Fast geodesic distances between sets
tupu - Fast geodesic distances in Python
This is a personal project centered around geodesic distances. Its goal is to be able to quickly compute, for every coordinate in a list:
- Distances to a given point (e.g. distances from each point to NYC)
- Nearest neighbors: distances to the closest point in another list (e.g. distances from each point to a city), and the identity of such point
- Number of neighbors: number of points of another list within a certain distance or buffer.
After cloning the repo and opening the panflute folder:
python setup.py install
: installs the package locally
python setup.py develop
: installs locally with a symlink so changes are automatically updated
import tupu # TODO...
From the command line:
tupu some_cities.csv?id=uid --output=augmented.tsv --distance=dist_ny,40.7143,-74.0060
(See also [examples/README.md])
libspatialindex). Windows binaries here
Tupu was one of the Inca measures of distance, equivalent to about 130 cm. I would have preferred to use "topo", but it's already a quite popular name on Github, and has other meanings.
Gordon McEwan, "The Incas: New Perspectives", p.179
Jerry D. Moore, "Incidence of Travel: Recent Journeys in Ancient South America", p.208
Why not geopandas, etc.?
Earlier tests deemed them too slow/complicated, but there might be workarounds. EG:
- Not parallelized, although that should be trivial
- Not Cython, although most of the heavy load is already in C.
- Only deals with points, not with lines/polygons
- Currently only stores distance to closest city (although allowing more is trivial)
- Currently does not compute number of points within a given distance (although allowing more is trivial)
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