Hi @haehn, great to hear from you. Eventually DPY should be the preferred option. However, we are not promoting it much for now as we want to add some crucial new options. Can you give us an example of your user case?
Thank you, @Garyfallidis ! The use case is standard fiber bundles with flexible numbers of scalars (per fiber point) and flexible number of properties (per fiber). TRK files limit it to 10 each.
Is that supported yet with DPY?
@haehn we can easily have support for any number of properties in DPY. However, not there yet. I will try to prioritize it so that others like you can be facilitated. Alternatively, you can create a very fast format with NPZ using the Streamlines API that we developed in Nibabel and use DIPY. Basically a Streamlines object can give you the arrays of the points and the metrics (of any number - look at the data_per_point attributes/parameters) as numpy arrays.
Look at the parameters that a Tractogram object can have data_per_point and per_streamline. You can then save these data as independent arrays and reconstruct the Tractogram on load.
Hi all! I'm a fairly new user of dipy, and I've been doing some playing around with the HARDI labeled dataset accessible via dipy.data.read_stanford_labels(). I saw that this is relabeled, such that white matter voxels have the label 1 or 2, regardless of what labels freesurfer originally gave them. I was wondering if it is possible to gain access to the original freesurfer labels for these voxels, however they were reduced from the original aparc+aseg file? Then I could delineate different white matter regions according to that parcellation. Thanks in advance for the help!
And don't forget to save the streamlines too. From them Tractogram.streamlines you will need the data, offsets and lengths arrays to be saved in the NPZ (numpy compressed) format. It's an idea. Let me know if you try it. Right now we are busy with the upcoming release but building a tutorial with these issues and updating the DPY format should be an easy and fun project.
@haehn issue added here nipy/dipy#1936 and targeted for release 1.1 aimed in October.
Thank you so much @Garyfallidis ! I will follow the DPY issue and also try the NPZ.. then I will compare file size against the TRK and VTP formats.
Casa Mofoekeng 'Moso
Hello, I am trying to implement Python code when given the names and GPS positions of 750 people (latitude, longitude and elevation) to find the names of the 10 closest neighbors of a randomly selected individual.
This is not the right channel for this question @CasaMofoekengMo_twitter but hey you can google KNN and python I am sure they will be many solutions available. Best of luck.