From 9b4a92ab28c3b28d4eea4aefc45d63929870dd23 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Tue, 9 Jun 2026 23:34:00 +0000 Subject: [PATCH 1/5] rearrange files --- ...pynb => 101_LSSTCam_visits_database.ipynb} | 612 +++++++----- .../102_LSSTCam_visits_metadata_2026.ipynb | 942 ------------------ ....ipynb => 102_Rubin_Schedule_Viewer.ipynb} | 0 ....ipynb => 103_3I_ATLAS_image_stamps.ipynb} | 0 4 files changed, 375 insertions(+), 1179 deletions(-) rename Commissioning/{101_LSSTCam_visits_database_2025.ipynb => 101_LSSTCam_visits_database.ipynb} (77%) delete mode 100644 Commissioning/102_LSSTCam_visits_metadata_2026.ipynb rename Commissioning/{103_Rubin_Schedule_Viewer.ipynb => 102_Rubin_Schedule_Viewer.ipynb} (100%) rename Commissioning/{104_3I_ATLAS_image_stamps.ipynb => 103_3I_ATLAS_image_stamps.ipynb} (100%) 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..8cfae44b 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", "
\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: github.com/lsst/tutorial-notebooks\\\n", "DOI: 10.11578/rubin/dc.20250909.20" ] @@ -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 Science Validation survey summary webpage .\n", + "**LSST data products:** The file `prelsst_20260607_visits.db` has been made temporarily available until it is superseded by upcoming data releases.\n", "\n", "**Packages:** `sqlite3`, `rubin_sim`\n", "\n", - "**Credit:** Developed by the Rubin Community Science team, using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": [ - "
\n", - "This tutorial notebook is superseded by \"DP2/102_Visit_and_tract_metadata\" and \"Commissioning/102_LSSTCam_visits_metadata\".
\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", - "
" - ] - }, { "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 Science Validation survey summary webpage 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 was first made available on the Science Validation survey summary webpage .\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." ] }, { "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 Science Validation survey summary webpage 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 Rubin First Look.\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 Rubin First Look.\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 Science Validation survey summary webpage 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." ] }, { - "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": [ - "
\n", + "**Pre-LSST, AOS commissioning, and engineering observations.**\n", "\n", - "![lsstcam_nvisits.png](attachment:22e5c212-bdbe-481a-b499-88319da07109.png)\n", - "\n", - "
\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 Science Validation survey summary webpage , and instructions for recreating a version of it are in Section 5." + "Throughout the first half of 2026, pre-LSST observations were obtained in the Deep Drilling Fields (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 observations 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", + "* **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 observations were obtained, the Active Optics System (AOS) was being commissioned.\n", + "* **Not all of these visits lead to scientifically validated data products.** Some will end up excluded from the Data Preview 2 (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", "* **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 (Metric Analysis Framework) 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 (Metric Analysis Framework) 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, such as `astropy`, 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 +153,8 @@ "metadata": {}, "outputs": [], "source": [ - "db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'" + "# db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'\n", + "db_filename = '/home/melissagraham/prelsst_20260607_visits.db'" ] }, { @@ -239,7 +226,7 @@ "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 +265,6 @@ "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 +311,8 @@ "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. " @@ -393,7 +379,7 @@ "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 +436,22 @@ "id": "4c2e5aeb-d61c-4c81-b8d1-d9a2f905752d", "metadata": {}, "source": [ - "## 3. Surveys and targets\n", + "## 3. Observation reason and target\n", + "\n", + "There are a few rows of the database that indicate the _motivation_ behind each observation.\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`.