first functional changes

Author: MelissaGraham

DP1/100_How_to_Use_RSP_Tools/105_Image_reprocessing/105_3_Forced_Photometry.ipynb

@@ -22,8 +22,9 @@
     "Data Release: <a href=\"https://dp1.lsst.io/\">Data Preview 1</a> <br>\n",
     "Container Size: large <br>\n",
     "LSST Science Pipelines version: r29.2.0 <br>\n",
-    "Last verified to run: 2025-11-03 <br>\n",
-    "Repository: <a href=\"https://github.com/lsst/tutorial-notebooks\">github.com/lsst/tutorial-notebooks</a> <br>"
+    "Last verified to run: 2025-12-04 <br>\n",
+    "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
+    "DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
    ]
   },
   {
@@ -42,9 +43,7 @@
     "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",
-    "<a href=\"https://community.lsst.org/c/support\">Support Category</a> \n",
-    "of the Rubin Community Forum.\n",
+    "Everyone is encouraged to ask questions or raise issues in the [Support Category](https://community.lsst.org/c/support) of the Rubin Community Forum.\n",
     "Rubin staff will respond to all questions posted there."
    ]
   },
@@ -58,7 +57,7 @@
     "\n",
     "This notebook performs forced photometry using the Data Preview 1 (DP1) data products on an input image, given a set of input IDs and RA/Dec coordinates. The `ForcedMeasurementDriverTask` serves as a convenience function, allowing users to measure sources without running a full-scale `pipetask` command or dealing with additional pipeline setup.  This task can measure fluxes, shapes, and other properties at known source positions (e.g., from a pre-existing catalog) on a given image, without running source detection. (Note that for brevity we disable shape measurement in this notebook.)\n",
     "\n",
-    "**Related tutorials:** 100-level Detect and measure sources"
+    "**Related tutorials:** The 100-level tutorial on how to detect and measure sources."
    ]
   },
   {
@@ -185,7 +184,9 @@
    "source": [
     "## 2. Load an image and table\n",
     "\n",
-    "Identify coadded images overlapping a particular sky position and select one to run forced photometry on. Also load the corresponding `object` table using the TAP service so that forced photometry can be run at the positions of already-detected objects."
+    "Identify coadded images overlapping a particular sky position and select one to run forced photometry on. Also load the corresponding `object` table using the TAP service so that forced photometry can be run at the positions of already-detected objects.\n",
+    "\n",
+    "Define the right ascension and declination as the center of the ECDFS field, and use the $r$ band."
    ]
   },
   {
@@ -200,6 +201,14 @@
     "band = 'r'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "35debe43-cd77-431b-a294-9e50a037738b",
+   "metadata": {},
+   "source": [
+    "Execute a butler query for deep coadd images."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -209,19 +218,26 @@
    "source": [
     "query = f\"band='{band}' AND \\\n",
     "          patch.region OVERLAPS POINT({ra}, {dec})\"\n",
-    "\n",
-    "coadd_img_refs = butler.query_datasets('deep_coadd', where=query)\n",
-    "\n",
-    "ref = coadd_img_refs[0]"
+    "coadd_img_refs = butler.query_datasets('deep_coadd', where=query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc662eff-84ed-4fa9-9baa-7901cda08038",
+   "metadata": {},
+   "source": [
+    "Assert that at least one matching image was returned, and retrieve the first in the list."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b8e8b42d-eda2-4c96-a412-22e99da51a5d",
+   "id": "7b1aa5b0-b59e-49c2-8891-f7d0d469851b",
    "metadata": {},
    "outputs": [],
    "source": [
+    "assert(len(coadd_img_refs) > 0)\n",
+    "ref = coadd_img_refs[0]\n",
     "exposure = butler.get(ref)"
    ]
   },
@@ -230,7 +246,7 @@
    "id": "3a57d04c-00ae-4278-b637-9e312084419d",
    "metadata": {},
    "source": [
-    "Extract a list of objects from the `Object` table in a small radius near the position of interest. These will be used both as an input list for forced measurement, and as a comparison for the results."
+    "Query for point-like objects from the `Object` table in a 0.3 degree radius near the position of interest that overlap the deep coadd image's tract and patch. "
    ]
   },
   {
@@ -243,7 +259,7 @@
     "query = f\"\"\"SELECT TOP 50 objectId, coord_ra, coord_dec, {band}_psfMag, {band}_psfFlux\n",
     "        FROM dp1.Object\n",
     "        WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec),\n",
-    "        CIRCLE('ICRS', {ra}, {dec}, 0.1)) = 1\n",
+    "        CIRCLE('ICRS', {ra}, {dec}, 0.3)) = 1\n",
     "        AND refExtendedness < 0.5\n",
     "        AND {band}_psfMag < 26 AND {band}_psfMag > 17\n",
     "        AND tract = {ref.dataId['tract']}\n",
@@ -254,11 +270,47 @@
     "job.wait(phases=['COMPLETED', 'ERROR'])\n",
     "print('Job phase is', job.phase)\n",
     "if job.phase == 'ERROR':\n",
-    "    job.raise_if_error()\n",
