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Is there a reasoning behind automatic scaling of quiverdata via setting vnormalize = true by default here?
For me, it would make sense to have the normalization false by default, but maybe I am missing something.
Background: Currently, I am using the quiverdata method for visualizing current densities for different set-ups. As I want to compare these vectorfields, it is important that I do not have any automatic scaling.
My results seemed to be a bit strange, I thought, because of some scaling from PyPlot, as I realized now there is some scaling happening through GridVisualize ...
The text was updated successfully, but these errors were encountered:
I recall that I wanted to have a default which works reasonably well out-of-the box. Automatic scaling should prevent arrows which are too long or too short compared to the size of the domain.
May be it is not good enough though. If you have an example I could look into this.
If I understood this correctly, the problem is not that the scaling is bad per se,
but that it makes comparing two fields with different maxima and minima very difficult.
Maybe we could add the option to supply a maximum and minimum for scaling?
Setting vnormalize to false and vscale to some common reasonable value should make this possible. If this doesn't work I would consider this a bug. Not sure how a better API could look like.
Is there a reasoning behind automatic scaling of quiverdata via setting vnormalize = true by default here?
For me, it would make sense to have the normalization false by default, but maybe I am missing something.
Background: Currently, I am using the quiverdata method for visualizing current densities for different set-ups. As I want to compare these vectorfields, it is important that I do not have any automatic scaling.
My results seemed to be a bit strange, I thought, because of some scaling from PyPlot, as I realized now there is some scaling happening through GridVisualize ...
The text was updated successfully, but these errors were encountered: