pyvisgen 0.9.0

Installation

You can install the necessary packages in a conda environment of your choice by executing

$ pip install -e .

Usage

There are 3 possible modes at the moment: simulate (default), slurm, and gridding. simulate and slurm both utilize the RIME formalism for creating visibilities data. With the option gridding, these visibilities get gridded and prepared as input images for training a neural network from the radionets framework. The necessary options and variables are set with a toml file. An exemplary file can be found in config/data_set.toml.

$ pyvisgen_create_dataset --mode=simulate some_file.toml

In the examples directory, you can find introductory jupyter notebooks which can be used as an entry point.

Input images

As input images for the RIME formalism, we use GAN-generated radio galaxies created by Rustige et. al. and Kummer et. al.. Below, you can see four example images consisting of FRI and FRII sources.

Any image can be used as input for the formalism, as long as they are stored in the h5 format, generated with h5py.

RIME

Currently, we use the following expression for the simulation process:

$$\mathbf{V}_{\mathrm{pq}}(l, m) = \sum_{l, m} \mathbf{E}_{\mathrm{p}}(l, m) \mathbf{K}_{\mathrm{p}}(l, m) \mathbf{B}(l, m) \mathbf{K}^{H}_{\mathrm{q}}(l, m) \mathbf{E}^{H}_{\mathrm{q}}(l, m)$$

Here, $\mathbf{B}(l, m)$ corresponds to the source distribution, $\mathbf{K}(l, m) = \exp(-2\pi\cdot i\cdot (ul + vm))$ represents the phase delay, and $\mathbf{E}(l, m) = \mathrm{jinc}\left(\frac{2\pi}{\lambda}d\cdot \theta_{lm}\right)$ the telescope properties, with $\mathrm{jinc(x)} = \frac{J_1(x)}{x}$ and $J_1(x)$ as the first Bessel function. An exemplary result can be found below.

Visualization of Jones matrices

In this section, you can see visualizations of the matrices $\mathbf{E}(l, m)$ and $\mathbf{K}(l, m)$.

Visualization of the $\mathbf{E}$ matrix

Visualization of the $\mathbf{K}$ matrix