+    "    job.raise_if_error()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0939bcb-a517-4ec3-b2c0-3fb3b8700681",
+   "metadata": {},
+   "source": [
+    "Retrieve the search results as `objtable`.\n",
+    "This table will be used both as an input list for forced measurement, and as a comparison for the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1803ae74-76d1-42f1-9e29-39231188f026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "assert job.phase == 'COMPLETED'\n",
     "objtable = job.fetch_result().to_table()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "930f2a06-8918-43f5-9a05-bca95da38b03",
+   "metadata": {},
+   "source": [
+    "Ensure that the table is not empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3234934-8c70-4a75-87b8-2b59e5c1eb1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert(len(objtable) > 0)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e4243773-e324-4415-884c-857cdb497688",
@@ -434,7 +486,7 @@
    "source": [
     "The forced measurement functionality is limited to a handful of measurement plugins (for example, model-fitting algorithms will not work with this task because they require more ancillary information than it is set up to receive). Nonetheless, it offers a quick and easy way to extract measurements at arbitrary positions.\n",
     "\n",
-    "### 3.1 Compare the forced measurements to the Object table\n",
+    "### 3.1. Compare the forced measurements to the Object table\n",
     "\n",
     "Make a plot showing the ratio of the measured PSF fluxes from the forced measurement task to the `psfFlux` for the same objects in the `Object` table. Plot these as a function of magnitude, using Astropy units to convert `slot_PsfFlux_flux` from the `result` table to AB magnitudes."
    ]
@@ -468,7 +520,9 @@
    "source": [
     "## 4. Forced measurement on a visit image\n",
     "\n",
-    "The forced measurement task can be run on any `ExposureF` object. To demonstrate this, retrieve a `visit_image` and execute the driver to extract forced photometry on that image. The appropriate input catalog for `visit_image` measurements is the `Source` table."
+    "The forced measurement task can be run on any `ExposureF` object. To demonstrate this, retrieve a `visit_image` and execute the driver to extract forced photometry on that image. The appropriate input catalog for `visit_image` measurements is the `Source` table.\n",
+    "\n",
+    "Query the butler for overlapping visit images and retrieve the first in the list."
    ]
   },
   {
@@ -480,20 +534,18 @@
    "source": [
     "query = f\"band='{band}' AND \\\n",
     "          visit.region OVERLAPS POINT({ra}, {dec})\"\n",
-    "\n",
     "visit_img_refs = butler.query_datasets('visit_image', where=query)\n",
-    "\n",
-    "ref = visit_img_refs[0]"
+    "assert(len(visit_img_refs) > 0)\n",
+    "ref = visit_img_refs[0]\n",
+    "exposure = butler.get(ref)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "171e7626-85a7-4974-b1e6-d4964f8b814b",
+   "cell_type": "markdown",
+   "id": "c0760743-4c48-4db1-95fb-b2d2dd05da30",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "exposure = butler.get(ref)"
+    "Query the TAP service for point-like sources detected in the visit image."
    ]
   },
   {
@@ -506,7 +558,7 @@
     "query = f\"\"\"SELECT TOP 50 sourceId, ra, dec, psfFlux\n",
     "        FROM dp1.Source\n",
     "        WHERE CONTAINS(POINT('ICRS', ra, dec),\n",
-    "        CIRCLE('ICRS', {ra}, {dec}, 0.1)) = 1\n",
+    "        CIRCLE('ICRS', {ra}, {dec}, 0.3)) = 1\n",
     "        AND extendedness < 0.5\n",
     "        AND visit = {ref.dataId['visit']}\n",
     "        AND detector = {ref.dataId['detector']}\"\"\"\n",
@@ -516,9 +568,27 @@
     "job.wait(phases=['COMPLETED', 'ERROR'])\n",
     "print('Job phase is', job.phase)\n",
     "if job.phase == 'ERROR':\n",
-    "    job.raise_if_error()\n",
+    "    job.raise_if_error()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ebe2ed5-1b67-4ad8-b798-d69726dfba4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "assert job.phase == 'COMPLETED'\n",
-    "srctable = job.fetch_result().to_table()"
+    "srctable = job.fetch_result().to_table()\n",
+    "assert(len(srctable) > 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc39c47-eb40-46a9-b130-c2873d14a41f",
+   "metadata": {},
+   "source": [
+    "Configure and run forced photometry."
    ]
   },
   {
@@ -588,7 +658,7 @@
    "id": "af970fba-e2b5-42db-95a5-b24e6ec11caa",
    "metadata": {},
    "source": [
-    "### 4.1 Compare the forced measurements to the Source table\n",
+    "### 4.1. Compare the forced measurements to the Source table\n",
     "\n",
     "Make a plot showing the ratio of the measured PSF fluxes from the forced measurement task to the `psfFlux` for the same objects in the `Source` table. Plot these as a function of magnitude, using Astropy units to convert `slot_PsfFlux_flux` from the `result` table to AB magnitudes."
    ]