From 98f69bed1fdd0b1774b7d02510aa75a6bf375e6c Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Thu, 13 Nov 2025 17:35:13 +0000 Subject: [PATCH 1/6] add file --- Commissioning/lsstcam_visits.ipynb | 1476 ++++++++++++++++++++++++++++ 1 file changed, 1476 insertions(+) create mode 100644 Commissioning/lsstcam_visits.ipynb diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb new file mode 100644 index 00000000..d2acbf39 --- /dev/null +++ b/Commissioning/lsstcam_visits.ipynb @@ -0,0 +1,1476 @@ +{ + "cells": [ + { + "attachments": { + "f692d198-af50-4c45-8f97-ab0252c10d73.png": { + "image/png": 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl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\nrkJggg==\n" + } + }, + "cell_type": "markdown", + "id": "94406489-923b-4f2e-a748-078fec0028c0", + "metadata": {}, + "source": [ + "# LSSTCam visits database\n", + "\n", + "
\n", + "\n", + "![logo_for_header.png](attachment:f692d198-af50-4c45-8f97-ab0252c10d73.png)\n", + "\n", + "
\n", + "\n", + "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: 2025-11-12
\n", + "Repository: github.com/lsst/tutorial-notebooks
\n", + "DOI: 10.11578/rubin/dc.20250909.20
" + ] + }, + { + "cell_type": "markdown", + "id": "9b153e12-db08-42cc-9551-806034aabbbb", + "metadata": {}, + "source": [ + "**Learning objective:** How to query and retrieve data from the commissioning visits database file.\n", + "\n", + "**LSST data products:** The `lsstcam_20250930.db` file available on the Science Validation survey summary webpage .\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", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "0f288152-c837-4515-91fc-d9e630a17fac", + "metadata": {}, + "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 database file with non-standard schema and formatting, provided as a convenience.** \n", + "Rubin data releases have similar information in their `Visit` and `CcdVisit` tables." + ] + }, + { + "cell_type": "markdown", + "id": "3478f32b-5dab-4f72-ad89-7a917d86d016", + "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", + "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", + "The contiguous part of this 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, and a secondary area within the low-dust WFD was included to provide targets when the primary or DDF fields were not available." + ] + }, + { + "attachments": { + "22e5c212-bdbe-481a-b499-88319da07109.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "b52fbccd-b4a5-486a-a0ed-a8a1431f853e", + "metadata": {}, + "source": [ + "
\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 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." + ] + }, + { + "cell_type": "markdown", + "id": "5c013110-7c44-4bc6-a295-5c7545dc2b13", + "metadata": {}, + "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.\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", + "* **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." + ] + }, + { + "cell_type": "markdown", + "id": "cd1c8f8b-622d-440b-9a72-c088bb4633e2", + "metadata": {}, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14fa7d25-c422-47d1-af9d-a523ff54e16b", + "metadata": {}, + "outputs": [], + "source": [ + "import sqlite3\n", + "from rubin_sim import maf\n", + "\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import getpass\n", + "from lsst.utils.plotting import (get_multiband_plot_colors,\n", + " get_multiband_plot_linestyles)" + ] + }, + { + "cell_type": "markdown", + "id": "8e38967c-b361-49ec-a39d-6a75a2fb08f5", + "metadata": {}, + "source": [ + "### 1.2. Define parameters\n", + "\n", + "Define the path and name of the database file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bb2a77c-a809-4198-bc60-599b7308cac7", + "metadata": {}, + "outputs": [], + "source": [ + "db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'" + ] + }, + { + "cell_type": "markdown", + "id": "6d41e823-f448-4a6f-8479-2265cf6eaa07", + "metadata": {}, + "source": [ + "Define the default path for output files. Use the shared scratch directory, `/deleted-sundays/`, and create a subdirectory with your username if one does not already exist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d145fad-36a4-4e51-88f4-b9ce08468337", + "metadata": {}, + "outputs": [], + "source": [ + "uname = getpass.getuser()\n", + "output_path = '/deleted-sundays/' + uname + '/'\n", + "if os.path.exists(output_path):\n", + " print('Already exists: ', output_path)\n", + "else:\n", + " os.system('mkdir ' + output_path)\n", + " print('Created: ', output_path)" + ] + }, + { + "cell_type": "markdown", + "id": "90e8b20d-1c6d-4dde-8300-eb6c5dad9938", + "metadata": {}, + "source": [ + "Define the LSST filter names and the colors and linestyles to represent the filters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "905c3825-0aeb-4e37-b751-d919d9763f9c", + "metadata": {}, + "outputs": [], + "source": [ + "filter_names = ['u', 'g', 'r', 'i', 'z', 'y']\n", + "filter_colors = get_multiband_plot_colors()\n", + "filter_linestyles = get_multiband_plot_linestyles()\n", + "filter_colors_list = [filter_colors['u'], filter_colors['g'],\n", + " filter_colors['r'], filter_colors['i'],\n", + " filter_colors['z'], filter_colors['y']]" + ] + }, + { + "cell_type": "markdown", + "id": "cb5391cb-67f0-44b8-8778-a6627e816d04", + "metadata": {}, + "source": [ + "## 2. Explore the database\n", + "\n", + "The information contained in the database is an aggregation of entries in the \"Consolidate Database\" (ConsDB), including per-visit summary values from summit quicklook processing (the ConsDB is not yet released to users). The database generally follows the current LSST scheduler output schema, but additional columns were added in post-processing. Rubin data releases have similar information in their `Visit` and `CcdVisit` tables.\n", + "\n", + "As this is an aggregate file, descriptions for its columns can be found among those at:\n", + "* ConsDB Schema\n", + "* Scheduler Output Schema\n" + ] + }, + { + "cell_type": "markdown", + "id": "1989e1d3-fa07-462a-ad37-3c57087b72b9", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "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", + "\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", + "* `exp_midpt_mjd` : The midpoint time of the exposure at the fiducial center of the focal plane (in TAI).\n", + "* `fieldRA` : The boresight Right Ascension for the visit (degrees).\n", + "* `fieldDec` : The boresight Declination for the visit (degrees).\n", + "* `band` : The LSST filter used for the visit, one of $ugrizy$.\n", + "* `airmass` : The airmass of the visit ($1/\\cos(\\Theta_z)$, where $\\Theta_z$ is the zenith angle).\n", + "* `seeingFwhmGeom` : The full-width half-max of the measured point spread function (PSF; arcseconds).\n", + "* `fiveSigmaDepth` : The magnitude of a five-sigma point source detection in the visit (magnitudes).\n" + ] + }, + { + "cell_type": "markdown", + "id": "9aca85b9-1569-40b7-8eb8-c72119320bc7", + "metadata": {}, + "source": [ + "### 2.2. Connect with sqlite3\n", + "\n", + "Connect to the database file using `sqlite3`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44b57fbb-0ce0-4c61-aa23-a67560d8ee1b", + "metadata": {}, + "outputs": [], + "source": [ + "db_conn = sqlite3.connect(db_filename)\n", + "cursor = db_conn.cursor()" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a202dc1b-521d-4138-aeab-54cab9646998", + "metadata": {}, + "outputs": [], + "source": [ + "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", + "tables = cursor.fetchall()\n", + "table_names = [table[0] for table in tables]\n", + "print(\"Tables:\", table_names)\n", + "del tables, table_names" + ] + }, + { + "cell_type": "markdown", + "id": "55b74091-d827-4716-b8f6-245256c42dfa", + "metadata": {}, + "source": [ + "Option to print the schema (column names and types) for the `observations` table. It is a long list, and not printed by default." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddf95091-21ab-4fac-abef-03d42cd12136", + "metadata": {}, + "outputs": [], + "source": [ + "# table_name = 'observations'\n", + "# query = f\"\"\"SELECT sql FROM sqlite_master WHERE type='table'\n", + "# AND name='{table_name}';\"\"\"\n", + "# cursor.execute(query)\n", + "# create_table_sql = cursor.fetchone()[0]\n", + "# print(f\"Schema for {table_name}:\\n{create_table_sql}\")\n", + "# del query, table_name, create_table_sql" + ] + }, + { + "cell_type": "markdown", + "id": "0eadf049-d535-43aa-b955-52b1ad91aebb", + "metadata": {}, + "source": [ + "#### 2.2.2. Query the database\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. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "021ea87f-d93e-472e-827e-f7ce9f48cdc4", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"SELECT fieldRA, fieldDec, band FROM observations\n", + " WHERE observation_reason = 'field_survey_science'\n", + " AND band='r' AND airmass < 1.5; \"\"\"\n", + "cursor.execute(query)\n", + "results = cursor.fetchall()\n", + "print('Number of rows returned: ', len(results))" + ] + }, + { + "cell_type": "markdown", + "id": "45617da1-e4f8-4e67-9391-f6846575353b", + "metadata": {}, + "source": [ + "Option to display the query results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de8266f1-2a50-44b5-a6da-7019f3f774a3", + "metadata": {}, + "outputs": [], + "source": [ + "# results" + ] + }, + { + "cell_type": "markdown", + "id": "4261095e-6fb4-435c-a44c-689b29121e0c", + "metadata": {}, + "source": [ + "Clean up, and close the connection with the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c9d89f5-a506-4ce5-8fad-3c95ff3aee08", + "metadata": {}, + "outputs": [], + "source": [ + "del query, results\n", + "\n", + "cursor.close()\n", + "del cursor" + ] + }, + { + "cell_type": "markdown", + "id": "bdb1dc93-dc19-44d7-99fc-1e0712da93fb", + "metadata": {}, + "source": [ + "### 2.3. Read as a pandas dataframe\n", + "\n", + "As there are only 21647 visits in the database, and the file itself is only 81 M, it 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53a4c6b5-203c-44f4-83a5-6f165c4d4d90", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_sql_query(\"SELECT * FROM observations\", db_conn)\n", + "print(len(df))" + ] + }, + { + "cell_type": "markdown", + "id": "c166f5cf-318d-490f-83f0-fbcf8b6d583c", + "metadata": {}, + "source": [ + "Option to display the table (it will automatically truncate)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80ccb531-62f2-404e-a534-ef0b3f00fb46", + "metadata": {}, + "outputs": [], + "source": [ + "# df" + ] + }, + { + "cell_type": "markdown", + "id": "0ca58438-72e2-4c5c-a906-8f7a6c251f2b", + "metadata": {}, + "source": [ + "Option to print the column names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fa7b4e9-9fe0-4378-b0a0-0c0ee6e514f8", + "metadata": {}, + "outputs": [], + "source": [ + "# for col in df.columns:\n", + "# print(col)" + ] + }, + { + "cell_type": "markdown", + "id": "4c2e5aeb-d61c-4c81-b8d1-d9a2f905752d", + "metadata": {}, + "source": [ + "## 3. Surveys and targets\n", + "\n", + "Every visit has both an `observation_reason` and a `target_name`.\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, e.g., the WFD configuration, the DDF program, or target of opportunity (TOO).\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.\n", + "\n", + "### 3.1. Observation reason\n", + "\n", + "Print the unique values of the `observation_reason` column and the number of visits for each." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac7c3db8-6e68-49ad-9a51-a697cda460a5", + "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))" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4951907a-574e-4c21-a8e2-bc5ab99ad3f7", + "metadata": {}, + "outputs": [], + "source": [ + "tx = np.where(df['observation_reason'] == 'field_survey_science')[0]\n", + "print('Number of visits done for small field surveys: ', len(tx))\n", + "Nfss = len(tx)\n", + "del tx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd9b4fcf-c7e7-43f5-8560-e99fe65eb45f", + "metadata": {}, + "outputs": [], + "source": [ + "temp = np.asarray([value[:3] for value in values], dtype='str')\n", + "tx = np.where(temp == 'ddf')[0]\n", + "print('Number of visits done for deep drilling fields: ', np.sum(counts[tx]))\n", + "Nddf = np.sum(counts[tx])\n", + "del temp, tx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44839462-a867-4340-be8e-e586fa5767c9", + "metadata": {}, + "outputs": [], + "source": [ + "tx = np.where(df['observation_reason'] == 'too')[0]\n", + "print('Number of visits done for target of opportuntity: ', len(tx))\n", + "Ntoo = len(tx)\n", + "del tx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fb08c55-a995-4396-a142-e052a532d835", + "metadata": {}, + "outputs": [], + "source": [ + "tx = np.where(df['observation_reason'] == 'template_area_singles_i')[0]\n", + "print('Number of visits done for templates: ', len(tx))\n", + "Ntas = len(tx)\n", + "del tx" + ] + }, + { + "cell_type": "markdown", + "id": "31354530-f6e3-44a6-898a-d676824f9516", + "metadata": {}, + "source": [ + "Print the number of visits done as part of LSST Science Validation (excluding DDFs)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "067671ca-7aac-4046-9e2d-8039d6998833", + "metadata": {}, + "outputs": [], + "source": [ + "temp = len(df) - Nfss - Nddf - Ntoo - Ntas\n", + "print('Number of visits done for LSST Science Validation: ', temp)\n", + "del temp" + ] + }, + { + "cell_type": "markdown", + "id": "0e48c9a6-5877-4777-8d7c-631a1dd7e363", + "metadata": {}, + "source": [ + "Clean up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d08eeaf-b166-4e3a-9237-856f0af1cdad", + "metadata": {}, + "outputs": [], + "source": [ + "del Nfss, Nddf, Ntoo, Ntas" + ] + }, + { + "cell_type": "markdown", + "id": "507b2056-c8bf-4516-ba91-4608b0417890", + "metadata": {}, + "source": [ + "### 3.2. Target names" + ] + }, + { + "cell_type": "markdown", + "id": "a83e7872-938e-4a64-922b-ac8749b26c14", + "metadata": {}, + "source": [ + "#### 3.2.1. Small field surveys\n", + "\n", + "Index the visits that were done for small field surveys." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108913ac-ad1a-4727-a927-88a8e29017ac", + "metadata": {}, + "outputs": [], + "source": [ + "tx = np.where(df['observation_reason'] == 'field_survey_science')[0]\n", + "print('Number of visits done for small field surveys: ', len(tx))" + ] + }, + { + "cell_type": "markdown", + "id": "fde3e068-2c81-4662-94ab-1fbb6e8b9247", + "metadata": {}, + "source": [ + "Print the number of visits done for each of the small fields." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "154df286-326e-402b-b255-4b3e30e29a7b", + "metadata": {}, + "outputs": [], + "source": [ + "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", + "for value, count in zip(values, counts):\n", + " print('%20s %5i' % (value, count))" + ] + }, + { + "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 >100 visits are shown.\n", + "\n", + "Clean up. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89f5d59a-2a6e-4d7b-a3a7-3e367c6218f3", + "metadata": {}, + "outputs": [], + "source": [ + "del tx, values, counts" + ] + }, + { + "cell_type": "markdown", + "id": "5e65b35d-c552-4301-ae1c-e9a64274204c", + "metadata": {}, + "source": [ + "#### 3.2.2. LSST regions\n", + "\n", + "Define the `observation_reason` values that **are not** part of the WFD LSST Science Validation survey program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9691a359-dddd-47fe-9ad1-8c7dae9976d2", + "metadata": {}, + "outputs": [], + "source": [ + "obs_reason_not_SV = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", + " 'ddf_elaiss1', 'ddf_xmm_lss', 'too',\n", + " 'field_survey_science', 'template_area_singles_i']" + ] + }, + { + "cell_type": "markdown", + "id": "71b9d1b0-a85d-4972-94ea-1ac9a43bd3c4", + "metadata": {}, + "source": [ + "Index the visits that are part of the WFD LSST Science Validation survey program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "731566e9-ece3-4844-aedf-f1d021515485", + "metadata": {}, + "outputs": [], + "source": [ + "mask = np.isin(df['observation_reason'], obs_reason_not_SV)\n", + "tx = np.where(mask == 0)[0]\n", + "print('Number of visits done for LSST Science Validation: ', len(tx))" + ] + }, + { + "cell_type": "markdown", + "id": "bc778940-7901-44c5-9382-36d35e0bacb1", + "metadata": {}, + "source": [ + "Print the unique target names for visits that are part of the WFD LSST Science Validation survey program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60ce628e-013e-41bb-a0d7-177eb50af885", + "metadata": {}, + "outputs": [], + "source": [ + "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", + "for value, count in zip(values, counts):\n", + " if count > 100:\n", + " print('%20s %5i' % (value, count))" + ] + }, + { + "cell_type": "markdown", + "id": "c9a74a2c-709a-4042-8c32-ab04f37f248f", + "metadata": {}, + "source": [ + "Separate out the comma-separated lists into one list of unique target names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "137f4d12-3193-416b-a24a-4e52f2195a63", + "metadata": {}, + "outputs": [], + "source": [ + "temp = []\n", + "for value in values:\n", + " temp2 = str(value).split(',')\n", + " for t2 in temp2:\n", + " temp.append(t2.strip())\n", + " del temp2\n", + "targets = np.unique(temp)\n", + "del temp\n", + "print(targets)" + ] + }, + { + "cell_type": "markdown", + "id": "f0dcbd4c-7329-452a-bc9a-24634a87050f", + "metadata": {}, + "source": [ + "The unique LSST region target names covered during commissioning are:\n", + "\n", + "* `bulgy` - Galactic bulge region\n", + "* `lowdust` - low dust sky region\n", + "* `dusty_plane` - dusty regions of the Galactic plane\n", + "* `nes` - North Ecliptic Spur (NES)\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": "6e60fa24-dbe6-4e12-8fcd-1a0bcdbf57ff", + "metadata": {}, + "outputs": [], + "source": [ + "all_sv_target_names = np.array(df['target_name'][tx], dtype='str')\n", + "for target in targets:\n", + " indices = np.char.find(all_sv_target_names, target)\n", + " x = np.where(indices >= 0)[0]\n", + " print('%15s %5i' % (target, len(x)))\n", + " del indices, x" + ] + }, + { + "cell_type": "markdown", + "id": "4181742f-125d-4e77-8ab1-dc6eaae6bd6d", + "metadata": {}, + "source": [ + "Clean up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3e01147-0711-439b-9b85-cddca31fd53f", + "metadata": {}, + "outputs": [], + "source": [ + "del obs_reason_not_SV, mask, tx, values, counts\n", + "del targets, all_sv_target_names" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6ab35f8a-d9a9-4f5e-b126-af768054edd4", + "metadata": {}, + "source": [ + "#### 3.2.3. Deep Drilling Fields (DDFs)\n", + "\n", + "The LSST will include five Deep Drilling Fields,\n", + "four of which (all except COSMOS) were observed during commissioning." + ] + }, + { + "attachments": { + "7a812ae3-1b7a-44f8-9bfa-bf043009a0ed.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "ab12e39d-7bfa-4ef4-9df3-d4432990d460", + "metadata": {}, + "source": [ + "
\n", + "\n", + "![Screenshot 2025-11-12 at 11.17.09 AM.png](attachment:7a812ae3-1b7a-44f8-9bfa-bf043009a0ed.png)\n", + "\n", + "
