ap_verify command-line reference

This page describes the command-line arguments and environment variables used by ap_verify.py. See Running ap_verify from the command line for an overview.

Signature and syntax

The basic call signature of ap_verify.py is:

ap_verify.py --dataset DATASET --output WORKSPACE

These two arguments are mandatory, all others are optional.

Status code

ap_verify.py returns 0 on success, and a non-zero value if there were any processing problems.

An uncaught exception may cause ap_verify.py to return an interpreter-dependent nonzero value instead of the above.

Named arguments

Required arguments are --dataset and --output.


Rerun ap_verify in a clean Gen 3 run even if the workspace already exists.

By default, when ap_verify is run multiple times with the same --output workspace, the previous run collection is reused to avoid repeating processing. If this is undesirable (e.g., experimental config changes), this flag creates a new run, and the pipeline is run from the beginning. This flag has no effect if --output is a fresh directory.


The --clean-run flag does not reset the alert production database, as this is not something that can be done without knowledge of the specific database system being used. If the database has been written to by a previous run, clear it by hand before running with --clean-run.

-d, --data-query <dataId>

Butler data ID.

Specify data ID to process. This should use dimension expression syntax, such as --data-query "visit=12345 and detector in (1..6) and band='g'".

Multiple copies of this argument are allowed.

If this argument is omitted, then all data IDs in the dataset will be processed.

--dataset <dataset_package>

Input dataset package.

The input dataset is required for all ap_verify runs except when using --help.

The argument is the name of the Git LFS repository containing the dataset to process. The repository must be set up before running ap_verify.

This documentation includes a list of supported datasets.

--db, --db_url

Target Alert Production Database

A URI string identifying the database in which to store source associations. The string must be in the format expected by lsst.dax.apdb.ApdbConfig.db_url, i.e. an SQLAlchemy connection string. The indicated database is created if it does not exist and this is appropriate for the database type.

If this argument is omitted, ap_verify creates an SQLite database inside the directory indicated by --output.

-h, --help

Print help.

The help is equivalent to this documentation page, describing command-line arguments.

-j <processes>, --processes <processes>

Number of processes to use.

When processes is larger than 1 the pipeline may use the Python multiprocessing module to parallelize processing of multiple datasets across multiple processors.

--output <workspace_dir>

Output and intermediate product path.

The output argument is required for all ap_verify runs except when using --help.

The workspace will be created if it does not exist, and will contain the repository required for processing the data. The path may be absolute or relative to the current working directory.

-p, --pipeline <filename>

Custom ap_verify pipeline.

A pipeline definition file containing a custom verification pipeline. This pipeline must be specialized as necessary for the instrument and dataset being processed. If omitted, <dataset>/pipelines/ApVerify.yaml will be used.

The most common use for a custom pipeline is adding or removing metrics to be run along with the AP pipeline.


At present, ap_verify assumes that the provided pipeline includes the diaPipe task from the AP pipeline, and configures it on the fly. It will likely crash if this task is missing.