Refactor mean_pressure_weighted

[dopplershift]

Right now this looks like:

    ret = []  # Returned variable means in layer
    layer_arg = get_layer(pressure, *args, height=height,
                          bottom=bottom, depth=depth)
    layer_p = layer_arg[0]
    layer_arg = layer_arg[1:]
    # Taking the integral of the weights (pressure) to feed into the weighting
    # function. Said integral works out to this function:
    pres_int = 0.5 * (layer_p[-1]**2 - layer_p[0]**2)
    for i, _datavar in enumerate(args):
        arg_mean = np.trapz((layer_arg[i] * layer_p),
                            x=layer_p) / pres_int
        ret.append(arg_mean)

So it's iterating over an enumeration but not actually using what it's looping over, so that could be better. I think this could probably be reduced to returning a list comprehension, but not completely sure. We should also double check any other places that are using enumerate.