### Navigate to Example Repository Directory

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/uber-cal.rst

Start your shell session in the top directory of the example git repository. This sets up the environment for subsequent commands.

```bash
cd $RC2_SUBSET_DIR
```

--------------------------------

### Setup Top-Level Package

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/setup.rst

After loading the environment, use the 'setup' command followed by a top-level package name to configure it for use. Examples include 'lsst_apps' or 'lsst_distrib'.

```bash
setup <package>
```

--------------------------------

### Install and Setup lsst_apps

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/top-level-packages.rst

Installs the lsst_apps package, providing core frameworks and algorithmic code. This is a more space-efficient option.

```bash
eups distrib install lsst_apps -t <tag>
setup lsst_apps
```

--------------------------------

### Setup Science Pipelines

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Set up the installed Science Pipelines environment using the 'setup' command. This makes the packages available in the current shell session.

```bash
setup lsst_distrib
```

--------------------------------

### Install and Setup lsst_distrib

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/top-level-packages.rst

Installs the lsst_distrib package, which includes core Science Pipelines functionality, measurement algorithms, and cluster execution middleware. Use this for a comprehensive installation.

```bash
eups distrib install lsst_distrib -t <tag>
setup lsst_distrib
```

--------------------------------

### Create and navigate to installation directory

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstinstall.rst

Create a directory for the LSST Science Pipelines installation and change into it.

```bash
mkdir -p lsst_stack
cd lsst_stack
```

--------------------------------

### Run LSST Science Pipelines Docker Image

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

Downloads a current version of the LSST Science Pipelines Docker image, starts a container, and opens a prompt. This is a quick way to get started with the LSST environment.

```bash
docker run -ti lsstsqre/centos:7-stack-lsst_distrib-{{ release_eups_tag }}
```

--------------------------------

### Verify LSST Distribution Setup

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/data-setup.rst

Run this command to ensure the LSST distribution environment is correctly set up. It should output a line containing the word 'setup'.

```bash
eups list lsst_distrib
```

--------------------------------

### List All Installed Packages

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/setup.rst

Execute 'eups list' to view all packages that are installed in your environment, regardless of whether they are currently set up.

```bash
eups list
```

--------------------------------

### Setup and Run Demo Script

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/demo.rst

Set up the processing package for the current directory and execute the demo script. This will process sample data and create a Butler repository.

```bash
setup -r .
./bin/run_demo.sh
```

--------------------------------

### Install git on Debian/Ubuntu

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs the 'git' version control system on Debian/Ubuntu systems using apt-get. This is recommended for users following the lsstsw instructions.

```bash
sudo apt-get update && sudo apt-get install git
```

--------------------------------

### Install git on AlmaLinux 9

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs the 'git' version control system on AlmaLinux 9 systems using dnf. This is recommended for users following the lsstsw instructions.

```bash
sudo dnf install git
```

--------------------------------

### Install xpa-tools on Debian/Ubuntu

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs 'xpa-tools' on Debian/Ubuntu systems to potentially resolve xpa-related errors when displaying images in DS9.

```bash
sudo apt install xpa-tools; xpans
```

--------------------------------

### Install LSST Science Pipelines packages

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstinstall.rst

Install the lsst_distrib EUPS package using the same release tag used for lsstinstall, and then set it up.

```bash
eups distrib install -t {{ release_eups_tag }} lsst_distrib
setup lsst_distrib
```

--------------------------------

### Example mask plane colors

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

This is an example output showing the colors assigned to different mask planes. It is for reference and does not execute code.

```text
BAD: red
CR: magenta
CROSSTALK: None
DETECTED: blue
DETECTED_NEGATIVE: cyan
EDGE: yellow
INTRP: green
NOT_DEBLENDED: None
NO_DATA: orange
SAT: green
SUSPECT: yellow
UNMASKEDNAN: None
```

--------------------------------

### Switch to Released LSST Science Pipelines Package

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/package-development.rst

Use this command to revert from a development package setup to the installed LSST Science Pipelines distribution. 'current' is the default tag for the installed software stack.

```bash
setup -j <package name> -t current
```

