mwa-hyperbeam 0.3.2

Primary beam code for the Murchison Widefield Array (MWA) radio telescope.

mwa_hyperbeam

Primary beam code for the Murchison Widefield Array (MWA) radio telescope.

This code exists to provide a single correct, convenient implementation of Marcin Sokolowski's Full Embedded Element (FEE) primary beam model of the MWA, a.k.a. "the 2016 beam". This code should be used over all others. If there are soundness issues, please raise them here so everyone can benefit.

Usage

hyperbeam requires the MWA FEE HDF5 file. This can be obtained with:

wget http://ws.mwatelescope.org/static/mwa_full_embedded_element_pattern.h5

When making a new beam object, hyperbeam needs to know where this HDF5 file is. The easiest thing to do is set the environment variable MWA_BEAM_FILE:

export MWA_BEAM_FILE=/path/to/mwa_full_embedded_element_pattern.h5

(On Pawsey systems, this should be export MWA_BEAM_FILE=/pawsey/mwa/mwa_full_embedded_element_pattern.h5)

hyperbeam can be used by any programming language providing FFI via C. In other words, most languages. See Rust, C and Python examples of usage in the examples directory. A simple Python example is:

import mwa_hyperbeam
beam = mwa_hyperbeam.FEEBeam()
print(beam.calc_jones(0, 0.7, 167e6, [0]*16, [1]*16, True))

[ 1.73003520e-05-1.53580286e-05j -2.23184781e-01-4.51051073e-02j
 -1.51506097e-01-4.35034884e-02j -9.76099405e-06-1.21699926e-05j]

Installation

Python PyPI

If you're using Python version >=3.6:

pip install mwa_hyperbeam

Pre-compiled

Have a look at the GitHub releases page. There is a Python wheel for all versions of Python 3.6+, as well as shared and static objects for C-style linking. To get an idea of how to link hyperbeam, see the beam_calcs.c file in the examples directory.

Because these hyperbeam objects have the HDF5 library compiled in, the HDF5 license is also distributed.

From source

Prerequisites

• Cargo and a Rust compiler. rustup is recommended:

  https://www.rust-lang.org/tools/install

  The Rust compiler must be at least version 1.47.0:

  $ rustc -V
  rustc 1.47.0 (18bf6b4f0 2020-10-07)
  

• hdf5

  • Optional; use the hdf5-static feature.
  • Ubuntu: libhdf5-dev
  • Arch: hdf5

Clone the repo, and run:

cargo build --release

For usage with other languages, an include file will be in the include directory, along with C-compatible shared and static objects in the target/release directory.

To make hyperbeam without a dependence on a system HDF5 library, give the build command a feature flag:

cargo build --release --features=hdf5-static

This will automatically compile the HDF5 source code and "bake" it into the hyperbeam products, meaning that HDF5 is not needed as a system dependency. CMake version 3.10 or higher is needed to build the HDF5 source.

Python

To install hyperbeam to your currently-in-use virtualenv or conda environment, you'll need the Python package maturin (can get it with pip), then run:

maturin develop --release -b pyo3 --cargo-extra-args="--features python" --strip

If you don't have or don't want to install HDF5 as a system dependency, include the hdf5-static feature:

maturin develop --release -b pyo3 --cargo-extra-args="--features python,hdf5-static" --strip

Comparing with other FEE beam codes

Below is a table comparing other implementations of the FEE beam code. All benchmarks were done with unique azimuth and zenith angle pointings, and all on the same system. The CPU is a Ryzen 9 3900X, which has 12 cores and SMT (24 threads). All benchmarks were done in serial, unless indicated by "parallel". Python times were taken by running time.time() before and after the calculations. Memory usage is measured by running time -v on the command (not the time associated with your shell; this is usually at /usr/bin/time).

Code	Number of pointings	Duration	Max. memory usage
mwa_pb	500	98.8 ms	134.6 MiB
	100000	13.4 s	5.29 GiB
	1000000	139.8 s	51.6 GiB
mwa-reduce (C++)	500	115.2 ms	48.9 MiB
	10000	2.417 s	6.02 GiB
mwa_hyperbeam	500	30.8 ms	9.82 MiB
	100000	2.30 s	17.3 MiB
	1000000	22.5 s	85.6 MiB
mwa_hyperbeam (parallel)	1000000	1.73 s	86.1 MiB
mwa_hyperbeam (via python)	500	28.5 ms	35.0 MiB
	100000	4.25 s	51.5 MiB
	1000000	44.0 s	203.8 MiB
mwa_hyperbeam (via python, parallel)	1000000	3.40 s	203.2 MiB

Not sure what's up with the C++ code. Maybe I'm calling CalcJonesArray wrong, but it uses a huge amount of memory. In any case, hyperbeam seems to be roughly 10x faster.

Troubleshooting

Run your code with hyperbeam again, but this time with the debug build. This should be as simple as running:

cargo build

and then using the results in ./target/debug.

If that doesn't help reveal the problem, report the version of the software used, your usage and the program output in a new GitHub issue.

hyperbeam?

AERODACTYL used HYPER BEAM!