Mathias Goncalves

@arokem thank you! there are a few outdated pieces, I was going to push my [slides from the coastal coding workshop](https://github.com/ReproNim/coco2019-training/blob/gh-pages/presentations/heudiconv/presentation.md), but fixing up the current slides would be nice!...

@effigies Totally forgot about this PR - I liked your suggestion in https://github.com/nipy/nibabel/pull/932#discussion_r468995046 so I went ahead and added that. Since that now includes IndexMap information, I reworked the original...

I believe the failing tests are unrelated - this is ready for review.

@MichielCottaar @satra thanks for the reviews 😄 > I would suggest to turn around the logic to use the map types as a ground truth of what the user intends...

yes, I was hoping to - but this failing test reveals a problem with the approach of looking up based on map configuration. > (CIFTI_INDEX_TYPE_SCALARS, CIFTI_INDEX_TYPE_BRAIN_MODELS) applies to dscalar.nii and...

@psadil yes, `group_dicoms_into_seqinfos` will need to be altered (or avoided within the pipeline altogether) when PAR/REC files are passed - maybe adding a new cl argument `--parrec` that diverges from...

@eduardklap I don't believe anyone is currently working on this issue. The way I see this implementation going is by adding a function that mimics https://github.com/nipy/heudiconv/blob/6b30c75333d3ee75aca8ae07026b8d034d765adb/heudiconv/dicoms.py#L13 (groups scans into series,...

Here's a talk that is relevant: https://www.youtube.com/watch?v=wbohVjhqg7c

Just writing down a thought I had: I wasn't sure whether to use `nibabel.filebasedimages.FileBasedImage` or `nibabel.spatialimages.SpatialImage` as a general base for an image I want to `load()`. Perhaps there should...

@dkp can you share a few things? - heudiconv version - heuristic - conversion log - dicominfo Thanks!