This page is currently under development: text may change at any time! The content has not yet been reviewed for accuracy and completeness.
You must have the LSST Stack installed on your system (see LSST Stack Installation) to proceed. The commands listed in the code blocks below primarily assume you are using the bash shell; analogous commands for (t)csh should work as well. If you have not already done so, load the LSST environment: where
Load the LSST Environment
$INSTALL_DIR is the directory where the LSST Stack was installed.
You must have the LSST Stack installed on your system (see LSST Stack Installation) to proceed. The commands listed in the code blocks below primarily assume you are using the bash shell; analogous commands for (t)csh should work as well. If you have not already done so, load the LSST environment:
Process a Single Image File
obs_file Task Currently Broken
The obs_file task does not work for v8.0 of the LSST Stack. The recent upgrade for the CameraGeom library rendered the obs_file task incompatible with these underlying utilities. This will be fixed in a future release.
In the mean time, skip to Processing with a Repository.
obs_file package from the git repository.
This package is written purely in python, so it needs no compilation. Now setup the package in your current working directory:
It is handy to define an environment variable for the
/bin directory of this package, and helpful to browse the task command-line options:
Process the Image
While the task
processFile.py has a number of options, most defaults are acceptable for this example.
Here we will process an SDSS
fpC file from Stripe 82 (an input file included in the Installing the Stack demo). Specify an output subdirectory (cleverly named
output), and allow any existing configuration files to be overwritten. In this case, we need to set the gain and build a variance plane in order for the down-stream measurements to be performed.
Examine the Output Catalog
/output directory will be created if it did not exist before, and will include the following contents:
fpC-004192-r4-0300/src.fits FITS table contains the final catalog of 640 sources detected at 5-σ significance. Source brightnesses are measured in a variety of ways; the units are counts (corrected for gain) above background. Quality flags are given for each source, the meanings for which appears in the table header. The
fpC-004192-r4-0300.fits FITS MEF image is the fully qualified calexp image used in all of LSST processing, and includes image extensions for the science array, the quality mask, and the variance array.
Processing with a Repository
It is possible to process a single file with the LSST Stack using built-in data access mechanisms. You will need two things:
- a data repository that contains the input image(s) and a registry of the contents
- a directory containing Astrometry.net indexes for the sky coordinate range that covers your image
This example will describe the processing of an SDSS image in Stripe 82, for which the Astrometry.net indexes have already been built. If you want to process an image for another region of sky, see Building Astrometry.net Index Files for an example of how to build the indexes. The recipe for processing an image obtained with another camera is very similar (see., e.g., the tutorial Process PhoSim Images), provided the camera is one of those supported in the LSST Stack.
This example makes use of a script in the tutorials package, which you can clone from the code repository:
Begin by creating a working directory and a handy environment variable:
Create a Data Repository
A repository is basically a directory structure that is understood by the software, with contents that include the input images, a mapper (i.e., an indication to the software of which camera model to use), and a registry of the relevant metadata. Create a directory for the input data repository, an environment variable for that location, and the mapper:
We will use an image from the SDSS Stripe 82 (actually, one of the images used in the LSST Stack test demo) as input. Capture the field identifier in a file, and generate the URLs (using
genRetrieveList.py) with which to retrieve the data from the SDSS archive:
Note that using the above wget command preserves the organization of the input data from the SDSS archive, which is critical. The data consist of a number of files, including a mask and data quality information, which are needed for processing. Now setup some packages for processing, and create the registry for the data repository:
Install Astrometry.net Index Files
Install the pre-built Astrometry.net index files for SDSS Stripe 82, and setup the astrometry_net_data package to use these indexes.
Process the Image
Now fetch the processing configuration file (download:
processConfig.py) and place it in
$DEMO_DIR. This will set some default processing parameters that are appropriate for SDSS data. Finally, process the image and direct the output in the
/calexp_dir subdirectory (which will be created if necessary).
Examine the Output Catalog
The output is organized in a directory hierarchy similar to that of the input, namely by SDSS run/rerun/filter. The catalog of 648 sources detected at 5-σ significance is contained in:
which is a FITS binary table.