From c98a328227676540a4199668669b5669d7ea1625 Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Thu, 10 Jul 2025 22:43:11 -0700
Subject: [PATCH 1/7] Initial notebook is a cutout factory demo from Melissa
 Graham

---
 dp1/euclid_q1_lenses.ipynb | 233 +++++++++++++++++++++++++++++++++++++
 1 file changed, 233 insertions(+)
 create mode 100644 dp1/euclid_q1_lenses.ipynb

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
new file mode 100644
index 0000000..54ed54d
--- /dev/null
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "440d7890-ef3d-4c14-9a2f-5bf70e05caf8",
+   "metadata": {},
+   "source": [
+    "# Cutout Factory\n",
+    "\n",
+    "* DP0.2\n",
+    "* data.lsst.cloud\n",
+    "* Weekly 2025_17\n",
+    "* Thu Jun 5 2025\n",
+    "\n",
+    "For when an image is in-hand, and many cutouts from it are wanted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "60cebcc1-9e56-4ff3-9ae3-d4580bf28e3d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:33:21.369407Z",
+     "iopub.status.busy": "2025-06-06T03:33:21.369093Z",
+     "iopub.status.idle": "2025-06-06T03:33:25.275587Z",
+     "shell.execute_reply": "2025-06-06T03:33:25.274458Z",
+     "shell.execute_reply.started": "2025-06-06T03:33:21.369373Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import lsst.afw.display as afw_display\n",
+    "from lsst.daf.butler import Butler\n",
+    "import lsst.geom as geom\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "afw_display.setDefaultBackend('matplotlib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8014a204-b2ed-42de-b1db-69df528e9349",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:33:25.280188Z",
+     "iopub.status.busy": "2025-06-06T03:33:25.279868Z",
+     "iopub.status.idle": "2025-06-06T03:33:31.986179Z",
+     "shell.execute_reply": "2025-06-06T03:33:31.984918Z",
+     "shell.execute_reply.started": "2025-06-06T03:33:25.280156Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "butler = Butler('dp02', collections='2.2i/runs/DP0.2')\n",
+    "dataId = {'visit': 192350, 'detector': 175, 'band': 'i'}\n",
+    "calexp = butler.get('calexp', **dataId)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47f755c2-36bf-4f2e-a225-7f519d274180",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:33:32.924258Z",
+     "iopub.status.busy": "2025-06-06T03:33:32.923724Z",
+     "iopub.status.idle": "2025-06-06T03:33:43.123134Z",
+     "shell.execute_reply": "2025-06-06T03:33:43.121925Z",
+     "shell.execute_reply.started": "2025-06-06T03:33:32.924210Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(3,3))\n",
+    "display = afw_display.Display(frame=fig)\n",
+    "display.scale('asinh', 'zscale')\n",
+    "display.mtv(calexp.image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "66bcfac7-d835-4068-9970-43b59e53473f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:33:57.154799Z",
+     "iopub.status.busy": "2025-06-06T03:33:57.154295Z",
+     "iopub.status.idle": "2025-06-06T03:33:57.162208Z",
+     "shell.execute_reply": "2025-06-06T03:33:57.160758Z",
+     "shell.execute_reply.started": "2025-06-06T03:33:57.154755Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cutoutSize = geom.ExtentI(301, 301)\n",
+    "\n",
+    "xy1 = geom.PointI(2250, 700)\n",
+    "bbox1 = geom.BoxI(xy1 - cutoutSize // 2, cutoutSize)\n",
+    "\n",
+    "xy2 = geom.PointI(400, 1750)\n",
+    "bbox2 = geom.BoxI(xy2 - cutoutSize // 2, cutoutSize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "430b2a34-ca4a-44e6-a537-badd39f36719",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:33:58.859382Z",
+     "iopub.status.busy": "2025-06-06T03:33:58.858864Z",
+     "iopub.status.idle": "2025-06-06T03:33:58.865097Z",
+     "shell.execute_reply": "2025-06-06T03:33:58.863783Z",
+     "shell.execute_reply.started": "2025-06-06T03:33:58.859336Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cutout1 = calexp.Factory(calexp, bbox1)\n",
+    "cutout2 = calexp.Factory(calexp, bbox2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "84f02bbe-0700-49dc-bb72-cfcbca463448",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:34:13.569347Z",
+     "iopub.status.busy": "2025-06-06T03:34:13.568851Z",
+     "iopub.status.idle": "2025-06-06T03:34:13.975016Z",
+     "shell.execute_reply": "2025-06-06T03:34:13.973911Z",
+     "shell.execute_reply.started": "2025-06-06T03:34:13.569308Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(3, 3))\n",
+    "display = afw_display.Display(frame=fig)\n",
+    "display.scale('asinh', 'zscale')\n",
+    "display.mtv(cutout1.image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86ad2255-44bd-4339-9866-c4ff723f7d01",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-06-06T03:34:22.589402Z",
+     "iopub.status.busy": "2025-06-06T03:34:22.588894Z",
+     "iopub.status.idle": "2025-06-06T03:34:23.065096Z",
+     "shell.execute_reply": "2025-06-06T03:34:23.064133Z",
+     "shell.execute_reply.started": "2025-06-06T03:34:22.589361Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(3, 3))\n",
+    "display = afw_display.Display(frame=fig)\n",
+    "display.scale('asinh', 'zscale')\n",
+    "display.mtv(cutout2.image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71929a34-504c-4942-8e3d-4f9075ea1c86",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0e6afb1ca77b30ae892779bf94e86779a5dcd5b1 Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Sat, 12 Jul 2025 03:54:04 +0000
Subject: [PATCH 2/7] Useful md doc cells to define goals etc

---
 dp1/euclid_q1_lenses.ipynb | 190 +++++++++++++++++++++++++++++++++----
 1 file changed, 170 insertions(+), 20 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index 54ed54d..ab2cdea 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -5,31 +5,72 @@
    "id": "440d7890-ef3d-4c14-9a2f-5bf70e05caf8",
    "metadata": {},
    "source": [
-    "# Cutout Factory\n",
+    "# Euclid Q1 Lenses\n",
     "\n",
-    "* DP0.2\n",
+    "* **Phil Marshall, Phil Holloway, Ralf Kaehler, Ferro Shao**\n",
+    "* DP1\n",
     "* data.lsst.cloud\n",
-    "* Weekly 2025_17\n",
-    "* Thu Jun 5 2025\n",
+    "* Weekly 2025_17?\n",
+    "* Fri July 11 2025"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65367d2a-7335-4752-9565-3861387a2a63",
+   "metadata": {},
+   "source": [
+    "## Goals\n",
     "\n",
-    "For when an image is in-hand, and many cutouts from it are wanted."
+    "* Extract _ugrizy_ coadd image cutouts for each Euclid Q1 strong lens candidate in the ECDFS and EDFS DP1 fields\n",
+    "* Visualize them as _gri_ color composites.\n",
+    "* Stretch: deconvolve them using the Rubin SharPy by Kaehler et al (in prep)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "60cebcc1-9e56-4ff3-9ae3-d4580bf28e3d",
+   "cell_type": "markdown",
+   "id": "f26c3831-a5d6-4358-ba5a-4b54d06a4ed7",
+   "metadata": {},
+   "source": [
+    "## Cutout Image Extraction\n",
+    "\n",
+    "First we need to make a list (or better, a table) of targets. Then, for each one, we find out which DP1 coadd patch it lies in. (We'll need to choose which patch, for systems that lie in the patch overlap regions and hence in multiple patches.) Then, we loop over patches and bands, uploading a patch image and extracting all the cutouts we can - which will mean getting the image coordinates for each system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d216083-ef68-4d13-861d-0466311b55e5",
+   "metadata": {},
+   "source": [
+    "## _gri_ Composite Image Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aed2a81-68aa-4fa6-9ce0-eee6c652de76",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2025-06-06T03:33:21.369407Z",
-     "iopub.status.busy": "2025-06-06T03:33:21.369093Z",
-     "iopub.status.idle": "2025-06-06T03:33:25.275587Z",
-     "shell.execute_reply": "2025-06-06T03:33:25.274458Z",
-     "shell.execute_reply.started": "2025-06-06T03:33:21.369373Z"
+     "iopub.execute_input": "2025-07-12T03:41:51.156141Z",
+     "iopub.status.busy": "2025-07-12T03:41:51.155432Z",
+     "iopub.status.idle": "2025-07-12T03:41:51.158677Z",
+     "shell.execute_reply": "2025-07-12T03:41:51.158064Z",
+     "shell.execute_reply.started": "2025-07-12T03:41:51.156112Z"
     }
    },
+   "source": [
+    "## Appendix\n",
+    "\n",
+    "The code below is from the Cutout Factory demo notebook by Melissa Graham, and is being used as a source in this notebook further up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35c08215-2fc7-40c2-a58d-8c39c655a130",
+   "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
+    "\n",
     "import lsst.afw.display as afw_display\n",
     "from lsst.daf.butler import Butler\n",
     "import lsst.geom as geom\n",
@@ -44,14 +85,123 @@
    "id": "8014a204-b2ed-42de-b1db-69df528e9349",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2025-06-06T03:33:25.280188Z",
-     "iopub.status.busy": "2025-06-06T03:33:25.279868Z",
-     "iopub.status.idle": "2025-06-06T03:33:31.986179Z",
-     "shell.execute_reply": "2025-06-06T03:33:31.984918Z",
-     "shell.execute_reply.started": "2025-06-06T03:33:25.280156Z"
+     "iopub.execute_input": "2025-07-11T17:25:31.115300Z",
+     "iopub.status.busy": "2025-07-11T17:25:31.114712Z",
+     "iopub.status.idle": "2025-07-11T17:29:43.181005Z",
+     "shell.execute_reply": "2025-07-11T17:29:43.179902Z",
+     "shell.execute_reply.started": "2025-07-11T17:25:31.115276Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"margin: 0.1em;\n",
+       "padding-left: 0.25em;\n",
+       "border-left-style: solid;\n",
+       "font-family: var(--jp-code-font-family);\n",
+       "font-size: var(--jp-code-font-size);\n",
+       "line-height: var(--jp-code-line-height);\n",
+       "\"><span style=\"color: var(--jp-warn-color2)\">urllib3.connectionpool</span> <span style=\"color: var(--jp-warn-color0)\">WARNING</span>: Retrying (Retry(total=5, connect=2, read=3, redirect=None, status=5)) after connection broken by &#x27;ConnectTimeoutError(&lt;urllib3.connection.HTTPSConnection object at 0x7ceb5f83a5a0&gt;, &#x27;Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)&#x27;)&#x27;: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&amp;Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&amp;Expires=1752258331</pre>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"margin: 0.1em;\n",
+       "padding-left: 0.25em;\n",
+       "border-left-style: solid;\n",
+       "font-family: var(--jp-code-font-family);\n",
+       "font-size: var(--jp-code-font-size);\n",
+       "line-height: var(--jp-code-line-height);\n",
+       "\"><span style=\"color: var(--jp-warn-color2)\">urllib3.connectionpool</span> <span style=\"color: var(--jp-warn-color0)\">WARNING</span>: Retrying (Retry(total=4, connect=1, read=3, redirect=None, status=5)) after connection broken by &#x27;ConnectTimeoutError(&lt;urllib3.connection.HTTPSConnection object at 0x7ceb5efb8380&gt;, &#x27;Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)&#x27;)&#x27;: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&amp;Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&amp;Expires=1752258331</pre>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"margin: 0.1em;\n",
+       "padding-left: 0.25em;\n",
+       "border-left-style: solid;\n",
+       "font-family: var(--jp-code-font-family);\n",
+       "font-size: var(--jp-code-font-size);\n",
+       "line-height: var(--jp-code-line-height);\n",
+       "\"><span style=\"color: var(--jp-warn-color2)\">urllib3.connectionpool</span> <span style=\"color: var(--jp-warn-color0)\">WARNING</span>: Retrying (Retry(total=3, connect=0, read=3, redirect=None, status=5)) after connection broken by &#x27;ConnectTimeoutError(&lt;urllib3.connection.HTTPSConnection object at 0x7ceb5efbb020&gt;, &#x27;Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)&#x27;)&#x27;: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&amp;Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&amp;Expires=1752258331</pre>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Failure from formatter 'lsst.obs.base.formatters.fitsExposure.FitsExposureFormatter' for dataset 8a953c03-21bd-4878-bfa6-94dbf628ea81 (calexp from https://sdfdatas3.slac.stanford.edu/rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331): HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:198\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 198\u001b[0m     sock \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m        \u001b[49m\u001b[43msocket_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msocket_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "\u001b[0;31mTimeoutError\u001b[0m: timed out",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    790\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:488\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    487\u001b[0m         new_e \u001b[38;5;241m=\u001b[39m _wrap_proxy_error(new_e, conn\u001b[38;5;241m.\u001b[39mproxy\u001b[38;5;241m.\u001b[39mscheme)\n\u001b[0;32m--> 488\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m new_e\n\u001b[1;32m    490\u001b[0m \u001b[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001b[39;00m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:464\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 464\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:1093\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[0;32m-> 1093\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1095\u001b[0m \u001b[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:753\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    752\u001b[0m sock: socket\u001b[38;5;241m.\u001b[39msocket \u001b[38;5;241m|\u001b[39m ssl\u001b[38;5;241m.\u001b[39mSSLSocket\n\u001b[0;32m--> 753\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m sock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    754\u001b[0m server_hostname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:207\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 207\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[1;32m    208\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    210\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[0;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)')",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:841\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    839\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 841\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    842\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    843\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    844\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/retry.py:519\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    518\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m    521\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
