Non-random Points initialization for fractal_flame method?

[leios]

Right now, all the initial points are generated with rand(), but we could do something like...

function grid_gen(n; dims=2, endpoints = [-1,1], ArrayType = Array,
                  FloatType = Float64)

    grid_extents = endpoints[2] - endpoints[1]

    # number of points along any given axis
    # For 2D, we take the sqrt(n) and then round up
    axis_num = ceil(Int, n^(1/dims))

    # Distance between each point
    dx = grid_extents / (axis_num)

    # This is warning in the case that we do not have a square number
    if n^(1/dims) != axis_num
        println("Cannot evenly divide ", n, " into ", dims, " dimensions!")
    end 

    # Initializing the array, particles along the column, dimensions along rows
    a = zeros(FloatType, n, dims)

    # This works by firxt generating an N dimensional tuple with the number
    # of particles to be places along each dimension ((10,10) for 2D and n=100)
    # Then we generate the list of all CartesianIndices and cast that onto a
    # grid by multiplying by dx and subtracting grid_extents/2
    k = 1
    for i in CartesianIndices(Tuple([axis_num for j = 0:(dims-1)]))
        if k <= size(a)[1]
            a[k,:] .= (Tuple(i).-0.5).*dx.+endpoints[1]
        end
        k += 1
    end 

    return Particles(ArrayType(a),
                     ArrayType(zeros(FloatType, n, dims)),
                     ArrayType(zeros(FloatType, n, dims)))

end

Another useful idea: use the points from the previous timestep as initial points for the next timestep (do we already do this?). This will allow us to cut the number of steps down as well. Related #26