From 852550c2259141ef9a77ff6a951a57e446d568e3 Mon Sep 17 00:00:00 2001 From: Brant Robertson Date: Wed, 1 Aug 2018 04:43:38 +0000 Subject: [PATCH 01/45] Starting work on a notebook to demo lsst.afw.display --- Visualization/AFW_Display_Demo.ipynb | 271 +++++++++++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 Visualization/AFW_Display_Demo.ipynb diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb new file mode 100644 index 00000000..2c1b10f0 --- /dev/null +++ b/Visualization/AFW_Display_Demo.ipynb @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Demo of lsst.afw.display\n", + "\n", + "[Brant Robertson](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brant)\n", + "\n", + "In this tutorial we will: \n", + "\n", + "* Show how to access the `lsst.afw.display` routines.\n", + "\n", + "* Use the LSST data Butler to access processed data and inspect it visually." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Import Common Python Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.visualization import ZScaleInterval\n", + "zscale = ZScaleInterval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Loading the LSST DM Stack" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.daf.persistence as dafPersist" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.afw.display as afwDisplay" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "datadir = \"/project/shared/data/Twinkles_subset/output_data_v2\"\n", + "butler = dafPersist.Butler(datadir)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Grab a calexp of interest\n", + "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235}\n", + "calexp = butler.get('calexp', **dataId)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Use lsst.afw.display with the matplotlib backend# Use l \n", + "afwDisplay.setDefaultBackend('matplotlib') " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "afw_display = afwDisplay.Display()\n", + "afw_display.scale('asinh', 'zscale')\n", + "afw_display.mtv(calexp.image)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "method_list = [func for func in dir(afw_display) if callable(getattr(afw_display, func))]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Buffering', '_Buffering', '__class__', '__del__', '__delattr__', '__dir__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattr__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'close', 'delAllDisplays', 'dot', 'erase', 'flush', 'getActiveCallbackKeys', 'getDefaultBackend', 'getDefaultFrame', 'getDisplay', 'getMaskPlaneColor', 'getMaskTransparency', 'incrDefaultFrame', 'interact', 'line', 'maskColorGenerator', 'mtv', 'pan', 'scale', 'setCallback', 'setDefaultBackend', 'setDefaultFrame', 'setDefaultMaskPlaneColor', 'setDefaultMaskTransparency', 'setMaskPlaneColor', 'setMaskTransparency', 'show', 'zoom']\n" + ] + } + ], + "source": [ + "print(method_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "afw_display = afwDisplay.Display()\n", + "afw_display.mtv(calexp.image)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "scale() missing 2 required positional arguments: 'algorithm' and 'min'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mafw_display\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: scale() missing 2 required positional arguments: 'algorithm' and 'min'" + ] + } + ], + "source": [ + "print(afw_display.scale().__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful Documentation\n", + "\n", + "[Info on image indexing](https://github.com/lsst/afw/blob/master/doc/lsst.afw.image/indexing-conventions.rst) \n", + "[afw.display Doxygen](http://doxygen.lsst.codes/stack/doxygen/x_masterDoxyDoc/namespacelsst_1_1afw_1_1display.html) \n", + "[afw.display GitHub](https://github.com/RobertLuptonTheGood/afw/tree/master/python/lsst/afw/display) \n", + "[Getting Started Image Display](https://pipelines.lsst.io/getting-started/display.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + }, + "livereveal": { + "scroll": true, + "start_slideshow_at": "selected" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c1cdb8d343f7a1d6c6e9d9753b0d6ecbb3020c34 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 10 Aug 2018 01:11:44 -0700 Subject: [PATCH 02/45] LSP accounts by club membership application only --- GettingStarted/GettingStarted.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index 9dd0878c..7d5ad2c3 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -10,10 +10,10 @@ notebook aspect of the LSST science platform. In these notes we provide: ## Accessing the LSST Science Platform The [LSST Science Platform (LSP) Notebook Aspect Documentation](https://nb.lsst.io/) provides an introduction to the NCSA system, including how to gain access and then how to use JupyterLab once you are in. -Getting in to NCSA takes involves getting an NCSA account, and then figuring out VPN access. +Getting on to the LSP involves getting an NCSA account, and then figuring out VPN access. #### Getting an NCSA Account -Contact Phil (DM @drphilmarshall on LSSTC Slack) to get an NCSA Stack Club account. You'll need to provide your full name (first and last) and your email address. You'll (eventually) get an email invitation to [create an account at NCSA](https://identity.ncsa.illinois.edu/) (including choosing a username of 8 characters or fewer). After you have submitted your form, it typically takes 24 hours for your account to be set up: set an alarm to come back the next day! +The Stack Club has a limited number of active NCSA accounts it can support. To join the Stack Club and get one of these accounts, please fill out the [Stack Club Membership Application Form](https://goo.gl/forms/588KlPTFfkEEFFUu2). You'll need to agree to abide by the [Rules](../Rules.md), and then provide your full name (first and last) and your email address. If your application is successful, you'll get an email with instructions on how to set up your LSP account. #### Accessing NCSA via its VPN At present, unless you are on an approved network, you must use the [NCSA virtual private network (VPN)](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service). From dbaa454bb763fc6e6bd31bcc618ce15238ddc005 Mon Sep 17 00:00:00 2001 From: David Shupe Date: Fri, 10 Aug 2018 10:02:47 -0700 Subject: [PATCH 03/45] Add the DM demo notebook "with-globular" Add a link to the with-globular notebook that has been updated for LSST 2018. --- Visualization/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Visualization/README.md b/Visualization/README.md index 75b787f6..91455a42 100644 --- a/Visualization/README.md +++ b/Visualization/README.md @@ -5,3 +5,5 @@ This folder contains: * Nothing, yet. However, you can find the SQuaRE team's Firefly tutorial notebook [here](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb), and watch the Phase 1 Session 1 video (in which Alex walks us through it) [here](https://www.youtube.com/watch?v=UjB0aaNd0MA). + +Another helpful notebook including image visualization with Firefly is Jim Bosch's [LSST2018 version](https://github.com/lsst-dm/dm-demo-notebooks/blob/master/workshops/lsst2018/intro-with-globular.ipynb) of the `with-globular` notebook. From a83f2c8ce6f7caa8a21db3ca133f2e62f7aa3db4 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Fri, 10 Aug 2018 17:52:37 +0000 Subject: [PATCH 04/45] First commit of brighter fatter notebook --- .../Brighter_fatter_correction.ipynb | 643 ++++++++++++++++++ 1 file changed, 643 insertions(+) create mode 100644 ImageProcessing/Brighter_fatter_correction.ipynb diff --git a/ImageProcessing/Brighter_fatter_correction.ipynb b/ImageProcessing/Brighter_fatter_correction.ipynb new file mode 100644 index 00000000..8d348caf --- /dev/null +++ b/ImageProcessing/Brighter_fatter_correction.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis of Beam Simulator Images and Brighter-fatter Correction\n", + "
Owner(s): **Andrew Bradshaw** ([@andrewkbradshaw](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@andrewkbradshaw))\n", + "
Last Verified to Run: **2018-08-10**\n", + "
Verified Stack Release: **16.0+22**\n", + "\n", + "This notebook demonstrates the [brighter-fatter systematic error](https://arxiv.org/abs/1402.0725) on images of stars and galaxies illuminated on an ITL-3800C-002 CCD at the [UC Davis LSST beam simulator laboratory](https://arxiv.org/abs/1411.5667). Using a series of images at increasing exposure times, we demonstrate the broadening of image profiles on DM stack shape measurements, and a [possible correction method](https://arxiv.org/abs/1711.06273) which iteratively applies a kernel to restore electrons to the pixels which they were deflected from. To keep things simple, for now we skip DM stack instrument signature removal (ISR) and work on a subset of images which are arrays (500x500) of electron counts in pixels.\n", + "\n", + "### Learning Objectives:\n", + "\n", + "After working through this tutorial you should be able to: \n", + "1. Characterize and measure objects (stars/galaxies) in LSST beam simulator images\n", + "2. Test the Brighter-Fatter kernel correction method on those images\n", + "3. Build your own tests of stack instrument signature removal algorithms\n", + "\n", + "### Logistics\n", + "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", + "\n", + "## Set-up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# What version of the Stack am I using?\n", + "! echo $HOSTNAME\n", + "! eups list -s | grep lsst_distrib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "import time,glob\n", + "\n", + "# if running stack v16, silence a long matplotlib Agg warning with:\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "# and also \n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# generate different portions of the full CCD by modifying the X/Y slices on the last line\n", + "for fitsfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*whole.fits')):\n", + " fooimg=fits.getdata(fitsfilename)\n", + " foohdr=fits.getheader(fitsfilename)\n", + " fitsfilename=fitsfilename.replace('whole','part')\n", + " fits.writeto(fitsfilename,fooimg[1500:2000,509*3:509*3+500],header=foohdr,overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in an image, then set the variance plane based upon it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we are skipping some ISR because these are preprocessed (bias subtracted, gain corrected)\n", + "import lsst.afw.image as afwImage\n", + "from lsst.ip.isr.isrFunctions import updateVariance\n", + "\n", + "fitsfilename = np.sort(glob.glob('/home/sarujin/testdata/ITL*part.fits'))[19] \n", + "#for fun: whole instead of part, with [2000:,:509],[:2000,1018:1527]=0.0\n", + "\n", + "print(\"Reading in \",fitsfilename.split('/')[-1])\n", + "hdr=fits.getheader(fitsfilename)\n", + "image_array=afwImage.ImageF.readFits(fitsfilename)\n", + "image = afwImage.ImageF(image_array)\n", + "\n", + "\n", + "exposure = afwImage.ExposureF(image.getBBox())\n", + "exposure.setImage(image)\n", + "\n", + "# Set the variance plane using the image plane via updateVariance function\n", + "gain = 1.0 #these images are already gain corrected\n", + "readNoise = 10.0 #in electrons\n", + "updateVariance(exposure.getMaskedImage(), gain, readNoise)\n", + "\n", + "# Optional other ways of setting variance or\n", + "#mask = afwImage.makeMaskFromArray(np.zeros((4000,4072)).astype('int32'))\n", + "#variance = afwImage.makeImageFromArray((readNoise**2 + image_array.array()**2)\n", + "#masked_image = afwImage.MaskedImageF(image, None, variance)\n", + "#exposure = afwImage.ExposureF(masked_image)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", + "plt.suptitle('Star/galaxy beam sim segment of '+hdr['CCD_SERN']+'\\n '+fitsfilename.split('/')[-1])\n", + "\n", + "plt.subplot(121)\n", + "plt.imshow(exposure.getImage().array,vmax=1e3,origin='lower')\n", + "plt.colorbar(label='electrons')\n", + "\n", + "plt.subplot(122)\n", + "plt.hist(exposure.getImage().array.flatten(),bins=1000,histtype='step')\n", + "plt.yscale('log')#,plt.xscale('log')\n", + "plt.xlabel('Number of electrons in pixel'),plt.ylabel('Number of pixels')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO++++ perhaps some other image stats from \n", + "# https://github.com/lsst/pipe_tasks/blob/master/python/lsst/pipe/tasks/exampleStatsTasks.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform image characterization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.pipe.tasks.characterizeImage import CharacterizeImageTask, CharacterizeImageConfig\n", + "\n", + "# first set a few configs that are specific to the data\n", + "charConfig = CharacterizeImageConfig()\n", + "#this set the fwhm of the simple PSF to that of the fwhm used in the simulation\n", + "charConfig.installSimplePsf.fwhm = .2\n", + "charConfig.doMeasurePsf = False\n", + "charConfig.doApCorr = False\n", + "charConfig.repair.doCosmicRay = False # we do have some cosmic rays, but we also subpixel features and an undersampled PSF\n", + "charConfig.detection.background.binSize = 10 \n", + "#charConfig.background.binSize = 50\n", + "charConfig.detection.minPixels = 5\n", + "charTask = CharacterizeImageTask(config=charConfig)\n", + "\n", + "charTask.run?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tstart=time.time()\n", + "charResult = charTask.run(exposure) # for stack v16, change to charTask.characterize(exposure)\n", + "print(\"Characterization took \",str(time.time()-tstart)[:4],\" seconds\")\n", + "print(\"Detected \",len(charResult.sourceCat),\" objects \")\n", + "\n", + "plt.title('X/Y locations of detections')\n", + "plt.plot(charResult.sourceCat['base_SdssCentroid_x'],charResult.sourceCat['base_SdssCentroid_y'],'r.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Looking at the mask plane, which started off as all zeros\n", + "# and now has some values of 2^5\n", + "maskfoo=exposure.getMask()\n", + "print(\"Unique mask plane values: \",np.unique(maskfoo.array))\n", + "print(\"Mask dictionary entries: \",maskfoo.getMaskPlaneDict())\n", + "\n", + "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", + "plt.subplot(121)\n", + "plt.imshow(maskfoo.array,origin='lower'),plt.colorbar()\n", + "plt.subplot(122)\n", + "plt.hist(maskfoo.array.flatten()),plt.xlabel('Mask plane values')\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "#Another option for analysis \n", + "#from https://gist.github.com/josePhoenix/8325c16b44fb5fa51f40261b184a78ef\n", + "\n", + "import lsst.afw.table\n", + "import lsst.afw.image\n", + "import lsst.afw.math\n", + "import lsst.meas.algorithms\n", + "import lsst.meas.base\n", + "import lsst.meas.deblender\n", + "\n", + "schema = lsst.afw.table.SourceTable.makeMinimalSchema()\n", + "detect = lsst.meas.algorithms.SourceDetectionTask(schema=schema)\n", + "deblend = lsst.meas.deblender.SourceDeblendTask(schema=schema)\n", + "measure = lsst.meas.base.SingleFrameMeasurementTask(schema=schema)\n", + "\n", + "tstart=time.time()\n", + "\n", + "table = lsst.afw.table.SourceTable.make(schema) # this is really just a factory for records, not a table\n", + "\n", + "detect_result = detect.run(table, exposure)\n", + "catalog = detect_result.sources # this is the actual catalog, but most of it's still empty\n", + "print(time.time()-tstart)\n", + "\n", + "#deblend.run(exposure, catalog)\n", + "#print(time.time()-tstart)\n", + "\n", + "measure.run(catalog, exposure)\n", + "print(time.time()-tstart)\n", + "\n", + "plt.figure(figsize=(15,15))\n", + "plt.scatter(catalog['base_SdssCentroid_x'],catalog['base_SdssCentroid_y'],marker='.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform further image calibration and measurement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# no need to do astrometry or photometry calibration\n", + "#since this is a lab image\n", + "from lsst.pipe.tasks.calibrate import CalibrateTask, CalibrateConfig\n", + "calConfig = CalibrateConfig()\n", + "calConfig.doAstrometry = False\n", + "calConfig.doPhotoCal = False\n", + "calConfig.detection.minPixels = 15\n", + "calConfig.doApCorr = False\n", + "calConfig.doDeblend = False # these are well-separated objects, deblending adds time & trouble\n", + "calConfig.detection.background.binSize = 50\n", + "calTask = CalibrateTask(config= calConfig, icSourceSchema=charResult.sourceCat.schema)\n", + "\n", + "calTask.run?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tstart=time.time()\n", + "# for stack v16, change to calTask.characterize(charResult.exposure)\n", + "calResult = calTask.run(charResult.exposure, background=charResult.background,\n", + " icSourceCat = charResult.sourceCat)\n", + "\n", + "print(\"Calibration took \",str(time.time()-tstart)[:4],\" seconds\")\n", + "print(\"Detected \",len(calResult.sourceCat),\" objects \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Looking at the source catalog which has now been attached to the \n", + "src=calResult.sourceCat #.copy(deep=True) ?\n", + "#print(src.asAstropy)\n", + "\n", + "src.writeFits(fitsfilename+'.cat')\n", + "# read back in and access via:\n", + "#catalog=fits.open(fitsfilename+'.cat')\n", + "#catalog[1].data['base_SdssShape_xx'] etc.\n", + "\n", + "plt.figure()\n", + "par_names=['base_SdssShape_xx','base_SdssShape_yy','base_SdssShape_flux']\n", + "par_mins=[0,0,0]\n", + "par_maxs=[5,5,1e6]\n", + "n_par=len(par_names)\n", + "\n", + "\n", + "plt.figure(figsize=(5*n_par,6)),plt.subplots_adjust(wspace=.25)\n", + "for par_name,par_min,par_max,i in zip(par_names,par_mins,par_maxs,range(n_par)):\n", + " plt.subplot(2,n_par,i+1)\n", + " plt.scatter(src['base_SdssCentroid_x'],src['base_SdssCentroid_y'],c=src[par_name],marker='o',vmin=par_min,vmax=par_max)\n", + " plt.xlabel('X'),plt.ylabel('Y'),plt.colorbar(label=par_name)\n", + "\n", + "\n", + " plt.subplot(2,n_par,n_par+i+1)\n", + " plt.hist(src[par_name],range=[par_min,par_max],bins=20,histtype='step')\n", + " plt.xlabel(par_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display the image with Firefly and overlay detected objects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.afw.display as afwDisplay\n", + "\n", + "# Firefly client imports\n", + "from firefly_client import FireflyClient\n", + "\n", + "# Standard libraries in support of Firefly display\n", + "from urllib.parse import urlparse, urlunparse, ParseResult\n", + "from IPython.display import IFrame, display, Markdown\n", + "import os\n", + "\n", + "# Own cell?\n", + "my_channel = '{}_test_channel'.format(os.environ['USER'])\n", + "server = 'https://lsst-lspdev.ncsa.illinois.edu'\n", + "\n", + "# This needs its own cell\n", + "ff='{}/firefly/slate.html?__wsch={}'.format(server, my_channel)\n", + "IFrame(ff,1000,600)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set the backend and attach to the waiting display channel\n", + "afwDisplay.setDefaultBackend('firefly')\n", + "afw_display = afwDisplay.getDisplay(frame=1, \n", + " name=my_channel)\n", + "\n", + "# Open the exposure (Firefly knows about mask planes)\n", + "afw_display.mtv(exposure)\n", + "\n", + "#Now we’ll overplot sources from the src table onto the image display using the Display’s dot method for plotting markers. \n", + "#Display.dot plots markers individually, so you’ll need to iterate over rows in the SourceTable. \n", + "#Next we display the first 100 sources. We limit the number of sources since plotting the whole catalog \n", + "#is a serial process and takes some time. Because of this, it is more efficient to send a batch of updates to the display, \n", + "#so we enclose the loop in a display.Buffering context, like this:\n", + "\n", + "afw_display.erase()\n", + "\n", + "with afw_display.Buffering():\n", + " for record in src[:]:\n", + " afw_display.dot('o', record.getX(), record.getY(), size=20, ctype='orange')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apply the brighter-fatter correction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.ip.isr.isrTask import IsrTask # brighterFatterCorrection lives here\n", + "isr=IsrTask()\n", + "\n", + "pre_bfcorr_exposure=exposure.clone() #save a copy of the pre-bf corrected image\n", + "\n", + "isr.brighterFatterCorrection?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the kernel (determined from e.g. simulations or flat fields)\n", + "kernel=fits.getdata('/home/sarujin/BF_kernel/BF_kernel-ITL_3800C_002.fits')\n", + "\n", + "# Perform the correction\n", + "tstart=time.time()\n", + "exposure=pre_bfcorr_exposure.clone()\n", + "isr.brighterFatterCorrection(exposure,kernel,20,10,False)\n", + "print(\"Brighter-fatter correction took\",time.time()-tstart,\" seconds\") #takes 99 seconds for 4kx4k exposure, 21x21 kernel, 20 iterations, 10 thresh\n", + "\n", + "# Plot kernel and image differences\n", + "plt.figure(),plt.title('BF kernel')\n", + "plt.imshow(kernel),plt.colorbar()\n", + "\n", + "imagediff=(pre_bfcorr_exposure.getImage().array-exposure.getImage().array)\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "plt.subplot(231),plt.title('Before')\n", + "plt.imshow(pre_bfcorr_exposure.getImage().array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", + "plt.subplot(232),plt.title('After')\n", + "plt.imshow(exposure.getImage().array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", + "plt.subplot(233),plt.title('Before - After')\n", + "vmin,vmax=-50,50\n", + "plt.imshow(imagediff,vmin=vmin,vmax=vmax,origin='lower'),plt.colorbar()\n", + "\n", + "nbins=1000\n", + "plt.subplot(234)\n", + "plt.hist(pre_bfcorr_exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='before'),plt.yscale('log')\n", + "plt.subplot(235)\n", + "plt.hist(exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='after'),plt.yscale('log')\n", + "plt.subplot(236)\n", + "plt.hist(imagediff.flatten(),bins=nbins,histtype='step',label='difference'),plt.yscale('log')\n", + "plt.legend()\n", + "plt.xlabel('Pixel values [e-]')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the characterization & measurement over the 20 exposures of increasing brightness" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This cell runs through all of the image parts, and should take around 1 second per image (20 sec total)\n", + "\n", + "do_bf_corr=True # True or False, makes catalogs (.cat) for all of the corrected and uncorrected images\n", + "for fitsfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part.fits')):\n", + " image_array=afwImage.ImageF.readFits(fitsfilename)\n", + " image = afwImage.ImageF(image_array)\n", + "\n", + " exposure = afwImage.ExposureF(image.getBBox())\n", + " exposure.setImage(image)\n", + "\n", + " updateVariance(exposure.getMaskedImage(), gain, readNoise)\n", + " \n", + " # start the characterization and measurement, optionally beginning with the brighter-fatter correction\n", + " tstart=time.time()\n", + " if do_bf_corr:\n", + " isr.brighterFatterCorrection(exposure,kernel,20,10,False)\n", + " #print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", + " charResult = charTask.run(exposure)\n", + " calResult = calTask.run(charResult.exposure, background=charResult.background,\n", + " icSourceCat = charResult.sourceCat)\n", + " src=calResult.sourceCat #.copy(deep=True) ?\n", + " \n", + " # write out the source catalog\n", + " catfilename=fitsfilename.replace('.fits','.cat')\n", + " if do_bf_corr: catfilename=catfilename.replace('.cat','-bfcorr.cat')\n", + " src.writeFits(catfilename)\n", + " \n", + " print(fitsfilename.split('/')[-1],\" char. & calib. took \",str(time.time()-tstart)[:4],\" seconds to measure \",len(calResult.sourceCat),\" objects \")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# display some of the source \n", + "for name in src.schema.getOrderedNames():\n", + " if 'shape' in name.lower():\n", + " print(name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the catalogs, both corrected and uncorrected (this could be improved with pandas)\n", + "cat_arr,bf_cat_arr = [],[]\n", + "for catfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part-bfcorr.cat')): bf_cat_arr.append(fits.getdata(catfilename))\n", + "for catfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part.cat')): cat_arr.append(fits.getdata(catfilename))\n", + "ncats=len(cat_arr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show possible issues with source matching which we will remedy with simple matching in the next cell\n", + "for i in range(ncats):\n", + " xfoo,yfoo=cat_arr[i]['base_SdssCentroid_x'],cat_arr[i]['base_SdssCentroid_y']\n", + " plt.plot(xfoo,yfoo,'o')\n", + "plt.title('Centroids of sequential exposures')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using a fiducial frame as reference, we simply match the catalogs by looking for single objects\n", + "# within a max distance\n", + "\n", + "fidframe=10\n", + "maxdist=.5\n", + "\n", + "x0s,y0s=cat_arr[fidframe]['base_SdssCentroid_x'],cat_arr[fidframe]['base_SdssCentroid_y']\n", + "nspots=len(x0s)\n", + "bf_dat=np.empty((ncats,nspots,6)) #x,y,xx,yy,bfxx,bfyy\n", + "bf_dat[:]=np.nan\n", + "\n", + "for i in range(ncats):\n", + " # get the centroids of objects in the bf-corrected and uncorrected images\n", + " x1,y1=cat_arr[i]['base_SdssCentroid_x'],cat_arr[i]['base_SdssCentroid_y']\n", + " x1_bf,y1_bf=bf_cat_arr[i]['base_SdssCentroid_x'],bf_cat_arr[i]['base_SdssCentroid_y']\n", + " for j in range(nspots): # loop over fiducial frame centroids to find matches\n", + " x0,y0=x0s[j],y0s[j]\n", + " # find the matches between the fiducial centroid (x0,y0) and the corrected/uncorrected ones\n", + " bf_gd=np.where(np.sqrt((x1_bf-x0)**2+(y1_bf-y0)**2) Date: Fri, 10 Aug 2018 18:10:56 +0000 Subject: [PATCH 05/45] Changed filename --- ...ter_fatter_correction.ipynb => BrighterFatterCorrection.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ImageProcessing/{Brighter_fatter_correction.ipynb => BrighterFatterCorrection.ipynb} (100%) diff --git a/ImageProcessing/Brighter_fatter_correction.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb similarity index 100% rename from ImageProcessing/Brighter_fatter_correction.ipynb rename to ImageProcessing/BrighterFatterCorrection.ipynb From 12d98957c9fcca4da7a484fda5315ce86d14e070 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Fri, 10 Aug 2018 18:14:35 +0000 Subject: [PATCH 06/45] Started including brighter fatter correction in table --- ImageProcessing/README.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/ImageProcessing/README.rst b/ImageProcessing/README.rst index 36bdd516..6114b769 100644 --- a/ImageProcessing/README.rst +++ b/ImageProcessing/README.rst @@ -24,3 +24,14 @@ This folder contains a set of tutorial notebooks exploring the image processing - `Alex Drlica-Wagner `_ + + * - **BrighterFatterCorrection.ipynb** + - How to process a simulated "e-image" using the DM Stack. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.log + + - `Alex Drlica-Wagner `_ + From e2ad652f794c5a06db1cb6261f2968902dc34912 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Fri, 10 Aug 2018 22:30:14 +0000 Subject: [PATCH 07/45] Now uses data in /projects/shared/data/, and edited README links for beavis-ci --- .../BrighterFatterCorrection.ipynb | 151 ++++++++---------- ImageProcessing/README.rst | 12 +- 2 files changed, 76 insertions(+), 87 deletions(-) diff --git a/ImageProcessing/BrighterFatterCorrection.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb index 8d348caf..a28402bf 100644 --- a/ImageProcessing/BrighterFatterCorrection.ipynb +++ b/ImageProcessing/BrighterFatterCorrection.ipynb @@ -7,7 +7,7 @@ "# Analysis of Beam Simulator Images and Brighter-fatter Correction\n", "
Owner(s): **Andrew Bradshaw** ([@andrewkbradshaw](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@andrewkbradshaw))\n", "
Last Verified to Run: **2018-08-10**\n", - "
Verified Stack Release: **16.0+22**\n", + "
Verified Stack Release: **16.0 and 16.0+22 (w_2018_31)**\n", "\n", "This notebook demonstrates the [brighter-fatter systematic error](https://arxiv.org/abs/1402.0725) on images of stars and galaxies illuminated on an ITL-3800C-002 CCD at the [UC Davis LSST beam simulator laboratory](https://arxiv.org/abs/1411.5667). Using a series of images at increasing exposure times, we demonstrate the broadening of image profiles on DM stack shape measurements, and a [possible correction method](https://arxiv.org/abs/1711.06273) which iteratively applies a kernel to restore electrons to the pixels which they were deflected from. To keep things simple, for now we skip DM stack instrument signature removal (ISR) and work on a subset of images which are arrays (500x500) of electron counts in pixels.\n", "\n", @@ -46,34 +46,23 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", "from astropy.io import fits\n", "import time,glob\n", "\n", - "# if running stack v16, silence a long matplotlib Agg warning with:\n", - "#import warnings\n", - "#warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "# and also \n", + "# if running stack v16.0, silence a long matplotlib Agg warning with:\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "\n", "%matplotlib inline" ] }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# generate different portions of the full CCD by modifying the X/Y slices on the last line\n", - "for fitsfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*whole.fits')):\n", - " fooimg=fits.getdata(fitsfilename)\n", - " foohdr=fits.getheader(fitsfilename)\n", - " fitsfilename=fitsfilename.replace('whole','part')\n", - " fits.writeto(fitsfilename,fooimg[1500:2000,509*3:509*3+500],header=foohdr,overwrite=True)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Read in an image, then set the variance plane based upon it" + "## Read in an image, then set the variance plane based upon it\n", + "Cut-outs of beam simulator star/galaxy images have been placed in the shared data directory at `/project/shared/data/beamsim/bfcorr/`. We skip (for now) most of the instrument signature removal (ISR) steps because these are preprocessed images (bias subtracted, gain corrected). We instead start by reading in one of those `.fits` files and making an image plane `afwImage.ExposureF` as well as a variance plane, which is then ready for characterization and calibration in the following cells." ] }, { @@ -82,33 +71,31 @@ "metadata": {}, "outputs": [], "source": [ - "# we are skipping some ISR because these are preprocessed (bias subtracted, gain corrected)\n", "import lsst.afw.image as afwImage\n", "from lsst.ip.isr.isrFunctions import updateVariance\n", "\n", - "fitsfilename = np.sort(glob.glob('/home/sarujin/testdata/ITL*part.fits'))[19] \n", - "#for fun: whole instead of part, with [2000:,:509],[:2000,1018:1527]=0.0\n", + "# where the data lives, choosing one image to start\n", + "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits'\n", + "fitsfilename = np.sort(glob.glob(fitsglob))[19] \n", "\n", - "print(\"Reading in \",fitsfilename.split('/')[-1])\n", - "hdr=fits.getheader(fitsfilename)\n", + "# Read in a single image to an afwImage.ImageF object\n", "image_array=afwImage.ImageF.readFits(fitsfilename)\n", "image = afwImage.ImageF(image_array)\n", - "\n", - "\n", "exposure = afwImage.ExposureF(image.getBBox())\n", "exposure.setImage(image)\n", + "hdr=fits.getheader(fitsfilename) # the header has some useful info in it\n", + "print(\"Read in \",fitsfilename.split('/')[-1])\n", "\n", "# Set the variance plane using the image plane via updateVariance function\n", - "gain = 1.0 #these images are already gain corrected\n", - "readNoise = 10.0 #in electrons\n", + "gain = 1.0 # because these images are already gain corrected\n", + "readNoise = 10.0 # in electrons\n", "updateVariance(exposure.getMaskedImage(), gain, readNoise)\n", "\n", - "# Optional other ways of setting variance or\n", + "# Another way of setting variance and/or masks?\n", "#mask = afwImage.makeMaskFromArray(np.zeros((4000,4072)).astype('int32'))\n", - "#variance = afwImage.makeImageFromArray((readNoise**2 + image_array.array()**2)\n", - "#masked_image = afwImage.MaskedImageF(image, None, variance)\n", - "#exposure = afwImage.ExposureF(masked_image)\n", - "\n" + "#variance = afwImage.makeImageFromArray((readNoise**2 + image_array.array())\n", + "#masked_image = afwImage.MaskedImageF(image, mask, variance)\n", + "#exposure = afwImage.ExposureF(masked_image)" ] }, { @@ -117,6 +104,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Visualize the image and its electron distribution\n", "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", "plt.suptitle('Star/galaxy beam sim segment of '+hdr['CCD_SERN']+'\\n '+fitsfilename.split('/')[-1])\n", "\n", @@ -136,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO++++ perhaps some other image stats from \n", + "# +TODO perhaps some other image stats from \n", "# https://github.com/lsst/pipe_tasks/blob/master/python/lsst/pipe/tasks/exampleStatsTasks.py" ] }, @@ -161,13 +149,15 @@ "charConfig.installSimplePsf.fwhm = .2\n", "charConfig.doMeasurePsf = False\n", "charConfig.doApCorr = False\n", - "charConfig.repair.doCosmicRay = False # we do have some cosmic rays, but we also subpixel features and an undersampled PSF\n", + "charConfig.repair.doCosmicRay = False \n", + "# we do have some cosmic rays, but we also subpixel features and an undersampled PSF\n", "charConfig.detection.background.binSize = 10 \n", "#charConfig.background.binSize = 50\n", "charConfig.detection.minPixels = 5\n", "charTask = CharacterizeImageTask(config=charConfig)\n", "\n", - "charTask.run?" + "# charTask.run? # works for v16.0+22\n", + "charTask.characterize?" ] }, { @@ -179,7 +169,7 @@ "outputs": [], "source": [ "tstart=time.time()\n", - "charResult = charTask.run(exposure) # for stack v16, change to charTask.characterize(exposure)\n", + "charResult = charTask.characterize(exposure) # charTask.run(exposure) stack v16.0+22\n", "print(\"Characterization took \",str(time.time()-tstart)[:4],\" seconds\")\n", "print(\"Detected \",len(charResult.sourceCat),\" objects \")\n", "\n", @@ -207,19 +197,12 @@ "plt.yscale('log')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "raw", "metadata": {}, "source": [ - "#Another option for analysis \n", - "#from https://gist.github.com/josePhoenix/8325c16b44fb5fa51f40261b184a78ef\n", + "# Another option for this analysis, from:\n", + "# https://gist.github.com/josePhoenix/8325c16b44fb5fa51f40261b184a78ef\n", "\n", "import lsst.afw.table\n", "import lsst.afw.image\n", @@ -251,13 +234,6 @@ "plt.scatter(catalog['base_SdssCentroid_x'],catalog['base_SdssCentroid_y'],marker='.')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -283,7 +259,8 @@ "calConfig.detection.background.binSize = 50\n", "calTask = CalibrateTask(config= calConfig, icSourceSchema=charResult.sourceCat.schema)\n", "\n", - "calTask.run?\n" + "#calTask.run? # for stack v16.0+22 \n", + "calTask.calibrate?" ] }, { @@ -295,8 +272,8 @@ "outputs": [], "source": [ "tstart=time.time()\n", - "# for stack v16, change to calTask.characterize(charResult.exposure)\n", - "calResult = calTask.run(charResult.exposure, background=charResult.background,\n", + "# for stack v16.0+22, change to calTask.run(charResult.exposure)\n", + "calResult = calTask.calibrate(charResult.exposure, background=charResult.background,\n", " icSourceCat = charResult.sourceCat)\n", "\n", "print(\"Calibration took \",str(time.time()-tstart)[:4],\" seconds\")\n", @@ -397,11 +374,6 @@ " afw_display.dot('o', record.getX(), record.getY(), size=20, ctype='orange')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -430,7 +402,7 @@ "outputs": [], "source": [ "# Read in the kernel (determined from e.g. simulations or flat fields)\n", - "kernel=fits.getdata('/home/sarujin/BF_kernel/BF_kernel-ITL_3800C_002.fits')\n", + "kernel=fits.getdata('/project/shared/data/beamsim/bfcorr/BF_kernel-ITL_3800C_002.fits')\n", "\n", "# Perform the correction\n", "tstart=time.time()\n", @@ -479,8 +451,9 @@ "source": [ "# This cell runs through all of the image parts, and should take around 1 second per image (20 sec total)\n", "\n", - "do_bf_corr=True # True or False, makes catalogs (.cat) for all of the corrected and uncorrected images\n", - "for fitsfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part.fits')):\n", + "do_bf_corr=False # True or False, makes catalogs (.cat) for all of the corrected and uncorrected images\n", + "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits'\n", + "for fitsfilename in np.sort(glob.glob(fitsglob)):\n", " image_array=afwImage.ImageF.readFits(fitsfilename)\n", " image = afwImage.ImageF(image_array)\n", "\n", @@ -494,8 +467,9 @@ " if do_bf_corr:\n", " isr.brighterFatterCorrection(exposure,kernel,20,10,False)\n", " #print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", - " charResult = charTask.run(exposure)\n", - " calResult = calTask.run(charResult.exposure, background=charResult.background,\n", + " # for stack v16.0+22 use charTask.run() and calTask.run()\n", + " charResult = charTask.characterize(exposure) \n", + " calResult = calTask.calibrate(charResult.exposure, background=charResult.background,\n", " icSourceCat = charResult.sourceCat)\n", " src=calResult.sourceCat #.copy(deep=True) ?\n", " \n", @@ -514,7 +488,7 @@ "metadata": {}, "outputs": [], "source": [ - "# display some of the source \n", + "# display some of the source catalog shape measurements\n", "for name in src.schema.getOrderedNames():\n", " if 'shape' in name.lower():\n", " print(name)" @@ -527,9 +501,13 @@ "outputs": [], "source": [ "# Read in the catalogs, both corrected and uncorrected (this could be improved with pandas)\n", - "cat_arr,bf_cat_arr = [],[]\n", - "for catfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part-bfcorr.cat')): bf_cat_arr.append(fits.getdata(catfilename))\n", - "for catfilename in np.sort(glob.glob('/home/sarujin/testdata/ITL*part.cat')): cat_arr.append(fits.getdata(catfilename))\n", + "cat_arr = []\n", + "catglob='/project/shared/data/beamsim/bfcorr/ITL*part.cat' # uncorrected catalogs\n", + "for catfilename in np.sort(glob.glob(catglob)): cat_arr.append(fits.getdata(catfilename))\n", + "\n", + "bf_cat_arr = []\n", + "catglob='/project/shared/data/beamsim/bfcorr/ITL*part-bfcorr.cat' # corrected catalogs\n", + "for catfilename in np.sort(glob.glob(catglob)): bf_cat_arr.append(fits.getdata(catfilename))\n", "ncats=len(cat_arr)" ] }, @@ -585,14 +563,19 @@ "outputs": [], "source": [ "# Plot the brighter-fatter effect on those shape measurements and the corrected version\n", - "# These are good indices to look at by default: [0,1,6,12,23,35,44,46,52,56,59,69,71,73,90]\n", - "# TODO - change the exposure number to some brightness measurement!\n", + "# These are good indices to look with defaults: [0,1,6,12,23,35,44,46,52,56,59,69,71,73,90]\n", + "\n", + "# +TODO - change the exposure number to some brightness measurement\n", + "# +TODO - get postage stamps in a stackly manner\n", "\n", - "nfoo=69\n", - "sz=5\n", - "xc,yc=bf_dat[10,nfoo,0],bf_dat[10,nfoo,1]\n", - "plt.figure(figsize=(12,5))\n", - "#stamp=exposure.getImage().array[yc-sz:yc+sz,xc-sz:xc+sz]\n", + "nfoo=44\n", + "plt.figure(figsize=(14,4)),plt.subplots_adjust(wspace=.3)\n", + "# grab a postage stamp, +TODO in a stackly manner\n", + "sz=11\n", + "xc,yc=bf_dat[10,nfoo,0].astype('int')+1,bf_dat[10,nfoo,1].astype('int')+1\n", + "stamp=exposure.getImage().array[yc-sz:yc+sz,xc-sz:xc+sz]\n", + "plt.subplot(131)\n", + "plt.imshow(stamp,origin='lower',norm=LogNorm(1,stamp.max())),plt.colorbar()\n", "\n", "plt.subplot(132)\n", "plt.plot(bf_dat[:,nfoo,2],'r.',label='Uncorrected')\n", @@ -602,8 +585,7 @@ "plt.subplot(133)\n", "plt.plot(bf_dat[:,nfoo,3],'r.',label='Uncorrected')\n", "plt.plot(bf_dat[:,nfoo,5],'g.',label='Corrected')\n", - "plt.xlabel('Exposure number'),plt.ylabel('base_SdssShape_yy')\n", - "#add stamp in a stackly manner" + "plt.xlabel('Exposure number'),plt.ylabel('base_SdssShape_yy')" ] }, { @@ -612,10 +594,17 @@ "metadata": {}, "outputs": [], "source": [ - "# add re-scaled stamp subtraction comparison!!\n", - "# other ways of doing matching, catalog stacking\n", - "# should this analysis focus on only one stamp? realism in wide-field correction, simplicity in stamp by stamp...\n" + "# +TODO add re-scaled stamp subtraction comparison\n", + "# +TODO other ways of doing matching, catalog stacking\n", + "# should this analysis focus on only one stamp? realism in wide-field correction, simplicity in stamp by stamp..." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/ImageProcessing/README.rst b/ImageProcessing/README.rst index 6114b769..9cb2e0e0 100644 --- a/ImageProcessing/README.rst +++ b/ImageProcessing/README.rst @@ -26,12 +26,12 @@ This folder contains a set of tutorial notebooks exploring the image processing * - **BrighterFatterCorrection.ipynb** - - How to process a simulated "e-image" using the DM Stack. - - `ipynb `_, - `rendered `_ + - Analysis of Beam Simulator Images and Brighter-fatter Correction. + - `ipynb `_, + `rendered `_ - .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.svg - :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.log + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/BrighterFatterCorrection.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/BrighterFatterCorrection.log - - `Alex Drlica-Wagner `_ + - `Andrew Bradshaw `_ From 4b4c9b26c3bdef493b1964a0723de5bf3e30bf86 Mon Sep 17 00:00:00 2001 From: David Shupe Date: Mon, 13 Aug 2018 15:39:15 +0000 Subject: [PATCH 08/45] patch comments from @paulprice on original PR --- Basics/Calexp_guided_tour.ipynb | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/Basics/Calexp_guided_tour.ipynb b/Basics/Calexp_guided_tour.ipynb index dc7f37d5..98a95e41 100644 --- a/Basics/Calexp_guided_tour.ipynb +++ b/Basics/Calexp_guided_tour.ipynb @@ -460,7 +460,7 @@ "bbox = afwGeom.Box2I()\n", "bbox.include(afwGeom.Point2I(2200,3200))\n", "bbox.include(afwGeom.Point2I(2800,3800))\n", - "cutout = calexp.Factory(calexp, bbox, afwImage.LOCAL)" + "cutout = calexp[bbox]" ] }, { @@ -510,7 +510,7 @@ "metadata": {}, "outputs": [], "source": [ - "cutout_calexp = butler.get('calexp_sub', bbox=bbox, immediate=True, dataId=dataId)\n", + "cutout_calexp = butler.get('calexp_sub', bbox=bbox, dataId=dataId)\n", "cutout_calexp.getDimensions()" ] }, @@ -654,7 +654,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The result of the `set` command above shows that a calexp and a coadd have the same methods." + "The result of the `set` command above shows that a calexp and a coadd have the same methods. This is expected, because they are the same class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(calexp.__class__, coadd.__class__)" ] }, { From cfd37e3adbd7949f044a5a7d2e1efe269086acc0 Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Tue, 14 Aug 2018 16:55:13 +0000 Subject: [PATCH 09/45] Updates after LSST PCW 2018 session --- SourceDetection/LowSurfaceBrightness.ipynb | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/SourceDetection/LowSurfaceBrightness.ipynb b/SourceDetection/LowSurfaceBrightness.ipynb index 50797916..672068ce 100644 --- a/SourceDetection/LowSurfaceBrightness.ipynb +++ b/SourceDetection/LowSurfaceBrightness.ipynb @@ -7,10 +7,10 @@ "# Low-Surface Brightness Source Detection\n", "\n", "
Owner: **Alex Drlica-Wagner** ([@kadrlica](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@kadrlica))\n", - "
Last Verified to Run: **2018-07-22**\n", - "
Verified Stack Release: **w201829**\n", + "
Last Verified to Run: **2018-08-13**\n", + "
Verified Stack Release: **v16.0**\n", "\n", - "This Notebook demonstrates how to run the source detection, measurment, and deblending algorithms with a focus on optimizing for low-surface brightness object detection. It attempts to split out the source detection and measurement algorithms from `processCCD` and apply them to the search for low surface brightness galaxies. The content of this notebook builds off of Robert Lupton's [Greco LSB.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Greco%20LSB.ipynb) with some source detection and measurement details from [Tune Detection.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Tune%20Detection.ipynb) and [Kron.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Kron.ipynb).\n", + "This Notebook demonstrates how to run the source detection, measurment, and deblending algorithms with a focus on optimizing for low-surface brightness object detection. It attempts to split out the source detection and measurement algorithms from `processCCD` and apply them to the search for low surface brightness galaxies. The content of this notebook builds off of an analysis from Johnny Greco, adapted into notebook form in Robert Lupton's [Greco LSB.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Greco%20LSB.ipynb). Some source detection and measurement details come from [Tune Detection.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Tune%20Detection.ipynb) and [Kron.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Kron.ipynb).\n", "Interaction with `lsst.afw.display` was also improved by studying Michael Wood-Vasey's [DC2_Postage Stamps.ipynb](https://github.com/LSSTDESC/DC2_Repo/blob/master/Notebooks/DC2_Postage_Stamps.ipynb).\n", "\n", "### Learning Objectives:\n", @@ -111,7 +111,7 @@ "source": [ "### Data access\n", "\n", - "Here we use the `butler` to access a `calexp` from the Twinkles data subset. More information on the `butler` can be found in [Butler_Tutorial.ipynb](), while a deeper examination of the `calexp` object can be found in [Calexp_Tutorial.ipynb](). We expect the user to have a working knowledge of these objects." + "Here we use the `butler` to access a `calexp` from the Twinkles data subset. More information on the `butler` will be available in `Butler_Tutorial.ipynb` (TBD), while a deeper examination of the `calexp` object can be found in [Calexp_guided_tour.ipynb](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Calexp_guided_tour.ipynb). We expect the user to have a working knowledge of these objects." ] }, { @@ -449,7 +449,7 @@ "source": [ "if False:\n", " sources.writeFits(\"outputTable.fits\")\n", - " exposure.writeFits(\"example1-out.fits\")" + " calexp.writeFits(\"example1-out.fits\")" ] }, { @@ -629,6 +629,13 @@ "plt.gca().axis('off')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The procedure above masks all `DETECTED` pixels with a simplistic selection. However, the Stack provides an alternative mechanism for more directed masking through the [meas.base.noiseReplacer](http://doxygen.lsst.codes/stack/doxygen/x_masterDoxyDoc/classlsst_1_1meas_1_1base_1_1noise_replacer_1_1_noise_replacer.html). The `noiseReplacer` takes as input the calexp object and the footprint set (`fpset`) returned `sourceDetectionTask`. With the `noiseReplacer`, it is possible to selectively replace a subset of the sources." + ] + }, { "cell_type": "markdown", "metadata": {}, From 9edbacdaf47efbf025d5de24591acbd40167ffde Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Tue, 14 Aug 2018 22:27:55 -0700 Subject: [PATCH 10/45] LSST Data Facility, request account rather than just get one --- GettingStarted/GettingStarted.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index 7d5ad2c3..a1a8f3bc 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -4,18 +4,18 @@ _Greg Madejski and [Phil Marshall](https://github.com/LSSTScienceCollaborations/ We are developing tutorial notebooks on remote JupyterLab instances, to short-circuit the DM stack installation process and get used to working in the notebook aspect of the LSST science platform. In these notes we provide: -* [Notes on how to get set up on the LSST Science Platform (LSP) JupyterLab Notebook Aspect at NCSA](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#accessing-the-lsst-science-platform) +* [Notes on how to get set up on the LSST Science Platform (LSP) JupyterLab Notebook Aspect at the LSST Data Facility at NCSA](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#accessing-the-lsst-science-platform) * [Help with getting set up to run and edit the Stack Club tutorial notebooks](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#running-and-contributing-to-the-stack-club-notebooks) ## Accessing the LSST Science Platform -The [LSST Science Platform (LSP) Notebook Aspect Documentation](https://nb.lsst.io/) provides an introduction to the NCSA system, including how to gain access and then how to use JupyterLab once you are in. +The [LSST Science Platform (LSP) Notebook Aspect Documentation](https://nb.lsst.io/) provides an introduction to the system, including how to gain access and then how to use JupyterLab once you are in. Getting on to the LSP involves getting an NCSA account, and then figuring out VPN access. -#### Getting an NCSA Account -The Stack Club has a limited number of active NCSA accounts it can support. To join the Stack Club and get one of these accounts, please fill out the [Stack Club Membership Application Form](https://goo.gl/forms/588KlPTFfkEEFFUu2). You'll need to agree to abide by the [Rules](../Rules.md), and then provide your full name (first and last) and your email address. If your application is successful, you'll get an email with instructions on how to set up your LSP account. +#### Getting an LSST Science Platform Account +The Stack Club has a limited number of active LSST Science Platform accounts it can support. To join the Stack Club and request one of these accounts, please fill out the [Stack Club Membership Application Form](https://goo.gl/forms/588KlPTFfkEEFFUu2). You'll need to agree to abide by the [Rules](../Rules.md), and then provide your full name (first and last) and your email address. If your application is successful, you'll get an email with instructions on how to set up your LSP account. -#### Accessing NCSA via its VPN +#### Accessing the LSP via its VPN At present, unless you are on an approved network, you must use the [NCSA virtual private network (VPN)](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service). The recommended method is to use Cisco's AnyConnect with DUO two-factor authentication. Detailed instructions are available on the [NCSA VPN site](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service#VirtualPrivateNetwork(VPN)Service-UsingtheCiscoAnyConnectVPNClient(Required)). From 3ab93dc944af426f6501f57cc2b2fe7fde7c2a1c Mon Sep 17 00:00:00 2001 From: fred3m Date: Wed, 15 Aug 2018 09:22:34 -0700 Subject: [PATCH 11/45] add blending tutorials from LSST 2018 blending workshop --- Deblending/lsst_stack_deblender.ipynb | 628 ++++++++++++++++++++++++++ Deblending/scarlet_tutorial.ipynb | 470 +++++++++++++++++++ 2 files changed, 1098 insertions(+) create mode 100755 Deblending/lsst_stack_deblender.ipynb create mode 100755 Deblending/scarlet_tutorial.ipynb diff --git a/Deblending/lsst_stack_deblender.ipynb b/Deblending/lsst_stack_deblender.ipynb new file mode 100755 index 00000000..4427f74a --- /dev/null +++ b/Deblending/lsst_stack_deblender.ipynb @@ -0,0 +1,628 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LSST Stack Multiband Deblender Tutorial\n", + "\n", + "This tutorial is designed to illustrate how to execture the multiband deblender (*scarlet*) in the LSST stack. This includes a brief introduction to LSST stack objects including:\n", + "\n", + " - Geometry classes from `lsst.geom`, such as points and boxes.\n", + " - Higher-level astronomical primitives from `lsst.afw`, such as the `Image`, `Exposure`, and `Psf` classes.\n", + " - Our core algorithmic `Task` classes, including those for source detection, deblending, and measurement.\n", + " \n", + "We'll be working with coadded images made from Subaru Hyper Suprime-Cam (HSC) data in the COSMOS field. We've taken a recent LSST reprocessing of the HSC-SSP UltraDeep COSMOS field (see [this page](https://confluence.lsstcorp.org/display/DM/S18+HSC+PDR1+reprocessing) for information on that reprocessing, and [this page](https://hsc-release.mtk.nao.ac.jp/doc/) for the data), and added simulated stars from a scaled [SDSS catalog](http://www.sdss.org/dr14/data_access/value-added-catalogs/?vac_id=photometry-of-crowded-fields-in-sdss-for-galactic-globular-and-open-clusters). The result is a very deep image (deeper than the 10-year LSST Deep-Wide-Fast survey, though not as deep as LSST Deep Drilling fields will be) with both a large number of galaxies and region full of stars.\n", + "\n", + "This tutorial is based on Jim Bosch's globular cluster tutorial, however in it's present state *scarlet* is unable to process the crowded field (most likely) due to poor initial conditions for the sources in the field. So instead we use a region of the image outside of the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "We'll start with some standard imports of both LSST and third-party packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from lsst.daf.persistence import Butler\n", + "from lsst.geom import Box2I, Box2D, Point2I, Point2D, Extent2I, Extent2D\n", + "from lsst.afw.image import Exposure, Image, PARENT, MultibandExposure, MultibandImage\n", + "from lsst.afw.detection import MultibandFootprint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Data\n", + "\n", + "We'll be retrieving data using the `Butler` tool, which manages where various datasets are stored on the filesystem (and can in principle manage datasets that aren't even stored as files, though all of these are).\n", + "\n", + "We start by creating a `Butler` instance, pointing it at a *Data Repository* (which here is just a root directory)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"/project/jbosch/tutorials/lsst2018/data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Datasets managed by a butler are identified by a dictionary *Data ID* (specifying things like the visit number or sky patch) and a string *DatasetType* (such as a particular image or catalog). Different DatasetTypes have different keys, while different instances of the same Dataset Type have different values. All of the datasets we use in this tutorial will correspond to the same patch of sky, so they'll have at least the keys in the dictionary in the next cell (they will also have `filter`, but with different values):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataId = {\"tract\": 9813, \"patch\": \"4,4\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use those to load a set of *grizy* coadds, which we'll put directly in a dictionary. The result of each `Butler.get` call is in this case an `lsst.afw.image.Exposure` object, an image that actually contains three \"planes\" (the main image, a bit mask, and a variance image) as well as many other objects that describe the image, such as its PSF and WCS. Note that we (confusingly) use `Exposures` to hold coadd images as well as true single-exposure images, but combine them into a `MultibandExposure`, which contains an exposure in each band.