+      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:220\u001b[0m, in \u001b[0;36m_read_artifact_into_memory\u001b[0;34m(getInfo, ref, cache_manager, isComponent)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    221\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcomponent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgetInfo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomponent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43misComponent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    222\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrecorded_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mFileNotFoundError\u001b[39;00m, FileIntegrityError):\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;66;03m# This is expected for the case where the resource is missing\u001b[39;00m\n\u001b[1;32m    227\u001b[0m     \u001b[38;5;66;03m# or the information we passed to the formatter about the file size\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# is incorrect.\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;66;03m# Allow them to propagate up.\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/_formatter.py:515\u001b[0m, in \u001b[0;36mFormatterV2.read\u001b[0;34m(self, component, expected_size, cache_manager)\u001b[0m\n\u001b[1;32m    514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcan_read_from_local_file \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcan_read_from_uri:\n\u001b[0;32m--> 515\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_from_possibly_cached_local_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcomponent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    518\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/_formatter.py:714\u001b[0m, in \u001b[0;36mFormatterV2.read_from_possibly_cached_local_file\u001b[0;34m(self, component, expected_size, cache_manager)\u001b[0m\n\u001b[1;32m    712\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 714\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43muri\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_local\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlocal_uri\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_resource_size\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_descriptor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muri\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_uri\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/_resourcePath.py:1333\u001b[0m, in \u001b[0;36mResourcePath.as_local\u001b[0;34m(self, multithreaded, tmpdir)\u001b[0m\n\u001b[1;32m   1332\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTemporary directory for as_local must be local resource not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemp_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1333\u001b[0m local_src, is_temporary \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_as_local\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmultithreaded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultithreaded\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtmpdir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemp_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1334\u001b[0m local_uri \u001b[38;5;241m=\u001b[39m ResourcePath(local_src, isTemporary\u001b[38;5;241m=\u001b[39mis_temporary)\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/http.py:1560\u001b[0m, in \u001b[0;36mHttpResourcePath._as_local\u001b[0;34m(self, multithreaded, tmpdir)\u001b[0m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_session \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m-> 1560\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeturl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1561\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m!=\u001b[39m requests\u001b[38;5;241m.\u001b[39mcodes\u001b[38;5;241m.\u001b[39mok:\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/http.py:2283\u001b[0m, in \u001b[0;36m_SessionWrapper.get\u001b[0;34m(self, url, timeout, allow_redirects, stream, headers)\u001b[0m\n\u001b[1;32m   2274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget\u001b[39m(\n\u001b[1;32m   2275\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   2276\u001b[0m     url: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2281\u001b[0m     headers: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m requests\u001b[38;5;241m.\u001b[39mResponse:\n\u001b[0;32m-> 2283\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2284\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2285\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2286\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2287\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2288\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_augment_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2289\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:602\u001b[0m, in \u001b[0;36mSession.get\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    601\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/adapters.py:688\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[0;32m--> 688\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n",
+      "\u001b[0;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m butler \u001b[38;5;241m=\u001b[39m Butler(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdp02\u001b[39m\u001b[38;5;124m'\u001b[39m, collections\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2.2i/runs/DP0.2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m dataId \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m192350\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetector\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m175\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m'\u001b[39m}\n\u001b[0;32m----> 3\u001b[0m calexp \u001b[38;5;241m=\u001b[39m \u001b[43mbutler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcalexp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdataId\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/remote_butler/_remote_butler.py:293\u001b[0m, in \u001b[0;36mRemoteButler.get\u001b[0;34m(self, datasetRefOrType, dataId, parameters, collections, storageClass, timespan, **kwargs)\u001b[0m\n\u001b[1;32m    290\u001b[0m         ref \u001b[38;5;241m=\u001b[39m ref\u001b[38;5;241m.\u001b[39mmakeComponentRef(componentOverride)\n\u001b[1;32m    291\u001b[0m ref \u001b[38;5;241m=\u001b[39m apply_storage_class_override(ref, datasetRefOrType, storageClass)\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_dataset_as_python_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/remote_butler/_remote_butler.py:303\u001b[0m, in \u001b[0;36mRemoteButler._get_dataset_as_python_object\u001b[0;34m(self, ref, model, parameters)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_dataset_as_python_object\u001b[39m(\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    297\u001b[0m     ref: DatasetRef,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    301\u001b[0m     \u001b[38;5;66;03m# This thin wrapper method is here to provide a place to hook in a mock\u001b[39;00m\n\u001b[1;32m    302\u001b[0m     \u001b[38;5;66;03m# mimicking DatastoreMock functionality for use in unit tests.\u001b[39;00m\n\u001b[0;32m--> 303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_dataset_as_python_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m        \u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_to_file_payload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    307\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    308\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/fileDatastoreClient.py:88\u001b[0m, in \u001b[0;36mget_dataset_as_python_object\u001b[0;34m(ref, payload, parameters, cache_manager)\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     cache_manager \u001b[38;5;241m=\u001b[39m DatastoreDisabledCacheManager()\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_dataset_as_python_object_from_get_info\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     89\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdatastore_file_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:443\u001b[0m, in \u001b[0;36mget_dataset_as_python_object_from_get_info\u001b[0;34m(allGetInfo, ref, parameters, cache_manager)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;66;03m# For an assembled composite this could be a derived\u001b[39;00m\n\u001b[1;32m    438\u001b[0m     \u001b[38;5;66;03m# component derived from a real component. The validity\u001b[39;00m\n\u001b[1;32m    439\u001b[0m     \u001b[38;5;66;03m# of the parameters is not clear. For now validate against\u001b[39;00m\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;66;03m# the composite storage class\u001b[39;00m\n\u001b[1;32m    441\u001b[0m     getInfo\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mfile_descriptor\u001b[38;5;241m.\u001b[39mstorageClass\u001b[38;5;241m.\u001b[39mvalidateParameters(parameters)\n\u001b[0;32m--> 443\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read_artifact_into_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgetInfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43misComponent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43misComponent\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:237\u001b[0m, in \u001b[0;36m_read_artifact_into_memory\u001b[0;34m(getInfo, ref, cache_manager, isComponent)\u001b[0m\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m notes:\n\u001b[1;32m    236\u001b[0m         notes \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m notes\n\u001b[0;32m--> 237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailure from formatter \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformatter\u001b[38;5;241m.\u001b[39mname()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for dataset \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mref\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mref\u001b[38;5;241m.\u001b[39mdatasetType\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00muri\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnotes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    240\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m post_process_get(\n\u001b[1;32m    243\u001b[0m     result, ref\u001b[38;5;241m.\u001b[39mdatasetType\u001b[38;5;241m.\u001b[39mstorageClass, getInfo\u001b[38;5;241m.\u001b[39massemblerParams, isComponent\u001b[38;5;241m=\u001b[39misComponent\n\u001b[1;32m    244\u001b[0m )\n",
+      "\u001b[0;31mValueError\u001b[0m: Failure from formatter 'lsst.obs.base.formatters.fitsExposure.FitsExposureFormatter' for dataset 8a953c03-21bd-4878-bfa6-94dbf628ea81 (calexp from https://sdfdatas3.slac.stanford.edu/rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331): HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))"
+     ]
+    }
+   ],
    "source": [
     "butler = Butler('dp02', collections='2.2i/runs/DP0.2')\n",
     "dataId = {'visit': 192350, 'detector': 175, 'band': 'i'}\n",
@@ -225,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

From 4d50f2b89d466ef4e8e7105aef4012a4640451d1 Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Mon, 14 Jul 2025 02:52:13 +0000
Subject: [PATCH 3/7] Working cutout code (just not with the factory method)

---
 dp1/euclid_q1_lenses.ipynb | 480 +++++++++++++++++++++----------------
 1 file changed, 272 insertions(+), 208 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index ab2cdea..803619b 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "440d7890-ef3d-4c14-9a2f-5bf70e05caf8",
