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
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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