diff --git a/Commissioning/101_LSSTCam_visits_database_2025.ipynb b/Commissioning/101_LSSTCam_visits_database.ipynb
similarity index 77%
rename from Commissioning/101_LSSTCam_visits_database_2025.ipynb
rename to Commissioning/101_LSSTCam_visits_database.ipynb
index 09084beb..0ce746f8 100644
--- a/Commissioning/101_LSSTCam_visits_database_2025.ipynb
+++ b/Commissioning/101_LSSTCam_visits_database.ipynb
@@ -10,7 +10,7 @@
    "id": "94406489-923b-4f2e-a748-078fec0028c0",
    "metadata": {},
    "source": [
-    "# 101. LSSTCam visits database (2025 only)\n",
+    "# 101. LSSTCam visits database\n",
     "\n",
     "<div style=\"max-width:300px; float: left; margin-right: 1em\">\n",
     "\n",
@@ -21,7 +21,7 @@
     "For the Rubin Science Platform at data.lsst.cloud.\\\n",
     "Container Size: Large\\\n",
     "LSST Science Pipelines version: v29.2.0\\\n",
-    "Last verified to run: 2026-05-01\\\n",
+    "Last verified to run: 2026-06-09\\\n",
     "Repository: <a href=\"https://github.com/lsst/tutorial-notebooks\">github.com/lsst/tutorial-notebooks</a>\\\n",
     "DOI: <a href=\"https://doi.org/10.11578/rubin/dc.20250909.20\">10.11578/rubin/dc.20250909.20</a>"
    ]
@@ -31,13 +31,13 @@
    "id": "9b153e12-db08-42cc-9551-806034aabbbb",
    "metadata": {},
    "source": [
-    "**Learning objective:** How to query and retrieve data from the commissioning visits database file.\n",
+    "**Learning objective:** How to query and retrieve data from the temporary LSSTCam visits database file.\n",
     "\n",
-    "**LSST data products:** The `lsstcam_20250930.db` file available on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a>.\n",
+    "**LSST data products:** The file `prelsst_20260607_visits.db` has been made temporarily available until it is superseded by upcoming data releases.\n",
     "\n",
     "**Packages:** `sqlite3`, `rubin_sim`\n",
     "\n",
-    "**Credit:** Developed by the Rubin Community Science team, using materials from the Rubin Survey Scheduling team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
+    "**Credit:** Developed by the Rubin Community Science team together with the Rubin Survey Scheduling team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
     "\n",
     "**Get Support:**\n",
     "Everyone is encouraged to ask questions or raise issues in the \n",
@@ -46,18 +46,6 @@
     "Rubin staff will respond to all questions posted there."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e41cccb2-c9ec-491b-ab3b-bc80e3a629ee",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>This tutorial notebook is superseded by \"DP2/102_Visit_and_tract_metadata\" and \"Commissioning/102_LSSTCam_visits_metadata\"</b>.<br>\n",
-    "    The DP2 notebook includes all visits and tracts that will be included in Data Preview 2, and should be used to make plots such as Figure 1, below.\n",
-    "    The Commissioning notebook presents metadata for LSSTCam visits, some of which will be released as part of the Prompt data products.\n",
-    "</div>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0f288152-c837-4515-91fc-d9e630a17fac",
@@ -65,47 +53,38 @@
    "source": [
     "## 1. Introduction\n",
     "\n",
-    "This tutorial demonstrates how to query and load data from the SQL-formatted table of commissioning visits that is available on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a> and also as a shared file in the Rubin Science Platform.\n",
-    "\n",
-    "**This is a temporary, static database file with non-standard schema and formatting, which only includes LSSTCam visits obtained prior to Sep 30 2025, and which is provided as a convenience.** \n",
-    "\n",
+    "This tutorial demonstrates how to query and load data from a temporary SQL-formatted table of LSSTCam visits that is available in the Rubin Science Platform.\n",
+    "An early version of this file, for visits up to the end of September 2025, was first made available on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a>.\n",
+    "**This is a temporary, static database file with non-standard schema and formatting. It only includes LSSTCam visit metadata from April 2025 to June 2026.** \n",
     "For more recent visits and a forecast of the Rubin scheduler, see the tutorial notebook for the Rubin Schedule Viewer.\n",
-    "The future Rubin data releases will similar information in their `Visit` and `CcdVisit` tables."
+    "The future Rubin data releases will have similar information in their `Visit` and `CcdVisit` tables."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3478f32b-5dab-4f72-ad89-7a917d86d016",
+   "id": "fae0e6c4-22cc-48c2-aa0b-6472c09b123a",
    "metadata": {},
    "source": [
     "**Science Validation surveys.**\n",
     "\n",
-    "It is recommended to review the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a> for details on the strategy and results of the commissioning surveys.\n",
-    "\n",
-    "Science images with LSSTCam began on 04 April 2025, at first acquiring small field survey visits in sequences of $\\sim10$ visits per filter with small dithers, similar to the LSSTComCam strategy which resulted in Data Preview 1. These small field survey visits included the images which contributed toward <a href=\"https://rubinobservatory.org/news/rubin-first-look\">Rubin First Look</a>.\n",
-    "\n",
+    "Science images with LSSTCam began on 04 April 2025, at first acquiring small field survey visits in sequences of $\\sim$10 visits per filter with small dithers.\n",
+    "These small field survey visits included the images which contributed toward <a href=\"https://rubinobservatory.org/news/rubin-first-look\">Rubin First Look</a>.\n",
     "The Science Validation (SV) survey began on 20 June 2025, acquiring visits in a manner consistent with the planned operations survey for the LSST, but within a limited area.\n",
+    "Review the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a> for details on the strategy. \n",
     "The contiguous part of the SV area follows the ecliptic plane from dense regions of the Galactic Bulge through low-dust regions within the planned LSST Wide Fast Deep (WFD).\n",
-    "Four of the planned LSST Deep Drilling Fields (DDFs) were included in the SV survey, and a secondary area within the low-dust WFD was included to provide targets when the primary or DDF fields were not available."
+    "Four of the planned LSST Deep Drilling Fields (DDFs) were included in the SV survey, and a secondary area within the low-dust WFD was included to provide targets when the primary or DDF fields were not available.\n",
+    "The scientifically validated subset of these images obtained prior to Jan 7 2026 will be released as Data Preview 2 (DP2)."
    ]
   },
   {
-   "attachments": {
-    "22e5c212-bdbe-481a-b499-88319da07109.png": {
-     "image/png": 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"
-    }
-   },
    "cell_type": "markdown",
-   "id": "b52fbccd-b4a5-486a-a0ed-a8a1431f853e",
+   "id": "3478f32b-5dab-4f72-ad89-7a917d86d016",
    "metadata": {},
    "source": [
-    "<div style=\"max-width:500px\">\n",
-    "\n",
-    "![lsstcam_nvisits.png](attachment:22e5c212-bdbe-481a-b499-88319da07109.png)\n",
-    "\n",
-    "</div>\n",
+    "**Pre-LSST, AOS commissioning, and engineering visits.**\n",
     "\n",
-    "> **Figure 1:** All science visits acquired during LSSTCam commissioning. Both the small field surveys and the four SV DDFs appear as non-contiguous yellow regions in this plot. This plot is from the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a>, and instructions for recreating a version of it are in Section 5."
+    "Throughout the first half of 2026, pre-LSST visits were obtained in the DDFs where templates exist, for the purpose of alert production, and for engineering and Active Optics System (AOS) commissioning with the goal of reaching stable image quality metrics which meet the conditions for starting the LSST.\n",
+    "Many of the latter visits have been tagged as suitable for science (pending processing and science validation)."
    ]
   },
   {
@@ -115,8 +94,8 @@
    "source": [
     "**Caveats.**\n",
     "\n",
-    "* **Image quality (IQ) is variable.** The database file includes a total of 21647 commissioning visits. This excludes bad visits, but includes visits with a wide range of data quality due to both cloud extinction and/or delivered IQ or engineering issues. Keep in mind that while these observations were obtained, the Active Optics System (AOS) was being commissioned and it was winter.\n",
-    "* **Not all of these visits will be in Data Preview 2 (DP2).** Although an initial cut of bad visits have been made, users should expect that additional cuts will be made to the visits that are included and released as part of DP2.\n",
+    "* **Not all of these visits lead to scientifically validated data products.** Some will end up excluded from the DP2 and the Prompt Products datasets. Although an initial cut of bad visits have been made on the inputs to the database, users should expect that additional cuts post-processing.\n",
+    "* **Image quality (IQ) is variable.** The database file excludes bad visits, but includes visits with a wide range of data quality due to both cloud extinction and/or delivered IQ or engineering issues. Keep in mind that while these visits were obtained, the AOS was being commissioned.\n",
     "* **Measured IQ values may change.** Some columns contain NaNs, where the summit quicklook processing did not provide a useful value. Many of these problems will be resolved with later processing. Users should anticipate that some measured IQ values will change."
    ]
   },
@@ -127,7 +106,9 @@
    "source": [
     "### 1.1. Import packages\n",
     "\n",
-    "Import `sqlite3` to read the SQL-formatted database file, and import the `maf` module from the `rubin_sim` package to use the (<a href='https://rubin-sim.lsst.io/maf.html'>Metric Analysis Framework</a>) functions. Also import standard python science packages and the `lsst.utils.plotting` package."
+    "Import `sqlite3` to read the SQL-formatted database file, and import the `maf` module from the `rubin_sim` package to use the (<a href='https://rubin-sim.lsst.io/maf.html'>Metric Analysis Framework</a>) functions.\n",
+    "Import the `skyproj` package ([skyproj.readthedocs.io](https://skyproj.readthedocs.io/en/latest/)) for plotting all-sky projection plots, the `healpy` package ([healpy.readthedocs.io](https://healpy.readthedocs.io/en/latest/)) for dealing with HEALPix.\n",
+    "Also import standard python science packages and the `lsst.utils.plotting` package."
    ]
   },
   {
@@ -140,10 +121,15 @@
     "import sqlite3\n",
     "from rubin_sim import maf\n",
     "\n",
+    "import skyproj\n",
+    "import healpy as hp\n",
+    "\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import astropy.units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
     "from tabulate import tabulate\n",
     "from astropy.time import Time\n",
     "from lsst.utils.plotting import (get_multiband_plot_colors,\n",
@@ -167,7 +153,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'"
+    "db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/prelsst_20260607_visits.db'"
    ]
   },
   {
@@ -239,7 +225,7 @@
    "source": [
     "### 2.1. Key columns\n",
     "\n",
-    "The database contains 217 columns in total, but these are the key columns used in this tutorial.\n",
+    "The database contains many columns, but these are the key columns used in this tutorial.\n",
     "\n",
     "* `observation_reason` : The source of the visit in the Feature Based Scheduler (FBS).\n",
     "* `target_name` : The name of the sky region for the visit.\n",
@@ -278,8 +264,6 @@
    "id": "e4a0284b-381c-4966-9e35-e48624410608",
    "metadata": {},
    "source": [
-    "#### 2.2.1. Explore the schema\n",
-    "\n",
     "Print the names of all tables in the database. There is only one, the `observations` table."
    ]
   },
@@ -326,7 +310,8 @@
    "id": "0eadf049-d535-43aa-b955-52b1ad91aebb",
    "metadata": {},
    "source": [
-    "#### 2.2.2. Query the database\n",
+    "\\\n",
+    "**Example query.**\n",
     "\n",
     "As an example, create a query to return columns `fieldRA`, `fieldDec`, and `band` from the `observations` table for all $r$-band visits obtained at an airmass less than 1.5 and with an `observation_reason` of \"field_survey_science\".\n",
     "The column `observation_reason` is discussed in Section 3.1. "
@@ -381,7 +366,6 @@
    "outputs": [],
    "source": [
     "del query, results\n",
-    "\n",
     "cursor.close()\n",
     "del cursor"
    ]
@@ -393,7 +377,7 @@
    "source": [
     "### 2.3. Read as a pandas dataframe\n",
     "\n",
-    "As the file is only 81 M, it is small enough to be loaded in its entirety as a `pandas` dataframe.\n",
+    "The file is small enough to be loaded in its entirety as a `pandas` dataframe.\n",
     "\n",
     "Read the SQL-formatted table as a pandas dataframe, `df`, and print the number of rows."
    ]
@@ -450,21 +434,22 @@
    "id": "4c2e5aeb-d61c-4c81-b8d1-d9a2f905752d",
    "metadata": {},
    "source": [
-    "## 3. Surveys and targets\n",
+    "## 3. Observation reason and target\n",
     "\n",
-    "Every visit has both an `observation_reason` and a `target_name`.\n",
+    "There are a few rows of the database that indicate the _motivation_ behind each observation (each visit).\n",
+    "All rows of the visits database have `img_type` = `science`, but as explained in Section 1 this does not necessarily mean they will pass science validation and be released.\n",
+    "The column `science_program` is an internal designation, `BLOCK-XXX`, and probably not useful for most users.\n",
+    "\n",
+    "Every visit has both an `observation_reason` and a `target_name`, and these are useful to explore.\n",
+    "\n",
+    "### 3.1. Observation reason\n",
     "\n",
     "The `observation_reason` is the motivation as to why the visit was obtained.\n",
     "In other words, it indicates the scheduler mode or the survey.\n",
-    "For this database, for example, observation reasons include the SV WFD configurations, the SV DDF programs, target of opportunity (ToO), and the small field surveys.\n",
-    "\n",
-    "The `target_name` is either a region of the LSST WFD (e.g., bulge, low-dust) or a proper name (for DDFs, or the commissioning small field survey fields).\n",
-    "Since regions can overlap, or a single visit can overlap the boundaries of multiple regions, the `target_name` is often a comma-separated list.\n",
-    "For example, most DDFs are also in the low-dust extragalactic regions of the LSST WFD.\n",
     "\n",
-    "### 3.1. Observation reason\n",
+    "Option to print _all_ unique values of the `observation_reason` column and the number of visits for each.\n",
     "\n",
-    "Print the unique values of the `observation_reason` column and the number of visits for each."
+    "> **Warning:** The feature-based scheduler (FBS) and Rubin operations were in the early stages when these visits were obtained, and the values of the `observation_reason` column exhibit diversity; explanations for every single value are not provided at this time."
    ]
   },
   {
@@ -474,9 +459,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "values, counts = np.unique(df['observation_reason'], return_counts=True)\n",
-    "for value, count in zip(values, counts):\n",
-    "    print('%25s %5i' % (value, count))"
+    "# values, counts = np.unique(df['observation_reason'], return_counts=True)\n",
+    "# for value, count in zip(values, counts):\n",
+    "#     print('%25s %5i' % (value, count))\n",
+    "# del values, counts"
    ]
   },
   {
@@ -484,7 +470,7 @@
    "id": "19418097-1dcc-4063-8ff5-3e60dd23c7f4",
    "metadata": {},
    "source": [
-    "Print the number of visits done for small field surveys, deep drilling fields, target of opportunity, and template creation."
+    "Print the number of visits done for small field surveys, deep drilling fields, target of opportunity, AOS commissioning, and alert production."
    ]
   },
   {
@@ -497,49 +483,15 @@
     "Nsfs = len(df.query(\"observation_reason == 'field_survey_science'\"))\n",
     "Nddf = len(df.query(\"observation_reason.str.contains('ddf')\"))\n",
     "Ntoo = len(df.query(\"observation_reason.str.contains('too')\"))\n",
-    "Ntas = len(df.query(\"observation_reason == 'template_area_singles_i'\"))\n",
-    "\n",
-    "print(\"Number of visits done for small field surveys: \", Nsfs)\n",
-    "print(\"Number of visits done for SV DDFs: \", Nddf)\n",
-    "print(\"Number of visits done for target of opportuntity: \", Ntoo)\n",
-    "print(\"Number of visits done for templates: \", Ntas)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31354530-f6e3-44a6-898a-d676824f9516",
-   "metadata": {},
-   "source": [
-    "Print the number of visits done as part of the primary wide LSST Science Validation (SV WFD -- excluding the SV DDFs and all the other observation reasons above)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "067671ca-7aac-4046-9e2d-8039d6998833",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Nwfd = len(df) - Nsfs - Nddf - Ntoo - Ntas\n",
-    "print(\"Number of visits done for the SV WFD: \", Nwfd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e48c9a6-5877-4777-8d7c-631a1dd7e363",
-   "metadata": {},
-   "source": [
-    "Clean up."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d08eeaf-b166-4e3a-9237-856f0af1cdad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del Nsfs, Nddf, Ntoo, Ntas, Nwfd"
+    "Naos = len(df.query(\"observation_reason.str.contains('aos')\"))\n",
+    "Nalrt = len(df.query(\"observation_reason.str.contains('alert')\"))\n",
+    "print(\"Number of visits done for\")\n",
+    "print(\"small field surveys:    \", Nsfs)\n",
+    "print(\"deep drilling fields:   \", Nddf)\n",
+    "print(\"target of opportuntity: \", Ntoo)\n",
+    "print(\"AOS commissioning:      \", Naos)\n",
+    "print(\"alert production:       \", Nalrt)\n",
+    "del Nsfs, Nddf, Ntoo, Naos, Nalrt"
    ]
   },
   {
@@ -547,7 +499,11 @@
    "id": "507b2056-c8bf-4516-ba91-4608b0417890",
    "metadata": {},
    "source": [
-    "### 3.2. Target names"
+    "### 3.2. Target names\n",
+    "\n",
+    "The `target_name` is either a region of the LSST WFD (e.g., bulge, low-dust) or a proper name (for DDFs, or the commissioning small field survey fields).\n",
+    "Since regions can overlap, or a single visit can overlap the boundaries of multiple regions, the `target_name` is often a comma-separated list.\n",
+    "For example, most DDFs are also in the low-dust extragalactic regions of the LSST WFD."
    ]
   },
   {
@@ -555,7 +511,8 @@
    "id": "a83e7872-938e-4a64-922b-ac8749b26c14",
    "metadata": {},
    "source": [
-    "#### 3.2.1. Small field surveys\n",
+    "\\\n",
+    "**Small field surveys.**\n",
     "\n",
     "Get the unique values of `target_name` for each of the small field survey areas, and print the number of visits done for each."
    ]
@@ -574,24 +531,15 @@
     "del df_sfs, values, counts"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "403d17c0-fa05-4203-83b2-92815c9ae780",
-   "metadata": {},
-   "source": [
-    "Note that in the table of small field survey visits on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html#small-field-survey-visits'> Science Validation survey summary webpage </a>, only survey fields with >50 visits are shown."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5e65b35d-c552-4301-ae1c-e9a64274204c",
    "metadata": {},
    "source": [
-    "#### 3.2.2. Wide-fast-deep regions\n",
+    "\\\n",
+    "**Wide-fast-deep regions.**\n",
     "\n",
-    "Get the unique values of `target_name` for visits done as part of the WFD LSST Science Validation survey program.\n",
-    "\n",
-    "For this database, it is simpler to start by defining the `observation_reason` values that **are not** part of the SV WFD  (i.e., exclude SV DDFs, ToO, small field surveys, and the template-building observations)."
+    "Option to print the `target_name` for all visits that were _not_ done with an `observation_reason` related to the small field surveys, the deep drilling fields, or targets of opportunity - the result will be inclusive of all WFD sky regions."
    ]
   },
   {
@@ -601,38 +549,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query = \"(observation_reason != 'field_survey_science') & \"\n",
-    "query += \"(observation_reason.str.contains('ddf') == 0) & \"\n",
-    "query += \"(observation_reason.str.contains('too') == 0) & \"\n",
-    "query += \"(observation_reason != 'template_area_singles_i')\"\n",
-    "df_wfd = df.query(query)\n",
+    "# query = \"(observation_reason != 'field_survey_science') & \"\n",
+    "# query += \"(observation_reason.str.contains('ddf') == 0) & \"\n",
+    "# query += \"(observation_reason.str.contains('too') == 0) \"\n",
+    "# df_wfd = df.query(query)\n",
     "\n",
-    "values, counts = np.unique(df_wfd['target_name'], return_counts=True)\n",
-    "for value, count in zip(values, counts):\n",
-    "    print('%30s %5i' % (value, count))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7dc6b98-368a-4d1e-a92f-2a15dad81381",
-   "metadata": {},
-   "source": [
-    "Separate out the comma-separated lists into one list of unique target names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d88ac98-d851-4eb8-99a0-d57504c4dfcd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp = []\n",
-    "for value in values:\n",
-    "    for name in str(value).split(','):\n",
-    "        temp.append(name.strip())\n",
-    "targets = np.unique(temp)\n",
-    "print(targets)"
+    "# print(\"All target_name values, omitting non-WFD observation reasons.\")\n",
+    "# values, counts = np.unique(df_wfd['target_name'], return_counts=True)\n",
+    "# for value, count in zip(values, counts):\n",
+    "#     print('%30s %5i' % (value, count))\n",
+    "# print(\" \")\n",
+    "# print(\"Unique target_name values for WFD observation reasons.\")\n",
+    "# temp = []\n",
+    "# for value in values:\n",
+    "#     for name in str(value).split(','):\n",
+    "#         temp.append(name.strip())\n",
+    "# targets = np.unique(temp)\n",
+    "# print(targets)\n",
+    "\n",
+    "# del query, values, counts, temp, targets, df_wfd"
    ]
   },
   {
@@ -640,45 +575,37 @@
    "id": "f0dcbd4c-7329-452a-bc9a-24634a87050f",
    "metadata": {},
    "source": [
-    "The unique LSST region target names covered during commissioning are:\n",
+    "The WFD sky regions covered during commissioning are:\n",
     "\n",
+    "* `LMC_SMC` - Large and Small Magellanic Clouds\n",
     "* `bulgy` - Galactic bulge region\n",
-    "* `lowdust` - low dust sky region\n",
     "* `dusty_plane` - dusty regions of the Galactic plane\n",
+    "* `euclid_overlap` - overlap with Euclid space telescope field overlap\n",
+    "* `field_m49` and `virgo` - Virgo cluster\n",
+    "* `lowdust` - low dust sky region\n",
     "* `nes` - North Ecliptic Spur (NES)\n",
+    "* `scp` - south celestial pole\n",
     "\n",
     "Learn more about all of the planned LSST WFD regions on the <a href=\"https://survey-strategy.lsst.io/baseline/index.html\">LSST Baseline Strategy webpage</a>.\n",
     "\n",
-    "Sum up the number of visits that overlap with each unique target name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5f25b04-b9ec-47fe-8dbf-4146f902f30d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for target in targets:\n",
-    "    print('%15s %5i' % (target, len(df_wfd.query(\"target_name.str.contains(@target)\"))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4181742f-125d-4e77-8ab1-dc6eaae6bd6d",
-   "metadata": {},
-   "source": [
-    "Clean up."
