From 729998faeef734d9914eabd3dddcdf7598043ed2 Mon Sep 17 00:00:00 2001 From: garrethmartin Date: Thu, 11 Jun 2026 15:46:12 +0000 Subject: [PATCH] SP-2645: added 301 notebook on image display for faint features --- .../312_1_visualisation.ipynb | 551 ++++++++++++++++++ .../312_1_visualization.ipynb | 543 +++++++++++++++++ 2 files changed, 1094 insertions(+) create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb new file mode 100644 index 0000000..b29554c --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "attachments": { + "1e2050a2-4e1c-4956-84ad-cc5f196353c8.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", + "metadata": {}, + "source": [ + "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", + "\n", + "
\n", + "\n", + "![image.png](attachment:1e2050a2-4e1c-4956-84ad-cc5f196353c8.png)\n", + "\n", + "
\n", + "\n", + "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", + "Container Size: large \\\n", + "LSST Science Pipelines version: r29.2.0 \\\n", + "Last verified to run: 2026-06-11 \\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-info", + "metadata": {}, + "source": [ + "**Learning objective:** Use the Butler to load a `deep_coadd` patch from DP1, display it with a stretch suited to faint extended emission, and understand how the choice of stretch function and display range determines what is visible in the data.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `astropy.visualization`\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](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", + "\n", + "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", + "\n", + "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." + ] + }, + { + "cell_type": "markdown", + "id": "b334cfae-3f6f-458e-a3e4-89a1265f8578", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "Import common scientific analysis packages `numpy`, `matplotlib`, and `astropy`.\n", + "\n", + "Import LSST Science Pipelines packages for sky geometry (`lsst.geom`), image display (`lsst.afw.display`), and data access via the Butler (`lsst.daf.butler`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.visualization import AsinhStretch, ImageNormalize, LinearStretch\n", + "\n", + "import lsst.geom as geom\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", + "\n", + "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-params", + "metadata": {}, + "outputs": [], + "source": [ + "PIXEL_SCALE = 0.2\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "\n", + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.dpi\": 150,\n", + " \"image.origin\": \"lower\",\n", + " \"axes.titlesize\": 10,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ac181cbb", + "metadata": {}, + "source": [ + "Define `build_sky_arr` and `sky_stats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-helpers", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + "\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", + "\n", + " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", + " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", + " sources do not bias the estimates.\n", + " \"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd patch\n", + "\n", + "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-butler", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "target_ra = 53.218\n", + "target_dec = -28.095\n", + "band = \"i\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "\n", + "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", + "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", + "coadd_arr = coadd.image.array.copy()\n", + "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "\n", + "print(\n", + " f\"Shape: {coadd_arr.shape} \"\n", + " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", + " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", + ")\n", + "print(\n", + " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", + " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", + ")\n", + "sky_val = np.nanmedian(coadd_sky)\n", + "print(\n", + " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s3", + "metadata": {}, + "source": [ + "## 3. Full-patch display\n", + "\n", + "Display the full coadd patch with a sky-noise anchored stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-display-compare", + "metadata": {}, + "outputs": [], + "source": [ + "vmin, vmax = sky_stats(coadd_sky)\n", + "fig = plt.figure(figsize=(4, 4))\n", + "disp = afwDisplay.Display(frame=fig)\n", + "disp.scale(\"asinh\", min=vmin, max=vmax)\n", + "disp.image(coadd.image)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cfced9ea", + "metadata": {}, + "source": [ + "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s4", + "metadata": {}, + "source": [ + "## 4. Postage stamp and stretch comparison\n", + "\n", + "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", + "\n", + "**Display tools.** Two display approaches are used in this section.