Bad Blogger

I've been a bad blogger this past week. Apologies for not posting. I could give excuses, but that would break two of my blog rules, so I'll quit while I am ahead (or behind as it were).

Here is a summary of where I am with this darn 3D correlation function. The Sloan data is in ra/dec/redshift coordinates. However, it is easier to calculate the 3d correlation function in x-y-z comoving coordinates. So I convert the data into comoving coordinates to calculate the correlation function. However the data lives in a ra/dec/redshift space (mask) and this corresponds to a non-boxlike x-y-z space. Therefore I need to apply the mask in ra/dec/redshift for both the randoms and the data, and then convert both to x-y-z to calculate the correlation function.

But the mock data I am currently testing my 3D correlation function with is, actually in x-y-z coordinates to begin with. This is causing an issue, because I am converting it to ra/dec/redshift, which results in non-uniform data distribution in ra/dec/redshift space (because the mock data, unlike the Sloan data, is a contiguous box in x-y-z space). However the randoms are generated in uniform ra/dec/redshift space and converted to x-y-z space (to match the Sloan mask). When I compare the mock data with the randoms they don't have the same masks because of this issue:

I think what I need to do is take the mock data in x-y-z, convert it to ra/dec/redshift and then apply a mask in that coordinate system. Then convert it back to x-y-z and use those points as my data, and then apply the same mask to the randoms. This will be more similar to what I will be doing with the Sloan data and should get my data and randoms to fall in the same location on my vector space.