+   "id": "f11fc55e-df06-47dd-8599-eb78bc578862",
    "metadata": {},
    "source": [
     "# Euclid Q1 Lenses\n",
@@ -10,7 +10,7 @@
     "* **Phil Marshall, Phil Holloway, Ralf Kaehler, Ferro Shao**\n",
     "* DP1\n",
     "* data.lsst.cloud\n",
-    "* Weekly 2025_17?\n",
+    "* r29.1.1\n",
     "* Fri July 11 2025"
    ]
   },
@@ -26,6 +26,22 @@
     "* Stretch: deconvolve them using the Rubin SharPy by Kaehler et al (in prep)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "729bff4a-1e33-458b-83c5-9921ca77fed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lsst.daf.butler import Butler\n",
+    "import lsst.afw.display as afw_display\n",
+    "import lsst.geom as geom\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "afw_display.setDefaultBackend('matplotlib')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f26c3831-a5d6-4358-ba5a-4b54d06a4ed7",
@@ -36,6 +52,236 @@
     "First we need to make a list (or better, a table) of targets. Then, for each one, we find out which DP1 coadd patch it lies in. (We'll need to choose which patch, for systems that lie in the patch overlap regions and hence in multiple patches.) Then, we loop over patches and bands, uploading a patch image and extracting all the cutouts we can - which will mean getting the image coordinates for each system"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba8ee8ee-7cb4-4a9d-a3e8-186c0c593095",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n",
+    "assert butler is not None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39bed3db-4271-4aeb-82c4-a23bab785d8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "butler.get_dataset_type('deep_coadd').dimensions.required"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fb6113c-2763-4424-8856-06d79b1c5de3",
+   "metadata": {},
+   "source": [
+    "## Single Sky Position Testing\n",
+    "\n",
+    "### Single Band\n",
+    "\n",
+    "Let's just try extracting a single 32x32 pixel cutout image in one band."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "165077b3-3ed9-40a0-b762-92689fc24b0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ra = 59.626134\n",
+    "dec = -49.06175\n",
+    "\n",
+    "band = 'i'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dd657db-3028-4897-ae44-a4e44c4aff63",
+   "metadata": {},
+   "source": [
+    "We need to find the tract and patch that this target is in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34975668-16b9-447e-b37a-56a9e78cc45e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radec = geom.SpherePoint(ra, dec, geom.degrees)\n",
+    "cutoutSize = geom.ExtentI(32, 32)\n",
+    "\n",
+    "skymap = butler.get(\"skyMap\")\n",
+    "tractInfo = skymap.findTract(radec)\n",
+    "patchInfo = tractInfo.findPatch(radec)\n",
+    "\n",
+    "patch = tractInfo.getSequentialPatchIndex(patchInfo)\n",
+    "tract = tractInfo.getId()\n",
+    "\n",
+    "dataId = {'tract': tract, 'patch': patch, 'band': band}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bd4189a-0cc7-4fa3-b219-ccfb57a614f2",
+   "metadata": {},
+   "source": [
+    "When testing, it can be useful to upload the whole patch image and inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "774cb05c-c831-4c79-b891-aa00db25f7be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# deep_coadd = butler.get('deep_coadd', band=band, tract=tract, patch=patch)\n",
+    "# coadd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8f5d83d-beb8-4a01-a6c5-9a9d16a867d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig = plt.figure(figsize=(6,6))\n",
+    "# display = afw_display.Display(frame=fig)\n",
+    "# display.scale('asinh', 'zscale')\n",
+    "# display.mtv(coadd.image)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d516446-ae23-46e1-bc00-0c8cdb3c6ce5",
+   "metadata": {},
+   "source": [
+    "Now to define a small bounding box, and extract the pixels in it. This first cell _should_ work, but doesn't - maybe some tract/patch confusion. There could be some speed up here at some point, making multiple cutouts from teh same patch image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e32d9519-f4ec-4499-b6bb-f15eb3bad538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# xy = geom.PointI(tractInfo.getWcs().skyToPixel(radec))\n",
+    "# bbox = geom.BoxI(xy - cutoutSize // 2, cutoutSize)\n",
+    "\n",
+    "# cutout = coadd.Factory(coadd, bbox)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb06ca5f-af1b-4c61-8196-0690e743028f",
+   "metadata": {},
+   "source": [
+    "Here's some code that does work: define the bounding box, then just grab that part of the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c09ccc4-d4a0-4091-953e-b0bec9f3e962",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xy = geom.PointI(tractInfo.getWcs().skyToPixel(radec))\n",
+    "bbox = geom.BoxI(xy - cutoutSize // 2, cutoutSize)\n",
+    "\n",
+    "parameters = {'bbox': bbox}\n",
+    "\n",
+    "cutout = butler.get(\"deep_coadd\", parameters=parameters, dataId=dataId)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b6105f8-20ad-4e6a-9000-ad468abc7978",
+   "metadata": {},
+   "source": [
+    "Quick look to check we got our object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dff2724-6f93-4e35-83da-f88a98d55c9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(3, 3))\n",
+    "display = afw_display.Display(frame=fig)\n",
+    "display.scale('asinh', 'zscale')\n",
+    "display.mtv(cutout_image.image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2f16999-1a5a-45fd-a9ef-cf1714aff8fb",
+   "metadata": {},
+   "source": [
+    "## Multiple Bands\n",
+    "\n",
+    "Loop over all 6 bands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8c2c390-2374-41ba-a19b-c3e99c8dcd53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bands = [\"u\",\"g\",\"r\",\"i\",\"z\",\"y\"]\n",
+    "cutout = {}\n",
+    "\n",
+    "for band in bands:\n",
+    "    dataId = {'tract': tract, 'patch': patch, 'band': band}\n",
+    "    cutout[band] = butler.get(\"deep_coadd\", parameters=parameters, dataId=dataId)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41514463-c5c6-405f-a25c-bec43041139f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "533d3a49-967c-4a61-af6b-9d96a49c0bb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(3, 3))\n",
+    "display = afw_display.Display(frame=fig)\n",
+    "display.scale('asinh', 'zscale')\n",
+    "display.mtv(cutout[\"y\"].image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9b12a89-ef2c-4a62-af7f-6b8630eb899b",
+   "metadata": {},
+   "source": [
+    "OK - we have 6 cutouts for this target, so can go ahead and make a color composite. It took about 5 secs to make them all: we'll need to keep an eye on this, and return to the `factory` approach to try and speed things up a bit."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7d216083-ef68-4d13-861d-0466311b55e5",
@@ -44,6 +290,14 @@
     "## _gri_ Composite Image Visualization"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4ebd56d6-1b8f-43b4-9063-0d8cb4a7bc15",
+   "metadata": {},
+   "source": [
+    "## Do-It-All Code"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3aed2a81-68aa-4fa6-9ce0-eee6c652de76",
@@ -81,127 +335,10 @@
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-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:198\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 198\u001b[0m     sock \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m        \u001b[49m\u001b[43msocket_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msocket_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
-      "\u001b[0;31mTimeoutError\u001b[0m: timed out",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    790\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:488\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    487\u001b[0m         new_e \u001b[38;5;241m=\u001b[39m _wrap_proxy_error(new_e, conn\u001b[38;5;241m.\u001b[39mproxy\u001b[38;5;241m.\u001b[39mscheme)\n\u001b[0;32m--> 488\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m new_e\n\u001b[1;32m    490\u001b[0m \u001b[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001b[39;00m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:464\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 464\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:1093\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[0;32m-> 1093\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1095\u001b[0m \u001b[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:753\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    752\u001b[0m sock: socket\u001b[38;5;241m.\u001b[39msocket \u001b[38;5;241m|\u001b[39m ssl\u001b[38;5;241m.\u001b[39mSSLSocket\n\u001b[0;32m--> 753\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m sock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    754\u001b[0m server_hostname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connection.py:207\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 207\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[1;32m    208\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    210\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "\u001b[0;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)')",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:871\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    868\u001b[0m     log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    869\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetrying (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) after connection broken by \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, retries, err, url\n\u001b[1;32m    870\u001b[0m     )\n\u001b[0;32m--> 871\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpool_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelease_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelease_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Handle redirect?\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/connectionpool.py:841\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    839\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 841\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    842\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    843\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    844\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/urllib3/util/retry.py:519\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    518\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m    521\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
-      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:220\u001b[0m, in \u001b[0;36m_read_artifact_into_memory\u001b[0;34m(getInfo, ref, cache_manager, isComponent)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    221\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcomponent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgetInfo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomponent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43misComponent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    222\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrecorded_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mFileNotFoundError\u001b[39;00m, FileIntegrityError):\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;66;03m# This is expected for the case where the resource is missing\u001b[39;00m\n\u001b[1;32m    227\u001b[0m     \u001b[38;5;66;03m# or the information we passed to the formatter about the file size\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# is incorrect.\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;66;03m# Allow them to propagate up.