I tend to work with Nx2 arrays representing coordinate geometry.
I have examined a number of packages and there is no guidelines as to why a
certain arrangement is preferred over the other.
For example: a rectangle, coordinates ordered clockwise with the first and
last the same to ensure closure of the geometry
as a numpy ndarray
array([[ 0.00, 0.00],
[ 0.00, 2.00],
[ 8.00, 2.00],
[ 8.00, 0.00],
[ 0.00, 0.00]])
same, but just ravelled
array([ 0.00, 0.00, 0.00, 2.00, 8.00, 2.00, 8.00, 0.00, 0.00,
0.00])
How about a T
array([[ 0.00, 0.00, 8.00, 8.00, 0.00],
[ 0.00, 2.00, 2.00, 0.00, 0.00]])
and of course there are the python list equivalents of the above.
Preference/history seems to be the only guiding principle as to one chooses a
certain coordinate layout over another.
Nx2 for 2D coordinates makes sense to me ( eg X, Y graphs, Longitude, Latitude)
If I were to profer a reason to another person why I chose a particular format
over another other than "works for me", would there be any other guiding
considerations?
In general I:
- work with the coordinates as a pair
- sometimes, just the 'X' or 'Y'
- I save the values to disk on occasion so I can recover a particular entity
without having to recreate it.
Curious... since I also worked with 3D coordinates (X, Y, Z as position and
elevation) but I am considering working with temporal representation of 2D and
3D data. This is still ndim=2, but adding time as a the 3rd dimension
array([[[ 0.00, 0.00], # locations at time 0
[ 0.00, 2.00],
[ 8.00, 2.00],
[ 8.00, 0.00],
[ 0.00, 0.00]],
[[ 10.00, 10.00], # locations at time 2, shifted by 10, 10 in X, and Y
[ 10.00, 12.00],
[ 18.00, 12.00],
[ 18.00, 10.00],
[ 10.00, 10.00]]])
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