def contrastcurve(self,ann_space=0,darkpix=0.95,thresh=5): Returns list of radii and corresponding noise (thresh*sigma+mean background noise) and point sources (if any). It detects point sources by fitting gaussians over regions that have, on average, greater intensity than the noise and searches for peaks in those regions.
And it........... still doesn't work right.
Here's an example though, of what the contrast curve looks like for KIC109:
Interestingly, however, if I calculate magnitude using log instead of log base 10, I get this result:
This agrees much more closely with Justin's data. Weird! I still haven't figured out why.
By the way, Justin's data is this:
KIC109 3.7, 5.6, 7.8, 8.6, 8.6 at 0.25'', 0.5'', 1'', 2'', 4'' respectively.
(This is for thresh=3; mine are produced with thresh=5, but I ran the code again and this barely makes any difference).
-Start testing how my code behaves around images with bright point sources (yes I said I would do this today, but then the contrast curves looked funny...) while I try to figure out why my contrast curves are behaving this way.
-I actually learned something interesting today: If you have a function that returns multiple things in an array, it's much more efficient to do thing1, thing2, thing3=function()
as opposed to thing1, thing2 = function(), function even if you only want to save thing1 and thing2. This is probably because in the first case, Python will run the function once and save the corresponding variables, whereas in the second case, Python will run the function once, save the 0th element, and then run the function again and save the first. So I guess that ultimately what I learned is that if you have a function that takes a while to run, it's better to use the first case to save time, but if you have a function that's relatively fast for large amounts of data, but that returns a bunch of stuff, it's better to use the second case to save memory. If you have a slow function that returns lots of data.... well then good luck to you.