\n", + "Every visit has both an `observation_reason` and a `target_name`, and these are interesting 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 observations 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 +461,9 @@ "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))" ] }, { @@ -484,7 +471,7 @@ "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." ] }, { @@ -497,31 +484,15 @@ "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", + "Naos = len(df.query(\"observation_reason.str.contains('aos')\"))\n", + "Nalrt = len(df.query(\"observation_reason.str.contains('alert')\"))\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)" + "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)" ] }, { @@ -539,7 +510,7 @@ "metadata": {}, "outputs": [], "source": [ - "del Nsfs, Nddf, Ntoo, Ntas, Nwfd" + "del Nsfs, Nddf, Ntoo, Naos, Nalrt" ] }, { @@ -547,7 +518,11 @@ "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 +530,8 @@ "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 +550,15 @@ "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 Science Validation survey summary webpage , 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 observations 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 +568,25 @@ "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 +594,37 @@ "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 LSST Baseline Strategy webpage.\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 total is greater than the number of observations." ] }, { "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" ] }, { @@ -687,7 +633,8 @@ "id": "6ab35f8a-d9a9-4f5e-b126-af768054edd4", "metadata": {}, "source": [ - "#### 3.2.3. Deep Drilling Fields (DDFs)\n", + "\\\n", + "**Deep Drilling Fields (DDFs).**\n", "\n", "The LSST will include five Deep Drilling Fields,\n", "four of which (all except COSMOS) were observed as part of the LSST Science Validation survey." @@ -714,48 +661,65 @@ }, { "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 observations 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", + "* `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", - "Separate out the comma-separated lists into one list of unique target names." + "Standardize the `target_name` strings for DDF-related observations." ] }, { "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 +727,7 @@ "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 +737,10 @@ "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 +780,22 @@ "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 +804,7 @@ "id": "942d90c7-b551-43dd-87e7-d86155138d79", "metadata": {}, "source": [ - "> **Figure 2:** The number of visits per filter over time, in days." + "> **Figure 2:** The number of visits over time, stacked by filter, in 7-day bins." ] }, { @@ -894,7 +825,7 @@ "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", @@ -929,7 +860,7 @@ "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", @@ -964,7 +895,7 @@ "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 +909,7 @@ "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 5:** The number of visits in bins of 5$\\sigma$ depth for point sources, by filter." ] }, { @@ -990,6 +921,9 @@ "\n", "Recreate parts of the tables on the Science Validation survey summary webpage . This section uses code from the LSSTCam summary notebook in the Sims SV Survey repo.\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 +943,7 @@ "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 +986,49 @@ "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 DDF.\n", + "\n", + "First, create a new column that contains only the DDF name, for observations 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 DDF with >50 observations, total, display the table of visits per filter." ] }, { "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", @@ -1081,8 +1042,8 @@ "metadata": {}, "outputs": [], "source": [ - "query = \"observation_reason.str.contains('ddf')\"\n", - "df_ddf = df.query(query).groupby(['observation_reason']).agg({'seq_num': 'count',\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", @@ -1093,6 +1054,7 @@ "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", @@ -1104,9 +1066,9 @@ "id": "fe049fcd-1c0c-4db0-9d2e-b15f7d21b2b6", "metadata": {}, "source": [ - "## 5. MAF sky map\n", + "## 5. MAF sky maps\n", "\n", - "Recreate the sky map diagram in Figure 1.\n", + "Create a sky map diagram.\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", @@ -1254,14 +1216,66 @@ "Read more about they sky coverage for the SV wide-area survey on the Science Validation survey summary webpage ." ] }, + { + "cell_type": "markdown", + "id": "b205f6c3-1119-4df5-a693-c649960eaf73", + "metadata": {}, + "source": [ + "\\\n", + "**AOS testing visits.