--------------------------------

### Build Science Pipelines Packages

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Build the 'lsst_distrib' package using the 'rebuild' command with the 'current' tag. This command automatically marks installed packages for easy setup.

```bash
rebuild -t current lsst_distrib
```

--------------------------------

### Check Git LFS Installation

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/git-lfs.rst

Verify if Git LFS is installed on your system. This command is useful for initial setup or troubleshooting.

```bash
git-lfs version
```

--------------------------------

### Setup Sample Data Package

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/data-setup.rst

After cloning the `rc2_subset` repository, set up the package to add it to the EUPS stack. The '-j' flag ensures only this package is set up without affecting others.

```bash
setup -j -r rc2_subset
```

--------------------------------

### Install patch on AlmaLinux 9

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs the 'patch' utility on AlmaLinux 9 systems using dnf.

```bash
sudo dnf install patch
```

--------------------------------

### lsstinstall Argument Reference

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstinstall.rst

Displays the usage and available options for the `lsstinstall` command-line tool. Use this to understand how to specify tags, environments, and other installation parameters.

```text
usage: lsstinstall [-n]
       [-T EUPS_TAG | -X EUPS_TAG | -v RUBINENV_VERSION]
       [-e ENV_NAME] [-u] [-d]
       [-p CONDA_PATH] [-P] [-C CHANNEL]
       [-E EUPS_URL]
       [-B] [-S]
       [-h]

.. option:: -n

    No-op.  Echo commands instead of running.

.. option:: -T <EUPS_TAG>

    Select the rubin-env version used to build the given EUPS_TAG.

.. option:: -X <EUPS_TAG>

    Select the exact environment used to build the given EUPS_TAG.

.. option:: -v <RUBINENV_VERSION>

    Select a particular rubin-env version (default=latest).

.. option:: -e <ENV_NAME>

    
```

--------------------------------

### Install Xcode Command Line Tools on macOS

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs the Xcode Command Line Tools on macOS systems. This is a prerequisite for certain development tasks.

```bash
xcode-select --install
```

--------------------------------

### Get Display Instance with Firefly Backend

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Explicitly get a display instance configured to use the Firefly viewer backend.

```python
display = afwDisplay.getDisplay(backend='firefly')
```

--------------------------------

### Setup and Build LSST Package in Container

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

Navigates to the cloned package directory within the container, sets up the package for development (without EUPS tagging), and builds it using scons.

```bash
cd mnt/pipe_tasks
setup -r .
scons -Q -j 6 opt=3
```

--------------------------------

### Install Git LFS (Read-Only Access)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/git-lfs.rst

Initialize Git LFS for read-only access after installing the binaries. This command must be run on each machine where LFS-stored artifacts will be accessed.

```bash
git-lfs install
```

--------------------------------

### Load LSST Environment in Docker Container

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

Loads the LSST environment and activates the 'lsst_distrib' package within a running Docker container. This is equivalent to the setup instructions for a source-based installation.

```bash
source /opt/lsst/software/stack/loadLSST.bash
setup lsst_distrib
```

--------------------------------

### Example Dataset Table Output

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

This is an example of the output from a dataset table, showing various metrics for different datasets. It is used to illustrate the state of quanta during processing.

```sh
                      Dataset                    Visible Shadowed Predicted Only Unsuccessful Cursed TOTAL EXPECTED
    -------------------------------------------- ------- -------- -------------- ------------ ------ ----- --------
                            deepCoadd_directWarp    6384        0            457            0      0  6841     6841
                        deepCoadd_psfMatchedWarp    6369        0            472            0      0  6841     6841
                               makeWarp_metadata    6841        0              0            0      0  6841     6841
                                    makeWarp_log    6841        0              0            0      0  6841     6841
                                 deepCoaddVisits     294        0              0            0      0   294      294
                  selectDeepCoaddVisits_metadata     294        0              0            0      0   294      294
                       selectDeepCoaddVisits_log     294        0              0            0      0   294      294
                                goodSeeingVisits     294        0              0            0      0   294      294
                 selectGoodSeeingVisits_metadata     294        0              0            0      0   294      294
                      selectGoodSeeingVisits_log     294        0              0            0      0   294      294
                                 goodSeeingCoadd     288        0              0            6      0   294      294
                          goodSeeingCoadd_nImage     288        0              0            6      0   294      294
                            templateGen_metadata     288        0              0            6      0   294      294
                                 templateGen_log     288        0              0            6      0   294      294
```

