From 07c46b8b65b4918e0a5da2b1d4ea1e69d25b9e51 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 22 Sep 2026 21:30:11 +0000 Subject: [PATCH 01/13] updating deblender nbs to DP2 --- .../206_1_Deblender_outputs.ipynb | 930 ++++++++++++++++++ .../206_2_Deblender_footprints.ipynb | 758 ++++++++++++++ 2 files changed, 1688 insertions(+) create mode 100644 DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb create mode 100644 DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb diff --git a/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb new file mode 100644 index 00000000..4961bc93 --- /dev/null +++ b/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "attachments": { + "5574c61a-d2bb-42ee-9ba5-856498881b08.png": { + "image/png": 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl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\nrkJggg==\n" + } + }, + "cell_type": "markdown", + "id": "325aa8a5-92fd-4913-a471-ad1617343be6", + "metadata": {}, + "source": [ + "# Coadd Deblender models\n", + "\n", + "\n", + "
\n", + "\n", + "![tutorial_nb_logo.png](attachment:5574c61a-d2bb-42ee-9ba5-856498881b08.png)\n", + "\n", + "
\n", + "\n", + "For the Rubin Science Platform at data.lsst.cloud.
\n", + "Data Release: DP2
\n", + "Container Size: Large
\n", + "LSST Science Pipelines version: v30.0.10
\n", + "Last verified to run: 2026-07-21
\n", + "Repository: github.com/lsst/tutorial-notebooks
" + ] + }, + { + "cell_type": "markdown", + "id": "9da1a210-d858-42fe-8591-570965b8be1a", + "metadata": {}, + "source": [ + "**Learning objective:** This notebook demonstrates the meaning and basic use of deblender data products cataloged in the `Object` table.\n", + "\n", + "**LSST data products:** `Object` table, `deep_coadd`\n", + "\n", + "**Packages:** `lsst.rsp`, `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`\n", + "\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks. \n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "cfc73be0", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Deblending is an active area of research and the data products associated with are likely to be one of the more dynamic components of the LSST Science Pipelines. This notebook aims to introduce users to the data products associated with deblending in the `Object` table. The deblender packages are based on Melchior et al. 2018, and the Scarlet software written by Peter Melchior and Fred Moolekamp. Further documentation on Scarlet is available at that site.\n", + "\n", + "**Note**: In DP2, visit images use a completely different deblending algorithm based on the SDSS deblender (citation needed). \n", + "\n", + "### 1.2 Detection\n", + "\n", + "Each island of pixels connected by flux, and the flux peaks connected by them is called a blend. Each blend (called a \"**parent**\" object in the catalog parlance) is identified as a region of connected pixels above a signal-to-noise ratio (S/N) of 5 in a `deep_coadd` image. Each parent is given a unique `parentObjectId`, and the region occupied by the parent is called a footprint. Each footprint may have one or more peaks, which the deblender uses to infer the number and positions of objects blended in each footprint.\n", + "The deblended sub-peaks are referred to as \"**children**\" of the parent, \n", + "The Scarlet deblender separates the flux among components by modeling the flux and shape of each source making up the blend. Their properties are saved by the deblender in the `Object` table.\n", + "\n", + "\n", + "### 1.3 Deblending\n", + "\n", + "In order to make measurements on individual objects the peaks in a blend must be deblended. The LSST Science Pipeline uses the multi-wavelength deblending scarlet lite package, which implements the scarlet (Melchior et al. 2018) deblending algorithm specially optimized for LSST images, to create a model for each individual object that optimizes the residual between the sum of the models and the observed flux. For each source, the fraction of flux from its model divided by the total of all models in the blend is multiplied by the observed flux to create the final model that is used for all measurements of the object.\n", + "\n", + "### 1.3 Deconvolution and the blend hierarchy\n", + "\n", + "In fields with high stellar density it is common for a single blend to be the size of an entire LSST patch, an area currently too large for scarlet lite to process in DP2 (although future work hopes to improve this). In DP1 this limited the number of patches that could be deblended. In DP2, improvements were made in coverage by performing a partial deconvolution prior to deblending. This convolves each coadd to a Gaussian PSF with sigma=0.8, a well sampled kernel that is smaller than the best seeing in any band. Deconvolution shrinks the above threshold pixels so most blends can then be sub-divided into smaller sub-blends that do not overlap in the deconvolved space. Each of these sub-blends also has it's own unique `objectId`, so that each child source in a blend has a `parentObjectId` and a `blendObjectId`, as well as it's own `objectId`. The `object` catalog consists only of isolated objects, objects that don't overlap with any other peaks, and the deblended child sources (parent objects are excluded).\n", + "\n", + "**Note**: Deblending is an active area of research, deblending at scale doubly so. \n", + "\n", + "**References:** \n", + "- An introduction to all of the available deblending flags, and the processes that produce the deblender data products discussed in this tutorial\n", + "can be found in this overview of the deblending flags.\n", + "- A general discussion of blending impacts to LSST can be found in this article titled \"The challenge of blending in large sky surveys\" by Melchior et al. 2021. \n", + "- Some more in-depth presentations on the deblending implementation in the LSST Science Pipelines are available from recorded talks during the Rubin Project and Community Workshop 2022's session on \"Deblending Plans and Challenges\". \n", + "\n", + "**Related tutorials:**\n", + "A notebook demonstrating how to work with blend footprints is available as 206.2 Deblender footprints. \n" + ] + }, + { + "cell_type": "markdown", + "id": "dc36f107", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\n", + "\n", + "The `lsst.rsp` package provides access to the Table Access Protocol (TAP) service for queries to the DP2 catalogs.\n", + "\n", + "The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cddc1458", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from lsst.daf.butler import Butler\n", + "\n", + "from lsst.rsp import RSPDiscovery\n", + "\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.images import Box, Interval\n", + "\n", + "from astropy.coordinates import SkyCoord" + ] + }, + { + "cell_type": "markdown", + "id": "c217adff-25ed-4fce-95e7-8aa04630f6cc", + "metadata": {}, + "source": [ + "### 1.2. Define functions and parameters\n", + "\n", + "The following `cutout_coadd` function enables the visualization of deblended products by making a cutout." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b239ff8-19ba-4742-a4cb-350965f09ef0", + "metadata": {}, + "outputs": [], + "source": [ + "def cutout_coadd(butler, ra, dec, band='r', datasetType='deep_coadd',\n", + " cutoutSideLength=51, **kwargs):\n", + " \"\"\"\n", + " Produce a cutout from a coadd at the given ra, dec position.\n", + "\n", + " Parameters\n", + " ----------\n", + " butler: lsst.daf.persistence.Butler\n", + " Helper object providing access to a data repository\n", + " ra: float\n", + " Right ascension of the center of the cutout, in degrees\n", + " dec: float\n", + " Declination of the center of the cutout, in degrees\n", + " band: string\n", + " Filter of the image to load\n", + " datasetType: string ['deep_coadd']\n", + " Which type of coadd to load. Doesn't support 'calexp'\n", + " cutoutSideLength: float [optional]\n", + " Size of the cutout region in pixels.\n", + "\n", + " Returns\n", + " -------\n", + " MaskedImage\n", + " \"\"\"\n", + " \n", + " dataset_refs = butler.query_datasets(\"deep_coadd\",\n", + " where=\"band.name=band AND\\\n", + " patch.region OVERLAPS POINT(ra, dec)\",\n", + " bind={\"band\": band, \"ra\": ra, \"dec\": dec},\n", + " with_dimension_records=True,\n", + " order_by=[\"patch.tract\"])\n", + "\n", + " dataId = dataset_refs[0].dataId\n", + "\n", + " point = SkyCoord(ra, dec, unit='deg')\n", + "\n", + " wcs = butler.get('deep_coadd.sky_projection', dataId=dataId)\n", + " xy = wcs.sky_to_pixel(point)\n", + " \n", + " selection_bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(cutoutSideLength)\n", + " params = {'bbox': selection_bbox}\n", + " subset = butler.get(dataset_refs[0], parameters=params)\n", + "\n", + " return subset" + ] + }, + { + "cell_type": "markdown", + "id": "7893b850-3a77-4ffb-b01a-6f89bfdd228a", + "metadata": {}, + "source": [ + "Set the backend for afwDisplay to matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5a16260-32c0-4896-87b3-e5b251f45027", + "metadata": {}, + "outputs": [], + "source": [ + "afwDisplay.setDefaultBackend('matplotlib')" + ] + }, + { + "cell_type": "markdown", + "id": "d3383f6e-8c34-4cb7-aa2f-12e9b7f8efc0", + "metadata": {}, + "source": [ + "Get an instance of the TAP service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8184089-8a3e-4666-a194-5362a8faa541", + "metadata": {}, + "outputs": [], + "source": [ + "discovery = RSPDiscovery(\"dp2\")\n", + "service = discovery.get_tap_client()" + ] + }, + { + "cell_type": "markdown", + "id": "c5a8892e-2a9f-486e-a32a-4884c790b450", + "metadata": {}, + "source": [ + "Instantiate the butler." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38cf5a19-b61a-4d58-b6b1-79fc92e12a2f", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler('dp2', collections=[\"dp2\"])\n", + "assert butler is not None" + ] + }, + { + "cell_type": "markdown", + "id": "16eeb034-6728-406e-bfc8-7b875c3a9416", + "metadata": {}, + "source": [ + "## 2. The deblender data products\n", + "\n", + "The object catalog contains a number of columns that can be used to identify and characterize blended objects in the `deep_coadd` images. Prior to deblending, blends are identified (referred to as parents), and are then deblended into child objects by the LSST deblender package, Scarlet Lite. Only the children are ultimately stored in the `Object` table; the parent objects are excluded. Of course, isolated single sources that do not require deblending are also stored in the `Object` table and are not considered parent sources.\n", + "\n", + "In this tutorial we will focus on the deblending data products for objects stored in the `Object` table. \n", + "\n", + "The following describes key parameters in the `Object` table: 1) boolean flags that are set by the deblender, Scarlet Lite; 2) measurements or properties of deblended sources that can be used to characterize (de)blended sources; 3) blendedness metrics performed after the deblender completes; 4) obsolete keywords still present in the `Object` table.\n", + "\n", + "\n", + "Scarlet blend flags:\n", + "\n", + "`detect_fromBlend` (boolean): If True, this source is deblended from a parent with more than one child.\n", + "\n", + "`detect_isIsolated` (boolean): If True, this source was not part of a blend. \n", + "\n", + "\n", + "Scarlet Lite blend measurements:\n", + "\n", + "`parentObjectId` (long): Unique ID of parent source. \n", + "\n", + "`deblend_parentNPeaks` (int): Total number of peaks in the parent Footprint, even if some of them were skipped or failed to deblend\n", + "\n", + "`deblend_parentNChild` (int): Number of objects with the same `parentObjectId` (ie. share the same parent Footprint)\n", + "\n", + "`deblend_blendId` (long): Unique ID of sub-blend. (see section 1.3)\n", + "\n", + "`deblend_blendNChild` (long): Number of objects with the same `deblend_blendId` (ie. from the same sub-blend).\n", + "\n", + "`footprintArea` (int): Number of pixels in the deblended object's detection \"footprint\", in the \"reference band\".\n", + "\n", + "`deblend_chi2` (float): reduced chi^2 for the scarlet lite fit\n", + "\n", + "`_deblend_fluxOverlap` (dbl): The total flux from neighboring objects that overlaps with this sources footprint in the deconvolved space. See also `_deblend_fluxOverlapFraction` Fraction of flux from neighbors / source flux in the deconvolved footprint.\n", + "\n", + "\n", + "Blendedness parameters:\n", + "\n", + "`_blendedness` (dbl): For each filter (f = u, g, r, i, z, or y), a measure of how much the flux is affected by neighbors, (1 - child\\_flux/parent\\_flux). The measurement is in the observed, not point spread function (PSF)-deconvolved space. Operates on the absolute value of the pixels to try to obtain a de-noised value. These are not performed by the deblender (Scarlet Lite) but rather from a blendedness algorithm (see section 4.9.11 of Bosch et al. 2018, PASJ, 70, S5 for details).\n", + "\n", + "`_deblend_blendedness` (dbl): Blendedness in filter in the PSF-deconvolved space.\n", + "\n", + "`_blendedness_flag` (boolean): Flag set for any failure in the blendedness algorithm. " + ] + }, + { + "cell_type": "markdown", + "id": "19f67659-29ec-4fd2-af4d-f30b4eabbebd", + "metadata": {}, + "source": [ + "### 2.1. Identify blended objects\n", + "\n", + "Set the location for the query for blends to be in the center of the Extended Chandra Deep Field South (ECDFS) field. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eb2bc6d-dfdf-414a-8f67-7544eaf7407f", + "metadata": {}, + "outputs": [], + "source": [ + "ra = 53.2\n", + "dec = -28.1\n", + "\n", + "ra = 53.4\n", + "dec = -27.9\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa180093-b8f2-4aed-8bb8-c19d910605a3", + "metadata": {}, + "source": [ + "Query for objects in a small region of ECDFS for exploration of the deblender data products stored in the `Object` table. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "427ad26a-d650-4b4b-882d-59dbcaafb712", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"SELECT objectId, coord_ra, coord_dec, tract, patch, \" + \\\n", + " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", + " \"refExtendedness, detect_fromBlend, detect_isIsolated, \" + \\\n", + " \"parentObjectId, i_deblend_fluxOverlapFraction, \" + \\\n", + " \"footprintArea, i_blendedness, i_deblend_blendedness, i_blendedness_flag \" + \\\n", + " \"FROM dp2.Object \" + \\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", + " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \"" + ] + }, + { + "cell_type": "markdown", + "id": "34f6143f-4621-42bd-bef5-88f1049c5834", + "metadata": {}, + "source": [ + "Run the TAP query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c9fdd22-dcd6-419c-985c-eae2cc90b6fc", + "metadata": {}, + "outputs": [], + "source": [ + "#job = service.submit_job(query)\n", + "#job.run()\n", + "#job.wait(phases=['COMPLETED', 'ERROR'])\n", + "#print('Job phase is', job.phase)\n", + "\n", + "\n", + "\n", + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)\n", + "if job.phase == 'ERROR':\n", + " job.raise_if_error()\n", + "assert job.phase == 'COMPLETED'\n", + "#results = job.fetch_result().to_table()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "820aeb85-3bff-4453-b2b5-f5008143fcd1", + "metadata": {}, + "source": [ + "Store the resulting objects and columns from the `Object` table to a table called results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f546a2-6607-45bd-af28-c831e83f2ca1", + "metadata": {}, + "outputs": [], + "source": [ + "#assert job.phase == 'COMPLETED'\n", + "results = job.fetch_result().to_table()" + ] + }, + { + "cell_type": "markdown", + "id": "187e39df-8ec1-4f41-8950-e5bbf7aa2a99", + "metadata": {}, + "source": [ + "### 2.2. Identify isolated objects\n", + "\n", + "A sample of objects that are not near any neighbors, and therefore were not deblended, can be identified using the `detect_isIsolated` keyword. Similarly, a set of objects that were identified as part of a blend can be identified using the `detect_fromBlend` keyword.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa1f6a28-4567-4886-8522-c171dc206523", + "metadata": {}, + "outputs": [], + "source": [ + "whiso = np.where(results['detect_isIsolated'] == 1)[0]\n", + "whblend = np.where(results['detect_fromBlend'] == 1)[0]" + ] + }, + { + "cell_type": "markdown", + "id": "7f682ec5-3092-4dcb-a11a-e73df0593f09", + "metadata": {}, + "source": [ + "Generate a cutout of the i-band `deep_coadd` imaging that overlaps the queried objects, centered at (RA, Dec) = 53.11, -28.07 degrees, with a cutout edge size of 200 pixels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ffd2bce-c801-4794-a5b9-6e2ba4da8d76", + "metadata": {}, + "outputs": [], + "source": [ + "cutout = cutout_coadd(butler, 53.11, -28.07,\n", + " band='i', datasetType='deep_coadd',\n", + " cutoutSideLength=200)" + ] + }, + { + "cell_type": "markdown", + "id": "c0b8322e-36ae-495e-b7a6-d5a87be86f1f", + "metadata": {}, + "source": [ + "Display the cutout with isolated objects identified by red circles and objects that were identified from blends identified as cyan plus signs. To ensure that `afwDisplay` will make the low surface brightness connecting the blends together visible, set the scale to linear, set the min and max pixel values to be relatively low and also indicate with units=Absolute that these should be in actual count units." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5d30736-3384-4931-ba68-e4ca6a164bb2", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(6, 6))\n", + "display = afwDisplay.Display(frame=fig)\n", + "\n", + "display.scale('linear', min=0.1, max=10, unit='Absolute')\n", + "\n", + "display.mtv(cutout.image)\n", + "with display.Buffering():\n", + " for ci in range(len(whiso)):\n", + " display.dot('o', results['deblend_peak_center_x'][whiso][ci], results['deblend_peak_center_y'][whiso][ci],\n", + " size=10, ctype=afwDisplay.RED)\n", + " for ci in range(len(whblend)):\n", + " display.dot('+', results['deblend_peak_center_x'][whblend][ci], results['deblend_peak_center_y'][whblend][ci],\n", + " size=8, ctype=afwDisplay.CYAN)\n", + "plt.gca().axis('off')\n", + "display.show_colorbar(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "72727e68-9405-4072-b190-a38d6cb02f0a", + "metadata": {}, + "source": [ + "> **Figure 1:** The cutout image displayed in greyscale, with cyan + marking objects that are blended in the `deep_coadd`, red circles marking objects that are isolated (and is not part of a blend). " + ] + }, + { + "cell_type": "markdown", + "id": "ed13f250-3458-48d5-a695-306288a07eac", + "metadata": {}, + "source": [ + "## 3. Explore blended objects\n", + "\n", + "Use the TAP service to find a group of objects that were heavily blended and part of the same parent object. \n", + "\n", + "### 3.1. Blend with several children\n", + "\n", + "First, identify a set of unique `parentObjectIds`, and the number of objects that are deblended from those `parentObjectId`s, (stored as `counts`). Then, select as an example a blend that was made up of 15 children. \n", + "\n", + "To identify the most blended object in the query, replace the second code line with:\n", + "`most_common_index_in_unique = np.argmax(counts)`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b8f7411-068f-4474-a067-2576e249de3a", + "metadata": {}, + "outputs": [], + "source": [ + "unique_IDs, counts = np.unique(results['parentObjectId'], return_counts=True)\n", + "\n", + "most_common_index_in_unique = np.where(counts == 15)[0]\n", + "\n", + "most_common_value = unique_IDs[most_common_index_in_unique[0]]\n", + "\n", + "whparent = np.where(results['parentObjectId'] == most_common_value)[0]\n", + "max_parentId = results['parentObjectId'][whparent][0]" + ] + }, + { + "cell_type": "markdown", + "id": "0bd72ed8-bf9a-4b57-9667-8140c4c79592", + "metadata": {}, + "source": [ + "Use TAP to retrieve all children for the selected parent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "812d0a19-6c1b-4bd3-a868-6c6f05bf30f8", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, i_blendedness_flag, \" + \\\n", + " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", + " \"i_cModelFlux/i_cModelFluxErr AS i_S2N, footprintArea, i_deblend_blendedness, \" + \\\n", + " \"i_blendedness, i_deblend_fluxOverlap, i_deblend_fluxOverlapFraction \" + \\\n", + " \"FROM dp2.Object \" + \\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", + " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \" + \\\n", + " \"AND parentObjectId = \" + str(max_parentId)" + ] + }, + { + "cell_type": "markdown", + "id": "5ba2bcaf-6352-4d08-9e1b-793afab534af", + "metadata": {}, + "source": [ + "Run the TAP query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "551a9856-f273-448f-895e-16cb17f5f27f", + "metadata": {}, + "outputs": [], + "source": [ + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)" + ] + }, + { + "cell_type": "markdown", + "id": "82ea114e-9b92-4f0a-a1cd-93718954e8aa", + "metadata": {}, + "source": [ + "Store the query results in a table called children." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51888269-2afd-4cd2-988c-a8585ac9605d", + "metadata": {}, + "outputs": [], + "source": [ + "assert job.phase == 'COMPLETED'\n", + "children = job.fetch_result().to_table()\n", + "children" + ] + }, + { + "cell_type": "markdown", + "id": "5546408c-6009-4230-9ef4-2faf1c302940", + "metadata": {}, + "source": [ + "The table above confirms that the children returned by the query were deblended from the same parent source, since they all show the same `parentObjectId`. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf9ffc32-7b03-4aa8-bc70-2cc051562123", + "metadata": {}, + "source": [ + "A single source is identified with high `i_deblend_fluxOverlapFraction` (i.e. flux contribution from neighbors) of almost 62%. Note however that in this case, the deblender indicates that the deblended flux is uncontaminated in the PSF-decovolved space, `i_deblend_blendedness` = 0, and the blendedness flag returns False, indicating this object was successfully deblended. However, these blendedness metrics before and after deblending can assist in quality assessment.