**\n", + "\n", + "In the top panel of 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": "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 7:** 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 +1304,12 @@ "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 +1318,130 @@ "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 8:** 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 observations 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 9:** 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, 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)." ] }, { @@ -1311,9 +1449,9 @@ "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 Science Validation survey summary webpage :\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": "iVBORw0KGgoAAAANSUhEUgAABIYAAAJ4CAYAAAAQp+hTAAABYWlDQ1BJQ0MgUHJvZmlsZQAAKJF1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\nrkJggg==\n" - } - }, - "cell_type": "markdown", - "id": "ff9c9615-4e89-4e35-93ee-029192b9a31b", - "metadata": {}, - "source": [ - "# 102. LSSTCam visits metadata 2026\n", - "\n", - "
\n", - "\n", - "![logo_for_header.png](attachment:1af63937-73ed-4ba3-acc1-ceb2391ffb34.png)\n", - "\n", - "
\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 100% rename from Commissioning/103_Rubin_Schedule_Viewer.ipynb rename to Commissioning/102_Rubin_Schedule_Viewer.ipynb diff --git a/Commissioning/104_3I_ATLAS_image_stamps.ipynb b/Commissioning/103_3I_ATLAS_image_stamps.ipynb similarity index 100% rename from Commissioning/104_3I_ATLAS_image_stamps.ipynb rename to Commissioning/103_3I_ATLAS_image_stamps.ipynb From 9c48e38d13a06442701695dc82f62ca3183982e0 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Tue, 9 Jun 2026 23:37:58 +0000 Subject: [PATCH 2/5] update last run on 102, 103 --- Commissioning/102_Rubin_Schedule_Viewer.ipynb | 4 ++-- Commissioning/103_3I_ATLAS_image_stamps.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/Commissioning/102_Rubin_Schedule_Viewer.ipynb b/Commissioning/102_Rubin_Schedule_Viewer.ipynb index 83c5fa51..a395d87b 100644 --- a/Commissioning/102_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", "
\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/103_3I_ATLAS_image_stamps.ipynb b/Commissioning/103_3I_ATLAS_image_stamps.ipynb index 212e0b5e..9a7955bb 100644 --- a/Commissioning/103_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", "
\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)\\" ] }, From 36e781a557ec105ff55585e1f60a43cbc4080338 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Tue, 9 Jun 2026 23:55:20 +0000 Subject: [PATCH 3/5] minor mods on 101 --- .../101_LSSTCam_visits_database.ipynb | 70 +++++++------------ 1 file changed, 25 insertions(+), 45 deletions(-) diff --git a/Commissioning/101_LSSTCam_visits_database.ipynb b/Commissioning/101_LSSTCam_visits_database.ipynb index 8cfae44b..7872ccad 100644 --- a/Commissioning/101_LSSTCam_visits_database.ipynb +++ b/Commissioning/101_LSSTCam_visits_database.ipynb @@ -54,7 +54,7 @@ "## 1. Introduction\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 was first made available on the Science Validation survey summary webpage .\n", + "An early version of this file, for visits up to the end of September 2026, was first made available on the Science Validation survey summary webpage .\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." @@ -81,10 +81,10 @@ "id": "3478f32b-5dab-4f72-ad89-7a917d86d016", "metadata": {}, "source": [ - "**Pre-LSST, AOS commissioning, and engineering observations.**\n", + "**Pre-LSST, AOS commissioning, and engineering visits.**\n", "\n", - "Throughout the first half of 2026, pre-LSST observations were obtained in the Deep Drilling Fields (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 observations have been tagged as suitable for science (pending processing and science validation)." + "Throughout the first half of 2026, pre-LSST visits were obtained in the Deep Drilling Fields (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)." ] }, { @@ -94,8 +94,8 @@ "source": [ "**Caveats.**\n", "\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 observations were obtained, the Active Optics System (AOS) was being commissioned.\n", "* **Not all of these visits lead to scientifically validated data products.** Some will end up excluded from the Data Preview 2 (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 Active Optics System (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." ] }, @@ -108,7 +108,7 @@ "\n", "Import `sqlite3` to read the SQL-formatted database file, and import the `maf` module from the `rubin_sim` package to use the (Metric Analysis Framework) 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, such as `astropy`, and the `lsst.utils.plotting` package." + "Also import standard python science packages and the `lsst.utils.plotting` package." ] }, { @@ -153,8 +153,7 @@ "metadata": {}, "outputs": [], "source": [ - "# db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'\n", - "db_filename = '/home/melissagraham/prelsst_20260607_visits.db'" + "db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/prelsst_20260607_visits.db'" ] }, { @@ -367,7 +366,6 @@ "outputs": [], "source": [ "del query, results\n", - "\n", "cursor.close()\n", "del cursor" ] @@ -438,11 +436,11 @@ "source": [ "## 3. Observation reason and target\n", "\n", - "There are a few rows of the database that indicate the _motivation_ behind each observation.