--------------------------------

### Get the Default Display Instance

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Obtain an instance of the display framework. By default, it uses the DS9 backend.

```python
display = afwDisplay.getDisplay()
```

--------------------------------

### Download and Extract Demo Project (Main Branch)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/demo.rst

Download the latest version of the demo project from the main branch using curl and extract it. This is recommended for users with newer installations.

```bash
curl -L https://github.com/lsst/pipelines_check/archive/main.tar.gz | tar xvzf -
cd pipelines_check-main
```

--------------------------------

### LSSTSW Deploy Command Usage

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Displays the help message for the 'deploy.sh' script, which automates LSST development environment setup.

```text
usage: deploy.sh [-2|-3] [-b] [-h]

```

--------------------------------

### Install curl and patch on Debian/Ubuntu

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/prereqs.rst

Installs 'curl' and 'patch' utilities on Debian/Ubuntu systems using apt-get. This command first updates the package list.

```bash
sudo apt-get update && sudo apt-get install curl patch
```

--------------------------------

### Example LSST Docker Image Tag

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

An example of a specific LSST Science Pipelines Docker image tag, corresponding to CentOS 7, lsst_distrib, and a particular EUPS tag.

```text
7-stack-lsst_distrib-{{ release_eups_tag }}
```

--------------------------------

### Query Datasets with Multiple Collections (CLI)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Demonstrates how to query for a specific dataset type across multiple collections using the butler command-line tool. This example shows the raw output before duplicate removal.

```sh
$ butler query-datasets /repo/main calexp \
    --collections HSC/runs/RC2/w_2021_06/DM-28654 \
    --collections HSC/runs/RC2/w_2021_02/DM-28282 \
    --where "instrument='HSC' AND visit=1228 AND detector=40"
```

--------------------------------

### Verify RC2_SUBSET_DIR Environment Variable

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/data-setup.rst

Check that the `$RC2_SUBSET_DIR` environment variable correctly points to the path of the `rc2_subset` directory after setup.

```bash
echo $RC2_SUBSET_DIR
```

--------------------------------

### Alternative: Get PhotoCalib from deepCoadd_calexp

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/multiband-analysis.rst

Demonstrates an alternative method to obtain the 'lsst.afw.image.PhotoCalib' object by first retrieving the 'deepCoadd_calexp' dataset and then calling its 'getPhotoCalib()' method.

```python
rCoaddCalexp = butler.get('deepCoadd_calexp', band='r', tract=9813, patch=41)
rCoaddPhotoCalib = rCoaddCalexp.getPhotoCalib()
```

--------------------------------

### Example Data ID Output

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Illustrates the format of data IDs returned when querying for 'calexp' datasets with the 'HSC-R' physical filter.

```text
{band: 'r', instrument: 'HSC', detector: 41, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 42, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 47, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 49, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 50, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 58, physical_filter: 'HSC-R', visit_system: 0, visit: 23718}
{band: 'r', instrument: 'HSC', detector: 41, physical_filter: 'HSC-R', visit_system: 0, visit: 1214}
{band: 'r', instrument: 'HSC', detector: 42, physical_filter: 'HSC-R', visit_system: 0, visit: 1214}
```

--------------------------------

### Clone Sample Data Repository

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/data-setup.rst

Clone the `rc2_subset` package, which contains sample Hyper Suprime-Cam (HSC) exposure data for the tutorial. Ensure Git LFS is installed and configured for LSST.

```bash
git clone -b {{ release_eups_tag }} https://github.com/lsst/rc2_subset
```

