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ImplementationOf3DRasterFlowLine

-              v6
+              v7
 Next, I will try to implement Runge-Kutta with adaptive step size.
+=== Week 6 ===
+I implemented Runge-Kutta with adaptive step size based on the VTK implementation and it produces satisfactory results. It is now used as default integration method.
+I started to work on on-the-fly gradient computation. Gradient is computed with central differencing scheme of second order approximation (also on edges). I tested it against numpy implementation, the newest version is required because the older implementations are less precise on the edges. Then I tried to incorporate the computation of the gradient in r3.flow and it still needs some improvements and optimization. Tests show that the resulting flowlines differ slightly from flowlines computed when I use precomputed gradient. So I will look into that.