\n", + "\n", + "The DatasetType here is `deepCoadd_calexp` (a coadd on which we've already done some additional processing, such as subtracting the background and setting some mask values), and the extra `filter` argument gets appended to the Data ID." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filters = \"grizy\"\n", + "coadds = [butler.get(\"deepCoadd_calexp\", dataId, filter=\"HSC-{}\".format(f.upper())) for f in filters]\n", + "coadds = MultibandExposure.fromExposures(filters, coadds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making and displaying color composite images\n", + "\n", + "We'll start by just looking at the images, as 3-color composites. We'll use astropy to build those as a nice way to demonstrate how to get NumPy arrays from the `MultibandImage` objects (the images in `coadds`). (LSST also has code to make 3-color composites using the same algorithm, and in fact the Astropy implementation is based on ours, but now that it's in Astropy we'll probably retire ours.)\n", + "\n", + "We'll just use matplotlib to display the images themselves. We'll use Firefly for other image display tasks later, but while Firefly itself supports color-composites, work on our preferred composition algorithm is still in progress, and we haven't quite finished connecting that functionality to the Python client we'll demonstrate here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.visualization import make_lupton_rgb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the following function a few times to display color images. It's worth reading through the implementation carefully to see what's going on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def showRGB(image, bgr=\"gri\", ax=None, fp=None, figsize=(8,8), stretch=1, Q=10):\n", + " \"\"\"Display an RGB color composite image with matplotlib.\n", + " \n", + " Parameters\n", + " ----------\n", + " image : `MultibandImage`\n", + " `MultibandImage` to display.\n", + " bgr : sequence\n", + " A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n", + " to use for each channel. If `image` only has three filters then this parameter is ignored\n", + " and the filters in the image are used.\n", + " ax : `matplotlib.axes.Axes`\n", + " Axis in a `matplotlib.Figure` to display the image.\n", + " If `axis` is `None` then a new figure is created.\n", + " fp: `lsst.afw.detection.Footprint`\n", + " Footprint that contains the peak catalog for peaks in the image.\n", + " If `fp` is `None` then no peak positions are plotted.\n", + " figsize: tuple\n", + " Size of the `matplotlib.Figure` created.\n", + " If `ax` is not `None` then this parameter is ignored.\n", + " stretch: int\n", + " The linear stretch of the image.\n", + " Q: int\n", + " The Asinh softening parameter.\n", + " \"\"\"\n", + " # If the image only has 3 bands, reverse the order of the bands to produce the RGB image\n", + " if len(image) == 3:\n", + " bgr = image.filters\n", + " # Extract the primary image component of each Exposure with the .image property, and use .array to get a NumPy array view.\n", + " rgb = make_lupton_rgb(image_r=image[bgr[2]].array, # numpy array for the r channel\n", + " image_g=image[bgr[1]].array, # numpy array for the g channel\n", + " image_b=image[bgr[0]].array, # numpy array for the b channel\n", + " stretch=stretch, Q=Q) # parameters used to stretch and scale the pixel values\n", + " if ax is None:\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = fig.add_subplot(1,1,1)\n", + " \n", + " # Exposure.getBBox() returns a Box2I, a box with integer pixel coordinates that correspond to the centers of pixels.\n", + " # Matplotlib's `extent` argument expects to receive the coordinates of the edges of pixels, which is what\n", + " # this Box2D (a box with floating-point coordinates) represents.\n", + " integerPixelBBox = image[bgr[0]].getBBox()\n", + " bbox = Box2D(integerPixelBBox)\n", + " ax.imshow(rgb, interpolation='nearest', origin='lower', extent=(bbox.getMinX(), bbox.getMaxX(), bbox.getMinY(), bbox.getMaxY()))\n", + " if fp is not None:\n", + " for peak in fp.getPeaks():\n", + " ax.plot(peak.getIx(), peak.getIy(), \"bx\", mew=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we can slice `MultibandImage` objects (as well a `MultibandExposure` objects) along the filter dimension using the filter names as indices. Like `Exposure` objects, `MultibandExposure` objects have `image`, `mask`, and `variance` properties that contain the image, mask plane, and variance of the `Exposure` respectively. For now we will only worry about the `image` property, although internal deblending and measurement algorithms make use of all three objects (when available)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(coadds[:\"z\"].image, figsize=(10, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(coadds[\"i\":].image, figsize=(10, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those images are a full \"patch\", which is our usual unit of processing for coadds - it's about the same size as a single LSST sensor (exactly the same in pixels, smaller in terms of area because these use HSC's smaller pixel scale). That's a bit unweildy (just because waiting for processing to happen isn't fun in a tutorial setting), so we'll reload our dict with sub-images centered on the region of interest.\n", + "\n", + "Note that we can load the sub-images directly with the `butler`, by appending `_sub` to the DatasetType and passing a `bbox` argument. If you want to see the region of the image with the cluster, use `clusterBBox` below, however as mentioned above, that region is too memory intensive for the current version of *scarlet*. Instead use `sampleBBox` to select a sub-region of the image (note that we add a small frame around each blend to include more background regions, which are important for the detection algorithm)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frame = 50\n", + "clusterBBox = Box2I(Point2I(18325, 17725), Extent2I(400, 350))\n", + "\n", + "#sampleBBox = Box2I(Point2I(18699-frame, 17138-frame), Extent2I(93+2*frame, 104+2*frame))\n", + "#sampleBBox = Box2I(Point2I(16424-frame, 17806-frame), Extent2I(55+2*frame, 62+2*frame))\n", + "#sampleBBox = Box2I(Point2I(17838-frame, 18945-frame), Extent2I(111+2*frame, 103+2*frame))\n", + "sampleBBox = Box2I(Point2I(19141-frame, 18228-frame), Extent2I(63+2*frame, 87+2*frame))\n", + "\n", + "subset = coadds[:, sampleBBox]\n", + "# Due to a bug in the code the PSF isn't copied properly.\n", + "# The code below copies the PSF into the `MultibandExposure`,\n", + "# but will be unecessary in the future\n", + "for f in subset.filters:\n", + " subset[f].setPsf(coadds[f].getPsf())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(subset.image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Processing\n", + "\n", + "Now we'll try the regular LSST processing tasks, with a simpler configuration than we usually use to process coadds, just to avoid being distracted by complexity. This includes\n", + "\n", + " - Detection (`SourceDetectionTask`): given an `Exposure`, find above-threshold regions and peaks within them (`Footprints`), and create a *parent* source for each `Footprint`.\n", + " - Deblending (`MultibandDeblendTask`): given a `MultibandExposure` and a catalog of parent sources, create a *child* source for each peak in every `Footprint` that contains more than one peak. Each child source is given a `HeavyFootprint`, which contains both the pixel region that source covers and the fractional pixel values associated with that source. A `SourceDeblendTask` is also available using the single band SDSS-HSC deblender that takes a single band `Exposure`).\n", + " - Measurment (`SingleFrameMeasurementTask`): given an `Exposure` and a catalog of sources, run a set of \"measurement plugins\" on each source, using deblended pixel values if it is a child. Notice that measurement is still performed on single band catalogs, since none of the measurement algorithms work for multiband data.\n", + "\n", + "We'll start by importing these, along with the `SourceCatalog` class we'll use to hold the outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.meas.algorithms import SourceDetectionTask\n", + "from lsst.meas.deblender import MultibandDeblendTask\n", + "from lsst.meas.base import SingleFrameMeasurementTask\n", + "from lsst.afw.table import SourceCatalog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now construct all of these `Tasks` before actually running any of them. That's because `SourceDeblendTask` and `SingleFrameMeasurementTask` are constructed with a `Schema` object that records what fields they'll produce, and they modify that schema when they're constructed by adding columns to it. When we run the tasks later, they'll need to be given a catalog that includes all of those columns, **but we can't add columns to a catalog that already exists**.\n", + "\n", + "To recap, the sequence looks like this:\n", + "\n", + " 1. Make a (mostly) empty schema.\n", + " 2. Construct all of the `Task`s (in the order you plan to run them), which adds columns to the schema.\n", + " 3. Make a `SourceCatalog` object from the *complete* schema.\n", + " 4. Pass the same `SourceCatalog` object to each `Task` when you run it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schema = SourceCatalog.Table.makeMinimalSchema()\n", + "\n", + "detectionTask = SourceDetectionTask(schema=schema)\n", + "\n", + "config = MultibandDeblendTask.ConfigClass()\n", + "config.usePsfConvolution = True\n", + "config.conserveFlux = True\n", + "config.maxIter = 100\n", + "deblendTask = MultibandDeblendTask(schema=schema, config=config)\n", + "\n", + "# We'll customize the configuration of measurement to just run a few plugins.\n", + "# The default list of plugins is much longer (and hence slower).\n", + "measureConfig = SingleFrameMeasurementTask.ConfigClass()\n", + "measureConfig.plugins.names = [\"base_SdssCentroid\", \"base_PsfFlux\", \"base_SkyCoord\"]\n", + "# \"Slots\" are aliases that provide easy access to certain plugins.\n", + "# Because we're not running the plugin these slots refer to by default,\n", + "# we need to disable them in the configuration.\n", + "measureConfig.slots.apFlux = None\n", + "measureConfig.slots.instFlux = None\n", + "measureConfig.slots.shape = None\n", + "measureConfig.slots.modelFlux = None\n", + "measureConfig.slots.calibFlux = None\n", + "measureTask = SingleFrameMeasurementTask(config=measureConfig, schema=schema)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step we'll run is detection, which actually returns a new `SourceCatalog` object rather than working on an existing one.\n", + "\n", + "Instead, it takes a `Table` object, which is sort of like a factory for records. We won't use it directly after this, and it isn't actually necessary to make a new `Table` every time you run `MultibandDetectionTask` (but you can only create one after you're done adding columns to the schema).\n", + "\n", + "`Task`s that return anything do so via a `lsst.pipe.base.Struct` object, which is just a simple collection of named attributes. The only return values we're interested is `sources`. That's our new `SourceCatalog`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = SourceCatalog.Table.make(schema)\n", + "detectionResult = detectionTask.run(table, subset[\"r\"])\n", + "catalog = detectionResult.sources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at what's in that catalog. First off, we can look at its schema:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "catalog.schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this includes a lot of columns that were actually added by the deblend or measurement steps; those will all still be blank (`0` for integers or flags, `NaN` for floating-point columns).\n", + "\n", + "In fact, the only columns filled by `SourceDetectionTask` are the IDs. But it also attaches `Footprint` objects, which don't appear in the schema. You can retrieve the `Footprint` by calling `getFootprint()` on a row:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "footprint = catalog[0].getFootprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Footprints` have two components:\n", + " - a `SpanSet`, which represents an irregular region on an image via a list of (y, x0, x1) `Spans`;\n", + " - a `PeakCatalog`, a slightly different kind of catalog whose rows represent peaks within that `Footprint`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(footprint.getSpans())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(footprint.getPeaks())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we actually look at the footprints in the catalog we see that some have only a single peak, while others have multiple peaks that need to be deblended.\n", + "\n", + "To display only the pixels contained in the footprint (and not other pixels in the bounding box) we create a `MultibandFootprint`, which is a `HeavyFootprint` that contains a `SpanSet`, `PeakCatalog`, and `flux` values for all of the pixels in the `SpanSet`. In this case the `flux` is the total measured flux in the image, since no deblending has taken place yet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "for src in catalog:\n", + " fp = src.getFootprint()\n", + " img = coadds[:,fp.getBBox()].image\n", + " mfp = MultibandFootprint.fromImages(coadds.filters, image=img, footprint=fp)\n", + " showRGB(mfp.getImage().image, fp=fp, figsize=(3,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's worth noting that while the peaks *can* have both an integer-valued position and a floating-point position, they're the same right now; `SourceDetectionTask` currently just finds the pixels that are local minima and doesn't try to find their sub-pixel locations. That's left to the centroider, which is part of the measurement stage.\n", + "\n", + "Before we can get to that point, we need to run the deblender:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fluxCatalog, templateCatalog = deblendTask.run(coadds, catalog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`MultibandDeblendTask` always returns two catalogs, a `templateCatalog` that contains the model outputs from *scarlet* and a `fluxCatalog`, which uses the *scarlet* models as weights to redistribute the flux from the input image (in other words they contain flux-conserved models). If `MultibandDeblendTask.config.saveTemplates` is `False`, then `templateCatalog` will be `None`. Similarly, if `MultibandDeblendTask.config.conserveFlux` is `False` then the `fluxCatalog` will be `None` (and the code will run slightly faster, since it doesn't have to reweight the flux, however this is a small fraction of the processing time).\n", + "\n", + "The deblender itself sets the `parent` column for each source, which is `0` for objects with no parent, and all of the columns that begin with `deblend_` and also adds new rows to the catalog for each child. It does *not* remove the parent rows it created those child rows from, and this is intentional, because we want to measure both \"interpretations\" of the blend family: one in which there is only one object (the parent version) and one in which there are several (the children). Before doing any science with the outputs of an LSST catalog, it's important to remove one of those interpretations (typically the parent one). That can be done by looking at the `deblend_nChild` and `parent` fields:\n", + "\n", + " - `parent` is the ID of the source from which this was deblended, or `0` if the source is itself a parent.\n", + " - `deblend_nChild` is the number of child sources this source has (so it's `0` for sources that are themselves children or were never blended).\n", + " \n", + "Together, these define two particularly useful filters:\n", + "\n", + " - `deblend_nChild == 0`: never-blended object or de-blended child\n", + " - `deblend_nChild == 0 and parent == 0`: never-blended object\n", + " \n", + "The first is what you'll usually want to use; the second is what to use if you're willing to throw away some objects (possibly many) because you don't trust the deblender.\n", + "\n", + "The last processing step for our purposes is running measurement, which must be done on each catalog, in each band (if we want measurements for all of them):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "measureTask.run(templateCatalog[\"r\"], coadds['r'])\n", + "measureTask.run(templateCatalog[\"i\"], coadds['i'])\n", + "measureTask.run(fluxCatalog[\"r\"], coadds['r'])\n", + "measureTask.run(fluxCatalog[\"i\"], coadds['i'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to an unfortunate bug in the deblender task, the resulting catalogs are not contiguous and we need to copy them into new objects to use them appropriately. This step can be avoided in the near future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.afw.table as afwTable\n", + "\n", + "for f in filters:\n", + " _catalog = afwTable.SourceCatalog(templateCatalog[f].table.clone())\n", + " _catalog.extend(templateCatalog[f], deep=True)\n", + " templateCatalog[f] = _catalog\n", + " _catalog = afwTable.SourceCatalog(fluxCatalog[f].table.clone())\n", + " _catalog.extend(fluxCatalog[f], deep=True)\n", + " fluxCatalog[f] = _catalog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we care about deblending (for the sake of this tutorial) lets look at the results from the 13th blend displayed above. We use the `HeavyFootprint`s from the catalog sources that have the same parent (parent 13 from above) to build a model for the entre scene, and to compare the results of the flux conserved and *scarlet* models. In the process we look at both the *scarlet* and flux conserved models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from lsst.afw.detection import MultibandFootprint\n", + "from lsst.afw.image import MultibandImage\n", + "\n", + "# Use the 13th parent in the blend\n", + "# Note: this is not the parent ID, but the 13th source in the catalog\n", + "parentIdx = 13\n", + "\n", + "# Create empty multiband images to model the entire scene\n", + "templateModel = MultibandImage.fromImages(coadds.filters,\n", + " [Image(fluxCatalog[\"r\"][parentIdx].getFootprint().getBBox(), dtype=np.float32)\n", + " for b in range(len(filters))])\n", + "fluxModel = MultibandImage.fromImages(coadds.filters,\n", + " [Image(fluxCatalog[\"r\"][parentIdx].getFootprint().getBBox(), dtype=np.float32)\n", + " for b in range(len(filters))])\n", + "\n", + "# Only use the subset catalogs with the same parent\n", + "parentId = fluxCatalog[\"r\"][parentIdx].get(\"id\")\n", + "fluxChildren = {b: fluxCatalog[b][fluxCatalog[b].get(\"parent\")==parentId] for b in filters}\n", + "templateChildren = {b: templateCatalog[b][templateCatalog[b].get(\"parent\")==parentId] for b in filters}\n", + "assert(len(fluxChildren)==len(templateChildren))\n", + "\n", + "for n in range(len(templateChildren[\"r\"])):\n", + " # Add the source model to the model of the entire scene\n", + " fp = MultibandFootprint(coadds.filters, [templateChildren[b][n].getFootprint() for b in filters])\n", + " _fp = MultibandFootprint(coadds.filters, [fluxChildren[b][n].getFootprint() for b in filters])\n", + " templateModel[:, fp.getBBox()].array += fp.getImage(fill=0).image.array\n", + " fluxModel[:, _fp.getBBox()].array += _fp.getImage(fill=0).image.array\n", + "\n", + " # Show the model\n", + " fig = plt.figure(figsize=(6, 3))\n", + " ax = [fig.add_subplot(1, 2, n+1) for n in range(2)]\n", + " ax[0].set_title(\"scarlet\")\n", + " ax[1].set_title(\"flux conserved\")\n", + " showRGB(fp.getImage().image, ax=ax[0])\n", + " showRGB(_fp.getImage().image, ax=ax[1])\n", + " plt.show()\n", + "\n", + "templateResidual = MultibandImage(coadds.filters,\n", + " coadds[:, templateModel.getBBox()].image.array - templateModel.array)\n", + "fluxResidual = MultibandImage(coadds.filters,\n", + " coadds[:, fluxModel.getBBox()].image.array - fluxModel.array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we look at the full models and the residuals. As expected, there are no residuals for the flux conserved model since all of the flux in the image (that is within the footprint) is added to one of the sources. In this particular case that works fine, but in instances where one or more sources were not detected this can cause one source to have its flux contaminated with its neighbor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for title, model, residual in [[\"scarlet\", templateModel, templateResidual], [\"flux conserved\", fluxModel, fluxResidual]]:\n", + " fig = plt.figure(figsize=(15,8))\n", + " ax = [fig.add_subplot(1, 2, n+1) for n in range(2)]\n", + " ax[0].set_title(\"{0} model\".format(title))\n", + " ax[1].set_title(\"{0} residual\".format(title))\n", + " showRGB(model, ax=ax[0])\n", + " showRGB(residual ,ax=ax[1], Q=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "- Use some of the other`sampleBBox` regions and run through the code again, from source detection through measurment and blending displays. Don't foget to change the parent index to view only the children of the correct blend.\n", + "- Play around with other *scarlet* constraints, such as adding an L0 penalty. This should help the code execute faster, as one of the main reasons for the slow down is unecessarily large boxes surrounding the smaller sources. See https://github.com/lsst/meas_deblender/blob/master/python/lsst/meas/deblender/deblend.py#L462-L545 for a description of the other configuration options that can be passed to the deblender\n", + "\n", + "Note: in order to try out different constraints the `meas_deblender` package requires an upgrade that has not been pushed to master yet. To use the latest changes, from your terminal session you must execute the following steps:\n", + "\n", + "```bash\n", + "~$ cp /project/fred3m/tutorials/lsst2018/.user_setups ~/notebooks\n", + "~$ source ~/notebooks/.user_setups\n", + "```\n", + "\n", + "You will have to restart the kernel for this notebook session in order for the changes to take place." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deblending/scarlet_tutorial.ipynb b/Deblending/scarlet_tutorial.ipynb new file mode 100755 index 00000000..4ea99f05 --- /dev/null +++ b/Deblending/scarlet_tutorial.ipynb @@ -0,0 +1,470 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scarlet deblending Tutorial\n", + "\n", + "The purpose of this tutorial is to familiarize the user with the basics of using *scarlet* to model blended scenes, and how tweaking various objects and parameters affects the resulting model. A tutorial that is more specific to using scarlet in the context of the LSST DM Science Pipelines is also available.\n", + "\n", + "Before attempting this tutorial it will be useful to read the [introduction](http://scarlet.readthedocs.io/en/latest/user_docs.html) to the *scarlet* User Guide, and many of the exercises below may require referencing the *scarlet* [docs](http://scarlet.readthedocs.io/en/latest/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import the necessary libraries\n", + "import os\n", + "\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "# don't interpolate the pixels\n", + "matplotlib.rc('image', interpolation='none')\n", + "\n", + "import numpy as np\n", + "import scarlet\n", + "import scarlet.display" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display functions\n", + "\n", + "Below are several usful functions used throughout this tutorial to visualize the data and models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Display the sources\n", + "def display_sources(sources, image, norm=None, subset=None, combine=False, show_sed=True, filter_indices=None):\n", + " \"\"\"Display the data and model for all sources in a blend\n", + "\n", + " This convenience function is used to display all (or a subset) of\n", + " the sources and (optionally) their SED's.\n", + " \"\"\"\n", + " if subset is None:\n", + " # Show all sources in the blend\n", + " subset = range(len(sources))\n", + " if filter_indices is None:\n", + " filter_indices = [3,2,1]\n", + " for m in subset:\n", + " # Load the model for the source\n", + " src = sources[m]\n", + " model = [comp.get_model() for comp in src]\n", + "\n", + " # Select the image patch the overlaps with the source and convert it to an RGB image\n", + " img_rgb = scarlet.display.img_to_rgb(image[src[0].bb], filter_indices=filter_indices, norm=norm)\n", + "\n", + " # Build a model for each component in the model\n", + " rgb = []\n", + " for _model in model:\n", + " # Convert the model to an RGB image\n", + " _rgb = scarlet.display.img_to_rgb(_model, filter_indices=filter_indices, norm=norm)\n", + " rgb.append(_rgb)\n", + "\n", + " # Display the image and model\n", + " figsize = [6,3]\n", + " columns = 2\n", + " # Calculate the number of columns needed and shape of the figure\n", + " if show_sed:\n", + " figsize[0] += 3\n", + " columns += 1\n", + " if not combine:\n", + " figsize[0] += 3*(len(model)-1)\n", + " columns += len(model)-1\n", + " # Build the figure\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = [fig.add_subplot(1,columns,n+1) for n in range(columns)]\n", + " ax[0].imshow(img_rgb)\n", + " ax[0].set_title(\"Data: Source {0}\".format(m))\n", + " for n, _rgb in enumerate(rgb):\n", + " ax[n+1].imshow(_rgb)\n", + " if combine:\n", + " ax[n+1].set_title(\"Initial Model\")\n", + " else:\n", + " ax[n+1].set_title(\"Component {0}\".format(n))\n", + " if show_sed:\n", + " for comp in src:\n", + " ax[-1].plot(comp.sed)\n", + " ax[-1].set_title(\"SED\")\n", + " ax[-1].set_xlabel(\"Band\")\n", + " ax[-1].set_ylabel(\"Intensity\")\n", + " # Mark the current source in the image\n", + " y,x = src[0].center\n", + " ax[0].plot(x-src[0].bb[2].start, y-src[0].bb[1].start, 'x', color=\"#5af916\", mew=2)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def display_model_residual(images, blend, peaks, norm, filter_indices=None):\n", + " \"\"\"Display the data, model, and residual for a given result\n", + " \"\"\"\n", + " if filter_indices is None:\n", + " filter_indices = [3,2,1]\n", + " model = blend.get_model()\n", + " residual = images-model\n", + " print(\"Data range: {0:.3f} to {1:.3f}\\nresidual range: {2:.3f} to {3:.3f}\\nrms: {4:.3f}\".format(\n", + " np.min(images),\n", + " np.max(images),\n", + " np.min(residual),\n", + " np.max(residual),\n", + " np.sqrt(np.std(residual)**2+np.mean(residual)**2)\n", + " ))\n", + " # Create RGB images\n", + " img_rgb = scarlet.display.img_to_rgb(images, filter_indices=filter_indices, norm=norm)\n", + " model_rgb = scarlet.display.img_to_rgb(model, filter_indices=filter_indices, norm=norm)\n", + " residual_norm = scarlet.display.Linear(img=residual)\n", + " residual_rgb = scarlet.display.img_to_rgb(residual, filter_indices=filter_indices, norm=residual_norm)\n", + "\n", + " # Show the data, model, and residual\n", + " fig = plt.figure(figsize=(15,5))\n", + " ax = [fig.add_subplot(1,3,n+1) for n in range(3)]\n", + " ax[0].imshow(img_rgb)\n", + " ax[0].set_title(\"Data\")\n", + " ax[1].imshow(model_rgb)\n", + " ax[1].set_title(\"Model\")\n", + " ax[2].imshow(residual_rgb)\n", + " ax[2].set_title(\"Residual\")\n", + " for k,component in enumerate(blend.components):\n", + " y,x = component.center\n", + " #px, py = peaks[k]\n", + " ax[0].plot(x, y, \"gx\")\n", + " #ax[0].plot(px, py, \"rx\")\n", + " ax[1].text(x, y, k, color=\"r\")\n", + " plt.show()\n", + "\n", + "def show_psfs(psfs, filters, norm=None):\n", + " rows = int(np.ceil(len(psfs)/3))\n", + " columns = min(len(psfs), 3)\n", + " figsize = (45/columns, rows*5)\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = [fig.add_subplot(rows, columns, n+1) for n in range(len(psfs))]\n", + " for n, psf in enumerate(psfs):\n", + " im = ax[n].imshow(psf, norm=norm)\n", + " ax[n].set_title(\"{0}-band PSF\".format(filters[n]))\n", + " plt.colorbar(im, ax=ax[n])\n", + " plt.show()\n", + "\n", + "def display_diff_kernels(psf_blend, diff_kernels):\n", + " model = psf_blend.get_model()\n", + " for b, component in enumerate(psf_blend.components):\n", + " fig = plt.figure(figsize=(15,2.5))\n", + " ax = [fig.add_subplot(1,4,n+1) for n in range(4)]\n", + " # Display the psf\n", + " ax[0].set_title(\"psf\")\n", + " _img = ax[0].imshow(psfs[b])\n", + " fig.colorbar(_img, ax=ax[0])\n", + " # Display the model\n", + " ax[1].set_title(\"modeled psf\")\n", + " _model = np.ma.array(model[b], mask=model[b]==0)\n", + " _img = ax[1].imshow(_model)\n", + " fig.colorbar(_img, ax=ax[1])\n", + " # Display the difference kernel\n", + " ax[2].set_title(\"difference kernel\")\n", + " _img = ax[2].imshow(np.ma.array(diff_kernels[b], mask=diff_kernels[b]==0))\n", + " fig.colorbar(_img, ax=ax[2])\n", + " # Display the residual\n", + " ax[3].set_title(\"residual\")\n", + " residual = psfs[b]-model[b]\n", + " vabs = np.max(np.abs(residual))\n", + " _img = ax[3].imshow(residual, vmin=-vabs, vmax=vabs, cmap='seismic')\n", + " fig.colorbar(_img, ax=ax[3])\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and Display the data\n", + "\n", + "The `file_path` points to a directory with 147 HSC blends from the COSMOS field detected by the LSST pipeline. Changing `idx` below will select a different blend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample images\n", + "idx = 53\n", + "file_path = \"/project/fred3m/code/testdata_deblender/real_data/hsc_cosmos/not_matched\"\n", + "files = os.listdir(file_path)\n", + "data = np.load(os.path.join(file_path, files[idx]))\n", + "image = data[\"images\"]\n", + "wmap = data[\"weights\"]\n", + "peaks = data[\"peaks\"]\n", + "psfs = data[\"psfs\"]\n", + "filters = [\"G\", \"R\", \"I\", \"Z\", \"Y\"]\n", + "# Only a rough estimate of the background is needed\n", + "# to initialize and resize the sources\n", + "bg_rms = np.std(image, axis=(1,2))\n", + "print(\"Background RMS: {0}\".format(bg_rms))\n", + "\n", + "# Use Asinh scaling for the images\n", + "norm = scarlet.display.Asinh(img=image, Q=10)\n", + "# Map i,r,g -> RGB\n", + "filter_indices = [3,2,1]\n", + "# Convert the image to an RGB image\n", + "img_rgb = scarlet.display.img_to_rgb(image, filter_indices=filter_indices, norm=norm)\n", + "plt.imshow(img_rgb)\n", + "plt.title(\"Image: {0}\".format(idx))\n", + "for src in peaks:\n", + " plt.plot(src[0], src[1], \"rx\", mew=2)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initializing Sources\n", + "\n", + "Astrophysical objects are modeled in scarlet as a collection of components, where each component has a single SED that is constant over it's morphology (band independent intensity). So a single source might have multiple components, like a bulge and disk, or a single component.\n", + "\n", + "The different classes that inherit from `Source` mainly differ in how they are initialized, and otherwise behave similarly during the optimization routine. This section illustrates the differences between different source initialization classes.\n", + "\n", + "The simplest source is a single component intialized with only a single pixel (at the center of the object) turned on.\n", + "\n", + "### *WARNING* \n", + "Scarlet accepts source positions using the numpy/C++ convention of (y,x), which is different than the astropy and LSST stack convention of (x,y)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sources = [scarlet.PointSource((peak[1], peak[0]), image) for peak in peaks]\n", + "\n", + "# Display the initial guess for each source\n", + "display_sources(sources, image, norm=norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise:\n", + "\n", + "* Experiment with the above code by using `ExtendedSource`, which initializes each object as a single component with maximum flux at the peak that falls off monotonically and has 180 degree symmetry; and using `MultiComponentSource`, which models a source as two components (a bulge and a disk) that are each symmetric and montonically decreasing from the peak.\n", + "\n", + "# Deblending a scene\n", + "\n", + "The `Blend` class contains the list of sources, the image, and any other configuration parameters necessary to fit the data, including routines to fit the center positions and resize the bounding box containing the sources (if necessary). Once a blend has been initialized with a list of sources, the image and background RMS values must be set (the background RMS is used to determine when to truncate the bounding box around a source)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blend = scarlet.Blend(sources)\n", + "blend.set_data(image, bg_rms=bg_rms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we can fit a model, given a maximum number of iterations and the relative error required for convergence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blend.fit(100, 1e-2)\n", + "print(\"Deblending completed in {0} iterations\".format(blend.it))\n", + "display_model_residual(image, blend, peaks, norm)\n", + "display_sources(sources, image, norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Experiment by running the above code using different source models (for example `ExtendedSource`) to see how initializtion affects the belnding results.\n", + "\n", + "* The code above initialized the sources at their exact centers. Try offsetting the initial positions by `0.5` pixels in `x` and/or `y` and passing a `shift_center=0` argument when initializing the source. This prevents the source from updating its position, so notice how that affects the resulting model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Constraints\n", + "\n", + "The above models used the default constraints: perfect symmetry and a weighted monotonicity that decreases from the peak. So each source is defined (internally during initialization) with the constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import scarlet.constraint as sc\n", + "constraints = (sc.SimpleConstraint(),\n", + " sc.DirectMonotonicityConstraint(use_nearest=False),\n", + " sc.DirectSymmetryConstraint())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where `SimpleConstraint` forces the SED and morphology to be non-negative, the SED to be normalized to unity, and the peak to have some (minimal) flux at the center." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Go back to the source initialization cell and pass a custom set of constraints. For example, pass `DirectSymmetryConstraint` a number between 0 and 1 to set the level of symmetry required, or eliminate the symmetry constraint altogether and see how that affects deblending.\n", + "\n", + "* Set `use_nearest=True` in the `DirectMonotonicityConstraint`.\n", + "\n", + "* Add `L0Constraint` or `L1Constraint` to the list of constraints and observe the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration\n", + "\n", + "There are additional configuration paramters that can be used to initialize a source, as described in http://scarlet.readthedocs.io/en/latest/config.html#Configuration-(scarlet.config).\n", + "\n", + "## Exercises\n", + "\n", + "* Initialize the sources with a custom configuration where `refine_skip=2`, which updates the positions and box sizes on every other step, and see how the results compare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PSF Deconvolution\n", + "\n", + "When analyzing real images the PSF will be different in each band unless they have been PSF matched. In general deblending should not be performed on PSF matched coadds, as matching will increase the blending in bands with better seeing. Instead scarlet can be used to build a deconvolved model which is a more sparse (and less blended) representation of the data, and convolve the model in each band to compare to the input data.\n", + "\n", + "To initialize a source with a PSF, pass the PSF as an input to the new source:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scarlet.ExtendedSource(peaks[0], image, bg_rms, psf=psfs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Partial PSF Deconvolution\n", + "\n", + "As discussed in the tutorial http://scarlet.readthedocs.io/en/latest/psf_matching.html, the data is noisy and the fully deconvolved scene is undersampled, making the application of the constraints and and full convolution kernel unstable and prone to biases. Instead we can create a target PSF and model the sources in the partially deconvolved target PSF scene.\n", + "\n", + "First we need to specify the target PSF. *scarlet* includes a `fit_target_psf` function to fit the PSF in each band to either a `moffat`, `gaussian`, or `double_gaussian` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scarlet.psf_match\n", + "\n", + "show_psfs(psfs, filters)\n", + "\n", + "# Find the target PSF\n", + "target_psf = scarlet.psf_match.fit_target_psf(psfs, scarlet.psf_match.moffat)\n", + "plt.imshow(target_psf)\n", + "plt.title(\"target PSF\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the target PSF we can find the difference kernel in each band using *scarlet*. The `build_diff_kernels` function basically treats the PSF image as a blend, where the PSF in each band is a monochromatic source, and fits the difference kernels using the minimum number of pixels necessary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diff_kernels, psf_blend = scarlet.psf_match.build_diff_kernels(psfs, target_psf)\n", + "display_diff_kernels(psf_blend, diff_kernels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Try building the difference kernels while varying the parameters in `build_diff_kernels`, for example using larger and smaller values for `l0_thresh`.\n", + "\n", + "* Go back up to source initialization and use `psf=psfs` to fully deconvolve the scene and fit the blend\n", + "\n", + "* Try the same thing but set `psf=diff_kernels` for each source to partially deconvolve the scene." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4c5158c5423812b8fc793b30b7ada24f82235f11 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Wed, 15 Aug 2018 09:55:14 -0700 Subject: [PATCH 12/45] Deblending in the READMEs --- Deblending/README.rst | 36 ++++++++++++++++++++++++++++++++++++ README.md | 5 +++-- 2 files changed, 39 insertions(+), 2 deletions(-) create mode 100644 Deblending/README.rst diff --git a/Deblending/README.rst b/Deblending/README.rst new file mode 100644 index 00000000..5b422d3a --- /dev/null +++ b/Deblending/README.rst @@ -0,0 +1,36 @@ +Deblending +========== + +This folder contains a set of tutorial notebooks exploring the deblending of LSST objects. See the index table below for links to the notebook code, and an auto-rendered view of the notebook with outputs. + + +.. list-table:: + :widths: 10 20 10 10 + :header-rows: 1 + + * - Notebook + - Short description + - Links + - Owner + + + * - **SCARLET Tutorial** + - Introduction to the SCARLET deblender, how to configure and run it. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/scarlet_tutorial.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/scarlet_tutorial.log + + - `Fred Moolekamp `_ + + + * - **Deblending in DRP ** + - Where and how the deblending happens, in the DRP pipeline. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.log + + - `Fred Moolekamp `_ diff --git a/README.md b/README.md index 9f4ec115..ec3f1f6d 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ the LSST Science Collaborations. | Visualization | Displaying images and catalogs. | [StackClub/Visualization](Visualization) | | Image Processing | From raw images to `calexp`s and `coadd`s. | [StackClub/ImageProcessing](ImageProcessing) | | SourceDetection | Detection of sources in images - including low surface brightness galaxies. | [StackClub/SourceDetection](SourceDetection) | +| Deblending | Deblending the objects | [StackClub/Deblending](Deblending) | | Validation | Tools for validating Stack outputs, example validation analyses | [StackClub/Validation](Validation) | * [Stack Club projects](https://github.com/LSSTScienceCollaborations/StackClub/labels/project), as defined by Stack Club members - follow [this link](https://github.com/LSSTScienceCollaborations/StackClub/labels/project) to see what people are working on. [Unassigned projects](https://github.com/LSSTScienceCollaborations/StackClub/issues?utf8=%E2%9C%93&q=is%3Aopen+label%3Aproject+no%3Aassignee) are available for new members to take on! @@ -26,7 +27,7 @@ the LSST Science Collaborations. ## Contributing New Stack Club members: please see the [notes on getting started](GettingStarted/GettingStarted.md) - they'll walk you onto you new LSST Science Platform account, and then show you how to work on your tutorial notebooks. Also, please note the [Stack Club Rules](Rules.md) that we all agree to abide by. -Everyone else: we welcome pull requests! Feel free to fork this repo and send us a pull request. And if you are interested in joining the Stack Club, please drop one of us a line, or come and find us in the [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM) LSSTC Slack channel. +Everyone else: we welcome pull requests! Feel free to fork this repo and send us a pull request. And if you are interested in joining the Stack Club, please drop one of us a line, or come and find us in the [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM) LSSTC Slack channel. > When preparing a pull request, please note the [standards](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#standards) that we are trying to uphold. @@ -52,4 +53,4 @@ but you can't blame us if it doesn't do what you want. ## More About This Project -Following a successful LSSTC "Enabling Science" proposal, we put together a 3-phase plan, which you can read about in more detail [here](https://docs.google.com/document/d/103kzjOklSUWo5MJP9B-EsnAdO7V6bstTC_mzBvd0NIk/edit#). Phase 0 involved collecting existing tutorials and identifying potential club members from around the LSST Science Collaborations. Then, in Phase 1 (late May 2018 to mid August 2018) we worked together in a small group to turn a subset of those existing "seed" tutorials into community-maintained Jupyter notebooks, for display at the August LSST 2018 Project and Community Workshop (PCW) in Tucson. At that meeting, we opened up to a larger group of LSST science collaboration members, extending and spinning off the initial set of notebooks. +Following a successful LSSTC "Enabling Science" proposal, we put together a 3-phase plan, which you can read about in more detail [here](https://docs.google.com/document/d/103kzjOklSUWo5MJP9B-EsnAdO7V6bstTC_mzBvd0NIk/edit#). Phase 0 involved collecting existing tutorials and identifying potential club members from around the LSST Science Collaborations. Then, in Phase 1 (late May 2018 to mid August 2018) we worked together in a small group to turn a subset of those existing "seed" tutorials into community-maintained Jupyter notebooks, for display at the August LSST 2018 Project and Community Workshop (PCW) in Tucson. At that meeting, we opened up to a larger group of LSST science collaboration members, extending and spinning off the initial set of notebooks. From 121c28075c0e67e143f59df8d096a30d195836b1 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 17 Aug 2018 00:15:26 -0700 Subject: [PATCH 13/45] August 10, August 13 (PCW) --- Meetings.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Meetings.md b/Meetings.md index effd7653..51d0e326 100644 --- a/Meetings.md +++ b/Meetings.md @@ -1,10 +1,10 @@ # Stack Club Meetings -Inidividual session recordings are linked below. +Individual session recordings are linked below. | Session | Date | Topic | Links | |---|---|---|---| -| Phase 1, Session 1 | Friday May 25, 2018 [(video)](https://stanford.zoom.us/recording/share/xA33Pv0oq_g5l6a0CaJ0az01mbROy_gyGLDEqIR92FOwIumekTziMw) | Visualization with Firefly | [SQuaRE notebook](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb) | +| Phase 1, Session 1 | Friday May 25, 2018 [(video)](https://stanford.zoom.us/recording/share/xA33Pv0oq_g5l6a0CaJ0az01mbROy_gyGLDEqIR92FOwIumekTziMw) | Visualization with Firefly | [SQuaRE Firefly demo](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb) | | Phase 1, Session 2 | Wednesday June 6, 2018 [(video)](https://stanford.zoom.us/recording/share/YZad6BLPZFCjhgLSckrpis7w6Ekyr61VhhIvtFnjR_-wIumekTziMw) | Commissioning Team Bootcamp Report | [LSST Commissioning Team Notebooks](https://github.com/lsst-com/notebooks) | | Phase 1, Session 3 | Friday June 22, 2018 | | | | Phase 1, Session 4 | Friday July 6, 2018 [(video)](https://stanford.zoom.us/recording/share/1ZHCNdwRZnhwq8sb1TPvznug-AusUCCIV55N0DUF-LawIumekTziMw) | Project Discussion | [Topic List](https://docs.google.com/document/d/1PSA1uWwTfs9CweatpxF8CEPGBYRY5ZaXB39JzXYE7_U/edit#heading=h.txq6h6bpxzkd) | @@ -12,5 +12,7 @@ Inidividual session recordings are linked below. | Phase 1, Session 6 | Friday July 20, 2018 [(video)](https://stanford.zoom.us/recording/share/XqFx95GJ7zlSZTOVBPZz4l8WGYUmj7EyNsuF6vofMtewIumekTziMw) | Hack Session | [CalExp Tour](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/calexp-tour/stargaser/Basics/Calexp_guided_tour.ipynb) | | Phase 1, Session 7 | Friday July 27, 2018 [(video)](https://stanford.zoom.us/recording/share/AOFd8Q8yH4lHI6aTLylqRBgcusMUERz-ksiULX4rRL2wIumekTziMw) | Hack Session | [VPN set-up change](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#accessing-ncsa-via-its-vpn) | | Phase 1, Session 8 | Friday August 3, 2018 [(video)](https://stanford.zoom.us/recording/share/Pnin7IjBNCyGrOCKgyXTvRFFbcuE_eG6tN6QWkQtsvmwIumekTziMw) | PCW Planning | [Source Detection: Low Surface Brightness Galaxies](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/SourceDetection/LowSurfaceBrightness.ipynb) | +| Phase 1, Session 9 | Friday August 10, 2018 [(video)](https://stanford.zoom.us/recording/share/d5skJMVG1L-XhtyV6xZA6gI0pN552NQjfNuFUMymocCwIumekTziMw) | Hack Session | [Rules, `stackclub` library package, CIT with `beavis-ci`](https://github.com/LSSTScienceCollaborations/StackClub/issues/85) | +| LSST2018 | Monday August 13, 2018 [(video)](https://stanford.zoom.us/recording/share/qyunKljpUWaFQBneuiL4PnbxTB-tf1BvttELFVPJHnuwIumekTziMw) | PCW welcome, discussion, hacking | [Introduction to the LSST Stack Club](https://docs.google.com/presentation/d/1LWShGi-YLqWoxPvewkg-JOpb67WKZyI0YQcSFrmAl14/edit#slide=id.p1) | From 255596330d6b8f4f29ac69f89e48528e54d34b48 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 17 Aug 2018 00:31:15 -0700 Subject: [PATCH 14/45] Application form link --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9f4ec115..ed98e4ee 100644 --- a/README.md +++ b/README.md @@ -23,10 +23,13 @@ the LSST Science Collaborations. * [Working list of target topics, with links to tutorial seeds](https://docs.google.com/document/d/1PSA1uWwTfs9CweatpxF8CEPGBYRY5ZaXB39JzXYE7_U/edit#), for help in defining a new Stack Club project. This list is a fairly comprehensive collection of existing project and community tutorial web pages and demo notebooks, from which seeds can be drawn. +## Joining the Stack Club +If you would like to join the Stack Club, please fill out this short **[application form](https://goo.gl/forms/588KlPTFfkEEFFUu2)**. (Basically you'll be asked to agree to abide by the [Stack Club Rules](Rules.md), and then give enough contact information to request an account on the LSST Science Platform.) If you are not ready to commit time to working on a Stack Club project, you can still follow along by [watching](https://github.com/LSSTScienceCollaborations/StackClub/subscription) this repo and joining the [#stack-club LSSTC Slack channel](https://lsstc.slack.com/messages/C9YRAS4HM/). + ## Contributing -New Stack Club members: please see the [notes on getting started](GettingStarted/GettingStarted.md) - they'll walk you onto you new LSST Science Platform account, and then show you how to work on your tutorial notebooks. Also, please note the [Stack Club Rules](Rules.md) that we all agree to abide by. +New Stack Club members: please see the [notes on getting started](GettingStarted/GettingStarted.md) - they'll walk you onto you new LSST Science Platform account, and then show you how to work on your tutorial notebooks. -Everyone else: we welcome pull requests! Feel free to fork this repo and send us a pull request. And if you are interested in joining the Stack Club, please drop one of us a line, or come and find us in the [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM) LSSTC Slack channel. +Everyone else: we welcome pull requests! Feel free to fork this repo, write us an issue, and send us a PR. > When preparing a pull request, please note the [standards](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#standards) that we are trying to uphold. @@ -38,7 +41,7 @@ We welcome your input! Please post questions and suggestions in the * Alex Drlica-Wagner (Fermilab, [@kadrlica](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@kadrlica)) * Phil Marshall (SLAC, [@drphilmarshall](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@drphilmarshall)) -The Club meets periodically via Zoom, but you can find us on LSSTC Slack at [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM). You can also watch the tutorial walkthroughs in the Club sessions in the videos linked from our [Meetings page](Meetings.md). +The Club meets periodically via Zoom, but you can find us on LSSTC Slack at [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM). You can also watch the tutorial walkthroughs in the Club sessions in the videos linked from our [Meetings page](Meetings.md). If you are just looking for the application form, it's [here](https://goo.gl/forms/588KlPTFfkEEFFUu2). ## License From 26bf19ef0dd0732b7016ff153ce4707432a793d3 Mon Sep 17 00:00:00 2001 From: fred3m Date: Wed, 15 Aug 2018 09:22:34 -0700 Subject: [PATCH 15/45] add blending tutorials from LSST 2018 blending workshop --- Deblending/lsst_stack_deblender.ipynb | 628 ++++++++++++++++++++++++++ Deblending/scarlet_tutorial.ipynb | 470 +++++++++++++++++++ 2 files changed, 1098 insertions(+) create mode 100755 Deblending/lsst_stack_deblender.ipynb create mode 100755 Deblending/scarlet_tutorial.ipynb diff --git a/Deblending/lsst_stack_deblender.ipynb b/Deblending/lsst_stack_deblender.ipynb new file mode 100755 index 00000000..4427f74a --- /dev/null +++ b/Deblending/lsst_stack_deblender.ipynb @@ -0,0 +1,628 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LSST Stack Multiband Deblender Tutorial\n", + "\n", + "This tutorial is designed to illustrate how to execture the multiband deblender (*scarlet*) in the LSST stack. This includes a brief introduction to LSST stack objects including:\n", + "\n", + " - Geometry classes from `lsst.geom`, such as points and boxes.\n", + " - Higher-level astronomical primitives from `lsst.afw`, such as the `Image`, `Exposure`, and `Psf` classes.\n", + " - Our core algorithmic `Task` classes, including those for source detection, deblending, and measurement.\n", + " \n", + "We'll be working with coadded images made from Subaru Hyper Suprime-Cam (HSC) data in the COSMOS field. We've taken a recent LSST reprocessing of the HSC-SSP UltraDeep COSMOS field (see [this page](https://confluence.lsstcorp.org/display/DM/S18+HSC+PDR1+reprocessing) for information on that reprocessing, and [this page](https://hsc-release.mtk.nao.ac.jp/doc/) for the data), and added simulated stars from a scaled [SDSS catalog](http://www.sdss.org/dr14/data_access/value-added-catalogs/?vac_id=photometry-of-crowded-fields-in-sdss-for-galactic-globular-and-open-clusters). The result is a very deep image (deeper than the 10-year LSST Deep-Wide-Fast survey, though not as deep as LSST Deep Drilling fields will be) with both a large number of galaxies and region full of stars.\n", + "\n", + "This tutorial is based on Jim Bosch's globular cluster tutorial, however in it's present state *scarlet* is unable to process the crowded field (most likely) due to poor initial conditions for the sources in the field. So instead we use a region of the image outside of the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "We'll start with some standard imports of both LSST and third-party packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from lsst.daf.persistence import Butler\n", + "from lsst.geom import Box2I, Box2D, Point2I, Point2D, Extent2I, Extent2D\n", + "from lsst.afw.image import Exposure, Image, PARENT, MultibandExposure, MultibandImage\n", + "from lsst.afw.detection import MultibandFootprint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Data\n", + "\n", + "We'll be retrieving data using the `Butler` tool, which manages where various datasets are stored on the filesystem (and can in principle manage datasets that aren't even stored as files, though all of these are).\n", + "\n", + "We start by creating a `Butler` instance, pointing it at a *Data Repository* (which here is just a root directory)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"/project/jbosch/tutorials/lsst2018/data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Datasets managed by a butler are identified by a dictionary *Data ID* (specifying things like the visit number or sky patch) and a string *DatasetType* (such as a particular image or catalog). Different DatasetTypes have different keys, while different instances of the same Dataset Type have different values. All of the datasets we use in this tutorial will correspond to the same patch of sky, so they'll have at least the keys in the dictionary in the next cell (they will also have `filter`, but with different values):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataId = {\"tract\": 9813, \"patch\": \"4,4\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use those to load a set of *grizy* coadds, which we'll put directly in a dictionary. The result of each `Butler.get` call is in this case an `lsst.afw.image.Exposure` object, an image that actually contains three \"planes\" (the main image, a bit mask, and a variance image) as well as many other objects that describe the image, such as its PSF and WCS. Note that we (confusingly) use `Exposures` to hold coadd images as well as true single-exposure images, but combine them into a `MultibandExposure`, which contains an exposure in each band.