\n", + "\n", + "Below, identify this child with heavy blendedness of 50%. TO FIX THIS (BLENDEDNESS IS NEGATIVE BUT SHOULD NOT BE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fc87bf7-d7c7-4609-9919-fc7b73f3aca9", + "metadata": {}, + "outputs": [], + "source": [ + "whheavy = np.where(children['i_blendedness'] > 0.5)[0]\n", + "\n", + "children[whheavy]" + ] + }, + { + "cell_type": "markdown", + "id": "47c57439-2cc4-4cb6-9273-dab60b3127da", + "metadata": {}, + "source": [ + "Next, visualize these test cases along with the other child sources of this `parentObjectId`. Use the `cutout_coadd` function to obtain a cutout image to visualize the blend. Set the size of the cutout to be 2x the square root of the `footprintArea`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2f6c287-7502-4343-89e3-e76cb3e98e87", + "metadata": {}, + "outputs": [], + "source": [ + "cutout = cutout_coadd(butler, children['coord_ra'][0],\n", + " children['coord_dec'][0],\n", + " band='i', datasetType='deepCoadd',\n", + " cutoutSideLength=2.0\n", + " * np.sqrt(np.sum(children['footprintArea'])))" + ] + }, + { + "cell_type": "markdown", + "id": "8730ddb3-923b-4d1a-bce7-193b13a5b1b3", + "metadata": {}, + "source": [ + "Display the `deepCoadd` cutout made for this parent, with all deblended children identified with red circles. Identify the children for which the blendedness metric (before deblending) was 50% with a cyan circle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba327e1e-9323-4c57-9dc7-49ae22cce397", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(6, 6))\n", + "display = afwDisplay.Display(frame=fig)\n", + "display.scale('linear', min=0.1, max=10, unit='Absolute')\n", + "display.mtv(cutout.image)\n", + "with display.Buffering():\n", + " for ci in range(len(children)):\n", + " display.dot('o', children['deblend_peak_center_x'][ci], children['deblend_peak_center_y'][ci],\n", + " size=10, ctype=afwDisplay.RED)\n", + "\n", + " for ci in range(len(whheavy)):\n", + " display.dot('o', children['deblend_peak_center_x'][whheavy][ci], children['deblend_peak_center_y'][whheavy][ci],\n", + " size=8, ctype=afwDisplay.CYAN)\n", + "\n", + "plt.gca().axis('off')\n", + "display.show_colorbar(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4dc608a8-cb6b-48c8-9aa9-e30338da2070", + "metadata": {}, + "source": [ + "> **Figure 2:** The cutout image displayed in greyscale, with red circles marking the deblended children, cyan circles indicating any children whose contamination from neighbors prior to deblending is high (>50% of flux from a neighbor). \n" + ] + }, + { + "cell_type": "markdown", + "id": "98ec6b3c-48b8-4f18-bfd0-ee2b5bdc5947", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "02e4266d-7c63-4171-867b-0c7858dd4534", + "metadata": {}, + "source": [ + "## 4. Heavy blends\n", + "It is useful to be able to quantify how much flux in the blended source is attributed to a neighbor. Mandelbaum et al. 2018 quantitatively explored the criteria for acceptable blendedness as a metric of deblender performance. In this section, explore examples where sources are heavily contaminated (`_blendedness` >50%) by the flux of neighbors, and then compare to the deblended contamination fraction `_deblend_blendedness`. \n", + "\n", + "Below, identify which child objects are above this high threshold of PSF-convolved blending, and see what fraction of objects in the catalog meet this criteria." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41da6b5c-3501-4cd4-9a60-142050b85334", + "metadata": {}, + "outputs": [], + "source": [ + "whheavy = np.where(results['i_blendedness'] > 0.5)[0]\n", + "\n", + "print('Fraction of objects that are heavily blended is', len(whheavy)/len(results))\n", + "\n", + "results[whheavy]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02b649ad-5e0f-402a-b378-d51fe807b7ff", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,\n", + " width_ratios=[0.8, 0.2], figsize=(10, 6))\n", + "\n", + "ax.plot(results['i_blendedness'][whheavy], results['i_deblend_blendedness'][whheavy],\n", + " '+', alpha=0.5, label='i_blendedness_flag = False')\n", + "\n", + "ax2.hist((results['i_deblend_blendedness'][whheavy]), orientation=\"horizontal\",\n", + " bins=np.linspace(0, 1.1, 11), align='left',\n", + " histtype=\"step\", color='blue', stacked=True, fill=False, label=None)\n", + "\n", + "ax.legend()\n", + "ax.set_ylim([-0.1, 1.1])\n", + "ax2.set_ylim([-0.1, 1.1])\n", + "ax.set_xlabel('i_blendedness')\n", + "ax.set_ylabel('i_deblend_blendedness')\n", + "ax2.set_xlabel('Number of objects')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c27b24b6-29ed-48ae-8a28-2959c0f0a157", + "metadata": {}, + "source": [ + "> **Figure 3:** This figure shows the `blendedness` metric (fraction of flux contributed by neighbors) before deblending vs the `deblend_blendedness` metric that identifies the fraction of flux contamination after deblending, for heavily blended objects (>50% flux from one or more neighbors). The right panel shows a histogram of the `deblend_blendedness`, indicating that the deblender is successful at separating flux, such that `deblend_blendedness` = 0, in the overwhelming majority of objects.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae1e3189-d3e8-4609-9e95-fda91988b825", + "metadata": {}, + "source": [ + "Below, take a closer look at the objects for which the deblender did not effectively separate the flux from neighbors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c976c7a0-d794-4196-b146-92e19ee831b0", + "metadata": {}, + "outputs": [], + "source": [ + "wh = np.where((results['i_blendedness'][whheavy] == 1)\n", + " & (results['i_deblend_blendedness'][whheavy] == 1))[0]\n", + "results[whheavy][wh]" + ] + }, + { + "cell_type": "markdown", + "id": "7888a7d6-72ad-45d4-b77f-50b213e277e8", + "metadata": {}, + "source": [ + "Note that by selecting objects where the `i_blendedness` and `i_deblend_blendedness` metrics are high, the `i_deblend_fluxOverlapFraction` is also high. Below, pick one of the above objects which has a high value of `i_deblend_fluxOverlapFraction` as an example. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccf9d7cc-44f5-40ad-9a00-908ccc7a4d5f", + "metadata": {}, + "outputs": [], + "source": [ + "max_parentId = results['parentObjectId'][whheavy][wh][3]\n", + "print(max_parentId)" + ] + }, + { + "cell_type": "markdown", + "id": "3505f18e-75d7-408a-b92e-89a0f2361764", + "metadata": {}, + "source": [ + "Define a TAP query that will return all children with the same `parentObjectId` as the above source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "446b5ffa-1dd4-4784-851b-96ef8728ffc8", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, i_blendedness_flag, \" + \\\n", + " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", + " \"i_cModelFlux/i_cModelFluxErr AS i_S2N, footprintArea, i_deblend_blendedness, \" + \\\n", + " \"i_blendedness, i_deblend_fluxOverlapFraction \" + \\\n", + " \"FROM dp2.Object \" + \\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", + " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \" + \\\n", + " \"AND parentObjectId = \" + str(max_parentId)" + ] + }, + { + "cell_type": "markdown", + "id": "4e1c8c11-eb9a-402b-8d25-f1509b382077", + "metadata": {}, + "source": [ + "Run the TAP query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c82287f3-936d-432c-9a9d-0f98a0e1ae49", + "metadata": {}, + "outputs": [], + "source": [ + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)" + ] + }, + { + "cell_type": "markdown", + "id": "da272521-a393-4ad7-aa01-ae4b56b05e29", + "metadata": {}, + "source": [ + "Save the results to a table named `bigblend`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "842c745a-466b-4f34-af73-b3074b03f8b2", + "metadata": {}, + "outputs": [], + "source": [ + "assert job.phase == 'COMPLETED'\n", + "bigblend = job.fetch_result().to_table()\n", + "bigblend" + ] + }, + { + "cell_type": "markdown", + "id": "75ee67ad-5623-4705-b761-20ef0247389e", + "metadata": {}, + "source": [ + "Use the cutout_coadd function to request an image cutout of this blend, and identify any objects which still have 1% of flux contributed by a neighbor after the deblending process (`i_deblend_blendedness` > 0.01)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89120f7e-1339-474b-9ea7-7ad19a09e3a1", + "metadata": {}, + "outputs": [], + "source": [ + "cutout = cutout_coadd(butler, bigblend['coord_ra'][0],\n", + " bigblend['coord_dec'][0],\n", + " band='i', datasetType='deep_coadd',\n", + " cutoutSideLength=2.0 * np.sqrt(np.sum(bigblend['footprintArea'])))\n", + "\n", + "whheavy = np.where(bigblend['i_deblend_blendedness'] > 0.01)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4afccee-7595-4760-81e7-40b590749e81", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(6, 6))\n", + "display = afwDisplay.Display(frame=fig)\n", + "display.scale('linear', min=0.01, max=5, unit='Absolute')\n", + "display.mtv(cutout.image)\n", + "with display.Buffering():\n", + " for ci in range(len(bigblend)):\n", + " display.dot('o', bigblend['x'][ci], bigblend['y'][ci],\n", + " size=10, ctype=afwDisplay.RED)\n", + "\n", + " for ci in range(len(whheavy)):\n", + " display.dot('o', bigblend['x'][whheavy][ci], bigblend['y'][whheavy][ci],\n", + " size=9, ctype=afwDisplay.CYAN)\n", + "\n", + "plt.gca().axis('off')\n", + "display.show_colorbar(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3e7906e2-7fd9-4dad-aea9-bfb2f6305a5f", + "metadata": {}, + "source": [ + "> **Figure 4:** This figure shows a heavily blended parent for which deblended objects have a fraction of flux contributed by neighbors (red circles). Cyan circles indicate objects whose `deblend_blendedness` metric is not 0, and remains > 1% contamination even after deblending. They are typically faint objects near the outskirts of the biggest and brightest objects where contamination might be high.\n" + ] + }, + { + "cell_type": "markdown", + "id": "1e7e393f-ac6d-4ad5-8294-5dac4bca258e", + "metadata": {}, + "source": [ + "## 5. Exercises for the learner\n", + "\n", + "Use the deblender data products and blend metrics in the `Object` table to identify what fraction of sources in ECDFS are contaminated by neighbor flux at the 5% level in the observed frame? What fraction have 5% flux contamination in the PSF-deconvolved (i.e. deblended) frame?\n" + ] + } + ], + "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": "3.13.9" + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb new file mode 100644 index 00000000..ec0ae75e --- /dev/null +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -0,0 +1,758 @@ +{ + "cells": [ + { + "attachments": { + "5574c61a-d2bb-42ee-9ba5-856498881b08.png": { + "image/png": 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl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\nrkJggg==\n" + } + }, + "cell_type": "markdown", + "id": "325aa8a5-92fd-4913-a471-ad1617343be6", + "metadata": {}, + "source": [ + "# Coadd Deblender models\n", + "\n", + "\n", + "
\n", + "\n", + "![tutorial_nb_logo.png](attachment:5574c61a-d2bb-42ee-9ba5-856498881b08.png)\n", + "\n", + "
\n", + "\n", + "For the Rubin Science Platform at data.lsst.cloud.
\n", + "Data Release: DP2
\n", + "Container Size: Large
\n", + "LSST Science Pipelines version: v30.0.10
\n", + "Last verified to run: 2026-07-21
\n", + "Repository: github.com/lsst/tutorial-notebooks
" + ] + }, + { + "cell_type": "markdown", + "id": "9da1a210-d858-42fe-8591-570965b8be1a", + "metadata": {}, + "source": [ + "**Learning objective:** This notebook demonstrates how to use the deblended models generated by scarlet lite.\n", + "\n", + "**LSST data products:** `Object` table, `deep_coadd`, `object_scarlet_models`\n", + "\n", + "**Packages:** `lsst.rsp`, `lsst.daf.butler`, `lsst.scarlet.lite`, `lsst.meas.extensions.scarlet`\n", + "\n", + "**Credit:**\n", + "Originally developed by Christina Williams and the Rubin Community Science team. This notebook is partly based on DP0.2 notebook 10 on deblender data products, and benefited from helpful discussions with Fred Moolekamp. The deblender packages are based on Melchior et al. 2018, and the Scarlet software written by Peter Melchior and Fred Moolekamp. Further documentation on Scarlet is available at that site.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "e175c6a7-b3d8-49e5-841a-ec12631065e7", + "metadata": {}, + "source": [ + "DP1 version:\n", + "## 1. Introduction\n", + "\n", + "This notebook demonstrates how to reconstruct per-pixel models of objects that are deblended by the LSST Science Pipeline when building the `Object` table.\n", + "The focus is on how to recover the model of an object from its information in the `object` catalog, and how to investigate the blend as a whole.\n", + "\n", + "To measure the photometry of objects in `deep_coadd` images, the LSST Science Pipeline uses the multi-band deblending scarlet algorithm [Melchior et al. 2018](https://ui.adsabs.harvard.edu/abs/2018A%26C....24..129M/abstract) implemented with optimizations for LSST data in [Scarlet Lite](https://github.com/lsst/scarlet_lite). The core algorithm models objects in a blend as a collection of components that have the same spectrum in each pixel of their morphology, assuming that flux monotonically decreases from the peak (maximum pixel value) of the component.\n", + "\n", + "This tutorial will begin with the coordinates of an object of interest and show how to obtain its model and the model of the other sources that are extracted from the same blend.\n", + "\n", + "**Note**: There have been updates to the deblending implementation since DP1 so _expect the API to be slightly different (improved) in future data releases_, including having more information available to understand how blended an object is with its neighbors and useful cuts to remove potentially more difficult blends from analysis.\n", + "\n", + "**References:** \n", + "- A general discussion of blending impacts to LSST can be found in this article titled \"The challenge of blending in large sky surveys\" by Melchior et al. 2021. \n", + "- Some more in-depth presentations on the deblending implementation in the LSST Science Pipeline are available from recorded talks during the Rubin Project and Community Workshop 2022's session on \"Deblending Plans and Challenges\". \n", + "- Further information about the available deblending flags, and the processes that produce the deblender data products discussed in this tutorial\n", + "can be found in this overview of the deblending flags.\n", + "\n", + "\n", + "**Related tutorials:**\n", + "A notebook introducing the deblender data products stored in the `Object` table is available as 206.1 Introduction to deblender outputs. A notebook introducing image subsets and defining bounding boxes is available as 104.5 titled Image subsets with the butler.\n" + ] + }, + { + "cell_type": "markdown", + "id": "818dcb37-6a56-4e8e-b964-e9405b030b32", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\n", + "\n", + "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "731e1efc-d0c9-412a-bd43-1ea4fecbf3fd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "# Get access tot he Table Access Protocol (TAP) service\n", + "#from lsst.rsp import get_tap_service\n", + "# Access to LSST data products\n", + "from lsst.daf.butler import Butler\n", + "# Access to the scarlet lite deblender and visualization tools\n", + "import lsst.scarlet.lite as scl\n", + "# Access to meas_extensions_scarlet helper functions\n", + "import lsst.meas.extensions.scarlet as mes\n", + "# Access to multi-band images\n", + "from lsst.afw.image import MultibandExposure\n", + "\n", + "from lsst.rsp import RSPDiscovery\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e17b7961-4a72-42f6-8764-b945595a3d1a", + "metadata": {}, + "source": [ + "### 1.2 Define parameters\n", + "\n", + "Set matplotlib parameters to define the lower left corner of images as the origin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40035e44-dc1b-4ed8-9d2a-4e83d3fa9881", + "metadata": {}, + "outputs": [], + "source": [ + "matplotlib.rcParams['image.origin'] = 'lower'" + ] + }, + { + "cell_type": "markdown", + "id": "262c6bb8-bf46-4668-ad5c-a790a5d65bd6", + "metadata": {}, + "source": [ + "Get an instance of the TAP service." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "979781a7-1a12-41bb-9aa5-ad6e14efe4d4", + "metadata": {}, + "outputs": [], + "source": [ + "discovery = RSPDiscovery(\"dp2\")\n", + "service = discovery.get_tap_client()" + ] + }, + { + "cell_type": "markdown", + "id": "8c1e8d60-f8ed-4330-a160-b6898e35f932", + "metadata": {}, + "source": [ + "Instantiate the butler." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7abc2aef-ac23-4d2c-b94c-dc67e206cfca", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler('dp2', collections=[\"dp2\"])\n", + "assert butler is not None" + ] + }, + { + "cell_type": "markdown", + "id": "28b3feee-7a78-49e0-994a-90d360685a98", + "metadata": {}, + "source": [ + "## 2. Deblender data products\n", + "\n", + "\n", + "### 1.5 Load an object of interest from the object table\n", + "First, load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to load the appropriate scarlet models associated with it:\n", + "\n", + "- **tract**: the LSST track that contains the object\n", + "- **patch**: the patch in the `tract` that contains the object\n", + "- **parentObjectId**: the object ID of the object's parent\n", + "- **deblend_blendId**: the index of the sub-blend inside the parent blend\n", + "- **deblend_blendNChild**: Not required for lookup but it will describe the number of children in the sub blend that were deblended together with the object\n", + "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of our object\n", + "\n", + "Other notebooks, like `201_1_Object_table`, describe how to find an object from the `Object` table, so rather than duplicate that content we'll quickly get the TAP service running and load the catalog for a given source of interest. See that notebook for details about the following code.\n", + "\n", + "Below, search for all objects within 1 arcsec of the object of interest (1/3600 ~ 0.000278) and there should only be a single result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d7d72af-516d-46a3-916a-032b54e6c419", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, \" \\\n", + " \"deblend_blendId, tract, patch, \" \\\n", + " \"deblend_blendNChild, deblend_peak_center_x, deblend_peak_center_y \" \\\n", + " \"FROM dp2.Object \" \\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \" \\\n", + " \"CIRCLE('ICRS', 151.78888574607532, 1.55284113175051, 0.000278)) = 1\"\n", + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)\n", + "if job.phase == 'ERROR':\n", + " job.raise_if_error()\n", + "\n", + "assert job.phase == 'COMPLETED'\n", + "results = job.fetch_result().to_table()\n", + "print(len(results))" + ] + }, + { + "cell_type": "markdown", + "id": "819ac397-37b9-472d-9d51-a210c583afe3", + "metadata": {}, + "source": [ + "\n", + "### 2.1 Load parent blend data\n", + "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41310eda-528d-4f96-baa4-5ba41193e47c", + "metadata": {}, + "outputs": [], + "source": [ + "obj = results[0]\n", + "\n", + "model_data = butler.get(\n", + " \"object_scarlet_models\",\n", + " tract=obj[\"tract\"],\n", + " patch=obj[\"patch\"],\n", + " parameters={\"blend_id\": [obj[\"parentObjectId\"]]}\n", + ")\n", + "\n", + "print(obj[\"objectId\"])" + ] + }, + { + "cell_type": "markdown", + "id": "11587043-bbb5-46a8-bafa-9fe94dac3bde", + "metadata": {}, + "source": [ + "### 2.2 Extracting the blend data\n", + "\n", + "In DP2 all blends use the same model PSF (the sigma=0.8 Gaussian mentioned in section 1.3), with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed. So we can extract their models from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. In this case we only loaded a single blend, but it is still returned from the Butler in the full container, as it contains metadata required to reconstruct the scarlet lite `Blend` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84dffb2a-632d-478f-953e-2b0cf8d3ca3c", + "metadata": {}, + "outputs": [], + "source": [ + "model_psf = model_data.metadata[\"model_psf\"]\n", + "psf = model_data.metadata[\"psf\"]\n", + "bands = model_data.metadata[\"bands\"]\n", + "print(bands)" + ] + }, + { + "cell_type": "markdown", + "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", + "metadata": {}, + "source": [ + "Notice that the observed bands are not in wavelength order! This is because the LSST science pipelines process each band in alphabetical order, not wavelength order, so we'll have to re-adjust the models shortly to ensure that the models are structured using the expected order. First, however, we need to convert the data class into a scarlet lite `Blend`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eccd1393-c592-440f-9d76-9f35be1677f2", + "metadata": {}, + "outputs": [], + "source": [ + "# This is the full blend from the `object_detection` catalog (HierarchicalBlendData)\n", + "parent_data = model_data.blends[obj[\"parentObjectId\"]]\n", + "\n", + "# This is the actual deconvolved blend that the object was deblended from\n", + "# There is an entry in `HierarchicalBlendData` for each sub-blend in the parent blend\n", + "blend_data = parent_data.children[obj[\"deblend_blendId\"]]\n", + "\n", + "# Convert the storage class into a scarlet_lite.Blend instance\n", + "blend = blend_data.minimal_data_to_blend(model_psf[None], psf, bands)" + ] + }, + { + "cell_type": "markdown", + "id": "17022656-f310-4c1b-8337-e1f1fb5125eb", + "metadata": {}, + "source": [ + "Above we use the `minimal_data_to_blend` method because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n", + "\n", + "### 2.3 Exploring the blend data\n", + "\n", + "#### 2.4.1 Index slicing\n", + "\n", + "Notice that the blend is still using the order from `model_data.bands`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "037dbbc4-2e10-4940-bb62-aa1288f8e482", + "metadata": {}, + "outputs": [], + "source": [ + "print(blend.observation.bands)" + ] + }, + { + "cell_type": "markdown", + "id": "b5661451-2796-4f44-9d38-74e182d352ea", + "metadata": {}, + "source": [ + "All of the band-dependent scarlet lite data products can be re-ordered using indexing by passing slices or tuples of bands, similar to numpy arrays, with the exception that the last band in the slice is *included* in the resulting sliced model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b1aac9f-e49f-44c2-b0bb-a12916e32da6", + "metadata": {}, + "outputs": [], + "source": [ + "blend = blend[tuple(\"ugrizy\")]\n", + "print(blend.observation.bands)\n", + "print(blend[\"g\":\"i\"].observation.bands)\n", + "print(blend[\"z\"].observation.bands)" + ] + }, + { + "cell_type": "markdown", + "id": "bf9510a0-d413-4fc6-a393-c6dc2c214c62", + "metadata": {}, + "source": [ + "#### 2.3.2 Objects\n", + "\n", + "We can see the object IDs for all of the objects in the blend by looking at `Blend.sources` and notice that the object that we were searching for is the second one in the list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "598dd8dd-2862-4fb3-ba0d-c2049111bfdd", + "metadata": {}, + "outputs": [], + "source": [ + "for source in blend.sources:\n", + " print(source.metadata[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "904f58e6-75f5-485a-ab1d-ccb708b5e15d", + "metadata": {}, + "source": [ + "## 3 Visualizing blends\n", + "\n", + "### 3.1 Creating a color mapping\n", + "\n", + "In order to display multi-band image data as an RGB image we have to have some way to map the data to RGB space. Scarlet lite includes helper functions to do this, for example we can use the Lupton RGB color mapping to preserve colors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "075e33a1-eb16-4247-9603-b3009ae3077c", + "metadata": {}, + "outputs": [], + "source": [ + "# Create an asinh mapping normalization\n", + "stretch = 50\n", + "Q = 10\n", + "norm = scl.display.AsinhMapping(minimum=0, stretch=stretch, Q=Q)" + ] + }, + { + "cell_type": "markdown", + "id": "9aede605-caa6-4690-a579-dc49594e26e2", + "metadata": {}, + "source": [ + "### 3.2 Extracting a model\n", + "\n", + "To extract the model from a blend we must convolve it with the difference kernel so that matches the observed seeing. This happens automatically if we pass `convolve=True` to `Blend.get_model`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0310ffd7-65a0-49ba-8ae5-3824a94828b8", + "metadata": {}, + "outputs": [], + "source": [ + "model = blend.get_model(convolve=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0c87f797-78f9-4d42-aa80-a862a7c99902", + "metadata": {}, + "source": [ + "### 3.3 Displaying the full blend model\n", + "\n", + "As we'll see in a moment, scarlet lite has helper functions to display a full blend, mark the location of peaks, show the residuals, etc. But as an exercise we see how to convert a blend model into an RGB image and display it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65ea7d30-1476-40c2-8d7d-7ece07c0f69e", + "metadata": {}, + "outputs": [], + "source": [ + "extent = scl.display.get_extent(model.bbox)\n", + "rgb = scl.display.img_to_rgb(model, norm=norm)\n", + "plt.imshow(rgb, extent=extent)\n", + "plt.title(\"blend data\")\n", + "# Mark the source that we searched for\n", + "plt.plot(obj[\"deblend_peak_center_x\"], obj[\"deblend_peak_center_y\"], \"wx\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e400222a-7e64-4b0b-a0d7-8d43a5e2d9d3", + "metadata": {}, + "outputs": [], + "source": [ + "> **Figure 1:** The" + ] + }, + { + "cell_type": "markdown", + "id": "3eda775a-c16f-4a4d-ab84-1f78c64e2552", + "metadata": {}, + "source": [ + "### 3.4 Displaying a single source model\n", + "\n", + "To display the model for the object, first extract the object from the blend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22b1226-4263-44f0-a9c3-6c22c8da3a12", + "metadata": {}, + "outputs": [], + "source": [ + "source = next((src for src in blend.sources if src.metadata[\"id\"] == obj[\"objectId\"]), None)" + ] + }, + { + "cell_type": "markdown", + "id": "568cf76a-ec90-4ee8-910a-b3f9f9c30bf1", + "metadata": {}, + "source": [ + "Recall that the object model is in the deconvolved space, so displaying it will show the internal scarlet lite model (intrinsic model) for the source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b313ac7-3b55-41e0-9cf8-abc6271d049a", + "metadata": {}, + "outputs": [], + "source": [ + "rgb = scl.display.img_to_rgb(source.get_model(), norm=norm)\n", + "plt.imshow(rgb, extent=extent)\n", + "plt.title(\"object data\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "010d1036-b23d-4d96-9547-54ec3d0ac009", + "metadata": {}, + "source": [ + "> **Figure 2:** The\n", + "\n", + "EDIT: To view the actual model, first convolve it with the observation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9ad5a5f-98ae-4b47-a6c5-07e1f55886fd", + "metadata": {}, + "outputs": [], + "source": [ + "model = blend.observation.convolve(source.get_model())\n", + "rgb = scl.display.img_to_rgb(model, norm=norm)\n", + "plt.imshow(rgb, extent=extent)\n", + "plt.title(\"object data\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "90769d6a-6bbf-4d5a-8f8c-06b43a1ea306", + "metadata": {}, + "source": [ + "> **Figure 3:** The" + ] + }, + { + "cell_type": "markdown", + "id": "19844f19-ab6f-48c0-98e8-918d8731ea70", + "metadata": {}, + "source": [ + "## 4 Comparing to observations\n", + "\n", + "Load the scarlet models to see how well they compare to the data.\n", + "\n", + "### 4.1 Loading a MultibandExposure\n", + "\n", + "First, load a multi-band exposure for the region that covers the blend.\n", + "\n", + "Note to Christina: the Butler is temporarily broken and won't properly load the `MultibandExposure`. This should be fixed in the next day or so but I will be on vacation so I have a temporary fix that can be replaced with the commented code on Thursday or Friday." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60e0b3eb-10b1-4ce7-af43-fbaae7c2fbb8", + "metadata": {}, + "outputs": [], + "source": [ + "bbox = mes.utils.scarletBoxToBBox(blend.observation.bbox)\n", + "\n", + "# Begin temporary fix while Butler is borken\n", + "coadds = []\n", + "for band in bands:\n", + " coadds.append(butler.get(\"deep_coadd\", tract=obj[\"tract\"], patch=obj[\"patch\"], band=band).to_legacy())\n", + "\n", + "coadd = MultibandExposure.fromExposures(bands, coadds)[:, bbox]\n", + "## end temporary fix\n", + "\n", + "#coadd = MultibandExposure.fromButler(\n", + "# butler,\n", + "# bands,\n", + "# \"deep_coadd\",\n", + "# tract=obj[\"tract\"],\n", + "# patch=obj[\"patch\"],\n", + "# storageClass=\"ExposureF\",\n", + "#)" + ] + }, + { + "cell_type": "markdown", + "id": "da05ae49-381a-4767-96d6-9c686804c59f", + "metadata": {}, + "source": [ + "### 4.2 Build a full Blend with an Observation\n", + "\n", + "Scarlet lite requires an `Observation` to contain the exposure data along with loss function calculation and convolutions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a4b3869-c7f1-4bfb-86b0-88bc943b83ee", + "metadata": {}, + "outputs": [], + "source": [ + "observation = mes.utils.buildObservation(\n", + " modelPsf=model_psf,\n", + " psfCenter=bbox.getCenter(),\n", + " mExposure=coadd,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "46b5e68d-dd64-410f-9058-dc52f0c1ad3f", + "metadata": {}, + "source": [ + "Now we can build the full Blend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afb54237-68c5-4950-ba02-581c0227a08a", + "metadata": {}, + "outputs": [], + "source": [ + "blend = blend_data.to_blend(observation)[tuple(\"ugrizy\")]" + ] + }, + { + "cell_type": "markdown", + "id": "dc9178ae-8fad-4c4f-b539-5bb0cf8e59aa", + "metadata": {}, + "source": [ + "### 4.3 Display full blend residuals\n", + "\n", + "Scarlet lite has a built in display function to show the image, blend, and residuals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86a0cab8-a24c-4ea7-a7c8-652059247544", + "metadata": {}, + "outputs": [], + "source": [ + "scl.display.show_scene(\n", + " blend,\n", + " norm=norm,\n", + " show_model=True,\n", + " show_rendered=True,\n", + " show_observed=True,\n", + " show_residual=True,\n", + " linear=False,\n", + " add_boxes=True,\n", + " add_labels=True,\n", + " figsize=(10, 10),\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f17620e7-3de6-4de2-a9b9-2963253d5392", + "metadata": {}, + "outputs": [], + "source": [ + "> **Figure 4:** The" + ] + }, + { + "cell_type": "markdown", + "id": "805fa9ef-504e-486f-80ec-f7992d888eee", + "metadata": {}, + "source": [ + "### 4.4 Display source models\n", + "\n", + "Similarly we can display the models of the individual sources" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35329c0b-5e9e-43dd-9db0-8f5fd2f22cbb", + "metadata": {}, + "outputs": [], + "source": [ + "scl.display.show_sources(\n", + " blend,\n", + " norm=norm,\n", + " show_observed=True,\n", + " add_boxes=True\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8b530ac2-c6ce-489d-99ae-47a67b215e99", + "metadata": {}, + "source": [ + "> **Figure 1:** The" + ] + }, + { + "cell_type": "markdown", + "id": "fe5f9c59-f7c0-4bad-b75c-b5d0c419f47a", + "metadata": {}, + "source": [ + "### 4.5 Display the measurement models\n", + "\n", + "As mentioned above, measurement is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux, which creates a `Source.flux_weighted_image` for each `Source` in the `Blend`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3427770e-c0de-40d0-beb6-f36a640b3e7f", + "metadata": {}, + "outputs": [], + "source": [ + "blend.conserve_flux()" + ] + }, + { + "cell_type": "markdown", + "id": "44f7ede2-b151-4246-bc00-8c1a7df18155", + "metadata": {}, + "source": [ + "Now we pass `use_flux=True` to `show_sources` to see the flux weighted models that were used in all of the measurement tasks. Notice that the `spectrum` is also different. This is because the spectrum is created by summing the flux in each band individually, since even for the scarlet models each source is often made up of multiple components (ie. a bulge and disk for galaxies)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38d54701-1f15-4e0e-9713-ae50b9ea9745", + "metadata": {}, + "outputs": [], + "source": [ + "scl.display.show_sources(\n", + " blend,\n", + " norm=norm,\n", + " show_observed=True,\n", + " add_boxes=True,\n", + " use_flux=True,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "938c8189-f60f-4c19-a5e0-f124c60fd7ea", + "metadata": {}, + "source": [ + "> **Figure 1:** The" + ] + } + ], + "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": "3.13.9" + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d20be748975beb76f654250e6c8ebd888c4c4dbb Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Mon, 5 Oct 2026 23:13:55 +0000 Subject: [PATCH 02/13] updating 206 2 --- .../206_2_Deblender_footprints.ipynb | 93 ++++++++++++------- 1 file changed, 58 insertions(+), 35 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index ec0ae75e..0087d369 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -56,7 +56,7 @@ "DP1 version:\n", "## 1. Introduction\n", "\n", - "This notebook demonstrates how to reconstruct per-pixel models of objects that are deblended by the LSST Science Pipeline when building the `Object` table.\n", + "This notebook demonstrates how to reconstruct per-pixel models of objects that are deblended by the LSST Science Pipeline when building the `object` table.\n", "The focus is on how to recover the model of an object from its information in the `object` catalog, and how to investigate the blend as a whole.\n", "\n", "To measure the photometry of objects in `deep_coadd` images, the LSST Science Pipeline uses the multi-band deblending scarlet algorithm [Melchior et al. 2018](https://ui.adsabs.harvard.edu/abs/2018A%26C....24..129M/abstract) implemented with optimizations for LSST data in [Scarlet Lite](https://github.com/lsst/scarlet_lite). The core algorithm models objects in a blend as a collection of components that have the same spectrum in each pixel of their morphology, assuming that flux monotonically decreases from the peak (maximum pixel value) of the component.\n", @@ -98,19 +98,13 @@ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", - "# Get access tot he Table Access Protocol (TAP) service\n", - "#from lsst.rsp import get_tap_service\n", - "# Access to LSST data products\n", + "\n", "from lsst.daf.butler import Butler\n", - "# Access to the scarlet lite deblender and visualization tools\n", - "import lsst.scarlet.lite as scl\n", - "# Access to meas_extensions_scarlet helper functions\n", - "import lsst.meas.extensions.scarlet as mes\n", - "# Access to multi-band images\n", "from lsst.afw.image import MultibandExposure\n", - "\n", "from lsst.rsp import RSPDiscovery\n", - "\n" + "\n", + "import lsst.scarlet.lite as scl\n", + "import lsst.meas.extensions.scarlet as mes\n" ] }, { @@ -176,10 +170,11 @@ "id": "28b3feee-7a78-49e0-994a-90d360685a98", "metadata": {}, "source": [ - "## 2. Deblender data products\n", + "TO DELETE?:\n", + "\n", "\n", + "## 2. Explore blend data\n", "\n", - "### 1.5 Load an object of interest from the object table\n", "First, load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to load the appropriate scarlet models associated with it:\n", "\n", "- **tract**: the LSST track that contains the object\n", @@ -189,9 +184,7 @@ "- **deblend_blendNChild**: Not required for lookup but it will describe the number of children in the sub blend that were deblended together with the object\n", "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of our object\n", "\n", - "Other notebooks, like `201_1_Object_table`, describe how to find an object from the `Object` table, so rather than duplicate that content we'll quickly get the TAP service running and load the catalog for a given source of interest. See that notebook for details about the following code.\n", - "\n", - "Below, search for all objects within 1 arcsec of the object of interest (1/3600 ~ 0.000278) and there should only be a single result" + "Search for all objects within 1 arcsec of the object of interest. There should only be a single result." ] }, { @@ -255,7 +248,11 @@ "source": [ "### 2.2 Extracting the blend data\n", "\n", - "In DP2 all blends use the same model PSF (the sigma=0.8 Gaussian mentioned in section 1.3), with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed. So we can extract their models from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. In this case we only loaded a single blend, but it is still returned from the Butler in the full container, as it contains metadata required to reconstruct the scarlet lite `Blend` object." + "In DP2, all blends use the same model PSF (a sigma=0.8 Gaussian) with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed). Their models can thus be extracted directly from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. \n", + "\n", + "`model_data` (above) only loaded a single blend, but it is still returned from the Butler in the full container, which is needed because it contains the metadata required to reconstruct the scarlet lite `Blend` object.\n", + "\n", + "Below, extract the `model_psf`, `psf`, and the bands in the blend." ] }, { @@ -276,7 +273,11 @@ "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", "metadata": {}, "source": [ - "Notice that the observed bands are not in wavelength order! This is because the LSST science pipelines process each band in alphabetical order, not wavelength order, so we'll have to re-adjust the models shortly to ensure that the models are structured using the expected order. First, however, we need to convert the data class into a scarlet lite `Blend`." + "Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", + "\n", + "`blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend.\n", + "\n", + "Finally, convert the `blend_data` storage class into a scarlet lite Blend instance called `blend`." ] }, { @@ -286,15 +287,9 @@ "metadata": {}, "outputs": [], "source": [ - "# This is the full blend from the `object_detection` catalog (HierarchicalBlendData)\n", "parent_data = model_data.blends[obj[\"parentObjectId\"]]\n", "\n", - "# This is the actual deconvolved blend that the object was deblended from\n", - "# There is an entry in `HierarchicalBlendData` for each sub-blend in the parent blend\n", - "blend_data = parent_data.children[obj[\"deblend_blendId\"]]\n", - "\n", - "# Convert the storage class into a scarlet_lite.Blend instance\n", - "blend = blend_data.minimal_data_to_blend(model_psf[None], psf, bands)" + "blend_data = parent_data.children[obj[\"deblend_blendId\"]]" ] }, { @@ -302,10 +297,35 @@ "id": "17022656-f310-4c1b-8337-e1f1fb5125eb", "metadata": {}, "source": [ - "Above we use the `minimal_data_to_blend` method because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n", - "\n", - "### 2.3 Exploring the blend data\n", + "Finally, use the `minimal_data_to_blend` method to access the blend model. \n", "\n", + "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba93f083-ef91-4ea5-bfda-2c3a6dd65899", + "metadata": {}, + "outputs": [], + "source": [ + "blend = blend_data.minimal_data_to_blend(model_psf[None], psf, bands)" + ] + }, + { + "cell_type": "markdown", + "id": "aa1fbb9a-04ec-4e6b-abd6-a3ace04a341a", + "metadata": {}, + "source": [ + "### 2.3 Explore the blend data\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "202e94f3-44b9-4350-a34f-361b08e19f69", + "metadata": {}, + "source": [ "#### 2.4.1 Index slicing\n", "\n", "Notice that the blend is still using the order from `model_data.bands`." @@ -435,11 +455,9 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "e400222a-7e64-4b0b-a0d7-8d43a5e2d9d3", "metadata": {}, - "outputs": [], "source": [ "> **Figure 1:** The" ] @@ -589,7 +607,14 @@ "id": "46b5e68d-dd64-410f-9058-dc52f0c1ad3f", "metadata": {}, "source": [ - "Now we can build the full Blend" + "Now we can build the full Blend\n", + "\n", + "\n", + "\n", + "from earlier:\n", + "Finally, use the `minimal_data_to_blend` method to access the blend model. \n", + "\n", + "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog." ] }, { @@ -636,11 +661,9 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "f17620e7-3de6-4de2-a9b9-2963253d5392", "metadata": {}, - "outputs": [], "source": [ "> **Figure 4:** The" ] From d1b686a925641f1813520f084ecab74c5502240d Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 6 Oct 2026 17:41:12 +0000 Subject: [PATCH 03/13] updating --- .../206_2_Deblender_footprints.ipynb | 82 +++++++++++-------- 1 file changed, 46 insertions(+), 36 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 0087d369..3b95b44d 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -173,18 +173,20 @@ "TO DELETE?:\n", "\n", "\n", - "## 2. Explore blend data\n", + "## 2. Identify a blend\n", "\n", - "First, load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to load the appropriate scarlet models associated with it:\n", + "First, identify a blended system in the DP2 data. This example will load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to access the blend models associated with it. \n", "\n", "- **tract**: the LSST track that contains the object\n", "- **patch**: the patch in the `tract` that contains the object\n", - "- **parentObjectId**: the object ID of the object's parent\n", + "- **parentObjectId**: the object ID of the object's parent blend\n", "- **deblend_blendId**: the index of the sub-blend inside the parent blend\n", - "- **deblend_blendNChild**: Not required for lookup but it will describe the number of children in the sub blend that were deblended together with the object\n", - "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of our object\n", + "- **deblend_blendNChild**: Describes the number of children that were in the parent blend\n", + "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of the object\n", "\n", - "Search for all objects within 1 arcsec of the object of interest. There should only be a single result." + "Search for all objects within 1 arcsec of the object of interest. There should only be a single result.\n", + "\n", + "### 2.1 Query for deblended object" ] }, { @@ -194,22 +196,28 @@ "metadata": {}, "outputs": [], "source": [ - "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, \" \\\n", - " \"deblend_blendId, tract, patch, \" \\\n", - " \"deblend_blendNChild, deblend_peak_center_x, deblend_peak_center_y \" \\\n", - " \"FROM dp2.Object \" \\\n", - " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \" \\\n", - " \"CIRCLE('ICRS', 151.78888574607532, 1.55284113175051, 0.000278)) = 1\"\n", + "ra = 151.78888574607532\n", + "dec = 1.55284113175051\n", + "\n", + "query = f\"\"\"SELECT objectId, coord_ra, coord_dec, parentObjectId, \n", + " deblend_blendId, tract, patch, \n", + " deblend_blendNChild, deblend_peak_center_x, deblend_peak_center_y \n", + " FROM dp2.Object \n", + " WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \n", + " CIRCLE('ICRS', {ra}, {dec}, 0.000278)) = 1\"\"\"\n", + "\n", "job = service.submit_job(query)\n", "job.run()\n", "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)\n", "if job.phase == 'ERROR':\n", " job.raise_if_error()\n", - "\n", - "assert job.phase == 'COMPLETED'\n", + " \n", "results = job.fetch_result().to_table()\n", - "print(len(results))" + "print(len(results))\n", + "\n", + "#children = job.fetch_result().to_table()\n", + "#print(f\"Found {len(children)} deblended sources from this parent.\")\n", + "#children" ] }, { @@ -217,8 +225,10 @@ "id": "819ac397-37b9-472d-9d51-a210c583afe3", "metadata": {}, "source": [ + "## 3. Blend and object models\n", "\n", - "### 2.1 Load parent blend data\n", + "\n", + "### 3.1 Load parent blend data\n", "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data. " ] }, @@ -246,7 +256,7 @@ "id": "11587043-bbb5-46a8-bafa-9fe94dac3bde", "metadata": {}, "source": [ - "### 2.2 Extracting the blend data\n", + "### 3.2 Extracting the blend data (convert to a usable blend instance in dp1 is now not needed)\n", "\n", "In DP2, all blends use the same model PSF (a sigma=0.8 Gaussian) with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed). Their models can thus be extracted directly from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. \n", "\n", @@ -312,21 +322,12 @@ "blend = blend_data.minimal_data_to_blend(model_psf[None], psf, bands)" ] }, - { - "cell_type": "markdown", - "id": "aa1fbb9a-04ec-4e6b-abd6-a3ace04a341a", - "metadata": {}, - "source": [ - "### 2.3 Explore the blend data\n", - "\n" - ] - }, { "cell_type": "markdown", "id": "202e94f3-44b9-4350-a34f-361b08e19f69", "metadata": {}, "source": [ - "#### 2.4.1 Index slicing\n", + "#### 3.3.1 Index slicing\n", "\n", "Notice that the blend is still using the order from `model_data.bands`." ] @@ -369,7 +370,7 @@ "source": [ "#### 2.3.2 Objects\n", "\n", - "We can see the object IDs for all of the objects in the blend by looking at `Blend.sources` and notice that the object that we were searching for is the second one in the list." + "We can see the object IDs for all of the objects in the blend by looking at `Blend.sources`. Notice that the target object is the second one in the list." ] }, { @@ -392,7 +393,7 @@ "\n", "### 3.1 Creating a color mapping\n", "\n", - "In order to display multi-band image data as an RGB image we have to have some way to map the data to RGB space. Scarlet lite includes helper functions to do this, for example we can use the Lupton RGB color mapping to preserve colors" + "In order to display multi-band image data as an RGB image we have to have some way to map the data to RGB space. Scarlet lite includes helper functions to do this, for example we can use the Lupton RGB color mapping to preserve colors. Below, create an `asinh` mapping normalization." ] }, { @@ -402,7 +403,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Create an asinh mapping normalization\n", "stretch = 50\n", "Q = 10\n", "norm = scl.display.AsinhMapping(minimum=0, stretch=stretch, Q=Q)" @@ -433,7 +433,7 @@ "id": "0c87f797-78f9-4d42-aa80-a862a7c99902", "metadata": {}, "source": [ - "### 3.3 Displaying the full blend model\n", + "### 3.3 Plot the blend model\n", "\n", "As we'll see in a moment, scarlet lite has helper functions to display a full blend, mark the location of peaks, show the residuals, etc. But as an exercise we see how to convert a blend model into an RGB image and display it." ] @@ -459,7 +459,7 @@ "id": "e400222a-7e64-4b0b-a0d7-8d43a5e2d9d3", "metadata": {}, "source": [ - "> **Figure 1:** The" + "> **Figure 1:** A griz RGB color image of the blend model. By default, Scarlet Lite maps the filters into an RGB scaling where shorter wavelength filters contribute to the blue color and longer wavelength filters contribute to red." ] }, { @@ -540,7 +540,7 @@ "id": "19844f19-ab6f-48c0-98e8-918d8731ea70", "metadata": {}, "source": [ - "## 4 Comparing to observations\n", + "## 4 Compare to observation\n", "\n", "Load the scarlet models to see how well they compare to the data.\n", "\n", @@ -699,7 +699,7 @@ "id": "8b530ac2-c6ce-489d-99ae-47a67b215e99", "metadata": {}, "source": [ - "> **Figure 1:** The" + "> **Figure 5:** The" ] }, { @@ -752,7 +752,17 @@ "id": "938c8189-f60f-4c19-a5e0-f124c60fd7ea", "metadata": {}, "source": [ - "> **Figure 1:** The" + "> **Figure 6:** The" + ] + }, + { + "cell_type": "markdown", + "id": "d0d701fd-52b4-4c99-b7a8-e277e02a2965", + "metadata": {}, + "source": [ + "## 5. Analyzing a single object\n", + "\n", + "copy section 5 from Dp1 nb here" ] } ], From 501b70fcaf1573653cd62ad16ffd462932f152df Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 6 Oct 2026 19:08:36 +0000 Subject: [PATCH 04/13] updating --- .../206_2_Deblender_footprints.ipynb | 84 ++++++++++++++++++- 1 file changed, 81 insertions(+), 3 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 3b95b44d..f2b23dd8 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -10,7 +10,7 @@ "id": "325aa8a5-92fd-4913-a471-ad1617343be6", "metadata": {}, "source": [ - "# Coadd Deblender models\n", + "# Deblender footprints\n", "\n", "\n", "