\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 interesting to explore.\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", @@ -451,7 +449,7 @@ "\n", "Option to print _all_ unique values of the `observation_reason` column and the number of visits for each.\n", "\n", - "> **Warning:** The feature-based scheduler (FBS) and Rubin operations were in the early stages when these observations were obtained, and the values of the `observation_reason` column exhibit diversity; explanations for every single value are not provided at this time." + "> **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." ] }, { @@ -463,7 +461,8 @@ "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))" + "# print('%25s %5i' % (value, count))\n", + "# del values, counts" ] }, { @@ -486,30 +485,12 @@ "Ntoo = len(df.query(\"observation_reason.str.contains('too')\"))\n", "Naos = len(df.query(\"observation_reason.str.contains('aos')\"))\n", "Nalrt = len(df.query(\"observation_reason.str.contains('alert')\"))\n", - "\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)" - ] - }, - { - "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": [ + "print(\"alert production: \", Nalrt)\n", "del Nsfs, Nddf, Ntoo, Naos, Nalrt" ] }, @@ -558,7 +539,7 @@ "\\\n", "**Wide-fast-deep regions.**\n", "\n", - "Option to print the `target_name` for all observations 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." + "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." ] }, { @@ -610,7 +591,7 @@ "> **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 total is greater than the number of observations." + "As sky regions are not mutually exclusive, the sum is greater than the total number of visits." ] }, { @@ -664,7 +645,7 @@ "id": "8c65cfb8-845f-4100-804e-74548f5a5fa1", "metadata": {}, "source": [ - "Option to print the `target_name` for all observations that were done with an `observation_reason` related to the deep drilling fields." + "Option to print the `target_name` for all visits that were done with an `observation_reason` related to the deep drilling fields." ] }, { @@ -705,7 +686,7 @@ "* `DDF ELAISS1` --> `ddf_elaiss1`\n", "* `DDF XMM_LSS` --> `ddf_xmm_lss`\n", "\n", - "Standardize the `target_name` strings for DDF-related observations." + "Standardize the `target_name` strings for DDF-related visits." ] }, { @@ -991,7 +972,7 @@ "\n", "Make similar versions of the two tables above, but for the DDF.\n", "\n", - "First, create a new column that contains only the DDF name, for observations of a DDF (and is a null string otherwise)." + "First, create a new column that contains only the DDF name, for visits of a DDF (and is a null string otherwise)." ] }, { @@ -1012,7 +993,7 @@ "id": "b2e51c5a-6d21-4bce-9644-12f2b0c140bd", "metadata": {}, "source": [ - "For DDF with >50 observations, total, display the table of visits per filter." + "For visits of DDF with >50 visits, display tables similar to the above." ] }, { @@ -1068,10 +1049,7 @@ "source": [ "## 5. MAF sky maps\n", "\n", - "Create a sky map diagram.\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 rubin_sim_notebook repository.\n", "\n", "Set the path to the folder containing `rubin_sim` data in the RSP at data.lsst.cloud." @@ -1113,7 +1091,9 @@ "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." ] }, { @@ -1172,7 +1152,7 @@ "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." ] }, { @@ -1329,7 +1309,7 @@ "## 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 observations and the second has not." + "The first has been covered by LSSTCam visits and the second has not." ] }, { @@ -1441,7 +1421,7 @@ "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)." + "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)." ] }, { From f102fc90e6a3c9c9701f25184c7a0d404516fa68 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Wed, 10 Jun 2026 00:31:01 +0000 Subject: [PATCH 4/5] final updates --- Commissioning/101_LSSTCam_visits_database.