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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 convolve(data, window, axis='time', length=1):
 """Design taper and convolve it with the signal. Parameters data : instance of Data the data to filter. window : str one of the windows in scipy, using get_window length : float, optional length of the window axis : str, optional axis to apply the filter on. Returns ------- instance of DataRaw data after convolution Notes ----- Most of the code is identical to fftconvolve(axis=data.index_of(axis)) but unfortunately fftconvolve in scipy 0.13 doesn't take that argument so we need to redefine it here. It's pretty slow too. Taper is normalized such that the integral of the function remains the same even after convolution. See Also -------- scipy.signal.get_window : function used to create windows """

    taper = get_window(window, int(length * data.s_freq))
    taper = taper / sum(taper)

    fdata = data._copy()
    idx_axis = data.index_of(axis)

    for i in range(data.number_of('trial')):
        orig_dat = data.data[i]

        sel_dim = []
        i_dim = []
        dat = empty(orig_dat.shape, dtype=orig_dat.dtype)
        for i_axis, one_axis in enumerate(data.list_of_axes):
            if one_axis != axis:
                i_dim.append(i_axis)
                sel_dim.append(range(data.number_of(one_axis)[i]))

        for one_iter in product(*sel_dim):
            # create the numpy indices for one value per dimension,
            # except for the dimension of interest
            idx = [[x] for x in one_iter]
            idx.insert(idx_axis, range(data.number_of(axis)[i]))
            indices = ix_(*idx)

            d_1dim = squeeze(orig_dat[indices], axis=i_dim)

            d_1dim = fftconvolve(d_1dim, taper, 'same')

            for to_squeeze in i_dim:
                d_1dim = expand_dims(d_1dim, axis=to_squeeze)
                dat[indices] = d_1dim
        fdata.data[0] = dat

    return fdata