--------------------------------

### Pipetask Report Quanta Table Example

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

This table displays the status of task quanta for a specific reprocessing run. It shows counts for Unknown, Successful, Blocked, Failed, and Wonky states for each task.

```sh
             Task          Unknown Successful Blocked Failed Wonky TOTAL EXPECTED
    ---------------------- ------- ---------- ------- ------ ----- ----- --------
                  makeWarp       0       6596       0      0     0  6596     6596
     selectDeepCoaddVisits       0        294       0      0     0   294      294
    selectGoodSeeingVisits       0        294       0      0     0   294      294
               templateGen       0        288       0      6     0   294      294
             assembleCoadd       0        280       0     14     0   294      294
                 detection       0        280      14      0     0   294      294
    healSparsePropertyMaps       0          1       5      0     0     6        6
           mergeDetections       0         36      13      0     0    49       49
                   deblend       0         36      13      0     0    49       49
                   measure       0        216      78      0     0   294      294
         mergeMeasurements       0         36      13      0     0    49       49
           forcedPhotCoadd       0        216      78      0     0   294      294
          writeObjectTable       0         36      13      0     0    49       49
      transformObjectTable       0         36      13      0     0    49       49
    consolidateObjectTable       0          0       1      0     0     1        1
        matchObjectToTruth       0          0       1      0     0     1        1
      compareObjectToTruth       0          0       1      0     0     1        1
```

--------------------------------

### Run LSST Docker Container

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

Starts an LSST Science Pipelines Docker container in detached mode, mounting the current working directory. The container is named 'lsst'.

```bash
docker run -itd -v `pwd`:/home/lsst/mnt --name lsst lsstsqre/centos:7-stack-lsst_distrib-{{ release_eups_tag }}
```

--------------------------------

### Create and Navigate to Demo Directory

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/demo.rst

Use these bash commands to create a directory for the demo data and change into it.

```bash
mkdir -p demo_data
cd demo_data
```

--------------------------------

### List a specific set up package

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/package-development.rst

Review the version of a single package that is currently set up in the active Science Pipelines software stack.

```bash
eups list <package name>
```

--------------------------------

### Set up a package in the EUPS stack

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/package-development.rst

From the package's directory, set up the package itself in the EUPS stack. This makes the package available for building and use.

```bash
setup -r . -t $USER
```

--------------------------------

### List Currently Set Up Packages

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/setup.rst

Run 'eups list -s' to display all packages that are currently set up and active in your shell session.

```bash
eups list -s
```

--------------------------------

### Download and run lsstinstall

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstinstall.rst

Download the lsstinstall script, make it executable, and run it with a specified release tag.

```bash
curl -OL https://ls.st/lsstinstall
chmod u+x lsstinstall
./lsstinstall -T {{ release_eups_tag }}
```

--------------------------------

### Import the Display Framework

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Import the necessary module for astronomical data visualization. This framework supports multiple display backends.

```python
import lsst.afw.display as afwDisplay
```

--------------------------------

### Set Permissions for Shared Installations

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstinstall.rst

Adjust permissions for shared LSST installations to allow group access and prevent conda from recording user environment information.

```bash
chmod go-w ${CONDA_EXE%bin/conda}/pkgs/urls*
```

--------------------------------

### Download and Extract Demo Project (Specific Version)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/demo.rst

Download a specific version of the demo project using curl and extract it. Replace {{ pipelines_demo_ref }} with the desired Git reference.

```bash
curl -L https://github.com/lsst/pipelines_check/archive/{{ pipelines_demo_ref }}.tar.gz | tar xvzf -
cd pipelines_check-{{ pipelines_demo_ref }}
```

--------------------------------

### Import Good Seeing Templates

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/dc2-guide.rst

Use the 'import_templates.py' script to import good seeing templates. This script requires the Butler repository path, a collection name, and a WHERE clause to filter data.

```bash
import_templates.py \
    -b /repo/dc2 \
    -t $MY_COLLECTION \
    -w "skymap='DC2_cells_v1' and tract=4431 and patch IN (9,10,16,17) and band='r'"
```

--------------------------------

### Upgrade lsstsw Installation

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Upgrade an existing lsstsw installation by pulling the latest changes, redeploying the environment, and rebuilding the Science Pipelines codebase. Ensure the shell environment is correctly configured after the upgrade.

```bash
git pull
bin/deploy
rebuild -u -t current lsst_distrib
```

