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I have the result of some computations made with numpy.matrix types

import numpy as np
import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)

# [...]

# Downsampling for plotting
# type(verts):  np.matrix
# verts.shape:  (3, 700000)

verts = verts_small[:, ::1000]

ax.plot(verts[0, :], verts[1, :], verts[2, :], 'o')

This has strange behavior. Plots the points all in a line.

If instead:

verts = np.array(verts[:, ::1000])

the 3D-plot works as expected. Is this an intended behavior or is it a bug?

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    Keep in mind that when you index a np.matrix the result will still be 2d. A np.ndarray maybe 1d (depending on how you index). Commented Feb 27, 2017 at 0:20
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    "Strange behaviour of ____ [with] np.matrix" is in general, not a surprise. Writing code that deals with np.matrix is difficult, because it resists conversion to 1d arrays. There's talk of deprecating np.matrix in an immintent release, but we're not there yet. Either way, I'd strongly suggest not using it anyway. Commented Feb 27, 2017 at 2:05
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    The problem with the "consider everything as a matrix" mindset is that every list of numbers can now be horizontal or vertical, and everything needs to cope with that. I think the real problem is that np.matrix bundles a semi-useful feature, that * always means np.dot, with the very frustrating one that everything is 2d Commented Feb 27, 2017 at 2:49
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    IMO, some_matrix.reshape((2, 1, 3, 1)).shape != (2, 1, 3, 1) is just downright evil Commented Feb 27, 2017 at 2:54
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    @hpaulij Yes, thanks. Saw this on the docs. :) @Eric In classical mathematics it's already like this. Vectors are column vectors (so vertical). And with this assumption is easy to do everything just using the definition of matrix multiplication. (e.g. Scalar product v.T*u, norm v.T*v, linear operator A*v, matrix x matrix A*B. Everything comes flawlessly. :). But I believe it's a matter of opinions, and maybe there are some applications where this is unwanted. Thanks for the fruitful discussion. Commented Feb 27, 2017 at 8:26

1 Answer 1

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As hpaulj mentions, the issue here is that the result is 2d:

>>> verts = np.zeros((3, 100))
>>> verts_m = np.matrix(verts)
>>> verts[0, :].shape
(100,)
>>> verts_m[0, :].shape
(1, 100)

Crucially, this breaks any code that expects to be able to iterate over a column or row of a 2d array:

>>> len(verts)
100
>>> len(verts_m)
1

Worse still, verts_m doesn't even behave as a list of lists:

>>> len(verts[0])
1
>>> len(verts[0][0])
1
>>> len(verts[0][0][0][0][0][0][0][0][0][0])
1

In essence, np.matrix breaks most of the rules about shapes when it comes to numpy operations. To support it in matplotlib, they would either need to deliberately work around these rule breakages, or force the inputs to be standard np.arrays.

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