\n", + "\n", + "The DatasetType here is `deepCoadd_calexp` (a coadd on which we've already done some additional processing, such as subtracting the background and setting some mask values), and the extra `filter` argument gets appended to the Data ID." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filters = \"grizy\"\n", + "coadds = [butler.get(\"deepCoadd_calexp\", dataId, filter=\"HSC-{}\".format(f.upper())) for f in filters]\n", + "coadds = MultibandExposure.fromExposures(filters, coadds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making and displaying color composite images\n", + "\n", + "We'll start by just looking at the images, as 3-color composites. We'll use astropy to build those as a nice way to demonstrate how to get NumPy arrays from the `MultibandImage` objects (the images in `coadds`). (LSST also has code to make 3-color composites using the same algorithm, and in fact the Astropy implementation is based on ours, but now that it's in Astropy we'll probably retire ours.)\n", + "\n", + "We'll just use matplotlib to display the images themselves. We'll use Firefly for other image display tasks later, but while Firefly itself supports color-composites, work on our preferred composition algorithm is still in progress, and we haven't quite finished connecting that functionality to the Python client we'll demonstrate here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.visualization import make_lupton_rgb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the following function a few times to display color images. It's worth reading through the implementation carefully to see what's going on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def showRGB(image, bgr=\"gri\", ax=None, fp=None, figsize=(8,8), stretch=1, Q=10):\n", + " \"\"\"Display an RGB color composite image with matplotlib.\n", + " \n", + " Parameters\n", + " ----------\n", + " image : `MultibandImage`\n", + " `MultibandImage` to display.\n", + " bgr : sequence\n", + " A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n", + " to use for each channel. If `image` only has three filters then this parameter is ignored\n", + " and the filters in the image are used.\n", + " ax : `matplotlib.axes.Axes`\n", + " Axis in a `matplotlib.Figure` to display the image.\n", + " If `axis` is `None` then a new figure is created.\n", + " fp: `lsst.afw.detection.Footprint`\n", + " Footprint that contains the peak catalog for peaks in the image.\n", + " If `fp` is `None` then no peak positions are plotted.\n", + " figsize: tuple\n", + " Size of the `matplotlib.Figure` created.\n", + " If `ax` is not `None` then this parameter is ignored.\n", + " stretch: int\n", + " The linear stretch of the image.\n", + " Q: int\n", + " The Asinh softening parameter.\n", + " \"\"\"\n", + " # If the image only has 3 bands, reverse the order of the bands to produce the RGB image\n", + " if len(image) == 3:\n", + " bgr = image.filters\n", + " # Extract the primary image component of each Exposure with the .image property, and use .array to get a NumPy array view.\n", + " rgb = make_lupton_rgb(image_r=image[bgr[2]].array, # numpy array for the r channel\n", + " image_g=image[bgr[1]].array, # numpy array for the g channel\n", + " image_b=image[bgr[0]].array, # numpy array for the b channel\n", + " stretch=stretch, Q=Q) # parameters used to stretch and scale the pixel values\n", + " if ax is None:\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = fig.add_subplot(1,1,1)\n", + " \n", + " # Exposure.getBBox() returns a Box2I, a box with integer pixel coordinates that correspond to the centers of pixels.\n", + " # Matplotlib's `extent` argument expects to receive the coordinates of the edges of pixels, which is what\n", + " # this Box2D (a box with floating-point coordinates) represents.\n", + " integerPixelBBox = image[bgr[0]].getBBox()\n", + " bbox = Box2D(integerPixelBBox)\n", + " ax.imshow(rgb, interpolation='nearest', origin='lower', extent=(bbox.getMinX(), bbox.getMaxX(), bbox.getMinY(), bbox.getMaxY()))\n", + " if fp is not None:\n", + " for peak in fp.getPeaks():\n", + " ax.plot(peak.getIx(), peak.getIy(), \"bx\", mew=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we can slice `MultibandImage` objects (as well a `MultibandExposure` objects) along the filter dimension using the filter names as indices. Like `Exposure` objects, `MultibandExposure` objects have `image`, `mask`, and `variance` properties that contain the image, mask plane, and variance of the `Exposure` respectively. For now we will only worry about the `image` property, although internal deblending and measurement algorithms make use of all three objects (when available)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(coadds[:\"z\"].image, figsize=(10, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(coadds[\"i\":].image, figsize=(10, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those images are a full \"patch\", which is our usual unit of processing for coadds - it's about the same size as a single LSST sensor (exactly the same in pixels, smaller in terms of area because these use HSC's smaller pixel scale). That's a bit unweildy (just because waiting for processing to happen isn't fun in a tutorial setting), so we'll reload our dict with sub-images centered on the region of interest.\n", + "\n", + "Note that we can load the sub-images directly with the `butler`, by appending `_sub` to the DatasetType and passing a `bbox` argument. If you want to see the region of the image with the cluster, use `clusterBBox` below, however as mentioned above, that region is too memory intensive for the current version of *scarlet*. Instead use `sampleBBox` to select a sub-region of the image (note that we add a small frame around each blend to include more background regions, which are important for the detection algorithm)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frame = 50\n", + "clusterBBox = Box2I(Point2I(18325, 17725), Extent2I(400, 350))\n", + "\n", + "#sampleBBox = Box2I(Point2I(18699-frame, 17138-frame), Extent2I(93+2*frame, 104+2*frame))\n", + "#sampleBBox = Box2I(Point2I(16424-frame, 17806-frame), Extent2I(55+2*frame, 62+2*frame))\n", + "#sampleBBox = Box2I(Point2I(17838-frame, 18945-frame), Extent2I(111+2*frame, 103+2*frame))\n", + "sampleBBox = Box2I(Point2I(19141-frame, 18228-frame), Extent2I(63+2*frame, 87+2*frame))\n", + "\n", + "subset = coadds[:, sampleBBox]\n", + "# Due to a bug in the code the PSF isn't copied properly.\n", + "# The code below copies the PSF into the `MultibandExposure`,\n", + "# but will be unecessary in the future\n", + "for f in subset.filters:\n", + " subset[f].setPsf(coadds[f].getPsf())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "showRGB(subset.image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Processing\n", + "\n", + "Now we'll try the regular LSST processing tasks, with a simpler configuration than we usually use to process coadds, just to avoid being distracted by complexity. This includes\n", + "\n", + " - Detection (`SourceDetectionTask`): given an `Exposure`, find above-threshold regions and peaks within them (`Footprints`), and create a *parent* source for each `Footprint`.\n", + " - Deblending (`MultibandDeblendTask`): given a `MultibandExposure` and a catalog of parent sources, create a *child* source for each peak in every `Footprint` that contains more than one peak. Each child source is given a `HeavyFootprint`, which contains both the pixel region that source covers and the fractional pixel values associated with that source. A `SourceDeblendTask` is also available using the single band SDSS-HSC deblender that takes a single band `Exposure`).\n", + " - Measurment (`SingleFrameMeasurementTask`): given an `Exposure` and a catalog of sources, run a set of \"measurement plugins\" on each source, using deblended pixel values if it is a child. Notice that measurement is still performed on single band catalogs, since none of the measurement algorithms work for multiband data.\n", + "\n", + "We'll start by importing these, along with the `SourceCatalog` class we'll use to hold the outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.meas.algorithms import SourceDetectionTask\n", + "from lsst.meas.deblender import MultibandDeblendTask\n", + "from lsst.meas.base import SingleFrameMeasurementTask\n", + "from lsst.afw.table import SourceCatalog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now construct all of these `Tasks` before actually running any of them. That's because `SourceDeblendTask` and `SingleFrameMeasurementTask` are constructed with a `Schema` object that records what fields they'll produce, and they modify that schema when they're constructed by adding columns to it. When we run the tasks later, they'll need to be given a catalog that includes all of those columns, **but we can't add columns to a catalog that already exists**.\n", + "\n", + "To recap, the sequence looks like this:\n", + "\n", + " 1. Make a (mostly) empty schema.\n", + " 2. Construct all of the `Task`s (in the order you plan to run them), which adds columns to the schema.\n", + " 3. Make a `SourceCatalog` object from the *complete* schema.\n", + " 4. Pass the same `SourceCatalog` object to each `Task` when you run it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schema = SourceCatalog.Table.makeMinimalSchema()\n", + "\n", + "detectionTask = SourceDetectionTask(schema=schema)\n", + "\n", + "config = MultibandDeblendTask.ConfigClass()\n", + "config.usePsfConvolution = True\n", + "config.conserveFlux = True\n", + "config.maxIter = 100\n", + "deblendTask = MultibandDeblendTask(schema=schema, config=config)\n", + "\n", + "# We'll customize the configuration of measurement to just run a few plugins.\n", + "# The default list of plugins is much longer (and hence slower).\n", + "measureConfig = SingleFrameMeasurementTask.ConfigClass()\n", + "measureConfig.plugins.names = [\"base_SdssCentroid\", \"base_PsfFlux\", \"base_SkyCoord\"]\n", + "# \"Slots\" are aliases that provide easy access to certain plugins.\n", + "# Because we're not running the plugin these slots refer to by default,\n", + "# we need to disable them in the configuration.\n", + "measureConfig.slots.apFlux = None\n", + "measureConfig.slots.instFlux = None\n", + "measureConfig.slots.shape = None\n", + "measureConfig.slots.modelFlux = None\n", + "measureConfig.slots.calibFlux = None\n", + "measureTask = SingleFrameMeasurementTask(config=measureConfig, schema=schema)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step we'll run is detection, which actually returns a new `SourceCatalog` object rather than working on an existing one.\n", + "\n", + "Instead, it takes a `Table` object, which is sort of like a factory for records. We won't use it directly after this, and it isn't actually necessary to make a new `Table` every time you run `MultibandDetectionTask` (but you can only create one after you're done adding columns to the schema).\n", + "\n", + "`Task`s that return anything do so via a `lsst.pipe.base.Struct` object, which is just a simple collection of named attributes. The only return values we're interested is `sources`. That's our new `SourceCatalog`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = SourceCatalog.Table.make(schema)\n", + "detectionResult = detectionTask.run(table, subset[\"r\"])\n", + "catalog = detectionResult.sources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at what's in that catalog. First off, we can look at its schema:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "catalog.schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this includes a lot of columns that were actually added by the deblend or measurement steps; those will all still be blank (`0` for integers or flags, `NaN` for floating-point columns).\n", + "\n", + "In fact, the only columns filled by `SourceDetectionTask` are the IDs. But it also attaches `Footprint` objects, which don't appear in the schema. You can retrieve the `Footprint` by calling `getFootprint()` on a row:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "footprint = catalog[0].getFootprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Footprints` have two components:\n", + " - a `SpanSet`, which represents an irregular region on an image via a list of (y, x0, x1) `Spans`;\n", + " - a `PeakCatalog`, a slightly different kind of catalog whose rows represent peaks within that `Footprint`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(footprint.getSpans())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(footprint.getPeaks())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we actually look at the footprints in the catalog we see that some have only a single peak, while others have multiple peaks that need to be deblended.\n", + "\n", + "To display only the pixels contained in the footprint (and not other pixels in the bounding box) we create a `MultibandFootprint`, which is a `HeavyFootprint` that contains a `SpanSet`, `PeakCatalog`, and `flux` values for all of the pixels in the `SpanSet`. In this case the `flux` is the total measured flux in the image, since no deblending has taken place yet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "for src in catalog:\n", + " fp = src.getFootprint()\n", + " img = coadds[:,fp.getBBox()].image\n", + " mfp = MultibandFootprint.fromImages(coadds.filters, image=img, footprint=fp)\n", + " showRGB(mfp.getImage().image, fp=fp, figsize=(3,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's worth noting that while the peaks *can* have both an integer-valued position and a floating-point position, they're the same right now; `SourceDetectionTask` currently just finds the pixels that are local minima and doesn't try to find their sub-pixel locations. That's left to the centroider, which is part of the measurement stage.\n", + "\n", + "Before we can get to that point, we need to run the deblender:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fluxCatalog, templateCatalog = deblendTask.run(coadds, catalog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`MultibandDeblendTask` always returns two catalogs, a `templateCatalog` that contains the model outputs from *scarlet* and a `fluxCatalog`, which uses the *scarlet* models as weights to redistribute the flux from the input image (in other words they contain flux-conserved models). If `MultibandDeblendTask.config.saveTemplates` is `False`, then `templateCatalog` will be `None`. Similarly, if `MultibandDeblendTask.config.conserveFlux` is `False` then the `fluxCatalog` will be `None` (and the code will run slightly faster, since it doesn't have to reweight the flux, however this is a small fraction of the processing time).\n", + "\n", + "The deblender itself sets the `parent` column for each source, which is `0` for objects with no parent, and all of the columns that begin with `deblend_` and also adds new rows to the catalog for each child. It does *not* remove the parent rows it created those child rows from, and this is intentional, because we want to measure both \"interpretations\" of the blend family: one in which there is only one object (the parent version) and one in which there are several (the children). Before doing any science with the outputs of an LSST catalog, it's important to remove one of those interpretations (typically the parent one). That can be done by looking at the `deblend_nChild` and `parent` fields:\n", + "\n", + " - `parent` is the ID of the source from which this was deblended, or `0` if the source is itself a parent.\n", + " - `deblend_nChild` is the number of child sources this source has (so it's `0` for sources that are themselves children or were never blended).\n", + " \n", + "Together, these define two particularly useful filters:\n", + "\n", + " - `deblend_nChild == 0`: never-blended object or de-blended child\n", + " - `deblend_nChild == 0 and parent == 0`: never-blended object\n", + " \n", + "The first is what you'll usually want to use; the second is what to use if you're willing to throw away some objects (possibly many) because you don't trust the deblender.\n", + "\n", + "The last processing step for our purposes is running measurement, which must be done on each catalog, in each band (if we want measurements for all of them):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "measureTask.run(templateCatalog[\"r\"], coadds['r'])\n", + "measureTask.run(templateCatalog[\"i\"], coadds['i'])\n", + "measureTask.run(fluxCatalog[\"r\"], coadds['r'])\n", + "measureTask.run(fluxCatalog[\"i\"], coadds['i'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to an unfortunate bug in the deblender task, the resulting catalogs are not contiguous and we need to copy them into new objects to use them appropriately. This step can be avoided in the near future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.afw.table as afwTable\n", + "\n", + "for f in filters:\n", + " _catalog = afwTable.SourceCatalog(templateCatalog[f].table.clone())\n", + " _catalog.extend(templateCatalog[f], deep=True)\n", + " templateCatalog[f] = _catalog\n", + " _catalog = afwTable.SourceCatalog(fluxCatalog[f].table.clone())\n", + " _catalog.extend(fluxCatalog[f], deep=True)\n", + " fluxCatalog[f] = _catalog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we care about deblending (for the sake of this tutorial) lets look at the results from the 13th blend displayed above. We use the `HeavyFootprint`s from the catalog sources that have the same parent (parent 13 from above) to build a model for the entre scene, and to compare the results of the flux conserved and *scarlet* models. In the process we look at both the *scarlet* and flux conserved models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from lsst.afw.detection import MultibandFootprint\n", + "from lsst.afw.image import MultibandImage\n", + "\n", + "# Use the 13th parent in the blend\n", + "# Note: this is not the parent ID, but the 13th source in the catalog\n", + "parentIdx = 13\n", + "\n", + "# Create empty multiband images to model the entire scene\n", + "templateModel = MultibandImage.fromImages(coadds.filters,\n", + " [Image(fluxCatalog[\"r\"][parentIdx].getFootprint().getBBox(), dtype=np.float32)\n", + " for b in range(len(filters))])\n", + "fluxModel = MultibandImage.fromImages(coadds.filters,\n", + " [Image(fluxCatalog[\"r\"][parentIdx].getFootprint().getBBox(), dtype=np.float32)\n", + " for b in range(len(filters))])\n", + "\n", + "# Only use the subset catalogs with the same parent\n", + "parentId = fluxCatalog[\"r\"][parentIdx].get(\"id\")\n", + "fluxChildren = {b: fluxCatalog[b][fluxCatalog[b].get(\"parent\")==parentId] for b in filters}\n", + "templateChildren = {b: templateCatalog[b][templateCatalog[b].get(\"parent\")==parentId] for b in filters}\n", + "assert(len(fluxChildren)==len(templateChildren))\n", + "\n", + "for n in range(len(templateChildren[\"r\"])):\n", + " # Add the source model to the model of the entire scene\n", + " fp = MultibandFootprint(coadds.filters, [templateChildren[b][n].getFootprint() for b in filters])\n", + " _fp = MultibandFootprint(coadds.filters, [fluxChildren[b][n].getFootprint() for b in filters])\n", + " templateModel[:, fp.getBBox()].array += fp.getImage(fill=0).image.array\n", + " fluxModel[:, _fp.getBBox()].array += _fp.getImage(fill=0).image.array\n", + "\n", + " # Show the model\n", + " fig = plt.figure(figsize=(6, 3))\n", + " ax = [fig.add_subplot(1, 2, n+1) for n in range(2)]\n", + " ax[0].set_title(\"scarlet\")\n", + " ax[1].set_title(\"flux conserved\")\n", + " showRGB(fp.getImage().image, ax=ax[0])\n", + " showRGB(_fp.getImage().image, ax=ax[1])\n", + " plt.show()\n", + "\n", + "templateResidual = MultibandImage(coadds.filters,\n", + " coadds[:, templateModel.getBBox()].image.array - templateModel.array)\n", + "fluxResidual = MultibandImage(coadds.filters,\n", + " coadds[:, fluxModel.getBBox()].image.array - fluxModel.array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we look at the full models and the residuals. As expected, there are no residuals for the flux conserved model since all of the flux in the image (that is within the footprint) is added to one of the sources. In this particular case that works fine, but in instances where one or more sources were not detected this can cause one source to have its flux contaminated with its neighbor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for title, model, residual in [[\"scarlet\", templateModel, templateResidual], [\"flux conserved\", fluxModel, fluxResidual]]:\n", + " fig = plt.figure(figsize=(15,8))\n", + " ax = [fig.add_subplot(1, 2, n+1) for n in range(2)]\n", + " ax[0].set_title(\"{0} model\".format(title))\n", + " ax[1].set_title(\"{0} residual\".format(title))\n", + " showRGB(model, ax=ax[0])\n", + " showRGB(residual ,ax=ax[1], Q=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "- Use some of the other`sampleBBox` regions and run through the code again, from source detection through measurment and blending displays. Don't foget to change the parent index to view only the children of the correct blend.\n", + "- Play around with other *scarlet* constraints, such as adding an L0 penalty. This should help the code execute faster, as one of the main reasons for the slow down is unecessarily large boxes surrounding the smaller sources. See https://github.com/lsst/meas_deblender/blob/master/python/lsst/meas/deblender/deblend.py#L462-L545 for a description of the other configuration options that can be passed to the deblender\n", + "\n", + "Note: in order to try out different constraints the `meas_deblender` package requires an upgrade that has not been pushed to master yet. To use the latest changes, from your terminal session you must execute the following steps:\n", + "\n", + "```bash\n", + "~$ cp /project/fred3m/tutorials/lsst2018/.user_setups ~/notebooks\n", + "~$ source ~/notebooks/.user_setups\n", + "```\n", + "\n", + "You will have to restart the kernel for this notebook session in order for the changes to take place." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deblending/scarlet_tutorial.ipynb b/Deblending/scarlet_tutorial.ipynb new file mode 100755 index 00000000..4ea99f05 --- /dev/null +++ b/Deblending/scarlet_tutorial.ipynb @@ -0,0 +1,470 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scarlet deblending Tutorial\n", + "\n", + "The purpose of this tutorial is to familiarize the user with the basics of using *scarlet* to model blended scenes, and how tweaking various objects and parameters affects the resulting model. A tutorial that is more specific to using scarlet in the context of the LSST DM Science Pipelines is also available.\n", + "\n", + "Before attempting this tutorial it will be useful to read the [introduction](http://scarlet.readthedocs.io/en/latest/user_docs.html) to the *scarlet* User Guide, and many of the exercises below may require referencing the *scarlet* [docs](http://scarlet.readthedocs.io/en/latest/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import the necessary libraries\n", + "import os\n", + "\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "# don't interpolate the pixels\n", + "matplotlib.rc('image', interpolation='none')\n", + "\n", + "import numpy as np\n", + "import scarlet\n", + "import scarlet.display" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display functions\n", + "\n", + "Below are several usful functions used throughout this tutorial to visualize the data and models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Display the sources\n", + "def display_sources(sources, image, norm=None, subset=None, combine=False, show_sed=True, filter_indices=None):\n", + " \"\"\"Display the data and model for all sources in a blend\n", + "\n", + " This convenience function is used to display all (or a subset) of\n", + " the sources and (optionally) their SED's.\n", + " \"\"\"\n", + " if subset is None:\n", + " # Show all sources in the blend\n", + " subset = range(len(sources))\n", + " if filter_indices is None:\n", + " filter_indices = [3,2,1]\n", + " for m in subset:\n", + " # Load the model for the source\n", + " src = sources[m]\n", + " model = [comp.get_model() for comp in src]\n", + "\n", + " # Select the image patch the overlaps with the source and convert it to an RGB image\n", + " img_rgb = scarlet.display.img_to_rgb(image[src[0].bb], filter_indices=filter_indices, norm=norm)\n", + "\n", + " # Build a model for each component in the model\n", + " rgb = []\n", + " for _model in model:\n", + " # Convert the model to an RGB image\n", + " _rgb = scarlet.display.img_to_rgb(_model, filter_indices=filter_indices, norm=norm)\n", + " rgb.append(_rgb)\n", + "\n", + " # Display the image and model\n", + " figsize = [6,3]\n", + " columns = 2\n", + " # Calculate the number of columns needed and shape of the figure\n", + " if show_sed:\n", + " figsize[0] += 3\n", + " columns += 1\n", + " if not combine:\n", + " figsize[0] += 3*(len(model)-1)\n", + " columns += len(model)-1\n", + " # Build the figure\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = [fig.add_subplot(1,columns,n+1) for n in range(columns)]\n", + " ax[0].imshow(img_rgb)\n", + " ax[0].set_title(\"Data: Source {0}\".format(m))\n", + " for n, _rgb in enumerate(rgb):\n", + " ax[n+1].imshow(_rgb)\n", + " if combine:\n", + " ax[n+1].set_title(\"Initial Model\")\n", + " else:\n", + " ax[n+1].set_title(\"Component {0}\".format(n))\n", + " if show_sed:\n", + " for comp in src:\n", + " ax[-1].plot(comp.sed)\n", + " ax[-1].set_title(\"SED\")\n", + " ax[-1].set_xlabel(\"Band\")\n", + " ax[-1].set_ylabel(\"Intensity\")\n", + " # Mark the current source in the image\n", + " y,x = src[0].center\n", + " ax[0].plot(x-src[0].bb[2].start, y-src[0].bb[1].start, 'x', color=\"#5af916\", mew=2)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def display_model_residual(images, blend, peaks, norm, filter_indices=None):\n", + " \"\"\"Display the data, model, and residual for a given result\n", + " \"\"\"\n", + " if filter_indices is None:\n", + " filter_indices = [3,2,1]\n", + " model = blend.get_model()\n", + " residual = images-model\n", + " print(\"Data range: {0:.3f} to {1:.3f}\\nresidual range: {2:.3f} to {3:.3f}\\nrms: {4:.3f}\".format(\n", + " np.min(images),\n", + " np.max(images),\n", + " np.min(residual),\n", + " np.max(residual),\n", + " np.sqrt(np.std(residual)**2+np.mean(residual)**2)\n", + " ))\n", + " # Create RGB images\n", + " img_rgb = scarlet.display.img_to_rgb(images, filter_indices=filter_indices, norm=norm)\n", + " model_rgb = scarlet.display.img_to_rgb(model, filter_indices=filter_indices, norm=norm)\n", + " residual_norm = scarlet.display.Linear(img=residual)\n", + " residual_rgb = scarlet.display.img_to_rgb(residual, filter_indices=filter_indices, norm=residual_norm)\n", + "\n", + " # Show the data, model, and residual\n", + " fig = plt.figure(figsize=(15,5))\n", + " ax = [fig.add_subplot(1,3,n+1) for n in range(3)]\n", + " ax[0].imshow(img_rgb)\n", + " ax[0].set_title(\"Data\")\n", + " ax[1].imshow(model_rgb)\n", + " ax[1].set_title(\"Model\")\n", + " ax[2].imshow(residual_rgb)\n", + " ax[2].set_title(\"Residual\")\n", + " for k,component in enumerate(blend.components):\n", + " y,x = component.center\n", + " #px, py = peaks[k]\n", + " ax[0].plot(x, y, \"gx\")\n", + " #ax[0].plot(px, py, \"rx\")\n", + " ax[1].text(x, y, k, color=\"r\")\n", + " plt.show()\n", + "\n", + "def show_psfs(psfs, filters, norm=None):\n", + " rows = int(np.ceil(len(psfs)/3))\n", + " columns = min(len(psfs), 3)\n", + " figsize = (45/columns, rows*5)\n", + " fig = plt.figure(figsize=figsize)\n", + " ax = [fig.add_subplot(rows, columns, n+1) for n in range(len(psfs))]\n", + " for n, psf in enumerate(psfs):\n", + " im = ax[n].imshow(psf, norm=norm)\n", + " ax[n].set_title(\"{0}-band PSF\".format(filters[n]))\n", + " plt.colorbar(im, ax=ax[n])\n", + " plt.show()\n", + "\n", + "def display_diff_kernels(psf_blend, diff_kernels):\n", + " model = psf_blend.get_model()\n", + " for b, component in enumerate(psf_blend.components):\n", + " fig = plt.figure(figsize=(15,2.5))\n", + " ax = [fig.add_subplot(1,4,n+1) for n in range(4)]\n", + " # Display the psf\n", + " ax[0].set_title(\"psf\")\n", + " _img = ax[0].imshow(psfs[b])\n", + " fig.colorbar(_img, ax=ax[0])\n", + " # Display the model\n", + " ax[1].set_title(\"modeled psf\")\n", + " _model = np.ma.array(model[b], mask=model[b]==0)\n", + " _img = ax[1].imshow(_model)\n", + " fig.colorbar(_img, ax=ax[1])\n", + " # Display the difference kernel\n", + " ax[2].set_title(\"difference kernel\")\n", + " _img = ax[2].imshow(np.ma.array(diff_kernels[b], mask=diff_kernels[b]==0))\n", + " fig.colorbar(_img, ax=ax[2])\n", + " # Display the residual\n", + " ax[3].set_title(\"residual\")\n", + " residual = psfs[b]-model[b]\n", + " vabs = np.max(np.abs(residual))\n", + " _img = ax[3].imshow(residual, vmin=-vabs, vmax=vabs, cmap='seismic')\n", + " fig.colorbar(_img, ax=ax[3])\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and Display the data\n", + "\n", + "The `file_path` points to a directory with 147 HSC blends from the COSMOS field detected by the LSST pipeline. Changing `idx` below will select a different blend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample images\n", + "idx = 53\n", + "file_path = \"/project/fred3m/code/testdata_deblender/real_data/hsc_cosmos/not_matched\"\n", + "files = os.listdir(file_path)\n", + "data = np.load(os.path.join(file_path, files[idx]))\n", + "image = data[\"images\"]\n", + "wmap = data[\"weights\"]\n", + "peaks = data[\"peaks\"]\n", + "psfs = data[\"psfs\"]\n", + "filters = [\"G\", \"R\", \"I\", \"Z\", \"Y\"]\n", + "# Only a rough estimate of the background is needed\n", + "# to initialize and resize the sources\n", + "bg_rms = np.std(image, axis=(1,2))\n", + "print(\"Background RMS: {0}\".format(bg_rms))\n", + "\n", + "# Use Asinh scaling for the images\n", + "norm = scarlet.display.Asinh(img=image, Q=10)\n", + "# Map i,r,g -> RGB\n", + "filter_indices = [3,2,1]\n", + "# Convert the image to an RGB image\n", + "img_rgb = scarlet.display.img_to_rgb(image, filter_indices=filter_indices, norm=norm)\n", + "plt.imshow(img_rgb)\n", + "plt.title(\"Image: {0}\".format(idx))\n", + "for src in peaks:\n", + " plt.plot(src[0], src[1], \"rx\", mew=2)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initializing Sources\n", + "\n", + "Astrophysical objects are modeled in scarlet as a collection of components, where each component has a single SED that is constant over it's morphology (band independent intensity). So a single source might have multiple components, like a bulge and disk, or a single component.\n", + "\n", + "The different classes that inherit from `Source` mainly differ in how they are initialized, and otherwise behave similarly during the optimization routine. This section illustrates the differences between different source initialization classes.\n", + "\n", + "The simplest source is a single component intialized with only a single pixel (at the center of the object) turned on.\n", + "\n", + "### *WARNING* \n", + "Scarlet accepts source positions using the numpy/C++ convention of (y,x), which is different than the astropy and LSST stack convention of (x,y)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sources = [scarlet.PointSource((peak[1], peak[0]), image) for peak in peaks]\n", + "\n", + "# Display the initial guess for each source\n", + "display_sources(sources, image, norm=norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise:\n", + "\n", + "* Experiment with the above code by using `ExtendedSource`, which initializes each object as a single component with maximum flux at the peak that falls off monotonically and has 180 degree symmetry; and using `MultiComponentSource`, which models a source as two components (a bulge and a disk) that are each symmetric and montonically decreasing from the peak.\n", + "\n", + "# Deblending a scene\n", + "\n", + "The `Blend` class contains the list of sources, the image, and any other configuration parameters necessary to fit the data, including routines to fit the center positions and resize the bounding box containing the sources (if necessary). Once a blend has been initialized with a list of sources, the image and background RMS values must be set (the background RMS is used to determine when to truncate the bounding box around a source)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blend = scarlet.Blend(sources)\n", + "blend.set_data(image, bg_rms=bg_rms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we can fit a model, given a maximum number of iterations and the relative error required for convergence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blend.fit(100, 1e-2)\n", + "print(\"Deblending completed in {0} iterations\".format(blend.it))\n", + "display_model_residual(image, blend, peaks, norm)\n", + "display_sources(sources, image, norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Experiment by running the above code using different source models (for example `ExtendedSource`) to see how initializtion affects the belnding results.\n", + "\n", + "* The code above initialized the sources at their exact centers. Try offsetting the initial positions by `0.5` pixels in `x` and/or `y` and passing a `shift_center=0` argument when initializing the source. This prevents the source from updating its position, so notice how that affects the resulting model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Constraints\n", + "\n", + "The above models used the default constraints: perfect symmetry and a weighted monotonicity that decreases from the peak. So each source is defined (internally during initialization) with the constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import scarlet.constraint as sc\n", + "constraints = (sc.SimpleConstraint(),\n", + " sc.DirectMonotonicityConstraint(use_nearest=False),\n", + " sc.DirectSymmetryConstraint())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where `SimpleConstraint` forces the SED and morphology to be non-negative, the SED to be normalized to unity, and the peak to have some (minimal) flux at the center." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Go back to the source initialization cell and pass a custom set of constraints. For example, pass `DirectSymmetryConstraint` a number between 0 and 1 to set the level of symmetry required, or eliminate the symmetry constraint altogether and see how that affects deblending.\n", + "\n", + "* Set `use_nearest=True` in the `DirectMonotonicityConstraint`.\n", + "\n", + "* Add `L0Constraint` or `L1Constraint` to the list of constraints and observe the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration\n", + "\n", + "There are additional configuration paramters that can be used to initialize a source, as described in http://scarlet.readthedocs.io/en/latest/config.html#Configuration-(scarlet.config).\n", + "\n", + "## Exercises\n", + "\n", + "* Initialize the sources with a custom configuration where `refine_skip=2`, which updates the positions and box sizes on every other step, and see how the results compare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PSF Deconvolution\n", + "\n", + "When analyzing real images the PSF will be different in each band unless they have been PSF matched. In general deblending should not be performed on PSF matched coadds, as matching will increase the blending in bands with better seeing. Instead scarlet can be used to build a deconvolved model which is a more sparse (and less blended) representation of the data, and convolve the model in each band to compare to the input data.\n", + "\n", + "To initialize a source with a PSF, pass the PSF as an input to the new source:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scarlet.ExtendedSource(peaks[0], image, bg_rms, psf=psfs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Partial PSF Deconvolution\n", + "\n", + "As discussed in the tutorial http://scarlet.readthedocs.io/en/latest/psf_matching.html, the data is noisy and the fully deconvolved scene is undersampled, making the application of the constraints and and full convolution kernel unstable and prone to biases. Instead we can create a target PSF and model the sources in the partially deconvolved target PSF scene.\n", + "\n", + "First we need to specify the target PSF. *scarlet* includes a `fit_target_psf` function to fit the PSF in each band to either a `moffat`, `gaussian`, or `double_gaussian` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scarlet.psf_match\n", + "\n", + "show_psfs(psfs, filters)\n", + "\n", + "# Find the target PSF\n", + "target_psf = scarlet.psf_match.fit_target_psf(psfs, scarlet.psf_match.moffat)\n", + "plt.imshow(target_psf)\n", + "plt.title(\"target PSF\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the target PSF we can find the difference kernel in each band using *scarlet*. The `build_diff_kernels` function basically treats the PSF image as a blend, where the PSF in each band is a monochromatic source, and fits the difference kernels using the minimum number of pixels necessary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diff_kernels, psf_blend = scarlet.psf_match.build_diff_kernels(psfs, target_psf)\n", + "display_diff_kernels(psf_blend, diff_kernels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "* Try building the difference kernels while varying the parameters in `build_diff_kernels`, for example using larger and smaller values for `l0_thresh`.\n", + "\n", + "* Go back up to source initialization and use `psf=psfs` to fully deconvolve the scene and fit the blend\n", + "\n", + "* Try the same thing but set `psf=diff_kernels` for each source to partially deconvolve the scene." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0d79fdbf71a52d679342ea1e98e805d9cd33c1fa Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Wed, 15 Aug 2018 09:55:14 -0700 Subject: [PATCH 16/45] Deblending in the READMEs --- Deblending/README.rst | 36 ++++++++++++++++++++++++++++++++++++ README.md | 5 +++-- 2 files changed, 39 insertions(+), 2 deletions(-) create mode 100644 Deblending/README.rst diff --git a/Deblending/README.rst b/Deblending/README.rst new file mode 100644 index 00000000..5b422d3a --- /dev/null +++ b/Deblending/README.rst @@ -0,0 +1,36 @@ +Deblending +========== + +This folder contains a set of tutorial notebooks exploring the deblending of LSST objects. See the index table below for links to the notebook code, and an auto-rendered view of the notebook with outputs. + + +.. list-table:: + :widths: 10 20 10 10 + :header-rows: 1 + + * - Notebook + - Short description + - Links + - Owner + + + * - **SCARLET Tutorial** + - Introduction to the SCARLET deblender, how to configure and run it. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/scarlet_tutorial.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/scarlet_tutorial.log + + - `Fred Moolekamp `_ + + + * - **Deblending in DRP ** + - Where and how the deblending happens, in the DRP pipeline. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.log + + - `Fred Moolekamp `_ diff --git a/README.md b/README.md index ed98e4ee..f02b30c6 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ the LSST Science Collaborations. | Visualization | Displaying images and catalogs. | [StackClub/Visualization](Visualization) | | Image Processing | From raw images to `calexp`s and `coadd`s. | [StackClub/ImageProcessing](ImageProcessing) | | SourceDetection | Detection of sources in images - including low surface brightness galaxies. | [StackClub/SourceDetection](SourceDetection) | +| Deblending | Deblending the objects | [StackClub/Deblending](Deblending) | | Validation | Tools for validating Stack outputs, example validation analyses | [StackClub/Validation](Validation) | * [Stack Club projects](https://github.com/LSSTScienceCollaborations/StackClub/labels/project), as defined by Stack Club members - follow [this link](https://github.com/LSSTScienceCollaborations/StackClub/labels/project) to see what people are working on. [Unassigned projects](https://github.com/LSSTScienceCollaborations/StackClub/issues?utf8=%E2%9C%93&q=is%3Aopen+label%3Aproject+no%3Aassignee) are available for new members to take on! @@ -29,7 +30,7 @@ If you would like to join the Stack Club, please fill out this short **[applicat ## Contributing New Stack Club members: please see the [notes on getting started](GettingStarted/GettingStarted.md) - they'll walk you onto you new LSST Science Platform account, and then show you how to work on your tutorial notebooks. -Everyone else: we welcome pull requests! Feel free to fork this repo, write us an issue, and send us a PR. +Everyone else: we welcome pull requests! Feel free to fork this repo and send us a pull request. And if you are interested in joining the Stack Club, please drop one of us a line, or come and find us in the [#stack-club](https://lsstc.slack.com/messages/C9YRAS4HM) LSSTC Slack channel. > When preparing a pull request, please note the [standards](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#standards) that we are trying to uphold. @@ -55,4 +56,4 @@ but you can't blame us if it doesn't do what you want. ## More About This Project -Following a successful LSSTC "Enabling Science" proposal, we put together a 3-phase plan, which you can read about in more detail [here](https://docs.google.com/document/d/103kzjOklSUWo5MJP9B-EsnAdO7V6bstTC_mzBvd0NIk/edit#). Phase 0 involved collecting existing tutorials and identifying potential club members from around the LSST Science Collaborations. Then, in Phase 1 (late May 2018 to mid August 2018) we worked together in a small group to turn a subset of those existing "seed" tutorials into community-maintained Jupyter notebooks, for display at the August LSST 2018 Project and Community Workshop (PCW) in Tucson. At that meeting, we opened up to a larger group of LSST science collaboration members, extending and spinning off the initial set of notebooks. +Following a successful LSSTC "Enabling Science" proposal, we put together a 3-phase plan, which you can read about in more detail [here](https://docs.google.com/document/d/103kzjOklSUWo5MJP9B-EsnAdO7V6bstTC_mzBvd0NIk/edit#). Phase 0 involved collecting existing tutorials and identifying potential club members from around the LSST Science Collaborations. Then, in Phase 1 (late May 2018 to mid August 2018) we worked together in a small group to turn a subset of those existing "seed" tutorials into community-maintained Jupyter notebooks, for display at the August LSST 2018 Project and Community Workshop (PCW) in Tucson. At that meeting, we opened up to a larger group of LSST science collaboration members, extending and spinning off the initial set of notebooks. From 42917ee5e2cbd4d78089667cc12c490f0b4673a4 Mon Sep 17 00:00:00 2001 From: fred3m Date: Fri, 17 Aug 2018 07:21:27 -0700 Subject: [PATCH 17/45] Fix path to use public version of testdata_deblender --- Deblending/scarlet_tutorial.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Deblending/scarlet_tutorial.ipynb b/Deblending/scarlet_tutorial.ipynb index 4ea99f05..56b500b9 100755 --- a/Deblending/scarlet_tutorial.ipynb +++ b/Deblending/scarlet_tutorial.ipynb @@ -203,7 +203,7 @@ "source": [ "# Load the sample images\n", "idx = 53\n", - "file_path = \"/project/fred3m/code/testdata_deblender/real_data/hsc_cosmos/not_matched\"\n", + "file_path = \"/project/shared/data/testdata_deblender/real_data/hsc_cosmos/not_matched\"\n", "files = os.listdir(file_path)\n", "data = np.load(os.path.join(file_path, files[idx]))\n", "image = data[\"images\"]\n", @@ -448,9 +448,9 @@ ], "metadata": { "kernelspec": { - "display_name": "LSST", + "display_name": "Python 3", "language": "python", - "name": "lsst" + "name": "python3" }, "language_info": { "codemirror_mode": { From 47fbe32953f23ff498edfa78a92dee113d77a06b Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 17 Aug 2018 17:10:40 +0000 Subject: [PATCH 18/45] Added standard nb header --- Deblending/lsst_stack_deblender.ipynb | 20 +++++++++++++++++--- Deblending/scarlet_tutorial.ipynb | 20 +++++++++++++++++--- 2 files changed, 34 insertions(+), 6 deletions(-) diff --git a/Deblending/lsst_stack_deblender.ipynb b/Deblending/lsst_stack_deblender.ipynb index 4427f74a..26a2f97b 100755 --- a/Deblending/lsst_stack_deblender.ipynb +++ b/Deblending/lsst_stack_deblender.ipynb @@ -4,9 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LSST Stack Multiband Deblender Tutorial\n", + "# Using the LSST Stack Multiband Deblender \n", + "
Owner(s): **Fred Moolekamp** ([@fred3m](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@fred3m))\n", + "
Last Verified to Run: **2018-08-17**\n", + "
Verified Stack Release: **16.0**\n", "\n", - "This tutorial is designed to illustrate how to execture the multiband deblender (*scarlet*) in the LSST stack. This includes a brief introduction to LSST stack objects including:\n", + "This tutorial is designed to illustrate how to execute the multiband deblender (*scarlet*) in the LSST stack. This includes a brief introduction to LSST stack objects including:\n", "\n", " - Geometry classes from `lsst.geom`, such as points and boxes.\n", " - Higher-level astronomical primitives from `lsst.afw`, such as the `Image`, `Exposure`, and `Psf` classes.\n", @@ -14,7 +17,18 @@ " \n", "We'll be working with coadded images made from Subaru Hyper Suprime-Cam (HSC) data in the COSMOS field. We've taken a recent LSST reprocessing of the HSC-SSP UltraDeep COSMOS field (see [this page](https://confluence.lsstcorp.org/display/DM/S18+HSC+PDR1+reprocessing) for information on that reprocessing, and [this page](https://hsc-release.mtk.nao.ac.jp/doc/) for the data), and added simulated stars from a scaled [SDSS catalog](http://www.sdss.org/dr14/data_access/value-added-catalogs/?vac_id=photometry-of-crowded-fields-in-sdss-for-galactic-globular-and-open-clusters). The result is a very deep image (deeper than the 10-year LSST Deep-Wide-Fast survey, though not as deep as LSST Deep Drilling fields will be) with both a large number of galaxies and region full of stars.\n", "\n", - "This tutorial is based on Jim Bosch's globular cluster tutorial, however in it's present state *scarlet* is unable to process the crowded field (most likely) due to poor initial conditions for the sources in the field. So instead we use a region of the image outside of the cluster." + "This tutorial is based on Jim Bosch's globular cluster tutorial, however in it's present state *scarlet* is unable to process the crowded field (most likely) due to poor initial conditions for the sources in the field. So instead we use a region of the image outside of the cluster.\n", + "\n", + "### Learning Objectives:\n", + "\n", + "After working through this tutorial you should be able to: \n", + "1. Configure and run the LSST multiband deblender on a test list of objects;\n", + "2. Understand its task context in the DRP pipeline.\n", + "\n", + "### Logistics\n", + "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", + "\n", + "## Set-up" ] }, { diff --git a/Deblending/scarlet_tutorial.ipynb b/Deblending/scarlet_tutorial.ipynb index 4ea99f05..ddfaf0e8 100755 --- a/Deblending/scarlet_tutorial.ipynb +++ b/Deblending/scarlet_tutorial.ipynb @@ -4,11 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Scarlet deblending Tutorial\n", + "# Deblending with *Scarlet*\n", + "
Owner(s): **Fred Moolekamp** ([@fred3m](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@fred3m))\n", + "
Last Verified to Run: **2018-08-17**\n", + "
Verified Stack Release: **16.0**\n", "\n", - "The purpose of this tutorial is to familiarize the user with the basics of using *scarlet* to model blended scenes, and how tweaking various objects and parameters affects the resulting model. A tutorial that is more specific to using scarlet in the context of the LSST DM Science Pipelines is also available.\n", + "The purpose of this tutorial is to familiarize you with the basics of using *scarlet* to model blended scenes, and how tweaking various objects and parameters affects the resulting model. A tutorial that is more specific to using scarlet in the context of the LSST DM Science Pipelines is also available.\n", "\n", - "Before attempting this tutorial it will be useful to read the [introduction](http://scarlet.readthedocs.io/en/latest/user_docs.html) to the *scarlet* User Guide, and many of the exercises below may require referencing the *scarlet* [docs](http://scarlet.readthedocs.io/en/latest/index.html)." + "### Learning Objectives:\n", + "\n", + "After working through this tutorial you should be able to: \n", + "1. Configure and run _scarlet_ on a test list of objects;\n", + "2. Understand its various model assumptions and applied constraints.\n", + "\n", + "Before attempting this tutorial it will be useful to read the [introduction](http://scarlet.readthedocs.io/en/latest/user_docs.html) to the *scarlet* User Guide, and many of the exercises below may require referencing the *scarlet* [docs](http://scarlet.readthedocs.io/en/latest/index.html).\n", + "\n", + "### Logistics\n", + "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", + "\n", + "## Set-up" ] }, { From 58520d894c3c578567edbdba7875978cb32f7bf7 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 17 Aug 2018 17:30:51 +0000 Subject: [PATCH 19/45] Verified to run --- Deblending/lsst_stack_deblender.ipynb | 7 ------- 1 file changed, 7 deletions(-) diff --git a/Deblending/lsst_stack_deblender.ipynb b/Deblending/lsst_stack_deblender.ipynb index 26a2f97b..1142ed15 100755 --- a/Deblending/lsst_stack_deblender.ipynb +++ b/Deblending/lsst_stack_deblender.ipynb @@ -609,13 +609,6 @@ "\n", "You will have to restart the kernel for this notebook session in order for the changes to take place." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 0cdefb2ef9f3c5cfce2d82eac060c6537c1becdf Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 17 Aug 2018 17:33:13 +0000 Subject: [PATCH 20/45] Verified on w_2018_32 --- Deblending/lsst_stack_deblender.ipynb | 2 +- Deblending/scarlet_tutorial.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Deblending/lsst_stack_deblender.ipynb b/Deblending/lsst_stack_deblender.ipynb index 1142ed15..bcfa264b 100755 --- a/Deblending/lsst_stack_deblender.ipynb +++ b/Deblending/lsst_stack_deblender.ipynb @@ -7,7 +7,7 @@ "# Using the LSST Stack Multiband Deblender \n", "
Owner(s): **Fred Moolekamp** ([@fred3m](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@fred3m))\n", "
Last Verified to Run: **2018-08-17**\n", - "
Verified Stack Release: **16.0**\n", + "
Verified Stack Release: **w_2018_32**\n", "\n", "This tutorial is designed to illustrate how to execute the multiband deblender (*scarlet*) in the LSST stack. This includes a brief introduction to LSST stack objects including:\n", "\n", diff --git a/Deblending/scarlet_tutorial.ipynb b/Deblending/scarlet_tutorial.ipynb index f260f2a0..b98af66c 100755 --- a/Deblending/scarlet_tutorial.ipynb +++ b/Deblending/scarlet_tutorial.ipynb @@ -7,7 +7,7 @@ "# Deblending with *Scarlet*\n", "
Owner(s): **Fred Moolekamp** ([@fred3m](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@fred3m))\n", "
Last Verified to Run: **2018-08-17**\n", - "
Verified Stack Release: **16.0**\n", + "
Verified Stack Release: **w_2018_32**\n", "\n", "The purpose of this tutorial is to familiarize you with the basics of using *scarlet* to model blended scenes, and how tweaking various objects and parameters affects the resulting model. A tutorial that is more specific to using scarlet in the context of the LSST DM Science Pipelines is also available.\n", "\n", From da3b0da60c9080a98f595228551eeff9646ae11b Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Mon, 20 Aug 2018 17:03:40 -0700 Subject: [PATCH 21/45] Fixed table alignment --- Deblending/README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Deblending/README.rst b/Deblending/README.rst index 5b422d3a..6fd8286c 100644 --- a/Deblending/README.rst +++ b/Deblending/README.rst @@ -25,10 +25,10 @@ This folder contains a set of tutorial notebooks exploring the deblending of LSS - `Fred Moolekamp `_ - * - **Deblending in DRP ** + * - **Deblending in DRP** - Where and how the deblending happens, in the DRP pipeline. - `ipynb `_, - `rendered `_ + `rendered `_ .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.svg :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Deblending/log/lsst_stack_deblender.log From 8f9172a553aede3fed48e08e269b2040d6c04dc3 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Wed, 22 Aug 2018 20:25:49 +0000 Subject: [PATCH 22/45] Update to full descriptions and figures --- .../BrighterFatterCorrection.ipynb | 992 +++++++++++++++--- 1 file changed, 846 insertions(+), 146 deletions(-) diff --git a/ImageProcessing/BrighterFatterCorrection.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb index a28402bf..36c17513 100644 --- a/ImageProcessing/BrighterFatterCorrection.ipynb +++ b/ImageProcessing/BrighterFatterCorrection.ipynb @@ -6,17 +6,17 @@ "source": [ "# Analysis of Beam Simulator Images and Brighter-fatter Correction\n", "
Owner(s): **Andrew Bradshaw** ([@andrewkbradshaw](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@andrewkbradshaw))\n", - "
Last Verified to Run: **2018-08-10**\n", + "
Last Verified to Run: **2018-08-22**\n", "
