CODEWITHSUNDEEP
pythonnumpy
mostsquares
mostsquares

Reputation: 928

Load data from generator into already allocated numpy array

I have a large array

data = np.empty((n, k))

where both n and k are large. I also have a lot of generators g, each with k elements, and I want to load each generator into a row in data. I can do:

data[i] = list(g)

or something similar, but this makes a copy of the data in g. I can load with a for loop:

for j, x in enumerate(g):
    data[i, j] = x

but I'm wondering if numpy has a way to do this already without copying or looping in Python.

I know that g have length k in advance and am happy to do some __len__ subclass patching if necessary. np.fromiter will accept something like that when creating a new array, but I'd rather load into this already existing array if possible, due to the constraints of my context.

Upvotes: 4

Views: 261

Answers (3)

Emile
Emile

Reputation: 1235

There's not much you can do, as stated in the comments.

Although you can consider these two solutions:

using numpy.fromiter

Instead of creating data = np.empty((n, k)) yourself, use numpy.fromiter and the count argument, which is made specifically from this case where you know the number of items in advance. This way numpy won't have to "guess" the size and re-allocate until the guess is large enough. Using fromiter allows to run the for loop in C instead of python. This might be a tiny bit faster, but the real bottleneck will likely be in your generators anyway.

Note that fromiter only deals with flat arrays, so you need to read everything flatten (e.g. using chain.from_iterable) and only then call reshape:

from itertools import chain

n = 20
k = 4
generators = (
   (i*j for j in range(k))
   for i in range(n)
)

flat_gen = chain.from_iterable(generators)
data = numpy.fromiter(flat_gen, 'int64', count=n*k)
data = data.reshape((n, k))
"""
array([[ 0,  0,  0,  0],
       [ 0,  1,  2,  3],
       [ 0,  2,  4,  6],
       [ 0,  3,  6,  9],
       [ 0,  4,  8, 12],
       [ 0,  5, 10, 15],
       [ 0,  6, 12, 18],
       [ 0,  7, 14, 21],
       [ 0,  8, 16, 24],
       [ 0,  9, 18, 27],
       [ 0, 10, 20, 30],
       [ 0, 11, 22, 33],
       [ 0, 12, 24, 36],
       [ 0, 13, 26, 39],
       [ 0, 14, 28, 42],
       [ 0, 15, 30, 45],
       [ 0, 16, 32, 48],
       [ 0, 17, 34, 51],
       [ 0, 18, 36, 54],
       [ 0, 19, 38, 57]])
"""

using cython

If you can re-use data and want to avoid re-allocation of the memory, you can't use numpy's fromiter anymore. IMHO the only way to avoid the python's for loop is to implement it in cython. Again, this is extremely likely overkill, since you still have to read the generators in python.

For reference, the C implementation of fromiter looks like that: https://github.com/numpy/numpy/blob/v1.18.3/numpy/core/src/multiarray/ctors.c#L4001-L4118

Upvotes: 1

theWanderer4865
theWanderer4865

Reputation: 871

Couple of things here:

1) You can just say

for whatever in g:
  do_stuff

Since g is a generator, the for loop understands how to get the data out of the generator.

2) You won't have to "copy" out of the generator necessarily (since it isn't doesn't have the entire sequence loaded in memory by design) but you will need to loop through it to fill up your numpy data structure. You might be able to squeeze out some performance (since your structures are large) with tools in numpy or itertools.

So the answer is "no" since you're using generators. If you don't need to have all of the data available at once, you can just use generators to keep the memory profile small but I don't have any context for what you are doing with the data.

Upvotes: 0

Alexis Pister
Alexis Pister

Reputation: 499

There is no faster way than the ones you described. You have to allocate each element of the numpy array, either by iterating the generator or by allocating the entire list.

Upvotes: 0

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