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FIX: Avoid diverting CIFTI dtype from original BOLD #532

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Merged
merged 2 commits into from
Jun 9, 2020
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FIX: Avoid diverting CIFTI dtype from original BOLD #532

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c5a257f
FIX: Avoid diverting dtype from original
mgxd Jun 2, 2020
915cb01
ENH: apply changes from @effigies review
mgxd Jun 2, 2020
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13 changes: 7 additions & 6 deletions niworkflows/interfaces/cifti.py
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Original file line number Diff line number Diff line change
Expand Up @@ -346,16 +346,16 @@ def _create_cifti_image(
# Create brain models
idx_offset = 0
brainmodels = []
bm_ts = np.empty((timepoints, 0))
bm_ts = np.empty((timepoints, 0), dtype="float32")

for structure, labels in CIFTI_STRUCT_WITH_LABELS.items():
if labels is None: # surface model
model_type = "CIFTI_MODEL_TYPE_SURFACE"
# use the corresponding annotation
hemi = structure.split("_")[-1]
# currently only supports L/R cortex
surf = nb.load(bold_surfs[hemi == "RIGHT"])
surf_verts = len(surf.darrays[0].data)
surf_ts = nb.load(bold_surfs[hemi == "RIGHT"])
surf_verts = len(surf_ts.darrays[0].data)
if annotation_files[0].endswith(".annot"):
annot = nb.freesurfer.read_annot(annotation_files[hemi == "RIGHT"])
# remove medial wall
Expand All @@ -364,7 +364,7 @@ def _create_cifti_image(
annot = nb.load(annotation_files[hemi == "RIGHT"])
medial = np.nonzero(annot.darrays[0].data)[0]
# extract values across volumes
ts = np.array([tsarr.data[medial] for tsarr in surf.darrays])
ts = np.array([tsarr.data[medial] for tsarr in surf_ts.darrays])
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@effigies effigies Jun 2, 2020

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Oh, here would be a good place to use agg_data().

Suggested change
ts = np.array([tsarr.data[medial] for tsarr in surf_ts.darrays])
ts = surf_ts.agg_data()[medial, :]

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@mgxd mgxd Jun 2, 2020

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doesn't agg_data() return a tuple?

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@effigies effigies Jun 2, 2020

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If the image has multiple types of data array, or you request multiple intents, then no.

If you want to be safe (e.g., if we start putting surfaces inside functional files...):

Suggested change
ts = np.array([tsarr.data[medial] for tsarr in surf_ts.darrays])
ts = surf_ts.agg_data("time series")[medial, :]

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@effigies effigies Jun 2, 2020

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FWIW this case is explicitly covered in the docs: https://nipy.org/nibabel/reference/nibabel.gifti.html#nibabel.gifti.gifti.GiftiImage.agg_data

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@mgxd mgxd Jun 2, 2020

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running fmriprep with this change leads to:

   ts = surf_ts.agg_data("time series")[medial, :]
TypeError: tuple indices must be integers or slices, not tuple

I think keeping it as it was is simplest ATM

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@effigies effigies Jun 2, 2020

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Sure. That seems really weird, though. If you can pdb it at some point, I would be interested in the details.

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@effigies effigies Jun 2, 2020

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Oh, if there are no darrays, you do get an empty tuple.


vert_idx = ci.Cifti2VertexIndices(medial)
bm = ci.Cifti2BrainModel(
Expand All @@ -391,7 +391,7 @@ def _create_cifti_image(
else np.concatenate((ts, bold_data[ijk]))
)
vox += [
[ijk[0][ix], ijk[1][ix], ijk[2][ix]] for ix, row in enumerate(ts)
[ijk[0][idx], ijk[1][idx], ijk[2][idx]] for idx in range(len(ts))
]

vox = ci.Cifti2VoxelIndicesIJK(vox)
Expand Down Expand Up @@ -439,7 +439,8 @@ def _create_cifti_image(
matrix.append(geometry_map)
matrix.metadata = ci.Cifti2MetaData(meta)
hdr = ci.Cifti2Header(matrix)
img = ci.Cifti2Image(bm_ts, hdr)
img = ci.Cifti2Image(dataobj=bm_ts, header=hdr)
img.set_data_dtype(bold_img.get_data_dtype())
img.nifti_header.set_intent("NIFTI_INTENT_CONNECTIVITY_DENSE_SERIES")

out_file = "{}.dtseries.nii".format(split_filename(bold_file)[1])
Expand Down