Verified Stack Release: **16.0 and 16.0+22 (w_2018_31)**\n", "\n", - "This notebook demonstrates the [brighter-fatter systematic error](https://arxiv.org/abs/1402.0725) on images of stars and galaxies illuminated on an ITL-3800C-002 CCD at the [UC Davis LSST beam simulator laboratory](https://arxiv.org/abs/1411.5667). Using a series of images at increasing exposure times, we demonstrate the broadening of image profiles on DM stack shape measurements, and a [possible correction method](https://arxiv.org/abs/1711.06273) which iteratively applies a kernel to restore electrons to the pixels which they were deflected from. To keep things simple, for now we skip DM stack instrument signature removal (ISR) and work on a subset of images which are arrays (500x500) of electron counts in pixels.\n", + "This notebook demonstrates the [brighter-fatter systematic error](https://arxiv.org/abs/1402.0725) on images of stars and galaxies illuminated on an ITL-3800C-002 CCD at the [UC Davis LSST beam simulator laboratory](https://arxiv.org/abs/1411.5667). Using a series of images at increasing exposure times, we demonstrate the broadening of image profiles on DM stack shape measurements, and a [possible correction method](https://arxiv.org/abs/1711.06273) which iteratively applies a kernel to restore electrons to the pixels from which they were deflected. To keep things simple, for now we skip most DM stack instrument signature removal (ISR) and work on a subset of images which are already processed arrays (500x500) of electrons.\n", "\n", "### Learning Objectives:\n", "\n", "After working through this tutorial you should be able to: \n", "1. Characterize and measure objects (stars/galaxies) in LSST beam simulator images\n", "2. Test the Brighter-Fatter kernel correction method on those images\n", - "3. Build your own tests of stack instrument signature removal algorithms\n", + "3. Build your own tests of stack ISR algorithms\n", "\n", "### Logistics\n", "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", @@ -26,57 +26,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "# What version of the Stack am I using?\n", - "! echo $HOSTNAME\n", - "! eups list -s | grep lsst_distrib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jld-lab-sarujin-r160\n", + "lsst_distrib 16.0+1 \tcurrent v16_0 setup\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", + "from itertools import cycle\n", "from astropy.io import fits\n", - "import time,glob\n", + "import time,glob,os\n", + "\n", "\n", "# if running stack v16.0, silence a long matplotlib Agg warning with:\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "\n", - "%matplotlib inline" + "%matplotlib inline\n", + "\n", + "# What version of the Stack am I using?\n", + "! echo $HOSTNAME\n", + "! eups list -s | grep lsst_distrib\n", + "\n", + "# make a directory to write the catalogs\n", + "username=os.environ.get('USERNAME')\n", + "cat_dir='/home/'+username+'/DATA/beamsim/'\n", + "if not os.path.exists(cat_dir):\n", + " ! mkdir /home/$USER/DATA/beamsim/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Read in an image, then set the variance plane based upon it\n", - "Cut-outs of beam simulator star/galaxy images have been placed in the shared data directory at `/project/shared/data/beamsim/bfcorr/`. We skip (for now) most of the instrument signature removal (ISR) steps because these are preprocessed images (bias subtracted, gain corrected). We instead start by reading in one of those `.fits` files and making an image plane `afwImage.ExposureF` as well as a variance plane, which is then ready for characterization and calibration in the following cells." + "## Step 1: Read in an image\n", + "Cut-outs of beam simulator star/galaxy images have been placed in the shared data directory at `/project/shared/data/beamsim/bfcorr/`. We skip (for now) most of the instrument signature removal (ISR) steps because these are preprocessed images (bias subtracted, gain corrected). We instead start by reading in one of those `.fits` files and making an image plane `afwImage.ExposureF` as well as a variance plane (based upon the image), which is then ready for characterization and calibration in the following cells." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in ITL-3800C-002_spot_spot_419_20171108115142part.fits\n" + ] + } + ], "source": [ "import lsst.afw.image as afwImage\n", "from lsst.ip.isr.isrFunctions import updateVariance\n", "\n", "# where the data lives, choosing one image to start\n", + "imnum=19 # for this dataset, choose 0-19 as an example\n", "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits'\n", - "fitsfilename = np.sort(glob.glob(fitsglob))[19] \n", + "fitsfilename = np.sort(glob.glob(fitsglob))[imnum] \n", "\n", "# Read in a single image to an afwImage.ImageF object\n", "image_array=afwImage.ImageF.readFits(fitsfilename)\n", @@ -98,13 +115,40 @@ "#exposure = afwImage.ExposureF(masked_image)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now visualize the image and its electron distribution using matplotlib. Things to note: 1) the array is (purposefully) tilted with respect to the pixel grid, 2) most pixel values are at the background/sky level (a function of the mask opacity and illumination), but there is a pileup of counts around ~200k electrons indicating full well and saturation in some of the brightest pixels of the image" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(Text(0.5,0,'Number of electrons in pixel'), Text(0,0.5,'Number of pixels'))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Visualize the image and its electron distribution\n", "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", "plt.suptitle('Star/galaxy beam sim segment of '+hdr['CCD_SERN']+'\\n '+fitsfilename.split('/')[-1])\n", "\n", @@ -120,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,41 +176,147 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Perform image characterization" + "## Step 2: Perform image characterization and initial measurement\n", + "We now perform a base-level characterization of the image using the stack. We set some configuration settings which are specific to our sestup which has a very small optical PSF, setting a PSF size and turning off some other aspects such as cosmic ray rejection because of this." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mcharTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcharacterize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexposureIdInfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackground\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "!Characterize a science image\n", + "\n", + "Peforms the following operations:\n", + "- Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:\n", + " - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)\n", + "- interpolate over cosmic rays\n", + "- perform final measurement\n", + "\n", + "@param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).\n", + " The following changes are made:\n", + " - update or set psf\n", + " - set apCorrMap\n", + " - update detection and cosmic ray mask planes\n", + " - subtract background and interpolate over cosmic rays\n", + "@param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).\n", + " If not provided, returned SourceCatalog IDs will not be globally unique.\n", + "@param[in,out] background initial model of background already subtracted from exposure\n", + " (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,\n", + " which is typical for image characterization.\n", + "\n", + "@return pipe_base Struct containing these fields, all from the final iteration\n", + "of detectMeasureAndEstimatePsf:\n", + "- exposure: characterized exposure; image is repaired by interpolating over cosmic rays,\n", + " mask is updated accordingly, and the PSF model is set\n", + "- sourceCat: detected sources (an lsst.afw.table.SourceCatalog)\n", + "- background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)\n", + "- psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)\n", + "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/characterizeImage.py\n", + "\u001b[0;31mType:\u001b[0m method\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from lsst.pipe.tasks.characterizeImage import CharacterizeImageTask, CharacterizeImageConfig\n", + "import lsst.meas.extensions.shapeHSM\n", "\n", - "# first set a few configs that are specific to the data\n", + "# first set a few configs that are specific to our beam simulator data\n", "charConfig = CharacterizeImageConfig()\n", - "#this set the fwhm of the simple PSF to that of the fwhm used in the simulation\n", + "#this set the fwhm of the simple PSF to that of optics\n", "charConfig.installSimplePsf.fwhm = .2\n", "charConfig.doMeasurePsf = False\n", - "charConfig.doApCorr = False\n", + "charConfig.doApCorr = False # necessary\n", "charConfig.repair.doCosmicRay = False \n", - "# we do have some cosmic rays, but we also subpixel features and an undersampled PSF\n", - "charConfig.detection.background.binSize = 10 \n", + "# we do have some cosmic rays, but we also have subpixel mask features and an undersampled PSF\n", + "charConfig.detection.background.binSize = 10 # worth playing around with\n", "#charConfig.background.binSize = 50\n", - "charConfig.detection.minPixels = 5\n", + "charConfig.detection.minPixels = 2 # also worth playing around with\n", + "\n", + "# Add the HSM (Hirata/Seljak/Mandelbaum) adaptive moments shape measurement plugin\n", + "charConfig.measurement.plugins.names |= [\"ext_shapeHSM_HsmSourceMoments\"]\n", + "# to configure hsm you would do something like\n", + "# charConfig.measurement.plugins[\"ext_shapeHSM_hsmSourceMoments\"].addFlux = True\n", + "# (see sfm.py in meas_base for all the configuration options for the measurement task)\n", + "\n", "charTask = CharacterizeImageTask(config=charConfig)\n", "\n", - "# charTask.run? # works for v16.0+22\n", - "charTask.characterize?" + "charTask.characterize?\n", + "# use charTask.run instead of characterize for v16.0+22\n", + "# could also perform similar functions with processCcdTask.run()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[lsst.meas.base.wrappers.PixelFlagsConfig(doMeasure=True, masksFpAnywhere=[], masksFpCenter=[]),\n", + " lsst.meas.base.wrappers.GaussianFluxConfig(doMeasure=True, background=0.0),\n", + " lsst.meas.base.wrappers.PsfFluxConfig(doMeasure=True, badMaskPlanes=[]),\n", + " lsst.meas.base.wrappers.SdssCentroidConfig(doMeasure=True, binmax=16, doFootprintCheck=True, maxDistToPeak=-1.0, peakMin=-1.0, wfac=1.5),\n", + " lsst.meas.base.wrappers.ApertureFluxConfig(doMeasure=True, maxSincRadius=10.0, radii=[3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0], shiftKernel='lanczos5'),\n", + " lsst.meas.base.wrappers.HsmSourceMomentsConfig(doMeasure=True),\n", + " lsst.meas.base.wrappers.SdssShapeConfig(doMeasure=True, background=0.0, doMeasurePsf=True, maxIter=100, maxShift=0.0, tol1=9.999999747378752e-06, tol2=9.999999747378752e-05)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display which plugins are being used for measurement\n", + "charConfig.measurement.plugins.active " + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Characterization took 1.30 seconds\n", + "Detected 126 objects \n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tstart=time.time()\n", "charResult = charTask.characterize(exposure) # charTask.run(exposure) stack v16.0+22\n", @@ -179,9 +329,79 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "base_SdssShape_xx\n", + "base_SdssShape_yy\n", + "base_SdssShape_xy\n", + "base_SdssShape_xxSigma\n", + "base_SdssShape_yySigma\n", + "base_SdssShape_xySigma\n", + "base_SdssShape_x\n", + "base_SdssShape_y\n", + "base_SdssShape_flux\n", + "base_SdssShape_fluxSigma\n", + "base_SdssShape_psf_xx\n", + "base_SdssShape_psf_yy\n", + "base_SdssShape_psf_xy\n", + "base_SdssShape_flux_xx_Cov\n", + "base_SdssShape_flux_yy_Cov\n", + "base_SdssShape_flux_xy_Cov\n", + "base_SdssShape_flag\n", + "base_SdssShape_flag_unweightedBad\n", + "base_SdssShape_flag_unweighted\n", + "base_SdssShape_flag_shift\n", + "base_SdssShape_flag_maxIter\n", + "base_SdssShape_flag_psf\n", + "ext_shapeHSM_HsmSourceMoments_x\n", + "ext_shapeHSM_HsmSourceMoments_y\n", + "ext_shapeHSM_HsmSourceMoments_xx\n", + "ext_shapeHSM_HsmSourceMoments_yy\n", + "ext_shapeHSM_HsmSourceMoments_xy\n", + "ext_shapeHSM_HsmSourceMoments_flag\n", + "ext_shapeHSM_HsmSourceMoments_flag_no_pixels\n", + "ext_shapeHSM_HsmSourceMoments_flag_not_contained\n", + "ext_shapeHSM_HsmSourceMoments_flag_parent_source\n" + ] + } + ], + "source": [ + "# display some of the source catalog measurements filtered by searchword\n", + "searchword='shape'\n", + "for name in charResult.sourceCat.schema.getOrderedNames():\n", + " if searchword in name.lower():\n", + " print(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique mask plane values: [ 0 32]\n", + "Mask dictionary entries: {'BAD': 0, 'CR': 3, 'DETECTED': 5, 'DETECTED_NEGATIVE': 6, 'EDGE': 4, 'INTRP': 2, 'NO_DATA': 8, 'SAT': 1, 'SUSPECT': 7}\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Looking at the mask plane, which started off as all zeros\n", "# and now has some values of 2^5\n", @@ -203,6 +423,9 @@ "source": [ "# Another option for this analysis, from:\n", "# https://gist.github.com/josePhoenix/8325c16b44fb5fa51f40261b184a78ef\n", + "# Parejko: these are run as part of ProcessCcdTask\n", + "# see https://ldm-151.lsst.io/\n", + "\n", "\n", "import lsst.afw.table\n", "import lsst.afw.image\n", @@ -238,25 +461,72 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Perform further image calibration and measurement" + "## Step 3: Further image calibration and measurement\n", + "This builds on the exposure output from characterization, using the new mask plane as well as the source catalog. Similarly to the characterization, we turn off some processing which is suited to our particular setup." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mcalTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalibrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexposureIdInfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackground\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0micSourceCat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "!Calibrate an exposure (science image or coadd)\n", + "\n", + "@param[in,out] exposure exposure to calibrate (an\n", + " lsst.afw.image.ExposureF or similar);\n", + " in:\n", + " - MaskedImage\n", + " - Psf\n", + " out:\n", + " - MaskedImage has background subtracted\n", + " - Wcs is replaced\n", + " - Calib zero-point is set\n", + "@param[in] exposureIdInfo ID info for exposure (an\n", + " lsst.obs.base.ExposureIdInfo) If not provided, returned\n", + " SourceCatalog IDs will not be globally unique.\n", + "@param[in,out] background background model already subtracted from\n", + " exposure (an lsst.afw.math.BackgroundList). May be None if no\n", + " background has been subtracted, though that is unusual for\n", + " calibration. A refined background model is output.\n", + "@param[in] icSourceCat A SourceCatalog from CharacterizeImageTask\n", + " from which we can copy some fields.\n", + "\n", + "@return pipe_base Struct containing these fields:\n", + "- exposure calibrate science exposure with refined WCS and Calib\n", + "- background model of background subtracted from exposure (an\n", + " lsst.afw.math.BackgroundList)\n", + "- sourceCat catalog of measured sources\n", + "- astromMatches list of source/refObj matches from the astrometry\n", + " solver\n", + "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/calibrate.py\n", + "\u001b[0;31mType:\u001b[0m method\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# no need to do astrometry or photometry calibration\n", - "#since this is a lab image\n", "from lsst.pipe.tasks.calibrate import CalibrateTask, CalibrateConfig\n", + "\n", "calConfig = CalibrateConfig()\n", "calConfig.doAstrometry = False\n", "calConfig.doPhotoCal = False\n", - "calConfig.detection.minPixels = 15\n", "calConfig.doApCorr = False\n", "calConfig.doDeblend = False # these are well-separated objects, deblending adds time & trouble\n", + "# these images should have a uniform background, so measure it\n", + "# on scales which are larger than the objects\n", "calConfig.detection.background.binSize = 50\n", + "calConfig.detection.minPixels = 15\n", + "calConfig.measurement.plugins.names |= [\"ext_shapeHSM_HsmSourceMoments\"]\n", + "# to configure hsm you would do something like\n", + "#charConfig.measurement.plugins[\"ext_shapeHSM_hsmSourceMoments\"].addFlux = True\n", + "\n", "calTask = CalibrateTask(config= calConfig, icSourceSchema=charResult.sourceCat.schema)\n", "\n", "#calTask.run? # for stack v16.0+22 \n", @@ -265,11 +535,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calibration took 1.00 seconds\n", + "Detected 137 objects \n" + ] + } + ], "source": [ "tstart=time.time()\n", "# for stack v16.0+22, change to calTask.run(charResult.exposure)\n", @@ -280,22 +559,69 @@ "print(\"Detected \",len(calResult.sourceCat),\" objects \")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we look at some of the measurements in the source catalog which has been attached to the calibration result. We also save the source catalog to `$fitsfilename.cat` in `/home/$USER/beamsim/`, which was created in the first cell. This will allow the results from each image to be read in after these measurements are performed on each image." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/sarujin/DATA/beamsim/'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/python/miniconda3-4.3.21/lib/python3.6/site-packages/numpy/lib/function_base.py:780: RuntimeWarning: invalid value encountered in greater_equal\n", + " keep = (tmp_a >= first_edge)\n", + "/opt/lsst/software/stack/python/miniconda3-4.3.21/lib/python3.6/site-packages/numpy/lib/function_base.py:781: RuntimeWarning: invalid value encountered in less_equal\n", + " keep &= (tmp_a <= last_edge)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Looking at the source catalog which has now been attached to the \n", "src=calResult.sourceCat #.copy(deep=True) ?\n", "#print(src.asAstropy)\n", "\n", - "src.writeFits(fitsfilename+'.cat')\n", + "# catalog directory\n", + "src.writeFits(cat_dir+fitsfilename.split('/')[-1].replace('.fits','.cat'))\n", "# read back in and access via:\n", "#catalog=fits.open(fitsfilename+'.cat')\n", "#catalog[1].data['base_SdssShape_xx'] etc.\n", "\n", - "plt.figure()\n", "par_names=['base_SdssShape_xx','base_SdssShape_yy','base_SdssShape_flux']\n", "par_mins=[0,0,0]\n", "par_maxs=[5,5,1e6]\n", @@ -318,14 +644,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Display the image with Firefly and overlay detected objects" + "### Optional step: Display the image with Firefly and overlay detected objects\n", + "This is a nice interface for looking at measurements and images together, and it is much more powerful than demonstrated below (see other stack club notebooks for demonstration). From this display, it is clear that some objects are detected as two or more, complicating downstream measurements." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import lsst.afw.display as afwDisplay\n", "\n", @@ -349,9 +698,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "Open your web browser to this link" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# set the backend and attach to the waiting display channel\n", "afwDisplay.setDefaultBackend('firefly')\n", @@ -361,13 +723,11 @@ "# Open the exposure (Firefly knows about mask planes)\n", "afw_display.mtv(exposure)\n", "\n", - "#Now we’ll overplot sources from the src table onto the image display using the Display’s dot method for plotting markers. \n", - "#Display.dot plots markers individually, so you’ll need to iterate over rows in the SourceTable. \n", - "#Next we display the first 100 sources. We limit the number of sources since plotting the whole catalog \n", - "#is a serial process and takes some time. Because of this, it is more efficient to send a batch of updates to the display, \n", - "#so we enclose the loop in a display.Buffering context, like this:\n", + "# Now overplot sources from the src table onto the image display using the Display’s dot method \n", + "# It is more efficient to send a batch of updates to the display, \n", + "# so we enclose the loop in a display.Buffering context, like this:\n", "\n", - "afw_display.erase()\n", + "afw_display.erase() #\n", "\n", "with afw_display.Buffering():\n", " for record in src[:]:\n", @@ -378,14 +738,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Apply the brighter-fatter correction" + "## Step 4: Apply the brighter-fatter kernel correction to an image\n", + "This brighter fatter correction method takes in a \"kernel\" (derived from theory or flat fields) which models the broadening of incident image profiles assuming the pixel boundary displacement can be represented as the gradient of a scalar field. Given a kernel and this assumption, the incident image profile can in theory be reconstructed using an iterative process, which we test here using our beam simulator images. See [this paper](https://arxiv.org/abs/1711.06273) and the IsrTask docstring below for more details about the theory and its assumptions." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0misr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbrighterFatterCorrection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxIter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapplyGain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Apply brighter fatter correction in place for the image\n", + "\n", + "This correction takes a kernel that has been derived from flat field images to\n", + "redistribute the charge. The gradient of the kernel is the deflection\n", + "field due to the accumulated charge.\n", + "\n", + "Given the original image I(x) and the kernel K(x) we can compute the corrected image Ic(x)\n", + "using the following equation:\n", + "\n", + "Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))\n", + "\n", + "To evaluate the derivative term we expand it as follows:\n", + "\n", + "0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y))) + I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) )\n", + "\n", + "Because we use the measured counts instead of the incident counts we apply the correction\n", + "iteratively to reconstruct the original counts and the correction. We stop iterating when the\n", + "summed difference between the current corrected image and the one from the previous iteration\n", + "is below the threshold. We do not require convergence because the number of iterations is\n", + "too large a computational cost. How we define the threshold still needs to be evaluated, the\n", + "current default was shown to work reasonably well on a small set of images. For more information\n", + "on the method see DocuShare Document-19407.\n", + "\n", + "The edges as defined by the kernel are not corrected because they have spurious values\n", + "due to the convolution.\n", + "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/ip_isr/16.0+1/python/lsst/ip/isr/isrTask.py\n", + "\u001b[0;31mType:\u001b[0m method\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from lsst.ip.isr.isrTask import IsrTask # brighterFatterCorrection lives here\n", "isr=IsrTask()\n", @@ -397,24 +796,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brighter-fatter correction took 3.433595657348633 seconds\n", + "-0.72 percent change in flux\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5,0,'Pixel values [e-]')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Read in the kernel (determined from e.g. simulations or flat fields)\n", "kernel=fits.getdata('/project/shared/data/beamsim/bfcorr/BF_kernel-ITL_3800C_002.fits')\n", + "exposure=pre_bfcorr_exposure.clone() # save the pre-bf correction image\n", + "\n", + "# define the maximum number of iterations and threshold for differencing convergence (e-)\n", + "bf_maxiter,bf_threshold=20,10\n", "\n", "# Perform the correction\n", "tstart=time.time()\n", - "exposure=pre_bfcorr_exposure.clone()\n", - "isr.brighterFatterCorrection(exposure,kernel,20,10,False)\n", - "print(\"Brighter-fatter correction took\",time.time()-tstart,\" seconds\") #takes 99 seconds for 4kx4k exposure, 21x21 kernel, 20 iterations, 10 thresh\n", + "isr.brighterFatterCorrection(exposure,kernel,bf_maxiter,bf_threshold,False)\n", + "print(\"Brighter-fatter correction took\",time.time()-tstart,\" seconds\")\n", + "#takes 99 seconds for 4kx4k exposure, 21x21 kernel, 20 iterations, 10 thresh\n", "\n", "# Plot kernel and image differences\n", "plt.figure(),plt.title('BF kernel')\n", "plt.imshow(kernel),plt.colorbar()\n", "\n", "imagediff=(pre_bfcorr_exposure.getImage().array-exposure.getImage().array)\n", + "imagediffpct=np.sum(imagediff)/np.sum(pre_bfcorr_exposure.getImage().array)*100.\n", + "print(str(imagediffpct)[:5],' percent change in flux')\n", "\n", "plt.figure(figsize=(16,10))\n", "plt.subplot(231),plt.title('Before')\n", @@ -427,11 +871,14 @@ "\n", "nbins=1000\n", "plt.subplot(234)\n", - "plt.hist(pre_bfcorr_exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='before'),plt.yscale('log')\n", + "plt.hist(pre_bfcorr_exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='before')\n", + "plt.yscale('log')\n", "plt.subplot(235)\n", - "plt.hist(exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='after'),plt.yscale('log')\n", + "plt.hist(exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='after')\n", + "plt.yscale('log')\n", "plt.subplot(236)\n", - "plt.hist(imagediff.flatten(),bins=nbins,histtype='step',label='difference'),plt.yscale('log')\n", + "plt.hist(imagediff.flatten(),bins=nbins,histtype='step',label='difference')\n", + "plt.yscale('log')\n", "plt.legend()\n", "plt.xlabel('Pixel values [e-]')\n" ] @@ -440,19 +887,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Run the characterization & measurement over the 20 exposures of increasing brightness" + "### Step 5: Run the above steps (with and without brighter-fatter correction) on the 20 exposures of increasing brightness\n", + "Making and saving catalogs should take (with do_bf_corr=True/False) around 1 & 5 seconds per image." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITL-3800C-002_spot_spot_400_20171108114719part.fits char. & calib. took 2.46 seconds to measure 73 objects \n", + "ITL-3800C-002_spot_spot_401_20171108114728part.fits char. & calib. took 2.94 seconds to measure 85 objects \n", + "ITL-3800C-002_spot_spot_402_20171108114738part.fits char. & calib. took 3.43 seconds to measure 97 objects \n", + "ITL-3800C-002_spot_spot_403_20171108114749part.fits char. & calib. took 3.56 seconds to measure 102 objects \n", + "ITL-3800C-002_spot_spot_404_20171108114800part.fits char. & calib. took 3.89 seconds to measure 106 objects \n", + "ITL-3800C-002_spot_spot_405_20171108114811part.fits char. & calib. took 3.97 seconds to measure 112 objects \n", + "ITL-3800C-002_spot_spot_406_20171108114823part.fits char. & calib. took 4.37 seconds to measure 115 objects \n", + "ITL-3800C-002_spot_spot_407_20171108114835part.fits char. & calib. took 4.52 seconds to measure 118 objects \n", + "ITL-3800C-002_spot_spot_408_20171108114848part.fits char. & calib. took 4.90 seconds to measure 118 objects \n", + "ITL-3800C-002_spot_spot_409_20171108114901part.fits char. & calib. took 4.93 seconds to measure 124 objects \n", + "ITL-3800C-002_spot_spot_410_20171108114915part.fits char. & calib. took 5.36 seconds to measure 123 objects \n", + "ITL-3800C-002_spot_spot_411_20171108114930part.fits char. & calib. took 5.35 seconds to measure 124 objects \n", + "ITL-3800C-002_spot_spot_412_20171108114945part.fits char. & calib. took 5.45 seconds to measure 127 objects \n", + "ITL-3800C-002_spot_spot_413_20171108115000part.fits char. & calib. took 5.45 seconds to measure 130 objects \n", + "ITL-3800C-002_spot_spot_414_20171108115016part.fits char. & calib. took 5.35 seconds to measure 131 objects \n", + "ITL-3800C-002_spot_spot_415_20171108115032part.fits char. & calib. took 5.45 seconds to measure 134 objects \n", + "ITL-3800C-002_spot_spot_416_20171108115049part.fits char. & calib. took 5.49 seconds to measure 132 objects \n", + "ITL-3800C-002_spot_spot_417_20171108115106part.fits char. & calib. took 5.55 seconds to measure 135 objects \n", + "ITL-3800C-002_spot_spot_418_20171108115124part.fits char. & calib. took 5.80 seconds to measure 141 objects \n", + "ITL-3800C-002_spot_spot_419_20171108115142part.fits char. & calib. took 5.95 seconds to measure 137 objects \n" + ] + } + ], "source": [ - "# This cell runs through all of the image parts, and should take around 1 second per image (20 sec total)\n", + "do_bf_corr=True # True or False, run this cell with both\n", + "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits' # fits files to read in\n", "\n", - "do_bf_corr=False # True or False, makes catalogs (.cat) for all of the corrected and uncorrected images\n", - "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits'\n", "for fitsfilename in np.sort(glob.glob(fitsglob)):\n", " image_array=afwImage.ImageF.readFits(fitsfilename)\n", " image = afwImage.ImageF(image_array)\n", @@ -465,16 +939,16 @@ " # start the characterization and measurement, optionally beginning with the brighter-fatter correction\n", " tstart=time.time()\n", " if do_bf_corr:\n", - " isr.brighterFatterCorrection(exposure,kernel,20,10,False)\n", - " #print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", + " isr.brighterFatterCorrection(exposure,kernel,bf_maxiter,bf_threshold,False)\n", + " # print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", " # for stack v16.0+22 use charTask.run() and calTask.run()\n", " charResult = charTask.characterize(exposure) \n", " calResult = calTask.calibrate(charResult.exposure, background=charResult.background,\n", " icSourceCat = charResult.sourceCat)\n", " src=calResult.sourceCat #.copy(deep=True) ?\n", " \n", - " # write out the source catalog\n", - " catfilename=fitsfilename.replace('.fits','.cat')\n", + " # write out the source catalog, appending -bfcorr for the corrected catalogs\n", + " catfilename=cat_dir+fitsfilename.replace('.fits','.cat').split('/')[-1]#\n", " if do_bf_corr: catfilename=catfilename.replace('.cat','-bfcorr.cat')\n", " src.writeFits(catfilename)\n", " \n", @@ -483,122 +957,348 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# display some of the source catalog shape measurements\n", - "for name in src.schema.getOrderedNames():\n", - " if 'shape' in name.lower():\n", - " print(name)" + "Now read in those catalogs, both corrected and uncorrected (this could be improved with e.g. pandas)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "# Read in the catalogs, both corrected and uncorrected (this could be improved with pandas)\n", "cat_arr = []\n", - "catglob='/project/shared/data/beamsim/bfcorr/ITL*part.cat' # uncorrected catalogs\n", + "catglob=cat_dir+'ITL*part.cat' # uncorrected catalogs\n", "for catfilename in np.sort(glob.glob(catglob)): cat_arr.append(fits.getdata(catfilename))\n", "\n", "bf_cat_arr = []\n", - "catglob='/project/shared/data/beamsim/bfcorr/ITL*part-bfcorr.cat' # corrected catalogs\n", + "catglob=cat_dir+'ITL*part-bfcorr.cat' # corrected catalogs\n", "for catfilename in np.sort(glob.glob(catglob)): bf_cat_arr.append(fits.getdata(catfilename))\n", "ncats=len(cat_arr)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Centroids of sequential exposures')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Show possible issues with source matching which we will remedy with simple matching in the next cell\n", + "# Show issues with multiply detected sources which which we remedy with matching rejection\n", "for i in range(ncats):\n", " xfoo,yfoo=cat_arr[i]['base_SdssCentroid_x'],cat_arr[i]['base_SdssCentroid_y']\n", - " plt.plot(xfoo,yfoo,'o')\n", + " plt.plot(xfoo,yfoo,'o',alpha=.4)\n", "plt.title('Centroids of sequential exposures')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using a fiducial frame as reference, we simply match the catalogs by looking for *single* object matches within a specified distance. We then collect a shape measurement (e.g. `base_SdssShape_xx/yy`) for that object as well as a brightness measurement (e.g. `base_SdssShape_flux`) to test for a trend in size vs. brightness." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "# Using a fiducial frame as reference, we simply match the catalogs by looking for single objects\n", - "# within a max distance\n", + "fidframe=10 # frame number to compare to\n", + "maxdist=.5 # max distance to match objects between frames\n", "\n", - "fidframe=10\n", - "maxdist=.5\n", + "# choose which stack measurements to use for centroids and shape\n", + "# +TODO use 'ext_shapeHSM_HsmSourceMoments_xx','ext_shapeHSM_HsmSourceMoments_yy'\n", + "cen_param1,cen_param2='base_SdssCentroid_x','base_SdssCentroid_y'\n", + "bf_param1,bf_param2='base_SdssShape_xx','base_SdssShape_yy'\n", + "flux_param='base_SdssShape_flux'\n", "\n", - "x0s,y0s=cat_arr[fidframe]['base_SdssCentroid_x'],cat_arr[fidframe]['base_SdssCentroid_y']\n", + "# get the centroids (used for matching) from the fiducial frame \n", + "x0s,y0s=cat_arr[fidframe][cen_param1],cat_arr[fidframe][cen_param2]\n", "nspots=len(x0s)\n", - "bf_dat=np.empty((ncats,nspots,6)) #x,y,xx,yy,bfxx,bfyy\n", - "bf_dat[:]=np.nan\n", "\n", + "# make an array to hold that number of objects and their centroid/shape/flux measurements\n", + "# the 8 rows collect x/y centroid, x/y shape, x/y corrected shape, flux, and corrected flux\n", + "bf_dat=np.empty((ncats,nspots,8))\n", + "bf_dat[:]=np.nan # so that un-matched objects aren't plotted/used by default\n", + "\n", + "\n", + "# loop over catalogs\n", "for i in range(ncats):\n", " # get the centroids of objects in the bf-corrected and uncorrected images\n", - " x1,y1=cat_arr[i]['base_SdssCentroid_x'],cat_arr[i]['base_SdssCentroid_y']\n", - " x1_bf,y1_bf=bf_cat_arr[i]['base_SdssCentroid_x'],bf_cat_arr[i]['base_SdssCentroid_y']\n", - " for j in range(nspots): # loop over fiducial frame centroids to find matches\n", - " x0,y0=x0s[j],y0s[j]\n", - " # find the matches between the fiducial centroid (x0,y0) and the corrected/uncorrected ones\n", + " x1,y1=cat_arr[i][cen_param1],cat_arr[i][cen_param2]\n", + " x1_bf,y1_bf=bf_cat_arr[i][cen_param1],bf_cat_arr[i][cen_param2]\n", + " # loop over fiducial frame centroids to find matches\n", + " for j in range(nspots): \n", + " x0,y0=x0s[j],y0s[j] # fiducial centroid to match\n", + " # find objects in both catalogs which are within maxdist\n", " bf_gd=np.where(np.sqrt((x1_bf-x0)**2+(y1_bf-y0)**2)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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BwwkCuV4EQ7wSVpptXAwkMVH+vaTydQRBScKbSXVpkbSUIBBpbS1BD0SyfnwScKwD/mVmD4T1vAr4EbC7mT0r6UvAtcAFwHzgTmBDqtcIvM8nAVIq2wOvJz1/neDfwrZhWfJr+ljSyohzHRvW8wpJzwEXmlkdMAS4P9UBWXwGCTsB20v6IGlbGUE2toS+bB4gFohopjDzAMJsb5cBLwJ3mFltQSpSKhLd4A0NQaNbUwNVVTm9ZFNTE08//TT3338/P/7xj5kzZw4zZsygvr6ehQsXssUWW7Bq1SrWr1/PySefTE1NDcOHD2fKlCn89re/5eyzzwZgwIABPPNM8H3CDTfcQENDA/PnB2uvnnDCCXzve9/jwAMP5I033uDwww9n8eLFXHbZZWy55ZY8/3zw3/D999/n2GOP5brrrmPhwoUALF68mNmzZ/PEE09QXl7Od77zHf70pz8xceJEpk2bxoIFC9hyyy059NBD2WeffXL6XrkYVVUF/75ra4Obixz/O3fOdUE5bGeKPpABMLPbgNsk9QNuBH5OMH9kk56EcBL83QSTw+81s0ZJf4UO3X0OSTp/N4KelVRB1IvApyT1TRpetjfBcDSA54AD0l0knC9zSNK1/g3ckmb354DvRdT5bYIgIWEo0EQQsL0D7Jp0nZ4EyQnS1WsecFQ4nO5MggBrCEEwtHPr/dv4GbwJvGZmu0S8lt0Jhp4VnAEtMQ4bk3QzwRC8ZWY2Imn7JODXBEHd783sivDyawl6BFPOjXJtkKobvIMNb7rJ7ontxxxzDAD77rsv9fX1AMyZM4fTTz+dLbYImuqtt96aZ599lmHDhjF8eJDr46STTuL666/fGMgcd9xxm5w/+fmcOXNYtGjRxuerV69m7dq1zJkzhzvuuGPj9q222mqzetbU1LBgwQLGjh0LwLp169hmm2146qmnqK6uZtCgQRuv98orr2T5rrhOoarKAxjnXG7lqJ0p+qFlknaVND68QV5P0LORGL3zHlAZBhgAFQTD0JYDTWHPwGEdrMK+ko4Jh2WdTdBL8mTrnczsFWAhME1SDwXpk0cS3NRDkGFsf0mflVQWnmsFwRwQJI0Mj+sVTqgfDMxMU6engf6S0s1tuR34nqRhkvrwyVyiJuAu4AuSPhMOa5tOmkBPUoWkEyVtGQ6tW80n7/1NwCkKEi90k7SDpN1o22fwNLBGQTKHnpLKJI2QNDZpn0MIMpd1Cs1hr0ymR5ZmApOSN4T/Nq4HPgfsARwfzmt63Mw+R9Bj9+PYXlBXlYNu8AEDBvD+++9vsm3VqlUMHDgQgO7duwNQVlbWrvksCb179077vKWlhSeffJKFCxeycOFC3nrrLfr06dP6FCmZGSeddNLGY19++WWmT5/e7no655xzHVX0gQzBTfEVBDf97wLbEMz5gGASPsBKSc+EPSHfJeg1eB84Abivg9e/FzguPN/XgWPCm/pUJgNjwn2vAL5sZssBzOxlgqQCN4TlRwFfDOepEJ77HYK5MhOAiWaWcmhZeMzM8Hyp3Az8EZgLvEYQAJ4VHvti+Psd4fXWhtdMN4zt6wRJClYDpxPMv8HMniacyE8w9OsxYKe2fAbhEL8jgVFhPVcAvyecEyOpB3AE6Xum8sqARuuW1QMYKGl+0mPqZuczmwusarV5HLAknN/UQPA5HWVmiQDyfYL/E64jEt3gl10W27CyPn36MHjwYB555BEgCGL++c9/cuCBB6Y9ZuLEidx4440bA5tVq1ax6667Ul9fz5IlSwD44x//yCGHHJL2HMkOO+ywjfNsgI3DxiZOnMj111+/cXsi4CovL6exMWjOJkyYwF133cWyZcs21uX1119nv/3247HHHmPlypU0Njby5z//Geeccy4fin5oWTjkalyaspXAga22XU/wjXaq/WsJhoYlnjfRqjfCzFrfdaw3s3QBQ+vz1wPVEeV/Af6Spux8ggxg2boSeFzSNWG6543XDW96Lw0fqa41k7C3J+yxmUaK4UrhjfSk1tuTyu8B7kmxvS2fwdvA8WkucRpwm5m9l6Y8rwza0tuywszGtOMyO5A0h4ngc9lP0jEEGfv6E2agcx2Ug27wWbNmccYZZ3DOOecAMG3aNHbeebMRmBt985vf5JVXXmHkyJGUl5dz2mmnceaZZ/KHP/yBr3zlKzQ1NTF27FhOP/30rK5/zTXXcMYZZzBy5Eiampo4+OCDueGGG/jRj37EGWecwYgRIygrK2PatGkcc8wxTJ06lZEjRzJ69Gj+9Kc/8ZOf/ITDDjuMlpYWysvLuf7669l///2ZPn06VVVV9O/fn1GjRsXyXjnnnHOZyHxxvnaTNB34dLaBTLGQ9AWghiCIu5pgXZrR5v9YIu0+srvN+vvgrPYdt9PrrxP0MCXMMLMZrfeTVAn8PTFHRtKXgUlm9s3w+deB/czMF+4IjRkzxhKT2xMWL17M7rvvXqAauQT/HEqDpAXt/CKmZKRqZ5xz8cm2nSmFoWUufkcRJAR4G9gFmOxBTHZaTFk9CHtkkh6bBTFpvEVSggmC3qu34n4dcZM0ScFitUskXZiifKikRyX9R9Jzko4It0+UtEDS8+HP8fmvvXPOOec6o6IfWlZIZja90HXIhfDb/m8Wuh7Fpo1DywZKSv46L2WPTArzgF3CRVnfIph3dUKbKppnSQkKJhIMhZsn6T4zW5S024+AO83st2HygvuBSoJeqy+Y2duSRgAPEr1Aq3POOee6CA9knIuNaLasOzkzzpGRdDvBnKqB4fpE08zsJklnEtzQlxEsOvpiByqdDxsTFABIuoOg1y85kDE+WWdpS8IU5mb2n6R9XgR6SuqeLtGFc84lS5fGPqn8RIJsjyJY1+3bZtYpUvo75zLzQMa5mBjQSFm2u2fskTGzlEkOzOx+0iw22kmlTFDQap/pwEOSzgJ6A59NcZ5jgWdSBTFh1repAEOHDo2hys65EjGTIAHKrDTlrwGHmNn74XIAM9i8fXLOdVJ5DWQGDhxolZWVacsX10cnn9q9ctvI8pf+F338bp+KPj7K4teiz539F/Gp7bFTdN0WvR59/W7N0ee3HC84v/uw9r+3mbz8yruR5bsO365D5//v89HrR65ueG+FmQ3KdB6zeHtkupjjgZlmdrWkKuCPkkYk0kpL2pNgoduUaw6FQeAMCCbh5qnOzrlOzszmhklT0pX/O+npkyRlzXTOdX55DWQqKyuJyvIx9uRfRh4/b+Y5keVVk6+OLK+749zI8ijjpkSfu6l7hkghw/3t/BujX9voqdHvTffV0fduLRk+6Uz338oQKD31p/a/t5kcOvGKyPJHH95s7nibfG7n8yLL//m/q1/P9lwt2c+R6UqySVDwDcJU3mZWF64RNBBYJmlHgjTeU8zs1TzU1znXNX2DTrTAsnMuM89a5lxMgsn+3bJ6kMWCmCVkY4ICSRUECQpaL4L6BsFCr0jaHegBLJfUH/gHcKGZPZHHOufEu+++y+TJk9l5553Zd999OeKII3jllVfycu2FCxdy//1tH5FYXV0d+QWUc6VA0qEEgcwFEftMTbTZy5cvz1/lnHNpeSDjXGyCoWXZPGh/+uWiEy4sm0hQsJggO9mLki6V9MVwt3OB0yQ9C9wOnBym/D4T+DRwiaSF4WObAryMDjMzjj76aKqrq3n11VdZsGABl19+Oe+9l3k916amps3O1dLS0qbrtzeQca7USRoJ/B44KlxIOyUzm5FoswcNyjja2DmXBx7IOBcTAxqtLKtHV2Nm95vZcDPb2cx+Gm67xMzuC39fZGYHmNneZjbKzB4Kt//EzHqH2xKPZfmoc92bdVz++OXUvVkXy/keffRRysvLOf300zdu23vvvTnwwAM5//zzGTFiBHvttRezZ88GoLa2loMOOogvfvGL7LHHHtTX17PrrrsyZcoURowYwZtvvslDDz1EVVUVo0eP5itf+Qpr164FYN68eXzmM59h7733Zty4cXz44YdccsklzJ49m1GjRjF79mw++ugjTj31VMaNG8c+++zDvffeC8C6deuYPHkyu+++O0cffTTr1q2L5fU71xlJGgr8Bfi6meWne9Q5FxvPWuZcTAwlho1lo73ryLg8qHuzjgmzJtDQ3EBFWQU1U2qoGlLVoXO+8MIL7Lvvvptt/8tf/sLChQt59tlnWbFiBWPHjuXggw8G4JlnnuGFF15g2LBh1NfX89///pdbbrmF/fffnxUrVvCTn/yEOXPm0Lt3b37+85/zy1/+kgsvvJDjjjuO2bNnM3bsWFavXk2vXr249NJLmT9/Ptdddx0AP/jBDxg/fjw333wzH3zwAePGjeOzn/0sN954I7169WLx4sU899xzjB49ukOv27lCSpXGHigHMLMbgEuAAcBvJAE0eSIW54qHBzLOxajFs5aVhNr6WhqaG2i2ZhqaG6itr+1wIJPOv/71L44//njKysrYdtttOeSQQ5g3bx79+vVj3LhxDBs2bOO+O+20E/vvvz8ATz75JIsWLeKAAw4AoKGhgaqqKl5++WUGDx7M2LFjAejXr9/mFwUeeugh7rvvPq666ioA1q9fzxtvvMHcuXP57ne/C8DIkSMZOXJkTl63c/mQLo19UrkvAO1cEctrILP4tffY78T02b/6vtsQefz48ZdHlm/53prI8jGnZsr8lX7MufWOvkF95nfRWcdGnRF97Uz6vdEYWd592ceR5Q8+8+PI8oO/cGVk+dy/fT+yPJMJh/wsbVnNYz+IPDbXWckeePWqyHIpOmNdQmKyvyt+1ZXVVJRVbOyRqa6s7vA599xzT+666642HdO7d++0z82MiRMncvvtt2+yz/PPP5/Vuc2Mu+++m1133bVNdXIloq4OamuhuhqqchOkO+dcsro366itr6W6sjq2Lwcz3nVJGiLpUUmLJL0o6f/C7VtLeljSf8OfW8VSI+eKlCGaLbuH69yqhlRRM6WGyw69LJZhZQDjx49nw4YNzJjxyQjC5557jv79+zN79myam5tZvnw5c+fOZdy4cRnPt//++/PEE0+wZMkSAD766CNeeeUVdt11V9555x3mzZsHwJo1a2hqaqJv376sWfPJlz2HH3441157LUFOBfjPf/4DwMEHH8xtt90GBMPhnnvuuQ6/dtfJ1NXBhAlw8cXBz7p45oE551w6iSHbFz96MRNmTYht/mk2Xx83Aeea2R7A/sAZkvYALgRqzGwXoCZ87lyX1kK3rB50rfTLRalqSBUXHXRRbN8aSeKee+5hzpw57Lzzzuy5555cdNFFnHDCCYwcOZK9996b8ePHc+WVV7LddpkXeR00aBAzZ87k+OOPZ+TIkVRVVfHSSy9RUVHB7NmzOeuss9h7772ZOHEi69ev59BDD2XRokUbJ/tffPHFNDY2MnLkSPbcc08uvvhiAL797W+zdu1adt99dy655JKU83pckauthYYGaG4OftbWFrpGzrkSl2rIdhwyDi0zs3eAd8Lf10haDOwAHEUwgQ7gFqCWiPzrzpU6M7UlI5nPkemCtt9+e+68887Ntv/iF7/gF7/4xSbbqqurqa6u3vi8srKSF154YZN9xo8fv7HnJdnYsWN58sknN9veet8bb7xxs3169uzJHXfcEfk6XJGrroaKiiCIqagInjvnXA7lYsg2tHGOjKRKYB/gKWDbMMgBeBfYNs0xU4GpABW9fPSZK10GiTVinHOu86qqgpoanyPjnMutpLl4VVXBkO2458hkHchI6gPcDZxtZqvDNIUAmJlJslTHhSllZwD0HjAk5T7OlQqf7O+cKwpVVR7AOOdyJzEXL9HzW1NDVVVV7BlAs7rrklROEMT8ycz+Em5+T9LgsHwwkJdF6pzrrAzRYtk9nHPOOedKVp7m4mWTtUzATcBiM0vOIXwfcFL4+0nAvfFXz7ni0ky3rB74ZP+8S2TncoXh779zznUhibl4ZWU5nYuXzdCyA4CvA89LWhhu+wFwBXCnpG8ArwNfzXSiskaj9zvp14p55JGLsqhO7hzw5fTricy7JXqdmOpJP48sL9uxvEPHN1dEx5yZ1onJpGx9+jV0AA4bOz2yfOXeqRfdS1iQYa2YjhhzSvQaPYPydANl+IKYnVWPHj1YuXIlAwYMIHlYrMsPM2PlypVKVR3SAAAgAElEQVT06NGj0FVxzjmXD3mai5dN1rJ/Aen+8k+ItzrOFS+jTVnLXB7tuOOOLF26lOXLlxe6Kl1Wjx492HHHHQtdDeecc/mSh7l4bcpa5pyL1pw25neFVF5ezrBhwwpdDeecc87FyAMZ52JiprYMLXPOOeeccx3ggYxzMfJ1ZJxzzjnn8sMDGediYkCLDy1zzjnnnMsLD2Sci4khGluynuw/UNL8pOczwsVjnXPOOedcFjyQcS5GzdmtMQueftk555xzrkPyGsgMH75d5Foxo0+LXg/kmd9Fr+Uy5tTo4+ffHH38E3edl7Zs3JSrI49t2il6nZjuH0avZfLRdtHHZ1oMftKe0eu0vHvIwMjy8h0qIst7Z7h+c/Th7Hdi+vevW3P0sX3/uyayfP6C6dHX/lr0ZxcXQ7Rk+qCcc84551wsvEfGuRi1ZN8j45xzzjnnOsADGediYgbN3iPjnHPOOZcXHsg4FyMfWuac65Tq6qC2Fqqrc77StnPO5YsHMs7FxBCNlnXWMuecy4+6OpgwARoaoKICamo8mHHOlQQf0O9cTIygRyabh3PO5U1tbRDENDcHP2trC10j55yLhffIOBcb0WL+3YBzrpOprg56YhI9MtXVha6Rc87Fwu+6nItRC8rqkQuSekuaL+nInFygAyRNkvSypCWSLkxRPlTSo5L+I+k5SUcklV0UHveypMPzW3PnSkBVVTCc7LLLfFiZc66k5LVHZtHr7zF6avq1XsoaO3b+niujFyTZ+6xfRZZvf/9bacsaJm0feWzZ+shi1BK9jkxjhoValGGtFa1viCxv7h59/m6N0fXb4qOmyPKFv9ns3nQTUWv89Hojuu4f7Nk3svyAL18VWd73nQwfTkzizlom6WbgSGCZmY1I2j4J+DVQBvzezK4Iiy4A7oytAjGRVAZcD0wElgLzJN1nZouSdvsRcKeZ/VbSHsD9QGX4+2RgT2B7YI6k4WaW4X+Ec24TVVUewDjnSo73yDgXoxbrltUjSzOBSckbkoKCzwF7AMdL2kPSRGARsCy+VxObccASM/ufmTUAdwBHtdrHgH7h71sCb4e/HwXcYWYbzOw1YEl4Puecc851cT5HxrmYGKIp+yBloKT5Sc9nmNmMTc5nNldSZavjNgYFAJISQUEfoDdBcLNO0v1m1tL2V5ETOwBvJj1fCuzXap/pwEOSziJ4HZ9NOvbJVsfu0PoCkqYCUwGGDh0aS6Wdc8451zF1b9ZRW19LdWU1VUPi7xX2QMa5mCSylmVphZmNacdlUgYFZnYmgKSTw3N3liAmW8cDM83saklVwB8ljch0UEIYBM4AGDNmTPQ4Seecc87lXN2bdUyYNYGG5gYqyiqomVITezDjQ8uci1EbhpYNDCfmJx5T47i+mc00s7/Hca4YvQUMSXq+Y7gt2TcI5/eYWR3QAxiY5bHOOZeSpJslLZP0Qpry3STVSdog6bx818+5klJXB5dfHvwEautraWhuoNmaaWhuoLa+NvZLeo+Mc3Fp2xox7e2RKcYb+3nALpKGEdR1MnBCq33eACYAMyXtThDILAfuA26T9EuCyf67AE/nq+LOuaI3E7gOmJWmfBXwXeBL+aqQcyUpxcK71ZXVVJRVbOyRqa6sjv2yHsg4FxODtqRWzjhHJo1sgoJOxcyaJJ0JPEiQae1mM3tR0qXAfDO7DzgX+J2k7xG8lSebmQEvSrqTIJFBE3CGZyxzzmUrzVzD5PJlwDJJn89bpZwrRSkW3q266CJqptT4HBnnioEBTS1Zj9bM2CMj6XagmiDoWQpMM7ObUgUF7a91fpjZ/QQplZO3XZL0+yLggDTH/hT4aU4r6JxzGXhSEecipFl4t2pIVU4CmIS8BjJbrG9hq5fWpS1v6lPeofOv27ossvzZa78XWT52Tfq1TsrXRl87U7KqjwdG75BpHZlMs5leO2GzRE6bKP84+nhlmB79/u69Isuj1gcCeObmc9KWTdr1gshjH53z88jyjhp7UnTd26INQ8syMrPj02zfLCjIF0nbhN9gJm/b1cxeLkR9nHNdh6QvAP8oVDITTyriXITEwru1tUEQk6d1q3yyv3MxMYI5Mtk8yNFk/zx4XNJXE08knQvcU8D6OOe6juOA/0q6UtJuha6Mc66Vqiq46KK8Lr7rQ8uci1Eb5si0d7J/oVUDMyR9BdgWWIwvUOlc51NXl/dvRnPNzL4mqR9hunZJBvwBuN3M1hS2ds65QvBAxrm4WJuGlrV3sn9Bmdk7kv4JXAS0ABeaWYaBl865vEqRPaiEgpnVku4CegJnA0cD50u6xsyubb1/qrmGQHl4rhskbQfMB/oBLZLOBvYws9V5eUHOuQ7xQMa5mORpQcyCkjQHeBsYQZAG+iZJc83M119wrrNIkT2oFAIZSV8ETgE+TZBOeZyZLZPUiyCz4WaBTLq5hknl7xKksXfOFSEPZJyLiaG2ZC0rVteZ2V/D3z+Q9BmC3hnnXGeRJntQCTgW+JWZzU3eaGYfS/pGgerknCsgD2Sci5HFmLWsk3ol+Um4RszjhaqMcy6FAmUPyoMFwPOpCsysJs91cc51AnkNZKybaO6V/pKNvTv2bXaftxoiyw8fdXFkednu/dOWtWTIDN3SLfoGdsPWHbvBzXR/rAzJKBujsyfTUh59gf7/jV6DcN3W0Z/dqO+kT3E8uKEx8tjx4y+PLH/kkegOgYOPvDKyvP+qDZHlbZGHBTEL7U5JfwSuBHqEP8cAJXOn5FxJqKoqpQAmYRtgnqRngJuBB8OFc51zXVTGyEHSzZKWSXohadt0SW9JWhg+jshtNZ3r/Cyc7J9l+uUVZjYm6VEMQQzAfgRzY/4NzCOYL5NyIUvnnIuTmf0I2AW4CTiZIBXzzyTtXNCKOecKJpsukJnApBTbf2Vmo8JHQRbnc66zMVNWjyLWCKwjyBjUA3itUIvTOee6nrAH5t3w0QRsBdwlKbrr3TlXkjIGMuGkulV5qItzRU40t3TL6lHE5hEEMmOBg4DjJf25sFVyznUFkv5P0gKCIa1PAHuZ2beBfQkSATjnupiOzJE5U9IUgvzr55rZ+zHVybmi1Mb0y8U6R+YbZpao9zvAUZK+niiUtJW3Bc65HNkaOMbMXk/eaGYtko709se5rqe9Xw3/FtgZGEVwM3N1uh0lTZU0X9L8xoaP2nk554qABfNksnlQpHNkkoKY5G1/THrqmYOcczlhZtNaBzFJZYvx9se5LqddgYyZvWdmzeHY+N8B4yL2nZG4WSuv6N3eejpXFFpQVo8SVtIvzrlOq64OLr88+Nl1efvjXBfTrqFlkgab2Tvh06OBF6L2d64rMLrEOjKZeCpU5/Ktrg4mTPhkAcyamlJMvZwNb3+c62IyBjKSbgeqCcb0LwWmAdWSRhE0GvXAt7K5mAy6NUQlOCqLPP6zB/4ksvyjnaMXS9mwVfTLXTcwfQdVt8bo9nHdoOgb2JaKyGIa+0Unftrio+jzN26V4fg10Z1v9nH0+VftFv3Z9FgV/f70fi99/R54Lf0aMwATu30lsjyTuX//fmT5/iekHRkZyPoLTrVljoxzzsWjtjYIYpqbg5+1tV01kHHOdTEZAxkzOz7F5ptyUBfnil5LS8lP9s/EIznn8q26OuiJSfTIVFcXukaF4u2Pc11MR7KWOeeSBBP5s/47usLMxuSyPrki6UBgFzP7g6RBQB8zey0snlDAqjnXNVVVBcPJamuDIKaEe2O8/XHOJfNAxrkYlfrQMknTgDHArsAfgHLgVuAAADPzNaecK4SqqpIOYMDbH+c6nbq6zb5AqXuzjtr6Wqorq6kakvs2yQMZ52JkpT/V9GhgH+AZADN7W1LfwlbJOddFePvjXGeRIslI3Y4wYdYEGpobqCiroGZKTc6DmaJeYty5zsZMWT2KWIOZGWF2IEmeU905ly/e/jjXWaRIMlJbX0tDcwPN1kxDcwO19bU5r4b3yDgXE6Pog5Rs3CnpRqC/pNOAUwnWknLOuVzz9se5ziJFkpHqHaGirGJjj0x1ZXXOq+GBjHNxsdKfI2NmV0maCKwGhgOXmNnDBa6Wc64L8PbHuU4kRZKRKqBmSk3pzpGxbtDYN/0lm7tH3wQ29YqubsVH0WupfByxTgwQuZRWS0V03Zp7Rk+OyLTOS98dVkeWf3/3hyLLZy2N/sfy6ruDIsub3+seWd79/ej3rqFfhvene/p1aPb9ZvQ6MoMGRdc90/GKfuuZf9u50cfffl70CZJlP0emmNMvPw/0JHi1zxe4Ls65rsXbH+c6ixRJRqqGVOUlgEnwOTLOxagNc2RWmNmYpEdRBDGSvgk8DRwDfBl4UtKpha2Vc64r8PbHOdeaDy1zLkZdIGvZ+cA+ZrYSQNIA4N/AzVEHSZoE/BooA35vZle0Kv8VcGj4tBewjZn1D8uuBD5P8MXLw8D/hRN+nXNdS7vaH+dc6fJAxrmYGG1aELNYrQTWJD1fE25LS1IZcD0wEVgKzJN0n5ktSuxjZt9L2v8sghSrSPoMwRoRI8PifwGHALUdfSHOFa0Uazd0EW1uf5xzpc0DGefiYmAtJR/ILAGeknQvQex2FPCcpHMAzCzVhKVxwBIz+x+ApDvC4xal2BfgeGBa+LsBPYAKQAQL4L0Xz0txrgilWLuhCwUz7Wl/nHMlzAMZ5+JU+gOeXg0fCfeGP6MWpdsBeDPp+VJgv1Q7StoJGAY8AmBmdZIeBd4hCGSuM7PF7au6cyUgxdoNXSiQaU/745wrYR7IOBeb0l9Hxsx+nONLTAbuMrNmAEmfBnYHdgzLH5Z0kJk9nnyQpKnAVIChQ4fmuIrOFVCKtRu6ijy0P865IuOBjHNxKvEeGUmDgO8DexIM+QLAzMZHHPYWMCTp+Y7htlQmA2ckPT8aeNLM1obXfwCoAjYJZMKsbzMAxowZU+KfgutSWs+HSbF2Q1fRzvbHOVfC8ryOjGiOWI+lKcM6Mhu2Lo8sb+wZfbxlWkYm/VInNPeMPrYlwzuZaZ2YM3Z9LLL8xL7R8xm/vNs9keU/6J9yJM9Gf/l438jypvXR72352vb3RGTqxFh9yKciy3u/2xRZvnaH6A/ngGOviq5Atqxwk/0l7Q78HzAQqDGz3+boUn8CZgNHAqcDJwHLMxwzD9hF0jCCAGYycELrnSTtBmwF1CVtfgM4TdLlBEPLDgH+Xwdfg3PFId18mBRrN3QR7Wl/nHMlzNeRcS5OluUjC5JulrRM0guttk+S9LKkJZIuBDCzxWZ2OvBVgixfuTLAzG4CGs3sMTM7FYj8NtTMmoAzgQeBxcCdZvaipEslfTFp18nAHa1SK99FMCb+eeBZ4Fkz+1uMr8e5zivVfJiurc3tT7p2NKlckq4J29PnJI3ORcWdc7nhQ8uci1O8PTIzgeuAWYkNUamMw6Dg28Af46xEK43hz3ckfR54G9g600Fmdj9wf6ttl7R6Pj3Fcc3At9pbWeeKWheeD5NGe9qfmbRqR1v5HLBL+NgP+C1pkpE45zofD2Sci1OMszPMbK6kylab06YyNrP7gPsk/QO4Lb6abOInkrYEzgWuBfoB34s+xDnXLl14PkwabW5/0rSjyY4CZoU9wU9K6i9psJm9E1OdnXM55IGMc3Ex2tIjM1DS/KTnM8IJ65mkTGUsqRo4BuhOq56POJnZ38NfPwQOzdV1nHOhrjsfZjM5an9Stak7EKR8d851ch7IOBcjy75HZoWZjYnvulZLHla7D7MGnQZUktR+hGPVnXMuZwrd/niad+c6Hw9knItT9oFMe3tk2pLKOBfuJUh9PAdozuN1nXMuF+1P1m2qp3l3rvPxQMa5OGU/tKy9PTJZpTLOoV5mdkEer+eccwm5aH/uA84M5xvuB3zo82OcKx55DWRayuGjwekXa+m5oiXy+A1bRt8kbv3Cx5Hlc574UWT5nhf8Km1ZS8QaMwDNvaPrvuvAZZHlB/VcEn0BekWWdlf0GjuH9Hspsvye7qMiy61b9D+Vpgzr7Gz31Ia0Zeu3jj73Fuui39vuq9KfG4L1i6L0WfBGZHnWDBRd1WQZe2Qk3Q5Uh/suBaaZ2U2SEqmMy4CbzezFDtc9e3+XdESYhcw55/Kpze1PqnYUKAcwsxsI5hQeASwBPgZOibvSzrnc8R4Z52KjWHtkzOz4NNs3S2Wca5LWEAycE/ADSRuAxEqkZmb98lkf51zX0ZH2J107mlRuwBlx1dW5klZXt1kWxbo366itr6W6spqqIflPTOKBjHNxyv0cmYIws76FroNzrmvy9se5TqCuDiZM+GRdq5oa6naECbMm0NDcQEVZBTVTavIezHgg41ycCpS1LNck7QR8YGYfhs8PBb4E1APXm1lDAavnnCth3v441wnU1gZBTHNz8LO2ltoDoaG5gWZrpqG5gdr62rwHMt3yejXnSp1l+Sg+dwK9ASSNAv4MvAGMAn5TwHo550qftz/OFVp1ddATU1YW/KyuprqymoqyCspURkVZBdWV1XmvlvfIOBcXA7XkfEHMQulpZm+Hv3+NIMnA1ZK6AQsLWC/nXOnz9se5QquqgpqaTebIVAE1U2p8joxzJaNEh5YRTLJNGA9cBGBmLVLWwZtzzrWHtz/OdQZVVRsn+W/cNKSqIAFMggcyzrlsPCLpTuAdYCvgEQBJgwEfn+6cyyVvf5xzKeU1kFETdP8g/UIbT952buTxY075ZWT5hgHdI8v3PjP9OjEA6hFRGL1My6bfF6XQTdFf1S9viV4nZrhFL1Cy1qLXUnl5w86R5WQYEpXpOy/LMNtq1a7pP5utX46u+8fbRL/5c/92cWT5uK9fHVlu+w2NLOee6OJkGT7mZMU2tOxs4DhgMHCgmTWG27cDfliwWjnnugJvf5xzKWUMZCTdDBwJLDOzEeG2rYHZQCVB1pCvmtn7uaumc0UixnVkOpNwrYU7JPUG1gFIGg4MAR4oZN2cKxkp1mhw3v4459LLJmvZTGBSq20XAjVmtgtQEz53rmvLNmNZcWYtS5gL9JC0A/AQ8HWCNsI51xGJNRouvjj4WVdX6Bp1Rt7+OOc2kTGQMbO5wKpWm48Cbgl/v4Ugn7tzXZ5asnsUMZnZx8AxwG/M7CvAiALXybnil2KNBrcZb3+cc5to7zoy25rZO+Hv7wLbpttR0lRJ8yXNb9rwUTsv51yRyL5HZmDi/0X4mFqYCreZJFUBJwL/CLf5elTOdVSKNRrcZrz9cc5tosOT/c3MpPRTnMMJzDMAeg8YUtyDapzLpHTTLyecTZD69B4ze1HSp4BHC1wn54pLqrkwKdZocJvx9sc5t4n2BjLvSRpsZu+E6Q+XxVkp54qRrE1Zy4qSmT0GPAYQLka3wsy+W9haOVdEEnNhGhqCnpeamk2DGQ9g0vL2xznXWnu7ZO8DTgp/Pwm4N57qOFfkTNk9ipSk2yT1C7MHvQAsknR+oevlXNHwuTDt5u2Pc661bNIv3w5UE4zpXwpMA64A7pT0DeB14KvZXKxbs9Hj/eZ2V7alIrp83YCyDBWILu7+fvqv09cPiL757LY+uvzlFdtElt/bY3Rk+fM9l0eWv9PQP7L8b/UZ5kN+GL1WS1mG11e2Pvr0W6xL/96u3T76g23sHX3ug4+8MrK894bo2fWPPhyddE/6fnQFkpV4jwywh5mtlnQiQdrTC4EFwC8KWy3nikRiLkyiR8bnwrSFtz/OuU1kDGTM7Pg0RRNirotzRa8NGcmKbUHMhHJJ5QSZCq8zs8aoOXLOuVZ8LkxHePvjnNtEhyf7O+dCbZsjU6yT/W8kWAT3WWCupJ2A1QWtkXPFxufCtJe3P865TXjaQufiVOILYprZNWa2g5kdYYHXgUMLXS/nOqW6Orj8cl/cMibe/jjnWvMeGefiVMRBShRJ52TY5Zd5qYhzxSIqO5lrE29/nHPpeI+MczFKpGDO9ChCfcPHGODbwA7h43QgMlOFpEmSXpa0RNJmmRUk/UrSwvDxiqQPksqGSnpI0mJJiyRVxvianMsdz04Wp3a3P8650uY9Ms7FqTiDlIzM7McAkuYCo81sTfh8Op+ssL0ZSWXA9cBEYCkwT9J9ZrYo6dzfS9r/LGCfpFPMAn5qZg9L6gNkn07BuXxJtcClZyeLTXvbH+dc6fNAxrm4FG9vS1tsCzQkPW8It6UzDlhiZv8DkHQHcBSwKM3+xxOkeEfSHsAWZvYwgJmt7VjVncuBdEPIPDtZLrS1/XHOlbi8BjImaClPvx7JqDOih7n2Xhn9ZWy3xui7yObu0evM9Fqefo2bddtEv1Xla6JH6X1YH73Oyz0fjIosLyuPfu1NH0fXr9vq6PIeq6LrrwzL//RYGf3eb9g6/ee+xUfRx/bM8Llv6B/9uT51a/Q6MJMGnxFZ3ibZBzLFmn55FvC0pHsAEQQlMyP23wF4M+n5UmC/VDuGGYiGAY+Em4YDH0j6S7h9DnChmW32r1HSVGAqwNChQ9vwcpzroFRDyBJBi2cni1tb2x/nXInzHhnn4lTi6ZfN7KeSHgAOIni1p5jZf2I6/WTgrqRAZYvwOvsAbwCzgZOBm1LUawYwA2DMmDGl3y/mOg8fQpY3OW5/nHMZ1L1ZR219LdWV1VQN6Rxf0ngg41xMROkOLZPUC2g0s0Yze0bSR8ARBD0lUTcSbwFDkp7vGG5LZTKQ3D22FFiYNCztr8D+pAhknCsYH0KWcx1of5xz7dVq7l/dm3VMmDWBhuYGKsoqqJlS0ymCGQ9knItTiQYywD+BbwD/lfRpoA74E3CkpHFmtlk2stA8YBdJwwgCmMnACa13krQbsFV43uRj+0saZGbLgfHA/NbHOldwPoQs19rb/jjn2iPF3L/aploamhtotmYamhuora/tFIGMp192Li4GasnuUYS2MrP/hr+fBNxuZmcBnwM+n+4gM2sCzgQeBBYDd5rZi5IulfTFpF0nA3eYmSUd2wycB9RIep6g0+t3cb4o51xRaFf745xrpxRz/6orq6koq6BMZVSUVVBdWV3oWgLeI+NcvEq3Ryb5lY0HfgFgZg1SdGhmZvcD97fadkmr59PTHPswMLId9XUuXqlSLLt8aXf745xrhxRz/6qGVFEzpcbnyDhXykp1jgzwnKSrCIaHfRp4CEBSdDo+50pBuhTLLl861P5ImgT8GigDfm9mV7Qq3wm4GRgErAK+ZmZL46u+c0Umzdy/qiFVnSaASfChZc7FybJ8FJ/TgBVAJXCYmX0cbt8DuKpQlXIuL1KlWHb51O72J2lR3s+F+x8frlGV7CpglpmNBC4FLo+v6s4VqaoquOiiTv+lTX57ZCRatki/nsjC68+JPLx60s8jy1fvVB5Z/p8bos8/+lvp17Hp/n703WfDlulfF0C3huiYsWl198jyTHquz3D9pgzHL8uwBk+P6OMzvf6KD9Ofv887GSqX4ca/oV/0OjIHH3llZHnPNTGts1i8QUpGZrYOuCLF9n8D/048l3S3mR2bz7o5l3OeYrmgOtj+ZLMo7x5A4gbhUeCv8dXeOZdL3iPjXIxk2T1iv670JUm/kzRb0mHxXyFrnyrgtZ1rv7o6uPzy4GdriWEWl13mw8o6t1TtT6pFeXdotc+zwDHh70cDfSUNiL96zrm4+RwZ52IU57RTSTcDRwLLzGxE0vbNxnub2V+Bv0raimCYxEPx1aRNSrRPypW0bObAlHiK5c640F07tLf9OQ+4TtLJwFyCuTjNrXeSNBWYCjB06NB2Xso5FycPZJyLU7y38TOB64BZiQ1J470nEnyzOE/SfWaWGCbxo7DcOZetVHNgSjhoaa2zLnQXk4yL8prZ24Q9MpL6AMea2QetT2RmM4AZAGPGjPEvbZzrBHxomXNxyXaif/Dnb6Ck+UmPqZudzmwuQQadZBvHe5tZA3AHcJQCPwceMLNncvL6shM9Wcq5zigxB6asrEvOgamt33yhuyKVqv3ZuCivpAqCNavu2+QgaaCkxP3QRQQZzJxzRcB7ZJyLiWjTXfwKMxvTjsukGu+9H3AW8FlgS0mfNrMb2nHurEjqCQw1s5dTFF+Qq+s6lzNpUo12FYmF7hI9Mp1lobtU2tr+mFmTpMSivGXAzYlFeYH5ZnYfUA1cLskIhpadkbMX4JyLlQcyzsUp+8EGAyXNT3o+Ixy20L7Lml0DXNPe47Ml6QsEc3AqgGGSRgGXmtkXw3oUam6Ocx1T4nNgonTWhe5aa2/7k2lRXjO7C7grV/V2zuWOBzLOxagNk/3b2yOTcbx3jk0nGN5WC2BmCyUNy+P1nXM50BkXukthOt7+OOeS5DWQ2e1T2/LvO89LW55pnZh1A6Kru8W66K/Do9aJAVDEciZlm+Uv2VT5x9F3sBv6R09HKv8oelBSc0X09TPdQHdrjC5ft0309cvWRx+fqSdiq5fWpS2rmfvDyGP3+Xb057bNE+9Hlj/43GWR5aPOiD4/v4ku3kTue2Q2jvcmCGAmAye0oYYd1WhmH0qb/HvxSa+u86ur67JDx0qItz/OuU14j4xzcWnbGjEZe2Qk3U4wdnugpKXANDO7KdV47/ZXus1elHQCUCZpF+C7JC1I51ynlE16ZVcMvP1xzm3Cs5Y5F6fss5ZlPpXZ8WY22MzKzWxHM7sp3H6/mQ03s53N7Kc5eBVRzgL2BDYAtwOrgbPzXAdXyqIWpmyvVOmVXTHy9sc5twnvkXEuRm3okYl1sn++mNnHwA+BH4Zr2vQ2s0wDD53LTq56ThLplRPn7WLplUuFtz/Ouda8R8a5OGXfI7PCzMYkPTp9EAMg6TZJ/ST1Bp4HFkk6v9D1ciUiVz0nifTKl13mw8qKmLc/zrnWPJBxLi4WJF3I5kEWC2J2UnuY2WrgS8ADwDDg64WtkisZuVyYsqoKLrrIg5ji5u2Pc24TPrTMuTjFONm/kyqXVE5wI3GdmTWGi8g513FdfGFKl5G3P865TXgg41xMRJvmyBSrG4F64FlgrqSdCCbcOupUFpMAAB09SURBVBePti5M6WmVuxJvf5xzm+hQICOpHlgDNANNHf2GeYs10YudPPXPCyLL9zvx6sjysg3R158385y0ZZ/bOf36NwArDto+srzHygzrzGzZsXVcmrtHH5/JwBei3/uGvmWR5U/edm5k+We+elWb65Twn9+m/1zi0HNF9qtYZlT6k/2vAa5J2vS6pEMLVR/XxXla5S7F2x/nXGtx9MgcamYrYjiPc0VPlnUkU6xDy5D0eYIUqD2SNl9aoOq4rixVcgAPZEqatz/OuWQ+tMy5uNjGifwlS9INQC/gUOD3wJeBpwtaKdd1eVrlLsXbH+dcax3NWmbAQ5IWFFHWJedyJ8YFMTupz5jZFOB9M/sxUAUML3CdXFflaZW7Gm9/nHOb6GiPzIFm9pakbYCHJb1kZnOTdwgDnKkAQ4cO7eDlnOvcSn1BTGBd+PNjSdsDK4HBBayP64zyOQG/rckB3EZ1b9ZRW19LdWU1VUOK4j309sc5t4kOBTJm9lb4c5mke4BxwNxW+8wAZgCMGTOmuL+Ldi6T0k+//HdJ/YErgQXhtt8XsD6us/EJ+EWh7s06JsyaQENzAxVlFdRMqSmGYMbbH+fcJto9tExSb0l9E78DhwEvxFUx54qOBT0y2Tz+f3t3Hy1XXd97/P1JIEQCASRKkRASulhSrm1Bs0izBHq4PNYLpnqpN0FUKpV2KbQXHy5YLaYgN2gFa4WiKLmIKKmlUCMgqEjKQ482EZFHQUoDOcEmQkEbrISc871/zD5hOMzs2TOzZ/beM5/XWrMye//2w29P9vqd/d2/pwr7FPBuapPQjVJ7oLig0BxZuTTqgG+ls2b9GraOb2U8xtk6vpU169cUnaUsXP6Y2Ut0UyOzF3C9pMnjfDUibk7b4aH1m1j4hxc3TV9310dTT3j0YR9PTZ89kf6EOD4z/XLfcFrzvL1q55lN0wB23rwtNf353dKHL579RPr+v9ojff8df5l+7bP/9b9S05+bm359z/1aesx76DvSh76e9Uzz6zvihE+m7tuqA/0/3fR/UtN/9/c+kX78FkNLt6XaQUoWX6I25PrkEKgnA1cBb0vbSdLxwGeA6cAXI+LCKemfptaBF2qdeV8dEbvXpc8GHgT+MSLOyOE6rFfcAb8SRuaPMGP6jO01MiPzR4rOUhYdlT9m1kKF5+PqOJCJiMeA384xL2aVJkAtgukB8LqIOKhu+TZJD6btIGk6cClwDDAGrJW0OiK27xcRZ9VtfyZwyJTDnM+UZqtWUpMd8Cv6R3FYLN53Mbe+89aq9ZFpu/wxsxYq3hzYwy+b5WgIOvvfLel3IuJ7AJIWAeta7HMo8Gjy8gNJq4Al1GpYGlkGfGxyQdIbqNUA3wxUsV/R8HEH/EpYvO/iqgQwkzopf8wsTcXn43IgY5aX9oZWrlRnf0n3Ubu6HYF/lvREsrwf8OMWu+8DbKhbHgMWNTnPfsAC4LvJ8jTgIuAU4OguLsHMKqrL8sfM0lS8ObADGbMcDfCEmCf06TxLgWsjYjxZfi9wU0SMJf3xGvIw72YDrV/lj9nwqXhzYAcyZnka0C4yEfF4F7tvBPatW56brGtkKfC+uuXFwOGS3gvsAsyQtCUizpmSPw/zbjaguix/zKyVCjcHdiBjlqOKD63cK2uBAyQtoBbALKU22tBLSDoQ2IPasKoARMTb69JPBRZODWLMzMwsf1WYNNeBjFleYihGLWtbRGyTdAZwC7Xhl1dGxAOSzgPWRcTqZNOlwKqI8I84VYWHxjQzs+qpyqS5fQ1kpm0Ndh3b2jT9mGl/kLr/s6em/4A7/yx9Lpb/3Cf9cnf6RfPnp/FZM1L3bTWXyTG/c15q+g6bnk1N32nvPVLTf7H/zqnp32kxR88b/qj5HDoAM59Of7ac2LF5/wWA8ZnN56G5/Rvpv92it6fPUdPKP33z7K72lz6UfWM/gjcUETcBN01Zd+6U5eUtjnElcGXOWSu/fg6N6YDJzMxoPGnu0AcyZoNMuGmZ9UC/hsas+FwCZmaWn6pMmutAxiwvEbVPNlWdR8b6rV9DY1Z8LgEzM8tPVSbNdSBjlqM2amQqNY+MFahfQ2NWfC4BMzPLVxUmzXUgY5ajAZ5HxorUj6ExKz6XgFkzko4HPkNtsJEvRsSFU9LnAV8Cdk+2OSfp12dmJedAxiwvAXjUMquyCs8lYNaIpOnApcAxwBiwVtLqiHiwbrOPAl+LiMskHURtYJL5fc+smbWt+VBSZta+yPgxM+uz0Q2jrLhjBaMbRltvPDgOBR6NiMciYiuwClgyZZsAZiffdwOe7GP+zKwLrpExy5FHLTOzMqrKnBA9sA+woW55DFg0ZZvlwLcknQnMAo5udCBJpwOnA8ybNy/3jJpZ+/oayIzvJH6+f/P5WO7+9t+n7j9y/CdS01vNZfLDz70/NT3N4W/+q9T0I074ZGr6C/ulz/Py3KJdU9NnbUrvfLFtZvq1H/In6fPE7L6++fw+ALd9p3eTqbf67b5/Q4t5Zk5Jn2fm+1d/IDX9sLem/9+2xXM5mlkJVWVOiIIsA66MiIskLQa+LOl1EfGSP7zJyJKXAyxcuNCFvVXTgM0X5hoZsxy5RsbMyqgqc0L0wEZg37rlucm6eqcBxwNExKikmcAcYHNfcmjWLwM4X5j7yJjlRAGaiEyf3M8t7S/pCknX5n7wQTQ6CitW1P41q5hO+rpMzglx/pHnD1OzMoC1wAGSFkiaASwFVk/Z5gngKABJvwHMBH7W11ya9UOj+cIqzjUyZnnKcfhlSSuBE4DNEfG6uvUvG0o0Ih4DTnMgk8EAvpGy4dFNX5cqzAmRt4jYJukM4BZqZebKiHhA0nnAuohYDXwA+IKks6h1/D81wu2EbQAN4HxhDmTMcqR8//ZdCVwCXLX9+NmGErU0nsHeKsx9XdqXzAlz05R159Z9fxB4Y7/zZdZTjfrCDOB8YQ5kzPKS89DKEXG7pPlTVm8fShRA0uRQog5ksqrgG6nRDaOsWb+GkfkjA//QOkzX2okh7utiZlmltTwYsPnCHMiY5SbaGbVsjqR1dcuXJyPitNJwKFFJewIXAIdI+nBErMiakaFTsTdSwzRs7jBda6cm+7pkCfYcFJoNqSFqeeBAxixHbYxa9lRELMzrvBHxNPAneR1v4FXojdQwNSUapmvtRpa+Lg4KzYZYBVsedKq/gYxgYnrz5Nf/cfpcJ3fffHZXp281F8zEjOZzsdy1+kOp+44ce2Fqeospbth1LD199toWG+yY/l/5zKK9U9PT5vcBeP170v9vZj+ePg/Nmm81n4fm9hbzxBy+JP3/bfaNP0hNX0T6PDMvzEm5KdsRoPGe18hkGUrUWqjSm+phako0TNfaaw4KzYbEkPSFacY1MmZ5yt60rNMame1DiVILYJYCJ3dwnKFVtTfV7TQlKotOA8UqXmtZOSg0GwJD1BemGQcyZnnK3rSsZY2MpGuAkWTbMeBjEXFFo6FEu873EKnim+oqDZvbbaBYpWstMweFZgOmUc3LEPWFacaBjFmO2hh+uWWNTEQsa7L+ZUOJWnZ+U91bVQwUB5WDQrOKaRSsTK5vVPMyRH1hmnEgY5an3o9aZl3ym+rm8ug75ECxGFXq92VmDaQ1E2tW8zJEfWGacSBjlhNFtNPZP9dRy6w9flP9cnn1HXKg2H9V6/dlZg2kNRNLq3nJoS9MlV+EOJAxy1P2GhmzUsmzSZgDxf5ycz6zCmnWfKxVsNKjmpeqvwhxIGOWJzcts4pyk7Dq8v+dWUk0C1Lq09NGGUsLVno0ClnVX4T0NZCZvjWY/cS2puk/33/Hro6/eGn6fCG8In0yl627Tmua1mqemLR5UgCOOPGTqekvzErP26bj5qWm7zr2Qmr69776gdT0bh1xQvr1HXl0899vp01bUve9476Pd5SnScdM+4PU9F+deGhXx98ugInMW7tpmZWKm4RVl//vzEogLUiZ1GqUsQKGTK76i5CuAhlJxwOfoTYM7BcjIv1p32zAtTFqmVnpuElYdfn/zqyHWtW0QLahkEs4yljVX4R0HMhImg5cChwDjAFrJa2OiAfzypxZ5bhpmVlfZe2kWtXOrFXNt9nAyFLTAtmClJKOMlblFyHd1MgcCjwaEY8BSFoFLAEcyNhwioCJzG3LhqZpWauaW0mfBo5MFncGXh0Ru0s6GLgMmA2MAxdExN/1L+dWdlk7qbazXZmChqp3wjUbCFknncwapBTQfGyQdRPI7ANsqFseAxZN3UjS6cDpADu9YvcuTmdWAdn7yAyFLDW3EXFW3fZnAocki78E3hkRP5H0GuAHkm6JiGf7dwVWZlk7qWbZroxBQ9U74ZoNhHaagzlI6bvmvdtzEhGXR8TCiFi444xZvT6dWaEUkekzRLbX3EbEVmCy5raZZcA1ABHxSET8JPn+JLAZeFWP82sVMtlJdbqmp3ZSzbJdo6ChaFmvz8zaNDoKK1bU/m1lsqbl/PObNyuzwnRTI7MR2LdueW6yzmx4uY/MVJlqbgEk7QcsAL7bIO1QYAbwr0323V7zO29e+gh/NjiydlLNsl0ZR+6peidcs1LK2uelnmtaSqubQGYtcICkBdQCmKXAybnkyqyKApjIHMgMTR+ZNiwFro2I8fqVkvYGvgy8KyIaNt5LgsDLARYuXDhUVV7DLmsn1VbbtQoaiuo/U+VOuGY9lWUksUay9nmxSug4kImIbZLOAG6h1ol3ZUQ8kLbPlp9vfOqOG89+vG7VHOCprOfUZ9/fUV471F7e9OEeZqWhNvOXPs9NztrKWyvSBXkdatJL87f62lbb75ftsNFOjcywaKfmdinwvvoVkmYDNwIfiYjv9SSHZolmQUOr/jNlGyTASqLTB21rrZNalUklHALZOtfVPDIRcRNwUxvbv6R9u6R1ZX0rXea8QbnzV+a8QY/zl33UsmGRqeZW0oHAHsBo3boZwPXAVRHRMto065W0TvdlHCTASqCbB+1Bl0eA102tSkmHQLbOdBXImFmd9pqWDYVmNbeSzgPWRcTqZNOlwKqIl1RpvQ04AthT0qnJulMj4p4+Zd8MSO8/45HFrKE1axh91fOsmTfByBPPs9jNl2ryCvC6rVVxn5eB4UDGLDcBjbtwNDIsnf0b1txGxLlTlpc32O9q4OqeZs4sg7T+M2UcJMCKN3rwnhz1jgm2TocZ4xPcevCelP6xuR9N4fLqn+JaFUsUHciU+cGtzHmDcuevzHmDXuYvex8Zd/Y3q5Bm/Wc8spg1smaXp9m64zTGmWDrtGms2eXpcgcy/WoKl2f/FNeqGAUHMmV+A13mvEG581fmvEEP8+emZWZDySOL2VQj80eYscNO1amp69dIXq5JsZwVXSNjNljc2d/MEh7NbHhVrqaunyN5uSalUINWLjmQMcuNh182sxqPZmaVqqlzTUmqrA//ZQ8SBrFcmlbESSUdL+lhSY+qzxOcZCFpvaT7JN0zpUN2UflZKWmzpPvr1r1S0rcl/ST5d48S5W25pI3J73ePpDcVlLd9Jd0m6UFJD0j6s2R9b367oFYjk+VjZgOt0Whmk0Y3jLLijhWMbhhtfgDLTatnDkmfrvt79YikZ4vIZ9FG58KKw2r/9vQ8Od3/3RynnX0nH/7/4ra/4Kirjmq6T9bt8spXJ9LKparqe42MpOnApcAxwBiwVtLqiHiw33lp4ciIyG1Sxy5dCVwCXFW37hzg1oi4MCmYzwHOLkneAD4dEZ/qf3ZeYhvwgYi4W9KuwA8kfRs4lV79dq6RMTOaj2Y2iG9EyyzLM0dEnFW3/ZnAIX3PaMH6dV/mdZ5ujtPuvlmHWM+6XbNam15MvDt1n0EcZbGIpmWHAo9GxGMAklYBS4CyBTKlERG3S5o/ZfUSYCT5/iVgDQUEMk3yVgoR8VPgp8n3/5T0ELAPvfztsgcyQzP8stkwatZHotXkmmVullJR7T5zLAM+1qe8lUa/5kPK6zzdHKfdfbM+/GfZLi1Y6Wbi3UZlR7N9KtV3K4MiApl9gA11y2PAogLykSaAb0kK4PMlfcDcK3lQB/h3YK8iM9PAGZLeCayjVivyTJGZSYKtQ4Dv07PfLtoZtczDL5sNuEZ9JFxT03eZnzkk7QcsAL7bh3yVSr/e1Od1nm6O0+6+WR/+s2yXFqx0OvFus7Kj2T6V6ruVgTv7N3ZYRGyU9Grg25J+HBG3F52pZiIikqCrLC4DzqcWEJ4PXAS8u6jMSNoF+Afgf0fELyRtT8v1twuI8fFcDmVmg6mTmhrrm6XAtRHRsCCXdDpwOsC8efP6ma+e69eb+rzO081xOtk368N/q+3SgpVOJ95tVnYMYjOyRooIZDYC+9Ytz03WlUZEbEz+3SzpempV02ULZDZJ2jsifippb2Bz0RmaFBGbJr9L+gJwQ1F5kbQjtSDmKxFxXbK6d7+d+8iYWQvt1NRY19p55lgKvK/ZgZLWGZcDLFy4cOAK+369qc/rPN0cp6haiVZBVCcT7zYrOwaxGVkjRQQya4EDJC2gVpgsBU4uIB8NSZoFTEv6VMwCjgXOKzhbjawG3gVcmPz79WKz86LJICFZfAtwf9r2PcyHgCuAhyLi4rqk3vx2ER6RzMw6MiwPHQXI9Mwh6UBgD8BDyVlPdRpEdRLkDFozskb6HshExDZJZwC3ANOBlRHxQL/zkWIv4Pqk+dEOwFcj4uYiMyTpGmqd0+dIGqPWEfFC4GuSTgMeB95WoryNSDqYWtOy9cAfF5E34I3AO4D7JN2TrPtzevnbuUbGzDo0DA8d/dbsmUPSecC6iFidbLoUWBXhQtyqZ5jLjkL6yETETcBNRZy7lWRkk98uOh/1ImJZk6Sj+pqRBprk7Yq+Z6SBiLgTUJPknvx24RoZM7NSafTMERHnTlle3s88mVk+3NnfLC8RMO5AxszMzKwfHMiY5SkcyJiZmZn1gwMZs5wEENnnkclVMjDF3wJbgTUR8ZVCMmJmZmbWJ9OKzoDZwIio1chk+WQgaaWkzZLun7L+eEkPS3pU0jnJ6rdSm//gPcCb870wMzMzs/JxIGOWo5iITJ+MrgSOr18haTpwKfB7wEHAMkkHUZsbYXL2as/KaWZmZgNPHmnQLB+SbgbmZNx8JvCruuXLk8nWph5zPnBDRLwuWV4MLI+I45LlDyebjgHPRMQNklZFxNLOrqL6JP2M2rDaALsBP2+yaVraHOCpnLPWa2nXU9bzdHqsdvfLun2r7bpJr9o9lXYt+0XEq/qZmbJxOVOp87icKbdm15OtnIkIf/zxp6QfYD5wf93yScAX65bfAVwCzAL+H3AZ8Pai812WD7UAsZO0dUXnPc9rLet5Oj1Wu/tl3b7Vdt2kV+2e6tf9NAgflzPlPo/LmXJ/uv2/dmd/swEQEc8Bf1h0PkroGx2mVVG/rifP83R6rHb3y7p9q+26Ta+SQbqWXnM5U+7zuJwpt66ux03LzEosa9OyiFhRVB4HkaR1EbGw6HzY4PA9ZVP5nrC8DeM95c7+ZtWyFjhA0gJJM4ClwOqC8zSIXtZfyaxLvqdsKt8Tlrehu6dcI2NWUpKuAUaodd7bBHwsIq6Q9Cbgr4HpwMqIuKC4XJqZmZkVw4GMmZmZmZlVjpuWmZmZmZlZ5TiQMTMzMzOzynEgY2bWgqQRSXdI+pykkaLzY9UmaZqkCyR9VtK7is6PlYPLGcvTsJQzDmTMbChJWilps6T7p6w/XtLDkh6VdE6yOoAtwExgrN95tfJr835aAswFXsD300BzOWN5cjnzcu7sb2ZDSdIR1B4arqqbp2c68AhwDLWCfy2wDPhxRExI2gu4OCLeXlC2raTavJ/eDDwTEZ+XdG1EnFRQtq3HXM5YnlzOvJxrZMxsKEXE7cB/TFl9KPBoRDwWEVuBVcCSiJhI0p8BdupjNq0i2rmfqD1sPJNsM96/XFq/uZyxPLmcebkdis6AmVmJ7ANsqFseAxZJeitwHLA7cEkRGbNKang/AZ8BPivpcOD2IjJmhXI5Y3ka6nLGgYyZWQsRcR1wXdH5sMEQEb8ETis6H1YuLmcsT8NSzrhpmZnZizYC+9Ytz03WmXXC95M14vvC8jTU95MDGTOzF60FDpC0QNIMYCmwuuA8WXX5frJGfF9Ynob6fnIgY2ZDSdI1wCjwWkljkk6LiG3AGcAtwEPA1yLigSLzadXg+8ka8X1hefL99HIeftnMzMzMzCrHNTJmZmZmZlY5DmTMzMzMzKxyHMiYmZmZmVnlOJAxMzMzM7PKcSBjZmZmZmaV40DGzMzMzMwqx4GMmZmZmZlVjgMZM7MKkTRf0v19PN/Okr4i6T5J90u6U9IuDbZbLumDbR57mqS/SY57n6S1khYkaVvyuoY28/Snkh5KrvlUSZcUkQ+zIrmc6S2XM/nZoegMmJlZqf0ZsCkifhNA0muBF3I69v8CXgP8VkRMSJoLPJfTsTv1XuDoiBiTdGrBeTEbFi5nrCOukTEzq54dkjd5D0m6NnmbeW7ypvF+SZdLEmx/8/egpHslrUrWzZK0UtK/SPqhpCUp59ob2Di5EBEPR8TzyXE+IukRSXcCr53cpsk5f1fSPcnnh5J2TY7904iYSI49FhHP1B3nAkk/kvQ9SXsl606U9P3kGN+pW79c0pcljUr6iaT31B3nQ8lvc6+kv2x2oZI+B+wPfFPSWVPSrpR0Ut3yluTft0i6VTV7J7/Hr6X8nmZV4XLG5Uz5RYQ//vjjjz8V+QDzgQDemCyvBD4IvLJumy8DJybfnwR2Sr7vnvz7f4FTJtcBjwCzmpzvYGAzMAp8HDggWf8G4D5gZ2A28CjwwZRzfqMuz7tQaxEwF1gP3ANcBBxSd96ou4ZPAh9Nvu8BKPn+R8BFyfflwI+AVwBzgA3U3sIeC1wOiNrLuxuAI1J+3/XAnOT7qcAlyfcrgZPqtttS9/1q4Izk2MuKvkf88afbj8sZlzNV+bhGxsysejZExF3J96uBw4AjkzeI9wH/HfhvSfq9wFcknQJsS9YdC5wj6R5gDTATmNfoRBFxD7W3h38FvBJYK+k3gMOB6yPilxHxC2B13W6NznkXcLGkP6X20LEtIsaovWH9MDAB3CrpqGT7rdT+YAP8gNqDFdQeSm5JrvNDddcJ8PWI+K+IeAq4DTg0udZjgR8CdwMHAgc0utYunJlcw/MRcU3OxzYrissZlzOl5z4yZmbVEw2W/xZYGBEbJC2n9tAA8D+AI4ATgY9I+k1qbw3/Z0Q8nOlkEVuA64DrJE0AbwLGU3Z52Tkj4kJJNyb73iXpuIj4cdSaj3yTWjOLTcDvA7cCL0TyGjI51+Tfq88CF0fEakkj1N6Qpv0uAlZExOezXGuKbSTNsSVNA2bUpc2l9oC0l6RpkTRhMas4lzMuZ0rPNTJmZtUzT9Li5PvJwJ3J96dUG+nnJNj+h3DfiLgNOBvYjVpzi1uAM+vatx/S7ESS3ihpj+T7DOAg4HHgduD3Jb0iaYd+Yto5Jf16RNwXEZ8A1gIHSnq9pNfU7fdbybHT7MaLbenfNSVtiaSZkvYERpLz3AK8O/ldkLSPpFe3OEcj66k1cwF4M7BjcrwdqDW7WQY8BLy/g2OblZHLmRqXMyXmGhkzs+p5GHifpJXAg8Bl1Np03w/8O7U/rADTgasl7UbtjeHfRMSzks4H/hq4N/nD/m/ACU3O9evAZcnDyDTgRuAfIiIk/R219uKbs5xT0pHU3ig+QO3t6JHAFyTtlOz7L0CrYUiXA38v6Rngu8CCurR7qTX1mAOcHxFPAk8mTVRGk+epLcApSZ7b8QXg65J+BNzMi6Me/TlwR0TcmaStlXRjRDzU5vHNysbljMuZ0tOLNWpmZmbVlDRz2RIRnyo6L2Y2mFzOlI+blpmZmZmZWeW4RsbMzJB0HPCJKav/LSLeUkR+eilp235rg6SjIuLpfufHbFi4nAFczuTKgYyZmZmZmVWOm5aZmZmZmVnlOJAxMzMzM7PKcSBjZmZmZmaV40DGzMzMzMwqx4GMmZmZmZlVzv8HppyFNBPnxRYAAAAASUVORK5CYII=\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sz=11 # stamp size\n", "\n", - "nfoo=44\n", - "plt.figure(figsize=(14,4)),plt.subplots_adjust(wspace=.3)\n", - "# grab a postage stamp, +TODO in a stackly manner\n", - "sz=11\n", - "xc,yc=bf_dat[10,nfoo,0].astype('int')+1,bf_dat[10,nfoo,1].astype('int')+1\n", - "stamp=exposure.getImage().array[yc-sz:yc+sz,xc-sz:xc+sz]\n", - "plt.subplot(131)\n", - "plt.imshow(stamp,origin='lower',norm=LogNorm(1,stamp.max())),plt.colorbar()\n", + "# loop over some objects and save a summary figure\n", + "# [0,6,12,18,23,35,44,46,52,56,69,71,114] are good indices to look with default values \n", + "# or e.g. np.random.choice(np.arange(nspots),size=10)\n", + "indexfoo=[0,6,12,18,23,35,44,46,52,56,69,71,114]\n", "\n", - "plt.subplot(132)\n", - "plt.plot(bf_dat[:,nfoo,2],'r.',label='Uncorrected')\n", - "plt.plot(bf_dat[:,nfoo,4],'g.',label='Corrected')\n", - "plt.xlabel('Exposure number'),plt.ylabel('base_SdssShape_xx')\n", + "for nfoo in indexfoo:\n", + " plt.figure(figsize=(14,4)),plt.subplots_adjust(wspace=.3)\n", + " \n", + " # grab a postage stamp, integerizing the centroid and shipping\n", + " # if it is near the edge, +TODO in a stackly manner\n", + " xc,yc=bf_dat[10,nfoo,0].astype('int')+1,bf_dat[10,nfoo,1].astype('int')+1\n", + " if ((np.abs(xc-250)>250 - sz ) | (np.abs(yc-250)>250 - sz )): continue\n", + " stamp=exposure.getImage().array[yc-sz:yc+sz,xc-sz:xc+sz]\n", + " \n", + " # show the stamp with log scale (1,max)\n", + " plt.subplot(131),plt.title('stamp '+str(nfoo).zfill(3)+' (log scale)')\n", + " plt.imshow(stamp,origin='lower',norm=LogNorm(1,stamp.max())),plt.colorbar()\n", + " \n", + " # x size vs flux\n", + " plt.subplot(132),plt.title('x (row) size vs. flux')\n", + " plt.plot(bf_dat[:,nfoo,6],bf_dat[:,nfoo,2],'r.',label='Uncorrected')\n", + " plt.plot(bf_dat[:,nfoo,7],bf_dat[:,nfoo,4],'g.',label='Corrected')\n", + " plt.xlabel(flux_param),plt.ylabel(bf_param1),plt.xscale('log')\n", + " plt.legend(loc='upper left')\n", "\n", - "plt.subplot(133)\n", - "plt.plot(bf_dat[:,nfoo,3],'r.',label='Uncorrected')\n", - "plt.plot(bf_dat[:,nfoo,5],'g.',label='Corrected')\n", - "plt.xlabel('Exposure number'),plt.ylabel('base_SdssShape_yy')" + " # y size vs flux\n", + " plt.subplot(133),plt.title('y (column) size vs. flux')\n", + " plt.plot(bf_dat[:,nfoo,6],bf_dat[:,nfoo,3],'r.',label='Uncorrected')\n", + " plt.plot(bf_dat[:,nfoo,7],bf_dat[:,nfoo,5],'g.',label='Corrected')\n", + " plt.xlabel(flux_param),plt.ylabel(bf_param2)\n", + " plt.xscale('log')\n", + " plt.savefig(cat_dir+str(nfoo).zfill(5)+'bfcorr.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now plot the flux lost/gained in the process of brighter-fatter correction" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colorpalette=cycle(plt.cm.viridis(np.linspace(0,1,len(indexfoo))))\n", + "stylepalette=cycle(['s','*','o'])\n", + "plt.figure(figsize=(8,5))\n", + "for nfoo in indexfoo:\n", + " flux_foo=bf_dat[:,nfoo,6]\n", + " fluxdiffpct_foo=(bf_dat[:,nfoo,6]-bf_dat[:,nfoo,7])/bf_dat[:,nfoo,6]*100.\n", + " plt.plot(flux_foo,fluxdiffpct_foo,label=str(nfoo).zfill(3),c=next(colorpalette),marker=next(stylepalette))\n", + "plt.xscale('log')\n", + "plt.legend()\n", + "plt.xlabel(flux_param,fontsize=20)\n", + "plt.ylabel('Flux change of correction \\n (before - after) [%]',fontsize=20)\n", + "plt.savefig(cat_dir+'BF_corr_flux_change.png',dpi=150)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# +TODO add re-scaled stamp subtraction comparison\n", - "# +TODO other ways of doing matching, catalog stacking\n", - "# should this analysis focus on only one stamp? realism in wide-field correction, simplicity in stamp by stamp..." + "# +TODO other ways of doing matching, catalog stacking" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 3ac54f0d3534a5253e8bdf6912db4c13e85fbab0 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Wed, 22 Aug 2018 20:33:30 +0000 Subject: [PATCH 23/45] Forgot to clear all outputs --- .../BrighterFatterCorrection.ipynb | 88 +++++++++---------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/ImageProcessing/BrighterFatterCorrection.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb index 36c17513..42c62b90 100644 --- a/ImageProcessing/BrighterFatterCorrection.ipynb +++ b/ImageProcessing/BrighterFatterCorrection.ipynb @@ -262,13 +262,13 @@ { "data": { "text/plain": [ - "[lsst.meas.base.wrappers.PixelFlagsConfig(doMeasure=True, masksFpAnywhere=[], masksFpCenter=[]),\n", - " lsst.meas.base.wrappers.GaussianFluxConfig(doMeasure=True, background=0.0),\n", - " lsst.meas.base.wrappers.PsfFluxConfig(doMeasure=True, badMaskPlanes=[]),\n", - " lsst.meas.base.wrappers.SdssCentroidConfig(doMeasure=True, binmax=16, doFootprintCheck=True, maxDistToPeak=-1.0, peakMin=-1.0, wfac=1.5),\n", + "[lsst.meas.base.wrappers.SdssCentroidConfig(doMeasure=True, binmax=16, doFootprintCheck=True, maxDistToPeak=-1.0, peakMin=-1.0, wfac=1.5),\n", " lsst.meas.base.wrappers.ApertureFluxConfig(doMeasure=True, maxSincRadius=10.0, radii=[3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0], shiftKernel='lanczos5'),\n", + " lsst.meas.base.wrappers.SdssShapeConfig(doMeasure=True, background=0.0, doMeasurePsf=True, maxIter=100, maxShift=0.0, tol1=9.999999747378752e-06, tol2=9.999999747378752e-05),\n", + " lsst.meas.base.wrappers.PixelFlagsConfig(doMeasure=True, masksFpAnywhere=[], masksFpCenter=[]),\n", + " lsst.meas.base.wrappers.GaussianFluxConfig(doMeasure=True, background=0.0),\n", " lsst.meas.base.wrappers.HsmSourceMomentsConfig(doMeasure=True),\n", - " lsst.meas.base.wrappers.SdssShapeConfig(doMeasure=True, background=0.0, doMeasurePsf=True, maxIter=100, maxShift=0.0, tol1=9.999999747378752e-06, tol2=9.999999747378752e-05)]" + " lsst.meas.base.wrappers.PsfFluxConfig(doMeasure=True, badMaskPlanes=[])]" ] }, "execution_count": 6, @@ -292,14 +292,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Characterization took 1.30 seconds\n", + "Characterization took 1.27 seconds\n", "Detected 126 objects \n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 7, @@ -329,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -588,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -650,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -667,10 +667,10 @@ " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -698,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -744,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -796,14 +796,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Brighter-fatter correction took 3.433595657348633 seconds\n", + "Brighter-fatter correction took 3.471991777420044 seconds\n", "-0.72 percent change in flux\n" ] }, @@ -813,7 +813,7 @@ "Text(0.5,0,'Pixel values [e-]')" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -893,33 +893,33 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "ITL-3800C-002_spot_spot_400_20171108114719part.fits char. & calib. took 2.46 seconds to measure 73 objects \n", - "ITL-3800C-002_spot_spot_401_20171108114728part.fits char. & calib. took 2.94 seconds to measure 85 objects \n", - "ITL-3800C-002_spot_spot_402_20171108114738part.fits char. & calib. took 3.43 seconds to measure 97 objects \n", - "ITL-3800C-002_spot_spot_403_20171108114749part.fits char. & calib. took 3.56 seconds to measure 102 objects \n", - "ITL-3800C-002_spot_spot_404_20171108114800part.fits char. & calib. took 3.89 seconds to measure 106 objects \n", - "ITL-3800C-002_spot_spot_405_20171108114811part.fits char. & calib. took 3.97 seconds to measure 112 objects \n", - "ITL-3800C-002_spot_spot_406_20171108114823part.fits char. & calib. took 4.37 seconds to measure 115 objects \n", - "ITL-3800C-002_spot_spot_407_20171108114835part.fits char. & calib. took 4.52 seconds to measure 118 objects \n", - "ITL-3800C-002_spot_spot_408_20171108114848part.fits char. & calib. took 4.90 seconds to measure 118 objects \n", + "ITL-3800C-002_spot_spot_400_20171108114719part.fits char. & calib. took 2.47 seconds to measure 73 objects \n", + "ITL-3800C-002_spot_spot_401_20171108114728part.fits char. & calib. took 2.93 seconds to measure 85 objects \n", + "ITL-3800C-002_spot_spot_402_20171108114738part.fits char. & calib. took 3.48 seconds to measure 97 objects \n", + "ITL-3800C-002_spot_spot_403_20171108114749part.fits char. & calib. took 3.54 seconds to measure 102 objects \n", + "ITL-3800C-002_spot_spot_404_20171108114800part.fits char. & calib. took 3.91 seconds to measure 106 objects \n", + "ITL-3800C-002_spot_spot_405_20171108114811part.fits char. & calib. took 3.99 seconds to measure 112 objects \n", + "ITL-3800C-002_spot_spot_406_20171108114823part.fits char. & calib. took 4.39 seconds to measure 115 objects \n", + "ITL-3800C-002_spot_spot_407_20171108114835part.fits char. & calib. took 4.43 seconds to measure 118 objects \n", + "ITL-3800C-002_spot_spot_408_20171108114848part.fits char. & calib. took 4.79 seconds to measure 118 objects \n", "ITL-3800C-002_spot_spot_409_20171108114901part.fits char. & calib. took 4.93 seconds to measure 124 objects \n", - "ITL-3800C-002_spot_spot_410_20171108114915part.fits char. & calib. took 5.36 seconds to measure 123 objects \n", - "ITL-3800C-002_spot_spot_411_20171108114930part.fits char. & calib. took 5.35 seconds to measure 124 objects \n", - "ITL-3800C-002_spot_spot_412_20171108114945part.fits char. & calib. took 5.45 seconds to measure 127 objects \n", - "ITL-3800C-002_spot_spot_413_20171108115000part.fits char. & calib. took 5.45 seconds to measure 130 objects \n", - "ITL-3800C-002_spot_spot_414_20171108115016part.fits char. & calib. took 5.35 seconds to measure 131 objects \n", - "ITL-3800C-002_spot_spot_415_20171108115032part.fits char. & calib. took 5.45 seconds to measure 134 objects \n", - "ITL-3800C-002_spot_spot_416_20171108115049part.fits char. & calib. took 5.49 seconds to measure 132 objects \n", - "ITL-3800C-002_spot_spot_417_20171108115106part.fits char. & calib. took 5.55 seconds to measure 135 objects \n", - "ITL-3800C-002_spot_spot_418_20171108115124part.fits char. & calib. took 5.80 seconds to measure 141 objects \n", - "ITL-3800C-002_spot_spot_419_20171108115142part.fits char. & calib. took 5.95 seconds to measure 137 objects \n" + "ITL-3800C-002_spot_spot_410_20171108114915part.fits char. & calib. took 5.19 seconds to measure 123 objects \n", + "ITL-3800C-002_spot_spot_411_20171108114930part.fits char. & calib. took 5.26 seconds to measure 124 objects \n", + "ITL-3800C-002_spot_spot_412_20171108114945part.fits char. & calib. took 5.28 seconds to measure 127 objects \n", + "ITL-3800C-002_spot_spot_413_20171108115000part.fits char. & calib. took 5.28 seconds to measure 130 objects \n", + "ITL-3800C-002_spot_spot_414_20171108115016part.fits char. & calib. took 5.30 seconds to measure 131 objects \n", + "ITL-3800C-002_spot_spot_415_20171108115032part.fits char. & calib. took 5.39 seconds to measure 134 objects \n", + "ITL-3800C-002_spot_spot_416_20171108115049part.fits char. & calib. took 5.37 seconds to measure 132 objects \n", + "ITL-3800C-002_spot_spot_417_20171108115106part.fits char. & calib. took 5.42 seconds to measure 135 objects \n", + "ITL-3800C-002_spot_spot_418_20171108115124part.fits char. & calib. took 5.83 seconds to measure 141 objects \n", + "ITL-3800C-002_spot_spot_419_20171108115142part.fits char. & calib. took 5.80 seconds to measure 137 objects \n" ] } ], @@ -965,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -981,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -990,7 +990,7 @@ "Text(0.5,1,'Centroids of sequential exposures')" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -1022,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1073,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1253,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1284,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ From 53cf8c4e920596d8cbfa0f2e4f6a4441eba1cb05 Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Wed, 22 Aug 2018 20:35:04 +0000 Subject: [PATCH 24/45] Forgot to clear all outputs AND save... --- .../BrighterFatterCorrection.ipynb | 653 ++---------------- 1 file changed, 44 insertions(+), 609 deletions(-) diff --git a/ImageProcessing/BrighterFatterCorrection.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb index 42c62b90..8fe3bff9 100644 --- a/ImageProcessing/BrighterFatterCorrection.ipynb +++ b/ImageProcessing/BrighterFatterCorrection.ipynb @@ -26,18 +26,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jld-lab-sarujin-r160\n", - "lsst_distrib 16.0+1 \tcurrent v16_0 setup\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -75,17 +66,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read in ITL-3800C-002_spot_spot_419_20171108115142part.fits\n" - ] - } - ], + "outputs": [], "source": [ "import lsst.afw.image as afwImage\n", "from lsst.ip.isr.isrFunctions import updateVariance\n", @@ -124,30 +107,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Text(0.5,0,'Number of electrons in pixel'), Text(0,0.5,'Number of pixels'))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", "plt.suptitle('Star/galaxy beam sim segment of '+hdr['CCD_SERN']+'\\n '+fitsfilename.split('/')[-1])\n", @@ -164,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,49 +144,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mcharTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcharacterize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexposureIdInfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackground\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "!Characterize a science image\n", - "\n", - "Peforms the following operations:\n", - "- Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:\n", - " - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)\n", - "- interpolate over cosmic rays\n", - "- perform final measurement\n", - "\n", - "@param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).\n", - " The following changes are made:\n", - " - update or set psf\n", - " - set apCorrMap\n", - " - update detection and cosmic ray mask planes\n", - " - subtract background and interpolate over cosmic rays\n", - "@param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).\n", - " If not provided, returned SourceCatalog IDs will not be globally unique.\n", - "@param[in,out] background initial model of background already subtracted from exposure\n", - " (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,\n", - " which is typical for image characterization.\n", - "\n", - "@return pipe_base Struct containing these fields, all from the final iteration\n", - "of detectMeasureAndEstimatePsf:\n", - "- exposure: characterized exposure; image is repaired by interpolating over cosmic rays,\n", - " mask is updated accordingly, and the PSF model is set\n", - "- sourceCat: detected sources (an lsst.afw.table.SourceCatalog)\n", - "- background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)\n", - "- psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)\n", - "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/characterizeImage.py\n", - "\u001b[0;31mType:\u001b[0m method\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from lsst.pipe.tasks.characterizeImage import CharacterizeImageTask, CharacterizeImageConfig\n", "import lsst.meas.extensions.shapeHSM\n", @@ -256,26 +178,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[lsst.meas.base.wrappers.SdssCentroidConfig(doMeasure=True, binmax=16, doFootprintCheck=True, maxDistToPeak=-1.0, peakMin=-1.0, wfac=1.5),\n", - " lsst.meas.base.wrappers.ApertureFluxConfig(doMeasure=True, maxSincRadius=10.0, radii=[3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0], shiftKernel='lanczos5'),\n", - " lsst.meas.base.wrappers.SdssShapeConfig(doMeasure=True, background=0.0, doMeasurePsf=True, maxIter=100, maxShift=0.0, tol1=9.999999747378752e-06, tol2=9.999999747378752e-05),\n", - " lsst.meas.base.wrappers.PixelFlagsConfig(doMeasure=True, masksFpAnywhere=[], masksFpCenter=[]),\n", - " lsst.meas.base.wrappers.GaussianFluxConfig(doMeasure=True, background=0.0),\n", - " lsst.meas.base.wrappers.HsmSourceMomentsConfig(doMeasure=True),\n", - " lsst.meas.base.wrappers.PsfFluxConfig(doMeasure=True, badMaskPlanes=[])]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Display which plugins are being used for measurement\n", "charConfig.measurement.plugins.active " @@ -283,40 +188,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Characterization took 1.27 seconds\n", - "Detected 126 objects \n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tstart=time.time()\n", "charResult = charTask.characterize(exposure) # charTask.run(exposure) stack v16.0+22\n", @@ -329,47 +205,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "base_SdssShape_xx\n", - "base_SdssShape_yy\n", - "base_SdssShape_xy\n", - "base_SdssShape_xxSigma\n", - "base_SdssShape_yySigma\n", - "base_SdssShape_xySigma\n", - "base_SdssShape_x\n", - "base_SdssShape_y\n", - "base_SdssShape_flux\n", - "base_SdssShape_fluxSigma\n", - "base_SdssShape_psf_xx\n", - "base_SdssShape_psf_yy\n", - "base_SdssShape_psf_xy\n", - "base_SdssShape_flux_xx_Cov\n", - "base_SdssShape_flux_yy_Cov\n", - "base_SdssShape_flux_xy_Cov\n", - "base_SdssShape_flag\n", - "base_SdssShape_flag_unweightedBad\n", - "base_SdssShape_flag_unweighted\n", - "base_SdssShape_flag_shift\n", - "base_SdssShape_flag_maxIter\n", - "base_SdssShape_flag_psf\n", - "ext_shapeHSM_HsmSourceMoments_x\n", - "ext_shapeHSM_HsmSourceMoments_y\n", - "ext_shapeHSM_HsmSourceMoments_xx\n", - "ext_shapeHSM_HsmSourceMoments_yy\n", - "ext_shapeHSM_HsmSourceMoments_xy\n", - "ext_shapeHSM_HsmSourceMoments_flag\n", - "ext_shapeHSM_HsmSourceMoments_flag_no_pixels\n", - "ext_shapeHSM_HsmSourceMoments_flag_not_contained\n", - "ext_shapeHSM_HsmSourceMoments_flag_parent_source\n" - ] - } - ], + "outputs": [], "source": [ "# display some of the source catalog measurements filtered by searchword\n", "searchword='shape'\n", @@ -380,28 +218,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique mask plane values: [ 0 32]\n", - "Mask dictionary entries: {'BAD': 0, 'CR': 3, 'DETECTED': 5, 'DETECTED_NEGATIVE': 6, 'EDGE': 4, 'INTRP': 2, 'NO_DATA': 8, 'SAT': 1, 'SUSPECT': 7}\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Looking at the mask plane, which started off as all zeros\n", "# and now has some values of 2^5\n", @@ -467,50 +286,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mcalTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalibrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexposureIdInfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackground\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0micSourceCat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "!Calibrate an exposure (science image or coadd)\n", - "\n", - "@param[in,out] exposure exposure to calibrate (an\n", - " lsst.afw.image.ExposureF or similar);\n", - " in:\n", - " - MaskedImage\n", - " - Psf\n", - " out:\n", - " - MaskedImage has background subtracted\n", - " - Wcs is replaced\n", - " - Calib zero-point is set\n", - "@param[in] exposureIdInfo ID info for exposure (an\n", - " lsst.obs.base.ExposureIdInfo) If not provided, returned\n", - " SourceCatalog IDs will not be globally unique.\n", - "@param[in,out] background background model already subtracted from\n", - " exposure (an lsst.afw.math.BackgroundList). May be None if no\n", - " background has been subtracted, though that is unusual for\n", - " calibration. A refined background model is output.\n", - "@param[in] icSourceCat A SourceCatalog from CharacterizeImageTask\n", - " from which we can copy some fields.\n", - "\n", - "@return pipe_base Struct containing these fields:\n", - "- exposure calibrate science exposure with refined WCS and Calib\n", - "- background model of background subtracted from exposure (an\n", - " lsst.afw.math.BackgroundList)\n", - "- sourceCat catalog of measured sources\n", - "- astromMatches list of source/refObj matches from the astrometry\n", - " solver\n", - "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/calibrate.py\n", - "\u001b[0;31mType:\u001b[0m method\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from lsst.pipe.tasks.calibrate import CalibrateTask, CalibrateConfig\n", "\n", @@ -535,20 +313,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calibration took 1.00 seconds\n", - "Detected 137 objects \n" - ] - } - ], + "outputs": [], "source": [ "tstart=time.time()\n", "# for stack v16.0+22, change to calTask.run(charResult.exposure)\n", @@ -568,50 +337,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/sarujin/DATA/beamsim/'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cat_dir" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/python/miniconda3-4.3.21/lib/python3.6/site-packages/numpy/lib/function_base.py:780: RuntimeWarning: invalid value encountered in greater_equal\n", - " keep = (tmp_a >= first_edge)\n", - "/opt/lsst/software/stack/python/miniconda3-4.3.21/lib/python3.6/site-packages/numpy/lib/function_base.py:781: RuntimeWarning: invalid value encountered in less_equal\n", - " keep &= (tmp_a <= last_edge)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "src=calResult.sourceCat #.copy(deep=True) ?\n", "#print(src.asAstropy)\n", @@ -650,31 +387,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import lsst.afw.display as afwDisplay\n", "\n", @@ -698,22 +413,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Open your web browser to this link" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# set the backend and attach to the waiting display channel\n", "afwDisplay.setDefaultBackend('firefly')\n", @@ -744,47 +446,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0misr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbrighterFatterCorrection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexposure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxIter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapplyGain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Apply brighter fatter correction in place for the image\n", - "\n", - "This correction takes a kernel that has been derived from flat field images to\n", - "redistribute the charge. The gradient of the kernel is the deflection\n", - "field due to the accumulated charge.\n", - "\n", - "Given the original image I(x) and the kernel K(x) we can compute the corrected image Ic(x)\n", - "using the following equation:\n", - "\n", - "Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))\n", - "\n", - "To evaluate the derivative term we expand it as follows:\n", - "\n", - "0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y))) + I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) )\n", - "\n", - "Because we use the measured counts instead of the incident counts we apply the correction\n", - "iteratively to reconstruct the original counts and the correction. We stop iterating when the\n", - "summed difference between the current corrected image and the one from the previous iteration\n", - "is below the threshold. We do not require convergence because the number of iterations is\n", - "too large a computational cost. How we define the threshold still needs to be evaluated, the\n", - "current default was shown to work reasonably well on a small set of images. For more information\n", - "on the method see DocuShare Document-19407.\n", - "\n", - "The edges as defined by the kernel are not corrected because they have spurious values\n", - "due to the convolution.\n", - "\u001b[0;31mFile:\u001b[0m /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/ip_isr/16.0+1/python/lsst/ip/isr/isrTask.py\n", - "\u001b[0;31mType:\u001b[0m method\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from lsst.ip.isr.isrTask import IsrTask # brighterFatterCorrection lives here\n", "isr=IsrTask()\n", @@ -796,48 +460,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brighter-fatter correction took 3.471991777420044 seconds\n", - "-0.72 percent change in flux\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5,0,'Pixel values [e-]')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Read in the kernel (determined from e.g. simulations or flat fields)\n", "kernel=fits.getdata('/project/shared/data/beamsim/bfcorr/BF_kernel-ITL_3800C_002.fits')\n", @@ -893,36 +518,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ITL-3800C-002_spot_spot_400_20171108114719part.fits char. & calib. took 2.47 seconds to measure 73 objects \n", - "ITL-3800C-002_spot_spot_401_20171108114728part.fits char. & calib. took 2.93 seconds to measure 85 objects \n", - "ITL-3800C-002_spot_spot_402_20171108114738part.fits char. & calib. took 3.48 seconds to measure 97 objects \n", - "ITL-3800C-002_spot_spot_403_20171108114749part.fits char. & calib. took 3.54 seconds to measure 102 objects \n", - "ITL-3800C-002_spot_spot_404_20171108114800part.fits char. & calib. took 3.91 seconds to measure 106 objects \n", - "ITL-3800C-002_spot_spot_405_20171108114811part.fits char. & calib. took 3.99 seconds to measure 112 objects \n", - "ITL-3800C-002_spot_spot_406_20171108114823part.fits char. & calib. took 4.39 seconds to measure 115 objects \n", - "ITL-3800C-002_spot_spot_407_20171108114835part.fits char. & calib. took 4.43 seconds to measure 118 objects \n", - "ITL-3800C-002_spot_spot_408_20171108114848part.fits char. & calib. took 4.79 seconds to measure 118 objects \n", - "ITL-3800C-002_spot_spot_409_20171108114901part.fits char. & calib. took 4.93 seconds to measure 124 objects \n", - "ITL-3800C-002_spot_spot_410_20171108114915part.fits char. & calib. took 5.19 seconds to measure 123 objects \n", - "ITL-3800C-002_spot_spot_411_20171108114930part.fits char. & calib. took 5.26 seconds to measure 124 objects \n", - "ITL-3800C-002_spot_spot_412_20171108114945part.fits char. & calib. took 5.28 seconds to measure 127 objects \n", - "ITL-3800C-002_spot_spot_413_20171108115000part.fits char. & calib. took 5.28 seconds to measure 130 objects \n", - "ITL-3800C-002_spot_spot_414_20171108115016part.fits char. & calib. took 5.30 seconds to measure 131 objects \n", - "ITL-3800C-002_spot_spot_415_20171108115032part.fits char. & calib. took 5.39 seconds to measure 134 objects \n", - "ITL-3800C-002_spot_spot_416_20171108115049part.fits char. & calib. took 5.37 seconds to measure 132 objects \n", - "ITL-3800C-002_spot_spot_417_20171108115106part.fits char. & calib. took 5.42 seconds to measure 135 objects \n", - "ITL-3800C-002_spot_spot_418_20171108115124part.fits char. & calib. took 5.83 seconds to measure 141 objects \n", - "ITL-3800C-002_spot_spot_419_20171108115142part.fits char. & calib. took 5.80 seconds to measure 137 objects \n" - ] - } - ], + "outputs": [], "source": [ "do_bf_corr=True # True or False, run this cell with both\n", "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits' # fits files to read in\n", @@ -965,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -981,30 +579,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5,1,'Centroids of sequential exposures')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Show issues with multiply detected sources which which we remedy with matching rejection\n", "for i in range(ncats):\n", @@ -1022,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1073,140 +650,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "colorpalette=cycle(plt.cm.viridis(np.linspace(0,1,len(indexfoo))))\n", "stylepalette=cycle(['s','*','o'])\n", @@ -1284,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From 5536f9264d718c22153f18a6488af519e5458550 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Thu, 23 Aug 2018 04:43:50 +0000 Subject: [PATCH 25/45] Verified on v16, added README --- Visualization/README.md | 7 -- Visualization/README.rst | 31 ++++++++ .../bokeh_holoviews_datashader.ipynb | 79 ++++++++++++++++--- 3 files changed, 99 insertions(+), 18 deletions(-) delete mode 100644 Visualization/README.md create mode 100644 Visualization/README.rst diff --git a/Visualization/README.md b/Visualization/README.md deleted file mode 100644 index 75b787f6..00000000 --- a/Visualization/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Visualization - -This folder contains: - -* Nothing, yet. - -However, you can find the SQuaRE team's Firefly tutorial notebook [here](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb), and watch the Phase 1 Session 1 video (in which Alex walks us through it) [here](https://www.youtube.com/watch?v=UjB0aaNd0MA). diff --git a/Visualization/README.rst b/Visualization/README.rst new file mode 100644 index 00000000..98cfc14d --- /dev/null +++ b/Visualization/README.rst @@ -0,0 +1,31 @@ +Visualization +============= + +This folder contains a set of tutorial notebooks exploring various data visualizations. See the index table below for links to the notebook code, and auto-rendered views of the notebooks with outputs. + + +.. list-table:: + :widths: 10 20 10 10 + :header-rows: 1 + + * - Notebook + - Short description + - Links + - Owner + + + * - **Firefly Visualization Demo** + - Introduction to the Firefly interactive plotter and image viewer. + - `ipynb `_, `video `_ + - `Simon Krughoff `_ + + + * - **Interactive Visualization with Bokeh, HoloViews, and Datashader** + - Simple demonstrations of interactive, linked plotting. + - `ipynb `_, + `rendered `_ + + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Visualization/log/bokeh_holoviews_datashader.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/Visualization/log/bokeh_holoviews_datashader.log + + - `Keith Bechtol `_ \ No newline at end of file diff --git a/Visualization/bokeh_holoviews_datashader.ipynb b/Visualization/bokeh_holoviews_datashader.ipynb index ddba5cd1..da7a0781 100644 --- a/Visualization/bokeh_holoviews_datashader.ipynb +++ b/Visualization/bokeh_holoviews_datashader.ipynb @@ -8,7 +8,7 @@ "\n", "
Owner: **Keith Bechtol** ([@bechtol](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@bechtol))\n", "
Last Verified to Run: **2018-08-10**\n", - "
Verified Stack Release: **w201831**\n", + "
Verified Stack Release: **v16.0, w201831**\n", "\n", "This notebook demonstrates a few of the interactive features of the Bokeh, HoloViews, and Datashader plotting packages in the notebook environment. These packages are part of the [PyViz](http://pyviz.org/) set of python tools intended for visualization use cases in a web browser, and can be used to create quite sophisticated dashboard-like interactive displays and widgets. The goal of this notebook is to provide an introduction and starting point from which to create more advanced, custom interactive visualizations. As a source of inspiration, check out this beautiful [example notebook](https://github.com/timothydmorton/qa_explorer) using HSC data created with the [qa_explorer](https://github.com/timothydmorton/qa_explorer) tools.\n", "\n", @@ -151,8 +151,10 @@ "metadata": {}, "outputs": [], "source": [ + "width = 300\n", + "\n", "# create a new plot and add a renderer\n", - "left = figure(tools=TOOLS_LEFT, plot_width=500, plot_height=500, output_backend=\"webgl\",\n", + "left = figure(tools=TOOLS_LEFT, plot_width=width, plot_height=width, output_backend=\"webgl\",\n", " title='Spatial: Centered on (RA, Dec) = (%.2f, %.2f)'%(ra_target, dec_target))\n", "left.circle('x0', 'y0', hover_color='firebrick', source=source,\n", " selection_fill_color='steelblue', selection_line_color='steelblue',\n", @@ -163,7 +165,7 @@ "left.yaxis.axis_label = 'Delta DEC'\n", "\n", "# create another new plot and add a renderer\n", - "right = figure(tools=TOOLS_RIGHT, plot_width=500, plot_height=500, output_backend=\"webgl\",\n", + "right = figure(tools=TOOLS_RIGHT, plot_width=width, plot_height=width, output_backend=\"webgl\",\n", " title='CMD')\n", "right.circle('x1', 'y1', hover_color='firebrick', source=source,\n", " selection_fill_color='steelblue', selection_line_color='steelblue',\n", @@ -192,7 +194,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Use the hover tool to see information about individual datapoints (e.g., the `coadd_object_id`). Notice the data points highlighted in one panel with the hover tool are also highlighted in the other panel. Next, use the selection box and selection lasso to make various selections in either panel. The selected data points will be displayed in the other panel.\n", + "Use the hover tool to see information about individual datapoints (e.g., the `coadd_object_id`). Notice the data points highlighted in one panel with the hover tool are also highlighted in the other panel. Next, use the selection box and selection lasso to make various selections in either panel. The selected data points will be displayed in the other panel. \n", + "\n", + "> Note that the default tool is \"Box Zoom\", not \"Box Select\"! And see the \"Reset\" button - that's useful.\n", "\n", "**Open Issue:** Bonus for someone can suggest how to access the indices of the selected points!" ] @@ -222,7 +226,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Experimental Section: Creating Same Plot with HoloViews" + "### Experimental Section: Creating the same plot with HoloViews" ] }, { @@ -236,7 +240,7 @@ "points = hv.Points((data['RA'] - ra_target, data['DEC'] - dec_target))\n", "#points = hv.Points(np.random.multivariate_normal((0, 0), [[1, 0.1], [0.1, 1]], (1000,)))\n", "\n", - "# Declare points selection selection\n", + "# Declare points selection:\n", "sel = streams.Selection1D(source=points)\n", "\n", "#boundsxy = (0, 0, 0, 0)\n", @@ -262,7 +266,7 @@ "metadata": {}, "outputs": [], "source": [ - "%help(hv.Points)" + "# help(hv.Points)" ] }, { @@ -272,7 +276,7 @@ "outputs": [], "source": [ "# Here I had a re-sized hv Points with custom colors for the selection\n", - "hv.Points()" + "# hv.Points()" ] }, { @@ -281,9 +285,28 @@ "metadata": {}, "outputs": [], "source": [ + "# help(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print(sel.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", "df = pd.DataFrame.from_records(data)\n", - "df['x'] = d.RA - ra_target\n", - "df['y'] = d.DEC - dec_target\n", + "df['x'] = data.RA - ra_target\n", + "df['y'] = data.DEC - dec_target\n", "\n", "ds = hv.Dataset(df)#, kdims)" ] @@ -342,13 +365,47 @@ "np.sum(selection)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selected_points = points[selection.values]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(selected_points)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selected_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> For more help on selecting points in HoloViews, see the [user guide](http://build.holoviews.org/User_Guide/Indexing_and_Selecting_Data.html)." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intermission: Rapid Data Access with Parquet\n", "\n", - "For the next example, we want to use a much larger dataset. Let's use Gata Data Release 2 (Gaia DR2). " + "For the next example, we want to use a much larger dataset. Let's use Gaia Data Release 2 (Gaia DR2). " ] }, { From 8f7a463cd337bfd36b931e11ad064cea3949f705 Mon Sep 17 00:00:00 2001 From: Brant Robertson Date: Fri, 24 Aug 2018 17:57:15 +0000 Subject: [PATCH 26/45] First complete draft of lsst.afw.display demo --- Visualization/AFW_Display_Demo.ipynb | 251 +++++++++++++++------------ 1 file changed, 140 insertions(+), 111 deletions(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index 2c1b10f0..18cb9f8b 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -8,15 +8,24 @@ } }, "source": [ - "# Demo of lsst.afw.display\n", + "# **Demo of lsst.afw.display -- displaying images using the LSST DM Astronomical Framework library**\n", "\n", - "[Brant Robertson](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brant)\n", + "**Owner:** Brant Robertson ([@brant](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brant)) \n", + "**Level:** Introductory \n", + "**Last Verified to Run:** 2018-08-24 \n", + "**Verified Stack Release:** v16.0 \n", "\n", - "In this tutorial we will: \n", + "## **Learning Objectives:**\n", + "\n", + "In this tutorial we will \n", "\n", "* Show how to access the `lsst.afw.display` routines.\n", "\n", - "* Use the LSST data Butler to access processed data and inspect it visually." + "* Use the LSST data Butler to access processed data and inspect it visually.\n", + "\n", + "This tutorial is designed to help users get a brief feel for the `lsst.afw.display` library that enables the visual inspection of data. The [`lsst.afw` library](https://github.com/lsst/afw) provides an \"Astronomical Framework\" (afw) while the `lsst.daf.*` libraries (see, e.g., [daf_base](https://github.com/lsst/daf_base)) provides a Data Access Framework (daf). Both libraries are used in this tutorial, with the `lsst.daf.persistence` library used to access a calibrated exposure (calexp) and the `lsst.afw.display` library used to show the exposure image on the screen.\n", + "\n", + "This tutorial made use of the [`LowSurfaceBrightness.ipynb` StackClub notebook](https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/SourceDetection/LowSurfaceBrightness.nbconvert.ipynb) by [Alex Drlica-Wagner](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@kadrlica)." ] }, { @@ -27,28 +36,31 @@ } }, "source": [ - "## Import Common Python Libraries" + "## **Step 0) Import Common Python Libraries**\n", + "\n", + "The [`matplotlib`](https://matplotlib.org/), [`numpy`](http://www.numpy.org/), and [`astropy`](http://www.astropy.org/) libraries are widely used Python libraries for plotting, scientific computing, and astronomical data analysis. We will use these packages in common ways below, including the `matplotlib.pyplot` plotting sublibrary." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" + "#allow for matplotlib to create inline plots in our notebook\n", + "%matplotlib inline \n", + "import numpy as np #imports numpy with the alias np\n", + "import matplotlib.pyplot as plt #imports matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from astropy.visualization import ZScaleInterval\n", - "zscale = ZScaleInterval()" + "from astropy.visualization import ZScaleInterval #This function allows use to use the `zscale()` rescaling function familiar from, e.g., DS9, to adjust the image stretch.\n", + "zscale = ZScaleInterval() #create an alias to the `ZScaleInterval()` function" ] }, { @@ -59,179 +71,182 @@ } }, "source": [ - "## Loading the LSST DM Stack" + "## **Step 1) Loading the LSST DM Stack**\n", + "\n", + "To manipulate data, the LSST DM Stack provides a `Butler` that enables generic access routines to DM-generated data. For more information, see [the Data Butler entry in the LSST Software User Guide](https://confluence.lsstcorp.org/display/LSWUG/Data+Butler). In order to access a calibrated exposure from data stored in the format required by the LSST Data Butler, we must load the `lsst.daf.persistence` library to produce a Butler instance from the data." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import lsst.daf.persistence as dafPersist" + "import lsst.daf.persistence as dafPersist #load lsst.daf.persistence to gain access to a Butler instance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to load the `lsst.afw.display` library to gain access to the image visualization routines we'd like to use." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import lsst.afw.display as afwDisplay" + "import lsst.afw.display as afwDisplay #load lsst.afw.display to gain access to image visualization routines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Step 2) Importing Data to Visualize**\n", + "\n", + "To plot an image to the screen, we must first load some data. In this tutorial, we will use the `Twinkles` simulated images available in the StackClub data repository. These data sit in the data directory `/project/shared/data/Twinkles_subset/output_data_v2` and contain a set of data produced in generating a calibrated exposure by the DM Stack. These data are organized in a structure that enables a DM Stack `Butler` instance to be generated and provide access to a single filter image (in this case `r` band), a specific detector raft (2,2), a specific sensor in the raft (`1,1`) and a specific visit (in this case, 235 -- note only one band is available per visit in this example).\n", + "\n", + "Once we define a string that contains the data directory, we start the `Butler` instance using the `lsst.daf.persistence` library alias `dafPersist` and its function `Butler()`. The function `Butler()` takes as an argument a string containing the data directory we wish to access." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "datadir = \"/project/shared/data/Twinkles_subset/output_data_v2\"\n", - "butler = dafPersist.Butler(datadir)" + "datadir = \"/project/shared/data/Twinkles_subset/output_data_v2\" #our data directory containing the Twinkles data organized as Butler expects\n", + "butler = dafPersist.Butler(datadir) #create an instance of the Butler, which we call `butler`, with access to our data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the `Butler` instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the butler which filter, raft, sensor, and visit we wish to view. To do this, we definie dictionary with the required information." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Grab a calexp of interest\n", - "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235}\n", - "calexp = butler.get('calexp', **dataId)" + "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235} #Define a dictionary with the filter, raft, sensor, and visit we wish to view\n", + "calexp = butler.get('calexp', **dataId) #retrieve the data using the `butler` instance and its function `get()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Step 3) Use AFWDisplay to Visualize the Image**\n", + "\n", + "Now, with a `Butler` instance defined and a calibrated exposure retrieved, we can use [`lsst.afw.display`](https://github.com/lsst/afw/tree/master/python/lsst/afw/display) to visualize the data. The next task is to let AFWDisplay know that we want it to enroll `matplotlib` as our default display backend. To do this, we use the `setDefaultBackend()` function. Remember that we made an alias to `lsst.afw.display` called `afwDisplay`, so we'll use that to call `setDefaultBackend()`." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Use lsst.afw.display with the matplotlib backend# Use l \n", - "afwDisplay.setDefaultBackend('matplotlib') " + "afwDisplay.setDefaultBackend('matplotlib') # Use lsst.afw.display with the matplotlib backend" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now set to display the image. To do this, we first create a `matplotlib.pyplot` figure using `plt.figure()` -- this will be familiar to anyone with experience using `matplotlib`." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plt.figure()\n", - "afw_display = afwDisplay.Display()\n", - "afw_display.scale('asinh', 'zscale')\n", - "afw_display.mtv(calexp.image)" + "plt.figure() #create a matplotlib.pyplot figure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create an alias to the `lsst.afw.display.Display` method that will allow us to display the data to the screen. This alias will be called `afw_display`." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "method_list = [func for func in dir(afw_display) if callable(getattr(afw_display, func))]" + "afw_display = afwDisplay.Display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before showing the data on the screen, we have to decide how to apply an image stretch given the data. The algorithm we'll use is `asinh` familiar from SDSS images, with a range of values set by `zscale`. To do this, we use the `scale()` function provided by `lsst.afw.display`. See the `scale()` function definition in the [`interface.py` file of the lsst.afw.display library](https://github.com/lsst/afw/blob/master/python/lsst/afw/display/interface.py)." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Buffering', '_Buffering', '__class__', '__del__', '__delattr__', '__dir__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattr__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'close', 'delAllDisplays', 'dot', 'erase', 'flush', 'getActiveCallbackKeys', 'getDefaultBackend', 'getDefaultFrame', 'getDisplay', 'getMaskPlaneColor', 'getMaskTransparency', 'incrDefaultFrame', 'interact', 'line', 'maskColorGenerator', 'mtv', 'pan', 'scale', 'setCallback', 'setDefaultBackend', 'setDefaultFrame', 'setDefaultMaskPlaneColor', 'setDefaultMaskTransparency', 'setMaskPlaneColor', 'setMaskTransparency', 'show', 'zoom']\n" - ] - } - ], + "outputs": [], "source": [ - "print(method_list)" + "afw_display.scale('asinh', 'zscale')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can display the image. Do do this, we provide the `mtv()` method the `image` member of our calibrated image retrieved by the `butler`. We can then use `plt.show()` to display our figure." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plt.figure()\n", - "afw_display = afwDisplay.Display()\n", "afw_display.mtv(calexp.image)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Step 4) More Information about lsst.afw.display**\n", + "\n", + "To get some more information about `lsst.afw.display`, we can print the method list to see what's available. The next cell will print `lsst.afw.display` methods to the screen." + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "scale() missing 2 required positional arguments: 'algorithm' and 'min'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mafw_display\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: scale() missing 2 required positional arguments: 'algorithm' and 'min'" - ] - } - ], + "outputs": [], "source": [ - "print(afw_display.scale().__doc__)" + "method_list = [func for func in dir(afw_display) if callable(getattr(afw_display, func))]\n", + "print(method_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Useful Documentation\n", - "\n", - "[Info on image indexing](https://github.com/lsst/afw/blob/master/doc/lsst.afw.image/indexing-conventions.rst) \n", - "[afw.display Doxygen](http://doxygen.lsst.codes/stack/doxygen/x_masterDoxyDoc/namespacelsst_1_1afw_1_1display.html) \n", - "[afw.display GitHub](https://github.com/RobertLuptonTheGood/afw/tree/master/python/lsst/afw/display) \n", - "[Getting Started Image Display](https://pipelines.lsst.io/getting-started/display.html)" + "If you'd like to learn more about any given function, we can retrieve the `__doc__` member of each method and print to the screen." ] }, { @@ -239,7 +254,21 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "print(afw_display.scale().