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

Like command-line tasks, ap_verify.py returns the number of data IDs that could not be processed (i.e., 0 on a complete success). However, an uncaught exception causes ap_verify.py to return an interpreter-dependent nonzero value instead (also as for command-line tasks).

Named arguments

Required arguments are --dataset and --output.

--id <dataId>

Butler data ID.

Specify data ID to process using data ID syntax. For example, --id "visit=12345 ccd=1..6 filter=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_name>

Input dataset designation.

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

The argument is a unique name for the dataset, which can be associated with a repository in the configuration file. See Datasets as input arguments for more information on dataset names.

Allowed names can be queried using the --help argument.

--dataset-metrics-config <filename>

Input dataset-level metrics config.

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. See also Configuring metrics for ap_verify.

-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.

--image-metrics-config <filename>

Input image-level metrics config.

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. See also Configuring metrics for ap_verify.

--metrics-file <filename>

Output metrics file.

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.

--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.