+    "> **Notice:** These WFD sky regions are not mutually exclusive; the Euclid fields are within the low-dust WFD region, for example, just like the DDFs are.\n",
+    "\n",
+    "List the number of visits that overlap with each WFD sky region.\n",
+    "As sky regions are not mutually exclusive, the sum is greater than the total number of visits."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3e01147-0711-439b-9b85-cddca31fd53f",
+   "id": "96657902-b6ec-4138-beba-13070c037daf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "del query, df_wfd, values, counts, temp, targets"
+    "WFD_targets = ['LMC_SMC', 'bulgy', 'dusty_plane', 'euclid_overlap',\n",
+    "               'field_m49', 'lowdust', 'nes', 'scp', 'virgo']\n",
+    "for target in WFD_targets:\n",
+    "    print('%15s %5i' % (target, len(df.query(\"target_name.str.contains(@target)\"))))\n",
+    "del WFD_targets"
    ]
   },
   {
@@ -687,10 +614,12 @@
    "id": "6ab35f8a-d9a9-4f5e-b126-af768054edd4",
    "metadata": {},
    "source": [
-    "#### 3.2.3. Deep Drilling Fields (DDFs)\n",
+    "\\\n",
+    "**Deep Drilling Fields.**\n",
     "\n",
     "The LSST will include five <a href=\"https://survey-strategy.lsst.io/baseline/ddf.html\">Deep Drilling Fields</a>,\n",
-    "four of which (all except COSMOS) were observed as part of the LSST Science Validation survey."
+    "four of which (all except COSMOS) were observed as part of the LSST Science Validation survey.\n",
+    "Note the EDFS (Euclid Deep Field South) field is actually two side-by-side fields."
    ]
   },
   {
@@ -714,48 +643,65 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87fe871f-d197-4aa0-b2a3-d5695d0ab733",
+   "id": "8c65cfb8-845f-4100-804e-74548f5a5fa1",
    "metadata": {},
    "source": [
-    "Print the unique DDF target names and the number of visits done for each."
+    "Option to print the `target_name` for all visits that were done with an `observation_reason` related to the deep drilling fields."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07bb4704-3668-4c6d-b15f-5910fae84492",
+   "id": "796776cb-527c-40c8-9e9c-288bb985f5b4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_ddf = df.query(\"observation_reason.str.contains('ddf')\")\n",
-    "values, counts = np.unique(df_ddf['target_name'], return_counts=True)\n",
-    "for value, count in zip(values, counts):\n",
-    "    print('%20s %5i' % (value, count))"
+    "# df_ddf = df.query(\"observation_reason.str.contains('ddf')\")\n",
+    "\n",
+    "# print(\"All target_name values with DDF observation reasons.\")\n",
+    "# values, counts = np.unique(df_ddf['target_name'], return_counts=True)\n",
+    "# for value, count in zip(values, counts):\n",
+    "#     print('%30s %5i' % (value, count))\n",
+    "# print(\" \")\n",
+    "# print(\"Unique target_name values for DDF observation reasons.\")\n",
+    "# temp = []\n",
+    "# for value in values:\n",
+    "#     for name in str(value).split(','):\n",
+    "#         temp.append(name.strip())\n",
+    "# targets = np.unique(temp)\n",
+    "# print(targets)\n",
+    "\n",
+    "# del values, counts, temp, targets, df_ddf"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fce95c75-cf56-43c4-a43d-a16379cd2270",
+   "id": "1b892553-8fc9-4e32-a3a8-adb6c66ac4ee",
    "metadata": {},
    "source": [
-    "The DDFs overlap the WFD lowdust region, and so the fields have both the DDF name and \"lowdust\" in their `target_name`.\n",
+    "The naming convention for the DDFs has changed since the start of LSSTCam observations.\n",
     "\n",
-    "Separate out the comma-separated lists into one list of unique target names."
+    "* `DDF ECDFS` --> `ddf_ecdfs`\n",
+    "* `DDF EDFS_a` --> `ddf_edfs_a`\n",
+    "* `DDF EDFS_b` --> `ddf_edfs_b`\n",
+    "* `DDF ELAISS1` --> `ddf_elaiss1`\n",
+    "* `DDF XMM_LSS` --> `ddf_xmm_lss`\n",
+    "\n",
+    "Standardize the `target_name` strings for DDF-related visits."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4164a71f-a938-4992-9763-1aaa9aeec508",
+   "id": "fd04433f-26d1-405b-82eb-41381095a2b6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "temp = []\n",
-    "for value in values:\n",
-    "    for name in str(value).split(','):\n",
-    "        temp.append(name.strip())\n",
-    "targets = np.unique(temp)\n",
-    "print(targets)"
+    "df['target_name'] = df['target_name'].str.replace('DDF ECDFS', 'ddf_ecdfs', regex=False)\n",
+    "df['target_name'] = df['target_name'].str.replace('DDF EDFS_a', 'ddf_edfs_a', regex=False)\n",
+    "df['target_name'] = df['target_name'].str.replace('DDF EDFS_b', 'ddf_edfs_b', regex=False)\n",
+    "df['target_name'] = df['target_name'].str.replace('DDF ELAISS1', 'ddf_elaiss1', regex=False)\n",
+    "df['target_name'] = df['target_name'].str.replace('DDF XMM_LSS', 'ddf_xmm_lss', regex=False)"
    ]
   },
   {
@@ -763,7 +709,7 @@
    "id": "120fb553-01f5-4561-960a-48834fef2fd1",
    "metadata": {},
    "source": [
-    "Define the list of only the DDF target names."
+    "List the number of visits that overlap with each DDF sky region."
    ]
   },
   {
@@ -773,44 +719,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ddf_names = ['DDF ECDFS', 'DDF EDFS_a', 'DDF EDFS_b', 'DDF ELAISS1', 'DDF XMM_LSS']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0dd26088-bc96-4703-981c-3c1c175b1c27",
-   "metadata": {},
-   "source": [
-    "Sum up the number of visits for each SV DDF."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "50106666-aebe-41ae-a48f-3dc08eb13648",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for name in ddf_names:\n",
-    "    print('%15s %5i' % (name, len(df_ddf.query(\"target_name.str.contains(@name)\"))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f3724ad-61c6-493e-9849-2b6fea21de3b",
-   "metadata": {},
-   "source": [
-    "Clean up."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3e9d4c26-f675-448c-9ec5-c449e9840e76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del df_ddf, values, counts, temp, targets, ddf_names"
+    "DDF_targets = ['ddf_cosmos', 'ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b', 'ddf_elaiss1', 'ddf_xmm_lss']\n",
+    "for target in DDF_targets:\n",
+    "    print('%15s %5i' % (target, len(df.query(\"target_name.str.contains(@target)\"))))\n",
+    "del DDF_targets"
    ]
   },
   {
@@ -850,21 +762,22 @@
     "mjd_to_jd = t.mjd - t.jd\n",
     "df.loc[:, 'jd'] = np.floor(df.observationStartMJD - mjd_to_jd)\n",
     "df.loc[:, 'jd'] = df.jd.astype(int)\n",
-    "jds = np.arange(df.jd.min(), df.jd.max()+1, 1)\n",
-    "jdsbins = np.arange(df.jd.min(), df.jd.max()+2, 1)\n",
+    "jds = np.arange(df.jd.min(), df.jd.max()+1, 7)\n",
+    "jdsbins = np.arange(df.jd.min(), df.jd.max()+7, 7)\n",
     "days = [t.split('T')[0] for t in Time(jds, format='jd', scale='tai').isot]\n",
     "bar_bottom = np.zeros(len(jds))\n",
     "\n",
     "plt.figure(figsize=(8, 4))\n",
     "for b in 'ugrizy':\n",
     "    heights, _ = np.histogram(df.query(\"band == @b \").jd, bins=jdsbins)\n",
-    "    plt.bar(jds, heights, bottom=bar_bottom, width=1, color=filter_colors[b], alpha=0.8, label=b)\n",
+    "    plt.bar(jds, heights, bottom=bar_bottom, width=4, color=filter_colors[b], alpha=0.8, label=b)\n",
     "    bar_bottom += heights\n",
-    "plt.legend()\n",
+    "plt.legend(loc='upper left', ncol=6)\n",
     "_ = plt.xticks(jds[::7], labels=days[::7], rotation=90)\n",
     "plt.grid(alpha=0.2)\n",
     "plt.ylabel(\"Number of visits\", fontsize='large')\n",
-    "plt.title(\"LSSTCam Science Visits\")\n",
+    "plt.ylim([0, 4500])\n",
+    "plt.title(\"LSSTCam Science Visits in 7-day bins\")\n",
     "plt.show()"
    ]
   },
@@ -873,7 +786,7 @@
    "id": "942d90c7-b551-43dd-87e7-d86155138d79",
    "metadata": {},
    "source": [
-    "> **Figure 2:** The number of visits per filter over time, in days."
+    "> **Figure 1:** The number of visits over time, stacked by filter, in 7-day bins."
    ]
   },
   {
@@ -894,7 +807,7 @@
     "fig = plt.figure(figsize=(8, 4))\n",
     "for f, filt in enumerate(filter_names):\n",
     "    plt.hist(df.query(\"band == @filt\")['airmass'],\n",
-    "             bins=40, histtype='step',\n",
+    "             bins=40, histtype='step', log=True,\n",
     "             ls=filter_linestyles[filt],\n",
     "             color=filter_colors_list[f], label=filt)\n",
     "plt.legend(loc='best')\n",
@@ -908,7 +821,7 @@
    "id": "0bd6b0e3-ac74-4add-b0a7-5c36b3734aab",
    "metadata": {},
    "source": [
-    "> **Figure 3:** The number of visits in bins of airmass, by filter."
+    "> **Figure 2:** The number of visits in bins of airmass, by filter."
    ]
   },
   {
@@ -929,7 +842,7 @@
     "fig = plt.figure(figsize=(8, 4))\n",
     "for f, filt in enumerate(filter_names):\n",
     "    plt.hist(df.query(\"band == @filt\")['seeingFwhmEff'],\n",
-    "             bins=40, histtype='step',\n",
+    "             bins=40, histtype='step', log=True,\n",
     "             ls=filter_linestyles[filt],\n",
     "             color=filter_colors_list[f], label=filt)\n",
     "plt.legend(loc='best')\n",
@@ -943,7 +856,7 @@
    "id": "2f165fa2-3c44-4b15-8e31-110a41b9bd88",
    "metadata": {},
    "source": [
-    "> **Figure 4:** The number of visits in bins of seeing, by filter."
+    "> **Figure 3:** The number of visits in bins of seeing, by filter."
    ]
   },
   {
@@ -964,7 +877,7 @@
     "fig = plt.figure(figsize=(8, 4))\n",
     "for f, filt in enumerate(filter_names):\n",
     "    plt.hist(df.query(\"band == @filt\")['fiveSigmaDepth'],\n",
-    "             bins=40, histtype='step',\n",
+    "             bins=40, histtype='step', log=True,\n",
     "             ls=filter_linestyles[filt],\n",
     "             color=filter_colors_list[f], label=filt)\n",
     "plt.legend(loc='best')\n",
@@ -978,7 +891,7 @@
    "id": "1a5f8873-bb48-4616-b438-451c68f179e0",
    "metadata": {},
    "source": [
-    "> **Figure 5:** The number of visits in bins of $5\\sigma$ depth for point sources, by filter."
+    "> **Figure 4:** The number of visits in bins of 5$\\sigma$ depth for point sources, by filter."
    ]
   },
   {
@@ -988,7 +901,10 @@
    "source": [
     "### 4.2. Summary statistics\n",
     "\n",
-    "Recreate parts of the tables on the  <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html#sv-wide'> Science Validation survey summary webpage </a>. This section uses code from the LSSTCam summary notebook in the <a href=\"https://github.com/lsst-sims/sims_sv_survey/blob/main/notebooks/lsstcam_summary.ipynb\">Sims SV Survey repo</a>.\n",
+    "Recreate parts of the tables on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html#sv-wide'> Science Validation survey summary webpage</a>. This section uses code from the LSSTCam summary notebook in the <a href=\"https://github.com/lsst-sims/sims_sv_survey/blob/main/notebooks/lsstcam_summary.ipynb\">Sims SV Survey repo</a>.\n",
+    "\n",
+    "\\\n",
+    "**Small field survey areas.**\n",
     "\n",
     "Display a table of the number of visits by band for each of the small field survey areas with at least 50 visits total."
    ]
@@ -1009,6 +925,7 @@
     "df_sfs = df_sfs.query(\"all > 50\").sort_values('all')\n",
     "table = tabulate(pd.DataFrame(df_sfs.round(0)), headers='keys')\n",
     "table = table.replace('nan', '  0')\n",
+    "print('Number of visits, for small fields with at least 50 visits total.')\n",
     "print(table)\n",
     "del query, df_sfs, table"
    ]
@@ -1051,23 +968,49 @@
    "id": "99aa1d6b-4c85-4d41-aafb-0b1b5f7d8faa",
    "metadata": {},
    "source": [
-    "Recreate the two tables above, but for the SV DDF, and include all fields (do not restrict to fields with >50 visits)."
+    "\\\n",
+    "**Deep drilling fields.**\n",
+    "\n",
+    "Make similar versions of the two tables above, but for the DDFs.\n",
+    "\n",
+    "First, create a new column that contains only the DDF name, for visits of a DDF (and is a null string otherwise)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d44daed1-2169-4aff-90d3-6aa5b655a9e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['target_name_ddf'] = df['target_name']\n",
+    "df.loc[~df['target_name_ddf'].str.contains(\"ddf\"), 'target_name_ddf'] = ''\n",
+    "df['target_name_ddf'] = df['target_name_ddf'].str.replace(', lowdust', '', regex=False)\n",
+    "df['target_name_ddf'] = df['target_name_ddf'].str.replace('lowdust, ', '', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2e51c5a-6d21-4bce-9644-12f2b0c140bd",
+   "metadata": {},
+   "source": [
+    "For visits of DDF with >50 visits, display tables similar to the above."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b862d38b-706d-48a6-a243-4c239f96ec13",
+   "id": "4ce3f609-a37a-43e7-bcc7-9da9452e4b74",
    "metadata": {},
    "outputs": [],
    "source": [
-    "query = \"observation_reason.str.contains('ddf')\"\n",
-    "df_ddf = df.query(query).groupby(['observation_reason', 'band']).agg({'seq_num': 'count'})\n",
+    "query = \"target_name_ddf.str.contains('ddf')\"\n",
+    "df_ddf = df.query(query).groupby(['target_name_ddf', 'band']).agg({'seq_num': 'count'})\n",
     "df_ddf.rename({'seq_num': 'count'}, axis=1, inplace=True)\n",
     "df_ddf = df_ddf.reset_index('band').pivot(columns=[\"band\"]).droplevel(0, axis=1)\n",
     "df_ddf = df_ddf[['u', 'g', 'r', 'i', 'z', 'y']]\n",
     "df_ddf['all'] = df_ddf.sum(axis=1)\n",
-    "df_ddf = df_ddf.query(\"all > 0\").sort_values('all')\n",
+    "df_ddf = df_ddf.query(\"all > 50\").sort_values('all')\n",
     "table = tabulate(pd.DataFrame(df_ddf.round(0)), headers='keys')\n",
     "table = table.replace('nan', '  0')\n",
     "print(table)\n",
@@ -1081,11 +1024,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query = \"observation_reason.str.contains('ddf')\"\n",
-    "df_ddf = df.query(query).groupby(['observation_reason']).agg({'seq_num': 'count',\n",
-    "                                                              'seeingFwhmEff': 'median',\n",
-    "                                                              'airmass': 'mean',\n",
-    "                                                              'exp_midpt_mjd': np.ptp})\n",
+    "query = \"target_name_ddf.str.contains('ddf')\"\n",
+    "df_ddf = df.query(query).groupby(['target_name_ddf']).agg({'seq_num': 'count',\n",
+    "                                                           'seeingFwhmEff': 'median',\n",
+    "                                                           'airmass': 'mean',\n",
+    "                                                           'exp_midpt_mjd': np.ptp})\n",
     "df_ddf.rename({'seq_num': 'nvisits',\n",
     "               'seeingFwhmEff': 'median fwhm (arcsec)',\n",
     "               'airmass': 'mean airmass',\n",
@@ -1093,6 +1036,7 @@
     "df_ddf = df_ddf.sort_values('nvisits')\n",
     "df_ddf['timespan (days)'] = df_ddf['timespan (days)'].astype(int) + 1\n",
     "df_ddf.round(2)\n",
+    "df_ddf = df_ddf.query(\"nvisits > 50\").sort_values('nvisits')\n",
     "table = tabulate(pd.DataFrame(df_ddf.round(2)), headers='keys')\n",
     "table = table.replace('nan', '  0')\n",
     "print(table)\n",
@@ -1104,12 +1048,9 @@
    "id": "fe049fcd-1c0c-4db0-9d2e-b15f7d21b2b6",
    "metadata": {},
    "source": [
-    "## 5. MAF sky map\n",
-    "\n",
-    "Recreate the sky map diagram in Figure 1.\n",
+    "## 5. MAF sky maps\n",
     "\n",
     "The commissioning visits database file has been generated using the same format and schema as the Operations Simulations (opsim) databases, and so can be read by the MAF (Metric Analysis Frameworks) package from the `rubin_sim` package.\n",
-    "\n",
     "This section follows the Jupyter Notebook tutorial for visualizing the survey footprint in the <a href=\"https://github.com/lsst/rubin_sim_notebooks\">rubin_sim_notebook repository</a>.\n",
     "\n",
     "Set the path to the folder containing `rubin_sim` data in the RSP at data.lsst.cloud."
@@ -1151,7 +1092,9 @@
    "id": "5c8b529e-77ad-45b8-8f66-79325d47b465",
    "metadata": {},
    "source": [
-    "Define the metric to be plotted, in this case, `Nvisits` the number of visits. Define the size of the healpix to use for the map. Do not define any constraints, and include all visits."
+    "Define the metric to be plotted, in this case, `Nvisits`: the number of visits.\n",
+    "Define the size (`nside`) of the `healpix` to use for the map.\n",
+    "Do not define any constraints, and include all visits."
    ]
   },
   {
@@ -1210,7 +1153,7 @@
    "id": "44760ebf-ba89-4fd9-ab26-3237131c7ea7",
    "metadata": {},
    "source": [
-    "Calculate the metric. The following step will generate the file `lsstcam_20250930_Nvisits_HEAL.npz` in the `output_path` defined in Section 1.2."
+    "Calculate the metric. The following step will generate the files starting with `prelsst_20260607` in the `output_path` defined in Section 1.2."