__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Further Documentation**\n", + "\n", + "* [Info on image indexing conventions.](https://github.com/lsst/afw/blob/master/doc/lsst.afw.image/indexing-conventions.rst) \n", + "* [afw.display Doxygen website](http://doxygen.lsst.codes/stack/doxygen/x_masterDoxyDoc/namespacelsst_1_1afw_1_1display.html) \n", + "* [afw.display GitHub website](https://github.com/RobertLuptonTheGood/afw/tree/master/python/lsst/afw/display) \n", + "* [The `pipelines.lsst.io` Getting Started on Image Display website.](https://pipelines.lsst.io/getting-started/display.html)" + ] } ], "metadata": { From bb643ae3ade4bd460730228c23365c53f7829e88 Mon Sep 17 00:00:00 2001 From: Brant Robertson Date: Fri, 24 Aug 2018 19:55:33 +0000 Subject: [PATCH 27/45] Draft notebook for pull request to merge into master branch. --- Visualization/AFW_Display_Demo.ipynb | 115 ++++++++++++++------------- 1 file changed, 59 insertions(+), 56 deletions(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index 18cb9f8b..d5fddc42 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -10,7 +10,7 @@ "source": [ "# **Demo of lsst.afw.display -- displaying images using the LSST DM Astronomical Framework library**\n", "\n", - "**Owner:** Brant Robertson ([@brant](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brant)) \n", + "**Owner:** Brant Robertson ([@brantr](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brantr)) \n", "**Level:** Introductory \n", "**Last Verified to Run:** 2018-08-24 \n", "**Verified Stack Release:** v16.0 \n", @@ -38,7 +38,7 @@ "source": [ "## **Step 0) Import Common Python Libraries**\n", "\n", - "The [`matplotlib`](https://matplotlib.org/), [`numpy`](http://www.numpy.org/), and [`astropy`](http://www.astropy.org/) libraries are widely used Python libraries for plotting, scientific computing, and astronomical data analysis. We will use these packages in common ways below, including the `matplotlib.pyplot` plotting sublibrary." + "The [`matplotlib`](https://matplotlib.org/), [`numpy`](http://www.numpy.org/), and [`astropy`](http://www.astropy.org/) libraries are widely used Python libraries for plotting, scientific computing, and astronomical data analysis. We will use these packages in common ways below, including the `matplotlib.pyplot` plotting sublibrary. We also import the [`warnings` library](https://docs.python.org/2/library/warnings.html) to prevent some routine warning messages from printing to the screen." ] }, { @@ -50,7 +50,15 @@ "#allow for matplotlib to create inline plots in our notebook\n", "%matplotlib inline \n", "import numpy as np #imports numpy with the alias np\n", - "import matplotlib.pyplot as plt #imports matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt #imports matplotlib.pyplot as plt\n", + "import warnings #imports the warnings library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's go ahead and import from `astropy` the image stretch limits from the familiar `zscale()` function." ] }, { @@ -59,21 +67,15 @@ "metadata": {}, "outputs": [], "source": [ - "from astropy.visualization import ZScaleInterval #This function allows use to use the `zscale()` rescaling function familiar from, e.g., DS9, to adjust the image stretch.\n", + "from astropy.visualization import ZScaleInterval #This function allows use to use the `zscale()` rescaling limits function familiar from, e.g., DS9, to adjust the image stretch.\n", "zscale = ZScaleInterval() #create an alias to the `ZScaleInterval()` function" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "metadata": {}, "source": [ - "## **Step 1) Loading the LSST DM Stack**\n", - "\n", - "To manipulate data, the LSST DM Stack provides a `Butler` that enables generic access routines to DM-generated data. For more information, see [the Data Butler entry in the LSST Software User Guide](https://confluence.lsstcorp.org/display/LSWUG/Data+Butler). In order to access a calibrated exposure from data stored in the format required by the LSST Data Butler, we must load the `lsst.daf.persistence` library to produce a Butler instance from the data." + "And let the kernel know that we're happy not to have some useful warnings printed during this tutorial." ] }, { @@ -82,14 +84,15 @@ "metadata": {}, "outputs": [], "source": [ - "import lsst.daf.persistence as dafPersist #load lsst.daf.persistence to gain access to a Butler instance" + "warnings.simplefilter(\"ignore\", category=FutureWarning) #prevent some helpful but ancillary warning messages from printing during LSST DM Release v16 calls\n", + "warnings.simplefilter(\"ignore\", category=UserWarning) #prevent some helpful but ancillary warning messages from printing during LSST DM Release v16 calls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to load the `lsst.afw.display` library to gain access to the image visualization routines we'd like to use." + "As a last preparatory task, we set the parameters of `matplotlib.pyplot` to give us a large default size for an image." ] }, { @@ -98,18 +101,20 @@ "metadata": {}, "outputs": [], "source": [ - "import lsst.afw.display as afwDisplay #load lsst.afw.display to gain access to image visualization routines." + "plt.rcParams['figure.figsize'] = (8.0, 8.0) #set a large default size for our images" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "## **Step 2) Importing Data to Visualize**\n", - "\n", - "To plot an image to the screen, we must first load some data. In this tutorial, we will use the `Twinkles` simulated images available in the StackClub data repository. These data sit in the data directory `/project/shared/data/Twinkles_subset/output_data_v2` and contain a set of data produced in generating a calibrated exposure by the DM Stack. These data are organized in a structure that enables a DM Stack `Butler` instance to be generated and provide access to a single filter image (in this case `r` band), a specific detector raft (2,2), a specific sensor in the raft (`1,1`) and a specific visit (in this case, 235 -- note only one band is available per visit in this example).\n", + "## **Step 1) Loading the LSST DM Stack**\n", "\n", - "Once we define a string that contains the data directory, we start the `Butler` instance using the `lsst.daf.persistence` library alias `dafPersist` and its function `Butler()`. The function `Butler()` takes as an argument a string containing the data directory we wish to access." + "To manipulate data, the LSST DM Stack provides a `Butler` that enables generic access routines to DM-generated data. For more information, see [the Data Butler entry in the LSST Software User Guide](https://confluence.lsstcorp.org/display/LSWUG/Data+Butler). In order to access a calibrated exposure from data stored in the format required by the LSST Data Butler, we must load the `lsst.daf.persistence` library to produce a Butler instance from the data." ] }, { @@ -118,15 +123,14 @@ "metadata": {}, "outputs": [], "source": [ - "datadir = \"/project/shared/data/Twinkles_subset/output_data_v2\" #our data directory containing the Twinkles data organized as Butler expects\n", - "butler = dafPersist.Butler(datadir) #create an instance of the Butler, which we call `butler`, with access to our data" + "import lsst.daf.persistence as dafPersist #load lsst.daf.persistence to gain access to a Butler instance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "With the `Butler` instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the butler which filter, raft, sensor, and visit we wish to view. To do this, we definie dictionary with the required information." + "Next, we need to load the `lsst.afw.display` library to gain access to the image visualization routines we'd like to use." ] }, { @@ -135,18 +139,18 @@ "metadata": {}, "outputs": [], "source": [ - "# Grab a calexp of interest\n", - "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235} #Define a dictionary with the filter, raft, sensor, and visit we wish to view\n", - "calexp = butler.get('calexp', **dataId) #retrieve the data using the `butler` instance and its function `get()`" + "import lsst.afw.display as afwDisplay #load lsst.afw.display to gain access to image visualization routines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## **Step 3) Use AFWDisplay to Visualize the Image**\n", + "## **Step 2) Importing Data to Visualize**\n", "\n", - "Now, with a `Butler` instance defined and a calibrated exposure retrieved, we can use [`lsst.afw.display`](https://github.com/lsst/afw/tree/master/python/lsst/afw/display) to visualize the data. The next task is to let AFWDisplay know that we want it to enroll `matplotlib` as our default display backend. To do this, we use the `setDefaultBackend()` function. Remember that we made an alias to `lsst.afw.display` called `afwDisplay`, so we'll use that to call `setDefaultBackend()`." + "To plot an image to the screen, we must first load some data. In this tutorial, we will use the `Twinkles` simulated images available in the StackClub data repository. These data sit in the data directory `/project/shared/data/Twinkles_subset/output_data_v2` and contain a set of data produced in generating a calibrated exposure by the DM Stack. These data are organized in a structure that enables a DM Stack `Butler` instance to be generated and provide access to a single filter image (in this case `r` band), a specific detector raft (2,2), a specific sensor in the raft (`1,1`) and a specific visit (in this case, 235 -- note only one band is available per visit in this example).\n", + "\n", + "Once we define a string that contains the data directory, we start the `Butler` instance using the `lsst.daf.persistence` library alias `dafPersist` and its function `Butler()`. The function `Butler()` takes as an argument a string containing the data directory we wish to access. Running the cell may take a few moments." ] }, { @@ -155,14 +159,15 @@ "metadata": {}, "outputs": [], "source": [ - "afwDisplay.setDefaultBackend('matplotlib') # Use lsst.afw.display with the matplotlib backend" + "datadir = \"/project/shared/data/Twinkles_subset/output_data_v2\" #our data directory containing the Twinkles data organized as Butler expects\n", + "butler = dafPersist.Butler(datadir) #create an instance of the Butler, which we call `butler`, with access to our data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We are now set to display the image. To do this, we first create a `matplotlib.pyplot` figure using `plt.figure()` -- this will be familiar to anyone with experience using `matplotlib`." + "With the `Butler` instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the butler which filter, raft, sensor, and visit we wish to view. To do this, we definie dictionary with the required information." ] }, { @@ -171,14 +176,18 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure() #create a matplotlib.pyplot figure" + "# Grab a calexp of interest\n", + "dataId = {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235} #Define a dictionary with the filter, raft, sensor, and visit we wish to view\n", + "calexp = butler.get('calexp', **dataId) #retrieve the data using the `butler` instance and its function `get()`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We then create an alias to the `lsst.afw.display.Display` method that will allow us to display the data to the screen. This alias will be called `afw_display`." + "## **Step 3) Use AFWDisplay to Visualize the Image**\n", + "\n", + "Now, with a `Butler` instance defined and a calibrated exposure retrieved, we can use [`lsst.afw.display`](https://github.com/lsst/afw/tree/master/python/lsst/afw/display) to visualize the data. The next task is to let AFWDisplay know that we want it to enroll `matplotlib` as our default display backend. To do this, we use the `setDefaultBackend()` function. Remember that we made an alias to `lsst.afw.display` called `afwDisplay`, so we'll use that to call `setDefaultBackend()`." ] }, { @@ -187,14 +196,21 @@ "metadata": {}, "outputs": [], "source": [ - "afw_display = afwDisplay.Display()" + "afwDisplay.setDefaultBackend('matplotlib') # Use lsst.afw.display with the matplotlib backend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Before showing the data on the screen, we have to decide how to apply an image stretch given the data. The algorithm we'll use is `asinh` familiar from SDSS images, with a range of values set by `zscale`. To do this, we use the `scale()` function provided by `lsst.afw.display`. See the `scale()` function definition in the [`interface.py` file of the lsst.afw.display library](https://github.com/lsst/afw/blob/master/python/lsst/afw/display/interface.py)." + "We are now set to display the image. To do this, we:\n", + "\n", + "* First create a `matplotlib.pyplot` figure using `plt.figure()` -- this will be familiar to anyone with experience using `matplotlib`.\n", + "* Then create an alias to the `lsst.afw.display.Display` method that will allow us to display the data to the screen. This alias will be called `afw_display`.\n", + "* Before showing the data on the screen, we have to decide how to apply an image stretch given the data. The algorithm we'll use is `asinh` familiar from SDSS images, with a range of values set by `zscale`. To do this, we use the `scale()` function provided by `lsst.afw.display`. See the `scale()` function definition in the [`interface.py` file of the lsst.afw.display library](https://github.com/lsst/afw/blob/master/python/lsst/afw/display/interface.py).\n", + "* Finally, we can display the image. Do do this, we provide the `mtv()` method the `image` member of our calibrated image retrieved by the `butler`. We can then use `plt.show()` to display our figure.\n", + "\n", + "All these tasks are best done within the same notebook cell." ] }, { @@ -203,24 +219,18 @@ "metadata": {}, "outputs": [], "source": [ - "afw_display.scale('asinh', 'zscale')" + "plt.figure() #create a matplotlib.pyplot figure\n", + "afw_display = afwDisplay.Display() #get an alias to the lsst.afw.display.Display() method\n", + "afw_display.scale('asinh', 'zscale') #set the image stretch algorithm and range\n", + "afw_display.mtv(calexp.image) #load the image into the display\n", + "plt.show() #show the corresponding pyplot figure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can display the image. Do do this, we provide the `mtv()` method the `image` member of our calibrated image retrieved by the `butler`. We can then use `plt.show()` to display our figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "afw_display.mtv(calexp.image)\n", - "plt.show()" + "**Congrats!** We've plotted an image using `lsst.afw.display`!" ] }, { @@ -246,16 +256,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you'd like to learn more about any given function, we can retrieve the `__doc__` member of each method and print to the screen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(afw_display.scale().__doc__)" + "If you'd like to learn more about any given function, please see the [`lsst.afw.display` source code](https://github.com/lsst/afw/tree/master/python/lsst/afw/display)." ] }, { @@ -264,6 +265,8 @@ "source": [ "### **Further Documentation**\n", "\n", + "If you'd like some more information on `lsst.afw.display`, please have a look at the following websites:\n", + "\n", "* [Info on image indexing conventions.](https://github.com/lsst/afw/blob/master/doc/lsst.afw.image/indexing-conventions.rst) \n", "* [afw.display Doxygen website](http://doxygen.lsst.codes/stack/doxygen/x_masterDoxyDoc/namespacelsst_1_1afw_1_1display.html) \n", "* [afw.display GitHub website](https://github.com/RobertLuptonTheGood/afw/tree/master/python/lsst/afw/display) \n", From 5f9e9bbaa341b22dd2b13736747663c849d0049a Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Sun, 26 Aug 2018 16:50:04 -0700 Subject: [PATCH 28/45] Re-ordered, added Phase 2, Session 2 --- Meetings.md | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/Meetings.md b/Meetings.md index 51d0e326..36b32f3f 100644 --- a/Meetings.md +++ b/Meetings.md @@ -1,18 +1,27 @@ # Stack Club Meetings -Individual session recordings are linked below. +Individual session links and recordings are given below, most recent meeting at the top. + +| Session | Date | Topic | Links | +|---|---|---|---| +| Phase 2, Session 2 | Friday August 24, 2018 [(video)](https://stanford.zoom.us/recording/share/Xii8Utw9RX5rqGUn8a_barg6NDBcRuzkmDIjDrUds82wIumekTziMw) | Interactive visualization, new member start-ups, hacking | [Bokeh/HoloViews Demo](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/bokeh_holoviews_datashader/bechtol/Visualization/bokeh_holoviews_datashader.ipynb) | +| LSST2018 Launch | Monday August 13, 2018 [(video)](https://stanford.zoom.us/recording/share/qyunKljpUWaFQBneuiL4PnbxTB-tf1BvttELFVPJHnuwIumekTziMw) | PCW welcome, discussion, hacking | [Introduction to the LSST Stack Club](https://docs.google.com/presentation/d/1LWShGi-YLqWoxPvewkg-JOpb67WKZyI0YQcSFrmAl14/edit#slide=id.p1) | + + +### Phase 1 Sessions + +Before the August 2018 PCW "launch", we met as a small group, putting together our first notebooks to get the Stack Club started. | Session | Date | Topic | Links | |---|---|---|---| -| Phase 1, Session 1 | Friday May 25, 2018 [(video)](https://stanford.zoom.us/recording/share/xA33Pv0oq_g5l6a0CaJ0az01mbROy_gyGLDEqIR92FOwIumekTziMw) | Visualization with Firefly | [SQuaRE Firefly demo](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb) | -| Phase 1, Session 2 | Wednesday June 6, 2018 [(video)](https://stanford.zoom.us/recording/share/YZad6BLPZFCjhgLSckrpis7w6Ekyr61VhhIvtFnjR_-wIumekTziMw) | Commissioning Team Bootcamp Report | [LSST Commissioning Team Notebooks](https://github.com/lsst-com/notebooks) | -| Phase 1, Session 3 | Friday June 22, 2018 | | | -| Phase 1, Session 4 | Friday July 6, 2018 [(video)](https://stanford.zoom.us/recording/share/1ZHCNdwRZnhwq8sb1TPvznug-AusUCCIV55N0DUF-LawIumekTziMw) | Project Discussion | [Topic List](https://docs.google.com/document/d/1PSA1uWwTfs9CweatpxF8CEPGBYRY5ZaXB39JzXYE7_U/edit#heading=h.txq6h6bpxzkd) | -| Phase 1, Session 5 | Friday July 13, 2018 [(video)](https://stanford.zoom.us/recording/share/QT8r75yuXR1sjZVkHh4MstfBLJ80wKubuqSvW4s3gfGwIumekTziMw) | Hack Session | [Project List](https://github.com/LSSTScienceCollaborations/StackClub/issues?q=is%3Aopen+label%3Aproject+sort%3Aupdated-desc) | -| Phase 1, Session 6 | Friday July 20, 2018 [(video)](https://stanford.zoom.us/recording/share/XqFx95GJ7zlSZTOVBPZz4l8WGYUmj7EyNsuF6vofMtewIumekTziMw) | Hack Session | [CalExp Tour](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/calexp-tour/stargaser/Basics/Calexp_guided_tour.ipynb) | -| Phase 1, Session 7 | Friday July 27, 2018 [(video)](https://stanford.zoom.us/recording/share/AOFd8Q8yH4lHI6aTLylqRBgcusMUERz-ksiULX4rRL2wIumekTziMw) | Hack Session | [VPN set-up change](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#accessing-ncsa-via-its-vpn) | -| Phase 1, Session 8 | Friday August 3, 2018 [(video)](https://stanford.zoom.us/recording/share/Pnin7IjBNCyGrOCKgyXTvRFFbcuE_eG6tN6QWkQtsvmwIumekTziMw) | PCW Planning | [Source Detection: Low Surface Brightness Galaxies](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/SourceDetection/LowSurfaceBrightness.ipynb) | | Phase 1, Session 9 | Friday August 10, 2018 [(video)](https://stanford.zoom.us/recording/share/d5skJMVG1L-XhtyV6xZA6gI0pN552NQjfNuFUMymocCwIumekTziMw) | Hack Session | [Rules, `stackclub` library package, CIT with `beavis-ci`](https://github.com/LSSTScienceCollaborations/StackClub/issues/85) | -| LSST2018 | Monday August 13, 2018 [(video)](https://stanford.zoom.us/recording/share/qyunKljpUWaFQBneuiL4PnbxTB-tf1BvttELFVPJHnuwIumekTziMw) | PCW welcome, discussion, hacking | [Introduction to the LSST Stack Club](https://docs.google.com/presentation/d/1LWShGi-YLqWoxPvewkg-JOpb67WKZyI0YQcSFrmAl14/edit#slide=id.p1) | +| Phase 1, Session 8 | Friday August 3, 2018 [(video)](https://stanford.zoom.us/recording/share/Pnin7IjBNCyGrOCKgyXTvRFFbcuE_eG6tN6QWkQtsvmwIumekTziMw) | PCW Planning | [Source Detection: Low Surface Brightness Galaxies](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/SourceDetection/LowSurfaceBrightness.ipynb) | +| Phase 1, Session 7 | Friday July 27, 2018 [(video)](https://stanford.zoom.us/recording/share/AOFd8Q8yH4lHI6aTLylqRBgcusMUERz-ksiULX4rRL2wIumekTziMw) | Hack Session | [VPN set-up change](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/GettingStarted/GettingStarted.md#accessing-ncsa-via-its-vpn) | +| Phase 1, Session 6 | Friday July 20, 2018 [(video)](https://stanford.zoom.us/recording/share/XqFx95GJ7zlSZTOVBPZz4l8WGYUmj7EyNsuF6vofMtewIumekTziMw) | Hack Session | [CalExp Tour](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/calexp-tour/stargaser/Basics/Calexp_guided_tour.ipynb) | +| Phase 1, Session 5 | Friday July 13, 2018 [(video)](https://stanford.zoom.us/recording/share/QT8r75yuXR1sjZVkHh4MstfBLJ80wKubuqSvW4s3gfGwIumekTziMw) | Hack Session | [Project List](https://github.com/LSSTScienceCollaborations/StackClub/issues?q=is%3Aopen+label%3Aproject+sort%3Aupdated-desc) | +| Phase 1, Session 4 | Friday July 6, 2018 [(video)](https://stanford.zoom.us/recording/share/1ZHCNdwRZnhwq8sb1TPvznug-AusUCCIV55N0DUF-LawIumekTziMw) | Project Discussion | [Topic List](https://docs.google.com/document/d/1PSA1uWwTfs9CweatpxF8CEPGBYRY5ZaXB39JzXYE7_U/edit#heading=h.txq6h6bpxzkd) | +| Phase 1, Session 3 | Friday June 22, 2018 | | | +| Phase 1, Session 2 | Wednesday June 6, 2018 [(video)](https://stanford.zoom.us/recording/share/YZad6BLPZFCjhgLSckrpis7w6Ekyr61VhhIvtFnjR_-wIumekTziMw) | Commissioning Team Bootcamp Report | [LSST Commissioning Team Notebooks](https://github.com/lsst-com/notebooks) | +| Phase 1, Session 1 | Friday May 25, 2018 [(video)](https://stanford.zoom.us/recording/share/xA33Pv0oq_g5l6a0CaJ0az01mbROy_gyGLDEqIR92FOwIumekTziMw) | Visualization with Firefly | [SQuaRE Firefly demo](https://github.com/lsst-sqre/notebook-demo/blob/master/Firefly.ipynb) | From 2673066099557a4fe8c11c7676ecc73b875181f8 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Mon, 27 Aug 2018 00:08:15 +0000 Subject: [PATCH 29/45] Added help() statements --- Visualization/AFW_Display_Demo.ipynb | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index d5fddc42..be21db3e 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -150,7 +150,7 @@ "\n", "To plot an image to the screen, we must first load some data. In this tutorial, we will use the `Twinkles` simulated images available in the StackClub data repository. These data sit in the data directory `/project/shared/data/Twinkles_subset/output_data_v2` and contain a set of data produced in generating a calibrated exposure by the DM Stack. These data are organized in a structure that enables a DM Stack `Butler` instance to be generated and provide access to a single filter image (in this case `r` band), a specific detector raft (2,2), a specific sensor in the raft (`1,1`) and a specific visit (in this case, 235 -- note only one band is available per visit in this example).\n", "\n", - "Once we define a string that contains the data directory, we start the `Butler` instance using the `lsst.daf.persistence` library alias `dafPersist` and its function `Butler()`. The function `Butler()` takes as an argument a string containing the data directory we wish to access. Running the cell may take a few moments." + "Once we define a string that contains the data directory, we start the `Butler` instance using the `lsst.daf.persistence` library alias `dafPersist` and its `Butler` class. The `Butler` object is initialized with a string containing the data directory we wish to access. Running the cell may take a few moments." ] }, { @@ -256,7 +256,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you'd like to learn more about any given function, please see the [`lsst.afw.display` source code](https://github.com/lsst/afw/tree/master/python/lsst/afw/display)." + "If you'd like to learn more about any given function, please see the [`lsst.afw.display` source code](https://github.com/lsst/afw/tree/master/python/lsst/afw/display).\n", + "\n", + "You can also read the API documentation about the above functions using the Jupyter notebook `help()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(afw_display.scale)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(afw_display.mtv)" ] }, { From 01953756fa14de798fca0aa1c3e3a02cdd0f0e75 Mon Sep 17 00:00:00 2001 From: Brant Robertson Date: Mon, 27 Aug 2018 02:15:01 +0000 Subject: [PATCH 30/45] Responding to @drphilmarshall comments --- Visualization/AFW_Display_Demo.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index be21db3e..7d372295 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -10,7 +10,7 @@ "source": [ "# **Demo of lsst.afw.display -- displaying images using the LSST DM Astronomical Framework library**\n", "\n", - "**Owner:** Brant Robertson ([@brantr](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@brantr)) \n", + "**Owner:** Brant Robertson ([@brantr](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@brantr)) \n", "**Level:** Introductory \n", "**Last Verified to Run:** 2018-08-24 \n", "**Verified Stack Release:** v16.0 \n", @@ -25,7 +25,7 @@ "\n", "This tutorial is designed to help users get a brief feel for the `lsst.afw.display` library that enables the visual inspection of data. The [`lsst.afw` library](https://github.com/lsst/afw) provides an \"Astronomical Framework\" (afw) while the `lsst.daf.*` libraries (see, e.g., [daf_base](https://github.com/lsst/daf_base)) provides a Data Access Framework (daf). Both libraries are used in this tutorial, with the `lsst.daf.persistence` library used to access a calibrated exposure (calexp) and the `lsst.afw.display` library used to show the exposure image on the screen.\n", "\n", - "This tutorial made use of the [`LowSurfaceBrightness.ipynb` StackClub notebook](https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/SourceDetection/LowSurfaceBrightness.nbconvert.ipynb) by [Alex Drlica-Wagner](https://github.com/LSSTScienceCollaborations/DMStackClub/issues/new?body=@kadrlica)." + "This tutorial made use of the [`LowSurfaceBrightness.ipynb` StackClub notebook](https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/SourceDetection/LowSurfaceBrightness.nbconvert.ipynb) by [Alex Drlica-Wagner](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@kadrlica)." ] }, { @@ -84,8 +84,8 @@ "metadata": {}, "outputs": [], "source": [ - "warnings.simplefilter(\"ignore\", category=FutureWarning) #prevent some helpful but ancillary warning messages from printing during LSST DM Release v16 calls\n", - "warnings.simplefilter(\"ignore\", category=UserWarning) #prevent some helpful but ancillary warning messages from printing during LSST DM Release v16 calls" + "warnings.simplefilter(\"ignore\", category=FutureWarning) #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls\n", + "warnings.simplefilter(\"ignore\", category=UserWarning) #prevent some helpful but ancillary warning messages from printing during some LSST DM Release calls" ] }, { From 7e565132e041c7f32e00f58bb82f24e70e6e7680 Mon Sep 17 00:00:00 2001 From: Robert Morgan Date: Mon, 27 Aug 2018 15:07:15 +0000 Subject: [PATCH 31/45] Added cell to bottom of notebook --- GettingStarted/HelloWorld.ipynb | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/GettingStarted/HelloWorld.ipynb b/GettingStarted/HelloWorld.ipynb index ee48b344..8fccd6fd 100644 --- a/GettingStarted/HelloWorld.ipynb +++ b/GettingStarted/HelloWorld.ipynb @@ -277,6 +277,22 @@ "source": [ "take_that_first_baby_step(and_follow_up=\"it's me, Phil, using python!\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Rob was here, thanks all!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From b28d7613e86fffa03db13cff31bbf836de696042 Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Wed, 29 Aug 2018 06:37:43 +0000 Subject: [PATCH 32/45] Adding masked image plotting --- Visualization/AFW_Display_Demo.ipynb | 57 +++++++++++++++++++++++++++- 1 file changed, 55 insertions(+), 2 deletions(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index 7d372295..be330777 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -25,7 +25,7 @@ "\n", "This tutorial is designed to help users get a brief feel for the `lsst.afw.display` library that enables the visual inspection of data. The [`lsst.afw` library](https://github.com/lsst/afw) provides an \"Astronomical Framework\" (afw) while the `lsst.daf.*` libraries (see, e.g., [daf_base](https://github.com/lsst/daf_base)) provides a Data Access Framework (daf). Both libraries are used in this tutorial, with the `lsst.daf.persistence` library used to access a calibrated exposure (calexp) and the `lsst.afw.display` library used to show the exposure image on the screen.\n", "\n", - "This tutorial made use of the [`LowSurfaceBrightness.ipynb` StackClub notebook](https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/SourceDetection/LowSurfaceBrightness.nbconvert.ipynb) by [Alex Drlica-Wagner](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@kadrlica)." + "This tutorial made use of the [`LowSurfaceBrightness.ipynb` StackClub notebook](https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/SourceDetection/LowSurfaceBrightness.nbconvert.ipynb) by [Alex Drlica-Wagner](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@kadrlica). More examples of the use of `lsst.afw.display` can be found in the [Stack ](https://pipelines.lsst.io/getting-started/display.html)." ] }, { @@ -185,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## **Step 3) Use AFWDisplay to Visualize the Image**\n", + "## **Step 3.1) Use AFWDisplay to Visualize the Image**\n", "\n", "Now, with a `Butler` instance defined and a calibrated exposure retrieved, we can use [`lsst.afw.display`](https://github.com/lsst/afw/tree/master/python/lsst/afw/display) to visualize the data. The next task is to let AFWDisplay know that we want it to enroll `matplotlib` as our default display backend. To do this, we use the `setDefaultBackend()` function. Remember that we made an alias to `lsst.afw.display` called `afwDisplay`, so we'll use that to call `setDefaultBackend()`." ] @@ -233,6 +233,59 @@ "**Congrats!** We've plotted an image using `lsst.afw.display`!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Step 3.2) Use AFWDisplay to Visualize the Image and Mask Plane**\n", + "\n", + "The `calexp` returned by the butler contains more than just the image pixel values (see the [calexp tutorial](https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Calexp_guided_tour.ipynb) for more details). One other component is the mask plane associated with the image. `AFWDisplay` provides a nice pre-packaged interface for overplotting the mask associated with an image. A mask is composed of a set of \"mask planes\", 2D binary bit maps corresponding to pixels that are masked for various reasons (see [here](https://pipelines.lsst.io/v/DM-11392/getting-started/display.html#interpreting-displayed-mask-colors) for more details)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll follow the same steps as above to display the image, but we'll add a few modifications\n", + "\n", + "* We explicitly set the transparency of the overplotted mask (0 = transparent, 1 = opaque)\n", + "* We explicitly set the color of the 'DETECTED' mask plane to 'blue' (i.e. all pixels associated with detected objects).\n", + "* We pass the full `calexp` object to `mtv` instead of just the image plane." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure() #create a matplotlib.pyplot figure\n", + "afw_display = afwDisplay.Display() #get an alias to the lsst.afw.display.Display() method\n", + "afw_display.scale('asinh', 'zscale') #set the image stretch algorithm and range\n", + "afw_display.setMaskTransparency(0.4) #set the transparency of the mask plane (1 = opaque)\n", + "afw_display.setMaskPlaneColor('DETECTED','blue') #set the color for a single plane in the mask\n", + "afw_display.mtv(calexp) #load the image and mask plane into the display\n", + "plt.show() #show the corresponding pyplot figure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `afw_display` object contains more information about the mask planes that can be accessed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Mask plane bit definitions:\\n\", afw_display.getMaskPlaneColor()) # Print the colors associated to each plane in the mask\n", + "print(\"\\nMask plane methods:\\n\")\n", + "help(afw_display.setMaskPlaneColor)" + ] + }, { "cell_type": "markdown", "metadata": {}, From 72e35e5b1de76d94e45dae5f538dd14a459dd416 Mon Sep 17 00:00:00 2001 From: Craig Lage Date: Thu, 30 Aug 2018 10:51:49 -0700 Subject: [PATCH 33/45] Pushing a notebook to extract the BF Kernel --- .../make_bfk_lage_more_flats_28Aug18.ipynb | 993 ++++++++++++++++++ 1 file changed, 993 insertions(+) create mode 100644 ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb diff --git a/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb b/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb new file mode 100644 index 00000000..251750c6 --- /dev/null +++ b/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb @@ -0,0 +1,993 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Starting with Michael Wood-Vasey's notebook as a starting point\n", + "## Craig Lage - 28Aug18\n", + "\n", + "## Make a brighter-fatter kernel from a set of high-intensity flats measured at UC Davis.\n", + "## Run correlations by amplifier. Now adding more flat pairs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows extracting the BF kernel from a set of measured flats.\n", + "\n", + "So far, this notebook is only runnable on the system at UC Davis. \n", + "\n", + "Before it can be run at NCSA, two things need to happen:\n", + "\n", + " (1) I need to upolad the fits files for the flats. Easily done.\n", + " (2) I need to understand how to run at NCSA when not using a released version of the stack. \n", + " The code currently requires several branches which are not released in the stack, \n", + " as detailed below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "lsst_distrib 16.0+3 \tcurrent w_2018_26 setup\n" + ] + } + ], + "source": [ + "# What version of the Stack am I using?\n", + "! echo $HOSTNAME\n", + "! eups list -s | grep lsst_distrib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# if running stack v16.0, silence a long matplotlib Agg warning with:\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Test to make sure that we can import obs_lsstCam\n", + "import lsst.obs.base\n", + "import lsst.obs.lsstCam" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.cp.pipe.makeBrighterFatterKernel import MakeBrighterFatterKernelTask\n", + "from lsst.daf.persistence import Butler\n", + "from lsst.pipe.tasks.ingest import IngestTask" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ingest 25 pairs of flats from UC Davis measurements" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514141655.fits\n" + ] + } + ], + "source": [ + "!ls /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514??????.fits" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root INFO: Loading config overrride file '/sandbox/cslage/Research/LSST/code/w_2018_26/obs_lsstCam/config/ingest.py'\n", + "LsstCamMapper WARN: Unable to find calib root directory\n", + "CameraMapper INFO: Loading Posix exposure registry from /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514141655.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/100/R21/00000100-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_101_20180514141706.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/101/R21/00000101-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_102_20180514141718.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/102/R21/00000102-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_103_20180514141729.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/103/R21/00000103-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_104_20180514141740.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/104/R21/00000104-R21-S11-det085-000.fits\n", + "ingest INFO: 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/mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_309_20180514150026.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/309/R21/00000309-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_400_20180514152344.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/400/R21/00000400-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_401_20180514152401.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/401/R21/00000401-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_402_20180514152419.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/402/R21/00000402-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_403_20180514152436.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/403/R21/00000403-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_404_20180514152454.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/404/R21/00000404-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_405_20180514152511.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/405/R21/00000405-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_406_20180514152529.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/406/R21/00000406-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_407_20180514152546.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/407/R21/00000407-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_408_20180514152603.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/408/R21/00000408-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_409_20180514152621.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/409/R21/00000409-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_500_20180514155238.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/500/R21/00000500-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_501_20180514155257.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/501/R21/00000501-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_502_20180514155317.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/502/R21/00000502-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_503_20180514155336.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/503/R21/00000503-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_504_20180514155355.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/504/R21/00000504-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_505_20180514155414.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/505/R21/00000505-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_506_20180514155434.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/506/R21/00000506-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_507_20180514155453.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/507/R21/00000507-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_508_20180514155513.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/508/R21/00000508-R21-S11-det085-000.fits\n", + "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_509_20180514155532.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/509/R21/00000509-R21-S11-det085-000.fits\n" + ] + } + ], + "source": [ + "# Don't need to run this each time\n", + "!rm -rf ucd_repo_2\n", + "! mkdir ucd_repo_2\n", + "! echo \"lsst.obs.lsstCam.LsstCamMapper\" > ucd_repo_2/_mapper\n", + "\n", + "# Ingest the flats. The ?00 and ?01 are the flat pairs\n", + "! ingestImages.py ucd_repo_2 /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_?0?_20180514??????.fits --mode link" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler('ucd_repo_2')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So I got this far, and it has successfully ingested the images. So far, all I have done is:\n", + "\n", + " (1) Copied MWV code and hacks as closely as I could.\n", + " (A) Using w_2018_26\n", + " (B) obs_base tickets/DM-13293\n", + " (C) obs_lsstCam tickets/DM-15509\n", + " (D) cp_pipe tickets/DM-13293\n", + " (E) Comment out the `exposure.setWcs` command in the raw assembly in `obs_lsstCam`.\n", + " (F) Define a wrapper `run` method in `makeBrighterFatterTask.py` that calls `runDataRef`.\n", + " (G) Comment out from .cpTask import * in the `cp_pipe/python/lsst/cp/pipe/__init__.py` file.\n", + "\n", + " (2) edit obs_lsstCam/config/ingest.py to use some different header values\n", + " (3) edit obs_lsstCam/python/lsst/obs/lsstCam/ingest.py to work with these different values and \n", + " to fudge the raft and sensor IDs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(100, 85, 'r', 1, 'R21', 'S11'), (101, 85, 'r', 2, 'R21', 'S11'), (102, 85, 'r', 3, 'R21', 'S11'), (103, 85, 'r', 4, 'R21', 'S11'), (104, 85, 'r', 5, 'R21', 'S11'), (105, 85, 'r', 6, 'R21', 'S11'), (106, 85, 'r', 7, 'R21', 'S11'), (107, 85, 'r', 8, 'R21', 'S11'), (108, 85, 'r', 9, 'R21', 'S11'), (109, 85, 'r', 10, 'R21', 'S11'), (200, 85, 'r', 11, 'R21', 'S11'), (201, 85, 'r', 12, 'R21', 'S11'), (202, 85, 'r', 13, 'R21', 'S11'), (203, 85, 'r', 14, 'R21', 'S11'), (204, 85, 'r', 15, 'R21', 'S11'), (205, 85, 'r', 16, 'R21', 'S11'), (206, 85, 'r', 17, 'R21', 'S11'), (207, 85, 'r', 18, 'R21', 'S11'), (208, 85, 'r', 19, 'R21', 'S11'), (209, 85, 'r', 20, 'R21', 'S11'), (300, 85, 'r', 21, 'R21', 'S11'), (301, 85, 'r', 22, 'R21', 'S11'), (302, 85, 'r', 23, 'R21', 'S11'), (303, 85, 'r', 24, 'R21', 'S11'), (304, 85, 'r', 25, 'R21', 'S11'), (305, 85, 'r', 26, 'R21', 'S11'), (306, 85, 'r', 27, 'R21', 'S11'), (307, 85, 'r', 28, 'R21', 'S11'), (308, 85, 'r', 29, 'R21', 'S11'), (309, 85, 'r', 30, 'R21', 'S11'), (400, 85, 'r', 31, 'R21', 'S11'), (401, 85, 'r', 32, 'R21', 'S11'), (402, 85, 'r', 33, 'R21', 'S11'), (403, 85, 'r', 34, 'R21', 'S11'), (404, 85, 'r', 35, 'R21', 'S11'), (405, 85, 'r', 36, 'R21', 'S11'), (406, 85, 'r', 37, 'R21', 'S11'), (407, 85, 'r', 38, 'R21', 'S11'), (408, 85, 'r', 39, 'R21', 'S11'), (409, 85, 'r', 40, 'R21', 'S11'), (500, 85, 'r', 41, 'R21', 'S11'), (501, 85, 'r', 42, 'R21', 'S11'), (502, 85, 'r', 43, 'R21', 'S11'), (503, 85, 'r', 44, 'R21', 'S11'), (504, 85, 'r', 45, 'R21', 'S11'), (505, 85, 'r', 46, 'R21', 'S11'), (506, 85, 'r', 47, 'R21', 'S11'), (507, 85, 'r', 48, 'R21', 'S11'), (508, 85, 'r', 49, 'R21', 'S11'), (509, 85, 'r', 50, 'R21', 'S11')]\n" + ] + } + ], + "source": [ + "print(butler.queryMetadata('src', ['visit', 'detector', 'filter', 'id', 'raftName', 'detectorName']))\n", + "# Note that the 'detector' value is 85, and I have hacked the CCD to be R21:S11" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Put in the approximate measured gain values\n", + "amp_names = ['C{:02d}'.format(i) for i in range(18)]\n", + "ucd_gain = 4.5\n", + "nominalGain = {a: ucd_gain for a in amp_names}\n", + "gain = nominalGain\n", + "dataRef = butler.dataRef('brighterFatterGain', dataId={'detector': 85}) \n", + "dataRef.put(gain, 'brighterFatterGain')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['100,101', '102,103', '104,105', '106,107', '108,109', '200,201', '202,203', '204,205', '206,207', '208,209', '300,301', '302,303', '304,305', '306,307', '308,309', '400,401', '402,403', '404,405', '406,407', '408,409', '500,501', '502,503', '504,505', '506,507', '508,509']\n" + ] + } + ], + "source": [ + "pairs = []\n", + "for firstDigit in range(1,6):\n", + " for lastDigit in range(5):\n", + " pairs.append('%s,%s'%(str(100*firstDigit+2*lastDigit),str(100*firstDigit+2*lastDigit+1)))\n", + " \n", + "print(pairs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try calculating the brighter-fatter kernel using `MakeBrighterFatterKernelTask`. By setting level='AMP', we force amplifier by amplifier calculations. The first attempt was with assumed gains." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now repeating it with calculated gains. I needed to make some edits to prevent it from exiting on the bad amps.\n", + "This processing took several hours." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "makeBrighterFatterKernel.py ucd_repo_2 --rerun test --id detector=85 --visit-pairs 100,101 102,103 104,105 106,107 108,109 200,201 202,203 204,205 206,207 208,209 300,301 302,303 304,305 306,307 308,309 400,401 402,403 404,405 406,407 408,409 500,501 502,503 504,505 506,507 508,509 -c xcorrCheckRejectLevel=2 doCalcGains=True level=\"AMP\" --clobber-config --clobber-versions\n", + "Finished image preparation for visit 100 101\n", + "Finished cross-correlation for C10 in visit 100 101\n", + "Finished cross-correlation for C11 in visit 100 101\n", + "Finished cross-correlation for C12 in visit 100 101\n", + "Finished cross-correlation for C13 in visit 100 101\n", + "Finished cross-correlation for C14 in visit 100 101\n", + "Finished cross-correlation for C15 in visit 100 101\n", + "Finished cross-correlation for C16 in visit 100 101\n", + "Finished cross-correlation for C17 in visit 100 101\n", + "Finished cross-correlation for C07 in visit 100 101\n", + "Finished cross-correlation for C06 in visit 100 101\n", + "Finished cross-correlation for C05 in visit 100 101\n", + "Finished cross-correlation for C04 in visit 100 101\n", + "Finished cross-correlation for C03 in visit 100 101\n", + "Finished cross-correlation for C02 in visit 100 101\n", + "Finished cross-correlation for C01 in visit 100 101\n", + "Finished cross-correlation for C00 in visit 100 101\n", + "Finished image preparation for visit 102 103\n", + "Finished cross-correlation for C10 in visit 102 103\n", + "Finished cross-correlation for C11 in visit 102 103\n", + "Finished cross-correlation for C12 in visit 102 103\n", + "Finished cross-correlation for C13 in visit 102 103\n", + "Finished cross-correlation for C14 in visit 102 103\n", + "Finished cross-correlation for C15 in visit 102 103\n", + "Finished cross-correlation for C16 in visit 102 103\n", + "Finished cross-correlation for C17 in visit 102 103\n", + "Finished cross-correlation for C07 in visit 102 103\n", + "Finished cross-correlation for C06 in visit 102 103\n", + "Finished cross-correlation for C05 in visit 102 103\n", + "Finished cross-correlation for C04 in visit 102 103\n", + "Finished cross-correlation for C03 in visit 102 103\n", + "Finished cross-correlation for C02 in visit 102 103\n", + "Finished cross-correlation for C01 in visit 102 103\n", + "Finished cross-correlation for C00 in visit 102 103\n", + "Finished image preparation for visit 104 105\n", + "Finished cross-correlation for C10 in visit 104 105\n", + "Finished cross-correlation for C11 in visit 104 105\n", + "Finished cross-correlation for C12 in visit 104 105\n", + "Finished cross-correlation for C13 in visit 104 105\n", + "Finished cross-correlation for C14 in visit 104 105\n", + "Finished cross-correlation for C15 in visit 104 105\n", + "Finished cross-correlation for C16 in visit 104 105\n", + "Finished cross-correlation for C17 in visit 104 105\n", + "Finished cross-correlation for C07 in visit 104 105\n", + "Finished cross-correlation for C06 in visit 104 105\n", + "Finished cross-correlation for C05 in visit 104 105\n", + "Finished cross-correlation for C04 in visit 104 105\n", + "Finished cross-correlation for C03 in visit 104 105\n", + "Finished cross-correlation for C02 in visit 104 105\n", + "Finished cross-correlation for C01 in visit 104 105\n", + "Finished cross-correlation for C00 in visit 104 105\n", + "Finished image preparation for visit 106 107\n", + "Finished cross-correlation for C10 in visit 106 107\n", + "Finished cross-correlation for C11 in visit 106 107\n", + "Finished cross-correlation for C12 in visit 106 107\n", + "Finished cross-correlation for C13 in visit 106 107\n", + "Finished cross-correlation for C14 in visit 106 107\n", + "Finished cross-correlation for C15 in visit 106 107\n", + "Finished cross-correlation for C16 in visit 106 107\n", + "Finished cross-correlation for C17 in visit 106 107\n", + "Finished cross-correlation for C07 in visit 106 107\n", + "Finished cross-correlation for C06 in visit 106 107\n", + "Finished cross-correlation for C05 in visit 106 107\n", + "Finished cross-correlation for C04 in visit 106 107\n", + "Finished cross-correlation for C03 in visit 106 107\n", + "Finished cross-correlation for C02 in visit 106 107\n", + "Finished cross-correlation for C01 in visit 106 107\n", + "Finished cross-correlation for C00 in visit 106 107\n", + "Finished image preparation for visit 108 109\n", + "Finished cross-correlation for C10 in visit 108 109\n", + "Finished cross-correlation for C11 in visit 108 109\n", + "Finished cross-correlation for C12 in visit 108 109\n", + "Finished cross-correlation for C13 in visit 108 109\n", + "Finished cross-correlation for C14 in visit 108 109\n", + "Finished cross-correlation for C15 in visit 108 109\n", + "Finished cross-correlation for C16 in visit 108 109\n", + "Finished cross-correlation for C17 in visit 108 109\n", + "Finished cross-correlation for C07 in visit 108 109\n", + "Finished cross-correlation for C06 in visit 108 109\n", + "Finished cross-correlation for C05 in visit 108 109\n", + "Finished cross-correlation for C04 in visit 108 109\n", + "Finished cross-correlation for C03 in visit 108 109\n", + "Finished cross-correlation for C02 in visit 108 109\n", + "Finished cross-correlation for C01 in visit 108 109\n", + "Finished cross-correlation for C00 in visit 108 109\n", + "Finished image preparation for visit 200 201\n", + "Finished cross-correlation for C10 in visit 200 201\n", + "Finished cross-correlation for C11 in visit 200 201\n", + "Finished cross-correlation for C12 in visit 200 201\n", + "Finished cross-correlation for C13 in visit 200 201\n", + "Finished cross-correlation for C14 in visit 200 201\n", + "Finished cross-correlation for C15 in visit 200 201\n", + "Finished cross-correlation for C16 in visit 200 201\n", + "Finished cross-correlation for C17 in visit 200 201\n", + "Finished cross-correlation for C07 in visit 200 201\n", + "Finished cross-correlation for C06 in visit 200 201\n", + "Finished cross-correlation for C05 in visit 200 201\n", + "Finished cross-correlation for C04 in visit 200 201\n", + "Finished cross-correlation for C03 in visit 200 201\n", + "Finished cross-correlation for C02 in visit 200 201\n", + "Finished cross-correlation for C01 in visit 200 201\n", + "Finished cross-correlation for C00 in visit 200 201\n", + "Finished image preparation for visit 202 203\n", + "Finished cross-correlation for C10 in visit 202 203\n", + "Finished cross-correlation for C11 in visit 202 203\n", + "Finished cross-correlation for C12 in visit 202 203\n", + "Finished cross-correlation for C13 in visit 202 203\n", + "Finished cross-correlation for C14 in visit 202 203\n", + "Finished cross-correlation for C15 in visit 202 203\n", + "Finished cross-correlation for C16 in visit 202 203\n", + "Finished cross-correlation for C17 in visit 202 203\n", + "Finished cross-correlation for C07 in visit 202 203\n", + "Finished cross-correlation for C06 in visit 202 203\n", + "Finished cross-correlation for C05 in visit 202 203\n", + "Finished cross-correlation for C04 in visit 202 203\n", + "Finished cross-correlation for C03 in visit 202 203\n", + "Finished cross-correlation for C02 in visit 202 203\n", + "Finished cross-correlation for C01 in visit 202 203\n", + "Finished cross-correlation for C00 in visit 202 203\n", + "Finished image preparation for visit 204 205\n", + "Finished cross-correlation for C10 in visit 204 205\n", + "Finished cross-correlation for C11 in visit 204 205\n", + "Finished cross-correlation for C12 in visit 204 205\n", + "Finished cross-correlation for C13 in visit 204 205\n", + "Finished cross-correlation for C14 in visit 204 205\n", + "Finished cross-correlation for C15 in visit 204 205\n", + "Finished cross-correlation for C16 in visit 204 205\n", + "Finished cross-correlation for C17 in visit 204 205\n", + "Finished cross-correlation for C07 in visit 204 205\n", + "Finished cross-correlation for C06 in visit 204 205\n", + "Finished cross-correlation for C05 in visit 204 205\n", + "Finished cross-correlation for C04 in visit 204 205\n", + "Finished cross-correlation for C03 in visit 204 205\n", + "Finished cross-correlation for C02 in visit 204 205\n", + "Finished cross-correlation for C01 in visit 204 205\n", + "Finished cross-correlation for C00 in visit 204 205\n", + "Finished image preparation for visit 206 207\n", + "Finished cross-correlation for C10 in visit 206 207\n", + "Finished cross-correlation for C11 in visit 206 207\n", + "Finished cross-correlation for C12 in visit 206 207\n", + "Finished cross-correlation for C13 in visit 206 207\n", + "Finished cross-correlation for C14 in visit 206 207\n", + "Finished cross-correlation for C15 in visit 206 207\n", + "Finished cross-correlation for C16 in visit 206 207\n", + "Finished cross-correlation for C17 in visit 206 207\n", + "Finished cross-correlation for C07 in visit 206 207\n", + "Finished cross-correlation for C06 in visit 206 207\n", + "Finished cross-correlation for C05 in visit 206 207\n", + "Finished cross-correlation for C04 in visit 206 207\n", + "Finished cross-correlation for C03 in visit 206 207\n", + "Finished cross-correlation for C02 in visit 206 207\n", + "Finished cross-correlation for C01 in visit 206 207\n", + "Finished cross-correlation for C00 in visit 206 207\n", + "Finished image preparation for visit 208 209\n", + "Finished cross-correlation for C10 in visit 208 209\n", + "Finished cross-correlation for C11 in visit 208 209\n", + "Finished cross-correlation for C12 in visit 208 209\n", + "Finished cross-correlation for C13 in visit 208 209\n", + "Finished cross-correlation for C14 in visit 208 209\n", + "Finished cross-correlation for C15 in visit 208 209\n", + "Finished cross-correlation for C16 in visit 208 209\n", + "Finished cross-correlation for C17 in visit 208 209\n", + "Finished cross-correlation for C07 in visit 208 209\n", + "Finished cross-correlation for