gisalgs / interpolation Goto Github PK

Function spherical has a small typo in docstring. Variable c0 is described as a sill and variable c is described as a nugget but they should be described inversely:

def spherical(h, c0, c, a):
    """
    Input
      h: distance
      c0: nugget
      c: sill
      a: range
    """

When we calculate theoretical semivariance then we must set a nugget for each model. As I understand from the book (and the code itself) a nugget value should be set to zero (?) even if our first lag distance from empirical semivariance is not 0.0. But I do not understand the else statement in a condition when we set a nugget. Why do we choose the second value of lag if the first is equal to 0? Then we will start with nugget different than 0. Is this a correct way of setting our nugget value?

Code related to the question:

if s[0][0] is not 0.0:      # c0, nugget
  c0 = 0.0
else:
  c0 = s[0][1]