NumbaQuadpack

NumbaQuadpack is a python wrapper to cquadpack, which is for solving integrals. Right now, this package only wraps the dqags method, which computes definite integrals.

For most definite integrals, with non-infinite bounds NumbaQuadpack.dqags does the same thing as scipy.integrate.quad, but NumbaQuadpack is much faster than scipy, because scipy uses the python interpreter to set up the problem. Also, NumbaQuadpack.dqags can be called from within numba-jited functions, and scipy's functions can not be.

Installation

NumbaQuadpack will probably only work on MacOS or Linux. You must have CMake and a C compiler. You must also have python >3.6.0 with numpy and numba.

After satisfying the dependencies, install with the pip command below

python -m pip install git+git://github.com/Nicholaswogan/NumbaQuadpack.git

Basic usage

from NumbaQuadpack import quadpack_sig, dqags
import numpy as np
import numba as nb

@nb.cfunc(quadpack_sig)
def f(x, data):
    return x**2
funcptr = f.address

a = 0
b = 1

# computes definite integral of function f 
# from a to b
sol, abserr, success = dqags(funcptr, a, b)

You can pass an array data to the your integrand in the following way

@nb.cfunc(quadpack_sig)
def f(x, data_):
    data = nb.carray(data_, (2,))
    return np.sum(data)*x**2
funcptr = f.address
a = 0
b = 1
data = np.array([1.0,2.0],np.float64)
sol, abserr, success = dqags(funcptr, a, b, data = data)

Above, data_, in the integrand is a pointer to an array of floats. Therefore, operations like np.sum(data_) or len(data_) will not work. However, you can use the function nb.carray() to make a numpy array out of the pointers. However, you can use the function nb.carray() to make a numpy array out of the pointers (which is what is done above).

Also, dqags can be used from within jitted functions, which is not the case with scipy.integrate.quad.

@nb.njit()
def test():
    sol, abserr, success = dqags(funcptr, a, b)
    return sol
sol = test() # this works!