\n", + "\n", + "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", + "\n", + "```python\n", + "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", + "disp.scale('asinh', min=vmin, max=vmax) # explicit range from sky_stats()\n", + "```\n", + "\n", + "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", + "\n", + "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." + ] + }, + { + "cell_type": "markdown", + "id": "79ec6be5", + "metadata": {}, + "source": [ + "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b3ec972", + "metadata": {}, + "outputs": [], + "source": [ + "_demo_targets = [\n", + " (53.3375, -27.810, \"(5,0)\"),\n", + " (53.010, -28.350, \"(5,1)\"),\n", + " (52.965, -28.184, \"(4,1)\"),\n", + " (53.220, -28.095, \"(3,3)\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2a028501", + "metadata": {}, + "source": [ + "Load a 4′ × 4′ stamp centred on each target galaxy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-bbox", + "metadata": {}, + "outputs": [], + "source": [ + "half_width = 600\n", + "\n", + "demo_stamps = []\n", + "for ra, dec, label in _demo_targets:\n", + " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", + " ti = skymap.findTract(sp)\n", + " pi = ti.findPatch(sp)\n", + " wcs = ti.getWcs()\n", + " cen = wcs.skyToPixel(sp)\n", + " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", + " bbox = geom.Box2I(\n", + " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", + " geom.Extent2I(2 * half_width, 2 * half_width),\n", + " )\n", + " bbox.clip(pi.getOuterBBox())\n", + " stamp = butler.get(\"deep_coadd\", dataId=did, parameters={\"bbox\": bbox})\n", + " sky_a = build_sky_arr(stamp.getMaskedImage(), stamp.image.array)\n", + " demo_stamps.append((sky_a, stamp, f\"RA={ra} Dec={dec}\"))\n", + "\n", + "stamp_arcmin = 2 * half_width * PIXEL_SCALE / 60\n", + "fig, axes = plt.subplots(4, 1, figsize=(4, 4 * 4), squeeze=False)\n", + "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", + " plt.sca(axes[idx][0])\n", + " disp = afwDisplay.Display(frame=fig)\n", + " vmin, vmax = sky_stats(sky_a)\n", + " disp.scale(\"asinh\", min=vmin, max=vmax)\n", + " disp.image(stamp.image)\n", + " axes[idx][0].set_title(label, fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "b7409ecb", + "metadata": {}, + "source": [ + "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." + ] + }, + { + "cell_type": "markdown", + "id": "4ec932ba", + "metadata": {}, + "source": [ + "### 4.1. Stretch functions\n", + "\n", + "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", + "\n", + "| Stretch | Transfer function | Behaviour |\n", + "|---------|-------------------|-----------|\n", + "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", + "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", + "\n", + "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", + "\n", + "```python\n", + "AsinhStretch(a=0.1) # default — stronger compression\n", + "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", + "AsinhStretch(a=2.0) # close to linear\n", + "```\n", + "\n", + "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6a51c6", + "metadata": {}, + "outputs": [], + "source": [ + "stretches = [\n", + " (\"Linear\", LinearStretch(), 30),\n", + " (\"Asinh\", AsinhStretch(a=0.05), 30),\n", + "]\n", + "\n", + "lo = 2.0\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, (name, stretch_fn, hi) in enumerate(stretches):\n", + " norm = ImageNormalize(\n", + " vmin=sky - lo * sigma,\n", + " vmax=sky + hi * sigma,\n", + " stretch=stretch_fn,\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " if row == 0:\n", + " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "id": "3d68d84d", + "metadata": {}, + "source": [ + "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." + ] + }, + { + "cell_type": "markdown", + "id": "92410f2d", + "metadata": {}, + "source": [ + "### 4.2. White point, black point, and contrast\n", + "\n", + "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", + "\n", + "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", + "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", + "\n", + "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", + "\n", + "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", + "\n", + "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95338f39", + "metadata": {}, + "outputs": [], + "source": [ + "hi_sigmas = [4, 8, 20, 50]\n", + "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", + "\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps),\n", + " len(hi_sigmas),\n", + " figsize=(1.5 * len(hi_sigmas), 1.5 * len(demo_stamps)),\n", + " squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " vmin = sky - 2.0 * sigma\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, hi in enumerate(hi_sigmas):\n", + " norm = ImageNormalize(\n", + " vmin=vmin,\n", + " vmax=sky + hi * sigma,\n", + " stretch=AsinhStretch(),\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])\n", + " if row == 0:\n", + " axes[row][col].set_title(col_labels[col], fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "47f64152", + "metadata": {}, + "source": [ + "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-summary", + "metadata": {}, + "source": [ + "## 5. Summary\n", + "\n", + "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", + "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", + "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5072fe-2cba-48f2-b780-5fccaa15eec3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb new file mode 100644 index 0000000..4e4fce1 --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb @@ -0,0 +1,543 @@ +{ + "cells": [ + { + "attachments": { + "1e2050a2-4e1c-4956-84ad-cc5f196353c8.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", + "metadata": {}, + "source": [ + "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", + "\n", + "
