scipy - How do I limit the interpolation region in the InterpolatedUnivariateSpline in Python when given non-uniform samples? -

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i'm trying nice upsampler using python when have non-uniform spaced inputs. suggestions helpful. i've tried number of interp functions. here's example:

from scipy.interpolate import interpolatedunivariatespline numpy import linspace, arange, append matplotlib.pyplot import plot f=[0, 1000,1500,2000,2500,3000,3500,4000,4500,5000,5500,22050] m=[0.,2.85,2.49,1.65,1.55,1.81,1.35,1.00,1.13,1.58,1.21,0.] ff=linspace(f[0],f[1],10) in arange(2, len(f)):     ff=append(ff,linspace(f[i-1],f[i], 10)) aa=interpolatedunivariatespline(x=f,y=m,k=2); mm=aa(ff) plot(f,m,'r-o'); plot(ff,mm,'bo'); show()

this plot get:

enter image description here

i need interpolated values don't go below 0. note blue dots go below zero. red line represents original f vs. m data. if use k=1 (piece-wise linear interp) values shown here:

aa=interpolatedunivariatespline(x=f,y=m,k=1) mm=aa(ff); plot(f,m,'r-o');plot(ff,mm,'bo'); show()

enter image description here

the problem need have "smooth" interpolation , not piece-wise value. know if bbox argument in interpolatedunivarientspline helps fix that? cant find documentation on bbox does. there easier way accomplish this?

thanks in advance help.


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