\n", + "\n", + "> **Table 1:** DDF locations, from the DDF webpage.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3258ceaf-0d45-4d2b-aa11-deab1b25592e", + "metadata": {}, + "source": [ + "Define the `observation_reason` values that indicate the DDF program was the motivation for the visit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7698fc41-13ce-405b-9c62-207e536cfd82", + "metadata": {}, + "outputs": [], + "source": [ + "obs_reason_ddf = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", + " 'ddf_elaiss1', 'ddf_xmm_lss']" + ] + }, + { + "cell_type": "markdown", + "id": "73d2c722-74f6-4c0a-a418-a74c3d05dbdc", + "metadata": {}, + "source": [ + "Index the visits that are part of the DDF program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ff0a523-455c-47a0-a90d-a77b43ec96bf", + "metadata": {}, + "outputs": [], + "source": [ + "mask = np.isin(df['observation_reason'], obs_reason_ddf)\n", + "tx = np.where(mask)[0]\n", + "print('Number of visits done for DDFs: ', len(tx))" + ] + }, + { + "cell_type": "markdown", + "id": "b1158c11-7851-4b3e-b324-919c34591ac9", + "metadata": {}, + "source": [ + "Print the unique target names for all DDF visits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8143bc14-9c01-4738-8491-8d71e26751b3", + "metadata": {}, + "outputs": [], + "source": [ + "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", + "for value, count in zip(values, counts):\n", + " print('%20s %5i' % (value, count))" + ] + }, + { + "cell_type": "markdown", + "id": "6de95ef8-618c-456c-8fec-0aa10b910e3b", + "metadata": {}, + "source": [ + "Separate out the comma-separated lists into one list of unique target names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f82eb5f1-a2b4-4452-88f1-213502f0aad6", + "metadata": {}, + "outputs": [], + "source": [ + "temp = []\n", + "for value in values:\n", + " temp2 = str(value).split(',')\n", + " for t2 in temp2:\n", + " temp.append(t2.strip())\n", + " del temp2\n", + "targets = np.unique(temp)\n", + "del temp" + ] + }, + { + "cell_type": "markdown", + "id": "cfd4012e-056f-4070-99c1-cdbf51c1d48b", + "metadata": {}, + "source": [ + "Sum up the number of visits for each DDF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04be8bd4-827e-4619-b5ea-eca1955cab89", + "metadata": {}, + "outputs": [], + "source": [ + "all_ddf_target_names = np.array(df['target_name'][tx], dtype='str')\n", + "for target in targets:\n", + " indices = np.char.find(all_ddf_target_names, target)\n", + " x = np.where(indices >= 0)[0]\n", + " print('%15s %5i' % (target, len(x)))\n", + " del indices, x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c4518f1-f6a0-4036-8b23-cfe11412098d", + "metadata": {}, + "outputs": [], + "source": [ + "del mask, tx, values, counts\n", + "del targets, all_ddf_target_names" + ] + }, + { + "cell_type": "markdown", + "id": "65f8143c-89f8-4d25-97a6-87ecf16f5761", + "metadata": {}, + "source": [ + "## 4. Visit metadata" + ] + }, + { + "cell_type": "markdown", + "id": "fe438fab-dfb7-44a0-83cb-5ca7987db2c9", + "metadata": {}, + "source": [ + "### 4.1. Plot histograms" + ] + }, + { + "cell_type": "markdown", + "id": "944105ff-4cce-4297-88f4-46de10d267bd", + "metadata": {}, + "source": [ + "Create a stacked histogram of the Modified Julian Dates of all visits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a57e62f7-5d35-49db-8236-aa9f92a48bb8", + "metadata": {}, + "outputs": [], + "source": [ + "Ndays = int(np.floor(np.max(df['exp_midpt_mjd']))\n", + " - np.floor(np.min(df['exp_midpt_mjd'])))\n", + "\n", + "tx_u = np.where(df['band'] == 'u')[0]\n", + "tx_g = np.where(df['band'] == 'g')[0]\n", + "tx_r = np.where(df['band'] == 'r')[0]\n", + "tx_i = np.where(df['band'] == 'i')[0]\n", + "tx_z = np.where(df['band'] == 'z')[0]\n", + "tx_y = np.where(df['band'] == 'y')[0]\n", + "\n", + "fig = plt.figure(figsize=(8, 4))\n", + "plt.hist([df['exp_midpt_mjd'][tx_u], df['exp_midpt_mjd'][tx_g],\n", + " df['exp_midpt_mjd'][tx_r], df['exp_midpt_mjd'][tx_i],\n", + " df['exp_midpt_mjd'][tx_z], df['exp_midpt_mjd'][tx_y]],\n", + " bins=Ndays, stacked=True,\n", + " color=filter_colors_list, label=filter_names)\n", + "plt.legend(loc='best')\n", + "plt.xlabel('Modified Julian Date (MJD)')\n", + "plt.ylabel('Number of visits')\n", + "plt.show()\n", + "\n", + "del tx_u, tx_g, tx_r, tx_i, tx_z, tx_y\n", + "del Ndays" + ] + }, + { + "cell_type": "markdown", + "id": "942d90c7-b551-43dd-87e7-d86155138d79", + "metadata": {}, + "source": [ + "> **Figure 2:** The number of visits per filter over time, in days." + ] + }, + { + "cell_type": "markdown", + "id": "ee84a313-c7c4-47f8-bed4-dd2a69769d07", + "metadata": {}, + "source": [ + "Create a histogram of the airmass of each visit, by filter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c6ddb4b-97bd-4c6d-aa81-3d3024a70808", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "for f, filt in enumerate(filter_names):\n", + " tx = np.where(df['band'] == filt)[0]\n", + " plt.hist(df['airmass'][tx], bins=40, histtype='step',\n", + " ls=filter_linestyles[filt],\n", + " color=filter_colors_list[f], label=filt)\n", + " del tx\n", + "plt.legend(loc='best')\n", + "plt.xlabel('Airmass')\n", + "plt.ylabel('Number of visits')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0bd6b0e3-ac74-4add-b0a7-5c36b3734aab", + "metadata": {}, + "source": [ + "> **Figure 3:** The number of visits in bins of airmass, by filter." + ] + }, + { + "cell_type": "markdown", + "id": "f51a94df-abe6-433f-8d7a-639256532138", + "metadata": {}, + "source": [ + "Create a histogram of the measured seeing of each visit, by filter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b1c2409-fd2f-43f7-804d-16b9d4c419d1", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "for f, filt in enumerate(filter_names):\n", + " tx = np.where(df['band'] == filt)[0]\n", + " plt.hist(df['seeingFwhmGeom'][tx], bins=40, histtype='step',\n", + " ls=filter_linestyles[filt],\n", + " color=filter_colors_list[f], label=filt)\n", + "plt.legend(loc='best')\n", + "plt.xlabel('Seeing (FWHM of the PSF; arcsec)')\n", + "plt.ylabel('Number of visits')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2f165fa2-3c44-4b15-8e31-110a41b9bd88", + "metadata": {}, + "source": [ + "> **Figure 4:** The number of visits in bins of seeing, by filter." + ] + }, + { + "cell_type": "markdown", + "id": "a383c026-f7ce-4ec7-b271-e6654ec8ea42", + "metadata": {}, + "source": [ + "Create a histogram of the $5\\sigma$ depth (for point sources) of each visit, by filter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3003c0a3-1473-425d-8f61-6693eeb86737", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "for f, filt in enumerate(filter_names):\n", + " tx = np.where(df['band'] == filt)[0]\n", + " plt.hist(df['fiveSigmaDepth'][tx], bins=40, histtype='step',\n", + " ls=filter_linestyles[filt],\n", + " color=filter_colors_list[f], label=filt)\n", + "plt.legend(loc='best')\n", + "plt.xlabel('5-Sigma Depth (mag)')\n", + "plt.ylabel('Number of visits')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "markdown", + "id": "cbf444d9-38d9-4432-95ba-59dbefb1e30e", + "metadata": {}, + "source": [ + "### 4.2. Summary statistics" + ] + }, + { + "cell_type": "markdown", + "id": "8dd25f8e-1800-4f3c-9d38-6822436625df", + "metadata": {}, + "source": [ + "Define a function to use in this section called `get_indices`. It will return the indices of `df` that match the input `observation_reason`, `target_name`, and `filter`, and enable the calculation of summary statistics for visit subsets (e.g., the mean seeing for all DDF visits of the ELAISS1 field in $r$-band)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6376bd60-7dea-459c-a67c-0651647176be", + "metadata": {}, + "outputs": [], + "source": [ + "def get_indices(reason, target, band, verbose=False):\n", + " \"\"\"\n", + " Given the observation reason, target name, and band,\n", + " return the indices in the dataframe.\n", + "\n", + " Parameters\n", + " ----------\n", + " reason: string\n", + " An observation reason (or None).\n", + " target: string\n", + " A target name (or None).\n", + " band: string\n", + " A filter name, ugrizy (or None).\n", + " verbose: boolean\n", + " If True, print warning messages.\n", + "\n", + " Returns\n", + " -------\n", + " indices: int\n", + " The index values of the rows.