    ]
   },
   {
@@ -1249,19 +1192,71 @@
    "id": "39b16195-a0c5-4c05-9e50-5bd700126272",
    "metadata": {},
    "source": [
-    "> **Figure 6:** Top, the sky map of the number of visits obtained during commissioning. Bottom, the sky area binned by number of visits, showing that most of the commissioning area was shallow.\n",
+    "> **Figure 5:** Top, the sky map of the number of visits obtained during commissioning. Bottom, the sky area binned by number of visits, showing that most of the commissioning area was shallow.\n",
     "\n",
     "Read more about they sky coverage for the SV wide-area survey on the  <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html#sv-wide'> Science Validation survey summary webpage </a>."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b205f6c3-1119-4df5-a693-c649960eaf73",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "**AOS testing visits.**\n",
+    "\n",
+    "In the top panel of Figure 5, stripes of visits with constant declination stand out in the sky distribution. The cause of this non-LSST-like survey pattern is in their observation reason: \"fbs_driven_aos_stability_test\". FBS stands for \"feature-based scheduler\" and AOS for \"active optics system\". These visits were obtained by the FBS for the purpose of testing the AOS, but are still anticipated to be, potentially, scientifically useful.\n",
+    "\n",
+    "Visualize only the FBS AOS testing visits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afc00e64-1dcd-4d70-b558-376b09c73b27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constraint = \"observation_reason like \\'%aos%\\'\"\n",
+    "bundle = maf.MetricBundle(metric, slicer, constraint, run_name=run_name)\n",
+    "group = maf.MetricBundleGroup({'nvisits': bundle},\n",
+    "                              opsim_fname, out_dir=output_path)\n",
+    "group.run_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1180118-70eb-4474-8cdc-521e7bf5e618",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ph = maf.PlotHandler(savefig=False, fig_format='png', thumbnail=False, dpi=270)\n",
+    "ph.set_metric_bundles([bundle])\n",
+    "ph.plot(plot_func=maf.plots.HealpixSkyMap(),\n",
+    "        plot_dicts={'color_min': 20, 'color_max': 100, 'figsize': (6, 4),\n",
+    "                    'labelsize': 'x-large', 'fontsize': 'x-large', 'extend': 'max',\n",
+    "                    'title': 'AOS Testing LSSTCam Visits'})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bef7bfd-3405-4969-ba0b-04a473caf76c",
+   "metadata": {},
+   "source": [
+    "> **Figure 6:** The FBS AOS testing visits."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f40d0511-12bc-451f-832d-ff5eabffee03",
    "metadata": {},
    "source": [
-    "#### 5.1. Sky map for DDFs only\n",
+    "\\\n",
+    "**DDF visits.**\n",
     "\n",
-    "Create a sky map for only the DDFs, which will be where alert production will start in early 2026."
+    "Create a sky map for only the DDFs."
    ]
   },
   {
@@ -1290,11 +1285,12 @@
     "ph.plot(plot_func=maf.plots.HealpixSkyMap(),\n",
     "        plot_dicts={'color_min': 20, 'color_max': 100, 'figsize': (6, 4),\n",
     "                    'labelsize': 'x-large', 'fontsize': 'x-large', 'extend': 'max',\n",
-    "                    'title': 'Deep Drilling Field LSSTCam Visits as of 2025-09-30'})\n",
+    "                    'title': 'Deep Drilling Field LSSTCam Visits'})\n",
     "plt.figtext(0.40, 0.62, 'XMM LSS', fontsize='large', fontweight='bold', color='white')\n",
     "plt.figtext(0.36, 0.50, 'ECDFS', fontsize='large', fontweight='bold', color='white')\n",
     "plt.figtext(0.36, 0.33, 'EDFS', fontsize='large', fontweight='bold', color='white')\n",
     "plt.figtext(0.47, 0.43, 'ELAIS-S1', fontsize='large', fontweight='bold', color='white')\n",
+    "plt.figtext(0.05, 0.65, 'COSMOS', fontsize='large', fontweight='bold', color='white')\n",
     "plt.show()"
    ]
   },
@@ -1303,7 +1299,192 @@
    "id": "2a9aaa25-6d90-41c7-acaf-485ea7dead82",
    "metadata": {},
    "source": [
-    "> **Figure 7:** The locations of the four Deep Drilling Fields (DDFs), with their names labeled. See Figure 1 for the field central coordinates."
+    "> **Figure 7:** The locations of the five Deep Drilling Fields (DDFs), with their names labeled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbb889dc-4273-4480-bca6-7f513fcb07aa",
+   "metadata": {},
+   "source": [
+    "## 6. Might my target be covered?\n",
+    "\n",
+    "Define two sets of coordinates for two hypothetical targets.\n",
+    "The first has been covered by LSSTCam visits and the second has not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c45fa5df-70cf-4184-b6db-3bd57bff489a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target1_ra, target1_dec = 330.0, -10.0\n",
+    "target2_ra, target2_dec = 330.0, +10.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a292ced-0bbd-44d0-b980-2559c779a7e2",
+   "metadata": {},
+   "source": [
+    "To show the location of the two targets on the sky on top of a 2D density plot of the number of visits,\n",
+    "instead of using the MAF metric to plot the sky map (as above), use the `healpy` and `skyproj` packages.\n",
+    "\n",
+    "Use the `healpy` package to show that the area of one 19-sided HEALPix is similar to the LSSTCam FOV of 9.6 square degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "487e8359-d55c-49b0-b61a-aae500971dff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('One 19-sided HEALPix is ', np.round(hp.nside2pixarea(19, degrees=True), 2),\n",
+    "      ' square degrees.')\n",
+    "print('It takes ', int(41253.0 / hp.nside2pixarea(19, degrees=True)),\n",
+    "      ' 19-sided HEALPix to cover the full sky.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d061aa7-cf9f-4263-96db-5e292cba51e5",
+   "metadata": {},
+   "source": [
+    "Create the 2D distribution of LSSTCam visits and mark the locations of the two targets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87d06080-f9f8-4696-9cf2-7a7ea748f86d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
+    "vras = np.asarray(df['fieldRA'], dtype='float')\n",
+    "vdecs = np.asarray(df['fieldDec'], dtype='float')\n",
+    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap='Greys', vmin=-10)\n",
+    "sp.ax.plot(target1_ra, target1_dec, 'o', ms=10, mec='darkorange', color='None',\n",
+    "           mew=2, label='target 1')\n",
+    "sp.ax.plot(target2_ra, target2_dec, 's', ms=10, mec='darkgreen', color='None',\n",
+    "           mew=2, label='target 2')\n",
+    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=14)\n",
+    "sp.ax.set_ylabel(\"Declination\", fontsize=14)\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcca0d28-6617-46d4-8eef-b9c26ce24dcb",
+   "metadata": {},
+   "source": [
+    "> **Figure 8:** A 2D histogram illustrating the distribution of visits on the sky (for all filters, combined), with the two hypothetical targets marked."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c2932a2-80fa-42ee-be25-4a39f078efb6",
+   "metadata": {},
+   "source": [
+    "Calculate the 2D sky separations of all visits from the two targets, and the subset to only visits within a sky distance of 1.75 degrees, the approximate radius of the LSSTCam field of view.\n",
+    "Print the number of visits that might overlap the two targets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccdf73d0-dd14-4fd1-81f7-310db4f76f45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords_target1 = SkyCoord(target1_ra, target1_dec, unit=\"deg\")\n",
+    "coords_target2 = SkyCoord(target2_ra, target2_dec, unit=\"deg\")\n",
+    "coords_df = SkyCoord(ra=df['fieldRA'].values * u.deg,\n",
+    "                     dec=df['fieldDec'].values * u.deg, frame='icrs')\n",
+    "df['sep_t1'] = coords_df.separation(coords_target1).degree\n",
+    "df['sep_t2'] = coords_df.separation(coords_target2).degree\n",
+    "\n",
+    "df_target1 = df.query('sep_t1 < 1.75')\n",
+    "df_target2 = df.query('sep_t2 < 1.75')\n",
+    "\n",
+    "print('Number of visits that potentially overlap')\n",
+    "print('target 1: ', len(df_target1))\n",
+    "print('target 2: ', len(df_target2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "622073bb-b635-4a13-ac08-ff736358b417",
+   "metadata": {},
+   "source": [
+    "As expected, target 1 has many potentially overlapping visits within a radius of 1.75 degrees, and target 2 has none.\n",
+    "Keep in mind that the LSSTCam FOV is not round and not exactly 1.75 degrees, and that there are gaps between the chips (detectors), so the number of visits that actually include target 1 might be different (but this estimate will be close)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e48c9d5-a383-4e00-b983-aeb0afede574",
+   "metadata": {},
+   "source": [
+    "### 6.1. DP1 fields\n",
+    "\n",
+    "Visualize the overlap of the [Data Preview 1](https://dp1.lsst.io/) (DP1) fields, which were observed with the LSST Commissioning Camera (LSSTComCam), with these LSSTCam visits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fa7f801-c300-4a59-b85e-1ae0cbc59bb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dp1_field_names = ['47 Tuc globular cluster',\n",
+    "                   'Low Ecliptic Latitude Field',\n",
+    "                   'Fornax Dwarf Spheroidal Galaxy',\n",
+    "                   'Extended Chandra Deep Field South',\n",
+    "                   'Euclid Deep Field South',\n",
+    "                   'Low Galactic Latitude Field',\n",
+    "                   'Seagull Nebula']\n",
+    "dp1_field_ras = [6.02, 37.86, 40.00, 53.13, 59.10, 95.00, 106.23]\n",
+    "dp1_field_decs = [-72.08, 6.98, -34.45, -28.10, -48.73, -25.00, -10.51]\n",
+    "dp1_field_symbols = ['o', 's', 'p', '*', '^', 'v', 'x']\n",
+    "dp1_field_symsizes = [6, 6, 6, 8, 6, 6, 8]\n",
+    "dp1_field_colors = ['red', 'lightseagreen', 'darkviolet',\n",
+    "                    'magenta', 'yellow', 'lime', 'darkorange']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4d8285c-bfa0-4a17-89d3-89d23ae77011",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
+    "vras = np.asarray(df['fieldRA'], dtype='float')\n",
+    "vdecs = np.asarray(df['fieldDec'], dtype='float')\n",
+    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap='Greys', vmin=-10)\n",
+    "for i in range(len(dp1_field_names)):\n",
+    "    sp.ax.plot(dp1_field_ras[i], dp1_field_decs[i], dp1_field_symbols[i], ms=dp1_field_symsizes[i],\n",
+    "               color=dp1_field_colors[i], label=dp1_field_names[i])\n",
+    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=14)\n",
+    "sp.ax.set_ylabel(\"Declination\", fontsize=14)\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8807d39-0cc6-4382-ad70-793e32e3c7d4",
+   "metadata": {},
+   "source": [
+    ">**Figure 9:** Same as Figure 8, but with the seven DP1 LSSTComCam fields marked."
    ]
   },
   {
@@ -1311,9 +1492,9 @@
    "id": "135c0da0-4d13-4230-9abe-2add68bb1266",
    "metadata": {},
    "source": [
-    "## 6. Exercises for the learner\n",
+    "## 7. Exercises for the learner\n",
     "\n",
-    "Of the 217 columns in the commissioning visits database, only nine were mentioned as key columns in Section 2.1. This did not include the column `cloud_extinction`.\n",
+    "Of the columns in the commissioning visits database, only nine were mentioned as key columns in Section 2.1. This did not include the column `cloud_extinction`.\n",
     "\n",
     "Review the description of the `cloud_extinction` on the <a href='https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html'> Science Validation survey summary webpage </a>:\n",
     "> The visit database *\"also includes an estimate of the mean cloud extinction in the images. These are estimates based on the measured zeropoints for the images, compared to the expected zeropoint for an image in that bandpass at that airmass. A potential issue here is that visits with very heavy cloud extinction (or other problem with the quicklook image processing occuring immediately after image acquisition) may not succeed in measuring a zeropoint for the image at all, and thus no estimate for the cloud extinction will be possible either.\"*\n",
diff --git a/Commissioning/102_LSSTCam_visits_metadata_2026.ipynb b/Commissioning/102_LSSTCam_visits_metadata_2026.ipynb
deleted file mode 100644
index 7ab6133f..00000000
--- a/Commissioning/102_LSSTCam_visits_metadata_2026.ipynb
+++ /dev/null
@@ -1,942 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {
-    "1af63937-73ed-4ba3-acc1-ceb2391ffb34.png": {