\n", + "\n", + "![image.png](attachment:1e2050a2-4e1c-4956-84ad-cc5f196353c8.png)\n", + "\n", + "
\n", + "\n", + "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", + "Container Size: large \\\n", + "LSST Science Pipelines version: r29.2.0 \\\n", + "Last verified to run: 2026-06-11 \\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-info", + "metadata": {}, + "source": [ + "**Learning objective:** Use the Butler to load a `deep_coadd` patch from DP1, display it with a stretch suited to faint extended emission, and understand how the choice of stretch function and display range determines what is visible in the data.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `astropy.visualization`\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](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", + "\n", + "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", + "\n", + "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." + ] + }, + { + "cell_type": "markdown", + "id": "b334cfae-3f6f-458e-a3e4-89a1265f8578", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "Import common scientific analysis packages `numpy`, `matplotlib`, and `astropy`.\n", + "\n", + "Import LSST Science Pipelines packages for sky geometry (`lsst.geom`), image display (`lsst.afw.display`), and data access via the Butler (`lsst.daf.butler`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.visualization import AsinhStretch, ImageNormalize, LinearStretch\n", + "\n", + "import lsst.geom as geom\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", + "\n", + "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-params", + "metadata": {}, + "outputs": [], + "source": [ + "PIXEL_SCALE = 0.2\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "\n", + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.dpi\": 150,\n", + " \"image.origin\": \"lower\",\n", + " \"axes.titlesize\": 10,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ac181cbb", + "metadata": {}, + "source": [ + "Define `build_sky_arr` and `sky_stats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-helpers", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + "\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", + "\n", + " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", + " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", + " sources do not bias the estimates.\n", + " \"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd patch\n", + "\n", + "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-butler", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "target_ra = 53.218\n", + "target_dec = -28.095\n", + "band = \"i\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "\n", + "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", + "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", + "coadd_arr = coadd.image.array.copy()\n", + "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "\n", + "print(\n", + " f\"Shape: {coadd_arr.shape} \"\n", + " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", + " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", + ")\n", + "print(\n", + " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", + " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", + ")\n", + "sky_val = np.nanmedian(coadd_sky)\n", + "print(\n", + " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s3", + "metadata": {}, + "source": [ + "## 3. Full-patch display\n", + "\n", + "Display the full coadd patch with a sky-noise anchored stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-display-compare", + "metadata": {}, + "outputs": [], + "source": [ + "vmin, vmax = sky_stats(coadd_sky)\n", + "fig = plt.figure(figsize=(4, 4))\n", + "disp = afwDisplay.Display(frame=fig)\n", + "disp.scale(\"asinh\", min=vmin, max=vmax)\n", + "disp.image(coadd.image)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cfced9ea", + "metadata": {}, + "source": [ + "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s4", + "metadata": {}, + "source": [ + "## 4. Postage stamp and stretch comparison\n", + "\n", + "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", + "\n", + "**Display tools.** Two display approaches are used in this section.