Dylan Marques

The scale function stretch the x-axis of the interpolation. Therefore, the weights are dependent on the scale because the weights are dependent on the x-axis of the interpolated function. The...

I am tying to confirm that I understand how it works because, to me, it doesn't seem to work properly with an itp scaled by a `StepRangeLength` (the function is...

I am interested on the weights as I need to calculate an interpolation based on a (very sparse) matrix. This matrix is constructed based on the weights. For my application,...

Sure, I think that something like this should work nicely: ``` using Interpolations x1 = range(0, 20, length = 11) x2 = range(0, 20, length = 11) y = x1...

I ran a few tests for the algorithm and it does not work for `Interpolations.gradient_weights`. For the gradients, the weight must be divided by `step(scaleditp.ranges)`. I tried to do this...

Answer: I ran the code in linux using the makefile to generate the binaries. In that case I have the output that I presented. If I run the code using...

Hi, Thanks it helps a lot! I see that there are a lot of small problems with python extension. I think that the best for me is to generate the...

It seems to work if the axes are set manually: ```julia using GLMakie, Unitful, Unitful.DefaultSymbols, DimensionalData dd = Observable(DimArray(collect(1V:1V:10V), X(1:10))) dd_2 = @lift identity($dd) fig, ax, plt = lines((@lift lookup($dd_2,...

Thanks, I will try to put a PR together for this

Hi, I looked a more into this and found a solution for this issue and #906. ```julia using GLMakie, Unitful, Unitful.DefaultSymbols, DimensionalData using GLMakie.Makie function Makie.convert_arguments(P::Makie.PointBased, dd::DimensionalData.AbstractDimVector) return Makie.convert_arguments(P, parent(lookup(dd,...