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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 _calculate_filter(n, spacing, shift, fI, r_def, reim, name):
 r"""Calculate filter for this spacing, shift, n."""


    # Base :: For this n/spacing/shift
    base = np.exp(spacing*(np.arange(n)-n//2) + shift)

    # r :: Start/end is defined by base AND r_def[0]/r_def[1]
    #      Overdetermined system if r_def[2] > 1
    r = np.logspace(np.log10(1/np.max(base)) - r_def[0],
                    np.log10(1/np.min(base)) + r_def[1], r_def[2]*n)

    # k :: Get required k-values (matrix of shape (r.size, base.size))
    k = base/r[:, None]

    # Create filter instance
    dlf = DigitalFilter(name.split('.')[0])
    dlf.base = base
    dlf.factor = np.around(np.average(base[1:]/base[:-1]), 15)

    # Loop over transforms
    for f in fI:
        # Calculate lhs and rhs for inversion
        lhs = reim(f.lhs(k))
        rhs = reim(f.rhs(r)*r)

        # Calculate filter values: Solve lhs*J=rhs using linalg.qr.
        # If factoring fails (qr) or if matrix is singular or square (solve) it
        # will raise a LinAlgError. Error is ignored and zeros are returned
        # instead.
        try:
            qq, rr = np.linalg.qr(lhs)
            J = np.linalg.solve(rr, rhs.dot(qq))
        except np.linalg.LinAlgError:
            J = np.zeros((base.size,))

        setattr(dlf, f.name, J)

    return dlf