about 4 months ago - No comments
Well, it's finally here: HerMES: point source catalogues from deep Herschel-SPIRE observations, by yours truly and lots of other people otherwise known as "Al". Some of the catalogues are available here, if you're interested. The biggest challenge was confusion (about which there is a great deal of confusion). For example, in this HerMES image, everything you…
about 2 years ago - No comments
You (both of you) might well be interested in the new Astropython site, which looks excellent. Here's the site's own description: Research in astronomy includes the analysis of astronomical images, parsing and manipulation of large catalogs, statistical yet often visual inference, and the creation of data visualizations for publication and dissemination of results. The purpose…
about 2 years ago - 1 comment
Yesterday I said that the PRF for a map in Jy/beam (or similar) should be normalized so that that peak is 1. But this is true only for an idealised (not pixelated) PRF, or if the map has infinitesimally small pixels. If the pixels are larger than infinitesimal, as is generally the case, then the…
about 2 years ago - 2 comments
You have a map and you know what a point source looks like. How do you filter the map so that the value of each pixel is now the most likely flux of a point source centred on that pixel? (An isolated point source, to be more precise.) Easy. First, find , which is the…
about 2 years ago - No comments
You have an image. Each pixel has a value with some uncertainty. How do you visualize the uncertainty in each pixel? Like this: Here's the Python code import numpy as np from matplotlib import pyplot as plt class FlickerImage(object): def __init__(self, im, err): self.im = im.copy() self.err = err.copy() finite = np.isfinite(self.im + self.err)…
about 2 years ago - No comments
Here's an easy way to display FITS images (or any array) in DS9 using Python (with PyFITS, NumPy and Numdisplay, which is part of stsci_python). First launch DS9, then in Python: import numdisplay import pyfits arr = pyfits.getdata('file.fits') numdisplay.display(arr) Easy! Alternatively, the Kapteyn package seems excellent, and uses Python's matplotlib for displaying images. It requires…
about 2 years ago - No comments
Two methods of approximating a point-spread function in IDL: 1. StarFinder seems to do a great job at finding point sources in crowded fields. It includes a routine for generating the Airy pattern. For a 51 x 51 array, with the peak at [25, 25], and an FWHM of 8.0 pixels, this is the command:…
about 4 years ago - No comments
Now Python and IDL can talk to each other (okay, Python talks to IDL and IDL does what it's told), using pIDLy (pronounce as you please). I experimented with a few other solutions available online but couldn't get them to work. So I cobbled this one together with surprisingly little trouble, thanks largely to pexpect.