C06 in visit 208 209\n", + "Finished cross-correlation for C05 in visit 208 209\n", + "Finished cross-correlation for C04 in visit 208 209\n", + "Finished cross-correlation for C03 in visit 208 209\n", + "Finished cross-correlation for C02 in visit 208 209\n", + "Finished cross-correlation for C01 in visit 208 209\n", + "Finished cross-correlation for C00 in visit 208 209\n", + "Finished image preparation for visit 300 301\n", + "Finished cross-correlation for C10 in visit 300 301\n", + "Finished cross-correlation for C11 in visit 300 301\n", + "Finished cross-correlation for C12 in visit 300 301\n", + "Finished cross-correlation for C13 in visit 300 301\n", + "Finished cross-correlation for C14 in visit 300 301\n", + "Finished cross-correlation for C15 in visit 300 301\n", + "Finished cross-correlation for C16 in visit 300 301\n", + "Finished cross-correlation for C17 in visit 300 301\n", + "Finished cross-correlation for C07 in visit 300 301\n", + "Finished cross-correlation for C06 in visit 300 301\n", + "Finished cross-correlation for C05 in visit 300 301\n", + "Finished cross-correlation for C04 in visit 300 301\n", + "Finished cross-correlation for C03 in visit 300 301\n", + "Finished cross-correlation for C02 in visit 300 301\n", + "Finished cross-correlation for C01 in visit 300 301\n", + "Finished cross-correlation for C00 in visit 300 301\n", + "Finished image preparation for visit 302 303\n", + "Finished cross-correlation for C10 in visit 302 303\n", + "Finished cross-correlation for C11 in visit 302 303\n", + "Finished cross-correlation for C12 in visit 302 303\n", + "Finished cross-correlation for C13 in visit 302 303\n", + "Finished cross-correlation for C14 in visit 302 303\n", + "Finished cross-correlation for C15 in visit 302 303\n", + "Finished cross-correlation for C16 in visit 302 303\n", + "Finished cross-correlation for C17 in visit 302 303\n", + "Finished cross-correlation for C07 in visit 302 303\n", + "Finished cross-correlation for C06 in visit 302 303\n", + "Finished cross-correlation for C05 in visit 302 303\n", + "Finished cross-correlation for C04 in visit 302 303\n", + "Finished cross-correlation for C03 in visit 302 303\n", + "Finished cross-correlation for C02 in visit 302 303\n", + "Finished cross-correlation for C01 in visit 302 303\n", + "Finished cross-correlation for C00 in visit 302 303\n", + "Finished image preparation for visit 304 305\n", + "Finished cross-correlation for C10 in visit 304 305\n", + "Finished cross-correlation for C11 in visit 304 305\n", + "Finished cross-correlation for C12 in visit 304 305\n", + "Finished cross-correlation for C13 in visit 304 305\n", + "Finished cross-correlation for C14 in visit 304 305\n", + "Finished cross-correlation for C15 in visit 304 305\n", + "Finished cross-correlation for C16 in visit 304 305\n", + "Finished cross-correlation for C17 in visit 304 305\n", + "Finished cross-correlation for C07 in visit 304 305\n", + "Finished cross-correlation for C06 in visit 304 305\n", + "Finished cross-correlation for C05 in visit 304 305\n", + "Finished cross-correlation for C04 in visit 304 305\n", + "Finished cross-correlation for C03 in visit 304 305\n", + "Finished cross-correlation for C02 in visit 304 305\n", + "Finished cross-correlation for C01 in visit 304 305\n", + "Finished cross-correlation for C00 in visit 304 305\n", + "Finished image preparation for visit 306 307\n", + "Finished cross-correlation for C10 in visit 306 307\n", + "Finished cross-correlation for C11 in visit 306 307\n", + "Finished cross-correlation for C12 in visit 306 307\n", + "Finished cross-correlation for C13 in visit 306 307\n", + "Finished cross-correlation for C14 in visit 306 307\n", + "Finished cross-correlation for C15 in visit 306 307\n", + "Finished cross-correlation for C16 in visit 306 307\n", + "Finished cross-correlation for C17 in visit 306 307\n", + "Finished cross-correlation for C07 in visit 306 307\n", + "Finished cross-correlation for C06 in visit 306 307\n", + "Finished cross-correlation for C05 in visit 306 307\n", + "Finished cross-correlation for C04 in visit 306 307\n", + "Finished cross-correlation for C03 in visit 306 307\n", + "Finished cross-correlation for C02 in visit 306 307\n", + "Finished cross-correlation for C01 in visit 306 307\n", + "Finished cross-correlation for C00 in visit 306 307\n", + "Finished image preparation for visit 308 309\n", + "Finished cross-correlation for C10 in visit 308 309\n", + "Finished cross-correlation for C11 in visit 308 309\n", + "Finished cross-correlation for C12 in visit 308 309\n", + "Finished cross-correlation for C13 in visit 308 309\n", + "Finished cross-correlation for C14 in visit 308 309\n", + "Finished cross-correlation for C15 in visit 308 309\n", + "Finished cross-correlation for C16 in visit 308 309\n", + "Finished cross-correlation for C17 in visit 308 309\n", + "Finished cross-correlation for C07 in visit 308 309\n", + "Finished cross-correlation for C06 in visit 308 309\n", + "Finished cross-correlation for C05 in visit 308 309\n", + "Finished cross-correlation for C04 in visit 308 309\n", + "Finished cross-correlation for C03 in visit 308 309\n", + "Finished cross-correlation for C02 in visit 308 309\n", + "Finished cross-correlation for C01 in visit 308 309\n", + "Finished cross-correlation for C00 in visit 308 309\n", + "Finished image preparation for visit 400 401\n", + "Finished cross-correlation for C10 in visit 400 401\n", + "Finished cross-correlation for C11 in visit 400 401\n", + "Finished cross-correlation for C12 in visit 400 401\n", + "Finished cross-correlation for C13 in visit 400 401\n", + "Finished cross-correlation for C14 in visit 400 401\n", + "Finished cross-correlation for C15 in visit 400 401\n", + "Finished cross-correlation for C16 in visit 400 401\n", + "Finished cross-correlation for C17 in visit 400 401\n", + "Finished cross-correlation for C07 in visit 400 401\n", + "Finished cross-correlation for C06 in visit 400 401\n", + "Finished cross-correlation for C05 in visit 400 401\n", + "Finished cross-correlation for C04 in visit 400 401\n", + "Finished cross-correlation for C03 in visit 400 401\n", + "Finished cross-correlation for C02 in visit 400 401\n", + "Finished cross-correlation for C01 in visit 400 401\n", + "Finished cross-correlation for C00 in visit 400 401\n", + "Finished image preparation for visit 402 403\n", + "Finished cross-correlation for C10 in visit 402 403\n", + "Finished cross-correlation for C11 in visit 402 403\n", + "Finished cross-correlation for C12 in visit 402 403\n", + "Finished cross-correlation for C13 in visit 402 403\n", + "Finished cross-correlation for C14 in visit 402 403\n", + "Finished cross-correlation for C15 in visit 402 403\n", + "Finished cross-correlation for C16 in visit 402 403\n", + "Finished cross-correlation for C17 in visit 402 403\n", + "Finished cross-correlation for C07 in visit 402 403\n", + "Finished cross-correlation for C06 in visit 402 403\n", + "Finished cross-correlation for C05 in visit 402 403\n", + "Finished cross-correlation for C04 in visit 402 403\n", + "Finished cross-correlation for C03 in visit 402 403\n", + "Finished cross-correlation for C02 in visit 402 403\n", + "Finished cross-correlation for C01 in visit 402 403\n", + "Finished cross-correlation for C00 in visit 402 403\n", + "Finished image preparation for visit 404 405\n", + "Finished cross-correlation for C10 in visit 404 405\n", + "Finished cross-correlation for C11 in visit 404 405\n", + "Finished cross-correlation for C12 in visit 404 405\n", + "Finished cross-correlation for C13 in visit 404 405\n", + "Finished cross-correlation for C14 in visit 404 405\n", + "Finished cross-correlation for C15 in visit 404 405\n", + "Finished cross-correlation for C16 in visit 404 405\n", + "Finished cross-correlation for C17 in visit 404 405\n", + "Finished cross-correlation for C07 in visit 404 405\n", + "Finished cross-correlation for C06 in visit 404 405\n", + "Finished cross-correlation for C05 in visit 404 405\n", + "Finished cross-correlation for C04 in visit 404 405\n", + "Finished cross-correlation for C03 in visit 404 405\n", + "Finished cross-correlation for C02 in visit 404 405\n", + "Finished cross-correlation for C01 in visit 404 405\n", + "Finished cross-correlation for C00 in visit 404 405\n", + "Finished image preparation for visit 406 407\n", + "Finished cross-correlation for C10 in visit 406 407\n", + "Finished cross-correlation for C11 in visit 406 407\n", + "Finished cross-correlation for C12 in visit 406 407\n", + "Finished cross-correlation for C13 in visit 406 407\n", + "Finished cross-correlation for C14 in visit 406 407\n", + "Finished cross-correlation for C15 in visit 406 407\n", + "Finished cross-correlation for C16 in visit 406 407\n", + "Finished cross-correlation for C17 in visit 406 407\n", + "Finished cross-correlation for C07 in visit 406 407\n", + "Finished cross-correlation for C06 in visit 406 407\n", + "Finished cross-correlation for C05 in visit 406 407\n", + "Finished cross-correlation for C04 in visit 406 407\n", + "Finished cross-correlation for C03 in visit 406 407\n", + "Finished cross-correlation for C02 in visit 406 407\n", + "Finished cross-correlation for C01 in visit 406 407\n", + "Finished cross-correlation for C00 in visit 406 407\n", + "Finished image preparation for visit 408 409\n", + "Finished cross-correlation for C10 in visit 408 409\n", + "Finished cross-correlation for C11 in visit 408 409\n", + "Finished cross-correlation for C12 in visit 408 409\n", + "Finished cross-correlation for C13 in visit 408 409\n", + "Finished cross-correlation for C14 in visit 408 409\n", + "Finished cross-correlation for C15 in visit 408 409\n", + "Finished cross-correlation for C16 in visit 408 409\n", + "Finished cross-correlation for C17 in visit 408 409\n", + "Finished cross-correlation for C07 in visit 408 409\n", + "Finished cross-correlation for C06 in visit 408 409\n", + "Finished cross-correlation for C05 in visit 408 409\n", + "Finished cross-correlation for C04 in visit 408 409\n", + "Finished cross-correlation for C03 in visit 408 409\n", + "Finished cross-correlation for C02 in visit 408 409\n", + "Finished cross-correlation for C01 in visit 408 409\n", + "Finished cross-correlation for C00 in visit 408 409\n", + "Finished image preparation for visit 500 501\n", + "Finished cross-correlation for C10 in visit 500 501\n", + "Finished cross-correlation for C11 in visit 500 501\n", + "Finished cross-correlation for C12 in visit 500 501\n", + "Finished cross-correlation for C13 in visit 500 501\n", + "Finished cross-correlation for C14 in visit 500 501\n", + "Finished cross-correlation for C15 in visit 500 501\n", + "Finished cross-correlation for C16 in visit 500 501\n", + "Finished cross-correlation for C17 in visit 500 501\n", + "Finished cross-correlation for C07 in visit 500 501\n", + "Finished cross-correlation for C06 in visit 500 501\n", + "Finished cross-correlation for C05 in visit 500 501\n", + "Finished cross-correlation for C04 in visit 500 501\n", + "Finished cross-correlation for C03 in visit 500 501\n", + "Finished cross-correlation for C02 in visit 500 501\n", + "Finished cross-correlation for C01 in visit 500 501\n", + "Finished cross-correlation for C00 in visit 500 501\n", + "Finished image preparation for visit 502 503\n", + "Finished cross-correlation for C10 in visit 502 503\n", + "Finished cross-correlation for C11 in visit 502 503\n", + "Finished cross-correlation for C12 in visit 502 503\n", + "Finished cross-correlation for C13 in visit 502 503\n", + "Finished cross-correlation for C14 in visit 502 503\n", + "Finished cross-correlation for C15 in visit 502 503\n", + "Finished cross-correlation for C16 in visit 502 503\n", + "Finished cross-correlation for C17 in visit 502 503\n", + "Finished cross-correlation for C07 in visit 502 503\n", + "Finished cross-correlation for C06 in visit 502 503\n", + "Finished cross-correlation for C05 in visit 502 503\n", + "Finished cross-correlation for C04 in visit 502 503\n", + "Finished cross-correlation for C03 in visit 502 503\n", + "Finished cross-correlation for C02 in visit 502 503\n", + "Finished cross-correlation for C01 in visit 502 503\n", + "Finished cross-correlation for C00 in visit 502 503\n", + "Finished image preparation for visit 504 505\n", + "Finished cross-correlation for C10 in visit 504 505\n", + "Finished cross-correlation for C11 in visit 504 505\n", + "Finished cross-correlation for C12 in visit 504 505\n", + "Finished cross-correlation for C13 in visit 504 505\n", + "Finished cross-correlation for C14 in visit 504 505\n", + "Finished cross-correlation for C15 in visit 504 505\n", + "Finished cross-correlation for C16 in visit 504 505\n", + "Finished cross-correlation for C17 in visit 504 505\n", + "Finished cross-correlation for C07 in visit 504 505\n", + "Finished cross-correlation for C06 in visit 504 505\n", + "Finished cross-correlation for C05 in visit 504 505\n", + "Finished cross-correlation for C04 in visit 504 505\n", + "Finished cross-correlation for C03 in visit 504 505\n", + "Finished cross-correlation for C02 in visit 504 505\n", + "Finished cross-correlation for C01 in visit 504 505\n", + "Finished cross-correlation for C00 in visit 504 505\n", + "Finished image preparation for visit 506 507\n", + "Finished cross-correlation for C10 in visit 506 507\n", + "Finished cross-correlation for C11 in visit 506 507\n", + "Finished cross-correlation for C12 in visit 506 507\n", + "Finished cross-correlation for C13 in visit 506 507\n", + "Finished cross-correlation for C14 in visit 506 507\n", + "Finished cross-correlation for C15 in visit 506 507\n", + "Finished cross-correlation for C16 in visit 506 507\n", + "Finished cross-correlation for C17 in visit 506 507\n", + "Finished cross-correlation for C07 in visit 506 507\n", + "Finished cross-correlation for C06 in visit 506 507\n", + "Finished cross-correlation for C05 in visit 506 507\n", + "Finished cross-correlation for C04 in visit 506 507\n", + "Finished cross-correlation for C03 in visit 506 507\n", + "Finished cross-correlation for C02 in visit 506 507\n", + "Finished cross-correlation for C01 in visit 506 507\n", + "Finished cross-correlation for C00 in visit 506 507\n", + "Finished image preparation for visit 508 509\n", + "Finished cross-correlation for C10 in visit 508 509\n", + "Finished cross-correlation for C11 in visit 508 509\n", + "Finished cross-correlation for C12 in visit 508 509\n", + "Finished cross-correlation for C13 in visit 508 509\n", + "Finished cross-correlation for C14 in visit 508 509\n", + "Finished cross-correlation for C15 in visit 508 509\n", + "Finished cross-correlation for C16 in visit 508 509\n", + "Finished cross-correlation for C17 in visit 508 509\n", + "Finished cross-correlation for C07 in visit 508 509\n", + "Finished cross-correlation for C06 in visit 508 509\n", + "Finished cross-correlation for C05 in visit 508 509\n", + "Finished cross-correlation for C04 in visit 508 509\n", + "Finished cross-correlation for C03 in visit 508 509\n", + "Finished cross-correlation for C02 in visit 508 509\n", + "Finished cross-correlation for C01 in visit 508 509\n", + "Finished cross-correlation for C00 in visit 508 509\n" + ] + } + ], + "source": [ + "args = ['ucd_repo_2', '--rerun', 'test',\n", + " '--id', 'detector=85',\n", + " '--visit-pairs', '100,101', '102,103', '104,105', '106,107', '108,109', '200,201', '202,203', '204,205', '206,207', '208,209', '300,301', '302,303', '304,305', '306,307', '308,309', '400,401', '402,403', '404,405', '406,407', '408,409', '500,501', '502,503', '504,505', '506,507', '508,509',\n", + " '-c',\n", + " 'xcorrCheckRejectLevel=2', 'doCalcGains=True', 'level=\"AMP\"',\n", + " '--clobber-config', '--clobber-versions'\n", + " ]\n", + "\n", + "command_line = 'makeBrighterFatterKernel.py ' + ' '.join(args)\n", + "print(command_line)\n", + "\n", + "ucd_pb_struct = MakeBrighterFatterKernelTask.parseAndRun(args=args)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#It now runs with doCalcGains=True and the gains are close to what we measured here.\n", + "ucd_detector = 85\n", + "test_butler = Butler('ucd_repo_2/rerun/test')\n", + "ucd_bf_kernel = test_butler.get('brighterFatterKernelNew', dataId={'raftName': 'R21', 'detectorName': 'S11', 'detector': ucd_detector})\n", + "gain_data = test_butler.get('brighterFatterGain', dataId={'raftName': 'R21', 'detectorName': 'S11', 'detector': 85})" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,8))\n", + "plt.subplots_adjust(hspace=0.5)\n", + "for i, key in enumerate(ucd_bf_kernel.keys()):\n", + " plt.subplot(4,8,i+1)\n", + " plt.title('AMP=%s'%key) \n", + " plt.imshow(ucd_bf_kernel[key])\n", + " ax1 = plt.subplot(4,8,i+17)\n", + " ax1.set_title('AMP=%s, gain = %.2f'%(key,gain_data[key]),fontsize = 8) \n", + " ax1.set_ylim(-3.0E-6,1.0E-6)\n", + " ax1.set_yticks([-1.0E-6, 0.0,1.0E-6])\n", + " ax1.plot(ucd_bf_kernel[key][:,8], color='blue', drawstyle='steps-mid')\n", + " ax1.plot(ucd_bf_kernel[key][8,:], linestyle='--', color='red', drawstyle='steps-mid')\n", + "\n", + " if (i == 0 or i == 8):\n", + " ax1.set_yticklabels([-1.0E-6, 0.0,1.0E-6])\n", + " else:\n", + " ax1.set_yticklabels([])\n", + " \n", + "\n", + "plt.savefig('BF_Kernel_25_Pairs_28Aug18.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now read in the kernel that I calculated with my code (BF_Kernel_Correction_07Jun18.ipynb) for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#Reading in the kernel that I calculated here.\n", + "\n", + "from pylab import *\n", + "import pickle as pkl\n", + "class Array2d:\n", + " def __init__(self,xmin,xmax,nx,ymin,ymax,ny):\n", + " # Each image is nx * ny pixels\n", + " self.nx=nx\n", + " self.ny=ny\n", + "\n", + " self.xmin=xmin\n", + " self.ymin=ymin\n", + " \n", + " self.xmax=xmax\n", + " self.ymax=ymax\n", + " \n", + " self.dx=(xmax-xmin)/nx\n", + " self.dy=(ymax-ymin)/ny\n", + " \n", + " self.x=linspace(xmin+self.dx/2,xmax-self.dx/2,nx)\n", + " self.y=linspace(ymin+self.dy/2,ymax-self.dy/2,ny)\n", + "\n", + " self.cov=zeros([nx,ny])\n", + " self.kernel=zeros([nx,ny]) \n", + " \n", + " def PrintKernel(self, filename):\n", + " file = open(filename, 'w')\n", + " line = 'X\\tY\\tCovariance\\t\\t\\tKernel\\n'\n", + " file.write(line)\n", + " for i in range(self.nx):\n", + " for j in range(self.ny):\n", + " line = '%d\\t%d\\t%0.12e\\t%0.12e\\n'%(i,j,self.cov[i,j],self.kernel[i,j])\n", + " file.write(line)\n", + " file.close()\n", + " return\n", + "\n", + " def ReadKernel(self, filename):\n", + " file = open(filename, 'r')\n", + " lines = file.readlines()\n", + " file.close()\n", + " lines.remove(lines[0])\n", + " for line in lines:\n", + " items = line.split()\n", + " i = int(items[0])\n", + " j = int(items[1])\n", + " cov = float(items[2])\n", + " ker = float(items[3])\n", + " self.cov[i,j] = cov\n", + " self.kernel[i,j] = ker\n", + " return\n", + "\n", + "NewNx = NewNy = 21\n", + "kernel = Array2d(0,NewNx,NewNx,0,NewNy,NewNy)\n", + "my_kernel_filename = \"/home/cslage/Research/LSST/code/notebooks/best_kernel/kernel_model_central_csteps_7_07jun18.txt\"\n", + "kernel.ReadKernel(my_kernel_filename)\n", + "#print(kernel.kernel)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "key = 'C04'\n", + "plt.figure(figsize=(16,8))\n", + "plt.suptitle(\"BF Kernel Comparison\", fontsize = 18)\n", + "plt.subplots_adjust(hspace=0.8)\n", + " \n", + "ax1 = plt.subplot(1,2,1)\n", + "ax1.set_title('X, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", + "ax1.set_ylim(-3.0E-6,1.0E-6)\n", + "ax1.set_yticks([-2.0E-6,-1.0E-6,0.0,1.0E-6])\n", + "ax1.plot(ucd_bf_kernel[key][:,8], color='blue', drawstyle='steps-mid', label='DM')\n", + "ax1.plot(kernel.kernel[2:19,10], color='green', drawstyle='steps-mid', label='UCD')\n", + "ax1.legend()\n", + "ax2 = plt.subplot(1,2,2)\n", + "ax2.set_title('Y, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", + "ax2.set_ylim(-3.0E-6,1.0E-6)\n", + "ax2.set_yticks([-2.0E-6,-1.0E-6,0.0,1.0E-6])\n", + "ax2.plot(ucd_bf_kernel[key][8,:], color='blue', drawstyle='steps-mid', label='DM')\n", + "ax2.plot(kernel.kernel[10,2:19], color='green', drawstyle='steps-mid', label='UCD')\n", + "ax2.legend()\n", + "plt.savefig('BF_Kernel_Comparison_29Aug18.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These don't agree very well at all. My hypothesis is that it is a gain issue, since if I divide the DM values by the gain of 4.31 then they agree pretty well. I suspect that this is just a difference of whether the correction is applied before or after the gain correction.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "key = 'C04'\n", + "plt.figure(figsize=(16,8))\n", + "plt.suptitle(\"BF Kernel Comparison - After Dividing by Gain\", fontsize = 18)\n", + "plt.subplots_adjust(hspace=0.8)\n", + " \n", + "ax1 = plt.subplot(1,2,1)\n", + "ax1.set_title('X, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", + "ax1.set_ylim(-1.0E-6,1.0E-6)\n", + "ax1.set_yticks([-1.0E-6,0.0,1.0E-6])\n", + "ax1.plot(ucd_bf_kernel[key][:,8]/gain_data[key], color='blue', drawstyle='steps-mid', label='DM')\n", + "ax1.plot(kernel.kernel[2:19,10], color='green', drawstyle='steps-mid', label='UCD')\n", + "ax1.legend()\n", + "ax2 = plt.subplot(1,2,2)\n", + "ax2.set_title('Y, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", + "ax2.set_ylim(-1.0E-6,1.0E-6)\n", + "ax2.set_yticks([-1.0E-6,0.0,1.0E-6])\n", + "ax2.plot(ucd_bf_kernel[key][8,:]/gain_data[key], color='blue', drawstyle='steps-mid', label='DM')\n", + "ax2.plot(kernel.kernel[10,2:19], color='green', drawstyle='steps-mid', label='UCD')\n", + "ax2.legend()\n", + "plt.savefig('BF_Kernel_Comparison_Gain_29Aug18.pdf')\n", + "plt.show()" + ] + }, + { + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0e49c04e961b085237e50f45e37f4495e04056b5 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Mon, 3 Sep 2018 12:11:57 -0700 Subject: [PATCH 34/45] Added August 31 session links --- Meetings.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Meetings.md b/Meetings.md index 36b32f3f..e7d92884 100644 --- a/Meetings.md +++ b/Meetings.md @@ -4,7 +4,8 @@ Individual session links and recordings are given below, most recent meeting at | Session | Date | Topic | Links | |---|---|---|---| -| Phase 2, Session 2 | Friday August 24, 2018 [(video)](https://stanford.zoom.us/recording/share/Xii8Utw9RX5rqGUn8a_barg6NDBcRuzkmDIjDrUds82wIumekTziMw) | Interactive visualization, new member start-ups, hacking | [Bokeh/HoloViews Demo](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/bokeh_holoviews_datashader/bechtol/Visualization/bokeh_holoviews_datashader.ipynb) | +| Phase 2, Session 3 | Friday August 31, 2018 [(video)](https://stanford.zoom.us/recording/share/U7_XJvwjNlUh4N7g3ytBbKtTQHl-fLS0tqiBhAxZrEmwIumekTziMw) | Live code review, hacking | [Brighter-Fatter Correction with Beamsim Data](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/beamsim/andrewkbradshaw/ImageProcessing/BrighterFatterCorrection.ipynb) | +| Phase 2, Session 2 | Friday August 24, 2018 [(video)](https://stanford.zoom.us/recording/share/share/Xii8Utw9RX5rqGUn8a_barg6NDBcRuzkmDIjDrUds82wIumekTziMw) | Interactive visualization, new member start-ups, hacking | [Bokeh/HoloViews Demo](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/bokeh_holoviews_datashader/bechtol/Visualization/bokeh_holoviews_datashader.ipynb) | | LSST2018 Launch | Monday August 13, 2018 [(video)](https://stanford.zoom.us/recording/share/qyunKljpUWaFQBneuiL4PnbxTB-tf1BvttELFVPJHnuwIumekTziMw) | PCW welcome, discussion, hacking | [Introduction to the LSST Stack Club](https://docs.google.com/presentation/d/1LWShGi-YLqWoxPvewkg-JOpb67WKZyI0YQcSFrmAl14/edit#slide=id.p1) | From 68f3594f3a2ba7e4205ca0353461ea5c3c6c5e0c Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Tue, 4 Sep 2018 00:46:09 -0500 Subject: [PATCH 35/45] Fixed typo --- Visualization/AFW_Display_Demo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Visualization/AFW_Display_Demo.ipynb b/Visualization/AFW_Display_Demo.ipynb index be330777..8a7b7ae1 100644 --- a/Visualization/AFW_Display_Demo.ipynb +++ b/Visualization/AFW_Display_Demo.ipynb @@ -167,7 +167,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With the `Butler` instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the butler which filter, raft, sensor, and visit we wish to view. To do this, we definie dictionary with the required information." + "With the `Butler` instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the butler which filter, raft, sensor, and visit we wish to view. To do this, we define dictionary with the required information." ] }, { From 8432e2ab44011144902387c2b1a9a6b430859cef Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Fri, 7 Sep 2018 11:43:19 -0500 Subject: [PATCH 36/45] Update link to close #117 --- SourceDetection/LowSurfaceBrightness.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SourceDetection/LowSurfaceBrightness.ipynb b/SourceDetection/LowSurfaceBrightness.ipynb index 672068ce..babc6873 100644 --- a/SourceDetection/LowSurfaceBrightness.ipynb +++ b/SourceDetection/LowSurfaceBrightness.ipynb @@ -11,7 +11,7 @@ "
Verified Stack Release: **v16.0**\n", "\n", "This Notebook demonstrates how to run the source detection, measurment, and deblending algorithms with a focus on optimizing for low-surface brightness object detection. It attempts to split out the source detection and measurement algorithms from `processCCD` and apply them to the search for low surface brightness galaxies. The content of this notebook builds off of an analysis from Johnny Greco, adapted into notebook form in Robert Lupton's [Greco LSB.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Greco%20LSB.ipynb). Some source detection and measurement details come from [Tune Detection.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Tune%20Detection.ipynb) and [Kron.ipynb](https://github.com/RobertLuptonTheGood/notebooks/blob/master/Demos/Kron.ipynb).\n", - "Interaction with `lsst.afw.display` was also improved by studying Michael Wood-Vasey's [DC2_Postage Stamps.ipynb](https://github.com/LSSTDESC/DC2_Repo/blob/master/Notebooks/DC2_Postage_Stamps.ipynb).\n", + "Interaction with `lsst.afw.display` was also improved by studying Michael Wood-Vasey's [DC2_Postage Stamps.ipynb](https://github.com/LSSTDESC/DC2-analysis/blob/master/tutorials/dm_butler_postage_stamps.ipynb).\n", "\n", "### Learning Objectives:\n", "After working through and studying this notebook you should be able to\n", From 763c3f65bc636cc92cb828ebc32dc4ee6473f999 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Wed, 12 Sep 2018 16:19:21 -0700 Subject: [PATCH 37/45] Links to video and notebook for Imran's afw table walkthrough --- Meetings.md | 1 + 1 file changed, 1 insertion(+) diff --git a/Meetings.md b/Meetings.md index e7d92884..8ef7f3a1 100644 --- a/Meetings.md +++ b/Meetings.md @@ -4,6 +4,7 @@ Individual session links and recordings are given below, most recent meeting at | Session | Date | Topic | Links | |---|---|---|---| +| Phase 2, Session 4 | Friday September 7, 2018 [(video)](https://stanford.zoom.us/recording/share/ZlkFudy5hMTeR-GZVOgo_oGd0R9Q4dkrN6-aJMfelGawIumekTziMw) | Notebook walkthrough, Hacking | [Guided Tour of an AFW Table](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/afw_table/ishasan/Basics/afw_table_guided_tour.ipynb) | | Phase 2, Session 3 | Friday August 31, 2018 [(video)](https://stanford.zoom.us/recording/share/U7_XJvwjNlUh4N7g3ytBbKtTQHl-fLS0tqiBhAxZrEmwIumekTziMw) | Live code review, hacking | [Brighter-Fatter Correction with Beamsim Data](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/beamsim/andrewkbradshaw/ImageProcessing/BrighterFatterCorrection.ipynb) | | Phase 2, Session 2 | Friday August 24, 2018 [(video)](https://stanford.zoom.us/recording/share/share/Xii8Utw9RX5rqGUn8a_barg6NDBcRuzkmDIjDrUds82wIumekTziMw) | Interactive visualization, new member start-ups, hacking | [Bokeh/HoloViews Demo](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/bokeh_holoviews_datashader/bechtol/Visualization/bokeh_holoviews_datashader.ipynb) | | LSST2018 Launch | Monday August 13, 2018 [(video)](https://stanford.zoom.us/recording/share/qyunKljpUWaFQBneuiL4PnbxTB-tf1BvttELFVPJHnuwIumekTziMw) | PCW welcome, discussion, hacking | [Introduction to the LSST Stack Club](https://docs.google.com/presentation/d/1LWShGi-YLqWoxPvewkg-JOpb67WKZyI0YQcSFrmAl14/edit#slide=id.p1) | From 6846fff916ab9c79b2ba269ee6104a329a9832ef Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Fri, 14 Sep 2018 11:23:50 -0500 Subject: [PATCH 38/45] Verified AnyConnect on Linux; added some details. --- GettingStarted/GettingStarted.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index a1a8f3bc..155957c4 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -17,7 +17,7 @@ The Stack Club has a limited number of active LSST Science Platform accounts it #### Accessing the LSP via its VPN At present, unless you are on an approved network, you must use the [NCSA virtual private network (VPN)](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service). -The recommended method is to use Cisco's AnyConnect with DUO two-factor authentication. Detailed instructions are available on the [NCSA VPN site](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service#VirtualPrivateNetwork(VPN)Service-UsingtheCiscoAnyConnectVPNClient(Required)). +The recommended method is to use Cisco's AnyConnect with DUO two-factor authentication (verified on Mac and Linux). Detailed instructions are available on the [NCSA VPN site](https://wiki.ncsa.illinois.edu/display/cybersec/Virtual+Private+Network+%28VPN%29+Service#VirtualPrivateNetwork(VPN)Service-UsingtheCiscoAnyConnectVPNClient(Required)). > You can get AnyConnect by pointing your browser at https://sslvpn.ncsa.illinois.edu/ and selecting the `ncsa-vpn-default` option (this will only work if you have a java-compatible browser, like firefox esr version<=52). If you already have the AnyConnect client installed, open it up and enter `sslvpn.ncsa.illinois.edu/` in its connection window. @@ -25,6 +25,8 @@ The recommended method is to use Cisco's AnyConnect with DUO two-factor authenti If you forget your password it can be reset following the instructions [here](https://developer.lsst.io/services/lsst-dev.html?highlight=reset#lsst-dev-password). If you have problems connecting to the NCSA services you can check their status and submit a help ticket [here](https://confluence.lsstcorp.org/display/DM/LSST+Service+Status+page). +For a Linux install, you may need to pre-install [`openconnect`](http://www.infradead.org/openconnect/) from your favorite package manager. + #### Starting up the LSST Science Platform JupyterLab Notebook Aspect Once the VPN connection is established, you should be able to navigate to the the JupyterLab instance at **https://lsst-lspdev.ncsa.illinois.edu/nb**. Select the `Release` and `medium` options on the Spawner Options landing page, and then hit the "Spawn" button. You'll (eventually) end up on the JupyterLab launcher, where you can use the file manager in the left hand side bar to open your Jupyter notebooks, or start terminal or notebook editor tabs from the buttons provided. You should see the pre-installed `notebook-demo` notebooks in the file manager, for example. From 76484de5c825265d91a288bd94d7121995d20be0 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 14 Sep 2018 17:29:01 +0000 Subject: [PATCH 39/45] Better emphasis on developing and using the stackclub package --- GettingStarted/GettingStarted.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index a1a8f3bc..636c7273 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -61,18 +61,20 @@ Broadly useful, small datasets are available in `/project/shared/data` - this i Larger datasets are available in `/datasets`. This is a read-only folder. #### The Stack Club Library -The [`stackclub` folder in this repo](../stackclub) is a python package containing a number of utility functions and classes for use in tutorial notebooks. You can browse its documentation at https://stackclub.readthedocs.io/. If you are not developing this package, you can install it using pip, like this: -``` -pip install git+git://github.com/LSSTScienceCollaborations/StackClub.git#egg=stackclub -``` -However, if you are contributing notebooks it is likely that you'll need to develop the `stackclub` package as well -(eg by adding modules to it), and so you'll need to make a local, editable installation. In the top level folder of your local clone of the StackClub repo, do: +The [`stackclub` folder in this repo](../stackclub) is a python package containing a number of utility functions and classes for use in tutorial notebooks. You can browse its documentation at https://stackclub.readthedocs.io/. +If you are contributing notebooks, you may want or need to develop the `stackclub` package as well +(eg by adding modules to it), and so its best to setup the package installation to be local and editable. In the top level folder of your local clone of the StackClub repo, do: ``` python setup.py -q develop --user ``` -This will put the `stackclub` folder on your path. You may find the following lines useful to add to your notebook as you develop the library: +This will put the repo's `stackclub` folder on your path. When developing the package, may find it useful to add the following lines to your notebook: ```python %load_ext autoreload %autoreload 2 ``` -This enables you to repeatedly `import stackclub` as you update the library code. +This enables you to repeatedly `import stackclub` as you update the library code. The above lines are in the [template notebook](templates/template_Notebook.ipynb), for your convenience. + +If you are not developing this package, you can install it using pip, like this: +``` +pip install git+git://github.com/LSSTScienceCollaborations/StackClub.git#egg=stackclub +``` \ No newline at end of file From 430071b63b0a8149066abd9a39a458e2d5b1b6ee Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Fri, 14 Sep 2018 10:30:12 -0700 Subject: [PATCH 40/45] Delete make_bfk_lage_more_flats_28Aug18.ipynb This will be added later --- .../make_bfk_lage_more_flats_28Aug18.ipynb | 993 ------------------ 1 file changed, 993 deletions(-) delete mode 100644 ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb diff --git a/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb b/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb deleted file mode 100644 index 251750c6..00000000 --- a/ImageProcessing/make_bfk_lage_more_flats_28Aug18.ipynb +++ /dev/null @@ -1,993 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Starting with Michael Wood-Vasey's notebook as a starting point\n", - "## Craig Lage - 28Aug18\n", - "\n", - "## Make a brighter-fatter kernel from a set of high-intensity flats measured at UC Davis.\n", - "## Run correlations by amplifier. Now adding more flat pairs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook shows extracting the BF kernel from a set of measured flats.\n", - "\n", - "So far, this notebook is only runnable on the system at UC Davis. \n", - "\n", - "Before it can be run at NCSA, two things need to happen:\n", - "\n", - " (1) I need to upolad the fits files for the flats. Easily done.\n", - " (2) I need to understand how to run at NCSA when not using a released version of the stack. \n", - " The code currently requires several branches which are not released in the stack, \n", - " as detailed below." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "lsst_distrib 16.0+3 \tcurrent w_2018_26 setup\n" - ] - } - ], - "source": [ - "# What version of the Stack am I using?\n", - "! echo $HOSTNAME\n", - "! eups list -s | grep lsst_distrib" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# if running stack v16.0, silence a long matplotlib Agg warning with:\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Test to make sure that we can import obs_lsstCam\n", - "import lsst.obs.base\n", - "import lsst.obs.lsstCam" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from lsst.cp.pipe.makeBrighterFatterKernel import MakeBrighterFatterKernelTask\n", - "from lsst.daf.persistence import Butler\n", - "from lsst.pipe.tasks.ingest import IngestTask" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks\n" - ] - } - ], - "source": [ - "!pwd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ingest 25 pairs of flats from UC Davis measurements" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514141655.fits\n" - ] - } - ], - "source": [ - "!ls /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514??????.fits" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root INFO: Loading config overrride file '/sandbox/cslage/Research/LSST/code/w_2018_26/obs_lsstCam/config/ingest.py'\n", - "LsstCamMapper WARN: Unable to find calib root directory\n", - "CameraMapper INFO: Loading Posix exposure registry from /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_100_20180514141655.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/100/R21/00000100-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_101_20180514141706.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/101/R21/00000101-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_102_20180514141718.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/102/R21/00000102-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_103_20180514141729.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/103/R21/00000103-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_104_20180514141740.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/104/R21/00000104-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_105_20180514141751.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/105/R21/00000105-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_106_20180514141802.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/106/R21/00000106-R21-S11-det085-000.fits\n", - 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"ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_302_20180514145838.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/302/R21/00000302-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_303_20180514145854.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/303/R21/00000303-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_304_20180514145909.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/304/R21/00000304-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_305_20180514145924.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/305/R21/00000305-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_306_20180514145940.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/306/R21/00000306-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_307_20180514145955.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/307/R21/00000307-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_308_20180514150011.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/308/R21/00000308-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_309_20180514150026.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/309/R21/00000309-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_400_20180514152344.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/400/R21/00000400-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_401_20180514152401.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/401/R21/00000401-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_402_20180514152419.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/402/R21/00000402-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_403_20180514152436.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/403/R21/00000403-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_404_20180514152454.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/404/R21/00000404-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_405_20180514152511.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/405/R21/00000405-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_406_20180514152529.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/406/R21/00000406-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_407_20180514152546.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/407/R21/00000407-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_408_20180514152603.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/408/R21/00000408-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_409_20180514152621.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/409/R21/00000409-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_500_20180514155238.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/500/R21/00000500-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_501_20180514155257.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/501/R21/00000501-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_502_20180514155317.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/502/R21/00000502-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_503_20180514155336.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/503/R21/00000503-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_504_20180514155355.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/504/R21/00000504-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_505_20180514155414.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/505/R21/00000505-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_506_20180514155434.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/506/R21/00000506-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_507_20180514155453.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/507/R21/00000507-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_508_20180514155513.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/508/R21/00000508-R21-S11-det085-000.fits\n", - "ingest INFO: /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_509_20180514155532.fits ----> /sandbox/cslage/Research/LSST/code/notebooks/DC2_Notebooks/ucd_repo_2/raw/509/R21/00000509-R21-S11-det085-000.fits\n" - ] - } - ], - "source": [ - "# Don't need to run this each time\n", - "!rm -rf ucd_repo_2\n", - "! mkdir ucd_repo_2\n", - "! echo \"lsst.obs.lsstCam.LsstCamMapper\" > ucd_repo_2/_mapper\n", - "\n", - "# Ingest the flats. The ?00 and ?01 are the flat pairs\n", - "! ingestImages.py ucd_repo_2 /mnt/storm-lsst/GUI/20180514_002_flats_3/ITL-3800C-002_flat_flat_?0?_20180514??????.fits --mode link" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "butler = Butler('ucd_repo_2')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So I got this far, and it has successfully ingested the images. So far, all I have done is:\n", - "\n", - " (1) Copied MWV code and hacks as closely as I could.\n", - " (A) Using w_2018_26\n", - " (B) obs_base tickets/DM-13293\n", - " (C) obs_lsstCam tickets/DM-15509\n", - " (D) cp_pipe tickets/DM-13293\n", - " (E) Comment out the `exposure.setWcs` command in the raw assembly in `obs_lsstCam`.\n", - " (F) Define a wrapper `run` method in `makeBrighterFatterTask.py` that calls `runDataRef`.\n", - " (G) Comment out from .cpTask import * in the `cp_pipe/python/lsst/cp/pipe/__init__.py` file.\n", - "\n", - " (2) edit obs_lsstCam/config/ingest.py to use some different header values\n", - " (3) edit obs_lsstCam/python/lsst/obs/lsstCam/ingest.py to work with these different values and \n", - " to fudge the raft and sensor IDs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(100, 85, 'r', 1, 'R21', 'S11'), (101, 85, 'r', 2, 'R21', 'S11'), (102, 85, 'r', 3, 'R21', 'S11'), (103, 85, 'r', 4, 'R21', 'S11'), (104, 85, 'r', 5, 'R21', 'S11'), (105, 85, 'r', 6, 'R21', 'S11'), (106, 85, 'r', 7, 'R21', 'S11'), (107, 85, 'r', 8, 'R21', 'S11'), (108, 85, 'r', 9, 'R21', 'S11'), (109, 85, 'r', 10, 'R21', 'S11'), (200, 85, 'r', 11, 'R21', 'S11'), (201, 85, 'r', 12, 'R21', 'S11'), (202, 85, 'r', 13, 'R21', 'S11'), (203, 85, 'r', 14, 'R21', 'S11'), (204, 85, 'r', 15, 'R21', 'S11'), (205, 85, 'r', 16, 'R21', 'S11'), (206, 85, 'r', 17, 'R21', 'S11'), (207, 85, 'r', 18, 'R21', 'S11'), (208, 85, 'r', 19, 'R21', 'S11'), (209, 85, 'r', 20, 'R21', 'S11'), (300, 85, 'r', 21, 'R21', 'S11'), (301, 85, 'r', 22, 'R21', 'S11'), (302, 85, 'r', 23, 'R21', 'S11'), (303, 85, 'r', 24, 'R21', 'S11'), (304, 85, 'r', 25, 'R21', 'S11'), (305, 85, 'r', 26, 'R21', 'S11'), (306, 85, 'r', 27, 'R21', 'S11'), (307, 85, 'r', 28, 'R21', 'S11'), (308, 85, 'r', 29, 'R21', 'S11'), (309, 85, 'r', 30, 'R21', 'S11'), (400, 85, 'r', 31, 'R21', 'S11'), (401, 85, 'r', 32, 'R21', 'S11'), (402, 85, 'r', 33, 'R21', 'S11'), (403, 85, 'r', 34, 'R21', 'S11'), (404, 85, 'r', 35, 'R21', 'S11'), (405, 85, 'r', 36, 'R21', 'S11'), (406, 85, 'r', 37, 'R21', 'S11'), (407, 85, 'r', 38, 'R21', 'S11'), (408, 85, 'r', 39, 'R21', 'S11'), (409, 85, 'r', 40, 'R21', 'S11'), (500, 85, 'r', 41, 'R21', 'S11'), (501, 85, 'r', 42, 'R21', 'S11'), (502, 85, 'r', 43, 'R21', 'S11'), (503, 85, 'r', 44, 'R21', 'S11'), (504, 85, 'r', 45, 'R21', 'S11'), (505, 85, 'r', 46, 'R21', 'S11'), (506, 85, 'r', 47, 'R21', 'S11'), (507, 85, 'r', 48, 'R21', 'S11'), (508, 85, 'r', 49, 'R21', 'S11'), (509, 85, 'r', 50, 'R21', 'S11')]\n" - ] - } - ], - "source": [ - "print(butler.queryMetadata('src', ['visit', 'detector', 'filter', 'id', 'raftName', 'detectorName']))\n", - "# Note that the 'detector' value is 85, and I have hacked the CCD to be R21:S11" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Put in the approximate measured gain values\n", - "amp_names = ['C{:02d}'.format(i) for i in range(18)]\n", - "ucd_gain = 4.5\n", - "nominalGain = {a: ucd_gain for a in amp_names}\n", - "gain = nominalGain\n", - "dataRef = butler.dataRef('brighterFatterGain', dataId={'detector': 85}) \n", - "dataRef.put(gain, 'brighterFatterGain')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['100,101', '102,103', '104,105', '106,107', '108,109', '200,201', '202,203', '204,205', '206,207', '208,209', '300,301', '302,303', '304,305', '306,307', '308,309', '400,401', '402,403', '404,405', '406,407', '408,409', '500,501', '502,503', '504,505', '506,507', '508,509']\n" - ] - } - ], - "source": [ - "pairs = []\n", - "for firstDigit in range(1,6):\n", - " for lastDigit in range(5):\n", - " pairs.append('%s,%s'%(str(100*firstDigit+2*lastDigit),str(100*firstDigit+2*lastDigit+1)))\n", - " \n", - "print(pairs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now try calculating the brighter-fatter kernel using `MakeBrighterFatterKernelTask`. By setting level='AMP', we force amplifier by amplifier calculations. The first attempt was with assumed gains." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now repeating it with calculated gains. I needed to make some edits to prevent it from exiting on the bad amps.\n", - "This processing took several hours." