.. py:currentmodule:: lsst.ap.verify .. program:: ap_verify.py .. _ap-verify-running: ####################################### Running ap_verify from the command line ####################################### :command:`ap_verify.py` is a Python script designed to be run on both developer machines and verification servers. This page describes the most common options used to run ``ap_verify``. For more details, see the :doc:`command-line-reference` or run :option:`ap_verify.py -h`. This guide assumes that the dataset(s) to be run are already installed on the machine. If this is not the case, see :doc:`datasets-install`. .. _ap-verify-run-output-gen3: How to run ap_verify in a new workspace ======================================= Using the `Cosmos PDR2`_ CI dataset as an example, first setup the dataset, if it isn't already. .. _Cosmos PDR2: https://github.com/lsst/ap_verify_ci_cosmos_pdr2/ .. prompt:: bash setup [-r] ap_verify_ci_cosmos_pdr2 You will need to setup the dataset once each session. You can then run :command:`ap_verify.py` as follows. .. prompt:: bash ap_verify.py --dataset ap_verify_ci_cosmos_pdr2 --data-query "visit in (59150, 59160)" -j4 --output workspaces/cosmos/ Here the inputs are: * :command:`ap_verify_ci_cosmos_pdr2` is the ``ap_verify`` :ref:`dataset <ap-verify-datasets>` to process, * :command:`visit in (59150, 59160)` is the :ref:`data ID query <daf_butler_dimension_expressions>` to process, * :option:`-j` causes the ingest and processing pipelines to use 4 processes: choose a value appropriate for your machine; the system does not automatically determine how many parallel processes to use. while the output is: * :command:`workspaces/cosmos/` is the location where the pipeline will create a Butler repository along with other outputs such as the alert production database. This call will create a new directory at :file:`workspaces/cosmos`, ingest the Cosmos data into a new repository, then run visits 59150 and 59160 through the entire AP pipeline. It's also possible to run an entire dataset by omitting the :option:`--data-query` argument (as some datasets are very large, do this with caution): .. prompt:: bash ap_verify.py --dataset ap_verify_ci_cosmos_pdr2 -j4 --output workspaces/cosmos/ .. warning:: Some datasets require particular data queries in order to successfully run through the pipeline, due to missing data or other limitations. Check the ``README.md`` in each dataset's main directory for what additional arguments might be necessary. .. _ap-verify-run-ingest: How to run ingestion by itself ============================== ``ap_verify`` includes a separate program, :command:`ingest_dataset.py`, that ingests datasets into repositories but does not run the pipeline on them. This is useful if the data need special processing or as a precursor to massive processing runs. Running :command:`ap_verify.py` with the same arguments as a previous run of :command:`ingest_dataset.py` will automatically skip ingestion. Using the `Cosmos PDR2`_ dataset as an example, one can run ``ingest_dataset`` as follows: .. prompt:: bash ingest_dataset.py --dataset ap_verify_ci_cosmos_pdr2 -j4 --output workspaces/cosmos/ The :option:`--dataset`, :option:`--output`, :option:`-j`, and :option:`--processes` arguments behave the same way as for :command:`ap_verify.py`. Other options from :command:`ap_verify.py` are not available. .. _ap-verify-results-gen3: How to use measurements of metrics =================================================== After ``ap_verify`` has run, it will produce Butler datasets named ``metricValue_<metric package>_<metric>``. These can be queried, like any Butler dataset, using methods like `~lsst.daf.butler.Registry.queryDatasetTypes` and `~lsst.daf.butler.Butler.get`. .. note:: Not all metric values need have the same data ID as the data run through the pipeline. For example, metrics describing the full focal plane have a visit but no detector. Further reading =============== - :doc:`datasets-install` - :doc:`new-metrics` - :doc:`command-line-reference`