[Neuroimaging] Thread-safe ArrayProxy/fileslice?

[Nathaniel Smith]

The best solution would be to use a pread-style interface that lets you
read from a given offset without seeking at all, and thus without needing
any locks. Unfortunately, Python hasn't historically had the best support
for this, but it's available if you're using Python 3 on unix:
https://docs.python.org/3/library/os.html#os.pread

-n

On Feb 8, 2017 5:55 AM, "paul mccarthy" <pauldmccarthy at gmail.com> wrote:

> Howdy all,
>
> Does anybody have experience accessing image data through the ArrayProxy
> class (or functions in the fileslice module) in a multi-threaded
> environment? I am visualising large 4D images which are kept on disk (via
> my indexed_gzip module), and am having trouble when accessing the data
> from multiple threads, as the seek/read pairs from different threads will
> occasionally become intertwined with each other.
>
>
> My hacky workaround is to patch the ArrayProxy.__getitem__ method, and
> add a threading.Lock to each instance, as follows:
>
>
> import threading
>
> import nibabel            as nib
> import nibabel.arrayproxy as ap
>
> def ArrayProxy__getitem__(self, slc):
>
>     if not hasattr(self, '_thread_lock'):
>         return self.__real_getitem__(slc)
>
>     self._thread_lock.acquire()
>
>     try:
>         return ap.ArrayProxy.__real_getitem__(self, slc)
>
>     finally:
>         self._thread_lock.release()
>
> # Patch ArrayProxy.__getitem__
> ap.ArrayProxy.__real_getitem__ = ap.ArrayProxy.__getitem__
> ap.ArrayProxy.__getitem__      = ArrayProxy__getitem__
>
>
> # Then add a lock to instances
> # which need to be thread-safe
> img = nib.load('MNI152_T1_2mm.nii.gz')
> img.dataobj._thread_lock = threading.Lock()
>
>
> This is the first thing I came up with, although I will probably end up
> adding the lock to the fileobj, and patching the fileslice.fileslice
> function instead. Unless there are any better ideas?
>
> Cheers,
>
> Paul
>
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