--------------------------------

### Get Static Dimensions

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Retrieves a list of static dimensions available in the Butler repository. This is a basic exploration command.

```python
>>> print(butler.dimensions.getStaticDimensions())
```

--------------------------------

### Query Datasets with findFirst (CLI)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Shows how to use the --find-first flag with the butler command-line tool to retrieve only the first matching dataset from the ordered list of collections. This is useful for avoiding duplicate results when collections are chained.

```sh
$ butler query-datasets /repo/main calexp --find-first \
    --collections HSC/runs/RC2/w_2021_06/DM-28654 \
    --collections HSC/runs/RC2/w_2021_02/DM-28282 \
    --where "instrument='HSC' AND visit=1228 AND detector=40"
```

--------------------------------

### Create Butler Client

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Initialize a Butler client to access data from the specified repository and collection. Ensure the LSST environment is set up.

```python
from lsst.daf.butler import Butler
collection = f"u/{os.environ['USER']}/single_frame"
butler = Butler('SMALL_HSC', collections=collection, instrument='HSC')
```

--------------------------------

### Get source catalog column names

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Extract and print only the names of the columns present in the source catalog schema.

```python
print(src.getSchema().getNames())
```

--------------------------------

### Get Dimension Schema

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Prints the schema for a specific dimension table, such as 'exposure'. This helps understand the available metadata fields.

```python
>>> print(butler.dimensions["exposure"].schema)
exposure:
  instrument: string
  id: int
  physical_filter: string
  obs_id: string
  exposure_time: float
      Duration of the exposure with shutter open (seconds).
  dark_time: float
      Duration of the exposure with shutter closed (seconds).
  observation_type: string
      The observation type of this exposure (e.g. dark, bias, science).
  observation_reason: string
      The reason this observation was taken. (e.g. science, filter scan,
      unknown).
  day_obs: int
      Day of observation as defined by the observatory (YYYYMMDD
      format).
  seq_num: int
      Counter for the observation within a larger sequence. Context of
      the sequence number is observatory specific. Can be a global
      counter or counter within day_obs.
  group_name: string
      String group identifier associated with this exposure by the
      acquisition system.
  group_id: int
      Integer group identifier associated with this exposure by the
      acquisition system.
  target_name: string
      Object of interest for this observation or survey field name.
  science_program: string
      Observing program (survey, proposal, engineering project)
      identifier.
  tracking_ra: float
      Tracking ICRS Right Ascension of boresight in degrees. Can be NULL
      for observations that are not on sky.
  tracking_dec: float
      Tracking ICRS Declination of boresight in degrees. Can be NULL for
      observations that are not on sky.
  sky_angle: float
      Angle of the instrument focal plane on the sky in degrees. Can be
      NULL for observations that are not on sky, or for observations
      where the sky angle changes during the observation.
  zenith_angle: float
      Angle in degrees from the zenith at the start of the exposure.
  timespan: timespan
```

--------------------------------

### Get metadata for a specific schema column

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Find and print metadata for a specific column in the source catalog schema, such as 'calib_psf_used'.

```python
print(src.schema.find("calib_psf_used"))
```

--------------------------------

### Get Instrumental Flux Units

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/multiband-analysis.rst

Access the schema of the 'iSources' dataset to find the 'base_PsfFlux_instFlux' field and retrieve its units, which are counts.

```python
iSources.getSchema().find('base_PsfFlux_instFlux').field.getUnits()
```

--------------------------------

### Initialize Butler with Data Collections

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/multiband-analysis.rst

Initialize the Butler to access data, specifying the collections where the forced photometry measurement catalogs are stored. Ensure the 'lsst_distrib' package is set up in your environment.

```python
import os
from lsst.daf.butler import Butler
collections = [f"u/{os.environ['USER']}/objects", f"u/{os.environ['USER']}/coadd_meas"]
butler = Butler('SMALL_HSC', collections=collections)
```

--------------------------------

### Get source catalog column values as Numpy array

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Access the values of a specific column from a SourceCatalog as a Numpy array for further analysis.

```python
print(src['base_PsfFlux_instFlux'])
```

--------------------------------

### Visualize Quantum Graph using Graphviz

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/singleframe.rst

Convert the generated dot file into a PDF for visualization using the graphviz 'dot' command.

```bash
dot single_frame.dot -Tpdf -o single_frame.pdf
```

