -150,60 +149,67 @@ "\n", "#### [GO Proposal 14949](https://stdatu.stsci.edu/proposal_search.php?mission=hst&id=14949): \"ACS External CTE Monitor\"\n", "\n", - "Using the python package `astroquery`, we can download files from the [MAST](http://archive.stsci.edu) archive." + "Using the python package `astroquery`, we can download files from the [MAST](http://archive.stsci.edu) archive.\n", + "\n", + "
\n", + "MAY CHANGE: The argument \"mrp_only\" stands for \"minimum recommended products only\". It currently needs to be set to False, although in the future, False is intended to be set as the default and can be left out.\n", + "
" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "obs_table = Observations.query_criteria(proposal_id=14949, filters='F606W')" + "obs_table = Observations.query_criteria(proposal_id=14949, filters='F606W')\n", + "\n", + "dl_table = Observations.download_products(obs_table['obsid'],\n", + " productSubGroupDescription=['FLC'],\n", + " mrp_only=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the package `os` to put all of these files in our working directory for convenience." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: Found cached file ./mastDownload/HST/jdg303d5q/jdg303d5q_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg303d7q/jdg303d7q_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg302clq/jdg302clq_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg302cnq/jdg302cnq_flc.fits with expected size 167964480. [astroquery.query]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg302cxq_flc.fits to ./mastDownload/HST/jdg302cxq/jdg302cxq_flc.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg302czq_flc.fits to ./mastDownload/HST/jdg302czq/jdg302czq_flc.fits ... [Done]\n", - "INFO: Found cached file ./mastDownload/HST/jdg301c4q/jdg301c4q_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg301c6q/jdg301c6q_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg303d9q/jdg303d9q_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg303dbq/jdg303dbq_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg301bwq/jdg301bwq_flc.fits with expected size 167964480. [astroquery.query]\n", - "INFO: Found cached file ./mastDownload/HST/jdg301byq/jdg301byq_flc.fits with expected size 167964480. [astroquery.query]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg303dlq_flc.fits to ./mastDownload/HST/jdg303dlq/jdg303dlq_flc.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg303dnq_flc.fits to ./mastDownload/HST/jdg303dnq/jdg303dnq_flc.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg301ccq_flc.fits to ./mastDownload/HST/jdg301ccq/jdg301ccq_flc.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg301ceq_flc.fits to ./mastDownload/HST/jdg301ceq/jdg301ceq_flc.fits ... [Done]\n", - "INFO: Found cached file ./mastDownload/HST/jdg302ctq/jdg302ctq_flc.fits with expected size 167964480. [astroquery.query]\n", - "Downloading URL https://mast.stsci.edu/api/v0/download/file?uri=mast:HST/product/jdg302cvq_flc.fits to ./mastDownload/HST/jdg302cvq/jdg302cvq_flc.fits ... [Done]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "dl_table = Observations.download_products(obs_table['obsid'],\n", - " productSubGroupDescription=['FLC'],\n", - " mrp_only=False)" + "for row in dl_table:\n", + " oldfname = row['Local Path']\n", + " newfname = os.path.basename(oldfname)\n", + " os.rename(oldfname, newfname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### File Information\n", - "\n", + "Now that all of our files are in the current working directory, we delete the leftover MAST file structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shutil.rmtree('mastDownload')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### File Information \n", + "The structure of the fits files from ACS may be different depending on what kind of observation was made. \n", "For more information, refer to Section Section 2.2 of the ACS Data Handbook.\n", "\n", "#### Raw Files\n", @@ -228,768 +234,909 @@ ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "fname_short = 'mastDownload/HST/jdg302ctq/jdg302ctq_flc.fits'\n", - "fname_long = 'mastDownload/HST/jdg301c4q/jdg301c4q_flc.fits'" + "You can always use `.info()` on an HDUlist for an overview of the structure." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Prepare Images\n", - "***\n", - "\n", - "* Apply Pixel Area Map\n", - "* Separate by long and short exposure\n", - "* Make sure you have images of the same field" + "with fits.open('jdg302ctq_flc.fits') as hdulist:\n", + " hdulist.info()" ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "fitsfile = fname_short\n", - "\n", - "dq_short = fits.getdata(fitsfile, ext=3)==256\n", - "raw_short = fits.getdata(fitsfile)\n", - "\n", - "pname = os.path.basename(fitsfile).split('.')[0] + '_pam.fits'\n", - "pamutils.pam_from_file(fitsfile, ext=1, output_pam=pname)\n", - "\n", - "pam_short = fits.getdata(pname)\n", + "## 1. Prepare Images \n", + "***\n", "\n", - "img_short = raw_short * pam_short" + "For this notebook, we will need two well-aligned images of the same field on the sky. One image should have a short exposure time (eg. 40 seconds) and the other should have a long exposure time (eg. 400 seconds). Here we assume you already know which images those are, and set those observation files to appropriate variable names." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "fitsfile = fname_long\n", - "\n", - "dq_long = fits.getdata(fitsfile, ext=3)==256\n", - "raw_long = fits.getdata(fitsfile)\n", - "\n", - "pname = os.path.basename(fitsfile).split('.')[0] + '_pam.fits'\n", - "pamutils.pam_from_file(fitsfile, ext=1, output_pam=pname)\n", - "\n", - "pam_long = fits.getdata(pname)\n", - "\n", - "img_long = raw_long * pam_long" + "fname_short = 'jdg302ctq_flc.fits'\n", + "fname_long = 'jdg301c4q_flc.fits'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Identify Saturated Stars\n", - "***\n", + "Before we use our images for photometry, we will need to apply a pixel area map (PAM) correction. This step corrects the difference in flux accross the CCD due to distortion. A dedicated notebook on PAM corrections can be found in the ACS notebook collection.\n", "\n", - "Here we have the local coordinates of three stars in our field. We also specify the pixel scale of WCS to help us with determining sizes for our apertures." + "First, we will work with the short exposure image." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "img_coord = [(1711, 225), (1205, 238), (3159, 312)]\n", - "\n", - "pix_per_arcsec = 20" + "fitsfile = fname_short" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will make cutouts around each souce with a radius of 100 pixels. This size cutout is typically big enough to contain saturation trails from the brightest stars. We will also assume that our extraction aperture has a radius of 0.5 arcseconds." + "Now we can extract the image from the fits file using the python package `fits`. Here, I use the name \"raw_short\" to indicate that this image has not had the PAM correction applied, and is the short exposure image." ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "cutout_radius = 100\n", - "aperture_radius = 0.5 * pix_per_arcsec" + "raw_short = fits.getdata(fitsfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's take a look at the first star in our list. We first can make a slice object with numpy to help make cutouts around our source." + "Now we need to obtain the PAM for this image using the python package `pamutils`. To contruct the new filename for the PAM, we will use the python package `os` to grab the basename of our fits file, and append '_pam.fits' at the end." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "x, y = img_coord[0]\n", - "\n", - "starty, endy = (y - cutout_radius), (y + cutout_radius)\n", - "startx, endx = (x - cutout_radius), (x + cutout_radius)\n", - "\n", - "cutter = np.s_[starty:endy, startx:endx]" + "pname = os.path.basename(fitsfile).split('.')[0] + '_pam.fits'\n", + "print(pname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can take a cutout of our image around the source." + "Now we can run `pam_from_file` on our fits file to create our PAM." ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "plot.ds9_imitate(plt, img_short[cutter])" + "pamutils.pam_from_file(fitsfile, ext=1, output_pam=pname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can visually confirm that this source is affected by saturation trails in the short exposure. What about the long exposure image?" + "Once our PAM has been written to file, we can extract it with `fits` for later use." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "plot.ds9_imitate(plt, img_long[cutter])" + "pam_short = fits.getdata(pname)" ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "plt.imshow(dq_short[cutter], cmap='bone')" + "Finally, we can apply the PAM corrections to our \"raw\" image." ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "plt.imshow(dq_long[cutter], cmap='bone')" + "img_short = raw_short * pam_short" ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "fig = plt.figure(figsize=[5,5])\n", - "ax = fig.add_subplot(111)\n", - "\n", - "circ_patch = Circle((cutout_radius, cutout_radius),\n", - " radius=aperture_radius,\n", - " color='C1',\n", - " linewidth=2,\n", - " fill=False)\n", - "ax.imshow(dq_short[cutter], cmap='bone')\n", - "ax.add_patch(circ_patch)" + "There is one more array we'll need to extract from our fits file. The data quality (DQ) array labels saturated pixels with the flag number 256. As seen from our [file information](#_fileinfo), the DQ array can be found in extension 3 of the HDU list." ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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aDNDdH737TElWJFmdZPUQapCkJzXwmB3wkqralORo4Nok35vMTFW1ElgJkKSGUIck7dXAe3ZVtam73wpcDZwObEmyGKC73zrociRpEAOFXZJDkxw2/hh4BbAOuAa4oJvsAuALgyxHkgY1aDf2GODqJOPv9cmq+ockNwCfTvI24C7gjQMuR5IGkqqZHy5zzE7SANb0Hfa2V55BIakJhp2kJhh2kppg2ElqgmEnqQmGnaQmGHaSmmDYSWqCYSepCYadpCYYdpKaYNhJaoJhJ6kJhp2kJhh2kppg2ElqgmEnqQmGnaQmGHaSmmDYSWqCYSepCYadpCYYdpKaYNhJaoJhJ6kJUw67JMcn+VqS25LcmuRdXfv7k9ydZG13Wz68ciVpauYNMO8O4Per6sYkhwFrklzbvfYXVfWhwcuTpOGYcthV1WZgc/f4oSS3AccOqzBJGqahjNklORF4IfCtruniJLckWZVk0V7mWZFkdZLVw6hBkp5MqmqwN0ieAnwD+NOq+lySY4B7gAL+BFhcVRdO8B6DFSGpZWuqatlEEw20Z5dkPvBZ4Iqq+hxAVW2pqrGq2gl8DDh9kGVI0jAM8m1sgI8Dt1XVn/e1L+6b7HXAuqmXJ0nDMci3sS8Bfgf4TpK1XdsfAucnWUqvG7sBePtAFUrSEAw8ZjeUIhyzkzR1ox+zk6T9hWEnqQmGnaQmGHaSmmDYSWqCYSepCYadpCYYdpKaYNhJaoJhJ6kJhp2kJhh2kppg2ElqgmEnqQmGnaQmGHaSmmDYSWqCYSepCYadpCYYdpKaYNhJaoJhJ6kJhp2kJhh2kppg2ElqwsjCLsnZSb6f5I4k7xnVciRpMkYSdknmApcC5wDPAc5P8pxRLEuSJmNUe3anA3dU1Q+r6jHgU8C5I1qWJE1oVGF3LPDjvucbu7YnJFmRZHWS1SOqQZKeMG9E75s9tNUuT6pWAisBkvwUeBi4Z0T17IsjsY5+1rEr69jVbKjjhMlMNKqw2wgc3/f8OGDT3iauqqOSrK6qZSOqZ9KswzqsY/+rYzJG1Y29ATglyZIkC4DzgGtGtCxJmtBI9uyqakeSi4EvA3OBVVV16yiWJUmTMapuLFX1ReCL+zDLylHVso+sY1fWsSvr2NVsqWNCqaqJp5Kk/Zyni0lqgmEnqQmzIuxm4jzaJMcn+VqS25LcmuRdXfv7k9ydZG13Wz4NtWxI8p1ueau7tiOSXJvk9u5+0YhreHbfv3ltkgeTvHu61keSVUm2JlnX17bHdZCev+y2l1uSnDrCGv4syfe65Vyd5PCu/cQkj/Stl48Oo4YnqWOvn0OS93br4vtJXjniOq7qq2FDkrVd+8jWx9BU1Yze6H1bux44CVgA3Aw8ZxqWuxg4tXt8GPADeufxvh/4z9O8DjYAR+7W9kHgPd3j9wAfmObP5Cf0DtaclvUBvBQ4FVg30ToAlgNfonfw+hnAt0ZYwyuAed3jD/TVcGL/dNOwLvb4OXTb7M3AQcCS7v/S3FHVsdvrHwb+aNTrY1i32bBnNyPn0VbV5qq6sXv8EHAbu53SNsPOBS7vHl8OvHYal/0yYH1V/Wi6FlhV1wH37da8t3VwLvCJ6rkeODzJ4lHUUFVfqaod3dPr6R0gP1J7WRd7cy7wqap6tKruBO6g939qpHUkCfAm4MphLGs6zIawm/A82lFLciLwQuBbXdPFXbdl1ai7j50CvpJkTZIVXdsxVbUZesEMHD0NdYw7j1034uleH+P2tg5mapu5kN4e5bglSW5K8o0kZ07D8vf0OczUujgT2FJVt/e1Tff62CezIewmPI92pAtPngJ8Fnh3VT0IfAQ4GVgKbKa3qz5qL6mqU+ldEusdSV46Dcvco+6Ml9cAn+maZmJ9TGTat5kk7wN2AFd0TZuBZ1bVC4H/BHwyyVNHWMLePoeZ+v9zPrv+QZzu9bHPZkPY7dN5tMOUZD69oLuiqj4HUFVbqmqsqnYCH2NIXYInU1WbuvutwNXdMreMd826+62jrqNzDnBjVW3papr29dFnb+tgWreZJBcArwbeXN0AVddtvLd7vIbeWNmzRlXDk3wO0/7/J8k84PXAVX31Tev6mIrZEHYzch5tN+bwceC2qvrzvvb+sZ/XAet2n3fIdRya5LDxx/QGxNfRWwcXdJNdAHxhlHX02eUv9nSvj93sbR1cA7yl+1b2DOCB8e7usCU5G/gD4DVVta2v/aj0LlJLkpOAU4AfjqKGbhl7+xyuAc5LclCSJV0d3x5VHZ2XA9+rqo199U3r+piSmf6GpPtDuZzet6HrgfdN0zL/Lb3d/VuAtd1tOfC3wHe69muAxSOu4yR636bdDNw6/u8Hng58Fbi9uz9iGtbJIcC9wNP62qZlfdAL2M3A4/T2Vt62t3VAr+t2abe9fAdYNsIa7qA3Jja+jXy0m/a3u8/rZuBG4N+NeF3s9XMA3teti+8D54yyjq79MuCi3aYd2foY1s3TxSQ1YTZ0YyVp5Aw7SU0w7CQ1wbCT1ATDTlITDDtJTTDsJDXh/wPvQDRO0WxDPwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "fig = plt.figure(figsize=[5,5])\n", - "ax = fig.add_subplot(111)\n", - "\n", - "circ_patch = Circle((cutout_radius, cutout_radius),\n", - " radius=aperture_radius,\n", - " color='C1',\n", - " linewidth=2,\n", - " fill=False)\n", - "ax.imshow(dq_long[cutter], cmap='bone', origin=1)\n", - "ax.add_patch(circ_patch)" + "dq_short = fits.getdata(fitsfile, ext=3)==256" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Since the saturated pixels appear to extend past our extraction radius, we can use a different method to improve photometry." + "Here I repeat all of the previous steps with the long exposure image, changing variable names where necessary." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Bleed the Saturation Mask" + "fitsfile = fname_long\n", + "\n", + "dq_long = fits.getdata(fitsfile, ext=3)==256\n", + "raw_long = fits.getdata(fitsfile)\n", + "\n", + "pname = os.path.basename(fitsfile).split('.')[0] + '_pam.fits'\n", + "pamutils.pam_from_file(fitsfile, ext=1, output_pam=pname)\n", + "\n", + "pam_long = fits.getdata(pname)\n", + "\n", + "img_long = raw_long * pam_long" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define a kernel. We can do this by hand. Since pixels affected by saturation will spill charge along columns, all we need is to convolve our image with a column kernel." + "## 2. Identify Saturated Stars \n", + "***\n", + "\n", + "Before we begin our modified aperture photometry routine, we should determine whether or not our sources are saturated. We can identify saturated stars by whether or not their saturation trails extend past a typical extraction radius.\n", + "\n", + "Here we have the local coordinates of a bright star in our field." ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "bleed_kernel = np.array([[0,1,0],\n", - " [0,1,0],\n", - " [0,1,0],\n", - " [0,1,0],\n", - " [0,1,0]])\n", - "\n", - "plt.imshow(bleed_kernel, origin=1)" + "local_coord = {'x':1711, 'y':225}" ] }, { - "cell_type": "code", - "execution_count": 19, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from scipy.signal import convolve2d\n", - "\n", - "# Here, mode='same' ensures that the returned array is the same shape as the input array\n", - "conv_sat = convolve2d(dq_long[cutter], bleed_kernel, mode='same')\n", - "sat_aperture = np.array([x > 0 for x in conv_sat]).astype(bool)" + "We will make cutouts around our source with a radius of 100 pixels. This size cutout is typically big enough to contain saturation trails from the brightest stars. We will also assume that our extraction aperture has a radius of 0.5 arcseconds. Knowing that the ACS pixel scale is ~20 pixels/arcsecond, we can calculate our aperture radius in pixels." ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ - "fig = plt.figure(figsize=[5,5])\n", - "ax = fig.add_subplot(111)\n", - "\n", - "circ_patch = Circle((cutout_radius, cutout_radius),\n", - " radius=aperture_radius,\n", - " color='C1',\n", - " linewidth=2,\n", - " fill=False)\n", - "ax.imshow(sat_aperture, cmap='bone', origin=1)\n", - "ax.add_patch(circ_patch)" + "pix_per_arcsec = 20\n", + "cutout_radius = 100\n", + "aperture_radius = 0.5 * pix_per_arcsec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Define a Custom Aperture\n", - "\n", - "Now we want to create a new aperture which includes the pixels with the spilled charge. If we want to use the saturation mask we just created, we need to isolate only the clump associated with our star.\n", - "\n", - "Here, we give you a function which will return a mask with only the central clump." + "We can make a slice object with numpy to help make cutouts around our source. It will be convenient for us to define a function to construct a cutter object with `numpy`." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Isolate associated clump from saturation mask\n", - "\n", - "def find_central_clump(boolean_mask):\n", - " \n", - " from scipy import ndimage\n", - "\n", - " central_index = tuple((np.array(np.shape(boolean_mask))/2).astype(int))\n", - "\n", - " label, num_label = ndimage.label(boolean_mask == True)\n", - " size = np.bincount(label.ravel())\n", + "def make_cutter(x, y, cutout_radius=100):\n", " \n", - " clump_labels = range(size[1:].shape[0])\n", + " # Makes a 2D array slice object centered around x, y\n", " \n", - " is_central_clump = False\n", + " starty, endy = (y - cutout_radius), (y + cutout_radius)\n", + " startx, endx = (x - cutout_radius), (x + cutout_radius)\n", " \n", - " for cl in clump_labels:\n", - " \n", - " clump_mask = label == (cl + 1)\n", - " idxs = [tuple(i) for i in np.argwhere(clump_mask)]\n", - " is_central_clump = central_index in idxs\n", - "\n", - " if is_central_clump:\n", - " \n", - " return clump_mask\n", - " \n", - " else: continue\n", - " \n", - " if not is_central_clump:\n", - " \n", - " return 0" + " return np.s_[starty:endy, startx:endx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can apply this function to our mask" + "Now we can take a cutout of our image around the source." ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "central_clump = find_central_clump(sat_aperture)\n", - "\n", - "plt.imshow(central_clump, origin=1)" + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cutter = make_cutter(local_coord['x'], local_coord['y'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can use the circular aperture in the package `photutils`. To combine it with our mask, we need the circular aperture in mask form. Luckily, this is a built-in feature of aperture objects!" + "Before we try out our cutter, let's take a look at our full frame image." ] }, { "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" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "aperture = CircularAperture((cutout_radius, cutout_radius), aperture_radius)\n", - "aperture_mask = np.array(aperture.to_mask()[0])\n", - "\n", - "plt.imshow(aperture_mask, origin=1)" + "plot.ds9_imitate(plt, img_short)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To match the size of our cutout, we can create a new array with our circular aperture at the center" + "Now by indexing our image with our cutter, we can grab just the cutout we need!" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(200, 200)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(sat_aperture)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "circular_mask = np.zeros(np.shape(sat_aperture))\n", - "\n", - "aperture_dim = np.shape(aperture_mask)\n", - "cutout_dim = np.shape(circular_mask)\n", - "\n", - "insert_start = int((cutout_dim[0] - aperture_dim[0]) / 2)\n", - "insert_end = int(insert_start + aperture_dim[0])\n", - "\n", - "circular_mask[insert_start:insert_end, insert_start:insert_end] = aperture_mask\n", - " \n", - "circular_mask = circular_mask.astype(bool)\n", - "plt.imshow(circular_mask, origin=1)" + "plot.ds9_imitate(plt, img_short[cutter])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we want to combine both of our masks to form one aperture." + "We can visually confirm that this source is affected by saturation trails in the short exposure. What about the long exposure image? Since our images are aligned, we can use the same coordinates (and the same cutter!) as before." ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "combined_aperture = np.logical_or(central_clump, circular_mask)\n", - "plt.imshow(combined_aperture, origin=1)" + "plot.ds9_imitate(plt, img_long[cutter])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Photometry with a Custom Aperture\n", - "\n", - "To get the local background for each source, we will use sigma-clipped cutouts of the image to obtain the median background value. We will then estimate the background in our new aperture by multiplying the median by the area covered by the aperture." + "We can also apply the same cutter to our DQ saturated pixel array!" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/molaes/Software/miniconda3/envs/astroconda/lib/python3.5/site-packages/numpy/core/fromnumeric.py:688: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", - " a.partition(kth, axis=axis, kind=kind, order=order)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "img_cutout = img_long[cutter]\n", - "flux_sum = np.sum(img_cutout[combined_aperture])\n", + "plt.imshow(dq_short[cutter], cmap='bone')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we expect, we do not see very much saturation in our short exposure image. What about our long exposure image?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(dq_long[cutter], cmap='bone')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we see a large clump of saturated pixels spilling along the y-axis!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For both of these images, we want to see whether or not the saturated pixels fall outside the range of our typical 0.5\" extraction radius." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[5,5])\n", + "ax = fig.add_subplot(111)\n", "\n", - "bkg_data = sigma_clip(img_cutout, sigma=3, iters=5)\n", + "circ_patch = Circle((cutout_radius, cutout_radius),\n", + " radius=aperture_radius,\n", + " color='C1',\n", + " linewidth=2,\n", + " fill=False)\n", "\n", - "new_aperture_area = np.sum(combined_aperture)\n", - "bkg_sum = np.median(bkg_data) * new_aperture_area\n", + "ax.imshow(dq_short[cutter], cmap='bone')\n", + "ax.add_patch(circ_patch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[5,5])\n", + "ax = fig.add_subplot(111)\n", "\n", - "final_sum = flux_sum - bkg_sum" + "circ_patch = Circle((cutout_radius, cutout_radius),\n", + " radius=aperture_radius,\n", + " color='C1',\n", + " linewidth=2,\n", + " fill=False)\n", + "ax.imshow(dq_long[cutter], cmap='bone', origin=1)\n", + "ax.add_patch(circ_patch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the saturated pixels extend past our extraction radius, we need to use a different method to improve photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Bleed the Saturation Mask \n", + "\n", + "First we need to define a kernel to bleed our saturation mask. We can do this by hand. Since pixels affected by saturation will spill charge along columns, all we need is to convolve our image with a column kernel." ] }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/molaes/Software/miniconda3/envs/astroconda/lib/python3.5/site-packages/numpy/core/fromnumeric.py:688: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", - " a.partition(kth, axis=axis, kind=kind, order=order)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "def make_cutter(x, y, cutout_radius=100):\n", + "bleed_kernel = np.array([[0,1,0],\n", + " [0,1,0],\n", + " [0,1,0],\n", + " [0,1,0],\n", + " [0,1,0]])\n", "\n", - " starty, endy = (y - cutout_radius), (y + cutout_radius)\n", - " startx, endx = (x - cutout_radius), (x + cutout_radius)\n", + "plt.imshow(bleed_kernel, origin=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the `scipy` function `convolve2d()` to convolve our cutout with our kernel. Here, `mode='same'` ensures that the returned array is the same shape as the input array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conv_sat = convolve2d(dq_long[cutter], bleed_kernel, mode='same')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After convolution, we need to convert to a boolean array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sat_aperture = np.array([x > 0 for x in conv_sat]).astype(bool)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's take a look at our mask to make sure it \"bled out\" properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[5,5])\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.imshow(sat_aperture, cmap='bone', origin=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define a Custom Aperture \n", + "\n", + "Now we want to create a new aperture which includes the pixels with the spilled charge. If we want to use the saturation mask we just created, we need to isolate only the clump associated with our star.\n", + "\n", + "Here, we give you a function which will return a mask with only the central clump." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Isolate associated clump from saturation mask\n", + "\n", + "def find_central_clump(boolean_mask):\n", " \n", - " return np.s_[starty:endy, startx:endx]\n", + " from scipy import ndimage\n", "\n", - "fitsfile = fname_long\n", + " central_index = tuple((np.array(np.shape(boolean_mask))/2).astype(int))\n", "\n", - "pname = os.path.basename(fitsfile).split('.')[0] + '_pam.fits'\n", - "pamutils.pam_from_file(fitsfile, ext=1, output_pam=pname)\n", + " label, num_label = ndimage.label(boolean_mask == True)\n", + " size = np.bincount(label.ravel())\n", + " \n", + " clump_labels = range(size[1:].shape[0])\n", + " \n", + " is_central_clump = False\n", + " \n", + " for cl in clump_labels:\n", + " \n", + " clump_mask = label == (cl + 1)\n", + " idxs = [tuple(i) for i in np.argwhere(clump_mask)]\n", + " is_central_clump = central_index in idxs\n", "\n", - "raw_array = fits.getdata(fitsfile)\n", - "pam_array = fits.getdata(pname)\n", - "img_array = raw_array * pam_array\n", + " if is_central_clump:\n", + " \n", + " return clump_mask\n", + " \n", + " else: continue\n", + " \n", + " if not is_central_clump:\n", + " \n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply this function to our mask to isolate the central clump." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "central_clump = find_central_clump(sat_aperture)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the resulting array to see the clump of interest isolated at the center." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(central_clump, origin=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the package `photutils` to define a circular aperture. To combine it with our mask, we need the circular aperture in mask form. Luckily, this is a built-in feature of aperture objects!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aperture = CircularAperture((cutout_radius, cutout_radius), aperture_radius)\n", + "aperture_mask = np.array(aperture.to_mask()[0])\n", "\n", - "sat_array = fits.getdata(fitsfile, ext=3)==256\n", + "plt.imshow(aperture_mask, origin=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To match the size of our cutout, we can create a new array with our circular aperture at the center" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "circular_mask = np.zeros(np.shape(sat_aperture))\n", "\n", - "cutter = make_cutter(x, y)\n", - "img_cutout = img_array[cutter]\n", - "sat_cutout = sat_array[cutter]\n", + "aperture_dim = np.shape(aperture_mask)\n", + "cutout_dim = np.shape(circular_mask)\n", "\n", - "sat_mask = find_central_clump(sat_cutout)\n", - "cir_mask = circular_mask\n", + "insert_start = int((cutout_dim[0] - aperture_dim[0]) / 2)\n", + "insert_end = int(insert_start + aperture_dim[0])\n", "\n", - "custom_aperture = np.logical_or(sat_mask, cir_mask)\n", - "flux_sum = np.sum(img_cutout[combined_aperture])\n", - "bkg_data = sigma_clip(img_cutout, sigma=3, iters=5)\n", + "circular_mask[insert_start:insert_end, insert_start:insert_end] = aperture_mask\n", + " \n", + "circular_mask = circular_mask.astype(bool)\n", "\n", + "plt.imshow(circular_mask, origin=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the `numpy` function `logical_or()` to combine both of our masks to form one boolean array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "combined_aperture = np.logical_or(central_clump, circular_mask)\n", + "\n", + "plt.imshow(combined_aperture, origin=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Photometry with a Custom Aperture \n", + "\n", + "Now that we have our custom aperture, let's use that aperture to perform photometry for one source on boht our short and long expsure images.\n", + "\n", + "We'll start with the short exposure image. As before, we will use our cutter to make a cutout around the source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "img_cutout = img_short[cutter]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To obtain the flux in the aperture, all we need to do is to apply the mask to the cutout, and then sum the values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flux_sum = np.sum(img_cutout[combined_aperture])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the local background for each source, we will sigma-clip the image and calculate the median background value. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bkg_data = sigma_clip(img_cutout, sigma=2, iters=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will then estimate the background in our new aperture by multiplying the median by the area covered by the aperture." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "new_aperture_area = np.sum(combined_aperture)\n", + "bkg_sum = np.median(bkg_data) * new_aperture_area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Subtract the estimated background from our flux sum, and you're finished!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_sum_short = flux_sum - bkg_sum\n", + "\n", + "print(final_sum_short)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, I repeat the photometry steps for this source on the long exposure image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "img_cutout = img_long[cutter]\n", + "flux_sum = np.sum(img_cutout[combined_aperture])\n", + "bkg_data = sigma_clip(img_cutout, sigma=2, iters=10)\n", "bkg_sum = np.median(bkg_data) * new_aperture_area\n", "\n", - "final_sum = flux_sum - bkg_sum" + "final_sum_long = flux_sum - bkg_sum\n", + "\n", + "print(final_sum_long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we have recovered the lost flux with our new aperture, our star in the 400 second exposure should have ~10 times the flux as our star in the 40 second exposure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_sum_long/final_sum_short" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Additional Results\n", + "\n", + "Here we perform all of the photometry steps on a list of three stars. This section of the notebook is intended as a worked example for multiple stars, and therefore will not guide you through each step.