CODEWITHSUNDEEP
pythonsortingnumpymatplotlib
James
James

Reputation: 63

Sorting data for plotting in mplot3d

I have been using mplot3d (part of matplotlib) for some various 3d plotting, and it has been performing the job admirably. However, I have run into a new problem.

Mplot3d expects data to be sorted in a certain fashion, to plot a wireframe. For example, it likes something like this:

x = array([[1, 2, 3],
          [1, 2, 3],
          [1, 2, 3]])

y = array([[1, 1, 1],
          [2, 2, 2],
          [3, 3, 3])

where z is then an array of the same dimensions, with data corresponding to each of those positions in space.

Unfortunately, my data isn't formatted like this - every other row is reversed, because the data is collected by scanning in a raster pattern.

So I have something more like:

x = array([[1, 2, 3],
          [3, 2, 1],
          [1, 2, 3]])

My current approach is a very ugly, brute-force "do a for loop then check if you're in an odd row or not" that builds a new array out of the old one, but I am hoping there is a more elegant way of doing this. The tricky part is that I have to re-arrange the Z array in the same way I do the X and Y, to ensure that the data corresponding with each point is space is preserved.

Ideally, I'd like something that's robust and specifically designed to sort a set of 2-d arrays that contain arbitrary random position points, but even a more pythonic way of doing what I'm already doing would be appreciated. If I could make it more robust, and not dependent on this specific raster scanning pattern, it would probably save me headaches in the long term.

Upvotes: 3

Views: 992

Answers (1)

Joe Kington
Joe Kington

Reputation: 284602

If I understand you correctly, you just want to do this: x[1::2, :] = x[1::2, ::-1].

There are a few kinks... If you don't make an intermediate copy of x it doesn't quite do what you'd expect due to the way broadcasting works in numpy.

Nonetheless, it's still pretty simple to do with basic indexing:

import numpy as np
x = np.array([[1,2,3],[3,2,1],[1,2,3],[3,2,1],[1,2,3]])
x_rev = x.copy()
x_rev[1::2, :] = x[1::2, ::-1]

This converts this (x):

array([[1, 2, 3],
       [3, 2, 1],
       [1, 2, 3],
       [3, 2, 1],
       [1, 2, 3]])

Into this (x_rev):

array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

In case you're not familiar with slicing in python, x[1::2] would select every other item of x, starting with the second item. (1 is the the start index, 2 is the increment) In contrast, x[::-1] just specifies an increment of -1, thus reversing the array. In this case we're only applying these slices to a particular axis, so we can select and reverse every other row, starting with the second row.

Upvotes: 2

Related Questions