Finding the correspondence of data from one data set in the other

Question

I have a catalogue of data and I want to use it in my MCMC code. What is crucial is the speed of implementation, in order to avoid slowing down my Markov ch

Answer 1

This is a perfect case where the scipy.spatial.cKDTree() class can be used to query all the points at once:

from scipy.spatial import cKDTree

k = cKDTree(data[:, 6:8]) # creating the KDtree using the Xpos and Ypos

xyCenters = np.array([[200.6, 310.9],
                      [300, 300],
                      [400, 400]])
print(k.query(xyCenters))
# (array([ 1.59740195,  1.56033234,  0.56352196]),
#  array([ 2662, 22789,  5932]))

where [ 2662, 22789, 5932] are the indices corresponding to the three closest points given in xyCenters. You can use these indices to get your ra and dec values very efficiently using np.take():

dists, indices = k.query(xyCenters)
myra = np.take(ra, indices)
mydec = np.take(dec, indices)