\n", + " \"\"\"\n", + "\n", + " if reason is not None:\n", + " tx = np.where(df['observation_reason'] == reason)[0]\n", + " else:\n", + " tx = np.arange(len(df['observation_reason']))\n", + " if len(tx) == 0:\n", + " if verbose:\n", + " print('Warning. No observation reasons match: ', reason)\n", + " return []\n", + "\n", + " if target is not None:\n", + " arr = np.array(df['target_name'][tx], dtype='str')\n", + " indices = np.char.find(arr, target)\n", + " ttx = np.where(indices >= 0)[0]\n", + " del arr, indices\n", + " else:\n", + " ttx = np.arange(len(df['target_name'][tx]))\n", + " if len(ttx) == 0:\n", + " if verbose:\n", + " print('Warning. No target names match: ', target)\n", + " return []\n", + "\n", + " if band is not None:\n", + " tttx = np.where(df['band'][tx[ttx]] == band)[0]\n", + " else:\n", + " tttx = np.arange(len(df['band'][tx[ttx]]))\n", + " if len(tttx) == 0:\n", + " if verbose:\n", + " print('Warning. No filter names match: ', band)\n", + " return []\n", + "\n", + " indices = tx[ttx[tttx]]\n", + "\n", + " return indices" + ] + }, + { + "cell_type": "markdown", + "id": "33e5cb2c-62e9-4188-ad1f-18462cba778b", + "metadata": {}, + "source": [ + "As an example, print the first and last MJD and the median airmass, seeing, and depth for all DDF visits of the ELAISS1 field in $r$-band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "651a123e-4b11-468d-a2c9-001718ca65e8", + "metadata": {}, + "outputs": [], + "source": [ + "x = get_indices('ddf_elaiss1', 'DDF ELAISS1', 'r')\n", + "print('Min and max MJD: %8.2f %8.2f' % (np.min(df['exp_midpt_mjd'][x]),\n", + " np.max(df['exp_midpt_mjd'][x])))\n", + "print('Median airmass: %4.2f' % np.nanmedian(df['airmass'][x]))\n", + "print('Median seeing: %4.2f arcsec' % np.nanmedian(df['seeingFwhmGeom'][x]))\n", + "print('Median depth: %5.2f mag' % np.nanmedian(df['fiveSigmaDepth'][x]))" + ] + }, + { + "cell_type": "markdown", + "id": "64453bcb-acc5-47f8-b70f-da347e660bb7", + "metadata": {}, + "source": [ + "Use bad inputs on purpose, to demonstrate the error messages that are returned and that an empty list is returned when there are no matches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b21c65a-0e95-47f0-a8c8-44ed743a0345", + "metadata": {}, + "outputs": [], + "source": [ + "x = get_indices('aliens', 'DDF ECDFS', 'r', verbose=True)\n", + "x = get_indices('ddf_ecdfs', 'ALIENS', 'r', verbose=True)\n", + "x = get_indices('ddf_ecdfs', 'DDF ECDFS', 'a', verbose=True)\n", + "print(x)\n", + "del x" + ] + }, + { + "cell_type": "markdown", + "id": "cfdb3d7c-f83b-41cd-9943-eb2f6ffa3dc1", + "metadata": {}, + "source": [ + "Create a table of the median seeing for all DDF visits, by filter and for all filters together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71be3460-0942-49c2-8f73-505dd5dc24c8", + "metadata": {}, + "outputs": [], + "source": [ + "reason_ddf = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", + " 'ddf_elaiss1', 'ddf_xmm_lss']\n", + "\n", + "print('Median seeing in the DDFs, by filter.')\n", + "print('%15s %5s %5s %5s %5s %5s %5s %5s ' %\n", + " ('DDF', 'u', 'g', 'r', 'i', 'z', 'y', 'all'))\n", + "for ddf in reason_ddf:\n", + " temp = []\n", + " for filt in filter_names:\n", + " x = get_indices(ddf, None, filt)\n", + " if len(x) > 0:\n", + " temp.append(float(np.nanmedian(df['seeingFwhmGeom'][x])))\n", + " else:\n", + " temp.append(float('NaN'))\n", + " del x\n", + " x = get_indices(ddf, None, None)\n", + " temp_all = float(np.nanmedian(df['seeingFwhmGeom'][x]))\n", + " del x\n", + " print('%15s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f' %\n", + " (ddf, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5], temp_all))" + ] + }, + { + "cell_type": "markdown", + "id": "a261906f-32fe-4ecd-b574-a3ae870c708f", + "metadata": {}, + "source": [ + "Create a table of the median airmass for all commissioning visits with an LSST region target name, by filter and for all filters together.\n", + "This will include DDF visits that overlap these regions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfef5e3b-0215-4057-871e-2682f20f3a03", + "metadata": {}, + "outputs": [], + "source": [ + "target_sv = ['bulgy', 'dusty_plane', 'lowdust', 'nes']\n", + "\n", + "print('Median airmass in the LSST SV regions, by filter.')\n", + "print('%15s %5s %5s %5s %5s %5s %5s %5s ' %\n", + " ('SV target', 'u', 'g', 'r', 'i', 'z', 'y', 'all'))\n", + "for sv in target_sv:\n", + " temp = []\n", + " for filt in filter_names:\n", + " x = get_indices(None, sv, filt)\n", + " if len(x) > 0:\n", + " temp.append(float(np.nanmedian(df['airmass'][x])))\n", + " else:\n", + " temp.append(float('NaN'))\n", + " del x\n", + " x = get_indices(None, sv, None)\n", + " temp_all = float(np.nanmedian(df['airmass'][x]))\n", + " del x\n", + " print('%15s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f' %\n", + " (sv, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5], temp_all))" + ] + }, + { + "cell_type": "markdown", + "id": "fe049fcd-1c0c-4db0-9d2e-b15f7d21b2b6", + "metadata": {}, + "source": [ + "## 5. MAF sky map\n", + "\n", + "Recreate the sky map diagram in Figure 1.\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", + "Define the \"opsim\" filename and the \"run name\" -- in this case, the name of the commissioning visits database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9084f4ae-adad-4da4-82c1-794adfd4bc4a", + "metadata": {}, + "outputs": [], + "source": [ + "opsim_fname = db_filename\n", + "temp = db_filename.split('/')[-1]\n", + "run_name = temp.split('.')[0]\n", + "print(run_name)" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b86a708-a2f3-4c65-856a-afd28166b29d", + "metadata": {}, + "outputs": [], + "source": [ + "metric = maf.metrics.CountMetric(col='observationStartMJD',\n", + " metric_name='Nvisits')\n", + "nside = 64\n", + "slicer = maf.slicers.HealpixSlicer(nside=nside)\n", + "constraint = None" + ] + }, + { + "cell_type": "markdown", + "id": "af54c591-330f-4269-902f-6357f914bd6f", + "metadata": {}, + "source": [ + "Bundle together the components of the metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd8e7205-21af-44e8-a615-606d7330cabf", + "metadata": {}, + "outputs": [], + "source": [ + "bundle = maf.MetricBundle(metric, slicer, constraint, run_name=run_name)" + ] + }, + { + "cell_type": "markdown", + "id": "8c03df30-3608-4041-ba48-9fd1a5703b64", + "metadata": {}, + "source": [ + "Define the bundle group to plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74da4456-431c-4424-926e-748912f05d00", + "metadata": {}, + "outputs": [], + "source": [ + "group = maf.MetricBundleGroup({'nvisits': bundle},\n", + " opsim_fname, out_dir=output_path)" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2200e8c9-0370-4b1a-a4d8-720784d6e2e4", + "metadata": {}, + "outputs": [], + "source": [ + "group.run_all()" + ] + }, + { + "cell_type": "markdown", + "id": "9de712b8-c436-42e3-979c-1e0198ffbc95", + "metadata": {}, + "source": [ + "Show the plots for this metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3f0b6fe-dc80-4ecc-a02f-73e4f7b0615a", + "metadata": {}, + "outputs": [], + "source": [ + "plot_dict = {'color_min': 10, 'color_max': 100,\n", + " 'x_min': -2, 'x_max': 100, 'bins': 50, \"extend\": \"both\"}\n", + "bundle.set_plot_dict(plot_dict)\n", + "_ = bundle.plot()" + ] + }, + { + "cell_type": "markdown", + "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", + "\n", + "Read more about they sky coverage for the SV wide-area survey on the Science Validation survey summary webpage ." + ] + }, + { + "cell_type": "markdown", + "id": "135c0da0-4d13-4230-9abe-2add68bb1266", + "metadata": {}, + "source": [ + "## 6. 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", + "\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", + "\n", + "As in Section 4.1, create a histogram of the `cloud_extinction` values. Use the function defined in Section 4.2 to calculate the mean cloud extinction in magnitudes for a subset of the visits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da3e68aa-a9f3-4817-90f6-ff83e8554957", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2ba54282c86fd1ea0785e821d76a7c458a692b74 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Thu, 13 Nov 2025 17:51:20 +0000 Subject: [PATCH 2/6] final test --- Commissioning/lsstcam_visits.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb index d2acbf39..cbd5acc5 100644 --- a/Commissioning/lsstcam_visits.ipynb +++ b/Commissioning/lsstcam_visits.ipynb @@ -101,7 +101,7 @@ "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.\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", "* **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." ] From 6577f1dc808683688a2a7393da66551de7746857 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Fri, 14 Nov 2025 03:18:17 +0000 Subject: [PATCH 3/6] after LJ review --- Commissioning/lsstcam_visits.ipynb | 115 ++++++++++++----------------- 1 file changed, 49 insertions(+), 66 deletions(-) diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb index cbd5acc5..258a0e01 100644 --- a/Commissioning/lsstcam_visits.ipynb +++ b/Commissioning/lsstcam_visits.ipynb @@ -71,8 +71,8 @@ "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", "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", - "The contiguous part of this 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, and a secondary area within the low-dust WFD was included to provide targets when the primary or DDF fields were not available." + "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." ] }, { @@ -91,7 +91,7 @@ "\n", "\n", "\n", - "> **Figure 1:** All science visits acquired during LSSTCam commissioning. Both the small field surveys and the four 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." + "> **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." ] }, { @@ -131,6 +131,7 @@ "import numpy as np\n", "import pandas as pd\n", "import getpass\n", + "from astropy.time import Time\n", "from lsst.utils.plotting import (get_multiband_plot_colors,\n", " get_multiband_plot_linestyles)" ] @@ -234,7 +235,7 @@ "* `fieldDec` : The boresight Declination for the visit (degrees).\n", "* `band` : The LSST filter used for the visit, one of $ugrizy$.\n", "* `airmass` : The airmass of the visit ($1/\\cos(\\Theta_z)$, where $\\Theta_z$ is the zenith angle).\n", - "* `seeingFwhmGeom` : The full-width half-max of the measured point spread function (PSF; arcseconds).\n", + "* `seeingFwhmEff` : The full-width half-max of the point spread function (PSF; arcseconds).\n", "* `fiveSigmaDepth` : The magnitude of a five-sigma point source detection in the visit (magnitudes).