[SciPy-dev] scipy.interpolate.fitpack patch
Zachary Pincus
zpincus@stanford....
Mon Feb 5 21:20:16 CST 2007
Hello folks,
I found a minor bug in some of the routines in
scipy.interpolate.fitpack that deal with parametric splines.
In the case of a parametric spline, the spline coefficients c (in the
'tck' tuple) are not simply a 1D array of coefficients as they are
for nonparametric splines. Instead, 'c' is a list of n different 1D
arrays, where n is the dimension of the space in which the spline
lives. To evaluate a parametric spline (or integrate it, or insert
points into it, or whatnot), one simply performs the operation n
times on the n different arrays that comprise c.
The general method for dealing with this in fitpack.py is:
try:
c[0][0]
<recurse on each element of c, collate the results, and return them>
except: pass
<process the 1D c parameter, returning something or raising a helpful
exception>
From this it should be clear what the problem is: if the spline is
parametric, and if one of the elements of the 'c' list causes an
exception to be raised, it will be silently swallowed by the 'except:
pass', and then the function will try to process c as a 1D array
(which it is not!), causing a very uninformative exception to be raised.
Attached is a patch that makes the functions look like this instead:
try:
c[0][0]
parametric = True
except:
parametric = False
if parametric:
<recurse on each element of c, collate the results, and return them>
else:
<process the 1D c parameter, returning something or raising a
helpful exception>
Thanks,
Zach Pincus
Program in Biomedical Informatics and Department of Biochemistry
Stanford University School of Medicine
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