Generate mock light cones with filamentary strucure or Gaussian blobs added

Note the structure does not intend to be realistic and the mock catalogs can be useful for teaching or testing correlation functions but not to do real science (except for routine uniform_sky())

• mcone_filam Generate a mock light cone of random 3D points emulating filamentary structure
• mcone_gaussblobs Generate a mock light cone of random 3D points with added gaussian blobs or "clusters"
• filament_box Generate random 3D points emulating filamentary structure over a box
• fill_cone Fill the volume of an observation light cone with small cubes of 3D points
• uniform_sky Generate uniform random points (ra,dec) in a given area of sky

# Create a 60x60 deg light cone with z=[0.01,0.15], filled with filaments
import mkone as mk
ralim  = [0,60]
declim = [0,60]
zlim   = [0.01,0.15]
kone = mk.mcone_filam(ralim,declim,zlim,npts=80000,nvoids=2000,nstep=100,b=150.)
# Send to Topcat (start Topcat first)
mk.send(kone,'kone')

# Create a 40x20 deg light cone with z=[0.01,0.15], filled with gaussian "clusters"
import mkone as mk
ralim   = [10,50]
declim  = [20,40]
zlim    = [0.01,0.15]
cradlim = [0.5,20]
kone = mk.mcone_gaussblobs(ralim,declim,zlim,n=20000,ufrac=0.7,ncen=500,
                           cradlim=cradlim,oformat='array')
# Send to Topcat (start Topcat first)
mk.send(kone,'kone',cols=['ra','dec','z','comd','px','py','pz'])

1. astropy : to provide table output and cosmological functions
2. matplotlib,mplot3d : to provide basic graphics
3. scipy.spatial : to provide cKDTree for fast searching nearest voids
4. sampc : to exchange data over SAMP with some VO apps (e.g. Topcat)

• Add seed random as input parameter where needed

E. Donoso (this package) Thanks to S. Woods and T. Sousbie (filament algorithm) and M. Bernyk (cone filling)