--------------------------------

### Command-line to query RUN collections matching a pattern

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Command-line equivalent to query RUN collections that match a pattern, potentially including datasets from CALIBRATION collections.

```sh
$ butler query-collections /repo/main --collection-type RUN \
        HSC/calib/gen2/20200115/*
```

--------------------------------

### Create a Simple Movie from Calibrated Exposures

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Iterates through calibrated exposures and displays each one sequentially to create a simple movie. Requires importing necessary modules and initializing the display. A short sleep is included between frames.

```python
import os
from time import sleep
import lsst.afw.display as afwDisplay

display = afwDisplay.getDisplay()
for ref in butler.registry.queryDatasets('calexp', physical_filter='HSC-R'):
    calexp = butler.get(ref)
    display.image(calexp)
    sleep(1)
```

--------------------------------

### Get Documentation for Classification Column

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/multiband-analysis.rst

This Python snippet retrieves the documentation string for the 'base_ClassificationExtendedness_value' column, which indicates if a source is extended (galaxy) or point-like (star).

```python
iSources.schema.find('base_ClassificationExtendedness_value').field.getDoc()
```

--------------------------------

### Manually Tag Packages

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Manually tag installed packages with a specific tag, such as 'current', using the 'eups tags' command. This is an alternative to the automatic tagging by 'rebuild'.

```bash
eups tags --clone bNNNN current
```

--------------------------------

### Load calibration dataset using Butler.get

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Directly loads the appropriate calibration dataset for a given raw data ID using Butler.get. This is the recommended approach for loading calibration data.

```python
flat = butler.get(
    "flat",
    instrument="HSC", exposure=903334, detector=0,
    collections="HSC/calib"
)
```

--------------------------------

### Get All Column Names from Schema

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/multiband-analysis.rst

Retrieve a list of all available column names from a table's schema using this Python command. This helps in exploring the table structure.

```python
refTable.schema.getNames()
```

--------------------------------

### Retrieve source catalog from Butler

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Get the source catalog generated by the single frame processing pipeline using the Butler. This catalog contains information about detected sources.

```python
import os
src = butler.get('src', visit=23718, detector=41)
```

--------------------------------

### Running Pipetask Report

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Use this command to analyze quantum graphs and store a full summary report. Ensure you provide the Butler repository and the sequence of quantum graphs.

```sh
pipetask report --full-output-filename <path/to/output_file>.json --force-v2 REPO QGRAPHS
```

--------------------------------

### Get and display mask plane colors

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Retrieve a dictionary of mask plane names and their corresponding colors as rendered by the display framework. This is useful for interpreting mask data.

```python
mask = calexp.getMask()
for maskName, maskBit in mask.getMaskPlaneDict().items():
    print('{}: {}'.format(maskName, display.getMaskPlaneColor(maskName)))
```

--------------------------------

### Deploy lsstsw

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Clone the lsstsw repository, navigate into it, deploy the environment, and configure the shell environment variables. Use the .csh version for csh/tcsh shells.

```bash
git clone https://github.com/lsst/lsstsw.git
cd lsstsw
./bin/deploy
source bin/envconfig
```

```bash
source bin/envconfig.csh
```

