Preparation of Running PFS 2D DRP

Environment Setup

First, set up the 2D DRP environment:

source $WORKDIR/$(whoami)/pfs/stack_30/loadLSST.bash
setup pfs_pipe2d

(Optional) Only when you are going to install curated calibs, and you are using a shared installation on a server, you will need to set up a local drp_pfs_data as follows:

setup -jr $WORKDIR/$(whoami)/packages/drp_pfs_data

Repository Setup

The data repository is a directory containing data products and configuration files for the Gen3 middleware. For this tutorial, we will store the data repository in the $DATASTORE directory on a local disk. However, the LSST Gen3 middleware supports storing data products on remote storage and various cloud services.

The first step is to make a copy of the default butler.yaml:

cp $OBS_PFS_DIR/gen3/butler.yaml $WORKDIR/$(whoami)/data/butler.yaml

This file specifies two configuration files that are important for the Gen3 middleware. butler.yaml contains the configuration for the data butler, specifying the registry and how the pipeline will read and write the various data products.

Most of this can be ignored by the pipeline user, except for the registry section, which specifies the location of the registry database. The default is to use a SQLite database in the repository directory (suitable for small-scale testing without extensive parallelization), but it is recommended to use a PostgreSQL database for shared data repositories or if you will be doing intensive processing with many cores.

To use a PostgreSQL database, change the registry section as follows:

registry:
  db: postgresql+psycopg2://dbserver:5432/dbname

dimensions.yaml contains the configuration for the dimensions of the data products. Dimensions are a very important concept in the LSST Gen3 middleware, as they are used to specify data products and control the parallelization levels of different operations. Some important dimensions are:

  • instrument: the instrument that produced the data. The datastore can contain data from multiple instruments, but we probably only care about the PFS instrument. Usually this will be PFS for data from Subaru, but in the case of the simulated data in the integration test it is PFS-F (the F stands for fake).
  • visit: the visit number. This is the unique identifier for an exposure.
  • arm: the spectrograph arm: b, r, n, or m.
  • spectrograph: the spectrograph module: 1, 2, 3, or 4.
  • detector: the detector number in the camera configuration. You shouldn’t need to worry about this dimension, since it is specified by the combination of arm and spectrograph.
  • pfs_design_id: the unique identifier for the PFS design. Note the spelling (not pfsDesignId), as this is a database table name.
  • dither: the slit dither. This is a new dimension that was not present in the Gen2 pipeline, which is used in creating fiber flats.
  • cat_id: the unique identifier for the catalog. Again, notice the spelling (not catId). There is no obj_id dimension, as the large number of objects would overwhelm the database.
  • skymap, tract, patch: these specify the sky tessellation. They are currently unused in the pipeline, but might be used in the future.

Next, create this directory and set up the Gen3 configuration files:

export DATASTORE="$WORKDIR/$(whoami)/data/datastore"
butler create $DATASTORE --seed-config $WORKDIR/$(whoami)/data/butler.yaml --dimension-config $OBS_PFS_DIR/gen3/dimensions.yaml --override

Note

Please always copy the latest default butler.yaml and modify it accordingly every time there is an update to the pipeline.

Then, register the instrument with butler. This also copies the camera configuration into the repository:

butler register-instrument $DATASTORE lsst.obs.pfs.PrimeFocusSpectrograph

The following commands import the defects files (and any other “curated” calibration files) from the drp_pfs_data package (you’ll need to be using your own copy of drp_pfs_data, not the shared version):

makePfsDefects --mko
butler write-curated-calibrations $DATASTORE PFS --collection PFS/calib

This creates a CALIBRATION collection PFS/calib in the butler. It is more flexible as a CHAIN collection:

butler remove-collections $DATASTORE PFS/calib # This command will prompt for confirmation
butler collection-chain $DATASTORE PFS/calib PFS/calib/curated/19700101T000000Z,PFS/calib/curated/20200101T000000Z,PFS/calib/curated/20230401T000000Z,PFS/calib/curated/20230701T000000Z,PFS/calib/curated/20240815T000000Z,PFS/calib/unbounded

There are different kinds of collections:

  • RUN collection always associates the datasets.
  • CALIBRATION collections associate datasets with a timespan indicating the validity range.
  • CHAINED collections provide a search path through multiple collections.