\n", @@ -104,7 +104,15 @@ "from lsst.rsp import RSPDiscovery\n", "\n", "import lsst.scarlet.lite as scl\n", - "import lsst.meas.extensions.scarlet as mes\n" + "import lsst.meas.extensions.scarlet as mes\n", + "\n", + "\n", + "import lsst.afw.display as afw_display\n", + "from lsst.images import Box, Interval\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.visualization import ZScaleInterval\n", + "from astropy.coordinates import SkyCoord\n", + "from lsst.images import Box, Interval" ] }, { @@ -220,6 +228,76 @@ "#children" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d35a78fc-5126-4ac2-b8de-e5dbe1ae07a7", + "metadata": {}, + "outputs": [], + "source": [ + "#parentId = 788017509725899960\n", + "parentId = results['parentObjectId'][0]\n", + "print(parentId)\n", + "query = f\"\"\"SELECT objectId, coord_ra, coord_dec, parentObjectId, footprintArea,\n", + " deblend_peak_center_x, deblend_peak_center_y\n", + " FROM dp2.Object\n", + " WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ),\n", + " CIRCLE('ICRS', {ra}, {dec}, .1)) = 1\n", + " AND parentObjectId = {parentId}\"\"\"\n", + "\n", + "\n", + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)\n", + "\n", + "assert job.phase == 'COMPLETED'\n", + "children = job.fetch_result().to_table()\n", + "children\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4aa160b3-78f9-4622-a8a4-6bfa24dba17b", + "metadata": {}, + "outputs": [], + "source": [ + "band = 'i'\n", + "\n", + "# Force the display to render inline using Matplotlib instead of DS9\n", + "afw_display.setDefaultBackend('matplotlib')\n", + "\n", + "# Note: The standard afw_display method to plot an image/exposure is .mtv()\n", + "#display.mtv(subset)\n", + "\n", + "#plt.show()\n", + "\n", + "total_footprint_area = np.sum(children['footprintArea'])\n", + "cutoutSideLength = 5.0 * np.sqrt(total_footprint_area)\n", + "\n", + "coadd = butler.get(\"deep_coadd\", tract=results['tract'][0], patch=results['patch'][0], band='i')\n", + "\n", + "point = SkyCoord(ra, dec, unit='deg')\n", + "xy = coadd.sky_projection.sky_to_pixel(point)\n", + "bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(200)\n", + "\n", + "\n", + "subset = coadd[bbox] #butler.get('deep_coadd', dataId=dataId, parameters={'bbox': bbox})\n", + "display = afw_display.Display(frame=1)\n", + "display.scale('asinh', 'zscale')\n", + "#display.image(subset.image)\n", + "display.mtv(subset.image)\n", + "with display.Buffering():\n", + " for ci in range(len(children)):\n", + " display.dot('o', children['deblend_peak_center_x'][ci], children['deblend_peak_center_y'][ci],\n", + " size=10)#, ctype=afwDisplay.RED)\n", + "plt.gca().axis('off')\n", + "display.show_colorbar(False)\n", + "\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "id": "819ac397-37b9-472d-9d51-a210c583afe3", @@ -247,7 +325,7 @@ " patch=obj[\"patch\"],\n", " parameters={\"blend_id\": [obj[\"parentObjectId\"]]}\n", ")\n", - "\n", + "print(obj[\"parentObjectId\"])\n", "print(obj[\"objectId\"])" ] }, From a0f919b819116336eaab8324aaa4c4d8b62ac12b Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 6 Oct 2026 19:32:42 +0000 Subject: [PATCH 05/13] updating --- .../206_2_Deblender_footprints.ipynb | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index f2b23dd8..9862b541 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -85,7 +85,9 @@ "\n", "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\n", "\n", - "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. " + "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. \n", + "\n", + "ADD astropy and lsst.images" ] }, { @@ -99,20 +101,19 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.visualization import ZScaleInterval\n", + "\n", "from lsst.daf.butler import Butler\n", "from lsst.afw.image import MultibandExposure\n", + "import lsst.afw.display as afw_display\n", + "from lsst.images import Box, Interval\n", "from lsst.rsp import RSPDiscovery\n", "\n", "import lsst.scarlet.lite as scl\n", "import lsst.meas.extensions.scarlet as mes\n", "\n", - "\n", - "import lsst.afw.display as afw_display\n", - "from lsst.images import Box, Interval\n", - "from astropy.coordinates import SkyCoord\n", - "from astropy.visualization import ZScaleInterval\n", - "from astropy.coordinates import SkyCoord\n", - "from lsst.images import Box, Interval" + "\n" ] }, { From c4644b9b1968c7d65144c385ca92e4cbdcebb145 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 6 Oct 2026 21:27:10 +0000 Subject: [PATCH 06/13] updating --- .../206_2_Deblender_footprints.ipynb | 208 +++++++++++++++--- 1 file changed, 173 insertions(+), 35 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 9862b541..867bf3af 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -174,6 +174,24 @@ "assert butler is not None" ] }, + { + "cell_type": "markdown", + "id": "db75cef5-6713-4d44-b2ec-338ad18acd47", + "metadata": {}, + "source": [ + "Set the backend for afwDisplay to matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd26977b-d5de-48ab-b599-bf7445296a99", + "metadata": {}, + "outputs": [], + "source": [ + "afw_display.setDefaultBackend('matplotlib')" + ] + }, { "cell_type": "markdown", "id": "28b3feee-7a78-49e0-994a-90d360685a98", @@ -236,9 +254,8 @@ "metadata": {}, "outputs": [], "source": [ - "#parentId = 788017509725899960\n", "parentId = results['parentObjectId'][0]\n", - "print(parentId)\n", + "\n", "query = f\"\"\"SELECT objectId, coord_ra, coord_dec, parentObjectId, footprintArea,\n", " deblend_peak_center_x, deblend_peak_center_y\n", " FROM dp2.Object\n", @@ -257,37 +274,80 @@ "children\n" ] }, + { + "cell_type": "markdown", + "id": "0857ab07-8cbb-4f9b-8506-585fe386d2ff", + "metadata": {}, + "source": [ + "### 2.2 Visualize the blend\n", + "\n", + "To visualize the blend, plot a `deep_coadd` image subset that contains the blend with children identified. First, find the patch and tract containing the blend, and set the band to be i-band." + ] + }, + { + "cell_type": "markdown", + "id": "9c395c34-d3c7-45df-b6cf-979a2b59fdbe", + "metadata": {}, + "source": [ + "Next, define the dimensions of the image subset to be 5x the square root of the total `footprintArea` of the blend." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "4aa160b3-78f9-4622-a8a4-6bfa24dba17b", + "id": "d3f71302-5a6c-46c7-b32f-3941167a3b2e", "metadata": {}, "outputs": [], "source": [ "band = 'i'\n", "\n", - "# Force the display to render inline using Matplotlib instead of DS9\n", - "afw_display.setDefaultBackend('matplotlib')\n", - "\n", - "# Note: The standard afw_display method to plot an image/exposure is .mtv()\n", - "#display.mtv(subset)\n", - "\n", - "#plt.show()\n", - "\n", "total_footprint_area = np.sum(children['footprintArea'])\n", - "cutoutSideLength = 5.0 * np.sqrt(total_footprint_area)\n", + "cutoutSideLength = 5.0 * np.sqrt(total_footprint_area) * 0.2\n", + "\n", + "coadd = butler.get(\"deep_coadd\", tract=results['tract'][0], patch=results['patch'][0], band='i')\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f0a7654-e8ed-4bb7-a989-5e1052ccee62", + "metadata": {}, + "source": [ + "Define the bounding box and construct the image subset. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d749b25-bc84-4c3d-9766-57b2dc3b6617", + "metadata": {}, + "outputs": [], + "source": [ "\n", - "coadd = butler.get(\"deep_coadd\", tract=results['tract'][0], patch=results['patch'][0], band='i')\n", "\n", "point = SkyCoord(ra, dec, unit='deg')\n", "xy = coadd.sky_projection.sky_to_pixel(point)\n", - "bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(200)\n", - "\n", - "\n", - "subset = coadd[bbox] #butler.get('deep_coadd', dataId=dataId, parameters={'bbox': bbox})\n", + "bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(cutoutSideLength)\n", + "subset = coadd[bbox]" + ] + }, + { + "cell_type": "markdown", + "id": "11ff6c06-ae1c-4b8c-8239-5f613a148651", + "metadata": {}, + "source": [ + "Finally, visualize blend and its child objects. First display the cutout, and then overplot circles to identify the child objects that were deblended. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4aa160b3-78f9-4622-a8a4-6bfa24dba17b", + "metadata": {}, + "outputs": [], + "source": [ "display = afw_display.Display(frame=1)\n", "display.scale('asinh', 'zscale')\n", - "#display.image(subset.image)\n", + "\n", "display.mtv(subset.image)\n", "with display.Buffering():\n", " for ci in range(len(children)):\n", @@ -299,6 +359,14 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "8eccf841-b143-4a7b-91d5-b1e2a965a2b1", + "metadata": {}, + "source": [ + "> **Figure 1:** The cutout of the i-band `deep_coadd` image displayed in greyscale, with green circles marking the deblended children. \n" + ] + }, { "cell_type": "markdown", "id": "819ac397-37b9-472d-9d51-a210c583afe3", @@ -306,11 +374,34 @@ "source": [ "## 3. Blend and object models\n", "\n", + "Scarlet Lite uses the multi-wavelength images to identify peaks. The scarlet models produced during deblending are available as a data product via the `object_scarlet_model` catalog. This contains all of the information needed to reconstruct the multi-band model for each source, including obtaining the flux re-distributed model. \n", + "\n", + "DELETE:\n", + "Below, find the `object_scarlet_model` data product in the butler by searching for all dataset types with the word \"scarlet\" in the name. \n", "\n", "### 3.1 Load parent blend data\n", "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data. " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d57fb219-88f5-4f5d-9a6a-7a702c340a93", + "metadata": {}, + "outputs": [], + "source": [ + "for dt in sorted(butler.registry.queryDatasetTypes('*scarlet*')):\n", + " print(dt)" + ] + }, + { + "cell_type": "markdown", + "id": "cda6a9dc-026b-45a7-a129-78f1d20e2cb4", + "metadata": {}, + "source": [ + "Use the butler to retrieve the `object_scarlet_models` data product that corresponds to the image with this dataId. `patch` and `tract` can also be used directly as keywords instead of `dataId`. UPDATE TEXT" + ] + }, { "cell_type": "code", "execution_count": null, @@ -326,7 +417,8 @@ " patch=obj[\"patch\"],\n", " parameters={\"blend_id\": [obj[\"parentObjectId\"]]}\n", ")\n", - "print(obj[\"parentObjectId\"])\n", + "\n", + "\n", "print(obj[\"objectId\"])" ] }, @@ -335,7 +427,7 @@ "id": "11587043-bbb5-46a8-bafa-9fe94dac3bde", "metadata": {}, "source": [ - "### 3.2 Extracting the blend data (convert to a usable blend instance in dp1 is now not needed)\n", + "### 3.2 Extracting the blend data\n", "\n", "In DP2, all blends use the same model PSF (a sigma=0.8 Gaussian) with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed). Their models can thus be extracted directly from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. \n", "\n", @@ -344,6 +436,18 @@ "Below, extract the `model_psf`, `psf`, and the bands in the blend." ] }, + { + "cell_type": "markdown", + "id": "f32934ec-2e89-4e8b-895b-9a7cb3ae02ba", + "metadata": {}, + "source": [ + "DP1 text: The LSST science pipelines use persistable `dataclass` objects as intermediaries between the storage containers and python classes. In the case of Scarlet Lite blends, this is the `ScarletBlendData` class. Some attributes of the blend model can be retrieved from `modelData`.\n", + "\n", + "In order to reconstruct the blend, the model PSF from the deconvolved model space is also required. The model PSF is the same for all blends, so extract that here.\n", + "\n", + "Also extract the set of blends associated with this `parentObjectId`.\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -353,8 +457,7 @@ "source": [ "model_psf = model_data.metadata[\"model_psf\"]\n", "psf = model_data.metadata[\"psf\"]\n", - "bands = model_data.metadata[\"bands\"]\n", - "print(bands)" + "bands = model_data.metadata[\"bands\"]\n" ] }, { @@ -362,7 +465,6 @@ "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", "metadata": {}, "source": [ - "Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", "\n", "`blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend.\n", "\n", @@ -406,7 +508,10 @@ "id": "202e94f3-44b9-4350-a34f-361b08e19f69", "metadata": {}, "source": [ - "#### 3.3.1 Index slicing\n", + "#### 3.2.1 Index slicing\n", + "\n", + "Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", + "\n", "\n", "Notice that the blend is still using the order from `model_data.bands`." ] @@ -426,7 +531,8 @@ "id": "b5661451-2796-4f44-9d38-74e182d352ea", "metadata": {}, "source": [ - "All of the band-dependent scarlet lite data products can be re-ordered using indexing by passing slices or tuples of bands, similar to numpy arrays, with the exception that the last band in the slice is *included* in the resulting sliced model." + "All of the band-dependent scarlet lite data products can be re-ordered using indexing by passing slices or tuples of bands, similar to numpy arrays, with the exception that the last band in the slice is *included* in the resulting sliced model.\n", + "\n" ] }, { @@ -442,12 +548,35 @@ "print(blend[\"z\"].observation.bands)" ] }, + { + "cell_type": "markdown", + "id": "5f7028a4-0b3e-4a75-bb84-cc88c7146abd", + "metadata": {}, + "source": [ + "DP1 text:\n", + "Printing `blend_data.bands` shows that this patch and tract in ECDFS was observed with all 6 bands but they are not in the order of increasing wavelength. To ensure that expected order, specify `model[tuple(\"ugrizy\")]` when retrieving the blend model to display the image with the expected color assignment in the cell below. If fewer bands of coverage exist (as is the case for some DP1 fields), simply replace this by the combination of observed bands, e.g. `tuple(\"griz\")`. For this example, select only 4 filters and exclude the `u` and `y` bands, which add unnecessary noise that makes it difficult to see the underlying objects.\n", + "\n", + "**Note:** this will change the order of the bands displayed in this instance of the the model but **not** in the `blend` instance. After DP1, a change is planned so that `blend = blend[display_bands]` can be used to reorganize all of the models so that they are displayed in a new order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7c9c301-c957-47d9-81f5-7c14afc40f41", + "metadata": {}, + "outputs": [], + "source": [ + "# this is from DP1 and not Fred\n", + "#display_bands = tuple(\"griz\")\n", + "#model = blend.get_model(convolve=True)[display_bands]" + ] + }, { "cell_type": "markdown", "id": "bf9510a0-d413-4fc6-a393-c6dc2c214c62", "metadata": {}, "source": [ - "#### 2.3.2 Objects\n", + "#### 3.2.2 Blend objects\n", "\n", "We can see the object IDs for all of the objects in the blend by looking at `Blend.sources`. Notice that the target object is the second one in the list." ] @@ -468,9 +597,9 @@ "id": "904f58e6-75f5-485a-ab1d-ccb708b5e15d", "metadata": {}, "source": [ - "## 3 Visualizing blends\n", + "### 3.3 Plot the blend model\n", "\n", - "### 3.1 Creating a color mapping\n", + "#### 3.3.1 Creating a color mapping\n", "\n", "In order to display multi-band image data as an RGB image we have to have some way to map the data to RGB space. Scarlet lite includes helper functions to do this, for example we can use the Lupton RGB color mapping to preserve colors. Below, create an `asinh` mapping normalization." ] @@ -492,7 +621,7 @@ "id": "9aede605-caa6-4690-a579-dc49594e26e2", "metadata": {}, "source": [ - "### 3.2 Extracting a model\n", + "#### 3.3.2 Extracting a model\n", "\n", "To extract the model from a blend we must convolve it with the difference kernel so that matches the observed seeing. This happens automatically if we pass `convolve=True` to `Blend.get_model`" ] @@ -512,9 +641,12 @@ "id": "0c87f797-78f9-4d42-aa80-a862a7c99902", "metadata": {}, "source": [ - "### 3.3 Plot the blend model\n", + "#### 3.3.3 Visualize\n", + "\n", + "Scarlet lite has helper functions to display a full blend, mark the location of peaks, show the residuals, etc. But as an exercise we see how to convert a blend model into an RGB image and display it.\n", "\n", - "As we'll see in a moment, scarlet lite has helper functions to display a full blend, mark the location of peaks, show the residuals, etc. But as an exercise we see how to convert a blend model into an RGB image and display it." + "DP1:\n", + "Finally, plot a color image of the blend model. Use the `AsinhMapping` to normalize: the asinh stretch preserves colors independent of brightness (for more information see Lupton et al. 2024). By default, Scarlet Lite will map the input filters into an red green blue (RGB) scaling where shorter wavelength filters map to blue and longer wavelength map to red (and assumes the order in the model is set as such, defined in the previous cell). This relative weighting between filters to RGB can be altered with the `channel_map` keyword to `img_to_rgb` (see documentation here)." ] }, { @@ -538,7 +670,7 @@ "id": "e400222a-7e64-4b0b-a0d7-8d43a5e2d9d3", "metadata": {}, "source": [ - "> **Figure 1:** A griz RGB color image of the blend model. By default, Scarlet Lite maps the filters into an RGB scaling where shorter wavelength filters contribute to the blue color and longer wavelength filters contribute to red." + "> **Figure 2:** A griz [check!] RGB color image of the blend model. By default, Scarlet Lite maps the filters into an RGB scaling where shorter wavelength filters contribute to the blue color and longer wavelength filters contribute to red." ] }, { @@ -587,7 +719,7 @@ "id": "010d1036-b23d-4d96-9547-54ec3d0ac009", "metadata": {}, "source": [ - "> **Figure 2:** The\n", + "> **Figure 3:** An RGB (PSF-deconvolved) image of the initial deblended source of interest in Section 2.\n", "\n", "EDIT: To view the actual model, first convolve it with the observation." ] @@ -611,7 +743,7 @@ "id": "90769d6a-6bbf-4d5a-8f8c-06b43a1ea306", "metadata": {}, "source": [ - "> **Figure 3:** The" + "> **Figure 4:** An RGB image of the same initial deblended source in Figure 3, but convolved with the PSF." ] }, { @@ -627,7 +759,13 @@ "\n", "First, load a multi-band exposure for the region that covers the blend.\n", "\n", - "Note to Christina: the Butler is temporarily broken and won't properly load the `MultibandExposure`. This should be fixed in the next day or so but I will be on vacation so I have a temporary fix that can be replaced with the commented code on Thursday or Friday." + "Note to Christina: the Butler is temporarily broken and won't properly load the `MultibandExposure`. This should be fixed in the next day or so but I will be on vacation so I have a temporary fix that can be replaced with the commented code on Thursday or Friday.\n", + "\n", + "DP1 text:\n", + "This section will make visual comparisons between the blend model and the observed data, and demonstrate some of the Scarlet Lite functionality. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n", + "\n", + "**Note:** the bands need to be in the same order as the `blend` band order in DP1.\n", + "\n" ] }, { From 6d4bb717e751f5b84bb6d8e2c26303e44740db2d Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 6 Oct 2026 22:14:39 +0000 Subject: [PATCH 07/13] updating --- .../206_2_Deblender_footprints.ipynb | 117 ++++++++++++++---- 1 file changed, 95 insertions(+), 22 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 867bf3af..28fe6fb0 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -759,8 +759,6 @@ "\n", "First, load a multi-band exposure for the region that covers the blend.\n", "\n", - "Note to Christina: the Butler is temporarily broken and won't properly load the `MultibandExposure`. This should be fixed in the next day or so but I will be on vacation so I have a temporary fix that can be replaced with the commented code on Thursday or Friday.