\n" ] }, @@ -379,7 +380,7 @@ "source": [ "### 2.3. Read as a pandas dataframe\n", "\n", - "As there are only 21647 visits in the database, and the file itself is only 81 M, it is small enough to be loaded in its entirety as a `pandas` dataframe.\n", + "As the file is only 81 M, it 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." ] @@ -424,12 +425,11 @@ { "cell_type": "code", "execution_count": null, - "id": "4fa7b4e9-9fe0-4378-b0a0-0c0ee6e514f8", + "id": "780d25ab-34c3-490d-b8e9-248c63acf8f7", "metadata": {}, "outputs": [], "source": [ - "# for col in df.columns:\n", - "# print(col)" + "# list(df.columns)" ] }, { @@ -442,11 +442,12 @@ "Every visit has both an `observation_reason` and a `target_name`.\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, e.g., the WFD configuration, the DDF program, or target of opportunity (TOO).\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.\n", + "For example, most DDFs are also in the low-dust extragalactic regions of the LSST WFD.\n", "\n", "### 3.1. Observation reason\n", "\n", @@ -495,7 +496,7 @@ "source": [ "temp = np.asarray([value[:3] for value in values], dtype='str')\n", "tx = np.where(temp == 'ddf')[0]\n", - "print('Number of visits done for deep drilling fields: ', np.sum(counts[tx]))\n", + "print('Number of visits done for SV DDFs: ', np.sum(counts[tx]))\n", "Nddf = np.sum(counts[tx])\n", "del temp, tx" ] @@ -531,7 +532,7 @@ "id": "31354530-f6e3-44a6-898a-d676824f9516", "metadata": {}, "source": [ - "Print the number of visits done as part of LSST Science Validation (excluding DDFs)." + "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)." ] }, { @@ -542,7 +543,7 @@ "outputs": [], "source": [ "temp = len(df) - Nfss - Nddf - Ntoo - Ntas\n", - "print('Number of visits done for LSST Science Validation: ', temp)\n", + "print('Number of visits done for the SV WFD: ', temp)\n", "del temp" ] }, @@ -640,7 +641,9 @@ "source": [ "#### 3.2.2. LSST regions\n", "\n", - "Define the `observation_reason` values that **are not** part of the WFD LSST Science Validation survey program." + "Index the visits that are 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)." ] }, { @@ -652,24 +655,7 @@ "source": [ "obs_reason_not_SV = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", " 'ddf_elaiss1', 'ddf_xmm_lss', 'too',\n", - " 'field_survey_science', 'template_area_singles_i']" - ] - }, - { - "cell_type": "markdown", - "id": "71b9d1b0-a85d-4972-94ea-1ac9a43bd3c4", - "metadata": {}, - "source": [ - "Index the visits that are part of the WFD LSST Science Validation survey program." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "731566e9-ece3-4844-aedf-f1d021515485", - "metadata": {}, - "outputs": [], - "source": [ + " 'field_survey_science', 'template_area_singles_i']\n", "mask = np.isin(df['observation_reason'], obs_reason_not_SV)\n", "tx = np.where(mask == 0)[0]\n", "print('Number of visits done for LSST Science Validation: ', len(tx))" @@ -782,7 +768,7 @@ "#### 3.2.3. Deep Drilling Fields (DDFs)\n", "\n", "The LSST will include five Deep Drilling Fields,\n", - "four of which (all except COSMOS) were observed during commissioning." + "four of which (all except COSMOS) were observed as part of the LSST Science Validation survey." ] }, { @@ -809,7 +795,7 @@ "id": "3258ceaf-0d45-4d2b-aa11-deab1b25592e", "metadata": {}, "source": [ - "Define the `observation_reason` values that indicate the DDF program was the motivation for the visit." + "Define the `observation_reason` values that indicate the SV DDF program was the motivation for the visit." ] }, { @@ -828,7 +814,7 @@ "id": "73d2c722-74f6-4c0a-a418-a74c3d05dbdc", "metadata": {}, "source": [ - "Index the visits that are part of the DDF program." + "Index the visits that are part of the SV DDF program." ] }, { @@ -840,7 +826,7 @@ "source": [ "mask = np.isin(df['observation_reason'], obs_reason_ddf)\n", "tx = np.where(mask)[0]\n", - "print('Number of visits done for DDFs: ', len(tx))" + "print('Number of visits done for SV DDFs: ', len(tx))" ] }, { @@ -848,7 +834,7 @@ "id": "b1158c11-7851-4b3e-b324-919c34591ac9", "metadata": {}, "source": [ - "Print the unique target names for all DDF visits." + "Print the unique target names for all SV DDF visits." ] }, { @@ -893,7 +879,7 @@ "id": "cfd4012e-056f-4070-99c1-cdbf51c1d48b", "metadata": {}, "source": [ - "Sum up the number of visits for each DDF." + "Sum up the number of visits for each SV DDF." ] }, { @@ -949,33 +935,30 @@ { "cell_type": "code", "execution_count": null, - "id": "a57e62f7-5d35-49db-8236-aa9f92a48bb8", + "id": "019919cf-97ad-4e90-a301-b7f3a82e5f3f", "metadata": {}, "outputs": [], "source": [ - "Ndays = int(np.floor(np.max(df['exp_midpt_mjd']))\n", - " - np.floor(np.min(df['exp_midpt_mjd'])))\n", - "\n", - "tx_u = np.where(df['band'] == 'u')[0]\n", - "tx_g = np.where(df['band'] == 'g')[0]\n", - "tx_r = np.where(df['band'] == 'r')[0]\n", - "tx_i = np.where(df['band'] == 'i')[0]\n", - "tx_z = np.where(df['band'] == 'z')[0]\n", - "tx_y = np.where(df['band'] == 'y')[0]\n", - "\n", - "fig = plt.figure(figsize=(8, 4))\n", - "plt.hist([df['exp_midpt_mjd'][tx_u], df['exp_midpt_mjd'][tx_g],\n", - " df['exp_midpt_mjd'][tx_r], df['exp_midpt_mjd'][tx_i],\n", - " df['exp_midpt_mjd'][tx_z], df['exp_midpt_mjd'][tx_y]],\n", - " bins=Ndays, stacked=True,\n", - " color=filter_colors_list, label=filter_names)\n", - "plt.legend(loc='best')\n", - "plt.xlabel('Modified Julian Date (MJD)')\n", - "plt.ylabel('Number of visits')\n", - "plt.show()\n", - "\n", - "del tx_u, tx_g, tx_r, tx_i, tx_z, tx_y\n", - "del Ndays" + "t = Time(\"2025-04-17T12:00:00\", scale='tai')\n", + "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", + "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", + " bar_bottom += heights\n", + "plt.legend()\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.show()" ] }, { @@ -1040,7 +1023,7 @@ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", " tx = np.where(df['band'] == filt)[0]\n", - " plt.hist(df['seeingFwhmGeom'][tx], bins=40, histtype='step',\n", + " plt.hist(df['seeingFwhmEff'][tx], bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", @@ -1191,7 +1174,7 @@ "print('Min and max MJD: %8.2f %8.2f' % (np.min(df['exp_midpt_mjd'][x]),\n", " np.max(df['exp_midpt_mjd'][x])))\n", "print('Median airmass: %4.2f' % np.nanmedian(df['airmass'][x]))\n", - "print('Median seeing: %4.2f arcsec' % np.nanmedian(df['seeingFwhmGeom'][x]))\n", + "print('Median seeing: %4.2f arcsec' % np.nanmedian(df['seeingFwhmEff'][x]))\n", "print('Median depth: %5.2f mag' % np.nanmedian(df['fiveSigmaDepth'][x]))" ] }, @@ -1243,12 +1226,12 @@ " for filt in filter_names:\n", " x = get_indices(ddf, None, filt)\n", " if len(x) > 0:\n", - " temp.append(float(np.nanmedian(df['seeingFwhmGeom'][x])))\n", + " temp.append(float(np.nanmedian(df['seeingFwhmEff'][x])))\n", " else:\n", " temp.append(float('NaN'))\n", " del x\n", " x = get_indices(ddf, None, None)\n", - " temp_all = float(np.nanmedian(df['seeingFwhmGeom'][x]))\n", + " temp_all = float(np.nanmedian(df['seeingFwhmEff'][x]))\n", " del x\n", " print('%15s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f' %\n", " (ddf, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5], temp_all))" From 08821cee7e23f023e0c66d8f2c52b1df24709a3d Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Fri, 14 Nov 2025 21:11:25 +0000 Subject: [PATCH 4/6] further LJ suggestions; pandasification --- Commissioning/lsstcam_visits.ipynb | 483 ++++++++--------------------- 1 file changed, 137 insertions(+), 346 deletions(-) diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb index 258a0e01..892a6840 100644 --- a/Commissioning/lsstcam_visits.ipynb +++ b/Commissioning/lsstcam_visits.ipynb @@ -130,7 +130,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import getpass\n", + "from tabulate import tabulate\n", "from astropy.time import Time\n", "from lsst.utils.plotting import (get_multiband_plot_colors,\n", " get_multiband_plot_linestyles)" @@ -171,8 +171,7 @@ "metadata": {}, "outputs": [], "source": [ - "uname = getpass.getuser()\n", - "output_path = '/deleted-sundays/' + uname + '/'\n", + "output_path = os.getenv(\"SCRATCH_DIR\")\n", "if os.path.exists(output_path):\n", " print('Already exists: ', output_path)\n", "else:\n", @@ -477,54 +476,19 @@ { "cell_type": "code", "execution_count": null, - "id": "4951907a-574e-4c21-a8e2-bc5ab99ad3f7", + "id": "fbb3c9f5-b619-49e2-b82a-f03650fb6548", "metadata": {}, "outputs": [], "source": [ - "tx = np.where(df['observation_reason'] == 'field_survey_science')[0]\n", - "print('Number of visits done for small field surveys: ', len(tx))\n", - "Nfss = len(tx)\n", - "del tx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd9b4fcf-c7e7-43f5-8560-e99fe65eb45f", - "metadata": {}, - "outputs": [], - "source": [ - "temp = np.asarray([value[:3] for value in values], dtype='str')\n", - "tx = np.where(temp == 'ddf')[0]\n", - "print('Number of visits done for SV DDFs: ', np.sum(counts[tx]))\n", - "Nddf = np.sum(counts[tx])\n", - "del temp, tx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44839462-a867-4340-be8e-e586fa5767c9", - "metadata": {}, - "outputs": [], - "source": [ - "tx = np.where(df['observation_reason'] == 'too')[0]\n", - "print('Number of visits done for target of opportuntity: ', len(tx))\n", - "Ntoo = len(tx)\n", - "del tx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fb08c55-a995-4396-a142-e052a532d835", - "metadata": {}, - "outputs": [], - "source": [ - "tx = np.where(df['observation_reason'] == 'template_area_singles_i')[0]\n", - "print('Number of visits done for templates: ', len(tx))\n", - "Ntas = len(tx)\n", - "del tx" + "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 == \"too\"'))\n", + "Ntas = len(df.query('observation_reason == \"template_area_singles_i\"'))\n", + "\n", + "print('Number of visits done for small field surveys: ', Nsfs)\n", + "print('Number of visits done for SV DDFs: ', Nddf)\n", + "print('Number of visits done for target of opportuntity: ', Ntoo)\n", + "print('Number of visits done for templates: ', Ntas)" ] }, { @@ -542,9 +506,8 @@ "metadata": {}, "outputs": [], "source": [ - "temp = len(df) - Nfss - Nddf - Ntoo - Ntas\n", - "print('Number of visits done for the SV WFD: ', temp)\n", - "del temp" + "Nwfd = len(df) - Nsfs - Nddf - Ntoo - Ntas\n", + "print('Number of visits done for the SV WFD: ', Nwfd)" ] }, { @@ -562,7 +525,7 @@ "metadata": {}, "outputs": [], "source": [ - "del Nfss, Nddf, Ntoo, Ntas" + "del Nsfs, Nddf, Ntoo, Ntas, Nwfd" ] }, { @@ -580,38 +543,21 @@ "source": [ "#### 3.2.1. Small field surveys\n", "\n", - "Index the visits that were done for small field surveys." