pgpkgto capture your ideas / findings that can be addressed in later PRs (mostly I'm thinking of these of capturing a few of the technical specifics while they are fresh). You can certainly link these into your proposal, but like @martinfleis said, your focus should be on the overall proposal and bigger picture: overall approach, goals, what is your sense of how hard it will be to go to from what is available to meet the goals for the project, etc. (I probably got overly excited by the specifics I listed off above, apologies if that was a bit of a distraction from the overall proposal)
dask-geopandasis in an early stage of development so there is a lot to do. I would say that the ideal roadmap now would be 1) spatial partitioning, 2) spatial indexing, 3) overlapping computation, based on my own experience and needs (which may be biased). Maybe IO... You can check the issues https://github.com/geopandas/dask-geopandas/issues for a discussion on some of these. If you have some needs yourself, feel free to embed them in the proposal. Also note that we have submitted a workshop proposal around dask-geopandas to Dask Summit, so there may be a chance to have a good discussion on priorities during that with a wider range of people.
bugand find some you're comfortable fixing. They require various level of expertise and some will be easier than other so you should be able to find some you're comfortable with. If you want to work on documentation, #1896 is a nice one to start with.
Still got some testing to do, but I ended up using https://github.com/dbworth/minimum-area-bounding-rectangle/blob/master/python/min_bounding_rect.py to make follow on operations easier.
It looks like
geom.minimum_rotated_rectangle doesn't guarantee right angles? They might be trying to accomplish different things.
Short answer everything works as intended - thank you again. FWIW the github link to
min_bounding_rect.py returns identical return as
Long answer is that the KML writer enforces geographic coordinates, which I did not know. So QGIS' project on the fly visually was not showing a true rectangle versus the underlying (in my case) local UTM. I had done quick tests with
min_bounding_rect.py at large scales only, and tests with
minimum_rotated_rectangle at small scale only. Obviously at large scale the difference in the projection were not as noticeable so the two calls appeared to have different results. Using the same projections for minimum rectangle and the underlying in a non-KML format works as intended.