\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/_formatter.py:515\u001b[0m, in \u001b[0;36mFormatterV2.read\u001b[0;34m(self, component, expected_size, cache_manager)\u001b[0m\n\u001b[1;32m    514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcan_read_from_local_file \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcan_read_from_uri:\n\u001b[0;32m--> 515\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_from_possibly_cached_local_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcomponent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    518\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/_formatter.py:714\u001b[0m, in \u001b[0;36mFormatterV2.read_from_possibly_cached_local_file\u001b[0;34m(self, component, expected_size, cache_manager)\u001b[0m\n\u001b[1;32m    712\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 714\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43muri\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_local\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlocal_uri\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_resource_size\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_descriptor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muri\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_uri\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/_resourcePath.py:1333\u001b[0m, in \u001b[0;36mResourcePath.as_local\u001b[0;34m(self, multithreaded, tmpdir)\u001b[0m\n\u001b[1;32m   1332\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTemporary directory for as_local must be local resource not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemp_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1333\u001b[0m local_src, is_temporary \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_as_local\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmultithreaded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultithreaded\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtmpdir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemp_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1334\u001b[0m local_uri \u001b[38;5;241m=\u001b[39m ResourcePath(local_src, isTemporary\u001b[38;5;241m=\u001b[39mis_temporary)\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/http.py:1560\u001b[0m, in \u001b[0;36mHttpResourcePath._as_local\u001b[0;34m(self, multithreaded, tmpdir)\u001b[0m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_session \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m-> 1560\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeturl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1561\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m!=\u001b[39m requests\u001b[38;5;241m.\u001b[39mcodes\u001b[38;5;241m.\u001b[39mok:\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/resources/gdd63cb302e+50e2446c94/python/lsst/resources/http.py:2283\u001b[0m, in \u001b[0;36m_SessionWrapper.get\u001b[0;34m(self, url, timeout, allow_redirects, stream, headers)\u001b[0m\n\u001b[1;32m   2274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget\u001b[39m(\n\u001b[1;32m   2275\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   2276\u001b[0m     url: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2281\u001b[0m     headers: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m requests\u001b[38;5;241m.\u001b[39mResponse:\n\u001b[0;32m-> 2283\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2284\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2285\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2286\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2287\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2288\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_augment_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2289\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:602\u001b[0m, in \u001b[0;36mSession.get\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    601\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/requests/adapters.py:688\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[0;32m--> 688\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n",
-      "\u001b[0;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m butler \u001b[38;5;241m=\u001b[39m Butler(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdp02\u001b[39m\u001b[38;5;124m'\u001b[39m, collections\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2.2i/runs/DP0.2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m dataId \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m192350\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetector\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m175\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m'\u001b[39m}\n\u001b[0;32m----> 3\u001b[0m calexp \u001b[38;5;241m=\u001b[39m \u001b[43mbutler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcalexp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdataId\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/remote_butler/_remote_butler.py:293\u001b[0m, in \u001b[0;36mRemoteButler.get\u001b[0;34m(self, datasetRefOrType, dataId, parameters, collections, storageClass, timespan, **kwargs)\u001b[0m\n\u001b[1;32m    290\u001b[0m         ref \u001b[38;5;241m=\u001b[39m ref\u001b[38;5;241m.\u001b[39mmakeComponentRef(componentOverride)\n\u001b[1;32m    291\u001b[0m ref \u001b[38;5;241m=\u001b[39m apply_storage_class_override(ref, datasetRefOrType, storageClass)\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_dataset_as_python_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/remote_butler/_remote_butler.py:303\u001b[0m, in \u001b[0;36mRemoteButler._get_dataset_as_python_object\u001b[0;34m(self, ref, model, parameters)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_dataset_as_python_object\u001b[39m(\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    297\u001b[0m     ref: DatasetRef,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    301\u001b[0m     \u001b[38;5;66;03m# This thin wrapper method is here to provide a place to hook in a mock\u001b[39;00m\n\u001b[1;32m    302\u001b[0m     \u001b[38;5;66;03m# mimicking DatastoreMock functionality for use in unit tests.\u001b[39;00m\n\u001b[0;32m--> 303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_dataset_as_python_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m        \u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_to_file_payload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    307\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    308\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/fileDatastoreClient.py:88\u001b[0m, in \u001b[0;36mget_dataset_as_python_object\u001b[0;34m(ref, payload, parameters, cache_manager)\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     cache_manager \u001b[38;5;241m=\u001b[39m DatastoreDisabledCacheManager()\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_dataset_as_python_object_from_get_info\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     89\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdatastore_file_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_manager\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:443\u001b[0m, in \u001b[0;36mget_dataset_as_python_object_from_get_info\u001b[0;34m(allGetInfo, ref, parameters, cache_manager)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;66;03m# For an assembled composite this could be a derived\u001b[39;00m\n\u001b[1;32m    438\u001b[0m     \u001b[38;5;66;03m# component derived from a real component. The validity\u001b[39;00m\n\u001b[1;32m    439\u001b[0m     \u001b[38;5;66;03m# of the parameters is not clear. For now validate against\u001b[39;00m\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;66;03m# the composite storage class\u001b[39;00m\n\u001b[1;32m    441\u001b[0m     getInfo\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mfile_descriptor\u001b[38;5;241m.\u001b[39mstorageClass\u001b[38;5;241m.\u001b[39mvalidateParameters(parameters)\n\u001b[0;32m--> 443\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read_artifact_into_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgetInfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43misComponent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43misComponent\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/share/eups/Linux64/daf_butler/g6dd59efbe6+dc2c386a65/python/lsst/daf/butler/datastores/file_datastore/get.py:237\u001b[0m, in \u001b[0;36m_read_artifact_into_memory\u001b[0;34m(getInfo, ref, cache_manager, isComponent)\u001b[0m\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m notes:\n\u001b[1;32m    236\u001b[0m         notes \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m notes\n\u001b[0;32m--> 237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailure from formatter \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformatter\u001b[38;5;241m.\u001b[39mname()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for dataset \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mref\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mref\u001b[38;5;241m.\u001b[39mdatasetType\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00muri\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnotes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    240\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m post_process_get(\n\u001b[1;32m    243\u001b[0m     result, ref\u001b[38;5;241m.\u001b[39mdatasetType\u001b[38;5;241m.\u001b[39mstorageClass, getInfo\u001b[38;5;241m.\u001b[39massemblerParams, isComponent\u001b[38;5;241m=\u001b[39misComponent\n\u001b[1;32m    244\u001b[0m )\n",
-      "\u001b[0;31mValueError\u001b[0m: Failure from formatter 'lsst.obs.base.formatters.fitsExposure.FitsExposureFormatter' for dataset 8a953c03-21bd-4878-bfa6-94dbf628ea81 (calexp from https://sdfdatas3.slac.stanford.edu/rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331): HTTPSConnectionPool(host='sdfdatas3.slac.stanford.edu', port=443): Max retries exceeded with url: /rubin-dp02-products/2.2i/runs/DP0.2/v23_0_0_rc5/PREOPS-905/20211218T144437Z/calexp/20220914/i/i_sim_1.4/192350/calexp_LSSTCam-imSim_i_i_sim_1_4_192350_R42_S11_2_2i_runs_DP0_2_v23_0_0_rc5_PREOPS-905_20211218T144437Z.fits?AWSAccessKeyId=dp02user&Signature=piKceDgoYEaMGi0rwf2%2BYcMJz0g%3D&Expires=1752258331 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ceb5efcce90>, 'Connection to sdfdatas3.slac.stanford.edu timed out. (connect timeout=60.0)'))"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "butler = Butler('dp02', collections='2.2i/runs/DP0.2')\n",
     "dataId = {'visit': 192350, 'detector': 175, 'band': 'i'}\n",
@@ -210,29 +347,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "47f755c2-36bf-4f2e-a225-7f519d274180",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-06-06T03:33:32.924258Z",
-     "iopub.status.busy": "2025-06-06T03:33:32.923724Z",
-     "iopub.status.idle": "2025-06-06T03:33:43.123134Z",
-     "shell.execute_reply": "2025-06-06T03:33:43.121925Z",
-     "shell.execute_reply.started": "2025-06-06T03:33:32.924210Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(3,3))\n",
     "display = afw_display.Display(frame=fig)\n",
@@ -243,17 +361,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "66bcfac7-d835-4068-9970-43b59e53473f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-06-06T03:33:57.154799Z",
-     "iopub.status.busy": "2025-06-06T03:33:57.154295Z",
-     "iopub.status.idle": "2025-06-06T03:33:57.162208Z",
-     "shell.execute_reply": "2025-06-06T03:33:57.160758Z",
-     "shell.execute_reply.started": "2025-06-06T03:33:57.154755Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cutoutSize = geom.ExtentI(301, 301)\n",
@@ -267,17 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "430b2a34-ca4a-44e6-a537-badd39f36719",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-06-06T03:33:58.859382Z",
-     "iopub.status.busy": "2025-06-06T03:33:58.858864Z",
-     "iopub.status.idle": "2025-06-06T03:33:58.865097Z",
-     "shell.execute_reply": "2025-06-06T03:33:58.863783Z",
-     "shell.execute_reply.started": "2025-06-06T03:33:58.859336Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cutout1 = calexp.Factory(calexp, bbox1)\n",
@@ -286,29 +388,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "84f02bbe-0700-49dc-bb72-cfcbca463448",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-06-06T03:34:13.569347Z",
-     "iopub.status.busy": "2025-06-06T03:34:13.568851Z",
-     "iopub.status.idle": "2025-06-06T03:34:13.975016Z",
-     "shell.execute_reply": "2025-06-06T03:34:13.973911Z",
-     "shell.execute_reply.started": "2025-06-06T03:34:13.569308Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(3, 3))\n",
     "display = afw_display.Display(frame=fig)\n",
@@ -319,29 +402,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "86ad2255-44bd-4339-9866-c4ff723f7d01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-06-06T03:34:22.589402Z",
-     "iopub.status.busy": "2025-06-06T03:34:22.588894Z",
-     "iopub.status.idle": "2025-06-06T03:34:23.065096Z",
-     "shell.execute_reply": "2025-06-06T03:34:23.064133Z",
-     "shell.execute_reply.started": "2025-06-06T03:34:22.589361Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(3, 3))\n",
     "display = afw_display.Display(frame=fig)\n",

From 553856fbc51530abdc8785249c18772cd1aabb23 Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Mon, 14 Jul 2025 04:00:40 +0000
Subject: [PATCH 4/7] Added gri color composite visualization

---
 dp1/euclid_q1_lenses.ipynb | 135 ++++++++++++++++++++++++++++++++++---
 1 file changed, 124 insertions(+), 11 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index 803619b..d895dca 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -36,7 +36,9 @@
     "from lsst.daf.butler import Butler\n",
     "import lsst.afw.display as afw_display\n",
     "import lsst.geom as geom\n",
+    "from lsst.afw.image import MultibandExposure\n",
     "import numpy as np\n",
+    "from astropy.visualization import make_lupton_rgb\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "afw_display.setDefaultBackend('matplotlib')"
@@ -78,9 +80,7 @@
    "id": "6fb6113c-2763-4424-8856-06d79b1c5de3",
    "metadata": {},
    "source": [
-    "## Single Sky Position Testing\n",
-    "\n",
-    "### Single Band\n",
+    "### Single Sky Position, Single Band\n",
     "\n",
     "Let's just try extracting a single 32x32 pixel cutout image in one band."