\n", - "\n", "DP1 text:\n", "This section will make visual comparisons between the blend model and the observed data, and demonstrate some of the Scarlet Lite functionality. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n", "\n", @@ -777,22 +775,15 @@ "source": [ "bbox = mes.utils.scarletBoxToBBox(blend.observation.bbox)\n", "\n", - "# Begin temporary fix while Butler is borken\n", - "coadds = []\n", - "for band in bands:\n", - " coadds.append(butler.get(\"deep_coadd\", tract=obj[\"tract\"], patch=obj[\"patch\"], band=band).to_legacy())\n", - "\n", - "coadd = MultibandExposure.fromExposures(bands, coadds)[:, bbox]\n", - "## end temporary fix\n", - "\n", - "#coadd = MultibandExposure.fromButler(\n", - "# butler,\n", - "# bands,\n", - "# \"deep_coadd\",\n", - "# tract=obj[\"tract\"],\n", - "# patch=obj[\"patch\"],\n", - "# storageClass=\"ExposureF\",\n", - "#)" + "mCoadd = MultibandExposure.fromButler(\n", + " butler,\n", + " bands,\n", + " \"deep_coadd\",\n", + " tract=obj[\"tract\"],\n", + " patch=obj[\"patch\"],\n", + " storageClass=\"ExposureF\",\n", + " parameters={'bbox': bbox}\n", + ")" ] }, { @@ -815,7 +806,7 @@ "observation = mes.utils.buildObservation(\n", " modelPsf=model_psf,\n", " psfCenter=bbox.getCenter(),\n", - " mExposure=coadd,\n", + " mExposure=mCoadd,\n", ")" ] }, @@ -882,7 +873,7 @@ "id": "f17620e7-3de6-4de2-a9b9-2963253d5392", "metadata": {}, "source": [ - "> **Figure 4:** The" + "> **Figure 5:** Visualization of the model generated by Scarlet Lite, with x and y axes indicating pixel number from the patch/tract. The panels show the blend model (not PSF-convolved; top left), the blend model rendered (PSF-convolved; top right), the observation (bottom left) and the residual image (observation - model rendered; bottom right). All deblended sources are identified with white boxes and a number indicating its ID within the set of deblended objects." ] }, { @@ -916,7 +907,7 @@ "id": "8b530ac2-c6ce-489d-99ae-47a67b215e99", "metadata": {}, "source": [ - "> **Figure 5:** The" + "> **Figure 6:** Visualization of the individual deblended sources (top to bottom show ID 0 to 8). For each source, left panel shows the PSF-deconvolved model footprint (where the color scale corresponds to the weighted RGB color), middle panel shows the observation of the blend with the source identified by a white box, and right panel shows the flux in each band. The bands are shown all 6 LSST filters, and are in observation order, i.e. `observed_bands`." ] }, { @@ -969,7 +960,7 @@ "id": "938c8189-f60f-4c19-a5e0-f124c60fd7ea", "metadata": {}, "source": [ - "> **Figure 6:** The" + "> **Figure 7:** The" ] }, { @@ -981,6 +972,88 @@ "\n", "copy section 5 from Dp1 nb here" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2214e15c-7477-40a3-acc0-21e48ed250db", + "metadata": {}, + "outputs": [], + "source": [ + "object1 = blend.sources[1]\n", + "print(object1.metadata[\"id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b66c158-ca36-436f-8027-564e6d6d56df", + "metadata": {}, + "outputs": [], + "source": [ + "blend.conserve_flux()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c37d978e-42f3-4b38-aeb4-5b78b5e101bf", + "metadata": {}, + "outputs": [], + "source": [ + "source_model = object1.get_model()\n", + "\n", + "display_bands = tuple(\"griz\") # needed??" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61932039-dc87-4185-857c-17c563613884", + "metadata": {}, + "outputs": [], + "source": [ + "psf_radius = int((np.max(blend.observation.psfs.shape)+1)//2)\n", + "enlarged_box = source_model.bbox.grow(psf_radius)\n", + "convolved_model = source_model.copy().project(bbox=enlarged_box)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54dd601b-e95c-48a7-b926-49770b9f5b95", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "extent = scl.display.get_extent(object1.bbox)\n", + "rgb = scl.display.img_to_rgb(source_model[display_bands], norm=norm)\n", + "ax[0].imshow(rgb, origin='lower', extent=extent)\n", + "ax[0].set_title(\"Deconvolved Model\")\n", + "\n", + "convolved_model = blend.observation.convolve(convolved_model)\n", + "extent = scl.display.get_extent(enlarged_box)\n", + "rgb = scl.display.img_to_rgb(convolved_model[display_bands], norm=norm)\n", + "ax[1].imshow(rgb, origin='lower', extent=extent)\n", + "ax[1].set_title(\"Convolved Model\")\n", + "\n", + "flux_model = object1.flux_weighted_image\n", + "extent = scl.display.get_extent(flux_model.bbox)\n", + "rgb = scl.display.img_to_rgb(flux_model[display_bands], norm=norm)\n", + "ax[2].imshow(rgb, origin='lower', extent=extent)\n", + "ax[2].set_title(\"Flux Re-distributed Model\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f35043e-3b21-42bd-b7fa-5baf59790030", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 4fb2e17119455a779b9dcad3672f7f3ab920fbd0 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Wed, 7 Oct 2026 16:22:06 +0000 Subject: [PATCH 08/13] updating --- .../206_2_Deblender_footprints.ipynb | 121 ++++++++---------- 1 file changed, 50 insertions(+), 71 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 28fe6fb0..8c9ff389 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -85,9 +85,7 @@ "\n", "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\n", "\n", - "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. \n", - "\n", - "ADD astropy and lsst.images" + "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` and `lsst.images` libraries provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. " ] }, { @@ -111,9 +109,7 @@ "from lsst.rsp import RSPDiscovery\n", "\n", "import lsst.scarlet.lite as scl\n", - "import lsst.meas.extensions.scarlet as mes\n", - "\n", - "\n" + "import lsst.meas.extensions.scarlet as mes" ] }, { @@ -138,58 +134,58 @@ }, { "cell_type": "markdown", - "id": "262c6bb8-bf46-4668-ad5c-a790a5d65bd6", + "id": "db75cef5-6713-4d44-b2ec-338ad18acd47", "metadata": {}, "source": [ - "Get an instance of the TAP service." + "Set the backend for afwDisplay to matplotlib." ] }, { "cell_type": "code", "execution_count": null, - "id": "979781a7-1a12-41bb-9aa5-ad6e14efe4d4", + "id": "cd26977b-d5de-48ab-b599-bf7445296a99", "metadata": {}, "outputs": [], "source": [ - "discovery = RSPDiscovery(\"dp2\")\n", - "service = discovery.get_tap_client()" + "afw_display.setDefaultBackend('matplotlib')" ] }, { "cell_type": "markdown", - "id": "8c1e8d60-f8ed-4330-a160-b6898e35f932", + "id": "262c6bb8-bf46-4668-ad5c-a790a5d65bd6", "metadata": {}, "source": [ - "Instantiate the butler." + "Get an instance of the TAP service." ] }, { "cell_type": "code", "execution_count": null, - "id": "7abc2aef-ac23-4d2c-b94c-dc67e206cfca", + "id": "979781a7-1a12-41bb-9aa5-ad6e14efe4d4", "metadata": {}, "outputs": [], "source": [ - "butler = Butler('dp2', collections=[\"dp2\"])\n", - "assert butler is not None" + "discovery = RSPDiscovery(\"dp2\")\n", + "service = discovery.get_tap_client()" ] }, { "cell_type": "markdown", - "id": "db75cef5-6713-4d44-b2ec-338ad18acd47", + "id": "8c1e8d60-f8ed-4330-a160-b6898e35f932", "metadata": {}, "source": [ - "Set the backend for afwDisplay to matplotlib." + "Instantiate the butler." ] }, { "cell_type": "code", "execution_count": null, - "id": "cd26977b-d5de-48ab-b599-bf7445296a99", + "id": "7abc2aef-ac23-4d2c-b94c-dc67e206cfca", "metadata": {}, "outputs": [], "source": [ - "afw_display.setDefaultBackend('matplotlib')" + "butler = Butler('dp2', collections=[\"dp2\"])\n", + "assert butler is not None" ] }, { @@ -197,9 +193,6 @@ "id": "28b3feee-7a78-49e0-994a-90d360685a98", "metadata": {}, "source": [ - "TO DELETE?:\n", - "\n", - "\n", "## 2. Identify a blend\n", "\n", "First, identify a blended system in the DP2 data. This example will load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to access the blend models associated with it. \n", @@ -211,9 +204,10 @@ "- **deblend_blendNChild**: Describes the number of children that were in the parent blend\n", "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of the object\n", "\n", - "Search for all objects within 1 arcsec of the object of interest. There should only be a single result.\n", "\n", - "### 2.1 Query for deblended object" + "### 2.1 Query for deblended object\n", + "\n", + "Search for all objects within 1 arcsec of the object of interest. There should only be a single result.\n" ] }, { @@ -240,11 +234,15 @@ " job.raise_if_error()\n", " \n", "results = job.fetch_result().to_table()\n", - "print(len(results))\n", - "\n", - "#children = job.fetch_result().to_table()\n", - "#print(f\"Found {len(children)} deblended sources from this parent.\")\n", - "#children" + "print(len(results))" + ] + }, + { + "cell_type": "markdown", + "id": "349c2567-e6dc-49d5-b46e-3d3c8c8cbc85", + "metadata": {}, + "source": [ + "Identify the parent object from which the target was deblended (using `parentObjectId` from the object catalog) and identify all the deblended `children`. This parent object was deblended into 11 separate objects." ] }, { @@ -271,7 +269,7 @@ "\n", "assert job.phase == 'COMPLETED'\n", "children = job.fetch_result().to_table()\n", - "children\n" + "children" ] }, { @@ -281,7 +279,7 @@ "source": [ "### 2.2 Visualize the blend\n", "\n", - "To visualize the blend, plot a `deep_coadd` image subset that contains the blend with children identified. First, find the patch and tract containing the blend, and set the band to be i-band." + "To visualize the blend, plot a `deep_coadd` image subset that contains the blend with children identified. First, retrieve the patch and tract coadd containing the blend, and set the band to be i-band. " ] }, { @@ -299,12 +297,10 @@ "metadata": {}, "outputs": [], "source": [ - "band = 'i'\n", - "\n", "total_footprint_area = np.sum(children['footprintArea'])\n", "cutoutSideLength = 5.0 * np.sqrt(total_footprint_area) * 0.2\n", "\n", - "coadd = butler.get(\"deep_coadd\", tract=results['tract'][0], patch=results['patch'][0], band='i')\n" + "coadd = butler.get(\"deep_coadd\", tract=results['tract'][0], patch=results['patch'][0], band='i')" ] }, { @@ -312,7 +308,7 @@ "id": "4f0a7654-e8ed-4bb7-a989-5e1052ccee62", "metadata": {}, "source": [ - "Define the bounding box and construct the image subset. " + "Define the center to be at the deblended target, and define the bounding box to construct the image subset. " ] }, { @@ -322,10 +318,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", - "point = SkyCoord(ra, dec, unit='deg')\n", - "xy = coadd.sky_projection.sky_to_pixel(point)\n", + "center = SkyCoord(ra, dec, unit='deg')\n", + "xy = coadd.sky_projection.sky_to_pixel(center)\n", "bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(cutoutSideLength)\n", "subset = coadd[bbox]" ] @@ -352,7 +346,7 @@ "with display.Buffering():\n", " for ci in range(len(children)):\n", " display.dot('o', children['deblend_peak_center_x'][ci], children['deblend_peak_center_y'][ci],\n", - " size=10)#, ctype=afwDisplay.RED)\n", + " size=10)\n", "plt.gca().axis('off')\n", "display.show_colorbar(False)\n", "\n", @@ -364,7 +358,7 @@ "id": "8eccf841-b143-4a7b-91d5-b1e2a965a2b1", "metadata": {}, "source": [ - "> **Figure 1:** The cutout of the i-band `deep_coadd` image displayed in greyscale, with green circles marking the deblended children. \n" + "> **Figure 1:** The image subset of the i-band `deep_coadd` image displayed in greyscale, with green circles marking the deblended children. \n" ] }, { @@ -376,11 +370,10 @@ "\n", "Scarlet Lite uses the multi-wavelength images to identify peaks. The scarlet models produced during deblending are available as a data product via the `object_scarlet_model` catalog. This contains all of the information needed to reconstruct the multi-band model for each source, including obtaining the flux re-distributed model. \n", "\n", - "DELETE:\n", - "Below, find the `object_scarlet_model` data product in the butler by searching for all dataset types with the word \"scarlet\" in the name. \n", - "\n", "### 3.1 Load parent blend data\n", - "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data. " + "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data.\n", + "\n", + "Below, find the `object_scarlet_model` data product in the butler by searching for all dataset types with the word \"scarlet\" in the name. \n" ] }, { @@ -399,7 +392,7 @@ "id": "cda6a9dc-026b-45a7-a129-78f1d20e2cb4", "metadata": {}, "source": [ - "Use the butler to retrieve the `object_scarlet_models` data product that corresponds to the image with this dataId. `patch` and `tract` can also be used directly as keywords instead of `dataId`. UPDATE TEXT" + "Use the butler to retrieve the `object_scarlet_models` data product that corresponds to the image that contains this blend." ] }, { @@ -416,10 +409,7 @@ " tract=obj[\"tract\"],\n", " patch=obj[\"patch\"],\n", " parameters={\"blend_id\": [obj[\"parentObjectId\"]]}\n", - ")\n", - "\n", - "\n", - "print(obj[\"objectId\"])" + ")" ] }, { @@ -429,23 +419,15 @@ "source": [ "### 3.2 Extracting the blend data\n", "\n", - "In DP2, all blends use the same model PSF (a sigma=0.8 Gaussian) with the same observed PSF (the PSF at the center of the patch), and the same bands (usually `ugrizy` except on the edges of the survey or crowded fields where not all bands successfully processed). Their models can thus be extracted directly from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch, so ommitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` above would load all of the models. \n", + "In DP2, all blends use the same model PSF (a Gaussian with sigma=0.8) and the same observed PSF (the PSF at the center of the patch), and the same bands (usually all bands, `ugrizy`, except on the edges of the survey or crowded fields where not all bands successfully processed). Their models can thus be extracted directly from the metadata (new in DP2). `LsstScarletModelData` is a container for all of the blends in a patch. Omitting the `parameters={\"blend_id\": [obj[\"parentObjectId\"]]}` in the command above would load all of the models. \n", "\n", "`model_data` (above) only loaded a single blend, but it is still returned from the Butler in the full container, which is needed because it contains the metadata required to reconstruct the scarlet lite `Blend` object.\n", "\n", - "Below, extract the `model_psf`, `psf`, and the bands in the blend." - ] - }, - { - "cell_type": "markdown", - "id": "f32934ec-2e89-4e8b-895b-9a7cb3ae02ba", - "metadata": {}, - "source": [ - "DP1 text: The LSST science pipelines use persistable `dataclass` objects as intermediaries between the storage containers and python classes. In the case of Scarlet Lite blends, this is the `ScarletBlendData` class. Some attributes of the blend model can be retrieved from `modelData`.\n", + "Below, extract the `model_psf`, `psf`, and the bands in the blend. The LSST science pipelines use persistable dataclass objects as intermediaries between the storage containers and python classes. In the case of Scarlet Lite blends, this is the `ScarletBlendData` class. Some attributes of the blend model can be retrieved from `model_data`.\n", "\n", "In order to reconstruct the blend, the model PSF from the deconvolved model space is also required. The model PSF is the same for all blends, so extract that here.\n", "\n", - "Also extract the set of blends associated with this `parentObjectId`.\n" + "Also extract the set of blends associated with this parentObjectId." ] }, { @@ -457,7 +439,7 @@ "source": [ "model_psf = model_data.metadata[\"model_psf\"]\n", "psf = model_data.metadata[\"psf\"]\n", - "bands = model_data.metadata[\"bands\"]\n" + "bands = model_data.metadata[\"bands\"]" ] }, { @@ -465,10 +447,7 @@ "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", "metadata": {}, "source": [ - "\n", - "`blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend.\n", - "\n", - "Finally, convert the `blend_data` storage class into a scarlet lite Blend instance called `blend`." + "Below, extract the parent data from `model_data`, and then the blend data from `parent_data`. `blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend." ] }, { @@ -488,9 +467,9 @@ "id": "17022656-f310-4c1b-8337-e1f1fb5125eb", "metadata": {}, "source": [ - "Finally, use the `minimal_data_to_blend` method to access the blend model. \n", + "Finally, convert the `blend_data` storage class into a scarlet lite `Blend` instance called `blend`. The `minimal_data_to_blend` method is used to access the blend model. \n", "\n", - "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" + "MOVE: because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" ] }, { @@ -510,10 +489,10 @@ "source": [ "#### 3.2.1 Index slicing\n", "\n", - "Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", + "UPDATE: Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", "\n", "\n", - "Notice that the blend is still using the order from `model_data.bands`." + "Print the Notice that the blend is still using the order from `model_data.bands`." ] }, { From 65a2604f45cbbe0e9f2fcd1368c90421f342af43 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Wed, 7 Oct 2026 20:10:36 +0000 Subject: [PATCH 09/13] updating deblender nbs to DP2 --- .../206_2_Deblender_footprints.ipynb | 48 ++++++++++++------- 1 file changed, 30 insertions(+), 18 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 8c9ff389..24d3973c 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -39,7 +39,7 @@ "**Packages:** `lsst.rsp`, `lsst.daf.butler`, `lsst.scarlet.lite`, `lsst.meas.extensions.scarlet`\n", "\n", "**Credit:**\n", - "Originally developed by Christina Williams and the Rubin Community Science team. This notebook is partly based on DP0.2 notebook 10 on deblender data products, and benefited from helpful discussions with Fred Moolekamp. The deblender packages are based on Melchior et al. 2018, and the Scarlet software written by Peter Melchior and Fred Moolekamp. Further documentation on Scarlet is available at that site.\n", + "Originally developed by the Rubin Community Science team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks. The deblender packages are based on Melchior et al. 2018, and the Scarlet software written by Peter Melchior and Fred Moolekamp. Further documentation on Scarlet is available at that site.\n", "\n", "**Get Support:**\n", "Everyone is encouraged to ask questions or raise issues in the \n", @@ -53,7 +53,6 @@ "id": "e175c6a7-b3d8-49e5-841a-ec12631065e7", "metadata": {}, "source": [ - "DP1 version:\n", "## 1. Introduction\n", "\n", "This notebook demonstrates how to reconstruct per-pixel models of objects that are deblended by the LSST Science Pipeline when building the `object` table.\n", @@ -195,7 +194,7 @@ "source": [ "## 2. Identify a blend\n", "\n", - "First, identify a blended system in the DP2 data. This example will load data for the object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to access the blend models associated with it. \n", + "First, identify a blended system in the DP2 data. This example will load data for the deblended object that is located at `(151.78888574607532, 1.55284113175051)` along with the relevant fields required to access the blend models associated with it. \n", "\n", "- **tract**: the LSST track that contains the object\n", "- **patch**: the patch in the `tract` that contains the object\n", @@ -204,7 +203,6 @@ "- **deblend_blendNChild**: Describes the number of children that were in the parent blend\n", "- **deblend_peak_center_x,y**: Not required but will be used later to mark the location of the object\n", "\n", - "\n", "### 2.1 Query for deblended object\n", "\n", "Search for all objects within 1 arcsec of the object of interest. There should only be a single result.\n" @@ -442,12 +440,30 @@ "bands = model_data.metadata[\"bands\"]" ] }, + { + "cell_type": "markdown", + "id": "1ddd3062-c672-43d5-aa41-acb09aee84a3", + "metadata": {}, + "source": [ + "Print the `bands` metadata associated with the blend model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8ed7d45-d27a-409c-8c1b-9a31e69fad9f", + "metadata": {}, + "outputs": [], + "source": [ + "print(bands)" + ] + }, { "cell_type": "markdown", "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", "metadata": {}, "source": [ - "Below, extract the parent data from `model_data`, and then the blend data from `parent_data`. `blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend." + "Below, extract the parent data from `model_data`. `parent_data` holds the full blend from the `object_detection` catalog. Then, extract the blend data from `parent_data`. `blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend." ] }, { @@ -467,9 +483,7 @@ "id": "17022656-f310-4c1b-8337-e1f1fb5125eb", "metadata": {}, "source": [ - "Finally, convert the `blend_data` storage class into a scarlet lite `Blend` instance called `blend`. The `minimal_data_to_blend` method is used to access the blend model. \n", - "\n", - "MOVE: because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" + "Finally, convert the `blend_data` storage class into a scarlet lite `Blend` instance called `blend`. The `minimal_data_to_blend` method is used to access the blend model. " ] }, { @@ -489,10 +503,7 @@ "source": [ "#### 3.2.1 Index slicing\n", "\n", - "UPDATE: Notice that the observed bands are not in wavelength order. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (to be reordered later). First, convert the data class into a scarlet lite `Blend`. `parent_data` holds the full blend from the `object_detection` catalog.\n", - "\n", - "\n", - "Print the Notice that the blend is still using the order from `model_data.bands`." + "Print the observed bands used in the deblending process. Notice that the observed bands are not in wavelength order, and they are still in the same order from `model_data.bands` above. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (this can be reordered later). " ] }, { @@ -510,8 +521,7 @@ "id": "b5661451-2796-4f44-9d38-74e182d352ea", "metadata": {}, "source": [ - "All of the band-dependent scarlet lite data products can be re-ordered using indexing by passing slices or tuples of bands, similar to numpy arrays, with the exception that the last band in the slice is *included* in the resulting sliced model.