python - np.piecewise() acting up with use of math.pow()? -

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i've been trying numpy.piecewise convert list of coefficients {a_ij} piecewise cubic polynomial. whenever run following code,

import math import numpy np  x = np.linspace(0.1,9.9,100) = [[i] * 4 in range(10)] x_i = [0,1,2,3,4,5,6,7,8,9,10] condlist = [(x[i] < x)*(x<x_i[i+1]) in range(len(x_i)-1)] funclist = [lambda y: sum([a[k][j] * math.pow(y - x_i[k],j) j in range(4)]) k in range(len(a))] print np.piecewise(x, condlist, funclist)

i error

...line 730, in piecewise y[condlist[k]] = item(vals, *args, **kw) file "test", line 8, in <lambda> funclist = [lambda y: sum([a[k][j] * math.pow(y - x_i[k],j) j in range(4)]) k in range(len(a))] typeerror: length-1 arrays can converted python scalars

the error goes away, however, if , if rid of math.pow(). reason, having funclist pass x math.pow() breaks everything.

what's going on? how can fix this?

the functions in math module expect numeric scalars arguments. don't expect numpy arrays. functions in funclist being passed numpy array, y. math.pow(y-constant, j) raising typeerror:

in [22]: y = np.arange(5) in [31]: math.pow(y - 1, 2) typeerror: length-1 arrays can converted python scalars

use np.power instead of math.pow:

funclist = [lambda y: ([a[k][j] * np.power(y - x_i[k],j) j in range(4)]).sum()             k in range(len(a))]

note: instead of using python sum function, should call numpy sum method better performance.


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