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "108913ac-ad1a-4727-a927-88a8e29017ac", - "metadata": {}, - "outputs": [], - "source": [ - "tx = np.where(df['observation_reason'] == 'field_survey_science')[0]\n", - "print('Number of visits done for small field surveys: ', len(tx))" - ] - }, - { - "cell_type": "markdown", - "id": "fde3e068-2c81-4662-94ab-1fbb6e8b9247", - "metadata": {}, - "source": [ - "Print the number of visits done for each of the small fields." + "Get the unique values of `target_name` for each of the small field survey areas, and print the number of visits done for each." ] }, { "cell_type": "code", "execution_count": null, - "id": "154df286-326e-402b-b255-4b3e30e29a7b", + "id": "9b845a63-2ecf-46d9-ba58-0f95f6ef81bd", "metadata": {}, "outputs": [], "source": [ - "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", + "df_sfs = df.query('observation_reason == \"field_survey_science\"')\n", + "values, counts = np.unique(df_sfs['target_name'], return_counts=True)\n", "for value, count in zip(values, counts):\n", - " print('%20s %5i' % (value, count))" + " print('%20s %5i' % (value, count))\n", + "del df_sfs, values, counts" ] }, { @@ -619,19 +565,7 @@ "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 >100 visits are shown.\n", - "\n", - "Clean up. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89f5d59a-2a6e-4d7b-a3a7-3e367c6218f3", - "metadata": {}, - "outputs": [], - "source": [ - "del tx, values, counts" + "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." ] }, { @@ -639,9 +573,9 @@ "id": "5e65b35d-c552-4301-ae1c-e9a64274204c", "metadata": {}, "source": [ - "#### 3.2.2. LSST regions\n", + "#### 3.2.2. Wide-fast-deep regions\n", "\n", - "Index the visits that are part of the WFD LSST Science Validation survey program.\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)." ] @@ -649,42 +583,24 @@ { "cell_type": "code", "execution_count": null, - "id": "9691a359-dddd-47fe-9ad1-8c7dae9976d2", + "id": "796d5892-e932-4b23-a598-3f2fd78231cb", "metadata": {}, "outputs": [], "source": [ - "obs_reason_not_SV = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", - " 'ddf_elaiss1', 'ddf_xmm_lss', 'too',\n", - " 'field_survey_science', 'template_area_singles_i']\n", - "mask = np.isin(df['observation_reason'], obs_reason_not_SV)\n", - "tx = np.where(mask == 0)[0]\n", - "print('Number of visits done for LSST Science Validation: ', len(tx))" - ] - }, - { - "cell_type": "markdown", - "id": "bc778940-7901-44c5-9382-36d35e0bacb1", - "metadata": {}, - "source": [ - "Print the unique target names for visits that are part of the WFD LSST Science Validation survey program." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60ce628e-013e-41bb-a0d7-177eb50af885", - "metadata": {}, - "outputs": [], - "source": [ - "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", + "query = \"(observation_reason != 'field_survey_science') & \"\n", + "query += \"(observation_reason.str.contains('ddf') == 0) & \"\n", + "query += \"(observation_reason != 'too') & \"\n", + "query += \"(observation_reason != 'template_area_singles_i')\"\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", - " if count > 100:\n", - " print('%20s %5i' % (value, count))" + " print('%30s %5i' % (value, count))" ] }, { "cell_type": "markdown", - "id": "c9a74a2c-709a-4042-8c32-ab04f37f248f", + "id": "e7dc6b98-368a-4d1e-a92f-2a15dad81381", "metadata": {}, "source": [ "Separate out the comma-separated lists into one list of unique target names." @@ -693,18 +609,15 @@ { "cell_type": "code", "execution_count": null, - "id": "137f4d12-3193-416b-a24a-4e52f2195a63", + "id": "0d88ac98-d851-4eb8-99a0-d57504c4dfcd", "metadata": {}, "outputs": [], "source": [ "temp = []\n", "for value in values:\n", - " temp2 = str(value).split(',')\n", - " for t2 in temp2:\n", - " temp.append(t2.strip())\n", - " del temp2\n", + " for name in str(value).split(','):\n", + " temp.append(name.strip())\n", "targets = np.unique(temp)\n", - "del temp\n", "print(targets)" ] }, @@ -728,16 +641,12 @@ { "cell_type": "code", "execution_count": null, - "id": "6e60fa24-dbe6-4e12-8fcd-1a0bcdbf57ff", + "id": "d5f25b04-b9ec-47fe-8dbf-4146f902f30d", "metadata": {}, "outputs": [], "source": [ - "all_sv_target_names = np.array(df['target_name'][tx], dtype='str')\n", "for target in targets:\n", - " indices = np.char.find(all_sv_target_names, target)\n", - " x = np.where(indices >= 0)[0]\n", - " print('%15s %5i' % (target, len(x)))\n", - " del indices, x" + " print('%15s %5i' % (target, len(df_wfd.query(\"target_name.str.contains(@target)\"))))" ] }, { @@ -755,8 +664,7 @@ "metadata": {}, "outputs": [], "source": [ - "del obs_reason_not_SV, mask, tx, values, counts\n", - "del targets, all_sv_target_names" + "del query, df_wfd, values, counts, temp, targets" ] }, { @@ -792,120 +700,103 @@ }, { "cell_type": "markdown", - "id": "3258ceaf-0d45-4d2b-aa11-deab1b25592e", + "id": "87fe871f-d197-4aa0-b2a3-d5695d0ab733", "metadata": {}, "source": [ - "Define the `observation_reason` values that indicate the SV DDF program was the motivation for the visit." + "Print the unique DDF target names and the number of visits done for each." ] }, { "cell_type": "code", "execution_count": null, - "id": "7698fc41-13ce-405b-9c62-207e536cfd82", + "id": "07bb4704-3668-4c6d-b15f-5910fae84492", "metadata": {}, "outputs": [], "source": [ - "obs_reason_ddf = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", - " 'ddf_elaiss1', 'ddf_xmm_lss']" + "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))" ] }, { "cell_type": "markdown", - "id": "73d2c722-74f6-4c0a-a418-a74c3d05dbdc", + "id": "fce95c75-cf56-43c4-a43d-a16379cd2270", "metadata": {}, "source": [ - "Index the visits that are part of the SV DDF program." + "The DDFs overlap the WFD lowdust region, and so the fields have both the DDF name and \"lowdust\" in their `target_name`.\n", + "\n", + "Separate out the comma-separated lists into one list of unique target names." ] }, { "cell_type": "code", "execution_count": null, - "id": "1ff0a523-455c-47a0-a90d-a77b43ec96bf", + "id": "4164a71f-a938-4992-9763-1aaa9aeec508", "metadata": {}, "outputs": [], "source": [ - "mask = np.isin(df['observation_reason'], obs_reason_ddf)\n", - "tx = np.where(mask)[0]\n", - "print('Number of visits done for SV DDFs: ', len(tx))" + "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)" ] }, { "cell_type": "markdown", - "id": "b1158c11-7851-4b3e-b324-919c34591ac9", + "id": "120fb553-01f5-4561-960a-48834fef2fd1", "metadata": {}, "source": [ - "Print the unique target names for all SV DDF visits." + "Define the list of only the DDF target names." ] }, { "cell_type": "code", "execution_count": null, - "id": "8143bc14-9c01-4738-8491-8d71e26751b3", + "id": "8ef8d2db-d4ef-4922-a29e-b61842025b7c", "metadata": {}, "outputs": [], "source": [ - "values, counts = np.unique(df['target_name'][tx], return_counts=True)\n", - "for value, count in zip(values, counts):\n", - " print('%20s %5i' % (value, count))" + "ddf_names = ['DDF ECDFS', 'DDF EDFS_a', 'DDF EDFS_b', 'DDF ELAISS1', 'DDF XMM_LSS']" ] }, { "cell_type": "markdown", - "id": "6de95ef8-618c-456c-8fec-0aa10b910e3b", + "id": "0dd26088-bc96-4703-981c-3c1c175b1c27", "metadata": {}, "source": [ - "Separate out the comma-separated lists into one list of unique target names." + "Sum up the number of visits for each SV DDF." ] }, { "cell_type": "code", "execution_count": null, - "id": "f82eb5f1-a2b4-4452-88f1-213502f0aad6", + "id": "50106666-aebe-41ae-a48f-3dc08eb13648", "metadata": {}, "outputs": [], "source": [ - "temp = []\n", - "for value in values:\n", - " temp2 = str(value).split(',')\n", - " for t2 in temp2:\n", - " temp.append(t2.strip())\n", - " del temp2\n", - "targets = np.unique(temp)\n", - "del temp" + "for name in ddf_names:\n", + " print('%15s %5i' % (name, len(df_ddf.query(\"target_name.str.contains(@name)\"))))" ] }, { "cell_type": "markdown", - "id": "cfd4012e-056f-4070-99c1-cdbf51c1d48b", + "id": "5f3724ad-61c6-493e-9849-2b6fea21de3b", "metadata": {}, "source": [ - "Sum up the number of visits for each SV DDF." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04be8bd4-827e-4619-b5ea-eca1955cab89", - "metadata": {}, - "outputs": [], - "source": [ - "all_ddf_target_names = np.array(df['target_name'][tx], dtype='str')\n", - "for target in targets:\n", - " indices = np.char.find(all_ddf_target_names, target)\n", - " x = np.where(indices >= 0)[0]\n", - " print('%15s %5i' % (target, len(x)))\n", - " del indices, x" + "Clean up." ] }, { "cell_type": "code", "execution_count": null, - "id": "8c4518f1-f6a0-4036-8b23-cfe11412098d", + "id": "3e9d4c26-f675-448c-9ec5-c449e9840e76", "metadata": {}, "outputs": [], "source": [ - "del mask, tx, values, counts\n", - "del targets, all_ddf_target_names" + "del df_ddf, values, counts, temp, targets, ddf_names" ] }, { @@ -913,7 +804,9 @@ "id": "65f8143c-89f8-4d25-97a6-87ecf16f5761", "metadata": {}, "source": [ - "## 4. Visit metadata" + "## 4. Visit metadata\n", + "\n", + "Examples of how to visualize and calculate statistics for a few key columns of visit metadata." ] }, { @@ -929,7 +822,7 @@ "id": "944105ff-4cce-4297-88f4-46de10d267bd", "metadata": {}, "source": [ - "Create a stacked histogram of the Modified Julian Dates of all visits." + "Create a histogram of the Modified Julian Dates of all visits, stacked by filter." ] }, { @@ -980,17 +873,16 @@ { "cell_type": "code", "execution_count": null, - "id": "9c6ddb4b-97bd-4c6d-aa81-3d3024a70808", + "id": "5d07b340-f151-4c1d-99c7-467c7db73aa7", "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " tx = np.where(df['band'] == filt)[0]\n", - " plt.hist(df['airmass'][tx], bins=40, histtype='step',\n", + " plt.hist(df.query('band == @filt')['airmass'],\n", + " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", - " del tx\n", "plt.legend(loc='best')\n", "plt.xlabel('Airmass')\n", "plt.ylabel('Number of visits')\n", @@ -1016,14 +908,14 @@ { "cell_type": "code", "execution_count": null, - "id": "9b1c2409-fd2f-43f7-804d-16b9d4c419d1", + "id": "29f9cfae-41f9-4919-a60e-e50b78f10aee", "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " tx = np.where(df['band'] == filt)[0]\n", - " plt.hist(df['seeingFwhmEff'][tx], bins=40, histtype='step',\n", + " plt.hist(df.query('band == @filt')['seeingFwhmEff'],\n", + " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", @@ -1051,14 +943,14 @@ { "cell_type": "code", "execution_count": null, - "id": "3003c0a3-1473-425d-8f61-6693eeb86737", + "id": "32b3a095-7f17-4a1f-8114-3b457e78a933", "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " tx = np.where(df['band'] == filt)[0]\n", - " plt.hist(df['fiveSigmaDepth'][tx], bins=40, histtype='step',\n", + " plt.hist(df.query('band == @filt')['fiveSigmaDepth'],\n", + " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", @@ -1080,198 +972,97 @@ "id": "cbf444d9-38d9-4432-95ba-59dbefb1e30e", "metadata": {}, "source": [ - "### 4.2. Summary statistics" - ] - }, - { - "cell_type": "markdown", - "id": "8dd25f8e-1800-4f3c-9d38-6822436625df", - "metadata": {}, - "source": [ - "Define a function to use in this section called `get_indices`. It will return the indices of `df` that match the input `observation_reason`, `target_name`, and `filter`, and enable the calculation of summary statistics for visit subsets (e.g., the mean seeing for all DDF visits of the ELAISS1 field in $r$-band)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6376bd60-7dea-459c-a67c-0651647176be", - "metadata": {}, - "outputs": [], - "source": [ - "def get_indices(reason, target, band, verbose=False):\n", - " \"\"\"\n", - " Given the observation reason, target name, and band,\n", - " return the indices in the dataframe.\n", - "\n", - " Parameters\n", - " ----------\n", - " reason: string\n", - " An observation reason (or None).\n", - " target: string\n", - " A target name (or None).\n", - " band: string\n", - " A filter name, ugrizy (or None).\n", - " verbose: boolean\n", - " If True, print warning messages.\n", - "\n", - " Returns\n", - " -------\n", - " indices: int\n", - " The index values of the rows.\n", - " \"\"\"\n", - "\n", - " if reason is not None:\n", - " tx = np.where(df['observation_reason'] == reason)[0]\n", - " else:\n", - " tx = np.arange(len(df['observation_reason']))\n", - " if len(tx) == 0:\n", - " if verbose:\n", - " print('Warning. No observation reasons match: ', reason)\n", - " return []\n", + "### 4.2. Summary statistics\n", "\n", - " if target is not None:\n", - " arr = np.array(df['target_name'][tx], dtype='str')\n", - " indices = np.char.find(arr, target)\n", - " ttx = np.where(indices >= 0)[0]\n", - " del arr, indices\n", - " else:\n", - " ttx = np.arange(len(df['target_name'][tx]))\n", - " if len(ttx) == 0:\n", - " if verbose:\n", - " print('Warning. No target names match: ', target)\n", - " return []\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", - " if band is not None:\n", - " tttx = np.where(df['band'][tx[ttx]] == band)[0]\n", - " else:\n", - " tttx = np.arange(len(df['band'][tx[ttx]]))\n", - " if len(tttx) == 0:\n", - " if verbose:\n", - " print('Warning. No filter names match: ', band)\n", - " return []\n", - "\n", - " indices = tx[ttx[tttx]]\n", - "\n", - " return indices" - ] - }, - { - "cell_type": "markdown", - "id": "33e5cb2c-62e9-4188-ad1f-18462cba778b", - "metadata": {}, - "source": [ - "As an example, print the first and last MJD and the median airmass, seeing, and depth for all DDF visits of the ELAISS1 field in $r$-band." + "Display a table of the number of visits by band for each of the small field survey areas with at least 50 visits total." ] }, { "cell_type": "code", "execution_count": null, - "id": "651a123e-4b11-468d-a2c9-001718ca65e8", + "id": "2084d2f5-8209-4ad7-a2b0-ee4b2632cf03", "metadata": {}, "outputs": [], "source": [ - "x = get_indices('ddf_elaiss1', 'DDF ELAISS1', 'r')\n", - "print('Min and max MJD: %8.2f %8.2f' % (np.min(df['exp_midpt_mjd'][x]),\n", - " np.max(df['exp_midpt_mjd'][x])))\n", - "print('Median airmass: %4.2f' % np.nanmedian(df['airmass'][x]))\n", - "print('Median seeing: %4.2f arcsec' % np.nanmedian(df['seeingFwhmEff'][x]))\n", - "print('Median depth: %5.2f mag' % np.nanmedian(df['fiveSigmaDepth'][x]))" + "query = \"observation_reason == 'field_survey_science'\"\n", + "df_sfs = df.query(query).groupby([\"target_name\", \"band\"]).agg({'seq_num': 'count'})\n", + "df_sfs.rename({\"seq_num\": \"count\"}, axis=1, inplace=True)\n", + "df_sfs = df_sfs.reset_index('band').pivot(columns=[\"band\"]).droplevel(0, axis=1)\n", + "df_sfs = df_sfs[['u', 'g', 'r', 'i', 'z', 'y']]\n", + "df_sfs['all'] = df_sfs.sum(axis=1)\n", + "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(table)\n", + "del query, df_sfs, table" ] }, { "cell_type": "markdown", - "id": "64453bcb-acc5-47f8-b70f-da347e660bb7", + "id": "06d64f74-567a-4033-bb03-bf74bf38e2f5", "metadata": {}, "source": [ - "Use bad inputs on purpose, to demonstrate the error messages that are returned and that an empty list is returned when there are no matches." + "Display a table of the number of visits, median seeing (FWHM), mean airmass, and timespan (time between first and last visit) for each of the small field survey areas with at least 50 visits total." ] }, { "cell_type": "code", "execution_count": null, - "id": "2b21c65a-0e95-47f0-a8c8-44ed743a0345", + "id": "5d04c003-519e-4223-88ad-a0544d85619b", "metadata": {}, "outputs": [], "source": [ - "x = get_indices('aliens', 'DDF ECDFS', 'r', verbose=True)\n", - "x = get_indices('ddf_ecdfs', 'ALIENS', 'r', verbose=True)\n", - "x = get_indices('ddf_ecdfs', 'DDF ECDFS', 'a', verbose=True)\n", - "print(x)\n", - "del x" + "query = \"observation_reason == 'field_survey_science'\"\n", + "df_sfs = df.query(query).groupby(['target_name']).agg({'seq_num': 'count',\n", + " 'seeingFwhmEff': 'median',\n", + " 'airmass': 'mean',\n", + " 'exp_midpt_mjd': np.ptp})\n", + "df_sfs.rename({\"seq_num\": \"nvisits\",\n", + " \"seeingFwhmEff\": \"median fwhm (arcsec)\",\n", + " \"airmass\": \"mean airmass\",\n", + " \"exp_midpt_mjd\": \"timespan (days)\"}, axis=1, inplace=True)\n", + "df_sfs = df_sfs.query(\"nvisits > 50\").sort_values(\"nvisits\")\n", + "df_sfs['timespan (days)'] = df_sfs['timespan (days)'].astype(int) + 1\n", + "df_sfs.round(2)\n", + "table = tabulate(pd.DataFrame(df_sfs.round(2)), headers='keys')\n", + "table = table.replace(\"nan\", \" 0\")\n", + "print(table)\n", + "del query, df_sfs, table" ] }, { "cell_type": "markdown", - "id": "cfdb3d7c-f83b-41cd-9943-eb2f6ffa3dc1", + "id": "99aa1d6b-4c85-4d41-aafb-0b1b5f7d8faa", "metadata": {}, "source": [ - "Create a table of the median seeing for all DDF visits, by filter and for all filters together." + "Recreate the table above, but for the SV DDF, and include all field (do not restrict to fields with >50 visits)." ] }, { "cell_type": "code", "execution_count": null, - "id": "71be3460-0942-49c2-8f73-505dd5dc24c8", + "id": "b2a9711a-d24a-427a-8661-6accb6fe0bb6", "metadata": {}, "outputs": [], "source": [ - "reason_ddf = ['ddf_ecdfs', 'ddf_edfs_a', 'ddf_edfs_b',\n", - " 'ddf_elaiss1', 'ddf_xmm_lss']\n", - "\n", - "print('Median seeing in the DDFs, by filter.')\n", - "print('%15s %5s %5s %5s %5s %5s %5s %5s ' %\n", - " ('DDF', 'u', 'g', 'r', 'i', 'z', 'y', 'all'))\n", - "for ddf in reason_ddf:\n", - " temp = []\n", - " for filt in filter_names:\n", - " x = get_indices(ddf, None, filt)\n", - " if len(x) > 0:\n", - " temp.append(float(np.nanmedian(df['seeingFwhmEff'][x])))\n", - " else:\n", - " temp.append(float('NaN'))\n", - " del x\n", - " x = get_indices(ddf, None, None)\n", - " temp_all = float(np.nanmedian(df['seeingFwhmEff'][x]))\n", - " del x\n", - " print('%15s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f' %\n", - " (ddf, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5], temp_all))" - ] - }, - { - "cell_type": "markdown", - "id": "a261906f-32fe-4ecd-b574-a3ae870c708f", - "metadata": {}, - "source": [ - "Create a table of the median airmass for all commissioning visits with an LSST region target name, by filter and for all filters together.