[Neuroimaging] Nibabel API change - always read as float
moloney at ohsu.edu
Mon Jul 6 22:50:30 CEST 2015
> Well, unless I am mistaken, there is no ambiguity in the
> datatype of a binary file.
I guess you mean a .npy file? A plain binary file is nothing
> Plus, numpy does not enforce anything, it is just a
> default value.
The latest proposition that I am running with is to include
a 'dtype' keyword for nibabel.load. I guess Matthew would
argue the default value should be float, and I am really
starting to agree with that point of view.
> Using np.genfromtxt(dtype=None), and you get a more
> logical behavior.
$ cat testfile
1 2 3 4 5
1 2.2 3 4 5
$ python -c 'from numpy import genfromtxt ; print genfromtxt("testfile", dtype=None)'
[(1, 2.0, 3, 4, 5) (1, 2.2000000000000002, 3, 4, 5)]
I don't find this behavior to be particularly logical. I guess
there is a reason the default is 'float'.
> Whatever you do, if you load a npy file using np.load,
> it will always be of the type saved in the first place.
Because it is a nice clean file format they control entirely.
> Also, numpy does not have to deal with the weirdness of using
> integers plus scale factors to represent floating point data.
> Well, in that case, why not adding a dtype value in the header ?
Because nibabel tries to load a bunch of formats that we have little
to no control over. Even in Nifti files where there is an "intent code"
that could be set to NIFTI_INTENT_LABEL for label files, I don't
think any software actually does this. Even if some software does,
we can't rely on it.
> Even without scale factors the data in an
> MRI isn't inherently integer data, it was just quantized that way for
> Except that Nifti can store other things than MRI data (such as masks,
> labels, or any voxel-related value).
I clearly acknowledged that in the next paragraph (which you trimmed),
and you don't address the fact that is almost always obvious from
context if the file is a mask/label. It isn't always obvious if an image
file is going to be float or not.
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