michaelwplde
Cartography/imaging: understanding projection between heterogeneous 2D coordinate systems
Windows Calculator reports
8192 / 1500000 being 0.00546448087431693989071038251366, which I need every single digit from that ratio for precision to be what we want, whereas dotnet csharp reporting more like 0.0054613333333333337, give or take depending decimal, float, etc, but all losing their stuff after about six places.10 replies
Cartography/imaging: understanding projection between heterogeneous 2D coordinate systems
That's exactly what's going on. How do I contend with that? Rounding issues beyond 6 places is not acceptable, especially in this scenario. Contingencies at my disposal are to start from larger resolution image source, but the performance hit is huge.
10 replies
Cartography/imaging: understanding projection between heterogeneous 2D coordinate systems
That's
1/183 assuming 1.5M world coordinate system (hard fact) versus 8192x bitmap canvas. Of course factors vary depending upon the bitmap resolution; I can start from as much as approximately ~64Kx if necessary, but that gets expensive to SKIA, etc.10 replies
Cartography/imaging: understanding projection between heterogeneous 2D coordinate systems
I was curious and ran it through for some GPT feedback. I am not hundred percent convinced this algorithm would necessarily provide the
t I want; owing to the fact, the scaling factor is something like 1/183 px/world, and that it seems as though X-Y the error is cumulatively increasing over that range; even more strange orthogonal to the distance the further Y-X travel from canvas zero.
https://math-gpt.org/3379928b-c8c1-4712-8a8f-43f6ed14205a10 replies