fix: IndexError in get_topk_rois when indexing dict_keys with numpy

[Shubham0209]

What's the bug?

When calling get_topk_rois with the default hemi="both" (or any value other than "both_separate"), the function crashes with:

IndexError: too many indices for array: array is 0-dimensional, but 1 were indexed

Root cause

The else branch assigns labels from a raw .keys() call:

labels = get_hcp_labels(mesh=mesh, combine=False, hemi=hemi).keys()

dict.keys() returns a view object, not a sequence. When passed to np.array(), numpy wraps the entire object into a 0-dimensional array instead of a 1D string array. Fancy-indexing that 0-d array with top_k then raises the IndexError.

Interestingly, the both_separate branch right above it already handles this correctly by constructing a plain Python list — the else branch was just missing the same treatment.

Fix

One character change — wrap .keys() in list():

# before
labels = get_hcp_labels(mesh=mesh, combine=False, hemi=hemi).keys()

# after
labels = list(get_hcp_labels(mesh=mesh, combine=False, hemi=hemi).keys())

This gives numpy a proper 1D sequence it can convert to a string array and fancy-index correctly.

Testing

Verified on a real brain encoding run with both text and video stimuli using the default hemi="both". get_topk_rois now returns the correct top-k parcel names without error.

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