    ]
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "165077b3-3ed9-40a0-b762-92689fc24b0f",
+   "id": "b20bc42a-e4af-4768-ba96-6f10879314d0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,6 +98,38 @@
     "band = 'i'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "248142d6-1e6c-473d-99a7-819b89d77034",
+   "metadata": {},
+   "source": [
+    "Turn the coordinates into an IAU standard object name, we'll need this later:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dc87bd9-271c-4205-a2af-c71381cee28e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "\n",
+    "# Example RA and Dec coordinates\n",
+    "rahms = ra * u.hourangle  # 12 hours, 30 minutes, 36 seconds\n",
+    "decdms = dec * u.deg    # +12 degrees, 24 minutes, 0 seconds\n",
+    "\n",
+    "# Create a SkyCoord object\n",
+    "coordinates = SkyCoord(ra=rahms, dec=decdms, frame='icrs')\n",
+    "\n",
+    "# Format the coordinates into an IAU-style string\n",
+    "name = (f'EUCLID J{coordinates.ra.to_string(unit=u.hourangle, sep=\"\", precision=1, pad=True)}'\n",
+    "                  f'{coordinates.dec.to_string(sep=\"\", precision=0, alwayssign=True, pad=True)}') #\n",
+    "\n",
+    "print(name)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7dd657db-3028-4897-ae44-a4e44c4aff63",
@@ -164,7 +196,7 @@
    "id": "1d516446-ae23-46e1-bc00-0c8cdb3c6ce5",
    "metadata": {},
    "source": [
-    "Now to define a small bounding box, and extract the pixels in it. This first cell _should_ work, but doesn't - maybe some tract/patch confusion. There could be some speed up here at some point, making multiple cutouts from teh same patch image."
+    "Now to define a small bounding box, and extract the pixels in it. This first cell below _should_ work, but doesn't - maybe some tract/patch confusion. There could be some speed up here at some point, making multiple cutouts from the same patch image using the calexp object's native factory method."
    ]
   },
   {
@@ -230,9 +262,9 @@
    "id": "a2f16999-1a5a-45fd-a9ef-cf1714aff8fb",
    "metadata": {},
    "source": [
-    "## Multiple Bands\n",
+    "### Single Object, Multiple Bands\n",
     "\n",
-    "Loop over all 6 bands:"
+    "Loop over all 6 bands and extract the cutout image in each one."
    ]
   },
   {
@@ -270,7 +302,7 @@
     "fig = plt.figure(figsize=(3, 3))\n",
     "display = afw_display.Display(frame=fig)\n",
     "display.scale('asinh', 'zscale')\n",
-    "display.mtv(cutout[\"y\"].image)\n",
+    "display.mtv(cutout[\"g\"].image)\n",
     "plt.show()"
    ]
   },
@@ -287,15 +319,96 @@
    "id": "7d216083-ef68-4d13-861d-0466311b55e5",
    "metadata": {},
    "source": [
-    "## _gri_ Composite Image Visualization"
+    "## _gri_ Composite Image Visualization\n",
+    "\n",
+    "Here's a useful function, adapted from https://github.com/lsst-sitcom/comcam_clusters/blob/main/ComCam_StarterKit.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bcfa278-84c5-4eda-852d-a31e9e1f2609",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def showRGB(image, bgr=\"gri\", ax=None, fp=None, figsize=(8,8), stretch=100, Q=1, name=None):\n",
+    "    \"\"\"Display an RGB color composite image with matplotlib.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    image : `MultibandImage`\n",
+    "        `MultibandImage` to display.\n",
+    "    bgr : sequence\n",
+    "        A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n",
+    "        to use for each channel. If `image` only has three filters then this parameter is ignored\n",
+    "        and the filters in the image are used.\n",
+    "    ax : `matplotlib.axes.Axes`\n",
+    "        Axis in a `matplotlib.Figure` to display the image.\n",
+    "        If `axis` is `None` then a new figure is created.\n",
+    "    figsize: tuple\n",
+    "        Size of the `matplotlib.Figure` created.\n",
+    "        If `ax` is not `None` then this parameter is ignored.\n",
+    "    stretch: int\n",
+    "        The linear stretch of the image.\n",
+    "    Q: int\n",
+    "        The Asinh softening parameter.\n",
+    "    \"\"\"\n",
+    "    # If the image only has 3 bands, reverse the order of the bands to produce the RGB image\n",
+    "    if len(image) == 3:\n",
+    "        bgr = image.filters\n",
+    "    # Extract the primary image component of each Exposure with the .image property, and use .array to get a NumPy array view.\n",
+    "    rgb = make_lupton_rgb(image_r=image[bgr[2]].array,  # numpy array for the r channel\n",
+    "                          image_g=image[bgr[1]].array,  # numpy array for the g channel\n",
+    "                          image_b=image[bgr[0]].array,  # numpy array for the b channel\n",
+    "                          stretch=stretch, Q=Q)  # parameters used to stretch and scale the pixel values\n",
+    "    if ax is None:\n",
+    "        fig = plt.figure(figsize=figsize)\n",
+    "        ax = fig.add_subplot(1,1,1)\n",
+    "    \n",
+    "    plt.axis(\"off\")\n",
+    "    ax.imshow(rgb, interpolation='nearest', origin='lower')\n",
+    "\n",
+    "    if name is not None:\n",
+    "        plt.text(0, 31, name, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "    \n",
+    "    plt.text(0, 2, bgr, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a51c625e-f017-423c-9f65-753e1f7a8fb5",
+   "metadata": {},
+   "source": [
+    "First we need to package our cutouts into a MultibandExposure object, then we pass that to the RGB composite generation function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f592790b-26cf-465c-9511-6c751799f39f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutouts = [cutout[band] for band in bands]\n",
+    "multibandexposure = MultibandExposure.fromExposures(bands, cutouts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23848e70-3f5d-40e4-bc6b-0e208af3f044",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "showRGB(multibandexposure.image, bgr='gri', figsize=(3,3), stretch=60, Q=1, name=name)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4ebd56d6-1b8f-43b4-9063-0d8cb4a7bc15",
+   "id": "37c0f387-d8a9-4723-9668-f227703fa105",
    "metadata": {},
    "source": [
-    "## Do-It-All Code"
+    "Choosing the stretch and Q can be a bit fiddly - this is best done when visualizing the whole set of cutouts in a gallery. This is what we will do next."
    ]
   },
   {

From 41f38d8d84b1cb7ce18c5da80d5f85ff0152d30d Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Mon, 14 Jul 2025 04:11:40 +0000
Subject: [PATCH 5/7] Links to source tutorial notebooks

---
 dp1/euclid_q1_lenses.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index d895dca..257a94e 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -135,7 +135,7 @@
    "id": "7dd657db-3028-4897-ae44-a4e44c4aff63",
    "metadata": {},
    "source": [
-    "We need to find the tract and patch that this target is in:"
+    "We need to find the tract and patch that this target is in. This approach was adopted from the CST Tutorial [\"03a Image Display and Manipulation\"](https://github.com/lsst/tutorial-notebooks/blob/main/DP0.2/03a_Image_Display_and_Manipulation.ipynb)."
    ]
   },
   {
@@ -311,7 +311,7 @@
    "id": "c9b12a89-ef2c-4a62-af7f-6b8630eb899b",
    "metadata": {},
    "source": [
-    "OK - we have 6 cutouts for this target, so can go ahead and make a color composite. It took about 5 secs to make them all: we'll need to keep an eye on this, and return to the `factory` approach to try and speed things up a bit."
+    "OK - we have 6 cutouts for this target, so can go ahead and make a color composite - see below for this. It took about 5 secs to make them all: we'll need to keep an eye on this, and return to the `factory` approach to try and speed things up a bit."
    ]
   },
   {
@@ -321,7 +321,7 @@
    "source": [
     "## _gri_ Composite Image Visualization\n",
     "\n",
-    "Here's a useful function, adapted from https://github.com/lsst-sitcom/comcam_clusters/blob/main/ComCam_StarterKit.ipynb"
+    "Here's a useful function, adapted from the CST Tutorial [\"03a Image Display and Manipulation\"](https://github.com/lsst/tutorial-notebooks/blob/main/DP0.2/03a_Image_Display_and_Manipulation.ipynb)."
    ]
   },
   {
@@ -426,7 +426,7 @@
    "source": [
     "## Appendix\n",
     "\n",
-    "The code below is from the Cutout Factory demo notebook by Melissa Graham, and is being used as a source in this notebook further up."
+    "The code below is from the Cutout Factory demo notebook by Melissa Graham at https://github.com/lsst/cst-dev/blob/main/MLG_sandbox/random/cutout_factory_demo_2025-06-05.ipynb, and is experimented with in this notebook further up."
    ]
   },
   {

From ce1bde67562d2d9ef1b1a9404b552bac6cab428c Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Mon, 14 Jul 2025 07:29:24 +0000
Subject: [PATCH 6/7] Download target table from Google sheets, make cutouts
 and gallery

---
 dp1/euclid_q1_lenses.ipynb | 284 +++++++++++++++++++++++++++++--------
 1 file changed, 227 insertions(+), 57 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index 257a94e..aadc232 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -39,6 +39,7 @@
     "from lsst.afw.image import MultibandExposure\n",
     "import numpy as np\n",
     "from astropy.visualization import make_lupton_rgb\n",
+    "from astropy.table import Table, Column\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "afw_display.setDefaultBackend('matplotlib')"
@@ -196,7 +197,7 @@
    "id": "1d516446-ae23-46e1-bc00-0c8cdb3c6ce5",
    "metadata": {},
    "source": [
-    "Now to define a small bounding box, and extract the pixels in it. This first cell below _should_ work, but doesn't - maybe some tract/patch confusion. There could be some speed up here at some point, making multiple cutouts from the same patch image using the calexp object's native factory method."
