[Numpy-discussion] Re: Format of arrays to facilitate analysis
So from a purely numpy perspective, there is no advantage if one of aforementioned coordinate arrangements is used (eg Nx2, ravelled, 2xN). ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: Format of arrays to facilitate analysis
On Tue, Feb 18, 2025 at 11:06 AM Dan Patterson wrote: > 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 > That's the great thing about conventions; there are so many to choose from! /sarcasm Usually, there is nothing substantial that would cause one to strongly prefer one convention over another. I usually take a look at existing libraries that I might want to use and pick a convention that makes it easy to use those libraries. So for use cases like this, I might want to use Shapely for some robust geometrical operations, so I'd follow Shapely conventions. But if I'm just drawing polygons onto images, I might follow scikit-image's convention instead. By and large, you can usually safely follow whatever is your personal preference without fearing that you are missing out on something vital. What makes these conventions are the low stakes. -- Robert Kern ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Format of arrays to facilitate analysis
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]]]) ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: Format of arrays to facilitate analysis
On Tue, Feb 18, 2025 at 3:00 PM Dan Patterson wrote: > So from a purely numpy perspective, there is no advantage if one of > aforementioned coordinate arrangements is used (eg Nx2, ravelled, 2xN). No, not from numpy's side. Some things you want to do will be easier/prettier in one of the arrangements than others (e.g. the ravelled will likely be the least convenient), but it has more to do with other code and file formats that you are interacting with that will be the largest factors in breaking the symmetry. -- Robert Kern ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
