Skip to main content

Visibility sampler

Project description

vis_sample - Python Package for visibility sampling
====================================================

Purpose:
vis_sample allows you to sample visibilities from a user-supplied sky-brightness image.

(u,v) grid points can either be supplied by the user, or can be retrieved from a template uvfits file / measurement set.

The results can be output either to a uvfits file or returned back to the user (for scripting)

Parameters
__________
imagefile : the input sky brightness image, needs to be in a valid FITS format with units of DEG for the RA and DEC or a RADMC3D image.out file (ascii format)

for uv points use:
uvfile - uvfits file or measurement set with visibilities that the sky brightness will be interpolated to
OR
uu, vv - numpy arrays - they need to be in units of lambda (i.e. number of wavelengths)

mu_RA - right ascension offset from phase center in arcseconds

mu_DEC - declination offset from phase center in arcseconds

src_distance - distance to source in parsecs - only required for RADMC3D input images

gcf_holder - (optional) gcf_holder object returned by previous call to vis_sample (see below return_gcf).
If you use this option DO NOT feed in a uvfile or uu, vv arrays. They will be used by default and you'll see no speed increase

corr_cache - (optional parameter) 2D corr_cache array output by previous call to vis_sample (see below return_corr_cache).

outfile - name of optional output file, needs to have either a .uvfits or .ms extension consistent with extension of uvfile

verbose - prints all progress output and timing

return_gcf - (boolean) flag to return the gcf cache to allow faster interpolation for many models

return_corr_cache - (boolean) flag to return the correction function cache to allow faster interpolation for many models


Usage::

>> from vis_sample import vis_sample # import the vis_sample command

>> vis_sample(imagefile="my_model.fits", uvfile="data.uvfits", outfile='interp.uvfits') # sample my_model using data (u,v) points and output to interp.uvfits

>> interp_vis = vis_sample(imagefile="my_model.fits", uvfile="data.uvfits") # sample my_model using data (u,v) points, interp_vis stores visibilities

In the second usage, interp_vis is the "raw" output visibility, ie just a numpy array of size [n_visibilities, n_chans]

We can also output the caches for faster future usage:

>> interp, gcf_holder = vis_sample(imagefile="my_model.fits", uvfile="data.uvfits", return_gcf = True) # sample my_model using data (u,v) points, also store the gcf_holder

>> interp2 = vis_sample(imagefile="second_model.fits", gcf_holder = gcf_holder) # sample a second model using the same (u,v) points, this is faster now

>> interp, gcf_holder, corr_cache = vis_sample(imagefile="my_model.fits", uvfile="data.uvfits", return_gcf = True, return_corr_cache = True) # sample my_model using data (u,v) points, also store the gcf_holder and corr_cache

>> interp2 = vis_sample(imagefile="second_model.fits", gcf_holder = gcf_holder, corr_cache=corr_cache) # sample a second model using the same (u,v) points, this is faster now

=================================



Source Structure
------------

Contents::

vis_sampler
|-- vis_sample
| |-- classes.py : Contains class information for visibilities and sky brightness images
| |-- constants.py : Physical and numerical constants
| |-- file_handling.py : File I/O
| |-- gridding.py : Functions for the gcf and correction function
| |-- interpolation.py : Functions for interpolating visibilities
| |-- transforms.py : FFTs for sky brightness -> visibility
| |-- vis_sampler.py : Wraps everything together into vis_sample() function
| |
| |-- __init__.py
|
|-- README

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for vis-sample, version 0.2
Filename, size File type Python version Upload date Hashes
Filename, size vis_sample-0.2.tar.gz (14.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page