[Neuroimaging] Nibabel API change - always read as float

Ariel Rokem arokem at gmail.com
Mon Jul 6 19:53:10 CEST 2015

On Mon, Jul 6, 2015 at 9:56 AM, Ben Cipollini <bcipolli at ucsd.edu> wrote:

> How about accepting a dtype parameter, with None meaning using the type
> determined from load (i.e. current behavior)? This makes it easy to
> document the 'arbitrary' behavior for the user (in explaining the
> parameter), to convert to whatever datatype you want (if you know your data
> or have particular needs like Alex). I also believe this is closer to a
> numpy semantic, to_float is a new semantic (even if relatively simple).
+1 for a `dtype` kwarg, rather than `as_float`, with the input being a
numpy dtype to which things get cast. That will give Alex the flexibility
to choose float32, rather than float64, if he doesn't need all that extra

> As for what the default should be (None or float32 or float64)... I would
> not touch that conversation quite yet :)
I think the default here should be whatever your system does when you
allocate an empty/zeros numpy array (that's float64 for me). Seems only
slightly less arbitrary than the current behavior (I think). As I
understand it, to mask with an array, you need to go to bool anyway, so
this could be nice for the masking use-case:


If the implementation can avoid going to a memory-consuming dtype along the
way, that would have a small advantage relative to a call to `as_type`
after loading into memory.



> On Mon, Jul 6, 2015 at 9:37 AM, Matthew Brett <matthew.brett at gmail.com>
> wrote:
>> On Mon, Jul 6, 2015 at 5:32 PM, Bertrand Thirion
>> <bertrand.thirion at inria.fr> wrote:
>> > +1 we (and more importantly, our students)  should rely as much as
>> possible
>> > on the standard behavior of numpy arrays and make adequate decisions,
>> rather
>> > than having to figure out the details of the API of neuroimaging
>> libraries.
>> > So the defaut should be unchanged.
>> Your reasoning implies the opposite.   Numpy tries very hard not to
>> return arrays of unknown or unpredictable data types, and that is the
>> situation we have here.   The returned datatype from a nibabel image
>> is essentially arbitrary, in that very few sources of nifti files
>> place any weight on whether there are non-default scalefactors or not.
>> At the moment, we do, depend on this, silently, and that is extremely
>> confusing, and quite contrary to the standard numpy way,
>> Cheers,
>> Matthew
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