\n", - "\n" + "All of the band-dependent scarlet lite data products can be re-ordered using indexing by passing slices or tuples of bands, similar to numpy arrays, with the exception that the last band in the slice is *included* in the resulting sliced model." ] }, { @@ -532,9 +542,9 @@ "id": "5f7028a4-0b3e-4a75-bb84-cc88c7146abd", "metadata": {}, "source": [ - "DP1 text:\n", - "Printing `blend_data.bands` shows that this patch and tract in ECDFS was observed with all 6 bands but they are not in the order of increasing wavelength. To ensure that expected order, specify `model[tuple(\"ugrizy\")]` when retrieving the blend model to display the image with the expected color assignment in the cell below. If fewer bands of coverage exist (as is the case for some DP1 fields), simply replace this by the combination of observed bands, e.g. `tuple(\"griz\")`. For this example, select only 4 filters and exclude the `u` and `y` bands, which add unnecessary noise that makes it difficult to see the underlying objects.\n", + "To ensure that expected order, specify `model[tuple(\"ugrizy\")]` when retrieving the blend model to display the image with the expected color assignment in the cell below. If fewer bands of coverage exist, simply replace this by the combination of observed bands, e.g. `tuple(\"griz\")`. For this example, select only 4 filters and exclude the `u` and `y` bands, which add unnecessary noise that makes it difficult to see the underlying objects.\n", "\n", + "TO CHECK (FROM DP1):\n", "**Note:** this will change the order of the bands displayed in this instance of the the model but **not** in the `blend` instance. After DP1, a change is planned so that `blend = blend[display_bands]` can be used to reorganize all of the models so that they are displayed in a new order." ] }, @@ -557,7 +567,7 @@ "source": [ "#### 3.2.2 Blend objects\n", "\n", - "We can see the object IDs for all of the objects in the blend by looking at `Blend.sources`. Notice that the target object is the second one in the list." + "Print the object IDs for all of the objects in the blend by looking at `Blend.sources`. Notice that the target object is the second one in the list." ] }, { @@ -801,7 +811,9 @@ "from earlier:\n", "Finally, use the `minimal_data_to_blend` method to access the blend model. \n", "\n", - "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog." + "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n", + "\n", + "MOVE: because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" ] }, { From 9a7ae370af53e09045180fab351c561ea11332d9 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Wed, 7 Oct 2026 23:11:27 +0000 Subject: [PATCH 10/13] updating deblender nbs to DP2 --- .../206_2_Deblender_footprints.ipynb | 81 +++++++++++-------- 1 file changed, 48 insertions(+), 33 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 24d3973c..920a443a 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -542,22 +542,20 @@ "id": "5f7028a4-0b3e-4a75-bb84-cc88c7146abd", "metadata": {}, "source": [ - "To ensure that expected order, specify `model[tuple(\"ugrizy\")]` when retrieving the blend model to display the image with the expected color assignment in the cell below. If fewer bands of coverage exist, simply replace this by the combination of observed bands, e.g. `tuple(\"griz\")`. For this example, select only 4 filters and exclude the `u` and `y` bands, which add unnecessary noise that makes it difficult to see the underlying objects.\n", + "To change to wavelength order, specify `model[tuple(\"ugrizy\")]` or `blend[tuple(\"ugrizy\")]` when retrieving the blend model to display the image with the expected color assignment. If fewer bands of coverage exist, simply replace this by the combination of observed bands, e.g. `tuple(\"griz\")`. \n", "\n", - "TO CHECK (FROM DP1):\n", - "**Note:** this will change the order of the bands displayed in this instance of the the model but **not** in the `blend` instance. After DP1, a change is planned so that `blend = blend[display_bands]` can be used to reorganize all of the models so that they are displayed in a new order." + "Demonstrate the new filter order below." ] }, { "cell_type": "code", "execution_count": null, - "id": "a7c9c301-c957-47d9-81f5-7c14afc40f41", + "id": "da8ffb55-4260-4cd9-8a77-c2451f5d8e35", "metadata": {}, "outputs": [], "source": [ - "# this is from DP1 and not Fred\n", - "#display_bands = tuple(\"griz\")\n", - "#model = blend.get_model(convolve=True)[display_bands]" + "print(blend.observation.bands)\n", + "#print(model.bands)" ] }, { @@ -588,9 +586,30 @@ "source": [ "### 3.3 Plot the blend model\n", "\n", - "#### 3.3.1 Creating a color mapping\n", + "#### 3.3.1 Extracting a model\n", "\n", - "In order to display multi-band image data as an RGB image we have to have some way to map the data to RGB space. Scarlet lite includes helper functions to do this, for example we can use the Lupton RGB color mapping to preserve colors. Below, create an `asinh` mapping normalization." + "To compare the extracted blend model to observations, first convolve it with the difference kernel so that it matches the observed seeing. This happens automatically when passing `convolve=True` to `Blend.get_model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0310ffd7-65a0-49ba-8ae5-3824a94828b8", + "metadata": {}, + "outputs": [], + "source": [ + "model = blend.get_model(convolve=True)\n", + "model.bands" + ] + }, + { + "cell_type": "markdown", + "id": "0c87f797-78f9-4d42-aa80-a862a7c99902", + "metadata": {}, + "source": [ + "#### 3.3.2 Visualize\n", + "\n", + "To display multi-band image data as a color (red green blue; RGB) image, use Scarlet lite helper functions. Below, use the Lupton RGB color mapping to preserve colors and display with an `asinh` normalization. The asinh stretch preserves colors independent of brightness (for more information see Lupton et al. 2024). " ] }, { @@ -607,35 +626,31 @@ }, { "cell_type": "markdown", - "id": "9aede605-caa6-4690-a579-dc49594e26e2", + "id": "52b6b8a7-5cc2-4f80-95b9-3a9804f38165", "metadata": {}, "source": [ - "#### 3.3.2 Extracting a model\n", + "By default, Scarlet Lite assumes the input filters are in wavelength order, and thus mapping into an RGB scaling where the first filter map to blue and last filter map to red. This relative weighting between filters to RGB can be altered with the `channel_map` keyword to `img_to_rgb` (see documentation here).\n", "\n", - "To extract the model from a blend we must convolve it with the difference kernel so that matches the observed seeing. This happens automatically if we pass `convolve=True` to `Blend.get_model`" + "Alternatively, " ] }, { "cell_type": "code", "execution_count": null, - "id": "0310ffd7-65a0-49ba-8ae5-3824a94828b8", + "id": "ee36b1a7-aea2-4e54-aa98-151cd6786031", "metadata": {}, "outputs": [], "source": [ - "model = blend.get_model(convolve=True)" + "#display_bands = tuple(\"ugrizy\")\n", + "#model_display = blend.get_model(convolve=True)[display_bands]" ] }, { "cell_type": "markdown", - "id": "0c87f797-78f9-4d42-aa80-a862a7c99902", + "id": "8577eb1f-1b37-4383-b325-60bb2155b240", "metadata": {}, "source": [ - "#### 3.3.3 Visualize\n", - "\n", - "Scarlet lite has helper functions to display a full blend, mark the location of peaks, show the residuals, etc. But as an exercise we see how to convert a blend model into an RGB image and display it.\n", - "\n", - "DP1:\n", - "Finally, plot a color image of the blend model. Use the `AsinhMapping` to normalize: the asinh stretch preserves colors independent of brightness (for more information see Lupton et al. 2024). By default, Scarlet Lite will map the input filters into an red green blue (RGB) scaling where shorter wavelength filters map to blue and longer wavelength map to red (and assumes the order in the model is set as such, defined in the previous cell). This relative weighting between filters to RGB can be altered with the `channel_map` keyword to `img_to_rgb` (see documentation here)." + "Below, convert the blend model into an RGB image and display it. (Scarlet lite also has helper functions to display a full blend, mark the location of peaks, show the residuals automatically. This will be demonstrated in Section X). \n" ] }, { @@ -659,7 +674,7 @@ "id": "e400222a-7e64-4b0b-a0d7-8d43a5e2d9d3", "metadata": {}, "source": [ - "> **Figure 2:** A griz [check!] RGB color image of the blend model. By default, Scarlet Lite maps the filters into an RGB scaling where shorter wavelength filters contribute to the blue color and longer wavelength filters contribute to red." + "> **Figure 2:** A ugrizy RGB color image of the blend model. The original target object from Section 2 is marked with a white x. By default, Scarlet Lite maps the filters into an RGB scaling where the first bands contribute to the blue color and last bands contribute to red." ] }, { @@ -679,7 +694,9 @@ "metadata": {}, "outputs": [], "source": [ - "source = next((src for src in blend.sources if src.metadata[\"id\"] == obj[\"objectId\"]), None)" + "source = next((src for src in blend.sources if\n", + " src.metadata[\"id\"] == obj[\"objectId\"]), None)\n", + "print(source)" ] }, { @@ -687,7 +704,7 @@ "id": "568cf76a-ec90-4ee8-910a-b3f9f9c30bf1", "metadata": {}, "source": [ - "Recall that the object model is in the deconvolved space, so displaying it will show the internal scarlet lite model (intrinsic model) for the source." + "Recall that the object model is in the deconvolved space, so displaying it will show the internal scarlet lite model (intrinsic model) for the object." ] }, { @@ -699,7 +716,7 @@ "source": [ "rgb = scl.display.img_to_rgb(source.get_model(), norm=norm)\n", "plt.imshow(rgb, extent=extent)\n", - "plt.title(\"object data\")\n", + "plt.title(\"Intrinsic (deconvolved) model\")\n", "plt.show()" ] }, @@ -710,7 +727,7 @@ "source": [ "> **Figure 3:** An RGB (PSF-deconvolved) image of the initial deblended source of interest in Section 2.\n", "\n", - "EDIT: To view the actual model, first convolve it with the observation." + "Now, to view the model as directly comparable to the data, first convolve it with the observation using the `convolve` method." ] }, { @@ -723,7 +740,7 @@ "model = blend.observation.convolve(source.get_model())\n", "rgb = scl.display.img_to_rgb(model, norm=norm)\n", "plt.imshow(rgb, extent=extent)\n", - "plt.title(\"object data\")\n", + "plt.title(\"Intrinsic (convolved) model\")\n", "plt.show()" ] }, @@ -732,7 +749,7 @@ "id": "90769d6a-6bbf-4d5a-8f8c-06b43a1ea306", "metadata": {}, "source": [ - "> **Figure 4:** An RGB image of the same initial deblended source in Figure 3, but convolved with the PSF." + "> **Figure 4:** An RGB image of the model of the same initial deblended source in Figure 3, but now convolved with the PSF." ] }, { @@ -742,15 +759,13 @@ "source": [ "## 4 Compare to observation\n", "\n", - "Load the scarlet models to see how well they compare to the data.\n", + "This section will make visual comparisons between the blend model and the observed data, and demonstrate some of the Scarlet Lite functionality. \n", "\n", "### 4.1 Loading a MultibandExposure\n", "\n", - "First, load a multi-band exposure for the region that covers the blend.\n", - "\n", - "DP1 text:\n", - "This section will make visual comparisons between the blend model and the observed data, and demonstrate some of the Scarlet Lite functionality. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n", + "First, load a multi-band exposure for the region that covers the blend. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n", "\n", + "DELETE?\n", "**Note:** the bands need to be in the same order as the `blend` band order in DP1.\n", "\n" ] From ef97ed2b84dc4a4e5e81e88568da88b5b7dd8c59 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Wed, 7 Oct 2026 23:59:26 +0000 Subject: [PATCH 11/13] finalizing 206 2 nb on deblender footprints --- .../206_2_Deblender_footprints.ipynb | 89 ++++++++++++------- 1 file changed, 57 insertions(+), 32 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 920a443a..58b01e53 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -763,11 +763,7 @@ "\n", "### 4.1 Loading a MultibandExposure\n", "\n", - "First, load a multi-band exposure for the region that covers the blend. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n", - "\n", - "DELETE?\n", - "**Note:** the bands need to be in the same order as the `blend` band order in DP1.\n", - "\n" + "First, load a multi-band exposure for the region that covers the blend. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n" ] }, { @@ -797,7 +793,7 @@ "source": [ "### 4.2 Build a full Blend with an Observation\n", "\n", - "Scarlet lite requires an `Observation` to contain the exposure data along with loss function calculation and convolutions." + "To compare the Scarlet Lite deblended models with the observed data, first create an Observation object. This bundles the actual images in each band (`mCoadd`) with the telescope's PSF (`modelPsf`). Scarlet Lite uses this to convolve the intrinsic model of the blend for comparison to the observed images." ] }, { @@ -819,16 +815,7 @@ "id": "46b5e68d-dd64-410f-9058-dc52f0c1ad3f", "metadata": {}, "source": [ - "Now we can build the full Blend\n", - "\n", - "\n", - "\n", - "from earlier:\n", - "Finally, use the `minimal_data_to_blend` method to access the blend model. \n", - "\n", - "because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n", - "\n", - "MOVE: because we haven't loaded the `Exposure` for the tract/patch combo that contains the blend, so it loads a `Blend` class where `blend.observation` is an empty image with the correct `psf`, `model_psf`, and `bands`, required to render a model of the blend in the observed seeing. Later we'll see how to load a full blend along with it's associated exposure, which is required if one wants to generate the flux re-distributed model that was used for measurements in the `Object` catalog.\n" + "Pass the `observation` into the `blend_data`, to build `full_blend`, which is a `scarlet.lite.Blend` object that contains all this information together. This enables both the model and real observations to be compared directly using Scarlet Lite's built-in `show_scene` function. " ] }, { @@ -838,7 +825,8 @@ "metadata": {}, "outputs": [], "source": [ - "blend = blend_data.to_blend(observation)[tuple(\"ugrizy\")]" + "observed_bands = tuple(\"ugrizy\")\n", + "full_blend = blend_data.to_blend(observation)[observed_bands]" ] }, { @@ -848,7 +836,7 @@ "source": [ "### 4.3 Display full blend residuals\n", "\n", - "Scarlet lite has a built in display function to show the image, blend, and residuals" + "Now, use the Scarlet Lite built in display function called `show_scene` to show the image, blend model, convolved blend model, and residuals between the model and observation. " ] }, { @@ -859,7 +847,7 @@ "outputs": [], "source": [ "scl.display.show_scene(\n", - " blend,\n", + " full_blend,\n", " norm=norm,\n", " show_model=True,\n", " show_rendered=True,\n", @@ -889,7 +877,7 @@ "source": [ "### 4.4 Display source models\n", "\n", - "Similarly we can display the models of the individual sources" + "Now, use the Scarlet Lite built in display function called `show_sources` to display the models of the individual sources compared to the observed image and its spectral energy distribution. " ] }, { @@ -900,7 +888,7 @@ "outputs": [], "source": [ "scl.display.show_sources(\n", - " blend,\n", + " full_blend,\n", " norm=norm,\n", " show_observed=True,\n", " add_boxes=True\n", @@ -913,7 +901,7 @@ "id": "8b530ac2-c6ce-489d-99ae-47a67b215e99", "metadata": {}, "source": [ - "> **Figure 6:** Visualization of the individual deblended sources (top to bottom show ID 0 to 8). For each source, left panel shows the PSF-deconvolved model footprint (where the color scale corresponds to the weighted RGB color), middle panel shows the observation of the blend with the source identified by a white box, and right panel shows the flux in each band. The bands are shown all 6 LSST filters, and are in observation order, i.e. `observed_bands`." + "> **Figure 6:** Visualization of the individual deblended sources (top to bottom show ID 0 to 7). For each source, left panel shows the PSF-deconvolved model footprint (where the color scale corresponds to the weighted RGB color), middle panel shows the observation of the blend with the source identified by a white box, and right panel shows the flux in each band. The bands are shown all 6 LSST bands, and are in order of increasing wavelength, defined in `observed_bands`." ] }, { @@ -923,7 +911,7 @@ "source": [ "### 4.5 Display the measurement models\n", "\n", - "As mentioned above, measurement is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux, which creates a `Source.flux_weighted_image` for each `Source` in the `Blend`." + "The measurement in the object catalog is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux by applying `conserve_flux()` to modify the `blend` object. Doing so will create a `Source.flux_weighted_image` for each `Source` in the `blend` and saves those internally." ] }, { @@ -941,7 +929,7 @@ "id": "44f7ede2-b151-4246-bc00-8c1a7df18155", "metadata": {}, "source": [ - "Now we pass `use_flux=True` to `show_sources` to see the flux weighted models that were used in all of the measurement tasks. Notice that the `spectrum` is also different. This is because the spectrum is created by summing the flux in each band individually, since even for the scarlet models each source is often made up of multiple components (ie. a bulge and disk for galaxies)." + "Now, pass `use_flux=True` to `show_sources` to see the flux weighted models that were used in all of the measurement tasks. Notice that the `spectrum` in Figure 7 is now also different from in Figure 6. This is because the spectrum is created by summing the flux in each band individually, since even for the scarlet models each source is often made up of multiple components (ie. a bulge and disk for galaxies)." ] }, { @@ -966,7 +954,7 @@ "id": "938c8189-f60f-4c19-a5e0-f124c60fd7ea", "metadata": {}, "source": [ - "> **Figure 7:** The" + "> **Figure 7:** Same as Figure 6, except after applying the `conserve_flux` function, to show the flux-redistributed model that the science pipelines use for measurements. For each source, left panel shows the PSF-deconvolved model footprint (where the color scale corresponds to the weighted RGB color), middle panel shows the observation of the blend with the source identified by a white box, and right panel shows the flux in each band. The bands are shown all 6 LSST bands, and are in order of increasing wavelength, defined in `observed_bands`." ] }, { @@ -976,7 +964,7 @@ "source": [ "## 5. Analyzing a single object\n", "\n", - "copy section 5 from Dp1 nb here" + "Individual objects can be inspected. Here, select the second brightest object in the blend (listed as Source 1 in the output in Section 4.1) and access its `objectId`." ] }, { @@ -990,6 +978,16 @@ "print(object1.metadata[\"id\"])" ] }, + { + "cell_type": "markdown", + "id": "9db7a6ca-d7f4-455b-86e3-e01e34376df7", + "metadata": {}, + "source": [ + "### 5.2 Creating the flux re-distributed models\n", + "\n", + "In the LSST science pipeline, measurements are not made on the Scarlet models directly. Imperfections in the PSF measurement and deviations of bright galaxies from a simple two-component monotonic morphology solution (see Melchior paper cited above) result in improper measurements. Instead, a deblending algorithm based on the SDSS deblender is employed, using the scarlet models as templates, and flux is re-distributed from the input image based on the ratio of values for overlapping templates in an image." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1000,6 +998,18 @@ "blend.conserve_flux()" ] }, + { + "cell_type": "markdown", + "id": "ee76fc64-3406-47ba-be91-6ac8e4d8f577", + "metadata": {}, + "source": [ + "### 5.3 Display the source model\n", + "\n", + "Three different models for the object are available for inspection. 1) The deconvolved scarlet model; 2) the convolution of the scarlet model with the difference kernel; and 3) the flux-redistributed model that the science pipelines use for measurement. The section demonstrates how to plot these three different models for source 1 in the blend.\n", + "\n", + "First, extract the deconvolved model from the object. The output is a Scarlet Lite image." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1008,8 +1018,15 @@ "outputs": [], "source": [ "source_model = object1.get_model()\n", - "\n", - "display_bands = tuple(\"griz\") # needed??" + "display_bands = tuple(\"griz\")" + ] + }, + { + "cell_type": "markdown", + "id": "65fdf2f8-cdfa-4725-810f-00906059aea3", + "metadata": {}, + "source": [ + "Next, project the model into a box large enough to fit the convolved model." ] }, { @@ -1024,6 +1041,14 @@ "convolved_model = source_model.copy().project(bbox=enlarged_box)" ] }, + { + "cell_type": "markdown", + "id": "763b3a30-b31f-4413-9530-628fe6841cc4", + "metadata": {}, + "source": [ + "Finally, plot the three models for the object for inspection." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1054,12 +1079,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "9f35043e-3b21-42bd-b7fa-5baf59790030", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "> **Figure 8:** Visualization of deblended source 1. Left panel shows the PSF-deconvolved model footprint (where the color scale corresponds to the weighted RGB color), middle panel shows the convolution of the scarlet model with the difference kernel and right panel shows the flux-redistributed model that the science pipelines use for measurements." + ] } ], "metadata": { From c6a9d8c9a4b56dd78a776ac1792b5de7e71ff490 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Tue, 13 Oct 2026 20:44:34 +0000 Subject: [PATCH 12/13] updating --- .../206_1_Deblender_outputs.ipynb | 930 ------------------ .../206_2_Deblender_footprints.ipynb | 51 +- 2 files changed, 18 insertions(+), 963 deletions(-) delete mode 100644 DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb diff --git a/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb deleted file mode 100644 index 4961bc93..00000000 --- a/DP2/200_Data_products/206_Deblender_Products/206_1_Deblender_outputs.ipynb +++ /dev/null @@ -1,930 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "5574c61a-d2bb-42ee-9ba5-856498881b08.png": { - "image/png": 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl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\nrkJggg==\n" - } - }, - "cell_type": "markdown", - "id": "325aa8a5-92fd-4913-a471-ad1617343be6", - "metadata": {}, - "source": [ - "# Coadd Deblender models\n", - "\n", - "\n", - "
\n", - "\n", - "![tutorial_nb_logo.png](attachment:5574c61a-d2bb-42ee-9ba5-856498881b08.png)\n", - "\n", - "
\n", - "\n", - "For the Rubin Science Platform at data.lsst.cloud.
