I often need to make a linear interpolation for a single scalar value but this is not very simple with numpy.interp :

...

...

import numpy as n n.__version__ '1.0.5.dev4420' xp = n.arange(10) yp = 2.5 + xp**2 -x x = 3.2 n.interp(x, xp, yp) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: object of too small depth for desired array

So I use the following trick (which is really ugly) :

...

...

n.interp([x], xp, yp)[0] 7.7000000000000011

Did I miss an obvious way to do it ? If not, I'd be tempted to patch interp to let it accept scalar values as first argument as follow :

...

...

def interp(x, *args, **kwds): ... if type(x) in (float, int): ... return n.interp([x], *args, **kwds).item() ... else : ... return n.interp(x, *args, **kwds) ...

interp(x, xp, yp) 7.7000000000000011 interp([x,2*x], xp, yp) array([ 7.7, 38.5])

-- LB