\n", + "\n", + "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", + "\n", + "```python\n", + "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", + "disp.scale('asinh', min=vmin, max=vmax) # explicit range from sky_stats()\n", + "```\n", + "\n", + "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", + "\n", + "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." + ] + }, + { + "cell_type": "markdown", + "id": "79ec6be5", + "metadata": {}, + "source": [ + "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b3ec972", + "metadata": {}, + "outputs": [], + "source": [ + "_demo_targets = [\n", + " (53.3375, -27.810, \"(5,0)\"),\n", + " (53.010, -28.350, \"(5,1)\"),\n", + " (52.965, -28.184, \"(4,1)\"),\n", + " (53.220, -28.095, \"(3,3)\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2a028501", + "metadata": {}, + "source": [ + "Load a 4′ × 4′ stamp centred on each target galaxy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-bbox", + "metadata": {}, + "outputs": [], + "source": [ + "half_width = 600\n", + "\n", + "demo_stamps = []\n", + "for ra, dec, label in _demo_targets:\n", + " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", + " ti = skymap.findTract(sp)\n", + " pi = ti.findPatch(sp)\n", + " wcs = ti.getWcs()\n", + " cen = wcs.skyToPixel(sp)\n", + " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", + " bbox = geom.Box2I(\n", + " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", + " geom.Extent2I(2 * half_width, 2 * half_width),\n", + " )\n", + " bbox.clip(pi.getOuterBBox())\n", + " stamp = butler.get(\"deep_coadd\", dataId=did, parameters={\"bbox\": bbox})\n", + " sky_a = build_sky_arr(stamp.getMaskedImage(), stamp.image.array)\n", + " demo_stamps.append((sky_a, stamp, f\"RA={ra} Dec={dec}\"))\n", + "\n", + "stamp_arcmin = 2 * half_width * PIXEL_SCALE / 60\n", + "fig, axes = plt.subplots(4, 1, figsize=(4, 4 * 4), squeeze=False)\n", + "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", + " plt.sca(axes[idx][0])\n", + " disp = afwDisplay.Display(frame=fig)\n", + " vmin, vmax = sky_stats(sky_a)\n", + " disp.scale(\"asinh\", min=vmin, max=vmax)\n", + " disp.image(stamp.image)\n", + " axes[idx][0].set_title(label, fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "b7409ecb", + "metadata": {}, + "source": [ + "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." + ] + }, + { + "cell_type": "markdown", + "id": "4ec932ba", + "metadata": {}, + "source": [ + "### 4.1. Stretch functions\n", + "\n", + "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", + "\n", + "| Stretch | Transfer function | Behaviour |\n", + "|---------|-------------------|-----------|\n", + "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", + "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", + "\n", + "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", + "\n", + "```python\n", + "AsinhStretch(a=0.1) # default — stronger compression\n", + "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", + "AsinhStretch(a=2.0) # close to linear\n", + "```\n", + "\n", + "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6a51c6", + "metadata": {}, + "outputs": [], + "source": [ + "stretches = [\n", + " (\"Linear\", LinearStretch(), 30),\n", + " (\"Asinh\", AsinhStretch(a=0.05), 30),\n", + "]\n", + "\n", + "lo = 2.0\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, (name, stretch_fn, hi) in enumerate(stretches):\n", + " norm = ImageNormalize(\n", + " vmin=sky - lo * sigma,\n", + " vmax=sky + hi * sigma,\n", + " stretch=stretch_fn,\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " if row == 0:\n", + " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "id": "3d68d84d", + "metadata": {}, + "source": [ + "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." + ] + }, + { + "cell_type": "markdown", + "id": "92410f2d", + "metadata": {}, + "source": [ + "### 4.2. White point, black point, and contrast\n", + "\n", + "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", + "\n", + "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", + "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", + "\n", + "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", + "\n", + "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", + "\n", + "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95338f39", + "metadata": {}, + "outputs": [], + "source": [ + "hi_sigmas = [4, 8, 20, 50]\n", + "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", + "\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps),\n", + " len(hi_sigmas),\n", + " figsize=(1.5 * len(hi_sigmas), 1.5 * len(demo_stamps)),\n", + " squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " vmin = sky - 2.0 * sigma\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, hi in enumerate(hi_sigmas):\n", + " norm = ImageNormalize(\n", + " vmin=vmin,\n", + " vmax=sky + hi * sigma,\n", + " stretch=AsinhStretch(),\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])\n", + " if row == 0:\n", + " axes[row][col].set_title(col_labels[col], fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "47f64152", + "metadata": {}, + "source": [ + "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-summary", + "metadata": {}, + "source": [ + "## 5. Summary\n", + "\n", + "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", + "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", + "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." + ] + } + ], + "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": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}