On Mon, Aug 2, 2010 at 3:35 PM, Alex Kraus <alex_work@live.de> wrote:

Hi,

I am trying to create a function that calculates the integral of another function. The integral function should later be used in scipy.optimize.leastsq(f, ...), so ideally it should have the format:

def f(x, *param)

so that it works for a variable number of parameters. While my code works for a fixed number of parameters I cannot get it to work with a variable number of parameters. It seems that numpy.vectorize fails here.

Is there a different/better way to do this?

from scipy.integrate import quad import numpy as np

def integrand_function(x, a, b, c):     result = a*x**2 + np.exp(b*x) + np.cos(a*c)     return result

def define_integral(f, lower, upper):     assert(lower < upper)

    def function(a, b, c):         result = quad(f, lower, upper, args=(a, b, c))[0]         return result     return np.vectorize(function)

def integrand_function_param(x, *param):     a, b, c = param     result = a*x**2 + np.exp(b*x) + np.cos(a*c)     return result

def define_integral_param(f, lower, upper):     assert(lower < upper)

    def function(a, *param):         print(param)         result = quad(f, lower, upper, args=(a, param))[0]         return result     return np.vectorize(function)

a = np.array([1,2,3,4])

print(integrand_function(1,2,3,4)) # 21.9400368894

f = define_integral(integrand_function, 0.0, 2.0) print(f(a, 1,2)) # [  8.22342909  10.41510219  16.30939667  16.7647227 ]

print(integrand_function_param(1,2,3,4)) # 21.9400368894

fp = define_integral_param(integrand_function_param, 0.0, 2.0) print(fp(a, 1,2)) # ValueError: mismatch between python function inputs and received arguments

vectorize cannot infer correctly the number of arguments with *params see http://projects.scipy.org/scipy/ticket/422 the solution that I used in scipy.stats.distributions was to explicitly specify the number of input arguments. nin = ... (I don't remember all the details) Josef

# fp should later be used in scipy.optimize.leastsq(fp, ...

Any help is very appreciated! Alexander

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