\n", + "\n", + "Since we are dealing with photometry of more than one star, it will be convenient to define a table to store information for each star. We will create a column each for x-position, y-position, and the final flux sum for each of the images. We set the table length at 'n' rows for each star, and fill it with zeros to start." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "local_coords = [(1711, 225), (1205, 238), (3159, 312)]\n", + "n = len(local_coords)\n", + "\n", + "dtype = [('x', 'i4'), \n", + " ('y', 'i4'), \n", + " ('flux_short', 'f8'), \n", + " ('flux_long', 'f8'), \n", + " ('flux_ratio', 'f8')]\n", + "\n", + "source_table = Table(data=np.zeros(n, dtype=dtype))\n", + "\n", + "source_table['x'] = [c[0] for c in local_coords]\n", + "source_table['y'] = [c[1] for c in local_coords]\n", + "\n", + "print(source_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below I have condensed the steps of this notebook into functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_images(fname):\n", + " \n", + " pname = os.path.basename(fname).split('.')[0] + '_pam.fits'\n", + " pamutils.pam_from_file(fname, ext=1, output_pam=pname)\n", + "\n", + " raw_array = fits.getdata(fname)\n", + " pam_array = fits.getdata(pname)\n", + " img_array = raw_array * pam_array\n", + "\n", + " sat_array = fits.getdata(fitsfile, ext=3)==256\n", + " \n", + " return img_array, sat_array\n", + "\n", + "def bleed_saturation_mask(sat_array):\n", + " \n", + " bleed_kernel = np.array([[0,1,0],\n", + " [0,1,0],\n", + " [0,1,0],\n", + " [0,1,0],\n", + " [0,1,0]])\n", + " \n", + " convolved = convolve2d(sat_array, bleed_kernel, mode='same')\n", + " bled_mask = np.array([x > 0 for x in conv_sat]).astype(bool)\n", + " \n", + " return bled_mask\n", + "\n", + "def photometry_on_cutout(img_cutout, custom_aperture):\n", + " \n", + " flux_sum = np.sum(img_cutout[custom_aperture])\n", + " bkg_data = sigma_clip(img_cutout, sigma=3, iters=10)\n", + " \n", + " aperture_area = np.sum(custom_aperture)\n", + " bkg_flux = np.median(bkg_data) * aperture_area\n", + " \n", + " return flux_sum-bkg_flux" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell performs photometry on the three stars." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for row in source_table:\n", + "\n", + " img_arr_s, _ = prepare_images(fname_short)\n", + " img_arr_l, sat_arr = prepare_images(fname_long)\n", + " sat_mask = bleed_saturation_mask(sat_arr)\n", + "\n", + " cutter = make_cutter(row['x'], row['y'])\n", + "\n", + " sat_aperture = find_central_clump(sat_mask[cutter])\n", + "\n", + " custom_aperture = np.logical_or(sat_mask, circular_mask)\n", + "\n", + " row['flux_short'] = photometry_on_cutout(img_arr_s[cutter], custom_aperture)\n", + " row['flux_long'] = photometry_on_cutout(img_arr_l[cutter], custom_aperture)\n", + " \n", + "source_table['flux_ratio'] = source_table['flux_long']/ source_table['flux_short']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our table..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "source_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this method is an improvement for some saturated stars, it still has limitations. We can make a quick plot to show that the percentage of recovered flux decreases for brighter stars." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8,5))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.plot(source_table['flux_short']/np.max(source_table['flux_short']),\n", + " source_table['flux_ratio']*10, 'o')\n", + "\n", + "ax.text(.7, 101, 'Perfect Recovery', color='C1', fontsize=12)\n", + "ax.set_ylim([60, 104])\n", + "ax.set_xlabel('Relative Flux (40s exposure)', fontsize=12)\n", + "ax.set_ylabel('% recovered flux (400s exposure)', fontsize=12)\n", + "ax.axhline(y=100, linestyle='--', color='C1')\n", + "ax.grid(True, linestyle=':')" ] }, { @@ -1006,6 +1153,15 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null,