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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def findbeam_azimuthal_fold(data, orig_initial, mask=None, maxiter=100, Ntheta=50, dmin=0, dmax=np.inf, extent=10, callback=None):
 """Find beam center using azimuthal integration and folding Inputs: data: scattering matrix orig_initial: estimated value for x (row) and y (column) coordinates of the beam center, starting from 1. mask: mask matrix. If None, nothing will be masked. Otherwise it should be of the same size as data. Nonzero means non-masked. maxiter: maximum number of iterations for scipy.optimize.fmin Ntheta: the number of theta points for the azimuthal integration. Should be even! dmin: pixels nearer to the origin than this will be excluded from the azimuthal integration dmax: pixels farther from the origin than this will be excluded from the azimuthal integration extent: approximate distance of the current and the real origin in pixels. Too high a value makes the fitting procedure unstable. Too low a value does not permit to move away the current origin. callback: callback function (expects no arguments) Output: a vector of length 2 with the x and y coordinates of the origin, starting from 1 """

    if Ntheta % 2:
        raise ValueError('Ntheta should be even!')
    if mask is None:
        mask = np.ones_like(data).astype(np.uint8)
    data = data.astype(np.double)
    # the function to minimize is the sum of squared difference of two halves of
    # the azimuthal integral.

    def targetfunc(orig, data, mask, orig_orig, callback):
        I = azimintpix(data, None, orig[
                       0] + orig_orig[0], orig[1] + orig_orig[1], mask, Ntheta, dmin, dmax)[1]
        if callback is not None:
            callback()
        return np.sum((I[:Ntheta / 2] - I[Ntheta / 2:]) ** 2) / Ntheta
    orig1 = scipy.optimize.fmin(targetfunc, np.array([extent, extent]),
                                args=(data, 1 - mask, np.array(orig_initial) - extent, callback), maxiter=maxiter, disp=0)
    return orig1 + np.array(orig_initial) - extent