Milan Klöwer

Just FYI this is the memory allocation that happens within Julia ```julia julia> using BitInformation julia> A = rand(Float32,100,200); julia> @time bitinformation(A); 0.001958 seconds (281 allocations: 13.328 KiB) julia> A...

> The easiest way I can think of, is to do the chunking on the python side and call BitInformation.jl on each of these chunks and combine the results afterwards....

Btw this here  From https://github.com/JuliaPy/PyCall.jl#readme tells me that it should be possible to call Julia code without copy from python by using `PyArray`. I've defined everything for `

figure from a talk I gave, showing how the bitwise real information content changes when using different binary encodings for the same data 

@Ishaanj18 @ayoubft As a benchmark to check that the bitinformation through xbitinfo works well with various formats, I suggest the following: 1) High precision data: create U(0,1) data of length...

Using BitInformation.jl produced with ```julia using BitInformation, LinLogQuantization, PyPlot x = rand(Float64,10_000) sort!(x) f16 = Float16.(x) f32 = Float32.(x) u16 = LinQuant16Array(x); fig,(ax1,ax2,ax3,ax4) = subplots(4,figsize=(8,3)) ax1.imshow((bitinformation(x)[1:32],)) ax1.set_xlim(-0.5,31.5) ax1.set_yticks([0],["Float64"]) # label...

Ideally `NaNs` should be masked and then ignored in the bit pair count algorithm so that in a bitstream of `1 0 NaN 0 1` it would only count two...

> An additional half-baked thought: What about using a convolution to compute info content pixel-by-pixel rather than chunk-by-chunk? You can technically do that, but it just gets really expensive. I...

Sounds great to me, I let Hauke comment though.

Thanks @nmassey001 !!! @observingClouds could you, at some point, compare this to xbitinfo performance?