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/Commissioning/101_LSSTCam_visits_database.ipynb b/Commissioning/101_LSSTCam_visits_database.ipynb index 7872ccad..d5a8bc91 100644 --- a/Commissioning/101_LSSTCam_visits_database.ipynb +++ b/Commissioning/101_LSSTCam_visits_database.ipynb @@ -1025,9 +1025,9 @@ "source": [ "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", + " '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", @@ -1403,7 +1403,8 @@ "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, dec=df['fieldDec'].values * u.deg, frame='icrs')\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", From 3117336626be0eff94b9d6221f262b61a9ca022e Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Sat, 13 Jun 2026 00:16:36 +0000 Subject: [PATCH 5/5] YC comments --- .../101_LSSTCam_visits_database.ipynb | 100 ++++++++++++++---- 1 file changed, 81 insertions(+), 19 deletions(-) diff --git a/Commissioning/101_LSSTCam_visits_database.ipynb b/Commissioning/101_LSSTCam_visits_database.ipynb index d5a8bc91..0ce746f8 100644 --- a/Commissioning/101_LSSTCam_visits_database.ipynb +++ b/Commissioning/101_LSSTCam_visits_database.ipynb @@ -54,10 +54,10 @@ "## 1. Introduction\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 2026, was first made available on the Science Validation survey summary webpage .\n", + "An early version of this file, for visits up to the end of September 2025, was first made available on the Science Validation survey summary webpage .\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." ] }, { @@ -73,7 +73,7 @@ "Review the Science Validation survey summary webpage 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.\n", - "The scientifically validated subset of these images obtained prior to Jan 7 2026 will be released as Data Preview 2." + "The scientifically validated subset of these images obtained prior to Jan 7 2026 will be released as Data Preview 2 (DP2)." ] }, { @@ -83,7 +83,7 @@ "source": [ "**Pre-LSST, AOS commissioning, and engineering visits.**\n", "\n", - "Throughout the first half of 2026, pre-LSST visits were obtained in the Deep Drilling Fields (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", + "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)." ] }, @@ -94,8 +94,8 @@ "source": [ "**Caveats.**\n", "\n", - "* **Not all of these visits lead to scientifically validated data products.** Some will end up excluded from the Data Preview 2 (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 Active Optics System (AOS) was being commissioned.\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." ] }, @@ -615,10 +615,11 @@ "metadata": {}, "source": [ "\\\n", - "**Deep Drilling Fields (DDFs).**\n", + "**Deep Drilling Fields.**\n", "\n", "The LSST will include five Deep Drilling Fields,\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." ] }, { @@ -785,7 +786,7 @@ "id": "942d90c7-b551-43dd-87e7-d86155138d79", "metadata": {}, "source": [ - "> **Figure 2:** The number of visits over time, stacked by filter, in 7-day bins." + "> **Figure 1:** The number of visits over time, stacked by filter, in 7-day bins." ] }, { @@ -820,7 +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." ] }, { @@ -855,7 +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." ] }, { @@ -890,7 +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." ] }, { @@ -900,7 +901,7 @@ "source": [ "### 4.2. Summary statistics\n", "\n", - "Recreate parts of the tables on the Science Validation survey summary webpage . This section uses code from the LSSTCam summary notebook in the Sims SV Survey repo.\n", + "Recreate parts of the tables on the Science Validation survey summary webpage. This section uses code from the LSSTCam summary notebook in the Sims SV Survey repo.\n", "\n", "\\\n", "**Small field survey areas.**\n", @@ -970,7 +971,7 @@ "\\\n", "**Deep drilling fields.**\n", "\n", - "Make similar versions of the two tables above, but for the DDF.\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)." ] @@ -1191,7 +1192,7 @@ "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 Science Validation survey summary webpage ." ] @@ -1204,7 +1205,7 @@ "\\\n", "**AOS testing visits.**\n", "\n", - "In the top panel of 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", + "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." ] @@ -1244,7 +1245,7 @@ "id": "6bef7bfd-3405-4969-ba0b-04a473caf76c", "metadata": {}, "source": [ - "> **Figure 7:** The FBS AOS testing visits." + "> **Figure 6:** The FBS AOS testing visits." ] }, { @@ -1298,7 +1299,7 @@ "id": "2a9aaa25-6d90-41c7-acaf-485ea7dead82", "metadata": {}, "source": [ - "> **Figure 8:** The locations of the five Deep Drilling Fields (DDFs), with their names labeled." + "> **Figure 7:** The locations of the five Deep Drilling Fields (DDFs), with their names labeled." ] }, { @@ -1382,7 +1383,7 @@ "id": "bcca0d28-6617-46d4-8eef-b9c26ce24dcb", "metadata": {}, "source": [ - "> **Figure 9:** A 2D histogram illustrating the distribution of visits on the sky (for all filters, combined), with the two hypothetical targets marked." + "> **Figure 8:** A 2D histogram illustrating the distribution of visits on the sky (for all filters, combined), with the two hypothetical targets marked." ] }, { @@ -1425,6 +1426,67 @@ "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." + ] + }, { "cell_type": "markdown", "id": "135c0da0-4d13-4230-9abe-2add68bb1266",