# Introduction to drive-casa¶

A Python package for scripting the NRAO CASA pipeline routines (casapy).

drive-casa provides an interface to allow dynamic interaction with CASA from a separate Python process, allowing utilization of CASA routines alongside other Python packages which may not easily be installed into the casapy environment.

For example, one can spawn an instance of casapy, send it some data reduction commands to run (while saving the logs for future reference), do some external analysis on the results, and then run some more casapy routines. All from within a standard Python script, and preferably from a virtualenv. This is particularly useful when you want to embed use of CASA within a larger pipeline which uses external Python libraries alongside CASA functionality.

drive-casa can be used to run plain-text casapy scripts directly; alternatively the package includes a set of convenience routines which try to adhere to a consistent style and make it easy to chain together successive CASA reduction commands to generate a casapy command-script programmatically; e.g.

importUVFITS -> Perform Clean on resulting MeasurementSet

is implemented like so:

ms = drivecasa.commands.import_uvfits(script, uvfits_path)
dirty_maps = drivecasa.commands.clean(script, ms, niter=0, threshold_in_jy=1,
other_clean_args=clean_args)


## Rationale¶

Newcomers to CASA should note that it is trivial to run simple Python scripts within the casapy environment, or even to launch casapy into a script directly from the command line, e.g.:

casapy --nologger -c hello_world.py


While this mostly works fine from a command line or within a shell script, things start to get messy if you want to run CASA functions alongside routines from external Python libraries.

casapy uses its own bundled-and-modified copy of the Python interpreter[*], so a first thought might be to try and install external libraries into the CASA environment directly, and then run everything via the casapy interpreter. Thanks to recent efforts, this is now possible. However it still breaks the virtualenv workflow, and requires that your external Python modules are compatible with the CASA-bundled version of Python.

Alternatively one can try to ‘break-out’ the casapy modules from the CASA environment, but this also requires binary compatibility and some monkeying around with embedded paths as detailed in this post from Peter Williams.

At a pinch, you might be tempted to try dumping CASA command scripts to file and then spawning a casapy instance via subprocess. Don’t. This was how drive-casa got started, and I quickly ran into issues with casapy filling the stdin / stdout pipe buffers and causing the whole process to freeze up.

Which leads us to the drive-casa approach - emulate terminal interaction with casapy via use of pexpect. drive-casa can be installed along with any other Python packages in the usual Python package fashion, since we only interface with casapy indirectly via the command line. The downside is that data has to be written to file to transfer it between the standard Python script and the casapy environment, but it brings some added benefits:

Error handling
CASA tasks do not, as far as I can tell, return useful values as standard (or even throw exceptions). Instead, since the over-riding assumption is that the package will be run in interactive mode, all information is written to stderr as part of the logging output, making it hard to programmatically verify if a task has completed sucessfully. drive-casa attempts to solve this by parsing the log output for ‘SEVERE’ warnings - the user may then choose to throw an exception when it is sensible to do so.
Logging / reproducibility
If scripting the reduction of large amounts of data in batches, it is often useful to record logging information along with the data output, both for purposes of debugging and data provenance. As far as I can tell, CASA does not provide an interface to control or redirect the logging output once the program has been instantiated. drive-casa can work-around this issue by simply restarting CASA with a fresh logging location specified for each dataset.
 [*] This provides dedicated functionality, such as displaying a logging window and providing access to plotting tools - useful in interactive usage but undesirable from a scripting perspective.

## Project status, licence and acknowledgement¶

drive-casa is BSD licensed. The package is now in use by a few people other than myself, and can reasonably be used ‘in production’. Any bug-fixes or interface changes should be accompanied by a version increment, so you can be assured of stability by specifying the PyPI version. I’d be interested to hear if others find it useful, and welcome any bug reports or pull requests. Any major changes should be recorded in the change-log.

If you make use of drive-casa in work leading to a publication, I ask that you cite Staley and Anderson (2015) and the relevant ASCL entry.

## Installation¶

Requirements:

• A working installation of casapy.
• pexpect (As listed in requirements.txt, installed automatically when using pip.)

drive-casa is pip installable, simply run:

pip install drive-casa


Warning

Multiprocessing bug with pexpect 3.3:

During 2015, the default version of pexpect available on PyPI was 3.3. If you wish to use drive-casa in a parallel-processing context, you should beware of this bug which means pexpect 3.3 is broken under multiprocessing. Fortunately, both the older pexpect 2.4 and the latest pexpect 4.0.1 seem to work fine.

## Developer setup¶

Those wanting to modify the source will need a git checkout, followed by a git-submodule checkout to grab the test-data for the unittests. So a setup script might look like this:

git clone git@github.com:timstaley/drive-casa.git
cd drive-casa
git submodule init
git submodule update
pip install -r requirements # (grab pexpect)
cd tests
nosetests -sv


## Documentation¶

Reference documentation can be found at http://drive-casa.readthedocs.org, or generated directly from the repository using Sphinx.

## Usage¶

Creating an instance of the drivecasa.interface.Casapy class will start up casapy in the background, awaiting instruction. Class init arguments determine details such as where to find casapy, where to write the casapy logfile, etc. The drivecasa.interface.Casapy.run_script() and drivecasa.interface.Casapy.run_script_from_file() commands can then be used to send casapy a list of commands or a script to execute (through use of the casapy execfile function). Logging output from the commands executed is returned for inspection.

You are free to create the casapy scripts by any method you like, but a number of convenience functions are provided that aim to make this process simpler and more programmatic. These functions try to adhere to a consistent calling signature, as detailed under drivecasa.commands.

## A Brief Example¶

Assuming you already have a uv-measurement dataset in uvFITS format, basic usage might go something like this:

from __future__ import print_function
import drivecasa
casa = drivecasa.Casapy()
script = []
uvfits_path = '/path/to/uvdata.fits'
vis = drivecasa.commands.import_uvfits(script, uvfits_path, out_dir='./')
clean_args = {
"imsize": [512, 512],
"cell": ['5.0arcsec'],
"weighting": 'briggs',
"robust": 0.5,
}
dirty_maps = drivecasa.commands.clean(script, vis, niter=0, threshold_in_jy=1,
other_clean_args=clean_args)
dirty_map_fits_image = drivecasa.commands.export_fits(script, dirty_maps.image)
print(script)
casa.run_script(script)


After which, there should be a dirty map converted to FITS format waiting for you.

The examples folder also contains example scripts demonstrating how to simulate and image a dataset from scratch.