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "makeBrighterFatterKernel.py ucd_repo_2 --rerun test --id detector=85 --visit-pairs 100,101 102,103 104,105 106,107 108,109 200,201 202,203 204,205 206,207 208,209 300,301 302,303 304,305 306,307 308,309 400,401 402,403 404,405 406,407 408,409 500,501 502,503 504,505 506,507 508,509 -c xcorrCheckRejectLevel=2 doCalcGains=True level=\"AMP\" --clobber-config --clobber-versions\n", - "Finished image preparation for visit 100 101\n", - "Finished cross-correlation for C10 in visit 100 101\n", - "Finished cross-correlation for C11 in visit 100 101\n", - "Finished cross-correlation for C12 in visit 100 101\n", - "Finished cross-correlation for C13 in visit 100 101\n", - "Finished cross-correlation for C14 in visit 100 101\n", - "Finished cross-correlation for C15 in visit 100 101\n", - "Finished cross-correlation for C16 in visit 100 101\n", - "Finished cross-correlation for C17 in visit 100 101\n", - "Finished cross-correlation for C07 in visit 100 101\n", - "Finished cross-correlation for C06 in visit 100 101\n", - "Finished cross-correlation for C05 in visit 100 101\n", - "Finished cross-correlation for C04 in visit 100 101\n", - "Finished cross-correlation for C03 in visit 100 101\n", - "Finished cross-correlation for C02 in visit 100 101\n", - "Finished cross-correlation for C01 in visit 100 101\n", - "Finished cross-correlation for C00 in visit 100 101\n", - "Finished image preparation for visit 102 103\n", - "Finished cross-correlation for C10 in visit 102 103\n", - "Finished cross-correlation for C11 in visit 102 103\n", - "Finished cross-correlation for C12 in visit 102 103\n", - "Finished cross-correlation for C13 in visit 102 103\n", - "Finished cross-correlation for C14 in visit 102 103\n", - "Finished cross-correlation for C15 in visit 102 103\n", - "Finished cross-correlation for C16 in visit 102 103\n", - "Finished cross-correlation for C17 in visit 102 103\n", - "Finished cross-correlation for C07 in visit 102 103\n", - "Finished cross-correlation for C06 in visit 102 103\n", - "Finished cross-correlation for C05 in visit 102 103\n", - "Finished cross-correlation for C04 in visit 102 103\n", - "Finished cross-correlation for C03 in visit 102 103\n", - "Finished cross-correlation for C02 in visit 102 103\n", - "Finished cross-correlation for C01 in visit 102 103\n", - "Finished cross-correlation for C00 in visit 102 103\n", - "Finished image preparation for visit 104 105\n", - "Finished cross-correlation for C10 in visit 104 105\n", - "Finished cross-correlation for C11 in visit 104 105\n", - "Finished cross-correlation for C12 in visit 104 105\n", - "Finished cross-correlation for C13 in visit 104 105\n", - "Finished cross-correlation for C14 in visit 104 105\n", - "Finished cross-correlation for C15 in visit 104 105\n", - "Finished cross-correlation for C16 in visit 104 105\n", - "Finished cross-correlation for C17 in visit 104 105\n", - "Finished cross-correlation for C07 in visit 104 105\n", - "Finished cross-correlation for C06 in visit 104 105\n", - "Finished cross-correlation for C05 in visit 104 105\n", - "Finished cross-correlation for C04 in visit 104 105\n", - "Finished cross-correlation for C03 in visit 104 105\n", - "Finished cross-correlation for C02 in visit 104 105\n", - "Finished cross-correlation for C01 in visit 104 105\n", - "Finished cross-correlation for C00 in visit 104 105\n", - "Finished image preparation for visit 106 107\n", - "Finished cross-correlation for C10 in visit 106 107\n", - "Finished cross-correlation for C11 in visit 106 107\n", - "Finished cross-correlation for C12 in visit 106 107\n", - "Finished cross-correlation for C13 in visit 106 107\n", - "Finished cross-correlation for C14 in visit 106 107\n", - "Finished cross-correlation for C15 in visit 106 107\n", - "Finished cross-correlation for C16 in visit 106 107\n", - "Finished cross-correlation for C17 in visit 106 107\n", - "Finished cross-correlation for C07 in visit 106 107\n", - "Finished cross-correlation for C06 in visit 106 107\n", - "Finished cross-correlation for C05 in visit 106 107\n", - "Finished cross-correlation for C04 in visit 106 107\n", - "Finished cross-correlation for C03 in visit 106 107\n", - "Finished cross-correlation for C02 in visit 106 107\n", - "Finished cross-correlation for C01 in visit 106 107\n", - "Finished cross-correlation for C00 in visit 106 107\n", - "Finished image preparation for visit 108 109\n", - "Finished cross-correlation for C10 in visit 108 109\n", - "Finished cross-correlation for C11 in visit 108 109\n", - "Finished cross-correlation for C12 in visit 108 109\n", - "Finished cross-correlation for C13 in visit 108 109\n", - "Finished cross-correlation for C14 in visit 108 109\n", - "Finished cross-correlation for C15 in visit 108 109\n", - "Finished cross-correlation for C16 in visit 108 109\n", - "Finished cross-correlation for C17 in visit 108 109\n", - "Finished cross-correlation for C07 in visit 108 109\n", - "Finished cross-correlation for C06 in visit 108 109\n", - "Finished cross-correlation for C05 in visit 108 109\n", - "Finished cross-correlation for C04 in visit 108 109\n", - "Finished cross-correlation for C03 in visit 108 109\n", - "Finished cross-correlation for C02 in visit 108 109\n", - "Finished cross-correlation for C01 in visit 108 109\n", - "Finished cross-correlation for C00 in visit 108 109\n", - "Finished image preparation for visit 200 201\n", - "Finished cross-correlation for C10 in visit 200 201\n", - "Finished cross-correlation for C11 in visit 200 201\n", - "Finished cross-correlation for C12 in visit 200 201\n", - "Finished cross-correlation for C13 in visit 200 201\n", - "Finished cross-correlation for C14 in visit 200 201\n", - "Finished cross-correlation for C15 in visit 200 201\n", - "Finished cross-correlation for C16 in visit 200 201\n", - "Finished cross-correlation for C17 in visit 200 201\n", - "Finished cross-correlation for C07 in visit 200 201\n", - "Finished cross-correlation for C06 in visit 200 201\n", - "Finished cross-correlation for C05 in visit 200 201\n", - "Finished cross-correlation for C04 in visit 200 201\n", - "Finished cross-correlation for C03 in visit 200 201\n", - "Finished cross-correlation for C02 in visit 200 201\n", - "Finished cross-correlation for C01 in visit 200 201\n", - "Finished cross-correlation for C00 in visit 200 201\n", - "Finished image preparation for visit 202 203\n", - "Finished cross-correlation for C10 in visit 202 203\n", - "Finished cross-correlation for C11 in visit 202 203\n", - "Finished cross-correlation for C12 in visit 202 203\n", - "Finished cross-correlation for C13 in visit 202 203\n", - "Finished cross-correlation for C14 in visit 202 203\n", - "Finished cross-correlation for C15 in visit 202 203\n", - "Finished cross-correlation for C16 in visit 202 203\n", - "Finished cross-correlation for C17 in visit 202 203\n", - "Finished cross-correlation for C07 in visit 202 203\n", - "Finished cross-correlation for C06 in visit 202 203\n", - "Finished cross-correlation for C05 in visit 202 203\n", - "Finished cross-correlation for C04 in visit 202 203\n", - "Finished cross-correlation for C03 in visit 202 203\n", - "Finished cross-correlation for C02 in visit 202 203\n", - "Finished cross-correlation for C01 in visit 202 203\n", - "Finished cross-correlation for C00 in visit 202 203\n", - "Finished image preparation for visit 204 205\n", - "Finished cross-correlation for C10 in visit 204 205\n", - "Finished cross-correlation for C11 in visit 204 205\n", - "Finished cross-correlation for C12 in visit 204 205\n", - "Finished cross-correlation for C13 in visit 204 205\n", - "Finished cross-correlation for C14 in visit 204 205\n", - "Finished cross-correlation for C15 in visit 204 205\n", - "Finished cross-correlation for C16 in visit 204 205\n", - "Finished cross-correlation for C17 in visit 204 205\n", - "Finished cross-correlation for C07 in visit 204 205\n", - "Finished cross-correlation for C06 in visit 204 205\n", - "Finished cross-correlation for C05 in visit 204 205\n", - "Finished cross-correlation for C04 in visit 204 205\n", - "Finished cross-correlation for C03 in visit 204 205\n", - "Finished cross-correlation for C02 in visit 204 205\n", - "Finished cross-correlation for C01 in visit 204 205\n", - "Finished cross-correlation for C00 in visit 204 205\n", - "Finished image preparation for visit 206 207\n", - "Finished cross-correlation for C10 in visit 206 207\n", - "Finished cross-correlation for C11 in visit 206 207\n", - "Finished cross-correlation for C12 in visit 206 207\n", - "Finished cross-correlation for C13 in visit 206 207\n", - "Finished cross-correlation for C14 in visit 206 207\n", - "Finished cross-correlation for C15 in visit 206 207\n", - "Finished cross-correlation for C16 in visit 206 207\n", - "Finished cross-correlation for C17 in visit 206 207\n", - "Finished cross-correlation for C07 in visit 206 207\n", - "Finished cross-correlation for C06 in visit 206 207\n", - "Finished cross-correlation for C05 in visit 206 207\n", - "Finished cross-correlation for C04 in visit 206 207\n", - "Finished cross-correlation for C03 in visit 206 207\n", - "Finished cross-correlation for C02 in visit 206 207\n", - "Finished cross-correlation for C01 in visit 206 207\n", - "Finished cross-correlation for C00 in visit 206 207\n", - "Finished image preparation for visit 208 209\n", - "Finished cross-correlation for C10 in visit 208 209\n", - "Finished cross-correlation for C11 in visit 208 209\n", - "Finished cross-correlation for C12 in visit 208 209\n", - "Finished cross-correlation for C13 in visit 208 209\n", - "Finished cross-correlation for C14 in visit 208 209\n", - "Finished cross-correlation for C15 in visit 208 209\n", - "Finished cross-correlation for C16 in visit 208 209\n", - "Finished cross-correlation for C17 in visit 208 209\n", - "Finished cross-correlation for C07 in visit 208 209\n", - "Finished cross-correlation for C06 in visit 208 209\n", - "Finished cross-correlation for C05 in visit 208 209\n", - "Finished cross-correlation for C04 in visit 208 209\n", - "Finished cross-correlation for C03 in visit 208 209\n", - "Finished cross-correlation for C02 in visit 208 209\n", - "Finished cross-correlation for C01 in visit 208 209\n", - "Finished cross-correlation for C00 in visit 208 209\n", - "Finished image preparation for visit 300 301\n", - "Finished cross-correlation for C10 in visit 300 301\n", - "Finished cross-correlation for C11 in visit 300 301\n", - "Finished cross-correlation for C12 in visit 300 301\n", - "Finished cross-correlation for C13 in visit 300 301\n", - "Finished cross-correlation for C14 in visit 300 301\n", - "Finished cross-correlation for C15 in visit 300 301\n", - "Finished cross-correlation for C16 in visit 300 301\n", - "Finished cross-correlation for C17 in visit 300 301\n", - "Finished cross-correlation for C07 in visit 300 301\n", - "Finished cross-correlation for C06 in visit 300 301\n", - "Finished cross-correlation for C05 in visit 300 301\n", - "Finished cross-correlation for C04 in visit 300 301\n", - "Finished cross-correlation for C03 in visit 300 301\n", - "Finished cross-correlation for C02 in visit 300 301\n", - "Finished cross-correlation for C01 in visit 300 301\n", - "Finished cross-correlation for C00 in visit 300 301\n", - "Finished image preparation for visit 302 303\n", - "Finished cross-correlation for C10 in visit 302 303\n", - "Finished cross-correlation for C11 in visit 302 303\n", - "Finished cross-correlation for C12 in visit 302 303\n", - "Finished cross-correlation for C13 in visit 302 303\n", - "Finished cross-correlation for C14 in visit 302 303\n", - "Finished cross-correlation for C15 in visit 302 303\n", - "Finished cross-correlation for C16 in visit 302 303\n", - "Finished cross-correlation for C17 in visit 302 303\n", - "Finished cross-correlation for C07 in visit 302 303\n", - "Finished cross-correlation for C06 in visit 302 303\n", - "Finished cross-correlation for C05 in visit 302 303\n", - "Finished cross-correlation for C04 in visit 302 303\n", - "Finished cross-correlation for C03 in visit 302 303\n", - "Finished cross-correlation for C02 in visit 302 303\n", - "Finished cross-correlation for C01 in visit 302 303\n", - "Finished cross-correlation for C00 in visit 302 303\n", - "Finished image preparation for visit 304 305\n", - "Finished cross-correlation for C10 in visit 304 305\n", - "Finished cross-correlation for C11 in visit 304 305\n", - "Finished cross-correlation for C12 in visit 304 305\n", - "Finished cross-correlation for C13 in visit 304 305\n", - "Finished cross-correlation for C14 in visit 304 305\n", - "Finished cross-correlation for C15 in visit 304 305\n", - "Finished cross-correlation for C16 in visit 304 305\n", - "Finished cross-correlation for C17 in visit 304 305\n", - "Finished cross-correlation for C07 in visit 304 305\n", - "Finished cross-correlation for C06 in visit 304 305\n", - "Finished cross-correlation for C05 in visit 304 305\n", - "Finished cross-correlation for C04 in visit 304 305\n", - "Finished cross-correlation for C03 in visit 304 305\n", - "Finished cross-correlation for C02 in visit 304 305\n", - "Finished cross-correlation for C01 in visit 304 305\n", - "Finished cross-correlation for C00 in visit 304 305\n", - "Finished image preparation for visit 306 307\n", - "Finished cross-correlation for C10 in visit 306 307\n", - "Finished cross-correlation for C11 in visit 306 307\n", - "Finished cross-correlation for C12 in visit 306 307\n", - "Finished cross-correlation for C13 in visit 306 307\n", - "Finished cross-correlation for C14 in visit 306 307\n", - "Finished cross-correlation for C15 in visit 306 307\n", - "Finished cross-correlation for C16 in visit 306 307\n", - "Finished cross-correlation for C17 in visit 306 307\n", - "Finished cross-correlation for C07 in visit 306 307\n", - "Finished cross-correlation for C06 in visit 306 307\n", - "Finished cross-correlation for C05 in visit 306 307\n", - "Finished cross-correlation for C04 in visit 306 307\n", - "Finished cross-correlation for C03 in visit 306 307\n", - "Finished cross-correlation for C02 in visit 306 307\n", - "Finished cross-correlation for C01 in visit 306 307\n", - "Finished cross-correlation for C00 in visit 306 307\n", - "Finished image preparation for visit 308 309\n", - "Finished cross-correlation for C10 in visit 308 309\n", - "Finished cross-correlation for C11 in visit 308 309\n", - "Finished cross-correlation for C12 in visit 308 309\n", - "Finished cross-correlation for C13 in visit 308 309\n", - "Finished cross-correlation for C14 in visit 308 309\n", - "Finished cross-correlation for C15 in visit 308 309\n", - "Finished cross-correlation for C16 in visit 308 309\n", - "Finished cross-correlation for C17 in visit 308 309\n", - "Finished cross-correlation for C07 in visit 308 309\n", - "Finished cross-correlation for C06 in visit 308 309\n", - "Finished cross-correlation for C05 in visit 308 309\n", - "Finished cross-correlation for C04 in visit 308 309\n", - "Finished cross-correlation for C03 in visit 308 309\n", - "Finished cross-correlation for C02 in visit 308 309\n", - "Finished cross-correlation for C01 in visit 308 309\n", - "Finished cross-correlation for C00 in visit 308 309\n", - "Finished image preparation for visit 400 401\n", - "Finished cross-correlation for C10 in visit 400 401\n", - "Finished cross-correlation for C11 in visit 400 401\n", - "Finished cross-correlation for C12 in visit 400 401\n", - "Finished cross-correlation for C13 in visit 400 401\n", - "Finished cross-correlation for C14 in visit 400 401\n", - "Finished cross-correlation for C15 in visit 400 401\n", - "Finished cross-correlation for C16 in visit 400 401\n", - "Finished cross-correlation for C17 in visit 400 401\n", - "Finished cross-correlation for C07 in visit 400 401\n", - "Finished cross-correlation for C06 in visit 400 401\n", - "Finished cross-correlation for C05 in visit 400 401\n", - "Finished cross-correlation for C04 in visit 400 401\n", - "Finished cross-correlation for C03 in visit 400 401\n", - "Finished cross-correlation for C02 in visit 400 401\n", - "Finished cross-correlation for C01 in visit 400 401\n", - "Finished cross-correlation for C00 in visit 400 401\n", - "Finished image preparation for visit 402 403\n", - "Finished cross-correlation for C10 in visit 402 403\n", - "Finished cross-correlation for C11 in visit 402 403\n", - "Finished cross-correlation for C12 in visit 402 403\n", - "Finished cross-correlation for C13 in visit 402 403\n", - "Finished cross-correlation for C14 in visit 402 403\n", - "Finished cross-correlation for C15 in visit 402 403\n", - "Finished cross-correlation for C16 in visit 402 403\n", - "Finished cross-correlation for C17 in visit 402 403\n", - "Finished cross-correlation for C07 in visit 402 403\n", - "Finished cross-correlation for C06 in visit 402 403\n", - "Finished cross-correlation for C05 in visit 402 403\n", - "Finished cross-correlation for C04 in visit 402 403\n", - "Finished cross-correlation for C03 in visit 402 403\n", - "Finished cross-correlation for C02 in visit 402 403\n", - "Finished cross-correlation for C01 in visit 402 403\n", - "Finished cross-correlation for C00 in visit 402 403\n", - "Finished image preparation for visit 404 405\n", - "Finished cross-correlation for C10 in visit 404 405\n", - "Finished cross-correlation for C11 in visit 404 405\n", - "Finished cross-correlation for C12 in visit 404 405\n", - "Finished cross-correlation for C13 in visit 404 405\n", - "Finished cross-correlation for C14 in visit 404 405\n", - "Finished cross-correlation for C15 in visit 404 405\n", - "Finished cross-correlation for C16 in visit 404 405\n", - "Finished cross-correlation for C17 in visit 404 405\n", - "Finished cross-correlation for C07 in visit 404 405\n", - "Finished cross-correlation for C06 in visit 404 405\n", - "Finished cross-correlation for C05 in visit 404 405\n", - "Finished cross-correlation for C04 in visit 404 405\n", - "Finished cross-correlation for C03 in visit 404 405\n", - "Finished cross-correlation for C02 in visit 404 405\n", - "Finished cross-correlation for C01 in visit 404 405\n", - "Finished cross-correlation for C00 in visit 404 405\n", - "Finished image preparation for visit 406 407\n", - "Finished cross-correlation for C10 in visit 406 407\n", - "Finished cross-correlation for C11 in visit 406 407\n", - "Finished cross-correlation for C12 in visit 406 407\n", - "Finished cross-correlation for C13 in visit 406 407\n", - "Finished cross-correlation for C14 in visit 406 407\n", - "Finished cross-correlation for C15 in visit 406 407\n", - "Finished cross-correlation for C16 in visit 406 407\n", - "Finished cross-correlation for C17 in visit 406 407\n", - "Finished cross-correlation for C07 in visit 406 407\n", - "Finished cross-correlation for C06 in visit 406 407\n", - "Finished cross-correlation for C05 in visit 406 407\n", - "Finished cross-correlation for C04 in visit 406 407\n", - "Finished cross-correlation for C03 in visit 406 407\n", - "Finished cross-correlation for C02 in visit 406 407\n", - "Finished cross-correlation for C01 in visit 406 407\n", - "Finished cross-correlation for C00 in visit 406 407\n", - "Finished image preparation for visit 408 409\n", - "Finished cross-correlation for C10 in visit 408 409\n", - "Finished cross-correlation for C11 in visit 408 409\n", - "Finished cross-correlation for C12 in visit 408 409\n", - "Finished cross-correlation for C13 in visit 408 409\n", - "Finished cross-correlation for C14 in visit 408 409\n", - "Finished cross-correlation for C15 in visit 408 409\n", - "Finished cross-correlation for C16 in visit 408 409\n", - "Finished cross-correlation for C17 in visit 408 409\n", - "Finished cross-correlation for C07 in visit 408 409\n", - "Finished cross-correlation for C06 in visit 408 409\n", - "Finished cross-correlation for C05 in visit 408 409\n", - "Finished cross-correlation for C04 in visit 408 409\n", - "Finished cross-correlation for C03 in visit 408 409\n", - "Finished cross-correlation for C02 in visit 408 409\n", - "Finished cross-correlation for C01 in visit 408 409\n", - "Finished cross-correlation for C00 in visit 408 409\n", - "Finished image preparation for visit 500 501\n", - "Finished cross-correlation for C10 in visit 500 501\n", - "Finished cross-correlation for C11 in visit 500 501\n", - "Finished cross-correlation for C12 in visit 500 501\n", - "Finished cross-correlation for C13 in visit 500 501\n", - "Finished cross-correlation for C14 in visit 500 501\n", - "Finished cross-correlation for C15 in visit 500 501\n", - "Finished cross-correlation for C16 in visit 500 501\n", - "Finished cross-correlation for C17 in visit 500 501\n", - "Finished cross-correlation for C07 in visit 500 501\n", - "Finished cross-correlation for C06 in visit 500 501\n", - "Finished cross-correlation for C05 in visit 500 501\n", - "Finished cross-correlation for C04 in visit 500 501\n", - "Finished cross-correlation for C03 in visit 500 501\n", - "Finished cross-correlation for C02 in visit 500 501\n", - "Finished cross-correlation for C01 in visit 500 501\n", - "Finished cross-correlation for C00 in visit 500 501\n", - "Finished image preparation for visit 502 503\n", - "Finished cross-correlation for C10 in visit 502 503\n", - "Finished cross-correlation for C11 in visit 502 503\n", - "Finished cross-correlation for C12 in visit 502 503\n", - "Finished cross-correlation for C13 in visit 502 503\n", - "Finished cross-correlation for C14 in visit 502 503\n", - "Finished cross-correlation for C15 in visit 502 503\n", - "Finished cross-correlation for C16 in visit 502 503\n", - "Finished cross-correlation for C17 in visit 502 503\n", - "Finished cross-correlation for C07 in visit 502 503\n", - "Finished cross-correlation for C06 in visit 502 503\n", - "Finished cross-correlation for C05 in visit 502 503\n", - "Finished cross-correlation for C04 in visit 502 503\n", - "Finished cross-correlation for C03 in visit 502 503\n", - "Finished cross-correlation for C02 in visit 502 503\n", - "Finished cross-correlation for C01 in visit 502 503\n", - "Finished cross-correlation for C00 in visit 502 503\n", - "Finished image preparation for visit 504 505\n", - "Finished cross-correlation for C10 in visit 504 505\n", - "Finished cross-correlation for C11 in visit 504 505\n", - "Finished cross-correlation for C12 in visit 504 505\n", - "Finished cross-correlation for C13 in visit 504 505\n", - "Finished cross-correlation for C14 in visit 504 505\n", - "Finished cross-correlation for C15 in visit 504 505\n", - "Finished cross-correlation for C16 in visit 504 505\n", - "Finished cross-correlation for C17 in visit 504 505\n", - "Finished cross-correlation for C07 in visit 504 505\n", - "Finished cross-correlation for C06 in visit 504 505\n", - "Finished cross-correlation for C05 in visit 504 505\n", - "Finished cross-correlation for C04 in visit 504 505\n", - "Finished cross-correlation for C03 in visit 504 505\n", - "Finished cross-correlation for C02 in visit 504 505\n", - "Finished cross-correlation for C01 in visit 504 505\n", - "Finished cross-correlation for C00 in visit 504 505\n", - "Finished image preparation for visit 506 507\n", - "Finished cross-correlation for C10 in visit 506 507\n", - "Finished cross-correlation for C11 in visit 506 507\n", - "Finished cross-correlation for C12 in visit 506 507\n", - "Finished cross-correlation for C13 in visit 506 507\n", - "Finished cross-correlation for C14 in visit 506 507\n", - "Finished cross-correlation for C15 in visit 506 507\n", - "Finished cross-correlation for C16 in visit 506 507\n", - "Finished cross-correlation for C17 in visit 506 507\n", - "Finished cross-correlation for C07 in visit 506 507\n", - "Finished cross-correlation for C06 in visit 506 507\n", - "Finished cross-correlation for C05 in visit 506 507\n", - "Finished cross-correlation for C04 in visit 506 507\n", - "Finished cross-correlation for C03 in visit 506 507\n", - "Finished cross-correlation for C02 in visit 506 507\n", - "Finished cross-correlation for C01 in visit 506 507\n", - "Finished cross-correlation for C00 in visit 506 507\n", - "Finished image preparation for visit 508 509\n", - "Finished cross-correlation for C10 in visit 508 509\n", - "Finished cross-correlation for C11 in visit 508 509\n", - "Finished cross-correlation for C12 in visit 508 509\n", - "Finished cross-correlation for C13 in visit 508 509\n", - "Finished cross-correlation for C14 in visit 508 509\n", - "Finished cross-correlation for C15 in visit 508 509\n", - "Finished cross-correlation for C16 in visit 508 509\n", - "Finished cross-correlation for C17 in visit 508 509\n", - "Finished cross-correlation for C07 in visit 508 509\n", - "Finished cross-correlation for C06 in visit 508 509\n", - "Finished cross-correlation for C05 in visit 508 509\n", - "Finished cross-correlation for C04 in visit 508 509\n", - "Finished cross-correlation for C03 in visit 508 509\n", - "Finished cross-correlation for C02 in visit 508 509\n", - "Finished cross-correlation for C01 in visit 508 509\n", - "Finished cross-correlation for C00 in visit 508 509\n" - ] - } - ], - "source": [ - "args = ['ucd_repo_2', '--rerun', 'test',\n", - " '--id', 'detector=85',\n", - " '--visit-pairs', '100,101', '102,103', '104,105', '106,107', '108,109', '200,201', '202,203', '204,205', '206,207', '208,209', '300,301', '302,303', '304,305', '306,307', '308,309', '400,401', '402,403', '404,405', '406,407', '408,409', '500,501', '502,503', '504,505', '506,507', '508,509',\n", - " '-c',\n", - " 'xcorrCheckRejectLevel=2', 'doCalcGains=True', 'level=\"AMP\"',\n", - " '--clobber-config', '--clobber-versions'\n", - " ]\n", - "\n", - "command_line = 'makeBrighterFatterKernel.py ' + ' '.join(args)\n", - "print(command_line)\n", - "\n", - "ucd_pb_struct = MakeBrighterFatterKernelTask.parseAndRun(args=args)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#It now runs with doCalcGains=True and the gains are close to what we measured here.\n", - "ucd_detector = 85\n", - "test_butler = Butler('ucd_repo_2/rerun/test')\n", - "ucd_bf_kernel = test_butler.get('brighterFatterKernelNew', dataId={'raftName': 'R21', 'detectorName': 'S11', 'detector': ucd_detector})\n", - "gain_data = test_butler.get('brighterFatterGain', dataId={'raftName': 'R21', 'detectorName': 'S11', 'detector': 85})" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(16,8))\n", - "plt.subplots_adjust(hspace=0.5)\n", - "for i, key in enumerate(ucd_bf_kernel.keys()):\n", - " plt.subplot(4,8,i+1)\n", - " plt.title('AMP=%s'%key) \n", - " plt.imshow(ucd_bf_kernel[key])\n", - " ax1 = plt.subplot(4,8,i+17)\n", - " ax1.set_title('AMP=%s, gain = %.2f'%(key,gain_data[key]),fontsize = 8) \n", - " ax1.set_ylim(-3.0E-6,1.0E-6)\n", - " ax1.set_yticks([-1.0E-6, 0.0,1.0E-6])\n", - " ax1.plot(ucd_bf_kernel[key][:,8], color='blue', drawstyle='steps-mid')\n", - " ax1.plot(ucd_bf_kernel[key][8,:], linestyle='--', color='red', drawstyle='steps-mid')\n", - "\n", - " if (i == 0 or i == 8):\n", - " ax1.set_yticklabels([-1.0E-6, 0.0,1.0E-6])\n", - " else:\n", - " ax1.set_yticklabels([])\n", - " \n", - "\n", - "plt.savefig('BF_Kernel_25_Pairs_28Aug18.pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now read in the kernel that I calculated with my code (BF_Kernel_Correction_07Jun18.ipynb) for comparison." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#Reading in the kernel that I calculated here.\n", - "\n", - "from pylab import *\n", - "import pickle as pkl\n", - "class Array2d:\n", - " def __init__(self,xmin,xmax,nx,ymin,ymax,ny):\n", - " # Each image is nx * ny pixels\n", - " self.nx=nx\n", - " self.ny=ny\n", - "\n", - " self.xmin=xmin\n", - " self.ymin=ymin\n", - " \n", - " self.xmax=xmax\n", - " self.ymax=ymax\n", - " \n", - " self.dx=(xmax-xmin)/nx\n", - " self.dy=(ymax-ymin)/ny\n", - " \n", - " self.x=linspace(xmin+self.dx/2,xmax-self.dx/2,nx)\n", - " self.y=linspace(ymin+self.dy/2,ymax-self.dy/2,ny)\n", - "\n", - " self.cov=zeros([nx,ny])\n", - " self.kernel=zeros([nx,ny]) \n", - " \n", - " def PrintKernel(self, filename):\n", - " file = open(filename, 'w')\n", - " line = 'X\\tY\\tCovariance\\t\\t\\tKernel\\n'\n", - " file.write(line)\n", - " for i in range(self.nx):\n", - " for j in range(self.ny):\n", - " line = '%d\\t%d\\t%0.12e\\t%0.12e\\n'%(i,j,self.cov[i,j],self.kernel[i,j])\n", - " file.write(line)\n", - " file.close()\n", - " return\n", - "\n", - " def ReadKernel(self, filename):\n", - " file = open(filename, 'r')\n", - " lines = file.readlines()\n", - " file.close()\n", - " lines.remove(lines[0])\n", - " for line in lines:\n", - " items = line.split()\n", - " i = int(items[0])\n", - " j = int(items[1])\n", - " cov = float(items[2])\n", - " ker = float(items[3])\n", - " self.cov[i,j] = cov\n", - " self.kernel[i,j] = ker\n", - " return\n", - "\n", - "NewNx = NewNy = 21\n", - "kernel = Array2d(0,NewNx,NewNx,0,NewNy,NewNy)\n", - "my_kernel_filename = \"/home/cslage/Research/LSST/code/notebooks/best_kernel/kernel_model_central_csteps_7_07jun18.txt\"\n", - "kernel.ReadKernel(my_kernel_filename)\n", - "#print(kernel.kernel)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "key = 'C04'\n", - "plt.figure(figsize=(16,8))\n", - "plt.suptitle(\"BF Kernel Comparison\", fontsize = 18)\n", - "plt.subplots_adjust(hspace=0.8)\n", - " \n", - "ax1 = plt.subplot(1,2,1)\n", - "ax1.set_title('X, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", - "ax1.set_ylim(-3.0E-6,1.0E-6)\n", - "ax1.set_yticks([-2.0E-6,-1.0E-6,0.0,1.0E-6])\n", - "ax1.plot(ucd_bf_kernel[key][:,8], color='blue', drawstyle='steps-mid', label='DM')\n", - "ax1.plot(kernel.kernel[2:19,10], color='green', drawstyle='steps-mid', label='UCD')\n", - "ax1.legend()\n", - "ax2 = plt.subplot(1,2,2)\n", - "ax2.set_title('Y, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", - "ax2.set_ylim(-3.0E-6,1.0E-6)\n", - "ax2.set_yticks([-2.0E-6,-1.0E-6,0.0,1.0E-6])\n", - "ax2.plot(ucd_bf_kernel[key][8,:], color='blue', drawstyle='steps-mid', label='DM')\n", - "ax2.plot(kernel.kernel[10,2:19], color='green', drawstyle='steps-mid', label='UCD')\n", - "ax2.legend()\n", - "plt.savefig('BF_Kernel_Comparison_29Aug18.pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These don't agree very well at all. My hypothesis is that it is a gain issue, since if I divide the DM values by the gain of 4.31 then they agree pretty well. I suspect that this is just a difference of whether the correction is applied before or after the gain correction.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "key = 'C04'\n", - "plt.figure(figsize=(16,8))\n", - "plt.suptitle(\"BF Kernel Comparison - After Dividing by Gain\", fontsize = 18)\n", - "plt.subplots_adjust(hspace=0.8)\n", - " \n", - "ax1 = plt.subplot(1,2,1)\n", - "ax1.set_title('X, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", - "ax1.set_ylim(-1.0E-6,1.0E-6)\n", - "ax1.set_yticks([-1.0E-6,0.0,1.0E-6])\n", - "ax1.plot(ucd_bf_kernel[key][:,8]/gain_data[key], color='blue', drawstyle='steps-mid', label='DM')\n", - "ax1.plot(kernel.kernel[2:19,10], color='green', drawstyle='steps-mid', label='UCD')\n", - "ax1.legend()\n", - "ax2 = plt.subplot(1,2,2)\n", - "ax2.set_title('Y, AMP=%s, gain = %.2f'%(key,gain_data[key])) \n", - "ax2.set_ylim(-1.0E-6,1.0E-6)\n", - "ax2.set_yticks([-1.0E-6,0.0,1.0E-6])\n", - "ax2.plot(ucd_bf_kernel[key][8,:]/gain_data[key], color='blue', drawstyle='steps-mid', label='DM')\n", - "ax2.plot(kernel.kernel[10,2:19], color='green', drawstyle='steps-mid', label='UCD')\n", - "ax2.legend()\n", - "plt.savefig('BF_Kernel_Comparison_Gain_29Aug18.pdf')\n", - "plt.show()" - ] - }, - { - "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": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 8e7c211c139142c8e58da31f7408f224426a2a84 Mon Sep 17 00:00:00 2001 From: Alex Drlica-Wagner Date: Fri, 14 Sep 2018 12:33:37 -0500 Subject: [PATCH 41/45] typo --- GettingStarted/GettingStarted.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index 636c7273..0ccbbc20 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -67,7 +67,7 @@ If you are contributing notebooks, you may want or need to develop the `stackcl ``` python setup.py -q develop --user ``` -This will put the repo's `stackclub` folder on your path. When developing the package, may find it useful to add the following lines to your notebook: +This will put the repo's `stackclub` folder on your path. When developing the package, you may find it useful to add the following lines to your notebook: ```python %load_ext autoreload %autoreload 2 @@ -77,4 +77,4 @@ This enables you to repeatedly `import stackclub` as you update the library code If you are not developing this package, you can install it using pip, like this: ``` pip install git+git://github.com/LSSTScienceCollaborations/StackClub.git#egg=stackclub -``` \ No newline at end of file +``` From 5af408c621d77f894286143af1cfcb3d9c908c8f Mon Sep 17 00:00:00 2001 From: Andrew Bradshaw Date: Fri, 14 Sep 2018 17:37:39 +0000 Subject: [PATCH 42/45] Responded to review, mostly adding markdown and python-ifying a few calls --- .../BrighterFatterCorrection.ipynb | 227 +++++++++--------- 1 file changed, 107 insertions(+), 120 deletions(-) diff --git a/ImageProcessing/BrighterFatterCorrection.ipynb b/ImageProcessing/BrighterFatterCorrection.ipynb index 8fe3bff9..abcb766e 100644 --- a/ImageProcessing/BrighterFatterCorrection.ipynb +++ b/ImageProcessing/BrighterFatterCorrection.ipynb @@ -6,7 +6,7 @@ "source": [ "# Analysis of Beam Simulator Images and Brighter-fatter Correction\n", "
Owner(s): **Andrew Bradshaw** ([@andrewkbradshaw](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@andrewkbradshaw))\n", - "
Last Verified to Run: **2018-08-22**\n", + "
Last Verified to Run: **2018-09-14**\n", "
Verified Stack Release: **16.0 and 16.0+22 (w_2018_31)**\n", "\n", "This notebook demonstrates the [brighter-fatter systematic error](https://arxiv.org/abs/1402.0725) on images of stars and galaxies illuminated on an ITL-3800C-002 CCD at the [UC Davis LSST beam simulator laboratory](https://arxiv.org/abs/1411.5667). Using a series of images at increasing exposure times, we demonstrate the broadening of image profiles on DM stack shape measurements, and a [possible correction method](https://arxiv.org/abs/1711.06273) which iteratively applies a kernel to restore electrons to the pixels from which they were deflected. To keep things simple, for now we skip most DM stack instrument signature removal (ISR) and work on a subset of images which are already processed arrays (500x500) of electrons.\n", @@ -89,7 +89,7 @@ "# Set the variance plane using the image plane via updateVariance function\n", "gain = 1.0 # because these images are already gain corrected\n", "readNoise = 10.0 # in electrons\n", - "updateVariance(exposure.getMaskedImage(), gain, readNoise)\n", + "updateVariance(exposure.maskedImage, gain, readNoise)\n", "\n", "# Another way of setting variance and/or masks?\n", "#mask = afwImage.makeMaskFromArray(np.zeros((4000,4072)).astype('int32'))\n", @@ -112,14 +112,14 @@ "outputs": [], "source": [ "plt.figure(figsize=(12,5)),plt.subplots_adjust(wspace=.3)\n", - "plt.suptitle('Star/galaxy beam sim segment of '+hdr['CCD_SERN']+'\\n '+fitsfilename.split('/')[-1])\n", + "plt.suptitle('Star/galaxy beam sim image and histogram \\n'+fitsfilename.split('/')[-1])\n", "\n", "plt.subplot(121)\n", - "plt.imshow(exposure.getImage().array,vmax=1e3,origin='lower')\n", + "plt.imshow(exposure.image.array,vmax=1e3,origin='lower')\n", "plt.colorbar(label='electrons')\n", "\n", "plt.subplot(122)\n", - "plt.hist(exposure.getImage().array.flatten(),bins=1000,histtype='step')\n", + "plt.hist(exposure.image.array.flatten(),bins=1000,histtype='step')\n", "plt.yscale('log')#,plt.xscale('log')\n", "plt.xlabel('Number of electrons in pixel'),plt.ylabel('Number of pixels')" ] @@ -129,10 +129,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# +TODO perhaps some other image stats from \n", - "# https://github.com/lsst/pipe_tasks/blob/master/python/lsst/pipe/tasks/exampleStatsTasks.py" - ] + "source": [] }, { "cell_type": "markdown", @@ -203,6 +200,13 @@ "plt.plot(charResult.sourceCat['base_SdssCentroid_x'],charResult.sourceCat['base_SdssCentroid_y'],'r.')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This figure illustrates the centroids of detections made during characterization. Note that most objects have been detected (except for one), and that there are several spurious detections which are not on our grid. Further visualization of these will be done in the Firefly window a few cells below." + ] + }, { "cell_type": "code", "execution_count": null, @@ -224,7 +228,7 @@ "source": [ "# Looking at the mask plane, which started off as all zeros\n", "# and now has some values of 2^5\n", - "maskfoo=exposure.getMask()\n", + "maskfoo=exposure.mask\n", "print(\"Unique mask plane values: \",np.unique(maskfoo.array))\n", "print(\"Mask dictionary entries: \",maskfoo.getMaskPlaneDict())\n", "\n", @@ -237,43 +241,10 @@ ] }, { - "cell_type": "raw", + "cell_type": "markdown", "metadata": {}, "source": [ - "# Another option for this analysis, from:\n", - "# https://gist.github.com/josePhoenix/8325c16b44fb5fa51f40261b184a78ef\n", - "# Parejko: these are run as part of ProcessCcdTask\n", - "# see https://ldm-151.lsst.io/\n", - "\n", - "\n", - "import lsst.afw.table\n", - "import lsst.afw.image\n", - "import lsst.afw.math\n", - "import lsst.meas.algorithms\n", - "import lsst.meas.base\n", - "import lsst.meas.deblender\n", - "\n", - "schema = lsst.afw.table.SourceTable.makeMinimalSchema()\n", - "detect = lsst.meas.algorithms.SourceDetectionTask(schema=schema)\n", - "deblend = lsst.meas.deblender.SourceDeblendTask(schema=schema)\n", - "measure = lsst.meas.base.SingleFrameMeasurementTask(schema=schema)\n", - "\n", - "tstart=time.time()\n", - "\n", - "table = lsst.afw.table.SourceTable.make(schema) # this is really just a factory for records, not a table\n", - "\n", - "detect_result = detect.run(table, exposure)\n", - "catalog = detect_result.sources # this is the actual catalog, but most of it's still empty\n", - "print(time.time()-tstart)\n", - "\n", - "#deblend.run(exposure, catalog)\n", - "#print(time.time()-tstart)\n", - "\n", - "measure.run(catalog, exposure)\n", - "print(time.time()-tstart)\n", - "\n", - "plt.figure(figsize=(15,15))\n", - "plt.scatter(catalog['base_SdssCentroid_x'],catalog['base_SdssCentroid_y'],marker='.')" + "The above figures illustrate the new mask plane of the exposure object which was added and modified during characterization. Values of 0 and 5 are now seen, which correspond to unassociated pixels and those which are \"detected\". Further visualization of the mask plane can be seen in the Firefly cell down below." ] }, { @@ -281,7 +252,7 @@ "metadata": {}, "source": [ "## Step 3: Further image calibration and measurement\n", - "This builds on the exposure output from characterization, using the new mask plane as well as the source catalog. Similarly to the characterization, we turn off some processing which is suited to our particular setup." + "This builds on the exposure output from characterization, using the new mask plane as well as the source catalog. Similar to the characterization, we turn off some processing which is suited to our particular setup. For this dataset a calibrate task is almost unncessary (as it is not on-sky data and we don't have a reference catalog), however, it does provide a background-subtracted image and for completeness it is included here. The steps in calibration that are turned on/off can be seen by printing the calibration config object." ] }, { @@ -341,7 +312,7 @@ "metadata": {}, "outputs": [], "source": [ - "cat_dir" + "print(\"Catalogs will be saved to: \"+cat_dir)" ] }, { @@ -377,6 +348,13 @@ " plt.xlabel(par_name)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above figures show the 2-dimensional distribution of detected objects measured parameter values and their histogram. By default, two shape parameters (in pixels) and a flux measurement (in electrons) are shown, but this can be modified through the `par_names` variable in the cell above. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -401,13 +379,11 @@ "from IPython.display import IFrame, display, Markdown\n", "import os\n", "\n", - "# Own cell?\n", + "# Info for Firefly server connection\n", "my_channel = '{}_test_channel'.format(os.environ['USER'])\n", "server = 'https://lsst-lspdev.ncsa.illinois.edu'\n", - "\n", - "# This needs its own cell\n", "ff='{}/firefly/slate.html?__wsch={}'.format(server, my_channel)\n", - "IFrame(ff,1000,600)\n", + "IFrame(ff,1000,600) # initiate the window\n", "\n" ] }, @@ -441,7 +417,7 @@ "metadata": {}, "source": [ "## Step 4: Apply the brighter-fatter kernel correction to an image\n", - "This brighter fatter correction method takes in a \"kernel\" (derived from theory or flat fields) which models the broadening of incident image profiles assuming the pixel boundary displacement can be represented as the gradient of a scalar field. Given a kernel and this assumption, the incident image profile can in theory be reconstructed using an iterative process, which we test here using our beam simulator images. See [this paper](https://arxiv.org/abs/1711.06273) and the IsrTask docstring below for more details about the theory and its assumptions." + "This brighter fatter correction method takes in a \"kernel\" (derived from theory or flat fields) which models the broadening of incident image profiles assuming the pixel boundary displacement can be represented as the gradient of a scalar field. Given a kernel and this assumption, the incident image profile can in theory be reconstructed using an iterative process, which we test here using our beam simulator images. See [this paper](https://arxiv.org/abs/1711.06273) and the IsrTask docstring below for more details about the theory and its assumptions. The kernel used here is not generated by the stack but rather through similar code which was written at UC Davis by Craig Lage. Future additions to the notebook will use a stack-generated kernel when available." ] }, { @@ -481,25 +457,25 @@ "plt.figure(),plt.title('BF kernel')\n", "plt.imshow(kernel),plt.colorbar()\n", "\n", - "imagediff=(pre_bfcorr_exposure.getImage().array-exposure.getImage().array)\n", - "imagediffpct=np.sum(imagediff)/np.sum(pre_bfcorr_exposure.getImage().array)*100.\n", + "imagediff=(pre_bfcorr_exposure.image.array-exposure.image.array)\n", + "imagediffpct=np.sum(imagediff)/np.sum(pre_bfcorr_exposure.image.array)*100.\n", "print(str(imagediffpct)[:5],' percent change in flux')\n", "\n", "plt.figure(figsize=(16,10))\n", "plt.subplot(231),plt.title('Before')\n", - "plt.imshow(pre_bfcorr_exposure.getImage().array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", + "plt.imshow(pre_bfcorr_exposure.image.array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", "plt.subplot(232),plt.title('After')\n", - "plt.imshow(exposure.getImage().array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", + "plt.imshow(exposure.image.array,vmin=0,vmax=1e3,origin='lower'),plt.colorbar()\n", "plt.subplot(233),plt.title('Before - After')\n", "vmin,vmax=-50,50\n", "plt.imshow(imagediff,vmin=vmin,vmax=vmax,origin='lower'),plt.colorbar()\n", "\n", "nbins=1000\n", "plt.subplot(234)\n", - "plt.hist(pre_bfcorr_exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='before')\n", + "plt.hist(pre_bfcorr_exposure.image.array.flatten(),bins=nbins,histtype='step',label='before')\n", "plt.yscale('log')\n", "plt.subplot(235)\n", - "plt.hist(exposure.getImage().array.flatten(),bins=nbins,histtype='step',label='after')\n", + "plt.hist(exposure.image.array.flatten(),bins=nbins,histtype='step',label='after')\n", "plt.yscale('log')\n", "plt.subplot(236)\n", "plt.hist(imagediff.flatten(),bins=nbins,histtype='step',label='difference')\n", @@ -512,8 +488,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Step 5: Run the above steps (with and without brighter-fatter correction) on the 20 exposures of increasing brightness\n", - "Making and saving catalogs should take (with do_bf_corr=True/False) around 1 & 5 seconds per image." + "The above figures illustrate the way that the brighter-fatter correction works: by iteratively convolving a physically-motivated kernel with the electron image to redistribute charge from the periphery to the center of objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5: Run the above steps (with and without brighter-fatter correction) on 20 exposures of increasing exposure time\n", + "Here we re-do all of the previous work, which was done with one image, on a series of images with increasing exposure times. We will generate this series of catalogs both with and without applying the brighter-fatter correction, allowing us to test the fidelity of the brighter-fatter correction with our beam simulator images. To do this in a simple way, we create a function to perform all of the above tasks, called `make_bf_catalogs`, which only takes in a list of filenames but uses some of the same global configuration values (`charTask.config` and `calTask.config`) which we set above." ] }, { @@ -522,36 +505,45 @@ "metadata": {}, "outputs": [], "source": [ - "do_bf_corr=True # True or False, run this cell with both\n", - "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits' # fits files to read in\n", + "fitsglob='/project/shared/data/beamsim/bfcorr/*part.fits' # fits filenames to read in\n", + "fitsfilelist=np.sort(glob.glob(fitsglob))\n", "\n", - "for fitsfilename in np.sort(glob.glob(fitsglob)):\n", - " image_array=afwImage.ImageF.readFits(fitsfilename)\n", - " image = afwImage.ImageF(image_array)\n", + "def make_bf_catalogs(fitsfilelist,do_bf_corr=False,do_verbose_print=True):\n", + " for fitsfilename in fitsfilelist:\n", + " tstart=time.time()\n", + " image_array=afwImage.ImageF.readFits(fitsfilename)\n", + " image = afwImage.ImageF(image_array)\n", "\n", - " exposure = afwImage.ExposureF(image.getBBox())\n", - " exposure.setImage(image)\n", + " exposure = afwImage.ExposureF(image.getBBox())\n", + " exposure.setImage(image)\n", "\n", - " updateVariance(exposure.getMaskedImage(), gain, readNoise)\n", - " \n", - " # start the characterization and measurement, optionally beginning with the brighter-fatter correction\n", - " tstart=time.time()\n", - " if do_bf_corr:\n", - " isr.brighterFatterCorrection(exposure,kernel,bf_maxiter,bf_threshold,False)\n", - " # print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", - " # for stack v16.0+22 use charTask.run() and calTask.run()\n", - " charResult = charTask.characterize(exposure) \n", - " calResult = calTask.calibrate(charResult.exposure, background=charResult.background,\n", - " icSourceCat = charResult.sourceCat)\n", - " src=calResult.sourceCat #.copy(deep=True) ?\n", - " \n", - " # write out the source catalog, appending -bfcorr for the corrected catalogs\n", - " catfilename=cat_dir+fitsfilename.replace('.fits','.cat').split('/')[-1]#\n", - " if do_bf_corr: catfilename=catfilename.replace('.cat','-bfcorr.cat')\n", - " src.writeFits(catfilename)\n", - " \n", - " print(fitsfilename.split('/')[-1],\" char. & calib. took \",str(time.time()-tstart)[:4],\" seconds to measure \",len(calResult.sourceCat),\" objects \")\n", - "\n" + " updateVariance(exposure.maskedImage, gain, readNoise)\n", + " \n", + " # start the characterization and measurement, \n", + " # optionally beginning with the brighter-fatter correction\n", + " if do_bf_corr:\n", + " isr.brighterFatterCorrection(exposure,kernel,bf_maxiter,bf_threshold,False)\n", + " # print(\"Brighter-fatter correction took\",str(time.time()-tstart)[:4],\" seconds\")\n", + " # for stack v16.0+22 use charTask.run() and calTask.run()\n", + " charResult = charTask.characterize(exposure) \n", + " calResult = calTask.calibrate(charResult.exposure, background=charResult.background,\n", + " icSourceCat = charResult.sourceCat)\n", + " src=calResult.sourceCat\n", + "\n", + " # write out the source catalog, appending -bfcorr for the corrected catalogs\n", + " catfilename=cat_dir+fitsfilename.replace('.fits','.cat').split('/')[-1]#\n", + " if do_bf_corr: catfilename=catfilename.replace('.cat','-bfcorr.cat')\n", + " src.writeFits(catfilename)\n", + "\n", + " if do_verbose_print: \n", + " print(fitsfilename.split('/')[-1],\" char. & calib. took \",\n", + " str(time.time()-tstart)[:4],\" seconds to measure \",\n", + " len(calResult.sourceCat),\" objects \")\n", + "\n", + " \n", + "# Run the catalog maker on the series of uncorrected and corrected images\n", + "make_bf_catalogs(fitsfilelist,do_bf_corr=True,do_verbose_print=True)\n", + "make_bf_catalogs(fitsfilelist,do_bf_corr=False,do_verbose_print=False)" ] }, { @@ -594,7 +586,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using a fiducial frame as reference, we simply match the catalogs by looking for *single* object matches within a specified distance. We then collect a shape measurement (e.g. `base_SdssShape_xx/yy`) for that object as well as a brightness measurement (e.g. `base_SdssShape_flux`) to test for a trend in size vs. brightness." + "The above image illustrates a problem with comparing images with different exposure times. Namely, that different sets of objects may be detected. To remedy this, we use a fiducial frame as reference and simply match the catalogs by looking for *single* object matches within a specified distance of those fiducial objects. We then collect a shape measurement (e.g. `base_SdssShape_xx/yy`) for that object as well as a brightness measurement (e.g. `base_SdssShape_flux`) to test for a trend in size vs. brightness." ] }, { @@ -610,7 +602,7 @@ "# +TODO use 'ext_shapeHSM_HsmSourceMoments_xx','ext_shapeHSM_HsmSourceMoments_yy'\n", "cen_param1,cen_param2='base_SdssCentroid_x','base_SdssCentroid_y'\n", "bf_param1,bf_param2='base_SdssShape_xx','base_SdssShape_yy'\n", - "flux_param='base_SdssShape_flux'\n", + "flux_param='base_GaussianFlux_flux' # could try 'base_CircularApertureFlux_17_0_flux' or 'base_SdssShape_flux'\n", "\n", "# get the centroids (used for matching) from the fiducial frame \n", "x0s,y0s=cat_arr[fidframe][cen_param1],cat_arr[fidframe][cen_param2]\n", @@ -618,7 +610,10 @@ "\n", "# make an array to hold that number of objects and their centroid/shape/flux measurements\n", "# the 8 rows collect x/y centroid, x/y shape, x/y corrected shape, flux, and corrected flux\n", - "bf_dat=np.empty((ncats,nspots,8))\n", + "bf_dat=np.empty((ncats,nspots),\n", + " dtype=np.dtype([('x', float), ('y', float),('shapex', float), ('shapey', float),\n", + " ('corrshapex', float), ('corrshapey', float),\n", + " ('flux', float), ('corrflux', float)]))\n", "bf_dat[:]=np.nan # so that un-matched objects aren't plotted/used by default\n", "\n", "\n", @@ -637,7 +632,7 @@ " xx,yy=cat_arr[i][bf_param1][gd],cat_arr[i][bf_param2][gd] # centroids\n", " xx_bf,yy_bf=bf_cat_arr[i][bf_param1][bf_gd],bf_cat_arr[i][bf_param2][bf_gd] # sizes\n", " flux,flux_bf=cat_arr[i][flux_param][gd],bf_cat_arr[i][flux_param][bf_gd] # fluxes\n", - " bf_dat[i,j,:]=x0,y0,xx,yy,xx_bf,yy_bf,flux,flux_bf # keep those above measurements" + " bf_dat[i,j]=x0,y0,xx,yy,xx_bf,yy_bf,flux,flux_bf # keep those above measurements" ] }, { @@ -645,7 +640,7 @@ "metadata": {}, "source": [ "# Plot the brighter-fatter effect on those shape measurements and the corrected version\n", - "Alongside stamps of each object, below we show the trend of X and Y sizes before and after brighter-fatter correction" + "Alongside stamps of each object, below we show the trend of X and Y sizes before and after brighter-fatter correction. By default this makes a dozen plots in as many seconds and saves them to the catalog directory." ] }, { @@ -654,47 +649,54 @@ "metadata": {}, "outputs": [], "source": [ - "sz=11 # stamp size\n", + "sz=13 # stamp size\n", "\n", "# loop over some objects and save a summary figure\n", "# [0,6,12,18,23,35,44,46,52,56,69,71,114] are good indices to look with default values \n", "# or e.g. np.random.choice(np.arange(nspots),size=10)\n", "indexfoo=[0,6,12,18,23,35,44,46,52,56,69,71,114]\n", "\n", - "for nfoo in indexfoo:\n", + "for index in indexfoo:\n", " plt.figure(figsize=(14,4)),plt.subplots_adjust(wspace=.3)\n", " \n", " # grab a postage stamp, integerizing the centroid and shipping\n", " # if it is near the edge, +TODO in a stackly manner\n", - " xc,yc=bf_dat[10,nfoo,0].astype('int')+1,bf_dat[10,nfoo,1].astype('int')+1\n", + " xc,yc=bf_dat['x'][fidframe,index].astype('int')+1,bf_dat['y'][fidframe,index].astype('int')+1\n", " if ((np.abs(xc-250)>250 - sz ) | (np.abs(yc-250)>250 - sz )): continue\n", " stamp=exposure.getImage().array[yc-sz:yc+sz,xc-sz:xc+sz]\n", " \n", " # show the stamp with log scale (1,max)\n", - " plt.subplot(131),plt.title('stamp '+str(nfoo).zfill(3)+' (log scale)')\n", + " plt.subplot(131),plt.title('stamp '+str(index).zfill(3)+' (log scale)')\n", " plt.imshow(stamp,origin='lower',norm=LogNorm(1,stamp.max())),plt.colorbar()\n", " \n", " # x size vs flux\n", " plt.subplot(132),plt.title('x (row) size vs. flux')\n", - " plt.plot(bf_dat[:,nfoo,6],bf_dat[:,nfoo,2],'r.',label='Uncorrected')\n", - " plt.plot(bf_dat[:,nfoo,7],bf_dat[:,nfoo,4],'g.',label='Corrected')\n", + " plt.plot(bf_dat['flux'][:,index],bf_dat['shapex'][:,index],'r.',label='Uncorrected')\n", + " plt.plot(bf_dat['corrflux'][:,index],bf_dat['corrshapex'][:,index],'g.',label='Corrected')\n", " plt.xlabel(flux_param),plt.ylabel(bf_param1),plt.xscale('log')\n", " plt.legend(loc='upper left')\n", "\n", " # y size vs flux\n", " plt.subplot(133),plt.title('y (column) size vs. flux')\n", - " plt.plot(bf_dat[:,nfoo,6],bf_dat[:,nfoo,3],'r.',label='Uncorrected')\n", - " plt.plot(bf_dat[:,nfoo,7],bf_dat[:,nfoo,5],'g.',label='Corrected')\n", + " plt.plot(bf_dat['flux'][:,index],bf_dat['shapey'][:,index],'r.',label='Uncorrected')\n", + " plt.plot(bf_dat['corrflux'][:,index],bf_dat['corrshapey'][:,index],'g.',label='Corrected')\n", " plt.xlabel(flux_param),plt.ylabel(bf_param2)\n", " plt.xscale('log')\n", - " plt.savefig(cat_dir+str(nfoo).zfill(5)+'bfcorr.png')" + " plt.savefig(cat_dir+str(index).zfill(5)+'bfcorr.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above figures illustrate the brighter-fatter effect (slight increasing size with flux) in the red dots, and the corrected image analysis in green. Curiously, some of the objects indicate that the default correction method is properly correcting star-like objects, but over- or under-correcting the effect in galaxy images. This could be due to a violation of some of the underlying assumptions in the method, including the small-pixel approximation or the linearity of kernel correction. Some of the remaining trends could be related to an increase in signal-to-noise in the images, however this is a universally applicable issue with shape measurement and is beyond the scope of this notebook. In some of the figures, a rapid increase in size can be seen at the highest fluxes, indicating saturation of pixel wells which is unrelated to the brighter-fatter effect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now plot the flux lost/gained in the process of brighter-fatter correction" + "Now plot the flux lost/gained in the process of brighter-fatter correction, by subtracting the flux of the corrected images from the uncorrected ones. The flux measurement is the same as the one used in the above figures and is measured in electrons." ] }, { @@ -707,14 +709,14 @@ "stylepalette=cycle(['s','*','o'])\n", "plt.figure(figsize=(8,5))\n", "for nfoo in indexfoo:\n", - " flux_foo=bf_dat[:,nfoo,6]\n", - " fluxdiffpct_foo=(bf_dat[:,nfoo,6]-bf_dat[:,nfoo,7])/bf_dat[:,nfoo,6]*100.\n", + " flux_foo=bf_dat['flux'][:,nfoo]\n", + " fluxdiffpct_foo=(bf_dat['flux'][:,nfoo]-bf_dat['corrflux'][:,nfoo])/bf_dat['flux'][:,nfoo]*100.\n", " plt.plot(flux_foo,fluxdiffpct_foo,label=str(nfoo).zfill(3),c=next(colorpalette),marker=next(stylepalette))\n", "plt.xscale('log')\n", "plt.legend()\n", "plt.xlabel(flux_param,fontsize=20)\n", "plt.ylabel('Flux change of correction \\n (before - after) [%]',fontsize=20)\n", - "plt.savefig(cat_dir+'BF_corr_flux_change.png',dpi=150)" + "#plt.savefig(cat_dir+'BF_corr_flux_change.png',dpi=150)" ] }, { @@ -723,23 +725,8 @@ "metadata": {}, "outputs": [], "source": [ - "# +TODO add re-scaled stamp subtraction comparison\n", "# +TODO other ways of doing matching, catalog stacking" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 75bd0f0ea49adc6c2d05978fec08c1e3ed676816 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 14 Sep 2018 21:25:51 -0700 Subject: [PATCH 43/45] Note about needing the right permissions for pip installing --- GettingStarted/GettingStarted.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GettingStarted/GettingStarted.md b/GettingStarted/GettingStarted.md index 0ccbbc20..0d10586e 100644 --- a/GettingStarted/GettingStarted.md +++ b/GettingStarted/GettingStarted.md @@ -74,7 +74,7 @@ This will put the repo's `stackclub` folder on your path. When developing the pa ``` This enables you to repeatedly `import stackclub` as you update the library code. The above lines are in the [template notebook](templates/template_Notebook.ipynb), for your convenience. -If you are not developing this package, you can install it using pip, like this: +If you are not developing this package, and you have permission to write to your base python site-packages, you can install it using pip, like this: ``` pip install git+git://github.com/LSSTScienceCollaborations/StackClub.git#egg=stackclub ``` From 83105d2b6d0c4246a66340da5b701cc703f2f270 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 14 Sep 2018 21:28:14 -0700 Subject: [PATCH 44/45] pip install permissions, where to do the setup --- GettingStarted/FindingDocs.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GettingStarted/FindingDocs.ipynb b/GettingStarted/FindingDocs.ipynb index 1fd994da..ca7c8947 100644 --- a/GettingStarted/FindingDocs.ipynb +++ b/GettingStarted/FindingDocs.ipynb @@ -27,11 +27,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We'll need the `stackclub` package to be installed. If you are not developing this package, you can install it using `pip`, like this:\n", + "We'll need the `stackclub` package to be installed. If you are not developing this package, and you have permission to write to your base python site-packages, you can install it using `pip`, like this:\n", "```\n", "pip install git+git://github.com/LSSTScienceCollaborations/StackClub.git#egg=stackclub\n", "```\n", - "If you are developing the `stackclub` package (eg by adding modules to it to support the Stack Club tutorial that you are writing, you'll need to make a local, editable installation. In the top level folder of the `StackClub` repo, do:" + "If you are developing the `stackclub` package (eg by adding modules to it to support the Stack Club tutorial that you are writing), you'll need to make a local, editable installation, like this:" ] }, { From 2050ef107c10ec093ae43b35482f92a008aec4ec Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Mon, 24 Sep 2018 11:06:24 -0700 Subject: [PATCH 45/45] Sept 14, 21 meetings --- Meetings.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Meetings.md b/Meetings.md index 8ef7f3a1..6bcb98b6 100644 --- a/Meetings.md +++ b/Meetings.md @@ -4,6 +4,8 @@ Individual session links and recordings are given below, most recent meeting at | Session | Date | Topic | Links | |---|---|---|---| +| Phase 2, Session 6 | Friday September 21, 2018 [(video)](https://stanford.zoom.us/recording/share/8_fQYpnZFh2jDLE4LHKjEfdiQ28kjFxGu5jSdTuzdE2wIumekTziMw) | Hacking, Syllabus discussion | ["Course" topic list](https://docs.google.com/document/d/1PSA1uWwTfs9CweatpxF8CEPGBYRY5ZaXB39JzXYE7_U/edit?ts=5ba52b5e#heading=h.txq6h6bpxzkd) | +| Phase 2, Session 5 | Friday September 14, 2018 [(video)](https://stanford.zoom.us/recording/share/-IiuluXvCcOdD-L8FNQSmnB29-f8lU2pTfPyyahcJ1uwIumekTziMw) | Tutorial walkthrough, Hacking | [HSC Re-Run Script and Notebook](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/hsc-re-run/ImageProcessing/Re-RunHSC.ipynb) | | Phase 2, Session 4 | Friday September 7, 2018 [(video)](https://stanford.zoom.us/recording/share/ZlkFudy5hMTeR-GZVOgo_oGd0R9Q4dkrN6-aJMfelGawIumekTziMw) | Notebook walkthrough, Hacking | [Guided Tour of an AFW Table](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/afw_table/ishasan/Basics/afw_table_guided_tour.ipynb) | | Phase 2, Session 3 | Friday August 31, 2018 [(video)](https://stanford.zoom.us/recording/share/U7_XJvwjNlUh4N7g3ytBbKtTQHl-fLS0tqiBhAxZrEmwIumekTziMw) | Live code review, hacking | [Brighter-Fatter Correction with Beamsim Data](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/beamsim/andrewkbradshaw/ImageProcessing/BrighterFatterCorrection.ipynb) | | Phase 2, Session 2 | Friday August 24, 2018 [(video)](https://stanford.zoom.us/recording/share/share/Xii8Utw9RX5rqGUn8a_barg6NDBcRuzkmDIjDrUds82wIumekTziMw) | Interactive visualization, new member start-ups, hacking | [Bokeh/HoloViews Demo](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/bokeh_holoviews_datashader/bechtol/Visualization/bokeh_holoviews_datashader.ipynb) |