numpy/scipy: calculate definite integral of sampled data

[Manuel Graune]

Hi everyone,

to calculate the definite integral of a function or an array of sampled
data scipy provides (among others) the quad and trapz functions.
So it is possible to compute e. g. the definite integral of cos(t) over
some range by doing

definite_integral= scipy.integrate.quad(cos,lower_limit,upper_limit) 

or

definite_integral= scipy.integrate.trapz(some_array).

Now, if I want to plot cos(t) and  the integral of cos(t) from 0 to t in
a graph, the necessary array can be calculated by:

@numpy.vectorize
def intfunc(fnc,upper_limit):
    return scipy.integrate.quad(fnc,0.0,upper_limit)

definite_inegral= intfunc(cos,t)

which seems (whithout knowing the actual code) a bit wasteful and slow
but is relatively concise.

Now for my question: scipy provides e. g. the trapz-function to
calculate definite integral of a complete array of sampled data.
However, I have no idea how to get achieve the same as above for
sampled data (apart from manually iterating in a for-loop). Is there
a function somewhere which delivers an array of the definite integrals
for each of the data-points in an array? 


Regards,

Manuel


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