+    "Now to define a small bounding box, and extract the pixels in it. This first cell below _should_ work, but doesn't - maybe some tract/patch confusion. There could be some speed up here at some point, making multiple cutouts from the same patch image using the calexp object's native factory method. See the Cutout Factory demo notebook by Melissa Graham at https://github.com/lsst/cst-dev/blob/main/MLG_sandbox/random/cutout_factory_demo_2025-06-05.ipynb for a working example of making multiple cutouts from the same patch image."
    ]
   },
   {
@@ -247,13 +248,15 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "1dff2724-6f93-4e35-83da-f88a98d55c9e",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(3, 3))\n",
     "display = afw_display.Display(frame=fig)\n",
     "display.scale('asinh', 'zscale')\n",
-    "display.mtv(cutout_image.image)\n",
+    "display.mtv(cutout.image)\n",
     "plt.show()"
    ]
   },
@@ -400,7 +403,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "showRGB(multibandexposure.image, bgr='gri', figsize=(3,3), stretch=60, Q=1, name=name)"
+    "showRGB(multibandexposure.image, bgr='gri', figsize=(3,3), stretch=60, Q=10, name=name)"
    ]
   },
   {
@@ -413,127 +416,294 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aed2a81-68aa-4fa6-9ce0-eee6c652de76",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-12T03:41:51.156141Z",
-     "iopub.status.busy": "2025-07-12T03:41:51.155432Z",
-     "iopub.status.idle": "2025-07-12T03:41:51.158677Z",
-     "shell.execute_reply": "2025-07-12T03:41:51.158064Z",
-     "shell.execute_reply.started": "2025-07-12T03:41:51.156112Z"
-    }
-   },
+   "id": "173ea5ab-2f3c-4a46-a65a-84430244f966",
+   "metadata": {},
    "source": [
-    "## Appendix\n",
+    "## Multiple Objects, Multiple Bands, Gallery of Images\n",
     "\n",
-    "The code below is from the Cutout Factory demo notebook by Melissa Graham at https://github.com/lsst/cst-dev/blob/main/MLG_sandbox/random/cutout_factory_demo_2025-06-05.ipynb, and is experimented with in this notebook further up."
+    "What we want is a function that takes in a table of targets, makes cutouts for them all (if they haven't been made already), and then enables visualization of them as a gallery of RGB color composite images (all on the same stretch)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35c08215-2fc7-40c2-a58d-8c39c655a130",
+   "id": "ecefadb1-5401-4573-a91f-3861b8f793bc",
    "metadata": {},
    "outputs": [],
    "source": [
+    "class StampCollector():\n",
+    "    def __init__(self):\n",
+    "        self.bands = ['u','g','r','i','z','y']\n",
+    "        self.cutoutSize = geom.ExtentI(32, 32)\n",
+    "        self.cutouts = {}\n",
+    "        butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n",
+    "        assert butler is not None\n",
+    "        butler.get_dataset_type('deep_coadd')\n",
+    "        self.skymap = butler.get(\"skyMap\")\n",
+    "        return\n",
+    "    \n",
+    "    def get_cutouts(self, targets, bands=None):\n",
+    "        \"\"\"Read in a table of target RA and Dec positions (in degrees) and make cutout images for them all\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        targets : `astropy.Table`\n",
+    "            Table of target coordinates.\n",
+    "        bands : list of strings\n",
+    "            Which bands to make cutouts in.\n",
+    "        \"\"\"\n",
+    "        self.targets = targets\n",
+    "        # Make IAU names if they don't exist already:\n",
+    "        if 'name' not in self.targets.columns:\n",
+    "            print(\"Adding IAU names...\")\n",
+    "            self.add_iau_names()\n",
     "\n",
+    "        if bands is not None:\n",
+    "            self.bands = bands\n",
     "\n",
-    "import lsst.afw.display as afw_display\n",
-    "from lsst.daf.butler import Butler\n",
-    "import lsst.geom as geom\n",
-    "import matplotlib.pyplot as plt\n",
+    "        print(\"Making cutouts:\")\n",
     "\n",
-    "afw_display.setDefaultBackend('matplotlib')"
+    "        # Loop over targets and bands, extracting cutouts:\n",
+    "        for i in range(len(self.targets)):\n",
+    "\n",
+    "            print(\"  \", self.targets['name'][i],\": \",end=\"\")\n",
+    "\n",
+    "            radec = geom.SpherePoint(self.targets['ra'][i], self.targets['dec'][i], geom.degrees)           \n",
+    "            tractInfo = self.skymap.findTract(radec)\n",
+    "            patchInfo = tractInfo.findPatch(radec)\n",
+    "            patch = tractInfo.getSequentialPatchIndex(patchInfo)\n",
+    "            tract = tractInfo.getId()\n",
+    "            \n",
+    "            xy = geom.PointI(tractInfo.getWcs().skyToPixel(radec))\n",
+    "            bbox = geom.BoxI(xy - cutoutSize // 2, cutoutSize)\n",
+    "            parameters = {'bbox': bbox}\n",
+    "\n",
+    "            if not self.targets['name'][i] in self.cutouts:\n",
+    "                self.cutouts[self.targets['name'][i]] = {}\n",
+    "            \n",
+    "            for band in self.bands:\n",
+    "\n",
+    "                if not band in self.cutouts[self.targets['name'][i]]:\n",
+    "                    dataId = {'tract': tract, 'patch': patch, 'band': band}\n",
+    "                    try:\n",
+    "                        self.cutouts[self.targets['name'][i]][band] = butler.get(\"deep_coadd\", parameters=parameters, dataId=dataId)\n",
+    "                        print(band,end=\"\")\n",
+    "                    except:\n",
+    "                        print(\".\",end=\"\")\n",
+    "                else:\n",
+    "                    print(\".\",end=\"\")\n",
+    "\n",
+    "            print()\n",
+    "        \n",
+    "        return\n",
+    "        \n",
+    "    def add_iau_names(self):\n",
+    "        names = []\n",
+    "        for i in range(len(self.targets)):\n",
+    "            rahms = self.targets['ra'][i] * u.hourangle\n",
+    "            decdms = self.targets['dec'][i] * u.deg\n",
+    "            coordinates = SkyCoord(ra=rahms, dec=decdms, frame='icrs')\n",
+    "            names.append((f'EUCLID J{coordinates.ra.to_string(unit=u.hourangle, sep=\"\", precision=1, pad=True)}'\n",
+    "                  f'{coordinates.dec.to_string(sep=\"\", precision=0, alwayssign=True, pad=True)}'))\n",
+    "        self.targets.add_column(names, name='name', index=0)\n",
+    "        return\n",
+    "    \n",
+    "    def make_gallery(self, nx=3, bgr=\"gri\", stretch=100, Q=1):\n",
+    "        \"\"\"Display a gallery of RGB color composite images with matplotlib.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        nx : integer\n",
+    "            Number of images in a row of the gallery.\n",
+    "        bgr : sequence\n",
+    "            A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n",
+    "            to use for each channel. If `image` only has three filters then this parameter is ignored\n",
+    "            and the filters in the image are used.\n",
+    "        stretch: int\n",
+    "            The linear stretch of the image.\n",
+    "        Q: int\n",
+    "            The Asinh softening parameter.\n",
+    "        \"\"\"\n",
+    "        # Figure out gallery dimensions:\n",
+    "        rem = len(self.targets) % nx\n",
+    "        ny = int((len(self.targets) - rem) / nx) + 1\n",
+    "        print(\"Making gallery, \",nx,\" wide by \",ny,\" deep.\")\n",
+    "\n",
+    "        # Create an nx by ny grid of subplots\n",
+    "        width = 12\n",
+    "        fig = plt.figure(figsize=(width, ny*(width/nx)))\n",
+    "        plt.axis(\"off\")\n",
+    "\n",
+    "        # Loop over targets, stepping through subplots:\n",
+    "        for k in range(len(self.targets)):\n",
+    "            ax = fig.add_subplot(ny,nx,k+1)\n",
+    "            self.show_RGB(self.targets['name'][k], bgr=bgr, ax=ax, stretch=stretch, Q=Q)\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        \n",
+    "        return\n",
+    "\n",
+    "    def show_RGB(self, name, bgr=\"gri\", ax=None, fp=None, figsize=(8,8), stretch=100, Q=5):\n",
+    "        \"\"\"Display an RGB color composite image with matplotlib.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        image : `MultibandImage`\n",
+    "            `MultibandImage` to display.\n",
+    "        bgr : sequence\n",
+    "            A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n",
+    "            to use for each channel. If `image` only has three filters then this parameter is ignored\n",
+    "            and the filters in the image are used.\n",
+    "        ax : `matplotlib.axes.Axes`\n",
+    "            Axis in a `matplotlib.Figure` to display the image.\n",
+    "            If `axis` is `None` then a new figure is created.\n",
+    "        figsize: tuple\n",
+    "            Size of the `matplotlib.Figure` created.\n",
+    "            If `ax` is not `None` then this parameter is ignored.\n",
+    "        stretch: int\n",
+    "            The linear stretch of the image.\n",
+    "        Q: int\n",
+    "            The Asinh softening parameter.\n",
+    "        \"\"\"\n",
+    "        bands = [bgr[0],bgr[1],bgr[2]]\n",
+    "        cutouts = [self.cutouts[name][band] for band in bands]\n",
+    "        image = MultibandExposure.fromExposures(bands, cutouts).image\n",
+    "        \n",
+    "        # Extract the primary image component of each Exposure with the .image property, and use .array to get a NumPy array view.\n",
+    "        rgb = make_lupton_rgb(image_r=image[bgr[2]].array,  # numpy array for the r channel\n",
+    "                              image_g=image[bgr[1]].array,  # numpy array for the g channel\n",
+    "                              image_b=image[bgr[0]].array,  # numpy array for the b channel\n",
+    "                              stretch=stretch, Q=Q)  # parameters used to stretch and scale the pixel values\n",
+    "        if ax is None:\n",
+    "            fig = plt.figure(figsize=figsize)\n",
+    "            ax = fig.add_subplot(1,1,1)\n",
+    "        \n",
+    "        plt.axis(\"off\")\n",
+    "        ax.imshow(rgb, interpolation='nearest', origin='lower')\n",
+    "    \n",
+    "        if name is not None:\n",
+    "            plt.text(0, 31, name, color='white', fontsize=16, horizontalalignment='left', verticalalignment='top')\n",
+    "        \n",
+    "        plt.text(0, 2, bgr, color='white', fontsize=16, horizontalalignment='left', verticalalignment='top')\n",
+    "\n",
+    "        del image, cutouts, rgb\n",
+    "        return\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c17463f-5e08-4cd9-a5d4-4a832cb2b430",
+   "metadata": {},
+   "source": [
+    "With our `StampCollector` ready, we can prepare and provide a table of targets. Here's a simple example with just two Euclid lens candidates. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8014a204-b2ed-42de-b1db-69df528e9349",