--------------------------------

### Release Notes Template

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/releases/README.md

Template for creating release notes, including versioning, build information, and sections for changes.

```rst
.. _release-latest:
.. _release-vNN-N:

##################################################
LSST Science Pipelines XX.Y.Z Release (YYYY-MM-DD)
##################################################

.. toctree::
   :hidden:

   tickets/vXX_Y_Z

+-------------------------------------------+------------+
| Source                                    | Identifier |
+===========================================+============+
| Git tag                                   | XX.Y.Z     |
+-------------------------------------------+------------+
| :doc:`EUPS distrib </install/newinstall>` | vXX_Y_Z  |
+-------------------------------------------+------------+

This release is based on the ``w_YYYY_NN`` weekly build.

These release notes highlight significant changes to the Science Pipelines codebase which are likely to be of wide interest.
For a complete list of changes made, see :doc:`tickets/vXX_Y_Z`.

- :ref:`release-vXX-Y-Z-functionality`
- :ref:`release-vXX-Y-Z-interface`
- :ref:`release-vXX-Y-Z-pending-deprecations`
- :ref:`release-vXX-Y-Z-deprecations`

*See also:*

.. todo::

   Insert link to the CMR when it is available.

.. todo::

   Insert link to Doxygen documentation when the release is frozen.

- :doc:`Installation instructions </install/index>`
- :doc:`Known issues </known-issues>`
- `Characterization Metric Report (DMTR-NNN) <https://ls.st/DMTR-NNN>`_
- `Doxygen Documentation`__

__ http://doxygen.lsst.codes/stack/doxygen/xlink_master_XXXX/


.. _release-vXX-Y-Z-functionality:

Major New Features
==================

.. Insert list of :ref:`..` to individual items

.. Add items here

.. _release-vXX-Y-Z-interface:

Significant Interface Changes
=============================

.. Insert list of :ref:`..` to individual items

.. Add items here

.. _release-vXX-Y-Z-pending-deprecations:

Pending Deprecations
====================

.. That is, items that we anticipate deprecating in the next release
.. Insert list of :ref:`..` to individual items

.. Add items here

.. _release-vXX-Y-Z-deprecations:

Deprecations
============

.. That is, items that are deprecated in this releas
.. Insert list of :ref:`..` to individual items

.. Add items here

```

--------------------------------

### Run Docker Container in Detached Mode

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/docker.rst

Starts a Docker container in detached mode with a specified name ('lsst'). The container runs in the background and does not immediately open a shell.

```bash
docker run -itd --name lsst lsstsqre/centos:7-stack-lsst_distrib-{{ release_eups_tag }}
```

--------------------------------

### Create SQLite APDB

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/dc2-guide.rst

Use the 'apdb-cli' command to create an empty SQLite APDB and set its instrument metadata. This is suitable for smaller data volumes.

```bash
apdb-cli create-sql sqlite:////path/to/my/database/apdb.sqlite3 apdb_config.yaml
apdb-cli metadata set apdb_config.yaml instrument LSSTCam-imSim

```

--------------------------------

### Create Butler Object for HSC Data

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/data-setup.rst

Instantiate a Butler object in Python to manage data. This example constructs a Butler for a local repository located within the `rc2_subset` directory.

```python
from lsst.daf.butler import Butler
import os
repo_path = os.path.join(os.environ['RC2_SUBSET_DIR'], 'SMALL_HSC')
butler = Butler(repo_path)
```

--------------------------------

### Apply asinh stretch with explicit thresholds

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Manually set the black and white thresholds for the 'asinh' stretch when automatic scaling is not desired.

```python
display.scale("asinh", -1, 30)
```

--------------------------------

### Access Skymap and Patch Information

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/coaddition.rst

Use the Butler to access the skymap and retrieve tract and patch information. This snippet shows how to get the skymap, generate a tract, and find the index of a specific patch.

```python
from lsst.daf.butler import Butler
butler = Butler('SMALL_HSC')
skymap = butler.get('skyMap', skymap='hsc_rings_v1', collections='HSC/RC2/defaults')
tractInfo = skymap.generateTract(9813)
patch = tractInfo[41]
patch.getIndex()
--> (5, 4)
```

--------------------------------

### Source Environment Configuration (csh/tcsh)

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/lsstsw.rst

Use this command to set up the LSST development environment if you are using csh or tcsh shells.

```bash
source bin/envconfig.csh
```

--------------------------------

### Retrieve a Calibrated Exposure Dataset

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Retrieve a specific 'calexp' dataset using the Butler's 'get' method, specifying the dataset type and relevant data ID components like visit and detector.

```python
import os
calexp = butler.get('calexp', visit=23718, detector=41)
```

--------------------------------

### Submit BPS Run

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/dc2-guide.rst

Use this command to submit a BPS run configuration file. Ensure the configuration file is correctly set up with necessary input collections.

```bash
bps submit ApPipe-DC2-bps.yaml
```

