Priorities about ipyniivue HOT 6 OPEN

Visualization

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@kolibril13 please have a look closely at this. Note how infrequently addVolume/addMesh are called in real world examples compared to loadVolumes/loadMeshes.

I think adding all the volumes/meshes should be the priority, the other "add" APIs can be added later (as an optimization, as i mentioned above).

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It is not clear how to implement lazy loading for local 4D data since I believe this relies on HTTP range requests.

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Great progress! I like your systematic approach, these charts give a good overview of the project!
I was thinking that the next todos could be to investigate this example and adding

• al_min, cal_max,visibility

to class Volume(ipywidgets.Widget)

https://niivue.github.io/niivue/features/additive.voxels.html

var volumeList1 = [
        { url: "../images/mni152.nii.gz" },
        {
          url: "../images/narps-4965_9U7M-hypo1_unthresh.nii.gz",
          colormap: "red",
          cal_min: 2,
          cal_max: 4,
        },
        {
          url: "../images/narps-4735_50GV-hypo1_unthresh.nii.gz",
          colormap: "green",
          cal_min: 2,
          cal_max: 4,
        },
      ];

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It is not clear how to implement lazy loading for local 4D data since I believe this relies on HTTP range requests.

@manzt , niivue does support loading specific volumes from 4D data using loadFromFile. It's just controlled by the limitFrames4D property.

However, this only works for NIFTI files (when the code detects). If not NIFTI, then the entire data range is loaded

However, i'm not sure if loadFromFile is used in the jupyter notebook implementation. Are files served via a local python server, or is data communicated by other means?

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Volumes are serialized to binary data and send in one shot to the front end (over a websocket). We are using the new NVImage(arrayBuffer, ...) API with the array buffer from the iamge.

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