\n", - "Data Release: DP2
\n", - "Container Size: Large
\n", - "LSST Science Pipelines version: v30.0.10
\n", - "Last verified to run: 2026-07-21
\n", - "Repository: github.com/lsst/tutorial-notebooks
" - ] - }, - { - "cell_type": "markdown", - "id": "9da1a210-d858-42fe-8591-570965b8be1a", - "metadata": {}, - "source": [ - "**Learning objective:** This notebook demonstrates the meaning and basic use of deblender data products cataloged in the `Object` table.\n", - "\n", - "**LSST data products:** `Object` table, `deep_coadd`\n", - "\n", - "**Packages:** `lsst.rsp`, `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`\n", - "\n", - "**Credit:** Originally developed by the Rubin Community Science team.\n", - "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks. \n", - "\n", - "**Get Support:**\n", - "Everyone is encouraged to ask questions or raise issues in the \n", - "Support Category \n", - "of the Rubin Community Forum.\n", - "Rubin staff will respond to all questions posted there." - ] - }, - { - "cell_type": "markdown", - "id": "cfc73be0", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "Deblending is an active area of research and the data products associated with are likely to be one of the more dynamic components of the LSST Science Pipelines. This notebook aims to introduce users to the data products associated with deblending in the `Object` table. The deblender packages are based on Melchior et al. 2018, and the Scarlet software written by Peter Melchior and Fred Moolekamp. Further documentation on Scarlet is available at that site.\n", - "\n", - "**Note**: In DP2, visit images use a completely different deblending algorithm based on the SDSS deblender (citation needed). \n", - "\n", - "### 1.2 Detection\n", - "\n", - "Each island of pixels connected by flux, and the flux peaks connected by them is called a blend. Each blend (called a \"**parent**\" object in the catalog parlance) is identified as a region of connected pixels above a signal-to-noise ratio (S/N) of 5 in a `deep_coadd` image. Each parent is given a unique `parentObjectId`, and the region occupied by the parent is called a footprint. Each footprint may have one or more peaks, which the deblender uses to infer the number and positions of objects blended in each footprint.\n", - "The deblended sub-peaks are referred to as \"**children**\" of the parent, \n", - "The Scarlet deblender separates the flux among components by modeling the flux and shape of each source making up the blend. Their properties are saved by the deblender in the `Object` table.\n", - "\n", - "\n", - "### 1.3 Deblending\n", - "\n", - "In order to make measurements on individual objects the peaks in a blend must be deblended. The LSST Science Pipeline uses the multi-wavelength deblending scarlet lite package, which implements the scarlet (Melchior et al. 2018) deblending algorithm specially optimized for LSST images, to create a model for each individual object that optimizes the residual between the sum of the models and the observed flux. For each source, the fraction of flux from its model divided by the total of all models in the blend is multiplied by the observed flux to create the final model that is used for all measurements of the object.\n", - "\n", - "### 1.3 Deconvolution and the blend hierarchy\n", - "\n", - "In fields with high stellar density it is common for a single blend to be the size of an entire LSST patch, an area currently too large for scarlet lite to process in DP2 (although future work hopes to improve this). In DP1 this limited the number of patches that could be deblended. In DP2, improvements were made in coverage by performing a partial deconvolution prior to deblending. This convolves each coadd to a Gaussian PSF with sigma=0.8, a well sampled kernel that is smaller than the best seeing in any band. Deconvolution shrinks the above threshold pixels so most blends can then be sub-divided into smaller sub-blends that do not overlap in the deconvolved space. Each of these sub-blends also has it's own unique `objectId`, so that each child source in a blend has a `parentObjectId` and a `blendObjectId`, as well as it's own `objectId`. The `object` catalog consists only of isolated objects, objects that don't overlap with any other peaks, and the deblended child sources (parent objects are excluded).\n", - "\n", - "**Note**: Deblending is an active area of research, deblending at scale doubly so. \n", - "\n", - "**References:** \n", - "- An introduction to all of the available deblending flags, and the processes that produce the deblender data products discussed in this tutorial\n", - "can be found in this overview of the deblending flags.\n", - "- A general discussion of blending impacts to LSST can be found in this article titled \"The challenge of blending in large sky surveys\" by Melchior et al. 2021. \n", - "- Some more in-depth presentations on the deblending implementation in the LSST Science Pipelines are available from recorded talks during the Rubin Project and Community Workshop 2022's session on \"Deblending Plans and Challenges\". \n", - "\n", - "**Related tutorials:**\n", - "A notebook demonstrating how to work with blend footprints is available as 206.2 Deblender footprints. \n" - ] - }, - { - "cell_type": "markdown", - "id": "dc36f107", - "metadata": {}, - "source": [ - "### 1.1. Import packages\n", - "\n", - "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\n", - "\n", - "The `lsst.rsp` package provides access to the Table Access Protocol (TAP) service for queries to the DP2 catalogs.\n", - "\n", - "The `lsst.afw.display` library provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cddc1458", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from lsst.daf.butler import Butler\n", - "\n", - "from lsst.rsp import RSPDiscovery\n", - "\n", - "import lsst.afw.display as afwDisplay\n", - "from lsst.images import Box, Interval\n", - "\n", - "from astropy.coordinates import SkyCoord" - ] - }, - { - "cell_type": "markdown", - "id": "c217adff-25ed-4fce-95e7-8aa04630f6cc", - "metadata": {}, - "source": [ - "### 1.2. Define functions and parameters\n", - "\n", - "The following `cutout_coadd` function enables the visualization of deblended products by making a cutout." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b239ff8-19ba-4742-a4cb-350965f09ef0", - "metadata": {}, - "outputs": [], - "source": [ - "def cutout_coadd(butler, ra, dec, band='r', datasetType='deep_coadd',\n", - " cutoutSideLength=51, **kwargs):\n", - " \"\"\"\n", - " Produce a cutout from a coadd at the given ra, dec position.\n", - "\n", - " Parameters\n", - " ----------\n", - " butler: lsst.daf.persistence.Butler\n", - " Helper object providing access to a data repository\n", - " ra: float\n", - " Right ascension of the center of the cutout, in degrees\n", - " dec: float\n", - " Declination of the center of the cutout, in degrees\n", - " band: string\n", - " Filter of the image to load\n", - " datasetType: string ['deep_coadd']\n", - " Which type of coadd to load. Doesn't support 'calexp'\n", - " cutoutSideLength: float [optional]\n", - " Size of the cutout region in pixels.\n", - "\n", - " Returns\n", - " -------\n", - " MaskedImage\n", - " \"\"\"\n", - " \n", - " dataset_refs = butler.query_datasets(\"deep_coadd\",\n", - " where=\"band.name=band AND\\\n", - " patch.region OVERLAPS POINT(ra, dec)\",\n", - " bind={\"band\": band, \"ra\": ra, \"dec\": dec},\n", - " with_dimension_records=True,\n", - " order_by=[\"patch.tract\"])\n", - "\n", - " dataId = dataset_refs[0].dataId\n", - "\n", - " point = SkyCoord(ra, dec, unit='deg')\n", - "\n", - " wcs = butler.get('deep_coadd.sky_projection', dataId=dataId)\n", - " xy = wcs.sky_to_pixel(point)\n", - " \n", - " selection_bbox = Box.factory[round(xy.y):round(xy.y)+1, round(xy.x):round(xy.x)+1].padded(cutoutSideLength)\n", - " params = {'bbox': selection_bbox}\n", - " subset = butler.get(dataset_refs[0], parameters=params)\n", - "\n", - " return subset" - ] - }, - { - "cell_type": "markdown", - "id": "7893b850-3a77-4ffb-b01a-6f89bfdd228a", - "metadata": {}, - "source": [ - "Set the backend for afwDisplay to matplotlib." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5a16260-32c0-4896-87b3-e5b251f45027", - "metadata": {}, - "outputs": [], - "source": [ - "afwDisplay.setDefaultBackend('matplotlib')" - ] - }, - { - "cell_type": "markdown", - "id": "d3383f6e-8c34-4cb7-aa2f-12e9b7f8efc0", - "metadata": {}, - "source": [ - "Get an instance of the TAP service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8184089-8a3e-4666-a194-5362a8faa541", - "metadata": {}, - "outputs": [], - "source": [ - "discovery = RSPDiscovery(\"dp2\")\n", - "service = discovery.get_tap_client()" - ] - }, - { - "cell_type": "markdown", - "id": "c5a8892e-2a9f-486e-a32a-4884c790b450", - "metadata": {}, - "source": [ - "Instantiate the butler." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38cf5a19-b61a-4d58-b6b1-79fc92e12a2f", - "metadata": {}, - "outputs": [], - "source": [ - "butler = Butler('dp2', collections=[\"dp2\"])\n", - "assert butler is not None" - ] - }, - { - "cell_type": "markdown", - "id": "16eeb034-6728-406e-bfc8-7b875c3a9416", - "metadata": {}, - "source": [ - "## 2. The deblender data products\n", - "\n", - "The object catalog contains a number of columns that can be used to identify and characterize blended objects in the `deep_coadd` images. Prior to deblending, blends are identified (referred to as parents), and are then deblended into child objects by the LSST deblender package, Scarlet Lite. Only the children are ultimately stored in the `Object` table; the parent objects are excluded. Of course, isolated single sources that do not require deblending are also stored in the `Object` table and are not considered parent sources.\n", - "\n", - "In this tutorial we will focus on the deblending data products for objects stored in the `Object` table. \n", - "\n", - "The following describes key parameters in the `Object` table: 1) boolean flags that are set by the deblender, Scarlet Lite; 2) measurements or properties of deblended sources that can be used to characterize (de)blended sources; 3) blendedness metrics performed after the deblender completes; 4) obsolete keywords still present in the `Object` table.\n", - "\n", - "\n", - "Scarlet blend flags:\n", - "\n", - "`detect_fromBlend` (boolean): If True, this source is deblended from a parent with more than one child.\n", - "\n", - "`detect_isIsolated` (boolean): If True, this source was not part of a blend. \n", - "\n", - "\n", - "Scarlet Lite blend measurements:\n", - "\n", - "`parentObjectId` (long): Unique ID of parent source. \n", - "\n", - "`deblend_parentNPeaks` (int): Total number of peaks in the parent Footprint, even if some of them were skipped or failed to deblend\n", - "\n", - "`deblend_parentNChild` (int): Number of objects with the same `parentObjectId` (ie. share the same parent Footprint)\n", - "\n", - "`deblend_blendId` (long): Unique ID of sub-blend. (see section 1.3)\n", - "\n", - "`deblend_blendNChild` (long): Number of objects with the same `deblend_blendId` (ie. from the same sub-blend).\n", - "\n", - "`footprintArea` (int): Number of pixels in the deblended object's detection \"footprint\", in the \"reference band\".\n", - "\n", - "`deblend_chi2` (float): reduced chi^2 for the scarlet lite fit\n", - "\n", - "`_deblend_fluxOverlap` (dbl): The total flux from neighboring objects that overlaps with this sources footprint in the deconvolved space. See also `_deblend_fluxOverlapFraction` Fraction of flux from neighbors / source flux in the deconvolved footprint.\n", - "\n", - "\n", - "Blendedness parameters:\n", - "\n", - "`_blendedness` (dbl): For each filter (f = u, g, r, i, z, or y), a measure of how much the flux is affected by neighbors, (1 - child\\_flux/parent\\_flux). The measurement is in the observed, not point spread function (PSF)-deconvolved space. Operates on the absolute value of the pixels to try to obtain a de-noised value. These are not performed by the deblender (Scarlet Lite) but rather from a blendedness algorithm (see section 4.9.11 of Bosch et al. 2018, PASJ, 70, S5 for details).\n", - "\n", - "`_deblend_blendedness` (dbl): Blendedness in filter in the PSF-deconvolved space.\n", - "\n", - "`_blendedness_flag` (boolean): Flag set for any failure in the blendedness algorithm. " - ] - }, - { - "cell_type": "markdown", - "id": "19f67659-29ec-4fd2-af4d-f30b4eabbebd", - "metadata": {}, - "source": [ - "### 2.1. Identify blended objects\n", - "\n", - "Set the location for the query for blends to be in the center of the Extended Chandra Deep Field South (ECDFS) field. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3eb2bc6d-dfdf-414a-8f67-7544eaf7407f", - "metadata": {}, - "outputs": [], - "source": [ - "ra = 53.2\n", - "dec = -28.1\n", - "\n", - "ra = 53.4\n", - "dec = -27.9\n" - ] - }, - { - "cell_type": "markdown", - "id": "fa180093-b8f2-4aed-8bb8-c19d910605a3", - "metadata": {}, - "source": [ - "Query for objects in a small region of ECDFS for exploration of the deblender data products stored in the `Object` table. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "427ad26a-d650-4b4b-882d-59dbcaafb712", - "metadata": {}, - "outputs": [], - "source": [ - "query = \"SELECT objectId, coord_ra, coord_dec, tract, patch, \" + \\\n", - " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", - " \"refExtendedness, detect_fromBlend, detect_isIsolated, \" + \\\n", - " \"parentObjectId, i_deblend_fluxOverlapFraction, \" + \\\n", - " \"footprintArea, i_blendedness, i_deblend_blendedness, i_blendedness_flag \" + \\\n", - " \"FROM dp2.Object \" + \\\n", - " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", - " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \"" - ] - }, - { - "cell_type": "markdown", - "id": "34f6143f-4621-42bd-bef5-88f1049c5834", - "metadata": {}, - "source": [ - "Run the TAP query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c9fdd22-dcd6-419c-985c-eae2cc90b6fc", - "metadata": {}, - "outputs": [], - "source": [ - "#job = service.submit_job(query)\n", - "#job.run()\n", - "#job.wait(phases=['COMPLETED', 'ERROR'])\n", - "#print('Job phase is', job.phase)\n", - "\n", - "\n", - "\n", - "job = service.submit_job(query)\n", - "job.run()\n", - "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)\n", - "if job.phase == 'ERROR':\n", - " job.raise_if_error()\n", - "assert job.phase == 'COMPLETED'\n", - "#results = job.fetch_result().to_table()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "820aeb85-3bff-4453-b2b5-f5008143fcd1", - "metadata": {}, - "source": [ - "Store the resulting objects and columns from the `Object` table to a table called results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9f546a2-6607-45bd-af28-c831e83f2ca1", - "metadata": {}, - "outputs": [], - "source": [ - "#assert job.phase == 'COMPLETED'\n", - "results = job.fetch_result().to_table()" - ] - }, - { - "cell_type": "markdown", - "id": "187e39df-8ec1-4f41-8950-e5bbf7aa2a99", - "metadata": {}, - "source": [ - "### 2.2. Identify isolated objects\n", - "\n", - "A sample of objects that are not near any neighbors, and therefore were not deblended, can be identified using the `detect_isIsolated` keyword. Similarly, a set of objects that were identified as part of a blend can be identified using the `detect_fromBlend` keyword.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa1f6a28-4567-4886-8522-c171dc206523", - "metadata": {}, - "outputs": [], - "source": [ - "whiso = np.where(results['detect_isIsolated'] == 1)[0]\n", - "whblend = np.where(results['detect_fromBlend'] == 1)[0]" - ] - }, - { - "cell_type": "markdown", - "id": "7f682ec5-3092-4dcb-a11a-e73df0593f09", - "metadata": {}, - "source": [ - "Generate a cutout of the i-band `deep_coadd` imaging that overlaps the queried objects, centered at (RA, Dec) = 53.11, -28.07 degrees, with a cutout edge size of 200 pixels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ffd2bce-c801-4794-a5b9-6e2ba4da8d76", - "metadata": {}, - "outputs": [], - "source": [ - "cutout = cutout_coadd(butler, 53.11, -28.07,\n", - " band='i', datasetType='deep_coadd',\n", - " cutoutSideLength=200)" - ] - }, - { - "cell_type": "markdown", - "id": "c0b8322e-36ae-495e-b7a6-d5a87be86f1f", - "metadata": {}, - "source": [ - "Display the cutout with isolated objects identified by red circles and objects that were identified from blends identified as cyan plus signs. To ensure that `afwDisplay` will make the low surface brightness connecting the blends together visible, set the scale to linear, set the min and max pixel values to be relatively low and also indicate with units=Absolute that these should be in actual count units." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5d30736-3384-4931-ba68-e4ca6a164bb2", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "display = afwDisplay.Display(frame=fig)\n", - "\n", - "display.scale('linear', min=0.1, max=10, unit='Absolute')\n", - "\n", - "display.mtv(cutout.image)\n", - "with display.Buffering():\n", - " for ci in range(len(whiso)):\n", - " display.dot('o', results['deblend_peak_center_x'][whiso][ci], results['deblend_peak_center_y'][whiso][ci],\n", - " size=10, ctype=afwDisplay.RED)\n", - " for ci in range(len(whblend)):\n", - " display.dot('+', results['deblend_peak_center_x'][whblend][ci], results['deblend_peak_center_y'][whblend][ci],\n", - " size=8, ctype=afwDisplay.CYAN)\n", - "plt.gca().axis('off')\n", - "display.show_colorbar(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "72727e68-9405-4072-b190-a38d6cb02f0a", - "metadata": {}, - "source": [ - "> **Figure 1:** The cutout image displayed in greyscale, with cyan + marking objects that are blended in the `deep_coadd`, red circles marking objects that are isolated (and is not part of a blend). " - ] - }, - { - "cell_type": "markdown", - "id": "ed13f250-3458-48d5-a695-306288a07eac", - "metadata": {}, - "source": [ - "## 3. Explore blended objects\n", - "\n", - "Use the TAP service to find a group of objects that were heavily blended and part of the same parent object. \n", - "\n", - "### 3.1. Blend with several children\n", - "\n", - "First, identify a set of unique `parentObjectIds`, and the number of objects that are deblended from those `parentObjectId`s, (stored as `counts`). Then, select as an example a blend that was made up of 15 children. \n", - "\n", - "To identify the most blended object in the query, replace the second code line with:\n", - "`most_common_index_in_unique = np.argmax(counts)`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b8f7411-068f-4474-a067-2576e249de3a", - "metadata": {}, - "outputs": [], - "source": [ - "unique_IDs, counts = np.unique(results['parentObjectId'], return_counts=True)\n", - "\n", - "most_common_index_in_unique = np.where(counts == 15)[0]\n", - "\n", - "most_common_value = unique_IDs[most_common_index_in_unique[0]]\n", - "\n", - "whparent = np.where(results['parentObjectId'] == most_common_value)[0]\n", - "max_parentId = results['parentObjectId'][whparent][0]" - ] - }, - { - "cell_type": "markdown", - "id": "0bd72ed8-bf9a-4b57-9667-8140c4c79592", - "metadata": {}, - "source": [ - "Use TAP to retrieve all children for the selected parent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "812d0a19-6c1b-4bd3-a868-6c6f05bf30f8", - "metadata": {}, - "outputs": [], - "source": [ - "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, i_blendedness_flag, \" + \\\n", - " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", - " \"i_cModelFlux/i_cModelFluxErr AS i_S2N, footprintArea, i_deblend_blendedness, \" + \\\n", - " \"i_blendedness, i_deblend_fluxOverlap, i_deblend_fluxOverlapFraction \" + \\\n", - " \"FROM dp2.Object \" + \\\n", - " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", - " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \" + \\\n", - " \"AND parentObjectId = \" + str(max_parentId)" - ] - }, - { - "cell_type": "markdown", - "id": "5ba2bcaf-6352-4d08-9e1b-793afab534af", - "metadata": {}, - "source": [ - "Run the TAP query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "551a9856-f273-448f-895e-16cb17f5f27f", - "metadata": {}, - "outputs": [], - "source": [ - "job = service.submit_job(query)\n", - "job.run()\n", - "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)" - ] - }, - { - "cell_type": "markdown", - "id": "82ea114e-9b92-4f0a-a1cd-93718954e8aa", - "metadata": {}, - "source": [ - "Store the query results in a table called children." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51888269-2afd-4cd2-988c-a8585ac9605d", - "metadata": {}, - "outputs": [], - "source": [ - "assert job.phase == 'COMPLETED'\n", - "children = job.fetch_result().to_table()\n", - "children" - ] - }, - { - "cell_type": "markdown", - "id": "5546408c-6009-4230-9ef4-2faf1c302940", - "metadata": {}, - "source": [ - "The table above confirms that the children returned by the query were deblended from the same parent source, since they all show the same `parentObjectId`. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "cf9ffc32-7b03-4aa8-bc70-2cc051562123", - "metadata": {}, - "source": [ - "A single source is identified with high `i_deblend_fluxOverlapFraction` (i.e. flux contribution from neighbors) of almost 62%. Note however that in this case, the deblender indicates that the deblended flux is uncontaminated in the PSF-decovolved space, `i_deblend_blendedness` = 0, and the blendedness flag returns False, indicating this object was successfully deblended. However, these blendedness metrics before and after deblending can assist in quality assessment.