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 (though use of either --gen2 or --gen3 is highly recommended).

Status code

ap_verify.py returns 0 on success, and a non-zero value if there were any processing problems. In --gen2 mode, the status code is the number of data IDs that could not be processed, like for command-line tasks.

With both --gen2 and --gen3, 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.

--clean-run

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

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.

Note

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, --id <dataId>

Butler data ID.

Specify data ID to process. If using --gen2, this should use data ID syntax, such as --data-query "visit=12345 ccd=1..6 filter=g". If using --gen3, 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. For compatibility with the syntax used by command line tasks, this flag with no argument processes all data IDs.

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

Warning

The --id form of this argument is for consistency with Gen 2 command-line tasks, and is deprecated. It will be removed after Science Pipelines release 23.

--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.

--dataset-metrics-config <filename>

Input dataset-level metrics config. (Gen 2 only)

A config file containing a MetricsControllerConfig, which specifies which metrics are measured and sets any options. If this argument is omitted, config/default_dataset_metrics.py will be used.

Use --image-metrics-config to configure image-level metrics instead. For the Gen 3 equivalent to this option, see --pipeline. See also Configuring metrics for ap_verify.

Warning

Support for Gen 2 processing is deprecated and will be removed after Science Pipelines release 23.

--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.

--gen2

--gen3

Choose Gen 2 or Gen 3 processing.

These optional flags run either the Gen 2 pipeline (ApPipeTask), or the Gen 3 pipeline (apPipe.yaml). If neither flag is provided, the Gen 3 pipeline will be run.

Warning

Support for Gen 2 processing is deprecated and will be removed after Science Pipelines release 23.

-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. In Gen 3 mode, data ingestion may also be parallelized.

--image-metrics-config <filename>

Input image-level metrics config. (Gen 2 only)

A config file containing a MetricsControllerConfig, which specifies which metrics are measured and sets any options. If this argument is omitted, config/default_image_metrics.py will be used.

Use --dataset-metrics-config to configure dataset-level metrics instead. For the Gen 3 equivalent to this option, see --pipeline. See also Configuring metrics for ap_verify.

Warning

Support for Gen 2 processing is deprecated and will be removed after Science Pipelines release 23.

--metrics-file <filename>

Output metrics file. (Gen 2 only)

The template for a file to contain metrics measured by ap_verify, in a format readable by the lsst.verify framework. The string {dataId} shall be replaced with the data ID associated with the job, and its use is strongly recommended. If omitted, the output will go to files named after ap_verify.{dataId}.verify.json in the user’s working directory.

Warning

Support for Gen 2 processing is deprecated and will be removed after Science Pipelines release 23.

--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 both input and output repositories required for processing the data. The path may be absolute or relative to the current working directory.

-p, --pipeline <filename>

Custom ap_verify pipeline. (Gen 3 only)

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.

Note

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.

For the Gen 2 equivalent to this option, see --dataset-metrics-config and --image-metrics-config.