Hi,
may be you could help me with this issue: For some reasons I want to
apply a mapper to my dataset which yields to one sample of each label
per chunk. As explained in the FAQs this could be easily performed with
the SampleGroupMapper:
# load dataset
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
labels=attr.labels,
chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
from mvpa.mappers.samplegroup import SampleGroupMapper
from mvpa.misc.transformers import FirstAxisMean
m = SampleGroupMapper(fx=FirstAxisMean)
mapped_data = ds.applyMapper(samplesmapper=m)
Its also possible to save the mapped Data like this:
m_img = ds.map2Nifti(mapped_data)
m_img.save(os.path.join(pymvpa_dataroot, 'bold_samples_mapped.nii'))
So far so good :-) Is it also possible to apply the same mapping to the
SamplesAttributes? Something like:
mapped_attr = attr.applyMapper(samplesmapper=m)
I just want a new txt-file with the corresponding (mapped) attributes.
If nothing like this exists, it should be very easy to recode my
existing attributes file externally (bash, python whatever)... but... I
was not able to find out how the mapper works internally. Especially
what the the 'new sample' order looks like. Is it depending on the first
appearance of a sample in each chunk?
e.g.
Sample Chunk
cat 1
cat 1
cat 1
dog 1
dog 1
dog 1
face 1
face 1
dog 2
dog 2
dog 2
face 2
face 2
face 2
. .
. .
. .
would be (after mapping)
Sample Chunk
cat 1
dog 1
face 1
dog 2
face 2
Would be very nice to hear your opinion on the following questions:
Would you perform the detrending before or after mapping?
The samplemapping really improves my decoding accuracy very much. I
assume this is because of noise reduction? Could you think of another
reason which might be not intended?
I really appreciate your help.
Bests,
Matthias
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