+   "id": "47fdc1aa-68ca-486d-b240-a2a090dfd702",
    "metadata": {},
    "outputs": [],
    "source": [
-    "butler = Butler('dp02', collections='2.2i/runs/DP0.2')\n",
-    "dataId = {'visit': 192350, 'detector': 175, 'band': 'i'}\n",
-    "calexp = butler.get('calexp', **dataId)"
+    "target_table = Table(names=('ra', 'dec'), dtype=('f4', 'f4'))\n",
+    "target_table.add_row((59.496380, -48.494726))\n",
+    "target_table.add_row((59.626134, -49.06175))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47f755c2-36bf-4f2e-a225-7f519d274180",
+   "id": "3f96ccfd-4018-43bd-81bb-0b757d5c8475",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = plt.figure(figsize=(3,3))\n",
-    "display = afw_display.Display(frame=fig)\n",
-    "display.scale('asinh', 'zscale')\n",
-    "display.mtv(calexp.image)\n",
-    "plt.show()"
+    "stamp_collector = StampCollector()\n",
+    "stamp_collector.get_cutouts(target_table,bands=['g','r','i'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "66bcfac7-d835-4068-9970-43b59e53473f",
+   "id": "b5517c76-d685-4d51-b455-4061a6e5672a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cutoutSize = geom.ExtentI(301, 301)\n",
-    "\n",
-    "xy1 = geom.PointI(2250, 700)\n",
-    "bbox1 = geom.BoxI(xy1 - cutoutSize // 2, cutoutSize)\n",
-    "\n",
-    "xy2 = geom.PointI(400, 1750)\n",
-    "bbox2 = geom.BoxI(xy2 - cutoutSize // 2, cutoutSize)"
+    "stamp_collector.targets"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "430b2a34-ca4a-44e6-a537-badd39f36719",
+   "id": "b6424825-9bde-4db7-89e4-1a55f307ad4b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cutout1 = calexp.Factory(calexp, bbox1)\n",
-    "cutout2 = calexp.Factory(calexp, bbox2)"
+    "stamp_collector.make_gallery(stretch=100,Q=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfee8c96-4229-4663-bcb7-b25bd3ab3491",
+   "metadata": {},
+   "source": [
+    "Now let's get all the Euclid lens candidates, in all DP1 fields. These can be downloaded as CSV from a Google sheet on the web, using the pandas native `read_csv` function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84f02bbe-0700-49dc-bb72-cfcbca463448",
+   "id": "356f0bba-95f5-4755-aa90-5f0e10e8e9ae",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = plt.figure(figsize=(3, 3))\n",
-    "display = afw_display.Display(frame=fig)\n",
-    "display.scale('asinh', 'zscale')\n",
-    "display.mtv(cutout1.image)\n",
-    "plt.show()"
+    "import pandas as pd\n",
+    "\n",
+    "sheet_id = '1OInIecou_c2NqVdpTBSYB2V_j2vanKvKrTBf0qtLCpE' \n",
+    "csv_url = f'https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv' \n",
+    "\n",
+    "# Read the data into a pandas DataFrame\n",
+    "df = pd.read_csv(csv_url)\n",
+    "\n",
+    "# Convert the DataFrame to an Astropy Table\n",
+    "all_targets = Table.from_pandas(df)[['Euclid_RA', 'Euclid_Dec']] \n",
+    "all_targets.rename_column('Euclid_RA', 'ra')\n",
+    "all_targets.rename_column('Euclid_Dec', 'dec')\n",
+    "print(all_targets)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86ad2255-44bd-4339-9866-c4ff723f7d01",
+   "id": "f07a7260-737f-4738-8b71-f49fcfe90a3c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = plt.figure(figsize=(3, 3))\n",
-    "display = afw_display.Display(frame=fig)\n",
-    "display.scale('asinh', 'zscale')\n",
-    "display.mtv(cutout2.image)\n",
-    "plt.show()"
+    "all_stamp_collector = StampCollector()\n",
+    "all_stamp_collector.get_cutouts(all_targets, bands=['u','g','r','i','z','y'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "71929a34-504c-4942-8e3d-4f9075ea1c86",
+   "id": "c4dd572c-53ce-4ed7-99ff-daa75a6c65c6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "all_stamp_collector.make_gallery(nx=3,stretch=100,Q=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84d8a66-f711-4bac-96bc-ba3b8e0579c6",
+   "metadata": {},
+   "source": [
+    "## Further Work\n",
+    "\n",
+    "* Investigate the Cutout Factory approach - if we can get the world coordinate to image coordinate transformation right, this should give us a bit of a speed up for cases where we have multiple targets in the same patch.\n",
+    "* The gallery needs work: better RGB representation (try and follow the First Look set up?), and a more efficient packing of small images (including scaled fontsize for the overlays).\n",
+    "* Add a method to package ugrizy cutouts into the right format for the Rubin SharPy - and then run that code to sharpen up the images and look for lensed arcs!"
+   ]
   }
  ],
  "metadata": {

From 7229e1d737f79cb070ec1a0b3c05e03c49fecf83 Mon Sep 17 00:00:00 2001
From: Phil Marshall <dr.phil.marshall@gmail.com>
Date: Wed, 16 Jul 2025 04:16:27 +0000
Subject: [PATCH 7/7] PrettyPicture adopted and compared

---
 dp1/euclid_q1_lenses.ipynb | 218 +++++++++++++++++++++++++++++++++++--
 1 file changed, 207 insertions(+), 11 deletions(-)

diff --git a/dp1/euclid_q1_lenses.ipynb b/dp1/euclid_q1_lenses.ipynb
index aadc232..f120ebe 100644
--- a/dp1/euclid_q1_lenses.ipynb
+++ b/dp1/euclid_q1_lenses.ipynb
@@ -355,6 +355,8 @@
     "        The linear stretch of the image.\n",
     "    Q: int\n",
     "        The Asinh softening parameter.\n",
+    "    name: str\n",
+    "        The name of the object/field to be displayed.\n",
     "    \"\"\"\n",
     "    # If the image only has 3 bands, reverse the order of the bands to produce the RGB image\n",
     "    if len(image) == 3:\n",
@@ -374,7 +376,11 @@
     "    if name is not None:\n",
     "        plt.text(0, 31, name, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
     "    \n",
-    "    plt.text(0, 2, bgr, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')"
+    "    plt.text(0, 2, 'astropy Lupton '+bgr, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "    \n",
+    "    plt.tight_layout();\n",
+    "\n",
+    "    return"
    ]
   },
   {
@@ -406,12 +412,123 @@
     "showRGB(multibandexposure.image, bgr='gri', figsize=(3,3), stretch=60, Q=10, name=name)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "38a9221a-1ecf-48f7-9584-991f9d8b512c",
+   "metadata": {},
+   "source": [
+    "We can use a more recent method for making color composite images, that was used in the First Look release! Here's an alternative RGB function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb37f19f-cb5f-4fb0-b2ef-7ee2308104ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lsst.pipe.tasks.prettyPictureMaker import PrettyPictureConfig, PrettyPictureTask\n",
+    "from lsst.pipe.tasks.prettyPictureMaker._task import ChannelRGBConfig\n",
+    "\n",
+    "def prettyRGB(images, ax=None, fp=None, figsize=(8,8), stretch=250, Q=0.7, name=None):\n",
+    "    \"\"\"Display an RGB (irg) color composite image with matplotlib using the prettyPictureMaker pipe task.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    images : dict\n",
+    "        Dictionary of images to display.\n",
+    "    ax : `matplotlib.axes.Axes`\n",
+    "        Axis in a `matplotlib.Figure` to display the image.\n",
+    "        If `axis` is `None` then a new figure is created.\n",
+    "    figsize: tuple\n",
+    "        Size of the `matplotlib.Figure` created.\n",
+    "        If `ax` is not `None` then this parameter is ignored.\n",
+    "    stretch: int\n",
+    "        The linear stretch of the image.\n",
+    "    Q: int\n",
+    "        The Asinh softening parameter.\n",
+    "    name: str\n",
+    "        The name of the object/field to be displayed.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    prettyPicConfig = PrettyPictureTask.ConfigClass()\n",
+    "    # Magic from Nate Lust:\n",
+    "    prettyPicConfig.localContrastConfig.doLocalContrast = False\n",
+    "    prettyPicConfig.localContrastConfig.sigma = 30\n",
+    "    prettyPicConfig.localContrastConfig.clarity = 0.8\n",
+    "    prettyPicConfig.localContrastConfig.shadows = 0\n",
+    "    prettyPicConfig.localContrastConfig.highlights = -1.5\n",
+    "    prettyPicConfig.localContrastConfig.maxLevel = 2\n",
+    "    prettyPicConfig.imageRemappingConfig.absMax = 11000\n",
+    "    prettyPicConfig.luminanceConfig.max = 100\n",
+    "    prettyPicConfig.luminanceConfig.stretch = stretch    # from kwargs\n",
+    "    prettyPicConfig.luminanceConfig.floor = 0\n",
+    "    prettyPicConfig.luminanceConfig.Q = Q                # from kwargs\n",
+    "    prettyPicConfig.luminanceConfig.highlight = 0.905882\n",
+    "    prettyPicConfig.luminanceConfig.shadow = 0.12\n",
+    "    prettyPicConfig.luminanceConfig.midtone = 0.25\n",
+    "    prettyPicConfig.doPSFDeconcovlve = False   # sic\n",
+    "    prettyPicConfig.exposureBrackets = None\n",
+    "    prettyPicConfig.colorConfig.maxChroma = 80\n",
+    "    prettyPicConfig.colorConfig.saturation = 0.6\n",
+    "    prettyPicConfig.cieWhitePoint = (0.28, 0.28)\n",
+    "    prettyPicConfig.channelConfig = dict(\n",
+    "        g=ChannelRGBConfig(r=0.0, g=0.0, b=1.0),\n",
+    "        r=ChannelRGBConfig(r=0.0, g=1.0, b=0.0),\n",
+    "        i=ChannelRGBConfig(r=1.0, g=0.0, b=0.0),\n",
+    "    )\n",
+    "    prettyPicTask = PrettyPictureTask(config=prettyPicConfig)\n",
+    "    \n",
+    "    bands = \"gri\"\n",
+    "    coaddG = images['g']\n",
+    "    coaddR = images['r']\n",
+    "    coaddI = images['i']\n",
+    "    \n",
+    "    prettyPicInputs = prettyPicTask.makeInputsFromExposures(i=coaddI, r=coaddR, g=coaddG)\n",
+    "    coaddRgbStruct = prettyPicTask.run(prettyPicInputs)\n",
+    "    coaddRgb = coaddRgbStruct.outputRGB\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig = plt.figure(figsize=figsize)\n",
+    "        ax = fig.add_subplot(1,1,1)\n",
+    "    \n",
+    "    plt.axis(\"off\")\n",
+    "    ax.imshow(coaddRgb, interpolation='nearest', origin='lower')\n",
+    "\n",
+    "    if name is not None:\n",
+    "        plt.text(0, 31, name, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "    \n",
+    "    plt.text(0, 2, 'PrettyPictureTask '+bands, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "    \n",
+    "    plt.tight_layout();\n",
+    "\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc1f388a-18d9-473d-bd5c-994863f324f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(8,4))\n",
+    "\n",
+    "ax = fig.add_subplot(1,2,1)\n",
+    "showRGB(multibandexposure.image, bgr='gri', ax=ax, stretch=60, Q=10, name=name)\n",
+    "\n",
+    "ax = fig.add_subplot(1,2,2)\n",
+    "prettyRGB(cutout, ax=ax, stretch=750, Q=0.7, name=name)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "37c0f387-d8a9-4723-9668-f227703fa105",
    "metadata": {},
    "source": [
-    "Choosing the stretch and Q can be a bit fiddly - this is best done when visualizing the whole set of cutouts in a gallery. This is what we will do next."