-     "image/png": 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl\nLS1tELiorq42KjMCLWx5Su8OWgwLPCkoKDDqR7Foa3Gsej733HMGKNJCV7aT7r77biMlI9BHEkMC\nTgSMCAhTOydMmIDs7Gzq8Z4YtLGkukSHn/70p1D7li5dive9731ntE8SJAICqqqqDG3xwQYt4gXI\nSC1Iklkq2wbxXHk1mRGPo9XsbLpEzqKRTyPuM2fONMCCO49cn9fX1xvAxR2va/FXUi9SIZRHuuuu\nu25IHVW/G264wdid+vnPf274oPem9qgterfiv0I8QMOmU9+QdIreR3RQGj2XZzyVo/ZY8EP1tyDc\nzTffTLFg2eFwpNuigTjRVZvUh9UfVE+Bmm4aSqN6S+pMwYI35sb1x01HKlfu9yfX8uqzOtT3rPSZ\nsos/4oX6lW3vrFmzcNNNN7moa5B2gFO1XcCsgBqp0SnePnNnUF9SP1m+fHnMfqg2fehDHzLfquoV\nDXSqXn/7t39r6qo2J8pjN5jrrk/yOj4HYkkN1eQHsbkujFePdeNQ3WE8sKMNR2hvSF6/0ura8TM+\nWzQtD++a5cNjNAr0na2UIE0J4JoFXpxs6kbDc6lo7KMRZo67AU8Gx9zYUxWjWkaarxzhTmBwOu5e\nEMLs4ma8YzY9gFL0Z2uLRhoeEhFyhWNtlFLJ82AJQaxX6jxGUkgpJDFUTtf1Aoi2UHLJhpump+O2\nafSa1tGE37zQj6f3TiMoRB01TS6GCZpL+CkVK1Do+sXtePsaeSHrwbde6MAmAmITqEJ2fZUPV8/0\n41XaP9rDTaYsSiBdMiGElqAX87nhtYnVeJpA1arJPiMt1Nzpx9TJazCJamTjFfopsWhBoeEmzu7y\npcJwioZPbYg3qR98zvmH0ihoAaOJfDIkzgEtWtzSAfFy9vb2U4L4FSxfOs8YmY2X7s2KjwYUz2c9\n1OckfWWlv2JJ30hSQGqD6qPxbE8NV+dJ5SVG4iEWbeUbCUB101Z9JbFmg1SHJDEWT2pMfUTSFDn0\nBnmugnuRH4umytSRDEM5INBD70sSfBs2bB20U6bfWQsQDadepnmX/U0ezh6b+ohAY4EzGp0EtIw2\nSErmyNFaU95IoH0lPahNEuBCFbFjLrA/XpnqG8NJn8qbWDk9q0lS6AQldWJ9N/Kmpj4vD2zia7z+\nJpBsUvmNeMcdbzPzTBmQV3CDbCP1V9nmEi9Vn0TaF6/dF1P8m/b19nWdQlv9NgMMdbadpOexE+in\nfaGhQd1aot8RbCLyMEgr7J3tfejs6IU/NQdl1StRWDqdswsaf355O3Y1NtCoo1yua1LIiQcBEt4Q\nyCGgQ9Uy87UwWrEmkL5KMjGMHMRBFE+ASECQiRQdY5U5QkuZSLOf8R09/ejN4A9i1ATUkI38aaf6\n2EnWu41p+wloiZwBZVQTe82zAWkMWKNrpXHAHgfAYYSTkQ8F5gg0ctoY/dzEm2dqLA8SU5pB9TPR\nNQXouQpWElOgLsx/E6d2Rh47jOLEdWoAK+dOwKpL1yC3qIrvrw6NJ3bj+L5NaGs4xI81TNE+WnQg\nv5RVJHRh5tH2rDg+8PkIkAUb0HDkFQR7WzCxch5Kpq1EdqEjiaJk5zpogSpgQACFFqFS47ELX6nu\n/PM//7Mp8q677hosWotZ2a4RaKLFswCZ6EWt7gXALFy40OSzz3UWkDF16tS4wJDqoUN2XBoaGgxA\nYQsXKCV7Kc8884yJkhSIBXbUFjcYMH36dKP6JTDGBlv+2rVrDZggsOEw1Yx0rnBJ/Kh8tU8Ag4Al\nW39LR2cLrOlaZSsIWJDajkC1N954wwA35gH/qO6KF3ih8rRIl3SLu2ybNpGzgIjPfe5zBmiLrp9o\nC1yT2pc7WOBDdRR/9e5s3d3pxDs3j75PCR7Z11E/UFm2PPe1O7+u9UySUjZt9HPdq2ylUVB7BDgJ\nmHIHpXEDSHpfUpFTn7VB70t8FYCi9xVNQ+lUD8Wrj8QLbjrRvFE9pk2bNih5pndpg+WDu72aOMbi\nrc2j/uPumzbenm1fEgglia5Y/VDlCrzS+9R3q+9h3bp1hjfqz9Ft1juPxZ9oHtu+qneeDIlzQFJD\nMoJ8dOcuTAyfMFJD2+ms8tc7mpFOAODYgBe9GkcZDvd6saIiA2+bX4DZtKmjceH+nV14ZOcAyrJT\ncefaAL2etOKn6zwEhwgY8XkgheCQL/50JUAPZa8co4v0nF6U5vRj8aROdAToCXNnGAfkF4E0zNjm\nVAFB7upsruW34ac0Ea+DfH4sIjUUrUJ26+xsfGJVNsp87Vi/pQOP7yjH8X46S/APD2R4QgH46TjD\n29+FxZPbcMcaXnMT5OGN7dh4LGS8kPk4X9jbQjCK0kKLJgCzqIb32AEvHj/pw5xy/q63BbGPxzwa\nw15YRACLdpJagrNRkzuffTw+P8TnsQYrOm/zjzRxtun02yup40RCdBnlF4kqmbttb+dO9bW0s6GQ\nRTuTb0YYrb2hkOaOF1jQwvPL//JJfOEfPj6s0d3xqrYW0pIsWP/y68ZWSnQ5Ai9l7+RB2jORRFy0\nNER0+uh7t+2t6Ge6P8oFpyTnZAtmtGGkb/Ni/K5Gy4OLKb3el6TUplVXUgqnbNB+jdog0MetXhYt\nPeRWvTXzLs6z4gWVk38W3h3djgEEwHz0f30hLvii3/zurh5TFX0rI9mWi1dnG+8GHkOkF62O7Aat\nygkiCRyKFyyQKvDIHdwgm2weffij/5hw+1Snt3oYn5lFHK4FBrohtbGu1iPo72lGb2cjOlpr0d3R\nQG/tfQQsJD3iDs4gZjELPemlXZ6+3lQUVSxBzfTlyMkvRVZuIb185XKSl4JTLe1YQXe524+Lll6g\n1L4o7ROh7VDk7FDiPq6gMoztAl4YMMgAQpIeihxENMKSFiKYYmwXCeGQ0WhNdqma1tcfMIdfPm8t\nadcY3EU7RLUUnW/s6o/skokWK6A/zn/nWnVinAPq6FLXShA5IqCQnjvxDqhzBuCjCZpJq3S6ds4O\nXeURKOTkFW0nnmU4M+jTZ0XZOF0bsI1qZPPKsXLNMsxcciXF/vworpyDyhlLeE+994Ov4uiO36Gn\nbT+BIHpZM/lF53QwpAZvyVP6renrOUwPZc3ooyHyhmM7kFlUjYpZawj6TR1MeS4vtCjUIckMLWyt\n9JAmuDLUqwW3VIO00NSCUbZJpLaiRaYW2sMtfvXDHR3Mj3mMeKVzL4a18FZZ+oG0QXXSj5nUxgoL\nCw2Y4JbWsel0jleO4rXA16LXAjSia4PAAQucjNQ+d9vFG6nPKdk6aHUAAEAASURBVK/s5YiO2mOD\nyrCqWlJfU9BZBpHHEtQOgW9SzYsVpKYlEM0dBETt2bPHSLxMJ/gjgC5WEO21a9cakEHSPJIg0TGe\noaqqyryTWGW462r5aNO5+4w7nX0+mrOMaH/sYx8zfToeuCTe6P2Wl5ePhvSo09p+qIz6Lt19zU1M\n8RawUn9eR2BI705SdNFBaS0QN9wz8dj9TUSnTd7H5oCkhvxZNfBkzEGgpx7XVXMnvDuEn+3l+Mnx\nNxQBhZT7ysnp+MvVhSigtOjjL56Ap30A84v82EQD0d96tR+fWJ6Gd11Ji0ChZtz/ItDYG6ZPM9oI\nCmdQiJdTFrO7cGY9AnQ08dvdEq8P453z9+OyyVSt5Vj8011U8aXqWHQYoPrzAAElS07gkII9l+f6\nccscGsyem4Yybxs2EBT64e+L8HrTJITShgcCPMEBBxTq68SiyhZ87MZeLKQnsm8924YndhL44tzB\nqJBRYujVRg9eawzh6pIQ1ek8+PByLxYRLGpoDhij2dks6s9qvJQW8qOFTSqevGpcpYXciwKHI2P7\nO9yiNLqM4XbAx1b6+OfSYiN6waFSpdrzne89jLPxApVo7bXwGY29IS2q+vojdiITLWSc06kNRqqF\nki3uoF19uaHWwlTqUB/7yDvcj8/ptVv9Jpb0jRa8Y7XX5JaoiFXpWIvfWOnixQ0nkXQxflfx2vlW\nibdghezXSEV3/frX4bbnJoBI6mXRtq3cqrfjzQszD4qA/FLXko2qaDs/8epw2aVLUFJSFO/xWce7\nx45zIWk62vaNFhg+6wa/CQTOKzDUcOQNHN/9LBe8RBc9BFJ6uigl1MGJeMR9pGZoAkAGA+85IbTR\nfT10NOubiJnL34bJs1YjK28Cxc/TBlPrYtHSK3Di6CHUUuLiqR0caEhO0JC5EBhiDFITEGGwE0IV\nGcE7TLxSq2QFBz8itMGJnAxLD0oQKYUykZ7o9FBct7vXj0x6KosO/RxUTgkUorRQwOzYMJ/AGFOA\nCjFXrrjIc6XRs6jDxOmjNfECfETDdR8NCqnd5jnTDAJLEbrm/nR7nUo5pFU9y3tdO0zx4NJZtAkx\nswLzFl9hQCE98qekwp9HY7U88iaUIz2NyPG2NtqNqj89EbFMtaR4Fi4hOS4ft4XD4T5KUrWgjbPf\n/voD6N7xKuW76NlinIAh1VtBi0ZJD0lyw9oH0kJTNkm04JaKjqSFZBRYUgxKJ1sv5zII9BFtgU6S\nRnJLhcQqR4v04SRAYuVRnPLpiA5ukGG07XPzRh64xK+RgiRGYtVjpHyJPNc7E0AgVT4bVEdJ2yhU\nV1fHBWL03A0iWAkSxY9XEB/cIKC7nHjvS2kEdh08eNAkH6lNbpqxrgW06RgpDFefkfIm8ny0/VDS\nTfpWBOJFq7klUl4yzbnjQF5BJfIm0kNZ43b+ojfimoo+nGiXm3U/JtGmUBmPFXRV/475eSjy9OEH\n6xvxk90BrJ6SgZsn+eE/0o8NkuLZMoCPL0vBu98GlBQ24sfPD2A33bj7aaQ6lJJpvJVFu7K3reij\nlNBjOytxso3gUs0+XEZQhiaN8NPdBN/rbKqRzwKF/mJ1Bd4+OwWBtqMEhbrwg5cmYUtbNQKpUstw\nDWZuchyMZWjaT0PT6OvAdYtace/NvZhU0I1fb2rBr7dxY4tAWU25D++jF7IMkmnd48GWVj9+3uTD\nIX7O9+QANM2ERw/2G1tHty1Kx4zcEPafDMKfMx8FOeNnW0hNcS8KdD8cwKPnNrh3tBU33KI0ugzz\nu0Jw8WIPAjMEIMhrVKJeoKKlp0bLAy00E7U3JMmXiyXIBshzL2w0bqbzzkL6IZH2JsJDLdjHYm9I\nElGrViygBPgbMdViTtAW12YakZbUUqKLb3ebVHdrk8Udr+u3yncV3a63wn1KRKpH3r10bb1fqW2x\n+pokcwRQnu+g/itD9yuW1yRUtNoiwGa8gnvsiKdqNpqyx9K+0dC/GNOeV2AoLbOAxqWDlArZTQAh\nnaADRdCoDtbX60gtGICDXNSPXGa2H2npzgLWxuujSE3LQgFt0ORNmBST38XlU7Fq9VVo7l2P3aea\ncZjeSISbGOCE35Smc+be+N7SDe+kHmZiBaD4DPhDuXUjZaOdULm8h5fPDDgUJTFEglJRC9EegOoX\nopqbgA6nDKCLkkQn2cYmSgoN6KOOPDAn2zBF6r/uI3Hm2ok0YI59Zs9qj6MSZsEj1V35VYYOxfNM\n4EeSWIMSQa7nVopIaU0w50gdItfmiY1nohVV/bh1STZWLqg2YJCTcejftIxcFEwoQTN3gHq6h/5A\nCAjq7fOwH1B8n7u1hjR5qA1leorlojxINZMeevuimmBGCbLzaMfhPASBAbJbcg+9aFk7PDKiLLsu\nAokk8aIfJIWzXYTHao4ACJUjgEA2cGRn6HwGC0ypzNEu/t28EVh1vusezSfV/wMf+AB/X/S1O6ps\nAoXs+xOPlSZecANjyhOtchcv3/mOd4NdI7XpbOomsOYXv/iF6Z/izXgGdz+sqoovSWXrcL5BPFtu\n8nwmBzRWTplF7yv9tIu150FU5Kfhtpm0p9cb4vcWxkcW5eDq2Xl4dW8LJXuaMbMkHf+03IODHZS4\nJViyujKN6mZe/PF4P39v+3EvPZVds0ISY234n6dC2ElgxKvxCwKH0s14fGYtiMcQHNp4rJg0PHjP\nwr2YNbEFn1rGDQ3aEnr0EKiaFSvX6bhlBK8+vjIPl5R3I9BCm0LraHB/QyWOBqoQ8ElSyPldOZ3D\nuZI9IS83ueR9rDi9BR+4vgW3X9aHprZufPvZdjy2PUAbRhxRJSFBNTQvx8HJlIK/c44XPfu92NHm\nw4YGD7J3MT6F3v2a/ATGYAxO91HIowuzMX/2e8dVWii6TbqXJyctPkcK7h1t7XZXcEERb4HgBpHs\nzvhI9C+G50eO1FK6+OSoqqp549ku+LQYS8Te0NmWM6qGnUVitw2QRPvfWRRnsibCw7F4JtK3M5Kq\nWjzvS2fbpmT+C58D6neSHhIAL1tW1gi6+poMHccKApNle2csQGIsesPF1dFuXDM1cTIz45siGC7/\neD47W2k71e1Cbt948m442ucVGMqbWIXC8rmoPbgdPV00QMCFm9eXiZT0ImRk5yM9K9/UteXUUZw6\ncQwZmTScVpBmACIBCP4ULwb6KIXS0Ry3TR6CN7MXXkbpgAO4YrYDDBkwRnM5A4rwgot8yv4M0jCP\nIvcSAnIAFlaPk1TBSsYFvYxR86FAINXbOThJZXm6lsRQL1XcwumyG6JcTmjtpgve9h5HUshG6pHK\nsakMOmLLVSI9dA7zyPkTqRfjBfaYtuisdBYUcq6V1wGFToNHirOHyUMa5t6pZuTaFWeY4nDGYQ2v\nSWPJnDKslgrZvFU2Z8xzKNDNd9VJWxFOK6XJ10fjoz0EhPxpVIWauwwTJ80kGuRFd3sLj0a+10Y0\nnTyIJu62CUOrmbcIlTPHz8BmdMUFFsgO0Nq1p+3wGHSa4IAFbpRHwJG8fknF7FwFu8jXwjtap/Zc\nlXE+6EgNTgDXueTNWOrttmFj32GidKKBsdHmT7Scs03nBuTOllZ0fvV368VOXh06OiTZ6QCj0WnP\n5b37O6utraVhToqQJBgu1PeUYPXfEsl8lJgtqrgO7a0n0Vr/ImbSwPMNU8L42b4BPLy7E02dAbx0\nqAud4RRkDPhoC7AXW0/1Iz3cjeun0nPLzAw8wPFsfS29YnKT5WOLgSsW0026rxPf+22I6Qkycfz2\ncAMh6KOzB19s1TKplW0+4dgeEDg0p7iFKmy0BUNc5+f7QPf2sdm9jLaPPrG6FCsm9uLI/lo8sTEV\nv905DUf7yyitNAwoFKQ9oYDsCXVjflkTPvy2dqyZT1WwwzSsvY6Gpo8GDfiVRkBIwNBe2jPaQXtB\n2dyUmkbw50PzgvjJPvKjxYuXjofwMucUk/PDVCGTwWk/jtQHUFG9DJNpe8+nNo9jcIM2iRajhbxs\nNVjgQbuww3kZc4NII6VNtA5vdjot1l7evG3Q01riklaOO+Szrf9I6lBnS/985rceg1TmSHZEzmW9\nxEO3++1o2urfY1EpE90pNNIbL2ygNJEMD4/FzlA8msn4N58DUis91dCMez9617AgjsAh2R6SZI5c\no0udUX0tnr0ePRvPdYLbIPyFZnDZXTdJ28UyTj2aN38uwKXRlHcxpB3fGUYUB3xU+yqpWoATB7ai\n4fheTJq+FFNmX4LsghKqEtF7EtWRFPqpYnby4BtortuH9qYjaK87gZy8VGIIHnS2nUJHS2wU1Rbn\npRHqRcvX4nh9G/bVHcf6vXpC0IMgjgAT48nEwDKcpBlwRlJCAlPkhUyQEdPquewLGWkh3lNiSPkd\nb2QOMOQYs1Y8DVhyd7Kb4FB/XwoNUAsscqSFWrt6jViganA6iDbvzKE/Cjyb/5F7A/wo2qbVQ5al\ne/2zwA7jHJBIae21k3bQMLUKU14BYvba0GWcguKMPSadGcy9c+lcO/FSIVs0PQfTZ84joBe/6/TQ\nhlRn8wGqdlB1jipSAoW6umTEuwhV81di6oLLUVwxA+m0CyVGBWlfSjamdDTXH8XJwzsIFDWhgjaL\n0jMpV3+WQRIP8pgklRPZDBouCBRw2+HRQlVgh2yryCbOwYMHzQL5XC+SLX1b3nB1HI9nbjfotg5j\nMcD7VlycC6x7s4Gukd75uQDk9N5lm0lqWaJ32223GW9mav/9999vvIiNVI+zfb5q1Srj8c9+Z6OZ\n/Kie51rF82zb86eYP79wCibPvAFHAo0IduykxEuQKlFB/P5QN7LCQSymUeVnj/Xja7TZo42DAXr/\nXF6ehiWzMzCNal9pAQ8eoDTpy/QKdvIPBIcW9eLG+emYVEhvXo+F8Nw2qpQH+qhaRoPU9AQa9KbF\nBIgsOLT9VCEWlTXiLtodWk3poRRKJ91PEGYnJXRskOrYzbMycNusXFSktmLDxmb8aF0BtjRWoS8l\nn6CQ5graKBkaPAKEgn3wcOwCjUwvqGijPaFuOmYIYPPednyLoNDmoyEMcANE6mPvqfFTPdqDDcdI\nLYVtbKJL+tYQrpkdxEdqQlh3xEOgjAa6e7y4q9KHBfROdqKRNtoyqUJWvHDcQSG1rrS0CCXFRXF3\nq4dywLmTpJ/UH2xYtXLBsItct1rEubATYct9M88vc3Gvw4Jjw6nSuetpxkzOzWwYq3qEVYfSQset\nlmLpXixnt7SQ6jya/nEu1PKGk+5RfdTPR6tSpnejb0JuyWPZMBLNl9a/ZtL8KdgwER//FEJXdw+e\nf2ETliyeg9tuuXLYJquPTJtKJw40Sm37iAFlaJxckkFuW1ICEZVmOPB92MJGeGgk0DlmKQwHUI1A\nZlwen4u6RYNLZ6PKOS6NfJOJxl/dj1PF8oursPy6j9C+UCcycwqNnSCBQtEht6gcfd2rUUsQacfL\nv0Zb83HkUnoohbZopIrWeGIvJkjiJE4onTQNl65civaWerR3dWJ7LcXgCHgY0EfzO4EfkrwxgAjv\nGedM+xzgxaQlSCSD0w4g49gYclzVC/hRBlGzGT1o7+5FW5ofE7JYFqO9Kk9laNCPYC5OdSM3Im1A\nGHvvSmRAnkg9I2kcUIf0dK/D1j9y7wBEincAoMF7gUJMM5jPVMbUfGi9jEElpw6Df8UUxq+sHsBt\ny6hCtnQx1fiGl5TpaNrDd7SNEkIEymg+qrubXpEKZmDuqttQNZdA4Bm2oU6DP6JdXj2fIFE/DYqf\njnf4Nra/paWl6OzsNKDOSMCQSnBLjCi9jDxr0SygRMandQhoGgtwEq8FtkxNEmXEWR6UEqlrPHqj\njbflK5/KVxtHsnNky3CDSuPBG1vOWM/RgMFoQBTl1XsWf9zBgmfuuPN9bfkuCS29MwGgsQwvJ1Iv\ntefLX/6yAYDkle4LX/iC8QhmvY25JbASoTfWNKNVDVO91X6FqqqRVc/GWq9kvsQ5IEnbotJlXEGd\nwoE36jExqx63zPSijirj6w52E3gJ4x+XZeKZPQE8sLsXk8szcO/SdHr9CuC7L3VjfyddyWd50US3\n8VL7+qc/hvBGXRfuWZaOf/kocP0bp/DjZzKx7VguvAO9lB6i5JA/gxJEadx84HeqsTkSBA51UFJ1\nw9ESglBeo1q2guDQklIfdnT5cf9eSrByQ+qji1KxpLAb4b5m/OYPPvx4wyQCW1MQ8Me2JyRnFt4A\n1cYoJeTp70FJTic+eFMHbrsshBTaT3p5Ryu++8cebKL0T4D8mDnBh+vobn5qlge5PJaVAk3tYZzo\n9GB9YwqeOe7BmtIgUmlo+1S3H9dWUVKK2vJ1FKzu9c3GvEXvQ2X1+ZGejTaWm4g7bes+WGwfyb19\n9OI9UcmayCu9IE8CMx755TOcJ5yWcDRjamRxNVyl3bxTurPZwbZqKZbHZ7ubPly9x+OZ+Pj//ff9\nBmAbC30tYi0wFyt/IhIGiUhejcWF/ZrViw2oFA+0E2ik8TbaG1WsdlzIcWMFNi/kNo21bgJzZOx9\n/ctbjTH6kVS/3KCHyiwpnTBoyFnAkbUlpf6XCE19T3X1jVi2NHHj0Sp3Unmxqa8k9/QbIsBbYNRI\n4NZYy1OZiQbVzc3HROv2q988j3qqxd1y81pISrVyUokpUr8XMgC+etXCC6J9ifJhPNOdd2BIxqIL\nSqpGbFNaRjbMQXBA6mHbN/yK4JDUy3xoqduF/a8/h8zcCQZcikVMeWYtuBStTcfQ2fMKTjT3oaWH\nE8eIpzKTh2AJrQMxjpNJgUDcubSeySQ5JGBFdIy6mIAV2kSSBFGY0kPGfomZgHISqjOP7p4QerNp\nO4nEZWsolXHZFB1vIR0ZoD4dCLu4QBgHNCJ958KAOAbIUYZB0Ed5nGMQ4DH3UVJAoiIgyEgUuQEh\nxamtETqWtr0XLRN0VhrdOHHzS7tww+JsrL3ybZhVs8bhiUl75p9uGupsb9iDDkr8dHZ2GdWxzPwZ\nWHDZOzF94doRJYAcI9YTziR8FjGaoGkB+corrxhvVKMBXKxXJDdwItfYMlItwGA4WipTP/SJSJvI\n6LRoyRaOvDJJasku/M+i6QlndYMnMuKrOsiwbyLtGwtvEq7YOUio+lVVVZn3kAiIYiXMVLTyuQFA\n+05EZzipMTdgMVITLFA1Urro59F8X7futLv26LQj3f/0pz/FD37wA+OBTYCgjLELpDnfwd0Pxd+R\n7Du51emqq4c3Kn6+2/KnXJ7HS2PS2QuRmlODpmP1mEW39PfUAN/fRk9le3rw5L5utFHKdlppOu6l\nqtj0tCDuf7UbvzwSRlF6iCAJRx+CPHtaA+jsC+MXO0M42tqJjyz24yqORZfU9OKPW7vxk2ezseN4\nDqWN++Gl9FCYm0whbwoPP4fYyAYOX0S09NA7aw5gaVk7SmvoNIF5J6d34vgRAlW/z8VTB6ahJ62E\nUkjaMdVo7gQDBtEFvYxLy+sY6Iq+JKsD11/SidvWhGgvqB/H6jvw2GvdVD8bwBECP1myk0g6Bygx\n+83dHlxJIOhqts3P+UEJh7nLZvpwimPE63sGcHB/AFvDmZhaFMbN1R5Myffijf39KJ6x6LyokNl2\nuifNihvJnbYWAxs2bjWLXoFCWthKOiJecHuVUZpEJWvi0RtNvOpqd+NHky9WWoEvv35sHX7z+Asc\nt09yA6xnWFAiFg037+zzszVGnIitHFvWWM9qu3bZpfZyLoLlpcC1/QeODpE+Gw1wWMt6DVenRKQf\nrOSVFpzx+orojNaFvQXtxK9Y4JCkhuSNSiERcEg8++a3HsTDj/yOa49ek+98/BlJ1fRCUz06HzyJ\nV4b6ktZRiQIP0f3X/dsoL4eSJjtOCSL1v4ceftoYrf7ze981BCixddFvi0DWFctqcNONl9vohM6q\nd9WUMiORJGAoEdtaZ1NeQpWKJFLdtHkhcCiRutnflsOHT+CWmy43QJukEEdDQ0Wfr/aNhhfjlfa8\nA0OjbUhaRg7Vj9YYHGXn+l+jv/sYJ3I0aHnkVdQfnovqmivikpRK2bLL34ljtGn09poj+MFml5tl\ngSceTvpkO8gYZ+YtLwUICVQx6mbsPEZiyDE8xOeUEDLXzKe8PAxwpBrwOsiju6cPvRl0jcy8mlJm\np/qRleqjlDknku5AIMaBXVQHPdCfyMGTAX94b/AaAwCZSMXoYeSwoFDkmdpkMkTOvB4iNWTzmrO7\nTF6rsiTjBLVNNx4UZvRi5eRmivFnU9e7clgVsoG+drSc2ILG2u00Lt3NRWYI2YWzMH9NYqCQLf1c\nn7XYlIttuYAfCexQ2QIGXn75ZeMhzAIjoiFpDHkm07Nnn5V3PS8+//nPxwRPREPSMwJ8EgGGpk+f\njrVrT9s20iJdtozkoczWwfJFtL/yla/giiuugNyMn4sgkEG8ef755037BAwJFEukfaqf2qk8AkyG\n440AE9muEe/e9a53JcQbtc8NtOhaYMpowvve9z5jSFx8E7A3HIhivX3pnb/3ve81bbNlucEYC+hE\nv1/Vz23s2uaNdxYAMhqVKUvHDSaKhtqkPhRLasjNP5vffe7t7TWgkNo8derUswKF4vHFXV68a/FX\n70rf2UMPPTSiJJT7W5VHPDeIF6+M4eJt3YdLk3yWGAfkpSy//Gq0tdYS3NmLmok+3DwjgB9sC6Kl\nn7aHOBy/Z6EPZbSx8zhBnqeOBNEGPxbk0gV3ESWG+oJI59hMyz00KM3f5aNhbDnRjxVlzbhnRTre\ntiwLa2q68dIbnfjJcw5A5CEw5PfR1p+PwBDH/zBBoqBAIoJMAaqndYRSBqWH7sY+zKb0UEMrVcte\nTMVz+6spJTQZ/dbrGIdAD1XfvC4wyCMPqpQUKs6hTaRLunDrmiABoQBOnGrH/3u6E08REDrWFjb2\nhOaV+nG3VMc4L3hgD7CD5TxTT3V1Sj1PZxuf2BFGyWHQSDfT56RhfUsmZheG8P7ZYcwjT9romr56\nzvWomnfjeVEhs29VE2+32osWehtpO0dx7p1apbeTZS2iZUT6EnpWmjZtslmwWHrRZ/1WSZXMBvOb\nyvFgvIO7rvHKEiijxfaTT78ULwmU5mRtg2lDb2+fkYzWQs0dEjGoHa8+dtHXQOcpctM+FtWiRKRe\n3PUdzbUWWvd982d49NFnhwXCJGHwD1/4bzPWx6Jv+ahn6g/xeOleHMeiY+NUr1/+6jlj68rGxTqr\nr8r2yx1vvzoubwXirGFfHgkckr0hzWXuvOOahKQyEgWHxDuptN16y1qaMHBspdm21J44hVde3YGN\nm7ZxLnOUzlz4mxQjJNIHY2QbNkp99tFfPzdoRytWYvXflykhI/6Npe/GonmxxyUCrKiN7t9Ggezy\nBGYN+Efbv+ql6ZIHfvYE1B/efttVg/3PAiECWQsLcrF40exBGqPh46qVCw3d4yccOz7q6wJKbb+U\nFJLWqQJnJPWYaHnnQqLsztuvoQbIMfy/7/zC/AbZ71BjlOx0qd+JD6rbI/ydEnj68Y+9k2MY1bEj\nY00sGpKuuoSSQ2rbWNs3Gh5fqGkveGBIjJPkUPW8NQQuPNj58q8Q6ONu/UADjuxch9yiMhSVz4zL\n30BvE6ZWl6D2+G5cO6MWv9vH2agBT4SE6JIDugF4hIs4AMugBzLZGGInsuBQmOXLk5pjZ4gTGeYz\nUkQ8OxuLHk7metCaloIiWnBXCZyawi+AhpOhocGU5kQJgzF10q3AHOesa92Ye/2JHI7EUGQiYp7z\nWmeTntdSMTNpnXjzTPcReuZsr01ZtjzX2aQHbpx+GDPyOF3vCtLTzHrjjj49q0AJzwhdzfsJ2G2k\nnaDD6ODurjySTZm/OCFJoTOIncMITTyrqqro9rQY//Iv/4K6ujp86lOfiglKaKEptZzly5cbUERg\niYJoXHPNNYMSDAJAfve73xmAQgDN6tWrB+2bCBCSRNFdd91l1NBsU9yL8+gFqOiv5aJei3vZeJFH\ntCeffNIMFKqDFuySVrG0J0yYgJoabsGPMrjrEJ1V6msCRdS26PYJ/Iiug21fdN0lcRSPN5JMURmf\n+cxnjM2m6DrEu3dLhmjwHEmSJJqOVKE+/OEPG8BGgEM8EEXv/2c/+5lJJ+BlxowZQ0AS8UC8EPCj\ntsgL22c/+9nBviT+SiVL79AGq17nBviGew82n85Kp74SKwhMfP/73294IbBSgNe9995rAJVPf/rT\ng4CiaKhdqrNCdN9TnJWEEhgp/syaNcvkV14BeYpT0L3ao74SDYiZBPwTrdamPPEk5/Qsun0yAP+h\nD33IxG/evDmudJ7ek8BeqYrKC53qpL5og2irHyvEanOsdOpbYwHpLK3k+TQHNHbKWHKIY+yxXQ+g\nrXsPrqQhZQmz/nh7EOvrQphd1IftdQM0Th3EsaAfi0u8dOPu4wZEGG90yBoPvXZNA2jaDodOhfFa\ncxi/PxzG5mO041PRg4+sSse1K3Nw6aIevLy9F6/v8+C1A2nYeTwbHgJDNEREWjwTGHIkiJxx+7VD\n2dh1YiHKMltpB6mZKl15GEibwFHRwzGbruY5/klCyBjIk3QQAaGSnB5cXdOBJTOCqK7gjuPEAdQ1\ndeG7z3Tiie19DiBEKagQ2y0pITBbmKphS6Z4kc1u+cg+4JU2D55v5LQrNYSaKWE0Us3txTpgO+Nn\nFoYNKLS0jDUI+NHjnUODtdeicMLk00w9T1dSezl8pNZIX2hyHWvH2wIbsreyeOFsA2IoX1r68JKG\nbomD8Vi8ulkk9YFvffchA+T0UyJDUj0DPMcLWtRKEuQAFx3xgjMWcW41TBjOoLZAie9872EMVx8t\n+uSqXcCEwAQt/EYyYOuujsA9ubA/V/aG9K6/xUWYFoYCcbo5z40HSNh6aDEsI+PxQiJ8VF79ptvF\nXDQt9/u19Rru/Sq/6iVQ6lGCSOKt+qCkLm6+6Yoh5KO/gSEPIzcqS1I+L29y3pPqOdK7SgQcam3r\nwDPPbcAfXnr1DGBNfBugetIAnUJEA5Kqltpz2y1XsR5XYgZB2rMJeu+PPbHOkQ4jGGr7bKxy3eVI\nLe71rXsG+atFttSQ/lSBIvHLghfxQET15fv++2dGYk7v8B13XjtE8lLf9F2Mkz03gdf6Xba/E44a\nouMm3oKsC2pm4uP3Cgw5U3rzBMEeK1kXD6hRP3V7OlRfd/dLPVdQ21Sn4cpzq8uORlVW32ks+z/p\nHGMuu3SJAYH1m2S/Q/EhlfVS3cQH1a2P4L3qJpDN1ln1Fo13v+t6HCMv5AlONHbvOWR++x98yJHG\nSrR9SvdWCj7akvjCxdAgqRjlFNJOAA0at9QdZpX7KD3UwmmcD/kTq5CSJlsAQ0N/dwM66l9DZ8MO\nBGk7wM88A5zrnWiLpOWsUxNPHU4YREmcOPNAU0Ub9Nymca6NzSKBKJHoABcWkhbKTk2Bj4ARp4c0\nNgn0yDgjxcc14XQOkeL1IIhj40WIkw4+c8AdxTv3Jk4FDdJwwKDTUkHOs2hQSOmNNSR7ZglOfQV0\nqf3uNp5u7YqKU5hf0ozSvB74wn0I8cPJLaxATtGZNoZ6Wo+i4dDzqDu8GS2NjfRiRDH6wpmYs+Im\nlFTOUolvamhpacHrr79uQImtW7diz549g4tFuYfXwlGL+S996UsQiCBJHQFBbnUaTVDk0l4LVy2y\ndTSyraL329/+1uTXQlUL9FtuuQV33303xRZLzGL6O9/5jgEMXnvtNQP2tLW1GfBHXpe0aNbCXG7e\n58yZY2ju3CmvBA4A8oc//AFPPPGEoS+JnrVr1+KLX/wiZAtGto4kBaNyDx48aPrM7t27jcSFAMz8\n/Hzk5uYO1uGb3/ym4YH6SHQ6lR+vfVrs2zpEt08vVnmlfiRATQvyeLwRPyXlpEW8JJKGC6IjcEy8\n+9a3vmX4LJ4o7Nq1a8g7tO0cjl5hYSFycnIMn7Zv346nn37aqBcqj/qAff/19fUGRPrc5z6HJUuW\nDAEbKioqDP8Ejshb17Zt2/D9738fDz/8ML797W8bGosXL8aiRYtMn7J9S8DKpk2bTPXUJoFHti/I\n45ckdtx9QQkF5qjd6wgWNjc3G89g7nQCqSQhI2k4ebVTH+7r6zP1U3n/9V//ZfqG+qYALD1TGvU9\n9X/VW3zUOxdf9X3Yfq38jzzyiLE5lJaWZtqjZ8qrvimpMPFPfXfevHl0ZZppyhVfW1tbzbNHH33U\nvD/xSHxTfdUm9dfHHnvM9HP1Q/UDefmz34HOtl3q0+L1xo0bTV3FT/uu7rvvPkycONGAvXfccQdU\nTwX1m0S+t1jp4r0LQzj5Z9Qc8BKQySsop/ROAdroNKKfGzWzKQ2USzN8rzYCG05yDOvow5qJYVw/\ny4+75vpQGA7gd6/3YENtGPOLPVhdHGJaj5HCWT2Zjh4GaJunJYSDLdytbArgZFM30rwBrJjhwSUL\n/bhpdQBXzm/H5IIOtLf1oaGJvxkB2iKi6pexSUTD1WHasBugGFJ7lw/tfZkI0vGEJ5LGQ89i6KO4\njtzOZ7Xh7cub8Nk7W3HvLf24fFEIU4p70M5v9sH1rfjGc514bt8AGuhtc4AixxNzaU8vw4cuqpwH\nIgvaHrqlLyWwdU01MJGYyTGWeZybJjMyw8jgnGBjgxdl+R58kJJCblBo8pz3YvLUFcS0ToOdo34B\nY8ygRcjsWVMpPVhhpAM0YV734it48qk/4Pl1m6k+tc7sEL++dbcBhaT6csXly8xk26tJxTDhqaf/\niN89u8GMVfPmTsP1167B5Erq141DeOK3L+Ix1rWTQIZsHiYC+uo3SeniHWZ+NUJdp9CArICFigrH\njoU7uYAEGaQdqT4qX+BLN21XzpxRZRZ4ubm0zp5g0DucTJWLAPvYrt0H0dHJfh0V5s+bYXbIR6Ir\nMOWF32/G3n1HOG+ho5DIOBxFbsjtueCjFsjXvW0N5s+bPoS2vXG/39HUSzyxvG2gF9y57IcrVwzd\naBP/cnKyaYS9luPlYVvkGWe9p34CNSo/0Xdl343AKIGqkm6wC3VbgKWrfuI+VJb47+6H4tP733sL\nvviFv8Bf/K93m0VzKe3TuBfClu5ozrIx8/NfPGXA0tF8Q+6+K/62tnZwbjsVc2ZPHU3x45M2QFty\np14GmrfDk10BT/FKIGvSuJS1fcd+o2abk5OFzIx07Nx1AC/w9/OBn/8W3yZgLdDtJw88jv/86g+h\n30VJrJSUFOEv//xuvPMd13L9MXSO7Pf7yMNpg7/L6jO2n9j+p7P61F/+xXsMsOPuAwKf/vNrP8L6\nDVvR1EQDdgwdHV2cux0zgIh+I1RXHQp5/L1Zy9/1RQtmDfZRd3kqS31zpPJkWL2R/UChs6vbGG/X\nWNLJ8sppM0jl2bo9+uvnsWPnftMu9fG9ew+b9AL1bVrRkQFpOUk4drzOfDuql8Ad+13but188xX4\n/Oc+YsAhfXfukJ+Xi1kzp6Cpuc38tqk8/TbY79nSiNc+N6230rUD+V0kLZLk0ORZy423suYTr3AS\nEoDOtXsnoHLuVUjNyBvSkp7242g+uQ1dHU2g6R/MLAtwN7DeACsbjxYZcMSY+jEoCSczuqEKlfFQ\nZlTGCMhwsudIBbFD8dqRFlK80kpiiOCKoCMzGXLUyVrZ2XOpQlaUmaEnyPJ5kEE1tC7tPLqDMCAG\nwTskYOplInTPDmqemDS6163ODhhkzpF7AUlmkLD35hyJE0HdOwSck6FvHphYE6kkgyEMgUJrppxE\nZX4X0vzauaVh7aZ92LflV/BxcV9StWQwdU/rYdTv+y0aDm+gy/lmflRB5BfPwpxV7zyv7uYHKxTj\nQlI3WoxK3Ulgihbp//RP/2QW/dXV1WbhKtT7pptuglRSZs+ePQQUsiQFGt14o0T7fUYyQotLeXLS\nYlaLX0mZCBCSpIkW4wpatHd10U7TDTcY8MTS0oK4qIgeYAgeKYimgKHvfe97Rh1I9XTTl+qVDtHO\nysoy6VXnvLw8U2/Rt0G0BSJYIMXWQe0bLt1Y2mfrLgDq5ptvNgt9AUnuulveSE0oHm9t3e1ZdZfk\nlNoXzTulURvFV3c7bd5YZ/FXYJ8kd2w/kPSYQDa/32/eg/hjeaz3pzzuIGDr7//+740kmCRp7LsX\nWGhV6vR+BNQpr8AQ0dMz2Y0STYEP0e2J7gvinUARgVm33367GSRVD9tmSWUpqD4C5GQXyLZJ4ItA\nJstz9Ue1T6CnQBZ5wFu4cKGpk0BGeduTNJ3AUElBudukuksiTv1bbfj3f/93Q1v97/rrrzcAqOox\nfbojvaTB2eZXW+fOnWu+J/HEtknv89ZbbzV9RXmj26S46Hbpe9W7+tGPfmTqIimhj3/847jsssvO\n6E+2r4/EY/UvAZojpVN9kmHsHPBRYqdquuMNUpJDHT17cHWlH8XpYfxwewi/a0vFSc5EFoUHKNlL\nqZ8OH17vTAWFaXB1XgBLS8Ko55r298c5hucCV1V4UJXvx67GEDaeCOJVurD//qYBLCnrwqJyL5ZV\nEcickoHZ1Wl45zVUkSTAc7LJi/pmD88e1PEcDHA3kZsy9W30Ucaht4TllFKNS4fPT6BG1xM8SOc4\nnprC8T7YSy+ntB1EyaCtx4M40U4pqFaqgPFRiPOCmVSTu6GaXsQo8XSqJ4yHDxL4avWhmzuWJ3oI\nPDd4cF1WEO9Y6EF1ZQhbj3kIbAFvsJ3FeWdKChlQ6Dy4ph/urWpH9eorV3LTopRj5la8Qpsy9VRv\namvrMNmuvmoV/usrn4FAkEzOddwLkOHo2p1cpZGERfRkfbi8o30mCYWP3fOO0WY76/RTppSfoQJk\niUpFRGoNownaHY9WKUokv96Jdv2PU91E6knRQa7gEwlahEniQwuk8xnU94Zz4X6u3m+8dlmVvPLS\nxPgk3iT6rvRu8vNy8LZrLqHE+SKzqN1MCR1JQBigqK6R5gTOdMctEKi8rNi8BsfuzBzzjU6tqjDS\nevGkq8by3uSh8IbrLh2cf4yFhvLoPUql6U8x6Hu30lJWNdG+35N8xwLSq6omGWPPS5c471KSXmlp\nsSUv7e+y3rPoWVqWt+oTd95+NaZHqfQqnUCcGdMqMZXlve3qVTaLmVOlaM1amGeAKfvAAUezTB9V\nf40uT6rFy5bNM4abR1Oe6GtOmcf+rzljdN0ERrmDO62NV33WXrGcm9HzOed0vP25eWG/DXl6m8Y2\nx/ou1L4Z06fgq//+Kbydv2/ub0/lDNc+W4+34pkYiEENLpq2Banff3T3y9j7yqO0/XgU6bTnU1g8\nFZOmX4qCiqVIzXR+MLtbD+Hk/mdph2gLutrpNjcYID4SQmt7Pw7VhfH0vhJsPEJwSMiN+cOLyLXA\nH4FADvjjgEHmmh1ZZz03skAmnTKdzqvn6oAFWRmYkEcD2rR10ElRtlNtneiMLOYGmT0IxkSAm8i9\nOQ2COUxtgB7BN3zCa+fMGamNZ7ucaxcdxjnpVJor3hRuSnCi7TMTr6QEhSadwlVTj6M8r4v2kSh2\nrXYyeDw+pKYVonTKMlTVvA0TpyymWl8b6vc8RlDoj2hqrOekkbup7R7MXvFOrLzhoyMamzaEz+Mf\nLagFpuhsF9dawFsAQAvSWIBAdBUtHS0u3XRi5bdpo2noXj94ymPLt2lsHjd9gTbRdbPpbD732U07\n0XQ2v03vLl91jNU+m8eezyavpaGzLVsDx3DB3c7h0rmfnW0dlV+AlM4K4osADvsuJZHT3d1tJMH0\n3vTMvmPlidUmdztGarstK1abRF/53e9L6SQdJKkhSeNE11fPY7VJ/U1p7XMBcZa27Y/mYYz87vJ1\nPZY22XL1zSq/DgXRszzQtTuoHUofKyTKY3e6WHSScaPnQJAbI4f3bzBqZZ6+Pcij4O62hgH8Dw1S\n723zwm+GUbqv5wZNFm39FdMIdVlGCHfMCGEW7fD8+LUwDrUDt86gXR7GE3PBUQJGz+4awBsng+hg\nPj/HvRTSqcj1YOEkH66fm4rFk9PgJTDqUT8xY7jzey/7gKKh0c3DjaAwbQnJppBE3YMB9h+qktW2\nBAwY9MxuglY0Jt0XZP2kLsY8IY6LK8qoOkJJpwHaL3rhcBjNrEMOG3KEEkHHac/o5qoQLi0hWHqC\n97Q99J4aDwrSaHOIa/TXCIBVFXioPhY6U1LoTQaF3G/XURUImG/K/bul709qY7Em3O787mtN3L/y\ntR9CovoK//uv3oe//qv3JwwquWklcq0dZB3nO+j3QwutWLwZS520CNIxlmDUKbggjPWbmOg7tH3A\n/f7HUpfR5hmOj6I1Fl7GqsNw/B1tGcPRilW2O86WpXFOvBbfBaS6g/qUBVP1/lSevFXF6mvufGO5\ntvUZS153npHeozvtuF/3NiK07esI7/85PCWr4J3/V0DxinEpVqpeUvlLJ8ij92T56X6/Kljvc7Tv\nMpqWbUC8bzqRb3i4vhurPEmzpnBsjfVbl0h5tl9oGSr6w/2+2LSx+nmsup0Lfg7XPsvvt+L5ogOG\n9BL6ezux/7WncHTnb4m2dhIcykQ+9fBzJ1QjPbvEQB0dTYfRdHInOlvrOMmzEwNO/gh8nAaHih1w\naAiwoxI0QzXTRZ50bUEigULR1056k86ZYprsZlFEsT8vtyP1gcgrWUhAzpCge9I38a5numTcYIxA\nHqUxEeah89zEnb43eSwtc7aFKa8O1/3gZSR/5P6OmnosKjuFvJRWZKVxoqx48sCcdUnVgLQ0qicV\nViInv4TqclwgdxyngdE64xlBoFBa7iysvO4ezFi0VrmTIcmBPzkOuAGMC6XxF2KdLhTeJOtxfjgw\nHDi0k8aZbVgyMYSFWQG8QRWysnwv/mxuAIfqA/j+dnr5pPdQL8fEO6h6dX1FCPVUK9tP0OZX+8LY\nRrtFnF1ShZsqiiSWRltFAoo09pXkeDAx20u1Lg+v5foXKKMRaA7NqGsjnQ4dlCLqpMv4TmcxRhzI\nGL6mHWwDBmVQ+leGsecXe5FPgKep14MagkPzJoTxgx1mqKS0sBePn0xBK9XTJlOFbEEGvZXRK1mj\nJwVTUgK0K0T5W06or5wMXD+FdaABImtT6EKQFLLvYDzO2nH+6n/9CLIFoZ30T/31B42ExXiUlaSZ\n5ECSA0kOnMGB8wgMnVF2MiLJgYuAA2PbgniTG5aank2X91Nx6mgJeto70N/Xjab6A+jubKatoSwC\nQdTJ7aHdju42bvoR3LDYC+stACc/NwXV6MctvhPITx/A03vLIi3iLJA7fkpvgJTIhNJMCRmvBzrp\nLDqOUWoTEVEpExnnmVD+HgtIKYkJgxeRe6c850ZAEO8VdCKQ49wpjY7BB7zVjY2PnM3jiK0gk1YR\nkWDvI8Wb7BHqEeTHeB+7btoRzCluR3FmP/y02eCurUjoXlJXfX2taGnoRkfzQSLdmocHaJROEjiS\nOvBR/3XxBaNCZlmQPCc5cD45EC3Fcj7LjlfWhVineHVNxr81ORCtVtbYtgPTM4P4DG33PHk4iBfq\n/Ggg2DIhNEAbPASDstLwB6qQzaO/g+pCPzcdfDhJtayFE6hWNiGAY/TS/BSlcYp9IUyiWQRPuR+3\nTqNkUVMIfzgcRhtd3TdJ34sDWEsT7RXQ3hCxHSM4pDipkSlQq8wcAoLMtSI1EdBznTlTqiKgdCPV\nxdoCXhztJOBDlbZF1BY+Sc2qzqAXN03nmWpkonl4wIP1zV509dPD2MwU3EZPqj/YFcArnSmYmx/C\nn9d4sag0hfOTMOpaaO/ENwPzltGm0AUkKSQWnOsgcNpKQAy3O32uy03SS3IgyYEkB5IcSHIgyYGR\nOXBRAkNqVjalVbLyStHVchAhH8Uu+2lPJXCKczgvJ3Y0zEZgJiyvIgyc65lgz5rtZdNAZEFmCFdP\na8WM4gDu31KMlm5HZWIwnRAUTQoNKCRCvJZXMv6TbSFn4sgz780RmUgO2h1SqXrk/NFFnEAQaLBQ\nJdGNjeO1fRYFCBnoyDxTGpsokt/e2+eKtsHSidxXFARxa00nKribmkLDdvLAIltLpn0mjQAn0xDe\nqawQDXT1URKq32GP0pBmiAY3C8umo2LGkgtOhcw0I/knyYEkB5IcSHLgTeXAUHDofnoN2YNM2g3/\nIO3vLK8ggEK7Q+taUtEU9GFhfgCXLggTlKEB6gNhHKetoDB3I+YWBFBNVbNn9gIvHea4k+XH3KIg\n3k1QaHkp7fkFfFhbDVzFQ/Y76ygJtJ/Gqo1EUFcY1Cg3Q5llhIZJSQppCF9T6cN0GoMuYJ3eoCe0\n3x4mwESgp4wmDDMoJbSV6mwbaaco64QHd9NsRl4q6REQqqD9owdqU7CbanE9VDcTsUJ6H6s9FcSG\n3hRs6fbhahrP/nBNKso51rZTDa4nPBFF1WtRMZ3exyZWc6Plop2SWVYOe3Z7p5FtiD9VL0XDMin5\nMMmBJAeSHEhyIMmBN4kDF+0sJDOXE6rS6Wg/tZ2AkKyrC5ggEESPJgpW3kbXxo60ziaeDkooO97W\n2k9L6OVYuPZWLOAksbR8Ix7b3IuNB8USg6Y4OXRpCfDSCQJHnImfmUlqNskgd/a6121EdsfcO3lO\n/82iXSSFrh7NTqMCARandJVhn0WuLaBj4hVnE+gcubZRNquqpnrZ5zrrVn+Yf9XUAO5cNoAaqn1V\nzVyG5qNv4MDWJ9HRtJe6owHurDoE9ddki/wVPUkuOfHOkwFOnjNTcgkKcYacDEkOJDmQ5ECSA0kO\nxOCABYfy8stwbN9TaKt7Ed19jVhYnIJ7ahy7Q1vb/DhEN+5lPo5D/HecLtzbaF9jYVEIl0wKoZHS\nQq92UMKIYEqOJ4giD731EPSpo9evUoI4nX10Eb8nREPVNPw81Svv8ZiST8mhZtqFC4RxyQwfjtAA\n9P4Guo6nPaLtjWGOyVQ9oxTPo9uDqCz2Y1YBjVMWApubKaFEx1lzyjzYzDoN0CbEFoJDC6nGNjU7\niONUoc7Ppm29ghC2tPhwtM+ZWh3u9eFEvRdFNLT9rpnA22f6qDrmRSudnnXTHf3kOe9GZfVyAkJp\nVNM+rUoXg2UXTZR1bS6bEHKXbMEfxW/YuNXYkpAa2SUrF47Zbs5Fw4xkRZMcSHIgyYEkB5IcuIg4\ncNECQz5/Kr2QZcOfmkZgyAEmLPZhoRX7Hhxow7nr75XaUzrKKLI9reZqlFbPp+GsdBQV5CEz/QV6\n7+nBS3vdOU5DIg64QmkhQ0pl8hh8LJka3psonXlE7p2Snb9lhVmYVVmCFs5atx444X50+toUoD88\nzH/nbO+dQm1ypWMYBImc2yH3JkkknUAuBZ5uXpKGm5flYdmKSzFn0eX0xpJGgIx2mrLysGP9IwSH\n9nC3tY9NieRxcp5ucuRepxB3SAMUsS+ixNDECloHTYYkB5IcSHIgyYEkB+JwQOBQ0cRpyC/8CI4c\nqMGx3T8nYLOHzg9SsJhSvBuODeCBvV7sbnU2UiSrW5wRxurCAUoLBfDHIx7slgQRbfnNnxBERSZt\n/tB+z2RqjjX2ePHYUS+aqcr1wQl0Y0svZOuPESziXCGbgEUe00pZei+BnRfrPdhHQImCx/TE6cNE\nupFHug87msNUb6M7X22s0KB0d38Iudwoqcry4BXeN9OG0BF6JyumOvUbrX6Eme/KKSRCQg8e8mJf\nJ+0XkdQcGs7+MFXH5hWSRm8Ie46TTumVmLf0AyiYUPWWkxKSi+vnXtho3ro8EVlgSMaPZSRUQV6Y\nVq1cYK6Tf5IcSHIgyYEkB5IcSHLgwuDARQsMiX1plExJz8qnMeoGaTcZHEaohQAaNzhkJHgY308L\nkv0DGZgy7zrMWnojVdEmwJ9CeXGGeYvX0qq/D/k56zFjihe/2dyJZnrYGiRqrgzqY6IMfZUl/GdQ\nIocRujZxvNaFrt2Bz1P9KawvtztlzGAwuK7NZeSeJwPMuOOYx7bPM1j2ICE+tE8jcUOAHQ8qi2i0\nc2EqrlhWjUtW0111xXQa4nS6gqR9ptWspaQQ3QFvfBTtjTvhC/ewDmJw/GBxKeVPz6TFzWRIciDJ\ngSQHkhxIcmAYDkhKxu/NQNWMNRzoKBW0+wH0tu1hDj/VyniU9BqA6MHDKdjZSjCnM4DDTcAOqo7J\nFtEAPaK8rSyIQm8ILzen0LU9gZr0ILZxXOzjeJ5JNa+8FDqcoHBu44APmwgkSS7n2slhgjYhFKZ7\n8I7ZHtR3e/DgXnpGIQZ1NyV73jmfZXGzY0+DjFpTSoiu5+v7vOigzcI5eSFMyfRhL9XVUtJCoOMy\nBDnQN9Lr2LdPpWBDix+n+mmgmtJD750ZwspK6rJx+JTqWFPvBORNWsv5xi0oJCj0VpESsq9YXseO\nHK0dBIAOH6lF7ckG4/bXqpG9/barcMfbr0lKC1mmJc9JDiQ5kORAkgNJDlwgHLiogSHtOEpyyAGC\nyNEIOhEFixgJl/4+SgoFOAGtuR5zV9xmQCH3O/CS1uyFl6Fi2lLM2LkJ88t/j0e56fXSHiIyg3SV\nQwiNRXtYkrm193rOawsO6dYAOroAinKzMauiBBUTciC3z+GI2pt56Epn7t1/9MwiL26CjHfaGpV5\nMK0l4jyvoueUG+d3Y96UDNSsvAazay5BTnYWQZ+hIuwC3KrnX47+nnbs3tyK/u7jbJVAMgW3vSEn\nJvk3yYEkB5IcSHIgyYGxcsCqllVWLcaxQ69QeuhBtHXv4biThlXVabikso8AUR8eOuzH8wRe/kg7\nP+X+EN4zqR+XEBh64aQX61tS0EYQqKJoAOkcpzI4FOfR61iJJIkG/GilN7MAVcAUwjRWrbGzLFt2\ngwjYUNIoQCDpJaqH1b5Gj2gcK+cR2JlN1bNrKYJ0asCLTe2pyD8Yxi3lAbybHtH29gSQxw2Tl5p8\n2NiaAi/V0rTXMysvjP+zwoM1U7O4ARRGS1sQdc2UT8qYg3mr3ofJVB3zUzr3rQYKia+lJROwZPFc\n/OGPrxpASF7IfvWb5/nEg1/+6jksXjgb77zzWlRXT1LyZEhyIMmBJAeSHEhyIMmBC4gDFzUwJM9g\ncgdvpHbE1Iho0BkSQ5ys9WoSVzwd1XMvOwMUsu9D4FBuXh6WrliLCRPLML16E27YtRtPvMJdywOc\nUBrJG4FAFoiJAEICYgYrEXlmk0SIZ6SlEpCZiKml+fSKwkkkJXIyaRWzuy+GnSHlicrvkHEiSwoI\n5jCirpmGCoYkjGSKVMvkiYBEl8wI4c7FfZg5YybmLLsJkybPHJQScmgP/ZtGNT2phNUdrkTj8TrQ\nnrcp0/4dmvr0XXd7M3Rk5tIwQzIkOZDkQJIDSQ4kOZAAB8xGD8fgqhmXcpihbZ7DryLUs5uGnU8D\nRFfO8WDD0QH8aAdVstp8OF7rxVPHB+ChqlghVblmEJQpoNTPKdoRzKSYTma6H+k0GF1Pw9NtBH88\nVDszmyxUCdNouYNSSK83eNHP8a2MhqKnZ0lhjcalG1LwZGMarq8IotQfpGRSCgboXOGZ1nQcoeTx\npNQgiPXgWD+NZNO2ngxVX1UWwA3T/Jg6kTck3tIW4BgdRFtPPjecrsbchTe+5Q1M+wi8rb5kEda/\n/Dp+/ZsXcPjICXzt6z/BpPJiXH/dpbj9tisxfdpkSmcP3YxKoHskkyQ5kORAkgNJDiQ5kOTAOHPg\nogaGujvq0NVey3leBBCJMGvonQOdaB6SkZ1L9bOR1ZwEEFVNm4uKydMxqeIPmJT+Q9w6qwWPvJ6P\nV2qLB1+JAJoS2ibKo9SNmWVK8oYAUUtHF13W0q07PXw1tPcgnQDQ5OIilBUW0T0tvabRvXvlxDyK\nW5fj9f3H0SNPYO7ABmSkpaC8KB9Ty4qQlyUbC2H0DXD2yrOXwFL/QAi+GZzsdvXy6EJrZzeOnuKW\nZVSYX9KMK6tOoHryJFx+/T2YNY+7lbSpFC0lFJXN3PZ0NKKv6xTLo2FvVwLx1409uR5hoL+HB2Xm\nkQSG3HxJXic5kORAkgNJDozMASs9JMmao4c2DwGIOqmBPTsriK9f7sFrVCnbUhvAThp73klpIU+/\nB48dCGJXrY82iID5dAs/q7COfbhgAAAaj0lEQVQflSkhhAgOVXDoP0kj0Rq90sOUKiJ45A9SFpYq\nY4srPTRgHcaRVi+WFgdxOT2eHe0YQC7VzV5u9hMAIqDE0E9p4F09fkoL+cyYOEFGpauB2+amUTIp\nA41NfTjREOSGD8fm3gIDCK1YcD0Kiia/ZaWEDGNcf6qmlOM//u2vce9H78JJqpEpVFaWYmpVBdLS\nU5OgkItXycskB5IcSHIgyYEkBy4kDly0wFBny2G0NexCcEAqWW7YwgEt3OCQhHlSUnzobDqI5tp9\nyCkoG/EdCDiRUeqZc5Ygy9uAV//4CN6/6CBumX0Uj++twv62KZhSXIxJRQU00EwbAgzeiJh6cW4O\nwR+6dOcxlYd2xzIpMZRCN7sDAYE7qqMfkwoLkTIrhbYJOmhnIYxeGmcsys0gzVyCQLSF4E0x+bQL\np5ASEX/3cjc1ixNShQzWsTA7G55SGt+cWIhdR+rRQqCohjudN808joKUZoqzBzC1fCJKJrC8tEyT\nb6Q/Hc3H0XpqNwZovykU7HFwrxiZLEAk72UpFONvqd+HhhP7kDehIkbqZFSSA0kOJDmQ5ECSA8Nz\nYFB6aPolRvXKAkRBShAFezkuUdB2No05Ly9PIeACI0W0pZYbMT00SE1D0G8QKFJ4vhP46eEQsrlR\n00Jj0QolHqqVdwTx+yNUH+NwvDQ3gBSme7UnFVu7/dhDg9V5/jDBHdoFInDUyLHYGbVB+0VhIxm0\nYKIH08ozMSnXx80pbs7wqKWHtA5qXHszZmPu6rsplbvIOHR4q6qNGWbG+KP5Sk5OFmrmzcC8OdNM\nCs2NklJCMZiVjEpyIMmBJAeSHEhy4ALiwEULDPV0nKLKEqWF6KJ+UGIoIjnkCImf5rIglJQ0Pwb6\nTmH/q49SYiYVk2asOp1gmKtQoAvB/lYapSZQE8xEehcNSi5l2cE+1FE0vSlYbCaXgnpC3IE0kA9t\nFRAlQip3IB3gxPkbMBI/FkoJEwyi/YO8HBRkZTAvjWESRNIEipAUslIpWURqQdJ06DpCSaoqIafB\nGyrTIU3AVDgFU/ObsCTvAIoymw1wlOHrRoaPM1XZVuhqQHebs3snGsOFrlYajNz3e4JobyDQ30H+\nyn/L6XqfvnaqoScC31Ipit/ddgwnDryGMnp7y8qdOFwxyWdJDiQ5kORAkgNJDsTlQDRApLHI2iBq\naNqBJqqSF2WFMY2ewmbNSUE+7QkJKdpIj2YWKJKR6ka6jW/udTZYGsJ+PEuX9l4JtrrCACcKAUoE\nHe7SqApMpNTQBNIuoh71ohIvrqkEKvK0YZOJFCboonr6CUos5eenc4gNoCs0A/MvvRsVVUvp1CLj\nLedtzMWqhC4FENlNrYQyJBMlOZDkQJIDSQ4kOZDkwJvKgYsSGOpsOYKWk6+jt7OBruppX4BzQWFC\ngQDVrXoHaE9oAAJhfH56PIkcabQ1kEKNrO62g9i76RfM40H59JUjMj840Im+7mZTjiY5eTke5DNv\nf7AXhd2vU11sK7ro6ezUwEzUD8wjSORI8gwl7MQJQLFQkQOpKBW9lFF1DWYz00kX5uQ0yAYNAbgs\nWYfIYHY/OpGfegoTUw/QDW8t0r3tSPMH4KfL3FCwn4dKoC2j9v+/vTt7buu+7gD+JQFiIwmQBHdK\nXESKlkRRoiU5oupYthIn6ThJx55pp50+9LGP/YP63j40mXYyfYjbvMR1Y8d2FSuyLYmbTHETF3ED\nQOxLv+dSEEGKFJyItkTweycQQGwX+FzPAPni/M5ZRDxaPhhKRB5i5cFHWJ7+GJuPpui43XQ6x3J7\nG0lv1naeZ78FN0v0a3gqbm7+yprNpvDgqw/Q3HEKQ9feLd6kcwlIQAISkMCfJVAMiOzB1oPoZN8V\nzNz/DHMPbnP51hcoJDlSjJ90SVYS+Wuy6K9jNdGVAH802W4IbR/L1hg6y3Hp8xwxb2HRAs8XWDlk\nVT8d/OHHNqsI6mTDaneNm0uo+bFsn7fc4gyBNiIMf+J2GzDNUWSRZAMK7mbUtL6KCzd+jPqGLgVC\n21z6VwISkIAEJCCBIyhw5IKhdGIDq/M3sfbwNsfUby8jsxBoK5pCdDPJMKiOjY/bUc9lUz5/iN8V\nC1hbnGbYMc0x6m4Eg15sbUzhy9/+M5b4xbJ3+Edo7HjlwEOXTW85gUo6tR2Q2B3tV0t3VQYhP4MQ\njrt1b26hib8sXuYkkxibUS5vujEXacF6vJYBSjHRKd3F42+bpVc5l3lfm2j2JBLajpGePAMvWDFS\nU2ALA433uc8MGps74WPFENsBIbqyzibR2z+DWiBU3PgqWXmUQCJiPYM24K3lqJV9tvjGA5p8gIdT\nn2D14V0GYpt8XIH9jNxIpdy8vP26LbiyIM6Wj1nvJguH/P6cs5TM62GZfnoF92+9j1C4AycGy4dv\n+7wUXSUBCUhAAhJ4SqAYEvUNvoGe/mv8jGWPPqskmr6F+Rl+L0jcdYKiNS4Ps08sP38QslM2zUpb\nbidDNajnDye9HDlfe8rD5eisOs7yM93DMIiPiLBpdA17EgVrXYgwaLKwaSPKB7rbEcuG0dA4jKuj\nDIJCXJLOJef2eo7bcjEHUv9IQAISkIAEJFBRAkcqGLJQaOnrD7H84GMkGVrkc1nEY2lsrG4hEOrC\nyFs/RlvvMCuDvHDV8MQvbLbMLJXYwtzk5xi/+V9YXLgPPwOi+rokvyiuw5aKta1f4sSy0wyTencd\nXGu8vLE8xtBpjb82liQtxXvxW2eB01DCrb0YvPxX6BwcRSrrxvz9W9h4eI89hlJY24hhld9QE4k0\noqlajsXt4HQUC4zsF0zWBFnq8yT54fdMPqeFLfZLZdAbY/i0xfM4wr5l1AZ8CAQ8CDa1cync33HC\nSRdquQwtl1jG13/8T0wmZhGL7KmPd14rd1CVw8biPQZqY+jYUyllfZqij+5ide4m5iY+xfLcXSTi\nacTZb6HGF4Y/2MypIoPoOXOVy8N2mkpvcfrYyvwkZsc+Yb+ncScc8noLbDCZQWx9Anc/+oVTSt7R\n/1pRTOcSkIAEJCCB5xYoBkSMfZznKgZF9sNNZH0OcwyKIptLYNshLG0uMhlaRZ4/WiC3ijp+B7DP\n2jibWXNRNyIMkaJxTi5taOco+zCi6zlUbfIzOMTP2p6LrCJqQ7CxE8GGDn4+Kwh67oOnJ5CABCTw\nogQ4wAdufm5wOrQ2CUhgt0AVg5OSWGL3jS/bXwsTH2P8/37BSpZ5VgZxjT8nfiXiLrScHEH/xbfR\nzr42vgCrhPbZUvEoNlcXMDP2GcZuvs9laPNobPKirj7EsCWM5u4LaD55EbWNPQxCOp3eOusPP8fi\n1P9gaeZLhkMsyaFUkcs5J12CfQv6ht/DpR/+A7z+oLPnfC7jhFZ2nxg7WMZiCX7RXOTyt6+wsTSJ\nRwvjsB5J9nMmF2VxMq+fDazZ4DITZXPqPHsNufn+atHR/z10DV5DbX0rQk0tHLvrgY9TPTyccmbB\nV3GymO1vfuwD3P39vzKoucPlXPyWy41P/2RzVXv5PJ3oGfohBi6/64Rg2XQM8fX7iCzfweKDPziB\nUGRthe81jWSqhgHSKM5zOZhV/vg4za021MxSeY7ifbxl2QE0FY/wcRMY+8NvMDf+CfsxPWJYxffA\n6WvZbD36zr+JkRt/j6b27SaUxcfqXAISkIAEJPBtCBT4y0uOn4vFwRR5p4SWv8Y4gyr4mctUKLKx\nyNOSs/v6UBvqeKq2klx+cha/FdlnrAVQVfy1xoY+2Lk2CUhAAhI4ogL2GcAVFMjyVwGG/NsBEc+1\nSUACjsCRikttyVdD2zCmbs1yKkmUS8Y6MfT6X6L7zOtPhRZ7j6+NqW8NvIIgQ45wRx9u/++/Y3Xh\nc37RizDESSA/k+RSqxlW43Sx+qiNXyqzDHPmsbb8NXsWRfY+nfO3La1qaDmNzv5LT0Ihu8HG3dvJ\ntkZOAWtsbEThRDvy54ZYWTONO7/7F8zce8Avn+wDxOZCruo0gyD2FUIanuoUx9hzdG5TB86/+n0G\nK2/wNfILKb+gHrTZvtrYc2F96WueptkPyaqGdnr/2OPyeYY4yTU8mv2cVU4xeoV5LX9ZXZvhsrEJ\nxDbZUHsr5fRQCDWfw8hr76D37DVOF+vcFQbZcxU3N5s2uRkWnQwE0dTWjene8/jyo1/xPY45X6xD\nrZ1oP3WJX7hbiw/RuQQkIAEJSOBbFbAAx80fQ561hVtDaGwZcO6i0OdZUrpNAhKQQIUI2P+X4g/v\nzqlC3pLehgQOU+BIBUP+uiYMXvk5g48cK1xu49SFH7Ba58aBVUL7QfkYYpwcfI0BURfDmY8w+cf/\nZvPKOTTklll9tM4gaIqhjpvBBqeEsbF1OhXncjM2uOaT7S2tynMSWGv3JU7gurDfrnZd5/zy6PbC\n3kOgPsyeBB5W1ERRzbL3Avv4ZHgqViNZY2dXTRAe9kgqBky7nmyfPzz+Br6nXngDrQyyNhkkcdZu\nyWaNrDNspP1o6S5DoFku9wow/ErzPUcZGCXZQ4iNO9lHqJPjgUfe/Ft09g07VUIlT3HgRQuIQs1d\nOHP5R06I9Plv/417K2Dk+t/g1PB1vo/6Ax+rGyQgAQlIQALftYCFR5xN9l3vVvuTgAQkIAEJSEAC\nL6XAkQqGTDDAfjdnR99F34UbzuWDlo49S9uCjHB7r9Mvx+Orx1e//w8u95pDfX2e4QkDFYZC2yFN\n8fzpZysUXAxD+tHWcx4eX93TdzjgGm+ggdVAnXyMH9mYdbTc2XbCJ5v+ZWXrpYvBdu530CXrBRQI\ntnLJ3NRTd3Gem+X02XQCW5wclohv8D2y3J5L1xJJVjh52jDw6ijOfe8ddJ4aPrBK6KknLrnCqrIG\nLlxn8NXkBEN/SrhU8jS6KAEJSEACEpCABCQgAQlIQAISkMB3JHDkgiFzsXDITs+7WfXQwMW32Feg\nmo2pf4VkcoaVNCW/IJaUCJVc3N4tyxH9dY3w1+7f0+ig12YVQF5/LSd4cUzKnq24j0KhmuFKM3sC\n2XKvb75ZL4RihZE9V2msVHxup+6JS9hyOTbT5nSxVJqPqWnHEMO2odF3yi7JK/dqLBw6OXiZd2NL\nz5J+ROUep9slIAEJSEACEpCABCQgAQlIQAIS+O4FSlKQ737nL8MevVzmZMudeoduMCdp5Kh1e1UW\no+xEKfu9TivmSW0tIbIyiUxyd+XPfvcvvc7t5lQTjpg/aLOeQjWsKLLpat90i3EKy8Lkx4isTjMR\nYnPNAx64c30VsmwQnU67+d7fwMU33nOWgx1GmOP0HlIodMAR0NUSkIAEJCABCUhAAhKQgAQkIIGX\nR+BIVgwdNp+FQ6df/QmSW2uYufNrFNzlgyF2cGYgtMoR75+Aw8LQ0H4OPk79cnnY1OwZWyq2hNjG\nNPsLsSv+AZubg1FcPFmfnnLb9qj5SU4l+xBzY7/jtLM5PiRz4MPsGav4j4VCiaSHk8eusVLop06l\n0IEP0g0SkIAEJCABCUhAAhKQgAQkIAEJVKSAgqHHh9X64nT1X0bk0QQSm1Ps8WPhysHBjIU2mVQU\ny7O3WTm0gpa1Sfhqm9louQU1gWZW/IS4ZIzLzDgW10bj8n9IJ1YQW7nLKqN7SCdtctjuzap5bI/s\nfY1kZBaxtVnUN3bsulMuE0c2s4V0/BESGw8YBD3E8swdLEx/gVhkxRnRW7VnIpk9QWmlkP2dybjY\n62gAZ678BK0nB+0qbRKQgAQkIAEJSEACEpCABCQgAQkcMwEFQyUHvLX7PMe9X8L0l7OsqsmxOTP7\n8Dxjy7OZcyoVwypH2kc3Frn0y88wqJHBToBLxQK8XM+ePxxH7+b0Ey4PYzSE6Po8lucnOQns6WCo\nGEMV8klsrU9icfx9JkRfswqpzgmMspkEHxdjwBTj8yzg0cIYq5zWnZDJmZ6Wfzyi/nEKtBMGlUZc\n1lC7mgFSNSeYsRF2Q6t6AT3jGOsmCUhAAhKQgAQkIAEJSEACEpBAJQsoGCo5ujZdzMau14ZakYot\ncoLX0+GN3T2XLXApmIUwNtLeLltjohRH0EcYBq1yGZhFMlWoZoNq26xayP6282wmDQtxCgyVDt7y\niMdWMPnFbzAz/uH21DM+OJWIIs+m0RZI2fMkEwme5/k3eGL4xP1WVxec/TjnvLx3s5eSy1Uhx/u3\ndJ1Gy4lX9t5Ff0tAAhKQgAQkIAEJSEACEpCABCRwTAQUDO050G42fLapYZnEbhoLVLIMgeLxHLZi\nFswU4K9v4FSyJlYKbT9JPLaG9fV1J5ix6zw1nMzFfkXWZ7pgGQ3/KdgF5489O97zZy6XYTi0jq1o\ngWGPVRvZ/vM8ubkMrJo9guxvF6MpFwKcjsYUClsR2zeDIV52MZOy/Xt9WQZGOyGU8zJsYRknktkE\nMR9P2iQgAQlIQAISkIAEJCABCUhAAhI4ngK704/jabDrXXv9QdRxeZVVDKWTvOlx0U18K4/IRhbV\nniB6zr2GnrOjCDZ1cBnW41SId7Uqnlw2hZX5Kaw+nMDa4iSXe03Ay+FiFg65XRYU8Y6WMtn2+Lm3\n/9i+es9VzJCqkUxWI8PpYVYV5K8LY+D8VYQ7TvNyE6ubwk9eg+3ftkcLk1iZm+CyuElsLo/D48mj\nxp1maGQBUXHnzl31jwQkIAEJSEACEpCABCQgAQlIQALHWEDB0J6D73Z7GOC4GchwfVYxFGKVUCpT\ni+6hKzh1/rrTrLku1Hxgb5627jNIxqNI8bS6NM2QaBKz458iwpDG59sOh6q3V5nt2vveUCjHiqBU\nqoaNrFswMDSKboZRIYZRFgZ5A0EnENpvvHx7z1nuO8KlZzHMT36GqVvvs5H1BKuIUnxf2V371B8S\nkIAEJCABCUhAAhKQgAQkIAEJHF8BBUN7jn0yvsLGzrOs0LFyISCxxVAoXYvTl36Gc1d/hmcFQsWn\nsqbTdkIYaGrvQ/fpyzg98jYWpm5i7OavOZVsEn6/jaTfWeJVfKydF/JVSKdd7EVUg86BUZx//T20\nnhh0RsrvFwSVPtYu2/Kw4hKxULgDgfpGjH/6S+73DueV5bnqLM/lZuxjFFnj8rNV1Ab5QrVJQAIS\nkIAEJCABCUhAAhKQgAQkcOwEFAyVHHIbP2/TvqIba2w8nWG1Tg6RzQxae8+i/8JbaGBj6j91s6Vm\nblYX1fLUEO7kOPsQbn/4S0RXx+Bn9VA1A5q9m4VCqG7FwMhVnBv9OTpODR9YnbT3sXv/tsqivqHr\n7Jm0jonPVhGPzqPgSrF6KM/wi82redImAQlIQAISkIAEJCABCUhAAhKQwPEU2GdB0/GEsHcdjy5j\nY2XWGf9uS64SXEIWCPXhlUtvI9ze+9ww1ux54MJ1XHzjrxFqeQXpjMfpIVR8Yuv+k2EolEy60Tv0\nJkZ/+o/PFQoVn9fD6qWu01fRcvICgyjrieRitVLB6YO0zF5E2iQgAQlIQAISkIAEJCABCUhAAhI4\nngIKhkqOuzWLXnrwFQOhFOJcQmbBUOepi+g9d+3PrtgpeXrnoi0x62c41DVwhc2kPTztFG1ls1VO\nKNTYOshg6BpCrFD6JkvH9u5jv7+DzT1o7RmBJ9DCMKrGmZwWWZ3B3OQtZznZfo/RdRKQgAQkIAEJ\nSEACEpCABCQgAQlUtoCCoZLj6/GF2HunDtFIGutrGdQ29uHE6UtP+vWU3PW5Llo4dKJ/BM0dZ9jf\nmuPKHm95jpDPs79Q18AlnBy8XLz6UM5dbKptwVC4a5hj7n2sVvKihhPWrNl2cZrZoexITyIBCUhA\nAhKQgAQkIAEJSEACEpDAkRHYKVc5Mi/523uhLSeGcPWdf3KqaBYf3EMXq4UOO6ApvvqG1m6nMXVs\nbZwT5NlTiEvXctlqNLQMMIwaOfQwyvZbH+5GW8+rWFt+iGBzH3rOfX+7qXWwqfiydC4BCUhAAhKQ\ngAQkIAEJSEACEpDAMRJQMFRysD2+eoQ76lHX0I7es3/BkfA7071K7nYoF+sb2xBiM2qXywcuIEM2\nl3OCoca2Xr6GvkPZx94nsaqh3vM30NI9DHuvAU4jO6ylanv3pb8lIAEJSEACEpCABCQgAQlIQAIS\nePkFFAztc4yejJvf57bDuspCmrqGVtSF2pCIxpHLJXmqQpDj5YMMjL6tzV8fhp20SUACEpCABCQg\nAQlIQAISkIAEJCAB9Rh6gf8N1Hi8qHa5GQjl2RCaq8n4Wtxur6p4XuAx0a4lIAEJSEACEpCABCQg\nAQlIQALHSUAVQy/4aGeyWSTTOTaEdsFX28BqnoYX/Iq0ewlIQAISkIAEJCABCUhAAhKQgASOi4Aq\nhl7gkbZKoVQqg3iiComUmz2o/XB7fC/wFWnXEpCABCQgAQlIQAISkIAEJCABCRwngaoCt+P0hl+m\n95qIrmFz9SEy6QSXkhWcJWTWX6gu1PwyvUy9FglIQAISkIAEJCABCUhAAhKQgAQqVEDBUIUeWL0t\nCUhAAhKQgAQkIAEJSEACEpCABCRQTkBLycoJ6XYJSEACEpCABCQgAQlIQAISkIAEJFChAgqGKvTA\n6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhAAhKQgAQkUKECCoYq\n9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXICCobKCel2CUhAAhKQgAQkIAEJSEACEpCABCRQoQIK\nhir0wOptSUACEpCABCQgAQlIQAISkIAEJCCBcgIKhsoJ6XYJSEACEpCABCQgAQlIQAISkIAEJFCh\nAgqGKvTA6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhAAhKQgAQk\nUKECCoYq9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXICCobKCel2CUhAAhKQgAQkIAEJSEACEpCA\nBCRQoQIKhir0wOptSUACEpCABCQgAQlIQAISkIAEJCCBcgIKhsoJ6XYJSEACEpCABCQgAQlIQAIS\nkIAEJFChAgqGKvTA6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhA\nAhKQgAQkUKECCoYq9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXIC/w/0zoxX9FG74gAAAABJRU5E\nrkJggg==\n"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "ff9c9615-4e89-4e35-93ee-029192b9a31b",
-   "metadata": {},
-   "source": [
-    "# 102. LSSTCam visits metadata 2026\n",
-    "\n",
-    "<div style=\"max-width:300px; float: left; margin-right: 1em\">\n",
-    "\n",
-    "![logo_for_header.png](attachment:1af63937-73ed-4ba3-acc1-ceb2391ffb34.png)\n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "For the Rubin Science Platform at data.lsst.cloud.\\\n",
-    "Container Size: Large\\\n",
-    "LSST Science Pipelines version: r29.2.0\\\n",
-    "Last verified to run: 2026-05-01\\\n",
-    "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
-    "DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1cc0f661-18b6-4e39-ad03-bd7a86f8d88b",
-   "metadata": {},
-   "source": [
-    "**Learning objective:** Explore and visualize metadata for visits obtained Dec 2025 - Apr 2026.\n",
-    "\n",
-    "**LSST data products:** A temporary, preliminary list of visits metadata.\n",
-    "\n",
-    "**Packages:** `skyproj`\n",
-    "\n",
-    "**Credit:** Originally developed by the Rubin Community Science team.\n",
-    "Please cite the DOI above and consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
-    "\n",
-    "**Get Support:**\n",
-    "Everyone is encouraged to ask questions or raise issues in the [Support Category](https://community.lsst.org/c/support) of the Rubin Community Forum.\n",
-    "Rubin staff will respond to all questions posted there."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f96626c-b786-411a-a64f-813f8730296f",
-   "metadata": {},
-   "source": [
-    "## 1. Introduction\n",
-    "\n",
-    "The purpose of this tutorial, and the associated metadata file, is to enable early exploration and visualization of some of the first science visits obtained between Dec 2025 and Apr 2026.\n",
-    "\n",
-    "**Visit:**\n",
-    "One visit is one observation (one image; one exposure) with the LSSTCam, centered on a sky coordinate, and obtained with a single filter and sky rotation.\n",
-    "\n",
-    "**Caveats:** This tutorial uses a *temporary*, *preliminary* metadata file for exposures that were labeled with an \"observation reason\" of \"science\" by the scheduler. The processed visit images and related catalogs *might* become available when Prompt images and the Prompt Products Database (PPDB) are released to users. However, the data validation stage of the data release preparations might imposed a different start date, and/or rejected some visits as not appropriate for scientific analysis. Furthermore, Prompt-processed *images* are only planned to be persisted for 30 days, and already that window has passed for many visits (but catalogs will persist). As a final reminder, difference image analysis (DIA) is only possible in the limited regions with templates.\n",
-    "\n",
-    "When the Prompt products are released, this static, temporary, preliminary file will be deprecated, and the visit metadata will be available in queryable catalogs which are continuously updated on ~24 hour timescales.\n",
-    "A new set of tutorials will accompany the Prompt data release.\n",
-    "\n",
-    "**Related tutorials:** See also the Commissioning tutorial 101 on the LSSTCam visits database, which uses a metadata file for science validation visits obtained up to the end of Sep 2025."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcc897ab-7f8a-4022-a62f-b9026ded7cd8",
-   "metadata": {},
-   "source": [
-    "### 1.1. Import packages\n",
-    "\n",
-    "Import standard python packages `astropy`, `matplotlib`, and `numpy`.\n",
-    "Import the `skyproj` package ([skyproj.readthedocs.io](https://skyproj.readthedocs.io/en/latest/)) for plotting all-sky projection plots, the `healpy` package ([healpy.readthedocs.io](https://healpy.readthedocs.io/en/latest/)) for dealing with HEALPix, and import from `lsst.utils` the standard colorblind-friendly colors for the LSST filters, $ugrizy$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cb7c5c9-689d-40b8-bb40-8745a667531c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from astropy.io import ascii\n",
-    "from astropy.time import Time\n",
-    "from astropy.coordinates import SkyCoord\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import skyproj\n",
-    "import healpy as hp\n",
-    "from lsst.utils.plotting import (get_multiband_plot_colors,\n",
-    "                                 get_multiband_plot_linestyles)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a0776bd-c482-483d-ba27-9c9acae07bb6",
-   "metadata": {},
-   "source": [
-    "### 1.2. Define parameters\n",
-    "\n",
-    "Define the colors and linestyles to use for each of the LSST filters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21919feb-fc78-429e-8225-81de243f0735",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filter_colors = get_multiband_plot_colors()\n",
-    "filter_names = list(filter_colors.keys())\n",
-    "filter_linestyles = get_multiband_plot_linestyles()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "860d2651-80d9-46c6-a947-ebad18e5a8a1",
-   "metadata": {},
-   "source": [
-    "Define the path to the data files used in this tutorial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a2213ba6-48ce-4037-a6a5-cbdbe94e9e0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_path = '/rubin/cst_repos/tutorial-notebooks-data/data/'\n",
-    "file_name = file_path + 'lsstcam_visits_by_2026-04-03.ecsv'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffa3f86f-92d5-48c5-9348-24283461aa68",
-   "metadata": {},
-   "source": [
-    "**Option**: to create interactive plots with zoom-in capabilities, un-comment and execute the following cell. All plots created after this will be interactive. To return to static plots, comment-out this cell, restart the kernel and clear all outputs, and re-execute the cells of this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a1bc642-72a1-4399-bf7b-bf4b427ddb5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "60748c6f-7847-4353-9d9b-f4f1d5a02fee",
-   "metadata": {},
-   "source": [
-    "## 2. Visits\n",
-    "\n",
-    "Column names, descriptions, and units.\n",
-    "\n",
-    "* `visitId`: A unique long integer identifier, composed of the year-month-day and a sequential image number.\n",
-    "* `ra`: Telescope tracking* Right Ascension, in degrees.\n",
-    "* `dec`: Telescope tracking* Declination, in degrees.\n",
-    "* `start_time`: Time at the start of the exposure, in ISO format (International Atomic Time; TAI).\n",
-    "* `filter`: The LSST filter. One of $ugrizy$.\n",
-    "* `exposure_time`: Exposure time, in seconds.\n",
-    "* `zenith`: Telescope pointing distance from zenith, in degrees.\n",
-    "* `observation_reason`: The source of the visit in the Feature Based Scheduler (FBS).\n",
-    "* `target_names`: The name of the sky region(s) for the visit, as a comma-separated list if more than one named sky region overlaps the pointing.\n",
-    "\n",
-    "> *The `ra` and `dec` coordinates are for the center of the visit, and were taken from the telescope pointing. They are not the result of an astrometric solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b1454b2-29d6-4174-8fd4-be97c6fa8748",
-   "metadata": {},
-   "source": [
-    "### 2.1. Read the visits table\n",
-    "\n",
-    "Read the prepared file of visit metadata, print the total number of rows (visits), and display the first five rows of the table.\n",
