CODEWITHSUNDEEP
pythonarraysnumpy
pafcu
pafcu

Reputation: 8128

Replace part of numpy 1D array with shorter array

I have a 1D numpy array containing some audio data. I'm doing some processing and want to replace certain parts of the data with white noise. The noise should, however, be shorter then the replaced part. Generating the noise is not a problem, but I'm wondering what the easiest way to replace the original data with the noise is. My first thought of doing data[10:110] = noise[0:10] does not work due to the obvious dimension mismatch.

What's the easiest way to replace a part of a numpy array with another part of different dimension?

edit: The data is uncompressed PCM data that can be up to an hour long, taking up a few hundred MB of memory. I would like to avoid creating any additional copies in memory.

Upvotes: 1

Views: 2680

Answers (2)

Andrew
Andrew

Reputation: 2892

What advantage does a numpy array have over a python list for your application? I think one of the weaknesses of numpy arrays is that they are not easy to resize:

http://mail.python.org/pipermail/python-list/2008-June/1181494.html

Do you really need to reclaim the memory from the segments of the array you're shortening? If not, maybe you can use a masked array:

http://docs.scipy.org/doc/numpy/reference/maskedarray.generic.html

When you want to replace a section of your signal with a shorter section of noise, replace the first chunk of the signal, then mask out the remainder of the removed signal.

EDIT: Here's some clunky numpy code that doesn't use masked arrays, and doesn't allocate more memory. It also doesn't free any memory for the deleted segments. The idea is to replace data that you want deleted by shifting the remainder of the array, leaving zeros (or garbage) at the end of the array.

import numpy
a = numpy.arange(10)
# [0 1 2 3 4 5 6 7 8 9]
## Replace a[2:7] with length-2 noise:
insert = -1 * numpy.ones((2))
new = slice(2, 4)
old = slice(2, 7)
#Just to indicate what we'll be replacing:
a[old] = 0
# [0 1 0 0 0 0 0 7 8 9]
a[new] = insert
# [0 1 -1 -1 0 0 0 7 8 9]
#Shift the remaining data over:
a[new.stop:(new.stop - old.stop)] = a[old.stop:]
# [0 1 -1 -1 7 8 9 7 8 9]
#Zero out the dangly bit at the end:
a[(new.stop - old.stop):] = 0
# [0 1 -1 -1 7 8 9 0 0 0]

Upvotes: 5

Ramy
Ramy

Reputation: 21261

not entirely familiar with numpy but can't you just break down the data array into pieces that are the same size as the noise array and set each data piece to the noise piece. for example:

data[10:20] = noise[0:10]
data[21:31] = noise[0:10]

etc., etc.?

you could loop like this:

for x in range(10,100,10):
    data[x:10+x] = noise[0:10]

UPDATE:

if you want to shorten the original data array, you could do this:

data = data[:10] + noise[:10]

this will truncate the data array and add the the noise to the original array after the 10th location, you could then add the rest of the data array to the new array if you need it.

Upvotes: 0

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