\n", - "This will include DDF visits that overlap these regions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfef5e3b-0215-4057-871e-2682f20f3a03", - "metadata": {}, - "outputs": [], - "source": [ - "target_sv = ['bulgy', 'dusty_plane', 'lowdust', 'nes']\n", - "\n", - "print('Median airmass in the LSST SV regions, by filter.')\n", - "print('%15s %5s %5s %5s %5s %5s %5s %5s ' %\n", - " ('SV target', 'u', 'g', 'r', 'i', 'z', 'y', 'all'))\n", - "for sv in target_sv:\n", - " temp = []\n", - " for filt in filter_names:\n", - " x = get_indices(None, sv, filt)\n", - " if len(x) > 0:\n", - " temp.append(float(np.nanmedian(df['airmass'][x])))\n", - " else:\n", - " temp.append(float('NaN'))\n", - " del x\n", - " x = get_indices(None, sv, None)\n", - " temp_all = float(np.nanmedian(df['airmass'][x]))\n", - " del x\n", - " print('%15s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f' %\n", - " (sv, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5], temp_all))" + "query = \"observation_reason.str.contains('ddf')\"\n", + "df_ddf = df.query(query).groupby(['observation_reason']).agg({'seq_num': 'count',\n", + " 'seeingFwhmEff': 'median',\n", + " 'airmass': 'mean',\n", + " 'exp_midpt_mjd': np.ptp})\n", + "df_ddf.rename({\"seq_num\": \"nvisits\",\n", + " \"seeingFwhmEff\": \"median fwhm (arcsec)\",\n", + " \"airmass\": \"mean airmass\",\n", + " \"exp_midpt_mjd\": \"timespan (days)\"}, axis=1, inplace=True)\n", + "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", + "table = tabulate(pd.DataFrame(df_ddf.round(2)), headers='keys')\n", + "table = table.replace(\"nan\", \" 0\")\n", + "print(table)\n", + "del query, df_ddf, table" ] }, { From 219c6d63b38be78cc4e4091650e510ba635b4ece Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Mon, 17 Nov 2025 19:42:56 +0000 Subject: [PATCH 5/6] redirect rubin sim data path --- Commissioning/lsstcam_visits.ipynb | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb index 892a6840..e7317c0a 100644 --- a/Commissioning/lsstcam_visits.ipynb +++ b/Commissioning/lsstcam_visits.ipynb @@ -1078,6 +1078,24 @@ "\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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "342b5cc3-3a46-4d19-8e5f-e73e81ff5fd2", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"RUBIN_SIM_DATA_DIR\"] = '/rubin/rubin_sim_data'" + ] + }, + { + "cell_type": "markdown", + "id": "5401c3ca-ca2f-488a-80e4-baac96f4a542", + "metadata": {}, + "source": [ "Define the \"opsim\" filename and the \"run name\" -- in this case, the name of the commissioning visits database." ] }, From f0789f956a04a290aad0024384663d205b72ce10 Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Mon, 17 Nov 2025 22:34:18 +0000 Subject: [PATCH 6/6] LJ comments re TOO --- Commissioning/lsstcam_visits.ipynb | 78 +++++++++++++++--------------- 1 file changed, 39 insertions(+), 39 deletions(-) diff --git a/Commissioning/lsstcam_visits.ipynb b/Commissioning/lsstcam_visits.ipynb index e7317c0a..d89816e1 100644 --- a/Commissioning/lsstcam_visits.ipynb +++ b/Commissioning/lsstcam_visits.ipynb @@ -171,7 +171,7 @@ "metadata": {}, "outputs": [], "source": [ - "output_path = os.getenv(\"SCRATCH_DIR\")\n", + "output_path = os.getenv('SCRATCH_DIR')\n", "if os.path.exists(output_path):\n", " print('Already exists: ', output_path)\n", "else:\n", @@ -330,7 +330,7 @@ " AND band='r' AND airmass < 1.5; \"\"\"\n", "cursor.execute(query)\n", "results = cursor.fetchall()\n", - "print('Number of rows returned: ', len(results))" + "print(\"Number of rows returned: \", len(results))" ] }, { @@ -480,15 +480,15 @@ "metadata": {}, "outputs": [], "source": [ - "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 == \"too\"'))\n", - "Ntas = len(df.query('observation_reason == \"template_area_singles_i\"'))\n", + "Nsfs = len(df.query(\"observation_reason == 'field_survey_science'\"))\n", + "Nddf = len(df.query(\"observation_reason.str.contains('ddf')\"))\n", + "Ntoo = len(df.query(\"observation_reason.str.contains('too')\"))\n", + "Ntas = len(df.query(\"observation_reason == 'template_area_singles_i'\"))\n", "\n", - "print('Number of visits done for small field surveys: ', Nsfs)\n", - "print('Number of visits done for SV DDFs: ', Nddf)\n", - "print('Number of visits done for target of opportuntity: ', Ntoo)\n", - "print('Number of visits done for templates: ', Ntas)" + "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)" ] }, { @@ -507,7 +507,7 @@ "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 the SV WFD: \", Nwfd)" ] }, { @@ -553,7 +553,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_sfs = df.query('observation_reason == \"field_survey_science\"')\n", + "df_sfs = df.query(\"observation_reason == 'field_survey_science'\")\n", "values, counts = np.unique(df_sfs['target_name'], return_counts=True)\n", "for value, count in zip(values, counts):\n", " print('%20s %5i' % (value, count))\n", @@ -589,7 +589,7 @@ "source": [ "query = \"(observation_reason != 'field_survey_science') & \"\n", "query += \"(observation_reason.str.contains('ddf') == 0) & \"\n", - "query += \"(observation_reason != 'too') & \"\n", + "query += \"(observation_reason.str.contains('too') == 0) & \"\n", "query += \"(observation_reason != 'template_area_singles_i')\"\n", "df_wfd = df.query(query)\n", "\n", @@ -879,13 +879,13 @@ "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " plt.hist(df.query('band == @filt')['airmass'],\n", + " plt.hist(df.query(\"band == @filt\")['airmass'],\n", " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", - "plt.xlabel('Airmass')\n", - "plt.ylabel('Number of visits')\n", + "plt.xlabel(\"Airmass\")\n", + "plt.ylabel(\"Number of visits\")\n", "plt.show()" ] }, @@ -914,13 +914,13 @@ "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " plt.hist(df.query('band == @filt')['seeingFwhmEff'],\n", + " plt.hist(df.query(\"band == @filt\")['seeingFwhmEff'],\n", " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", - "plt.xlabel('Seeing (FWHM of the PSF; arcsec)')\n", - "plt.ylabel('Number of visits')\n", + "plt.xlabel(\"Seeing (FWHM of the PSF; arcsec)\")\n", + "plt.ylabel(\"Number of visits\")\n", "plt.show()" ] }, @@ -949,13 +949,13 @@ "source": [ "fig = plt.figure(figsize=(8, 4))\n", "for f, filt in enumerate(filter_names):\n", - " plt.hist(df.query('band == @filt')['fiveSigmaDepth'],\n", + " plt.hist(df.query(\"band == @filt\")['fiveSigmaDepth'],\n", " bins=40, histtype='step',\n", " ls=filter_linestyles[filt],\n", " color=filter_colors_list[f], label=filt)\n", "plt.legend(loc='best')\n", - "plt.xlabel('5-Sigma Depth (mag)')\n", - "plt.ylabel('Number of visits')\n", + "plt.xlabel(\"5-Sigma Depth (mag)\")\n", + "plt.ylabel(\"Number of visits\")\n", "plt.show()" ] }, @@ -987,14 +987,14 @@ "outputs": [], "source": [ "query = \"observation_reason == 'field_survey_science'\"\n", - "df_sfs = df.query(query).groupby([\"target_name\", \"band\"]).agg({'seq_num': 'count'})\n", - "df_sfs.rename({\"seq_num\": \"count\"}, axis=1, inplace=True)\n", + "df_sfs = df.query(query).groupby(['target_name', 'band']).agg({'seq_num': 'count'})\n", + "df_sfs.rename({'seq_num': 'count'}, axis=1, inplace=True)\n", "df_sfs = df_sfs.reset_index('band').pivot(columns=[\"band\"]).droplevel(0, axis=1)\n", "df_sfs = df_sfs[['u', 'g', 'r', 'i', 'z', 'y']]\n", "df_sfs['all'] = df_sfs.sum(axis=1)\n", "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", + "table = table.replace('nan', ' 0')\n", "print(table)\n", "del query, df_sfs, table" ] @@ -1019,15 +1019,15 @@ " 'seeingFwhmEff': 'median',\n", " 'airmass': 'mean',\n", " 'exp_midpt_mjd': np.ptp})\n", - "df_sfs.rename({\"seq_num\": \"nvisits\",\n", - " \"seeingFwhmEff\": \"median fwhm (arcsec)\",\n", - " \"airmass\": \"mean airmass\",\n", - " \"exp_midpt_mjd\": \"timespan (days)\"}, axis=1, inplace=True)\n", - "df_sfs = df_sfs.query(\"nvisits > 50\").sort_values(\"nvisits\")\n", + "df_sfs.rename({'seq_num': 'nvisits',\n", + " 'seeingFwhmEff': 'median fwhm (arcsec)',\n", + " 'airmass': 'mean airmass',\n", + " 'exp_midpt_mjd': 'timespan (days)'}, axis=1, inplace=True)\n", + "df_sfs = df_sfs.query(\"nvisits > 50\").sort_values('nvisits')\n", "df_sfs['timespan (days)'] = df_sfs['timespan (days)'].astype(int) + 1\n", "df_sfs.round(2)\n", "table = tabulate(pd.DataFrame(df_sfs.round(2)), headers='keys')\n", - "table = table.replace(\"nan\", \" 0\")\n", + "table = table.replace('nan', ' 0')\n", "print(table)\n", "del query, df_sfs, table" ] @@ -1052,15 +1052,15 @@ " '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", - " \"exp_midpt_mjd\": \"timespan (days)\"}, axis=1, inplace=True)\n", - "df_ddf = df_ddf.sort_values(\"nvisits\")\n", + "df_ddf.rename({'seq_num': 'nvisits',\n", + " 'seeingFwhmEff': 'median fwhm (arcsec)',\n", + " 'airmass': 'mean airmass',\n", + " 'exp_midpt_mjd': 'timespan (days)'}, axis=1, inplace=True)\n", + "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", "table = tabulate(pd.DataFrame(df_ddf.round(2)), headers='keys')\n", - "table = table.replace(\"nan\", \" 0\")\n", + "table = table.replace('nan', ' 0')\n", "print(table)\n", "del query, df_ddf, table" ] @@ -1088,7 +1088,7 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ[\"RUBIN_SIM_DATA_DIR\"] = '/rubin/rubin_sim_data'" + "os.environ['RUBIN_SIM_DATA_DIR'] = '/rubin/rubin_sim_data'" ] }, { @@ -1205,7 +1205,7 @@ "outputs": [], "source": [ "plot_dict = {'color_min': 10, 'color_max': 100,\n", - " 'x_min': -2, 'x_max': 100, 'bins': 50, \"extend\": \"both\"}\n", + " 'x_min': -2, 'x_max': 100, 'bins': 50, 'extend': 'both'}\n", "bundle.set_plot_dict(plot_dict)\n", "_ = bundle.plot()" ]