--------------------------------

### Dataset Query for Coaddition Subset

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/coaddition.rst

Specify a subset of data for coaddition, including tract, skymap, and patch information. This example filters for tract 9813, skymap 'hsc_rings_v1', and patches 38, 39, 40, 41.

```bash
-d "tract = 9813 AND skymap = 'hsc_rings_v1' AND patch in (38, 39, 40, 41)"
```

--------------------------------

### Query Collections in Data Repository

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/middleware/faq.rst

Use 'butler query-collections' to get a high-level view of a data repository's contents. The '--chains=tree' format provides a hierarchical view. It's recommended to use wildcards or specific paths to avoid overwhelming results in large repositories.

```sh
$ butler query-collections /repo/main HSC/runs/RC2/* --chains=tree
                 Name                      Type
-------------------------------------- -----------
HSC/runs/RC2/w_2021_02/DM-28282/sfm    RUN
HSC/runs/RC2/w_2021_02/DM-28282/rest   RUN
HSC/runs/RC2/w_2021_06/DM-28654        CHAINED
  HSC/runs/RC2/w_2021_06/DM-28654/rest RUN
  HSC/runs/RC2/w_2021_06/DM-28654/sfm  RUN
  HSC/raw/RC2/9615                     TAGGED
  HSC/raw/RC2/9697                     TAGGED
  HSC/raw/RC2/9813                     TAGGED
  HSC/calib/gen2/20180117              CALIBRATION
  HSC/calib/DM-28636                   CALIBRATION
  HSC/calib/gen2/20180117/unbounded    RUN
  HSC/calib/DM-28636/unbounded         RUN
  HSC/masks/s18a                       RUN
  skymaps                              RUN
  refcats/DM-28636                     RUN
HSC/runs/RC2/w_2021_02/DM-28282        CHAINED
  HSC/runs/RC2/w_2021_02/DM-28282/rest RUN
  HSC/runs/RC2/w_2021_02/DM-28282/sfm  RUN
  HSC/raw/RC2/9615                     TAGGED
  HSC/raw/RC2/9697                     TAGGED
  HSC/raw/RC2/9813                     TAGGED
  HSC/calib/gen2/20180117              CALIBRATION
  HSC/calib/DM-28636                   CALIBRATION
  HSC/calib/gen2/20180117/unbounded    RUN
  HSC/calib/DM-28636/unbounded         RUN
  HSC/masks/s18a                       RUN
  skymaps                              RUN
  refcats/DM-28636                     RUN
HSC/runs/RC2/w_2021_06/DM-28654/sfm    RUN
HSC/runs/RC2/w_2021_06/DM-28654/rest   RUN
```

--------------------------------

### Run the AP Pipeline

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/dc2-guide.rst

Execute the AP Pipeline with specified parameters, including input/output collections, pipeline configuration, and APDB configuration. Ensure the APDB exists and is empty before running.

```bash
pipetask run -j 4 -b /repo/dc2 -d "skymap='DC2_cells_v1' AND band='r'" -i u/USERNAME/OUTPUT-COLLECTION-1,u/mrawls/DM-34827/defaults/4patch_4431 -o u/USERNAME/OUTPUT-COLLECTION-2 -p $AP_PIPE_DIR/pipelines/LSSTCam-imSim/ApPipe.yaml -c parameters:apdb_config=apdb_config.yaml
```

--------------------------------

### Clone an LSST package repository

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/install/package-development.rst

Use Git to clone the package you want to work on from the LSST GitHub organization. This is the first step before setting up and building the package.

```bash
git clone https://github.com/lsst/pipe_tasks
cd pipe_tasks
```

--------------------------------

### Apply asinh stretch with zscale algorithm

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/display.rst

Use the 'asinh' stretch with the 'zscale' algorithm to automatically determine black and white thresholds for image display.

```python
display.scale("asinh", "zscale")
```

--------------------------------

### Display AP Pipeline YAML

Source: https://github.com/lsst/pipelines_lsst_io/blob/main/getting-started/dc2-guide.rst

Display the YAML configuration file for the AP Pipeline using the 'cat' command.

```bash
cat $AP_PIPE_DIR/pipelines/LSSTCam-imSim/ApPipe.yaml
```