+    "The `prettyPictureMaker` code does a better job at bringing out the color contrast.\n",
+    "\n",
+    "Choosing the stretch and Q, in either method, can be a bit fiddly - this is best done when visualizing the whole set of cutouts in a gallery. This is what we will do next."
    ]
   },
   {
@@ -508,13 +625,15 @@
     "        self.targets.add_column(names, name='name', index=0)\n",
     "        return\n",
     "    \n",
-    "    def make_gallery(self, nx=3, bgr=\"gri\", stretch=100, Q=1):\n",
+    "    def make_gallery(self, nx=3, style='Pretty', bgr=\"gri\", stretch=750, Q=0.7):\n",
     "        \"\"\"Display a gallery of RGB color composite images with matplotlib.\n",
     "        \n",
     "        Parameters\n",
     "        ----------\n",
     "        nx : integer\n",
     "            Number of images in a row of the gallery.\n",
+    "        style : str\n",
+    "            Method to use, Lupton (astropy) or Pretty (Rubin)\n",
     "        bgr : sequence\n",
     "            A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n",
     "            to use for each channel. If `image` only has three filters then this parameter is ignored\n",
@@ -537,8 +656,12 @@
     "        # Loop over targets, stepping through subplots:\n",
     "        for k in range(len(self.targets)):\n",
     "            ax = fig.add_subplot(ny,nx,k+1)\n",
-    "            self.show_RGB(self.targets['name'][k], bgr=bgr, ax=ax, stretch=stretch, Q=Q)\n",
-    "\n",
+    "            if style == 'Lupton':\n",
+    "                self.show_RGB(self.targets['name'][k], bgr=bgr, ax=ax, stretch=stretch, Q=Q)\n",
+    "            else:\n",
+    "                assert bgr == \"gri\"\n",
+    "                self.pretty_RGB(self.targets['name'][k], ax=ax, stretch=stretch, Q=Q)\n",
+    "        \n",
     "        plt.tight_layout()\n",
     "        \n",
     "        return\n",
@@ -548,8 +671,8 @@
     "        \n",
     "        Parameters\n",
     "        ----------\n",
-    "        image : `MultibandImage`\n",
-    "            `MultibandImage` to display.\n",
+    "        name : str\n",
+    "            Name of the object/field to display.\n",
     "        bgr : sequence\n",
     "            A 3-element sequence of filter names (i.e. keys of the exps dict) indicating what band\n",
     "            to use for each channel. If `image` only has three filters then this parameter is ignored\n",
@@ -588,7 +711,80 @@
     "\n",
     "        del image, cutouts, rgb\n",
     "        return\n",
-    "        "
+    "\n",
+    "    def pretty_RGB(self, name, ax=None, fp=None, figsize=(8,8), stretch=750, Q=0.7):\n",
+    "        \"\"\"Display an RGB (irg) color composite image with matplotlib using the prettyPictureMaker pipe task.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        name : str\n",
+    "            Name of the object/field to display.\n",
+    "        ax : `matplotlib.axes.Axes`\n",
+    "            Axis in a `matplotlib.Figure` to display the image.\n",
+    "            If `axis` is `None` then a new figure is created.\n",
+    "        figsize: tuple\n",
+    "            Size of the `matplotlib.Figure` created.\n",
+    "            If `ax` is not `None` then this parameter is ignored.\n",
+    "        stretch: int\n",
+    "            The linear stretch of the image.\n",
+    "        Q: int\n",
+    "            The Asinh softening parameter.\n",
+    "        name: str\n",
+    "            The name of the object/field to be displayed.\n",
+    "        \"\"\"\n",
+    "    \n",
+    "        prettyPicConfig = PrettyPictureTask.ConfigClass()\n",
+    "        # Magic from Nate Lust:\n",
+    "        prettyPicConfig.localContrastConfig.doLocalContrast = False\n",
+    "        prettyPicConfig.localContrastConfig.sigma = 30\n",
+    "        prettyPicConfig.localContrastConfig.clarity = 0.8\n",
+    "        prettyPicConfig.localContrastConfig.shadows = 0\n",
+    "        prettyPicConfig.localContrastConfig.highlights = -1.5\n",
+    "        prettyPicConfig.localContrastConfig.maxLevel = 2\n",
+    "        prettyPicConfig.imageRemappingConfig.absMax = 11000\n",
+    "        prettyPicConfig.luminanceConfig.max = 100\n",
+    "        prettyPicConfig.luminanceConfig.stretch = stretch    # from kwargs\n",
+    "        prettyPicConfig.luminanceConfig.floor = 0\n",
+    "        prettyPicConfig.luminanceConfig.Q = Q                # from kwargs\n",
+    "        prettyPicConfig.luminanceConfig.highlight = 0.905882\n",
+    "        prettyPicConfig.luminanceConfig.shadow = 0.12\n",
+    "        prettyPicConfig.luminanceConfig.midtone = 0.25\n",
+    "        prettyPicConfig.doPSFDeconcovlve = False   # sic\n",
+    "        prettyPicConfig.exposureBrackets = None\n",
+    "        prettyPicConfig.colorConfig.maxChroma = 80\n",
+    "        prettyPicConfig.colorConfig.saturation = 0.6\n",
+    "        prettyPicConfig.cieWhitePoint = (0.28, 0.28)\n",
+    "        prettyPicConfig.channelConfig = dict(\n",
+    "            g=ChannelRGBConfig(r=0.0, g=0.0, b=1.0),\n",
+    "            r=ChannelRGBConfig(r=0.0, g=1.0, b=0.0),\n",
+    "            i=ChannelRGBConfig(r=1.0, g=0.0, b=0.0),\n",
+    "        )\n",
+    "        prettyPicTask = PrettyPictureTask(config=prettyPicConfig)\n",
+    "        \n",
+    "        bands = \"gri\"\n",
+    "        coaddG = self.cutouts[name]['g']\n",
+    "        coaddR = self.cutouts[name]['r']\n",
+    "        coaddI = self.cutouts[name]['i']\n",
+    "        \n",
+    "        prettyPicInputs = prettyPicTask.makeInputsFromExposures(i=coaddI, r=coaddR, g=coaddG)\n",
+    "        coaddRgbStruct = prettyPicTask.run(prettyPicInputs)\n",
+    "        coaddRgb = coaddRgbStruct.outputRGB\n",
+    "    \n",
+    "        if ax is None:\n",
+    "            fig = plt.figure(figsize=figsize)\n",
+    "            ax = fig.add_subplot(1,1,1)\n",
+    "        \n",
+    "        plt.axis(\"off\")\n",
+    "        ax.imshow(coaddRgb, interpolation='nearest', origin='lower')\n",
+    "    \n",
+    "        if name is not None:\n",
+    "            plt.text(0, 31, name, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "        \n",
+    "        plt.text(0, 2, bands, color='white', fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
+    "        \n",
+    "        plt.tight_layout();\n",
+    "    \n",
+    "        return"
    ]
   },
   {
@@ -639,7 +835,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stamp_collector.make_gallery(stretch=100,Q=5)"
+    "stamp_collector.make_gallery(stretch=750,Q=0.7)"
    ]
   },
   {
@@ -680,7 +876,7 @@
    "outputs": [],
    "source": [
     "all_stamp_collector = StampCollector()\n",
-    "all_stamp_collector.get_cutouts(all_targets, bands=['u','g','r','i','z','y'])"
+    "all_stamp_collector.get_cutouts(all_targets, bands=['g','r','i'])"
    ]
   },
   {
@@ -690,7 +886,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_stamp_collector.make_gallery(nx=3,stretch=100,Q=5)"
+    "all_stamp_collector.make_gallery(nx=3,style=\"Pretty\",stretch=750,Q=0.7)"
    ]
   },
   {