CODEWITHSUNDEEP
pythonnumpy
ejang
ejang

Reputation: 4062

numpy multiple slicing booleans

I'm having trouble editing values in a numpy array

import numpy as np
foo = np.ones(10,10,2)

foo[row_criteria, col_criteria, 0] += 5
foo[row_criteria,:,0][:,col_criteria] += 5

row_criteria and col_criteria are boolean arrays (1D). In the first case I get a

"shape mismatch: objects cannot be broadcast to a single shape" error

In the second case, += 5 doesn't get applied at all. When I do 

foo[row_criteria,:,0][:,col_criteria] + 5

I get a modified return value but modifying the value in place doesn't seem to work...

Can someone explain how to fix this? Thanks!

Upvotes: 3

Views: 3804

Answers (1)

Bi Rico
Bi Rico

Reputation: 25833

You want:

foo[np.ix_(row_criteria, col_criteria, [0])] += 5

To understand how this works take this example:

import numpy as np
A = np.arange(25).reshape([5, 5])
print A[[0, 2, 4], [0, 2, 4]]
# [0, 12, 24]

# The above example gives the the elements A[0, 0], A[2, 2], A[4, 4]
# But what if I want the "outer product?" ie for [[0, 2, 4], [1, 3]] i want
# A[0, 1], A[0, 3], A[2, 1], A[2, 3], A[4, 1], A[4, 3]
print A[np.ix_([0, 2, 4], [1, 3])]
# [[ 1  3]
#  [11 13]
#  [21 23]]

The same thing works with boolean indexing. Also np.ix_ doesn't do anything really amazing, it just reshapes it's arguments so they can be broadcast against each other:

i, j = np.ix_([0, 2, 4], [1, 3])
print i.shape
# (3, 1)
print j.shape
# (1, 2)

Upvotes: 3

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