CODEWITHSUNDEEP
pythonmultidimensional-arraynumpy
Milla Well
Milla Well

Reputation: 3313

remove zero lines 2-D numpy array

I run a qr factorization in numpy which returns a list of ndarrays, namely Qand R:

>>> [q,r] = np.linalg.qr(np.array([1,0,0,0,1,1,1,1,1]).reshape(3,3))

R is a two-dimensional array, having pivoted zero-lines at the bottom (even proved for all examples in my test set):

>>> print r
[[ 1.41421356  0.70710678  0.70710678]
 [ 0.          1.22474487  1.22474487]
 [ 0.          0.          0.        ]]

. Now, I want to divide R in two matrices R_~:

[[ 1.41421356  0.70710678  0.70710678]
 [ 0.          1.22474487  1.22474487]]

and R_0:

[[ 0.          0.          0.        ]]

(extracting all zero-lines). It seems to be close to this solution: deleting rows in numpy array.

EDIT:
Even more interesting: np.linalg.qr() returns a n x n-matrix. Not, what I would have expected:

A := n x m
Q := n x m
R := n x m

Upvotes: 45

Views: 71552

Answers (4)

yspreen
yspreen

Reputation: 1981

Since this is among the first google results to trim a 2D array of zero lines, I want to add my implementation to only remove leading and trailing zeros, in two dimensions:

p = np.where(t != 0)
t = t[min(p[0]) : max(p[0]) + 1, min(p[1]) : max(p[1]) + 1]

This assumes your array is called t and numpy is imported as np.

Upvotes: 5

travelingbones
travelingbones

Reputation: 8418

If you want to eliminate rows that have negligible entries, i'd use np.allclose. 

zero_row_indices = [i for i in r.shape[0] if np.allclose(r[i,:],0)]
nonzero_row_indices =[i for i in r.shape[0] if not np.allclose(r[i,:],0)]
r_new = r[nonzero_row_indices,:]

Upvotes: 2

HYRY
HYRY

Reputation: 97291

Because the data are not equal zero exactly, we need set a threshold value for zero such as 1e-6, use numpy.all with axis=1 to check the rows are zeros or not. Use numpy.where and numpy.diff to get the split positions, and call numpy.split to split the array into a list of arrays.

import numpy as np
[q,r] = np.linalg.qr(np.array([1,0,0,0,1,1,1,1,1]).reshape(3,3))
mask = np.all(np.abs(r) < 1e-6, axis=1)
pos = np.where(np.diff(mask))[0] + 1
result = np.split(r, pos)

Upvotes: 5

ecatmur
ecatmur

Reputation: 157344

Use np.all with an axis argument:

>>> r[np.all(r == 0, axis=1)]
array([[ 0.,  0.,  0.]])
>>> r[~np.all(r == 0, axis=1)]
array([[-1.41421356, -0.70710678, -0.70710678],
       [ 0.        , -1.22474487, -1.22474487]])

Upvotes: 108

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