\n", - "\n", - "Below, identify this child with heavy blendedness of 50%. TO FIX THIS (BLENDEDNESS IS NEGATIVE BUT SHOULD NOT BE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fc87bf7-d7c7-4609-9919-fc7b73f3aca9", - "metadata": {}, - "outputs": [], - "source": [ - "whheavy = np.where(children['i_blendedness'] > 0.5)[0]\n", - "\n", - "children[whheavy]" - ] - }, - { - "cell_type": "markdown", - "id": "47c57439-2cc4-4cb6-9273-dab60b3127da", - "metadata": {}, - "source": [ - "Next, visualize these test cases along with the other child sources of this `parentObjectId`. Use the `cutout_coadd` function to obtain a cutout image to visualize the blend. Set the size of the cutout to be 2x the square root of the `footprintArea`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2f6c287-7502-4343-89e3-e76cb3e98e87", - "metadata": {}, - "outputs": [], - "source": [ - "cutout = cutout_coadd(butler, children['coord_ra'][0],\n", - " children['coord_dec'][0],\n", - " band='i', datasetType='deepCoadd',\n", - " cutoutSideLength=2.0\n", - " * np.sqrt(np.sum(children['footprintArea'])))" - ] - }, - { - "cell_type": "markdown", - "id": "8730ddb3-923b-4d1a-bce7-193b13a5b1b3", - "metadata": {}, - "source": [ - "Display the `deepCoadd` cutout made for this parent, with all deblended children identified with red circles. Identify the children for which the blendedness metric (before deblending) was 50% with a cyan circle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba327e1e-9323-4c57-9dc7-49ae22cce397", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "display = afwDisplay.Display(frame=fig)\n", - "display.scale('linear', min=0.1, max=10, unit='Absolute')\n", - "display.mtv(cutout.image)\n", - "with display.Buffering():\n", - " for ci in range(len(children)):\n", - " display.dot('o', children['deblend_peak_center_x'][ci], children['deblend_peak_center_y'][ci],\n", - " size=10, ctype=afwDisplay.RED)\n", - "\n", - " for ci in range(len(whheavy)):\n", - " display.dot('o', children['deblend_peak_center_x'][whheavy][ci], children['deblend_peak_center_y'][whheavy][ci],\n", - " size=8, ctype=afwDisplay.CYAN)\n", - "\n", - "plt.gca().axis('off')\n", - "display.show_colorbar(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4dc608a8-cb6b-48c8-9aa9-e30338da2070", - "metadata": {}, - "source": [ - "> **Figure 2:** The cutout image displayed in greyscale, with red circles marking the deblended children, cyan circles indicating any children whose contamination from neighbors prior to deblending is high (>50% of flux from a neighbor). \n" - ] - }, - { - "cell_type": "markdown", - "id": "98ec6b3c-48b8-4f18-bfd0-ee2b5bdc5947", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "02e4266d-7c63-4171-867b-0c7858dd4534", - "metadata": {}, - "source": [ - "## 4. Heavy blends\n", - "It is useful to be able to quantify how much flux in the blended source is attributed to a neighbor. Mandelbaum et al. 2018 quantitatively explored the criteria for acceptable blendedness as a metric of deblender performance. In this section, explore examples where sources are heavily contaminated (`_blendedness` >50%) by the flux of neighbors, and then compare to the deblended contamination fraction `_deblend_blendedness`. \n", - "\n", - "Below, identify which child objects are above this high threshold of PSF-convolved blending, and see what fraction of objects in the catalog meet this criteria." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41da6b5c-3501-4cd4-9a60-142050b85334", - "metadata": {}, - "outputs": [], - "source": [ - "whheavy = np.where(results['i_blendedness'] > 0.5)[0]\n", - "\n", - "print('Fraction of objects that are heavily blended is', len(whheavy)/len(results))\n", - "\n", - "results[whheavy]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02b649ad-5e0f-402a-b378-d51fe807b7ff", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,\n", - " width_ratios=[0.8, 0.2], figsize=(10, 6))\n", - "\n", - "ax.plot(results['i_blendedness'][whheavy], results['i_deblend_blendedness'][whheavy],\n", - " '+', alpha=0.5, label='i_blendedness_flag = False')\n", - "\n", - "ax2.hist((results['i_deblend_blendedness'][whheavy]), orientation=\"horizontal\",\n", - " bins=np.linspace(0, 1.1, 11), align='left',\n", - " histtype=\"step\", color='blue', stacked=True, fill=False, label=None)\n", - "\n", - "ax.legend()\n", - "ax.set_ylim([-0.1, 1.1])\n", - "ax2.set_ylim([-0.1, 1.1])\n", - "ax.set_xlabel('i_blendedness')\n", - "ax.set_ylabel('i_deblend_blendedness')\n", - "ax2.set_xlabel('Number of objects')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c27b24b6-29ed-48ae-8a28-2959c0f0a157", - "metadata": {}, - "source": [ - "> **Figure 3:** This figure shows the `blendedness` metric (fraction of flux contributed by neighbors) before deblending vs the `deblend_blendedness` metric that identifies the fraction of flux contamination after deblending, for heavily blended objects (>50% flux from one or more neighbors). The right panel shows a histogram of the `deblend_blendedness`, indicating that the deblender is successful at separating flux, such that `deblend_blendedness` = 0, in the overwhelming majority of objects.\n" - ] - }, - { - "cell_type": "markdown", - "id": "ae1e3189-d3e8-4609-9e95-fda91988b825", - "metadata": {}, - "source": [ - "Below, take a closer look at the objects for which the deblender did not effectively separate the flux from neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c976c7a0-d794-4196-b146-92e19ee831b0", - "metadata": {}, - "outputs": [], - "source": [ - "wh = np.where((results['i_blendedness'][whheavy] == 1)\n", - " & (results['i_deblend_blendedness'][whheavy] == 1))[0]\n", - "results[whheavy][wh]" - ] - }, - { - "cell_type": "markdown", - "id": "7888a7d6-72ad-45d4-b77f-50b213e277e8", - "metadata": {}, - "source": [ - "Note that by selecting objects where the `i_blendedness` and `i_deblend_blendedness` metrics are high, the `i_deblend_fluxOverlapFraction` is also high. Below, pick one of the above objects which has a high value of `i_deblend_fluxOverlapFraction` as an example. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccf9d7cc-44f5-40ad-9a00-908ccc7a4d5f", - "metadata": {}, - "outputs": [], - "source": [ - "max_parentId = results['parentObjectId'][whheavy][wh][3]\n", - "print(max_parentId)" - ] - }, - { - "cell_type": "markdown", - "id": "3505f18e-75d7-408a-b92e-89a0f2361764", - "metadata": {}, - "source": [ - "Define a TAP query that will return all children with the same `parentObjectId` as the above source." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "446b5ffa-1dd4-4784-851b-96ef8728ffc8", - "metadata": {}, - "outputs": [], - "source": [ - "query = \"SELECT objectId, coord_ra, coord_dec, parentObjectId, i_blendedness_flag, \" + \\\n", - " \"deblend_peak_center_x, deblend_peak_center_y, \" + \\\n", - " \"i_cModelFlux/i_cModelFluxErr AS i_S2N, footprintArea, i_deblend_blendedness, \" + \\\n", - " \"i_blendedness, i_deblend_fluxOverlapFraction \" + \\\n", - " \"FROM dp2.Object \" + \\\n", - " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec ), \" + \\\n", - " \"CIRCLE('ICRS', \" + str(ra) + \", \" + str(dec) + \", .1)) = 1 \" + \\\n", - " \"AND parentObjectId = \" + str(max_parentId)" - ] - }, - { - "cell_type": "markdown", - "id": "4e1c8c11-eb9a-402b-8d25-f1509b382077", - "metadata": {}, - "source": [ - "Run the TAP query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c82287f3-936d-432c-9a9d-0f98a0e1ae49", - "metadata": {}, - "outputs": [], - "source": [ - "job = service.submit_job(query)\n", - "job.run()\n", - "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)" - ] - }, - { - "cell_type": "markdown", - "id": "da272521-a393-4ad7-aa01-ae4b56b05e29", - "metadata": {}, - "source": [ - "Save the results to a table named `bigblend`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "842c745a-466b-4f34-af73-b3074b03f8b2", - "metadata": {}, - "outputs": [], - "source": [ - "assert job.phase == 'COMPLETED'\n", - "bigblend = job.fetch_result().to_table()\n", - "bigblend" - ] - }, - { - "cell_type": "markdown", - "id": "75ee67ad-5623-4705-b761-20ef0247389e", - "metadata": {}, - "source": [ - "Use the cutout_coadd function to request an image cutout of this blend, and identify any objects which still have 1% of flux contributed by a neighbor after the deblending process (`i_deblend_blendedness` > 0.01)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89120f7e-1339-474b-9ea7-7ad19a09e3a1", - "metadata": {}, - "outputs": [], - "source": [ - "cutout = cutout_coadd(butler, bigblend['coord_ra'][0],\n", - " bigblend['coord_dec'][0],\n", - " band='i', datasetType='deep_coadd',\n", - " cutoutSideLength=2.0 * np.sqrt(np.sum(bigblend['footprintArea'])))\n", - "\n", - "whheavy = np.where(bigblend['i_deblend_blendedness'] > 0.01)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4afccee-7595-4760-81e7-40b590749e81", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "display = afwDisplay.Display(frame=fig)\n", - "display.scale('linear', min=0.01, max=5, unit='Absolute')\n", - "display.mtv(cutout.image)\n", - "with display.Buffering():\n", - " for ci in range(len(bigblend)):\n", - " display.dot('o', bigblend['x'][ci], bigblend['y'][ci],\n", - " size=10, ctype=afwDisplay.RED)\n", - "\n", - " for ci in range(len(whheavy)):\n", - " display.dot('o', bigblend['x'][whheavy][ci], bigblend['y'][whheavy][ci],\n", - " size=9, ctype=afwDisplay.CYAN)\n", - "\n", - "plt.gca().axis('off')\n", - "display.show_colorbar(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3e7906e2-7fd9-4dad-aea9-bfb2f6305a5f", - "metadata": {}, - "source": [ - "> **Figure 4:** This figure shows a heavily blended parent for which deblended objects have a fraction of flux contributed by neighbors (red circles). Cyan circles indicate objects whose `deblend_blendedness` metric is not 0, and remains > 1% contamination even after deblending. They are typically faint objects near the outskirts of the biggest and brightest objects where contamination might be high.\n" - ] - }, - { - "cell_type": "markdown", - "id": "1e7e393f-ac6d-4ad5-8294-5dac4bca258e", - "metadata": {}, - "source": [ - "## 5. Exercises for the learner\n", - "\n", - "Use the deblender data products and blend metrics in the `Object` table to identify what fraction of sources in ECDFS are contaminated by neighbor flux at the 5% level in the observed frame? What fraction have 5% flux contamination in the PSF-deconvolved (i.e. deblended) frame?\n" - ] - } - ], - "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": "3.13.9" - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 58b01e53..4613159c 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -58,7 +58,7 @@ "This notebook demonstrates how to reconstruct per-pixel models of objects that are deblended by the LSST Science Pipeline when building the `object` table.\n", "The focus is on how to recover the model of an object from its information in the `object` catalog, and how to investigate the blend as a whole.\n", "\n", - "To measure the photometry of objects in `deep_coadd` images, the LSST Science Pipeline uses the multi-band deblending scarlet algorithm [Melchior et al. 2018](https://ui.adsabs.harvard.edu/abs/2018A%26C....24..129M/abstract) implemented with optimizations for LSST data in [Scarlet Lite](https://github.com/lsst/scarlet_lite). The core algorithm models objects in a blend as a collection of components that have the same spectrum in each pixel of their morphology, assuming that flux monotonically decreases from the peak (maximum pixel value) of the component.\n", + "To measure the photometry of objects in `deep_coadd` images, the LSST Science Pipeline uses the multi-band deblending scarlet algorithm [Melchior et al. 2018](https://ui.adsabs.harvard.edu/abs/2018A%26C....24..129M/abstract) implemented with optimizations for LSST data in [Scarlet Lite](https://github.com/lsst/scarlet_lite). The algorithm models objects in a blend as a collection of components that have the same spectrum in each pixel of their morphology, assuming that flux monotonically decreases from the peak (maximum pixel value) of the component.\n", "\n", "This tutorial will begin with the coordinates of an object of interest and show how to obtain its model and the model of the other sources that are extracted from the same blend.\n", "\n", @@ -82,7 +82,7 @@ "source": [ "### 1.1. Import packages\n", "\n", - "The [matplotlib](https://matplotlib.org/) (and especially sublibrary `matplotlib.pyplot`), [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.\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.\n", "\n", "`lsst.afw.image` provides access to some of the extended products created by the deblender pipeline. The `lsst.afw.display` and `lsst.images` libraries provides access to image visualization routines and the `lsst.daf.butler` library is used to access data products via the butler. The `RSPDiscovery` class in the `lsst.rsp` package is for accessing the TAP service. Finally, `lsst.scarlet` and `lsst.meas.extensions.scarlet` are two packages containing the scarlet software infrastructure, to enable displaying and analyzing deblended models and object footprints. " ] @@ -277,7 +277,7 @@ "source": [ "### 2.2 Visualize the blend\n", "\n", - "To visualize the blend, plot a `deep_coadd` image subset that contains the blend with children identified. First, retrieve the patch and tract coadd containing the blend, and set the band to be i-band. " + "To visualize the blend, plot a `deep_coadd` image subset that contains the blend with children identified. First, retrieve the patch and tract for the coadd containing the blend, and set the band to be i-band. " ] }, { @@ -327,7 +327,7 @@ "id": "11ff6c06-ae1c-4b8c-8239-5f613a148651", "metadata": {}, "source": [ - "Finally, visualize blend and its child objects. First display the cutout, and then overplot circles to identify the child objects that were deblended. " + "Finally, visualize the blend and its child objects. First display the cutout, and then over plot circles to identify the child objects that were deblended. " ] }, { @@ -343,8 +343,8 @@ "display.mtv(subset.image)\n", "with display.Buffering():\n", " for ci in range(len(children)):\n", - " display.dot('o', children['deblend_peak_center_x'][ci], children['deblend_peak_center_y'][ci],\n", - " size=10)\n", + " display.dot('o', children['deblend_peak_center_x'][ci],\n", + " children['deblend_peak_center_y'][ci], size=10)\n", "plt.gca().axis('off')\n", "display.show_colorbar(False)\n", "\n", @@ -366,10 +366,10 @@ "source": [ "## 3. Blend and object models\n", "\n", - "Scarlet Lite uses the multi-wavelength images to identify peaks. The scarlet models produced during deblending are available as a data product via the `object_scarlet_model` catalog. This contains all of the information needed to reconstruct the multi-band model for each source, including obtaining the flux re-distributed model. \n", + "Scarlet Lite uses the multi-wavelength images to identify peaks. The scarlet models produced during deblending are available as a data product via the `object_scarlet_model` catalog. This contains all of the information needed to reconstruct the multi-band model for each source. \n", "\n", "### 3.1 Load parent blend data\n", - "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend and its data.\n", + "Scarlet lite models are stored in a zip archive with keys for metadata and each blend, allowing efficient retrieval of data for a single parent blend.\n", "\n", "Below, find the `object_scarlet_model` data product in the butler by searching for all dataset types with the word \"scarlet\" in the name. \n" ] @@ -503,7 +503,7 @@ "source": [ "#### 3.2.1 Index slicing\n", "\n", - "Print the observed bands used in the deblending process. Notice that the observed bands are not in wavelength order, and they are still in the same order from `model_data.bands` above. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order (this can be reordered later). " + "Print the observed bands used in the deblending process. Notice that the observed bands are not in wavelength order, and they are still in the same order from `model_data.bands` above. This is because the LSST science pipelines process each band in alphabetical order, not wavelength order. " ] }, { @@ -554,8 +554,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(blend.observation.bands)\n", - "#print(model.bands)" + "print(blend.observation.bands)" ] }, { @@ -629,20 +628,7 @@ "id": "52b6b8a7-5cc2-4f80-95b9-3a9804f38165", "metadata": {}, "source": [ - "By default, Scarlet Lite assumes the input filters are in wavelength order, and thus mapping into an RGB scaling where the first filter map to blue and last filter map to red. This relative weighting between filters to RGB can be altered with the `channel_map` keyword to `img_to_rgb` (see documentation here).\n", - "\n", - "Alternatively, " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee36b1a7-aea2-4e54-aa98-151cd6786031", - "metadata": {}, - "outputs": [], - "source": [ - "#display_bands = tuple(\"ugrizy\")\n", - "#model_display = blend.get_model(convolve=True)[display_bands]" + "By default, Scarlet Lite assumes the input filters are in wavelength order, and thus mapping into an RGB scaling where the first filter map to blue and last filter map to red. This relative weighting between filters to RGB can be altered with the `channel_map` keyword to `img_to_rgb` (see documentation here)." ] }, { @@ -650,7 +636,7 @@ "id": "8577eb1f-1b37-4383-b325-60bb2155b240", "metadata": {}, "source": [ - "Below, convert the blend model into an RGB image and display it. (Scarlet lite also has helper functions to display a full blend, mark the location of peaks, show the residuals automatically. This will be demonstrated in Section X). \n" + "Below, convert the blend model into an RGB image and display it, and mark the object of interest with an x. (Scarlet lite also has helper functions to display a full blend, mark the location of peaks, show the residuals automatically. This will be demonstrated in Section 4.3). \n" ] }, { @@ -664,7 +650,6 @@ "rgb = scl.display.img_to_rgb(model, norm=norm)\n", "plt.imshow(rgb, extent=extent)\n", "plt.title(\"blend data\")\n", - "# Mark the source that we searched for\n", "plt.plot(obj[\"deblend_peak_center_x\"], obj[\"deblend_peak_center_y\"], \"wx\")\n", "plt.show()" ] @@ -725,7 +710,7 @@ "id": "010d1036-b23d-4d96-9547-54ec3d0ac009", "metadata": {}, "source": [ - "> **Figure 3:** An RGB (PSF-deconvolved) image of the initial deblended source of interest in Section 2.\n", + "> **Figure 3:** An RGB (PSF-deconvolved) image of the initial deblended source of interest.\n", "\n", "Now, to view the model as directly comparable to the data, first convolve it with the observation using the `convolve` method." ] @@ -763,7 +748,7 @@ "\n", "### 4.1 Loading a MultibandExposure\n", "\n", - "First, load a multi-band exposure for the region that covers the blend. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent, which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n" + "First, load a multi-band exposure for the region that covers the blend. To load the observation, the following information is required: 1) the `tract` and `patch` that defines the region of the sky imaged in the `deep_coadd`; 2) the bounding box of the parent blend (`bbox`), which allows extraction a smaller region from the full `deep_coadd`; and 3) the bands desired for display.\n" ] }, { @@ -877,7 +862,7 @@ "source": [ "### 4.4 Display source models\n", "\n", - "Now, use the Scarlet Lite built in display function called `show_sources` to display the models of the individual sources compared to the observed image and its spectral energy distribution. " + "Now, use the Scarlet Lite built-in display function called `show_sources` to display the models of the individual sources compared to the observed image and its spectral energy distribution. " ] }, { @@ -911,7 +896,7 @@ "source": [ "### 4.5 Display the measurement models\n", "\n", - "The measurement in the object catalog is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux by applying `conserve_flux()` to modify the `blend` object. Doing so will create a `Source.flux_weighted_image` for each `Source` in the `blend` and saves those internally." + "The measurement in the object catalog is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux by applying `conserve_flux()` to modify the `full_blend` object. Doing so will create a `Source.flux_weighted_image` for each `Source` in the `full_blend`, and save those internally." ] }, { @@ -921,7 +906,7 @@ "metadata": {}, "outputs": [], "source": [ - "blend.conserve_flux()" + "full_blend.conserve_flux()" ] }, { @@ -940,7 +925,7 @@ "outputs": [], "source": [ "scl.display.show_sources(\n", - " blend,\n", + " full_blend,\n", " norm=norm,\n", " show_observed=True,\n", " add_boxes=True,\n", From 2dabd2d6c98c9a0e4e4ded6f028d9cf8eff7bda0 Mon Sep 17 00:00:00 2001 From: christinawilliams Date: Thu, 15 Oct 2026 18:19:30 +0000 Subject: [PATCH 13/13] freds comments on nb 2 --- .../206_2_Deblender_footprints.ipynb | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb index 4613159c..9ad51f16 100644 --- a/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb +++ b/DP2/200_Data_products/206_Deblender_Products/206_2_Deblender_footprints.ipynb @@ -463,7 +463,9 @@ "id": "5e2a5b77-4918-42a9-975e-e5afc63789ec", "metadata": {}, "source": [ - "Below, extract the parent data from `model_data`. `parent_data` holds the full blend from the `object_detection` catalog. Then, extract the blend data from `parent_data`. `blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend." + "Below, extract the parent data from `model_data`. `parent_data` holds the full blend from the `object_detection` catalog. Then, extract the blend data from `parent_data`. `blend_data` holds the deconvolved blend of the object. A model can be extracted for each deblended child from the parent blend.\n", + "\n", + "Multiple parent IDs can also be passed in the list in order to load multiple blends." ] }, { @@ -896,7 +898,7 @@ "source": [ "### 4.5 Display the measurement models\n", "\n", - "The measurement in the object catalog is not done on the scarlet models directly but instead on a model created by using the scarlet models as templates to re-distribute the flux from the observed image, thus conserving the overall flux of scene. The first step is to conserve the flux by applying `conserve_flux()` to modify the `full_blend` object. Doing so will create a `Source.flux_weighted_image` for each `Source` in the `full_blend`, and save those internally." + "In the LSST science pipeline, the measurement in the object catalog is not done on the scarlet models directly. Instead, a deblending algorithm based on the SDSS deblender is employed on a model created by using the scarlet models as templates. Then the flux is re-distributed from the observed image to conserve the overall flux of scene. The first step is to conserve the flux by applying `conserve_flux()` to modify the `full_blend` object. Doing so will create a `Source.flux_weighted_image` for each `Source` in the `full_blend`, and save those internally." ] }, { @@ -965,12 +967,10 @@ }, { "cell_type": "markdown", - "id": "9db7a6ca-d7f4-455b-86e3-e01e34376df7", + "id": "97876f84-def0-42b5-b0e3-2fa7c90a2212", "metadata": {}, "source": [ - "### 5.2 Creating the flux re-distributed models\n", - "\n", - "In the LSST science pipeline, measurements are not made on the Scarlet models directly. Imperfections in the PSF measurement and deviations of bright galaxies from a simple two-component monotonic morphology solution (see Melchior paper cited above) result in improper measurements. Instead, a deblending algorithm based on the SDSS deblender is employed, using the scarlet models as templates, and flux is re-distributed from the input image based on the ratio of values for overlapping templates in an image." + "As in Section 4.5, conserve the flux of the overall scene." ] }, { @@ -988,8 +988,6 @@ "id": "ee76fc64-3406-47ba-be91-6ac8e4d8f577", "metadata": {}, "source": [ - "### 5.3 Display the source model\n", - "\n", "Three different models for the object are available for inspection. 1) The deconvolved scarlet model; 2) the convolution of the scarlet model with the difference kernel; and 3) the flux-redistributed model that the science pipelines use for measurement. The section demonstrates how to plot these three different models for source 1 in the blend.\n", "\n", "First, extract the deconvolved model from the object. The output is a Scarlet Lite image."