[Neuroimaging] iteraxis API - we need feedback

Satrajit Ghosh satra at mit.edu
Sat Sep 5 04:22:31 CEST 2015

hi matthew,

> for vol in img.iteraxis(3):  # iterate over 4th axis
>     # do something with vol
> where `iteraxis` would use `databobj` slicing under the hood.
> The questions are:
> * should this be a method on the image (`img.iteraxis`), the dataobj
> (`img.dataobj.iteraxis`) or should it be a standalone function that
> knows about arrays and array proxies? (`nibabel.iteraxis`);

img.iteraxis seems like a good place.

> * how should the iterator optimize speed or memory?   Should this be
> configurable?  For example, if you are iterating over the first axis
> of a Nifti, then it will probably be most efficient to read all the
> data into memory and return the slices from the numpy array.   This
> will be very expensive in memory.   If a file is compressed, it may be
> most efficient to uncompress the file and use the uncompressed version
> with `dataobj` file slicing - but this will involve a temporary file
> that may be very large.   Options are:
>     * find some heuristic to chose joint optimization for memory and speed;
>     * always optimize for memory;
>     * always optimize for speed, saving memory where possible;
>     * have a tuning kwarg selecting between these options.

i don't know if there is a common heuristic - it really depends on the data
characteristics as well as the system configuration.

> The upside of image.iteraxis would be to embed knowledge we've gained
> on these objects and simplify the interface for users.

could you please clarify what you mean by "these objects"?

Any thoughts?   Use-cases?


- would iteraxis be for volume only or support surface and streamline
- recommend testing these with hcp data. they are closer to resolution and
size of what most datasets will look like in 5 years.
- stay away from labels for axes or dimensions - this would be dependent on
phase encoding direction (for epi images) as well as placement of object in
the scanner. i think nibabel should not have to figure that out. if during
construction the user labels these axes, then nibabel could use that
- [forget i'm saying this, but this is a general solution to the
optimization problem] one could just change the format and store nii as an
hdf5 dataset and you get both memory and speed optimization!


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