geodesic | 🚀 Geodesic is a DevOps Linux Toolbox | Continuous Deployment library

I think that the problem could be the following line:

I am no expert in the matter so read with prejudice. Also sorry this is more of a comment than answer...

First You should clarify some things:

• the mesh is convex or concave?
• are the path always on surface or can fly between faces on the outside (if concave) but never inside?
• are the start/end points on edges of faces or can be inside?

Assuming concave, points on edges and only surface paths...

I think the graph A* approach is unusable as there is infinite possible paths between point and any edge of the same face so how you test all of them?

If you really want A* then you can do something similar to raster A*

1. so resample all your edges to more points

   so either n points or use some density like 10 points per average edge length or some detail size.

2. use graph A* on resampled points (do not handle them as edges anymore)

However this will produce only close to shortest path so in order to improve the accuracy you should recursively resample the edges near used point with higher and higher density until the distance between resampled points get smaller than accuracy.

Another option would be using something similar to CCD (cyclic coordinate descent) so:

1. create plane that goes through your 2 points and center of your mesh
2. create path that goes through all intersection of plane and faces between the 2 points (use the shorter on from the 2 options)
3. iterativelly move intersections back and forward and use greedy approach to get the result

However this might get stuck in local minima... You could use search/fitting approaches instead but those will get very slow with increasing number of faces

I got the feeling you might also do this using RANSAC ...

From my point of view I think the first A* approach is the most promising, you just need linked list of points per each edge and one cost counter per each its point from this you can simply encode even the recursive improvement of accuracy. It can be done even in-place so no reallocation needed in the recursion ... And also the algo is not complicated so you should have no problems implementing it, and the result is guaranteed which is not the case with other approaches I mention... Another pros is that it can be used even if start/endpoint does not belong to edge...

Answering my own question. I was only able to get a working solution by creating a C wrapper package around the system library; that C wrapper package, in turn, is then wrapped with another Swift wrapper to expose 'Swifty-style' code - I was not able to get a single package that included all the required parts.

My working solution is as follows...

Folder structure for the the system library's C wrapper is:

Found something that works: setting the projection to be "RotatedPole" with the pole being about 90 degrees away at an azimuth perpendicular to the river. More generally, pick a pole so that the map's "up" points toward the pole and the map's left/right runs along the pole's equator.

Insert the tailwind class z-10 to the navbar container to give the navbar a higher z-Index

You did not get the plots of the circles because of wrong coordinate transformation you specifies in .add_geometries() statement.

To get it right, suppose I use this data file: 'obs_station.csv':

Thanks to the comment from Andre Borie, I decided to focus on sorting the queryset after-the-fact, and fixed it with this:

You can create RDDs from the users_dict and places_dict then join with ratings_rdd to get the coordinates of the user and the rated place. Then using map, call geodesic to calculate the distance.

Here's an example:

You need to define a custom user-defined-function: