Resizing numpy.memmap arrays

Question

I\'m working with a bunch of large numpy arrays, and as these started to chew up too much memory lately, I wanted to replace them with numpy.memmap instances. The p

Answer 1

The issue is that the flag OWNDATA is False when you create your array. You can change that by requiring the flag to be True when you create the array:

>>> a = np.require(np.memmap('bla.bin', dtype=int), requirements=['O'])
>>> a.shape
(10,)
>>> a.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
>>> a.resize(20, refcheck=False)
>>> a.shape
(20,)

The only caveat is that it may create the array and make a copy to be sure the requirements are met.

Edit to address saving:

If you want to save the re-sized array to disk, you can save the memmap as a .npy formatted file and open as a numpy.memmap when you need to re-open it and use as a memmap:

>>> a[9] = 1
>>> np.save('bla.npy',a)
>>> b = np.lib.format.open_memmap('bla.npy', dtype=int, mode='r+')
>>> b
memmap([0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Edit to offer another method:

You may get close to what you're looking for by re-sizing the base mmap (a.base or a._mmap, stored in uint8 format) and "reloading" the memmap:

>>> a = np.memmap('bla.bin', dtype=int)
>>> a
memmap([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> a[3] = 7
>>> a
memmap([0, 0, 0, 7, 0, 0, 0, 0, 0, 0])
>>> a.flush()
>>> a = np.memmap('bla.bin', dtype=int)
>>> a
memmap([0, 0, 0, 7, 0, 0, 0, 0, 0, 0])
>>> a.base.resize(20*8)
>>> a.flush()
>>> a = np.memmap('bla.bin', dtype=int)
>>> a
memmap([0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])