Update data_per_streamline capabilities

[skoudoro]

The goal of this PR is to allow inhomogeneous array for data_per_streamlines in a Tractogram.

I encounter this case when I wanted to save the labels of a cluster using Quickbundles from DIPY.

All saved centroids have a different number of streamlines, which make the array inhomogeneous.

data_per_streamline = {}
data_per_streamline['clusters_labels'] = np.array([c.indices
                                                   for c in clusters],
                                                  dtype=object)
data_per_streamline['clusters_sizes'] = clusters.clusters_sizes()

See the following tutorial: https://docs.dipy.org/stable/examples_built/segmentation/segment_quickbundles.html

[codecov]

Codecov Report

Attention: Patch coverage is 84.61538% with 2 lines in your changes are missing coverage. Please review.

Project coverage is 92.26%. Comparing base (0e925ab) to head (c907b45).

Files	Patch %	Lines
nibabel/streamlines/tractogram.py	84.61%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1291      +/-   ##
==========================================
- Coverage   92.27%   92.26%   -0.01%     
==========================================
  Files          99       99              
  Lines       12460    12470      +10     
  Branches     2561     2565       +4     
==========================================
+ Hits        11497    11505       +8     
- Misses        641      642       +1     
- Partials      322      323       +1     

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[effigies]

Is it possible to test this case? I think passing something like np.array([1, np.array([0, 1])], dtype=object)?

[effigies]

Hi Serge, thanks, and apologies for the slow response. I'm going to look into what's going on with the pre-release tests.

[skoudoro]

Hi @effigies,

Thank you for the review. no worries.

I will come back on this next week. What is the ETA of the next nibabel release?

[effigies]

ETA April. Might aim for late March or early May, as April will be busy for me.