-    "\n",
-    "> **Warning:** The following cell produces a pink `InvalidEcsvDatatypeWarning` but the `datetime64` format for the `start_time` column is actually read just fine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de3e68d4-f69d-4922-af5e-6409eb0967b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "visits_table = ascii.read(file_name)\n",
-    "print('Number of visits: ', len(visits_table))\n",
-    "visits_table[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8c97c12-c3c8-410f-8f98-8054718f3ccd",
-   "metadata": {},
-   "source": [
-    "### 2.2. MJD distribution\n",
-    "\n",
-    "Plot the cumulative distribution of the modified Julian dates (MJDs) at the midpoint time of all visits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef738cdc-a096-470b-a422-17055c5fdf57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "visits_table['mjd'] = Time(visits_table['start_time']).mjd\n",
-    "\n",
-    "notable_mjds = [np.floor(np.min(visits_table['mjd'])),\n",
-    "                61095.0, 61110.0, 61125.0,\n",
-    "                np.ceil(np.max(visits_table['mjd']))]\n",
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "for i, mjd in enumerate(notable_mjds):\n",
-    "    plt.axvline(mjd, ls='dotted', color='grey')\n",
-    "    t = Time(mjd, format='mjd', scale='utc')\n",
-    "    s = str(t.to_datetime())\n",
-    "    plt.text(mjd+2, 20000, s[0:10], rotation=90)\n",
-    "    del t, s\n",
-    "plt.plot(np.sort(visits_table['mjd']), np.arange(len(visits_table)),\n",
-    "         ls='solid', lw=1, color='black')\n",
-    "plt.xlabel('MJD')\n",
-    "plt.ylabel('Total number of visits')\n",
-    "plt.title('Cumulative distribution of visit MJDs')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4395967-7d9d-4925-85cc-2de4e757dece",
-   "metadata": {},
-   "source": [
-    "> **Figure 1:** The cumulative distribution of visit MJDs (modified Julian dates) for all visits, for all filters combined. Notable dates are marked with vertical dotted lines: the start and end dates included in the file, the date of first alerts (Feb 24 2026), and a period of engineering time when no alerts were released."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d856850-f9ea-4bea-8450-8a02324a1c00",
-   "metadata": {},
-   "source": [
-    "Plot the binned cumulative distribution of MJDs for each filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ac0c8ff-c18f-4fcf-8299-c5a6d4a4f83d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "for f, filt in enumerate(filter_names):\n",
-    "    tx = np.where(visits_table['filter'] == filt)[0]\n",
-    "    plt.hist(visits_table['mjd'][tx], histtype='step', bins=20,\n",
-    "             range=(notable_mjds[0], notable_mjds[-1]), cumulative=True,\n",
-    "             linestyle=filter_linestyles[filt], color=filter_colors[filt], label=filt)\n",
-    "    del tx\n",
-    "plt.legend(loc='upper left')\n",
-    "plt.xlabel('MJD')\n",
-    "plt.ylabel('Total number of visits per filter')\n",
-    "plt.title('Binned cumulative distribution of visit MJDs, by filter')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be2e0bf6-8a4c-4f73-9c7c-6e6a01a25407",
-   "metadata": {},
-   "source": [
-    "> **Figure 2:** The binned cumulative distribution of visit MJDs by filter (see figure legend)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3087e3f6-5de9-4d78-98b4-d8df51d2a784",
-   "metadata": {},
-   "source": [
-    "### 2.3. Exposure times\n",
-    "\n",
-    "The exposure times in column `exposure_time` are in seconds.\n",
-    "For filters $grizy$, the *standard* exposure time is 30 seconds, whereas for $u$-band it is 38 seconds.\n",
-    "\n",
-    "Create a stacked bar plot of the exposure time distribution by filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "588e4deb-6038-4913-815b-2b19292d7058",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xvals = []\n",
-    "clrs = []\n",
-    "lbls = []\n",
-    "for filt in filter_names:\n",
-    "    tx = np.where(visits_table['filter'] == filt)[0]\n",
-    "    if len(tx) > 0:\n",
-    "        xvals.append(visits_table['exposure_time'][tx])\n",
-    "        clrs.append(filter_colors[filt])\n",
-    "        lbls.append(filt)\n",
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "plt.hist(xvals, 10, histtype='bar', stacked=True, facecolor=clrs, label=lbls)\n",
-    "plt.legend(loc='upper left')\n",
-    "plt.xlabel('Exposure time [s]')\n",
-    "plt.ylabel('Number of visits')\n",
-    "plt.show()\n",
-    "del xvals, clrs, lbls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90cb4a14-5e39-41b5-b84f-0ce0f8a9628e",
-   "metadata": {},
-   "source": [
-    "> **Figure 3:** Stacked bar chart of exposure times per visit, by filter.\n",
-    "\n",
-    "Although it is clear from the plot above that _most_ visits with exposure times >30s are in the $u$-band, and most with <30s are in the $z$-band, it is unclear if any other filters have a very small number visits in these bins.\n",
-    "The bin width furthermore makes it unclear whether all exposure times are exactly the same, or if there is scatter within a bin.\n",
-    "\n",
-    "For visits with 30s, <30s, and >30s exposure time, print the mean and standard deviation in the exposure time, and the number of visits per filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5c5f7c14-ed1a-4c4b-9dc1-4db7a09d1da5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tx1 = np.where((visits_table['exposure_time'] < 29))[0]\n",
-    "tx2 = np.where((visits_table['exposure_time'] >= 29) & (visits_table['exposure_time'] <= 31))[0]\n",
-    "tx3 = np.where((visits_table['exposure_time'] > 31))[0]\n",
-    "print(f\"{'exposure time': <15} {' mean': <4}  {'std': <3}\")\n",
-    "print(f\"{'<29 s ': <15} {np.round(np.mean(visits_table['exposure_time'][tx1]), 1): 4.1f}  \"\n",
-    "      f\"{np.round(np.std(visits_table['exposure_time'][tx1]), 1): 3.1f}\")\n",
-    "print(f\"{' 30 s ': <15} {np.round(np.mean(visits_table['exposure_time'][tx2]), 1): 4.1f}  \"\n",
-    "      f\"{np.round(np.std(visits_table['exposure_time'][tx2]), 1): 3.1f}\")\n",
-    "print(f\"{'>31 s ': <15} {np.round(np.mean(visits_table['exposure_time'][tx3]), 1): 4.1f}  \"\n",
-    "      f\"{np.round(np.std(visits_table['exposure_time'][tx3]), 1): 3.1f}\")\n",
-    "print(' ')\n",
-    "vals1 = ['<29 s ']\n",
-    "vals2 = [' 30 s ']\n",
-    "vals3 = ['>31 s ']\n",
-    "\n",
-    "for filt in filter_names:\n",
-    "    vals1.append(len(np.where(visits_table['filter'][tx1] == filt)[0]))\n",
-    "    vals2.append(len(np.where(visits_table['filter'][tx2] == filt)[0]))\n",
-    "    vals3.append(len(np.where(visits_table['filter'][tx3] == filt)[0]))\n",
-    "print(f\"{'exposure time': <15} {'u': <4}  {'g': <4}  {'r': <4}  {'i': <4}  {'z': <4}  {'y': <4}\")\n",
-    "print(f\"{vals1[0]: <15} {vals1[1]: <4}  {vals1[2]: <4}  {vals1[3]: <4}  \"\n",
-    "      f\"{vals1[4]: <4}  {vals1[5]: <4}  {vals1[6]: <4}\")\n",
-    "print(f\"{vals2[0]: <15} {vals2[1]: <4}  {vals2[2]: <4}  {vals2[3]: <4}  \"\n",
-    "      f\"{vals2[4]: <4}  {vals2[5]: <4}  {vals2[6]: <4}\")\n",
-    "print(f\"{vals3[0]: <15} {vals3[1]: <4}  {vals3[2]: <4}  {vals3[3]: <4}  \"\n",
-    "      f\"{vals3[4]: <4}  {vals3[5]: <4}  {vals3[6]: <4}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67737e8c-9bb2-4238-9761-c6f9ad040f2e",
-   "metadata": {},
-   "source": [
-    "The output above shows that visits are either 15, 30, or 38 seconds exactly (because the standard deviations are 0), and that only $u$-band visits are 38 seconds, and only some $y$-band visits are 15 seconds."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f4592b6-2e5d-4f0e-98e9-3a51827f3051",
-   "metadata": {},
-   "source": [
-    "### 2.4. Airmass distribution\n",
-    "\n",
-    "Plot the cumulative distribution of airmasses for all visits. Convert the `zenith` column to `airmass`, first."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a6fde4c-e3d2-4ff8-be1d-394786b87822",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "visits_table['airmass'] = 1.0 / np.cos(np.deg2rad(visits_table['zenith']))\n",
-    "\n",
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "plt.plot(np.sort(visits_table['airmass']), np.arange(len(visits_table)),\n",
-    "         ls='solid', lw=1, color='black')\n",
-    "plt.xlabel('Airmass')\n",
-    "plt.ylabel('Total number of visits')\n",
-    "plt.title('Cumulative distribution of visit airmass (full)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8f41d7b-ef22-4004-8649-7b094e28c645",
-   "metadata": {},
-   "source": [
-    "> **Figure 4:** The cumulative distribution of visit airmass for all visits, for all filters combined."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "453399a0-3a0a-4e14-a5b6-70e1f641968b",
-   "metadata": {},
-   "source": [
-    "Plot the binned cumulative distribution of airmasses for each filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "750d7db4-8830-4a02-ab4d-39940e82d0b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "for f, filt in enumerate(filter_names):\n",
-    "    tx = np.where(visits_table['filter'] == filt)[0]\n",
-    "    plt.hist(visits_table['airmass'][tx], histtype='step', bins=100,\n",
-    "             range=(1.0, np.max(visits_table['airmass'])), cumulative=True, log=True,\n",
-    "             linestyle=filter_linestyles[filt], color=filter_colors[filt], label=filt)\n",
-    "    del tx\n",
-    "plt.legend(loc='best', ncol=2)\n",
-    "plt.xlim([0.98, 1.6])\n",
-    "plt.xlabel('Airmass')\n",
-    "plt.ylabel('Total number of visits per filter')\n",
-    "plt.title('Binned cumulative distribution of visit airmass, by filter (cropped)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4de6fbd-9b2c-4d5b-9470-f1ed40b29f05",
-   "metadata": {},
-   "source": [
-    "> **Figure 5:** The binned cumulative distribution of visit airmass by filter (see figure legend). The x-axis has been cropped to show only airmass $\\leq 1.6$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6b75f4c-4063-4b58-adbc-e8f8db4ae30e",
-   "metadata": {},
-   "source": [
-    "### 2.5. Sky distribution\n",
-    "\n",
-    "Use the `healpy` package to print the area of one 19-sided HEALPix.\n",
-    "It is similar to the LSSTCam FOV of 9.6 square degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f396c5a-f26b-4c41-af3b-84963d6a23d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('One 19-sided HEALPix is ', np.round(hp.nside2pixarea(19, degrees=True), 2),\n",
-    "      ' square degrees.')\n",
-    "print('It takes ', int(41253.0 / hp.nside2pixarea(19, degrees=True)),\n",
-    "      ' 19-sided HEALPix to cover the full sky.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2df32c57-4063-45ae-888a-1acf4704e475",
-   "metadata": {},
-   "source": [
-    "Create a two-dimensional (2D) histogram of the visits on the sky, for all filters together.\n",
-    "Use 19-sided HEALPix to approximately match the area of one visit on the sky."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "853e7f0a-526b-487d-9079-b21d62acbffc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
-    "vras = np.asarray(visits_table['ra'], dtype='float')\n",
-    "vdecs = np.asarray(visits_table['dec'], dtype='float')\n",
-    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap='Greys', vmin=-10)\n",
-    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=14)\n",
-    "sp.ax.set_ylabel(\"Declination\", fontsize=14)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7147dc2-8c53-4d5a-b9d7-f607ada424b4",
-   "metadata": {},
-   "source": [
-    "> **Figure 6:** A 2D histogram illustrating the distribution of visits on the sky (for all filters, combined)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ea698f8-3c76-4466-b55f-f47b73a88d8b",
-   "metadata": {},
-   "source": [
-    "Print the approximate total sky area covered by visits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6ae6af3-662b-4da0-b907-a465eab97a2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hpx_bins = sp.draw_hpxbin(vras, vdecs, nside=19)\n",
-    "array = hpx_bins[0]\n",
-    "tx = np.where(array > 0)[0]\n",
-    "print('Number of HEALPix with non-zero visits: ', len(tx))\n",
-    "print('Approximate area of all visits: ', int(len(tx) * hp.nside2pixarea(19, degrees=True)),\n",
-    "      ' square degrees.')\n",
-    "del sp, vras, vdecs, hpx_bins, array, tx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffa18e32-4153-4c5d-a4c2-5d3f908631c3",
-   "metadata": {},
-   "source": [
-    "Show a similar 2D histogram as above, but only for the g-band filter, and use the \"Greens\" [colormap](https://matplotlib.org/stable/gallery/color/colormap_reference.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95d413d4-f047-44e3-b68e-1a5654398d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "show_filter = 'g'\n",
-    "use_colormap = 'Greens'\n",
-    "fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
-    "tx = np.where(visits_table['filter'] == show_filter)[0]\n",
-    "vras = np.asarray(visits_table['ra'][tx], dtype='float')\n",
-    "vdecs = np.asarray(visits_table['dec'][tx], dtype='float')\n",
-    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap=use_colormap, vmin=-10)\n",
-    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=14)\n",
-    "sp.ax.set_ylabel(\"Declination\", fontsize=14)\n",
-    "plt.show()\n",
-    "del sp, tx, vras, vdecs, show_filter, use_colormap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a9f5399-336e-4a79-be40-29030fec5833",
-   "metadata": {},
-   "source": [
-    "> **Figure 7:** Similar to Figure 6, but for $g$-band visits only."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0257975e-4788-4350-a831-82de76bf5b78",
-   "metadata": {},
-   "source": [
-    "In Figure 6, stripes of visits with constant declination stand out in the sky distribution. The cause of this non-LSST-like survey pattern is in their observation reason: \"fbs_driven_aos_stability_test\". FBS stands for \"feature-based scheduler\" and AOS for \"active optics system\". These visits were obtained by the FBS for the purpose of testing the AOS, but are still anticipated to be, potentially, scientifically useful.\n",
-    "\n",
-    "Visualize only the FBS AOS testing visits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8cfd3379-3dd8-4fc6-99c6-bee954d31c09",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "use_colormap = 'Purples'\n",
-    "fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
-    "tx = np.where(visits_table['observation_reason'] == 'fbs_driven_aos_stability_test')[0]\n",
-    "vras = np.asarray(visits_table['ra'][tx], dtype='float')\n",
-    "vdecs = np.asarray(visits_table['dec'][tx], dtype='float')\n",
-    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap=use_colormap, vmin=-10)\n",
-    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=12)\n",
-    "sp.ax.set_ylabel(\"Declination\", fontsize=12, labelpad=22)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81d07ce7-793c-4f52-ab45-c0638158f329",
-   "metadata": {},
-   "source": [
-    "> **Figure 8:** Similar to Figure 6, but for visits with an observation reason related to AOS stability testing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "884d5437-e917-48c1-a1e0-db84388ea14a",
-   "metadata": {},
-   "source": [
-    "Instead of a 2D histogram, the cell below offers the option to mark each visit as a separate, semi-transparent marker the size of the LSSTCam field of view (FOV) on the sky (radius ~1.75 deg).\n",
-    "\n",
-    "> **Warning:** The mode of plotting in the following cell takes a few minutes to render, is not as informative as a histogram, and will not scale well as the number of visits increases. But it is possible, as there are only ~30,000 visits in this temporary file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3bc834c5-bff4-4acf-abbd-a82e29b05bba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "# sp = skyproj.McBrydeSkyproj(ax=ax)\n",
-    "# vras = np.asarray(visits_table['ra'], dtype='float')\n",
-    "# vdecs = np.asarray(visits_table['dec'], dtype='float')\n",
-    "# for ra, dec in zip(vras, vdecs):\n",
-    "#     sp.ax.circle(ra, dec, 1.75, edgecolor=None, color='grey', alpha=0.1, fill=True)\n",
-    "# plt.show()\n",
-    "# del sp, vras, vdecs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81cb7506-ee30-4c07-a47d-c8f5cb62b221",
-   "metadata": {},
-   "source": [
-    "## 3. Templates overlap\n",
-    "\n",
-    "At the time this notebook was made, template images for alert production only existed in the [deep drilling fields](https://prompt-products.lsst.io/observations/index.html#template-coverage) (DDFs).\n",
-    "\n",
-    "Define the coordinates, names, and filter coverage for the DDF templates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6ade96ec-a482-4252-9a91-6d1868b5e639",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ddf_coords = SkyCoord([[150.11, 2.23], [52.98, -28.12], [9.45, -44.02],\n",
-    "                       [58.9, -49.32], [63.6, -47.6], [35.57, -4.82],\n",
-    "                       [187.4, +8]], frame=\"icrs\", unit=\"deg\")\n",
-    "ddf_names = [\"COSMOS\", \"ECDFS\", \"ELAIS-S1\", \"EDFS_a\", \"EDFS_b\",\n",
-    "             \"XMM-LSS\", \"M49 (Virgo)\"]\n",
-    "ddf_filters = [\"ugrizy\", \"riz\", \"griz\", \"griz\", \"griz\", \"iz\", \"ugri\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78285ba0-20b1-4461-abca-3a55ac4d821f",
-   "metadata": {},
-   "source": [
-    "Define the radius of a visit. This is the radius of the LSSTCam's FOV in degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be129d15-586a-4d19-8798-4a9927ac891d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radius = 1.75"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1479df53-5978-493b-be78-f658f09fb026",
-   "metadata": {},
-   "source": [
-    "Tally the number of visits for which the boresight RA, Dec are within one LSSTCam radius of the center of a DDF, for each of the filters for which that DDF has a template image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25adf44d-390b-480a-8ff3-4d58eadae5aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ddf_tally = np.zeros((len(ddf_coords), 6), dtype='int')\n",
-    "for visit in visits_table:\n",
-    "    coord = SkyCoord(visit['ra'], visit['dec'], frame=\"icrs\", unit=\"deg\")\n",
-    "    seps = coord.separation(ddf_coords).deg\n",
-    "    tx = np.where(seps < radius)[0]\n",
-    "    if len(tx) == 1:\n",
-    "        temp_list = ddf_filters[tx[0]]\n",
-    "        if temp_list.find(visit['filter']) >= 0:\n",
-    "            fx = np.where(visit['filter'] == np.asarray(filter_names))[0]\n",
-    "            ddf_tally[tx[0], fx[0]] += 1\n",
-    "            del fx\n",
-    "        del temp_list\n",
-    "    del coord, seps, tx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a7f7d3e-15cc-4667-9081-e9a3acf31275",
-   "metadata": {},
-   "source": [
-    "Print the number of overlapping visits per filter for each DDF, with a `-` if the field has no template in that filter. Also print the total number of overlapping visits.\n",
-    "\n",
-    "> **Notice:** This tally is *indicative* of the number of visits that produced alerts, but alert production a specific step of Prompt processing and did not necessarily proceed (or succeed) for each overlapping visit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8102b99-faf0-4770-9d71-144ebf2174a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "line = f\"{'field': <15} \"\n",
-    "for f, filt in enumerate(filter_names):\n",
-    "    line += f\"{filt: <4} \"\n",
-    "line += f\"{'sum ': <5}\"\n",
-    "print(line)\n",
-    "for d, ddf_name in enumerate(ddf_names):\n",
-    "    temp_list = ddf_filters[d]\n",
-    "    line = f\"{ddf_name: <15} \"\n",
-    "    for f, filt in enumerate(filter_names):\n",
-    "        if temp_list.find(filt) >= 0:\n",
-    "            line += f\"{ddf_tally[d, f]: <4} \"\n",
-    "        else:\n",
-    "            line += f\"{'-   ': <4} \"\n",
-    "    line += f\"{np.sum(ddf_tally[d, :]): <5}\"\n",
-    "    print(line)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3c0b9b0-c9d9-47af-acbc-0c61cfe0cc34",
-   "metadata": {},
-   "source": [
-    "Note that the `observation_reason` and `target_names` columns, instead of or in addition to the spatial overlap, also indicate whether a given visit was an observation of a DDF and thus may have produced alerts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d79f1394-7d91-44ba-b4ee-9cbe7124414e",
-   "metadata": {},
-   "source": [
-    "Recreate Figure 6, but overplot the locations of the template fields."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4227f9ee-79bc-4aae-a0d3-3257f78e8718",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ddf_sym = ['o', 's', 'p', '^', '^', 'v', '*']\n",
-    "ddf_size = [10, 10, 10, 10, 10, 10, 15]\n",
-    "ddf_color = ['green', 'darkorange', 'purple', 'red', 'magenta', 'blue', 'brown']\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "sp = skyproj.McBrydeSkyproj(ax=ax)\n",
-    "vras = np.asarray(visits_table['ra'], dtype='float')\n",
-    "vdecs = np.asarray(visits_table['dec'], dtype='float')\n",
-    "sp.draw_hpxbin(vras, vdecs, nside=19, alpha=1, cmap='Greys', vmin=-10)\n",
-    "for d, coord in enumerate(ddf_coords):\n",
-    "    sp.ax.plot(coord.ra.deg, coord.dec.deg, ddf_sym[d], ms=ddf_size[d],\n",
-    "               mec=ddf_color[d], color='None', mew=2, label=ddf_names[d])\n",
-    "sp.ax.set_xlabel(\"Right Ascension\", fontsize=14)\n",
-    "sp.ax.set_ylabel(\"Declination\", fontsize=14)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "756aa94b-bff1-4c2a-9111-4afccd11457f",
-   "metadata": {},
-   "source": [
-    "> **Figure 9:** Similar to Figure 6, but with the locations of the template fields marked with symbols as in the legend."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34ec6824-3299-455c-9120-6fe9e7662fb3",
-   "metadata": {},
-   "source": [
-    "## 4. Was my object in a visit?\n",
-    "\n",
-    "Recall the **caveat** from Section 1: this temporary, preliminary metadata file includes visits that were obtained in the specific time frame, and it cannot be guaranteed that the visit will be successfully processed and pass data validation for inclusion in the Prompt data release.\n",
-    "\n",
-    "> **Caveat:** furthermore, coordinates that are *near the edge* of a visit might seem to be included using the code below, but this is not a guarantee that there will be high-quality data exactly at that location.\n",
-    "\n",
-    "Define two target coordinates.\n",
-    "Based on the figures above, the first one will, but the second one will not, be included."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "70faf803-d046-44b5-8789-79cc58d01380",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target1_ra, target1_dec = 90.0, -15.0\n",
-    "target2_ra, target2_dec = 90.0, +30.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01ea93ff-5ac0-45cc-b726-7f6dcef6222a",
-   "metadata": {},
-   "source": [
-    "Convert the targets' coordinates to the `astropy` `SkyCoord` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e6ae2374-7085-4f0a-b92c-296d0859bcd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c1 = SkyCoord(target1_ra, target1_dec, unit=\"deg\")\n",
-    "c2 = SkyCoord(target2_ra, target2_dec, unit=\"deg\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cef89e9-1665-43e5-8ebf-bcdeadc69e3d",
-   "metadata": {},
-   "source": [
-    "Use the radius of a visit defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8446b5f2-9f67-4e1a-a0a1-f4230d112cff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(radius)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "638ad941-42c1-4292-8eee-bcd956c44c79",
-   "metadata": {},
-   "source": [
-    "Convert the visits' coordinates to the `astropy` `SkyCoord` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d824f18-b47c-42a6-a8a6-7f7e1dc47685",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vc = SkyCoord(visits_table['ra'], visits_table['dec'], unit=\"deg\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a7edfe5-a687-4a3f-9bdb-223f898449d0",
-   "metadata": {},
-   "source": [
-    "Calculate the on-sky separations between each target and all of the visits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "846b9232-7b87-4a58-8b9b-3c7cae393a7f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s1 = vc.separation(c1)\n",
-    "s2 = vc.separation(c2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9f881e1-7b71-4704-9a01-5c9e2200dadf",
-   "metadata": {},
-   "source": [
-    "Sum up the number of visits that are within the LSSTCam radius of each target.\n",
-    "\n",
-    "Assert the expectation that target 1 has overlapping visits and target 2 does not, and print the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a9eb590-401a-47cb-9701-9ddbbd48f144",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tx1 = np.where(s1.degree < radius)[0]\n",
-    "tx2 = np.where(s2.degree < radius)[0]\n",
-    "assert len(tx1) != 0\n",
-    "assert len(tx2) == 0\n",
-    "print('Number of visits containing')\n",
-    "print('  target 1: ', len(tx1))\n",
-    "print('  target 2: ', len(tx2))\n",
-    "del tx1, tx2"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "LSST",
-   "language": "python",
-   "name": "lsst"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Commissioning/103_Rubin_Schedule_Viewer.ipynb b/Commissioning/102_Rubin_Schedule_Viewer.ipynb
similarity index 99%
rename from Commissioning/103_Rubin_Schedule_Viewer.ipynb
rename to Commissioning/102_Rubin_Schedule_Viewer.ipynb
index 83c5fa51..a395d87b 100644
--- a/Commissioning/103_Rubin_Schedule_Viewer.ipynb
+++ b/Commissioning/102_Rubin_Schedule_Viewer.ipynb
@@ -10,7 +10,7 @@
    "id": "0",
    "metadata": {},
    "source": [
-    "# 103. Rubin Schedule Viewer\n",
+    "# 102. Rubin Schedule Viewer\n",
     "\n",
     "<div style=\"max-width:300px; float: left; margin-right: 1em\">\n",
     "\n",
@@ -21,7 +21,7 @@
     "For the Rubin Science Platform at data.lsst.cloud.\\\n",
     "Container Size: Large\\\n",
     "LSST Science Pipelines version: v29.2.0\\\n",
-    "Last verified to run: 2026-06-05\\\n",
+    "Last verified to run: 2026-06-09\\\n",
     "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
     "DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
    ]
diff --git a/Commissioning/104_3I_ATLAS_image_stamps.ipynb b/Commissioning/103_3I_ATLAS_image_stamps.ipynb
similarity index 99%
rename from Commissioning/104_3I_ATLAS_image_stamps.ipynb
rename to Commissioning/103_3I_ATLAS_image_stamps.ipynb
index 212e0b5e..9a7955bb 100644
--- a/Commissioning/104_3I_ATLAS_image_stamps.ipynb
+++ b/Commissioning/103_3I_ATLAS_image_stamps.ipynb
@@ -10,7 +10,7 @@
    "id": "325aa8a5-92fd-4913-a471-ad1617343be6",
    "metadata": {},
    "source": [
-    "# 104. Image stamps for 3I/ATLAS (C/2025 N1)\n",
+    "# 103. Image stamps for 3I/ATLAS (C/2025 N1)\n",
     "\n",
     "<div style=\"max-width:300px; float: left; margin-right: 1em\">\n",
     "\n",
@@ -22,7 +22,7 @@
     "Data Release: [Chandler et al. (2026)](https://ui.adsabs.harvard.edu/abs/2025arXiv250713409C/abstract)\\\n",
     "Container Size: Large\\\n",
     "LSST Science Pipelines version: v29.2.0\\\n",
-    "Last verified to run: 2026-05-01\\\n",
+    "Last verified to run: 2026-06-09\\\n",
     "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\"
    ]
   },