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ENH: Improve PlaneSegmentation.add_roi doc #1308

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29 changes: 24 additions & 5 deletions src/pynwb/ophys.py
Original file line number Diff line number Diff line change
Expand Up @@ -238,21 +238,40 @@
self.reference_images = reference_images

@docval({'name': 'pixel_mask', 'type': 'array_data', 'default': None,
'doc': 'pixel mask for 2D ROIs: [(x1, y1, weight1), (x2, y2, weight2), ...]',
'doc': ('Pixel mask for 2D ROIs: [(x1, y1, weight1), (x2, y2, weight2), ...]. '),
'shape': (None, 3)},
{'name': 'voxel_mask', 'type': 'array_data', 'default': None,
'doc': 'voxel mask for 3D ROIs: [(x1, y1, z1, weight1), (x2, y2, z2, weight2), ...]',
'doc': ('Voxel mask for 3D ROIs: [(x1, y1, z1, weight1), (x2, y2, z2, weight2), ...].'),
'shape': (None, 4)},
{'name': 'image_mask', 'type': 'array_data', 'default': None,
'doc': 'image with the same size of image where positive values mark this ROI',
'doc': ('Image with the same size of image where positive values mark the ROI weights for '
'each pixel of the image.'),
'shape': [[None]*2, [None]*3]},
{'name': 'id', 'type': int, 'doc': 'the ID for the ROI', 'default': None},
allow_extra=True)
def add_roi(self, **kwargs):
"""Add a Region Of Interest (ROI) data to this"""
"""Add a Region Of Interest (ROI) data to this PlaneSegmentation table

Add an ROI using an image mask, a pixel mask, or a voxel mask. An image mask is an image with
the same size of the data image where positive values mark the ROI weights for each pixel of
the image. A pixel mask is a list of triplets [(x1, y1, weight1), (x2, y2, weight2), ...] or
Nx3 array that indicates the ROI weights for each pixel. Pixels without a weight have a
weight of 0. A voxel mask is a list of quadruplets or Nx4 array, that is similar to the pixel
mask, except the format is (x1, y1, z1, weight1).

If the NWB file will be compressed, then using an image mask will usually result in a smaller
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Are we suggesting that users compress the entire NWB file? Is there a way of just compressing the image masks?

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Good point. I will add 'data' to the allowed types for the masks and test this out.

file than using a pixel mask. However, note that the image mask will be represented as an
array of the same size as the image when read into memory.

If the NWB file will not be compressed and there are relatively many pixels that comprise the
mask (i.e., a dense mask), then using an image mask will usually result in a smaller file and
memory usage than using a pixel mask. Conversely, if the NWB file will not be compressed and
there are relatively few pixels that comprise the mask (i.e., a sparse mask), then using a
pixel mask will usually result in a smaller file and memory usage than using an image mask.
"""
pixel_mask, voxel_mask, image_mask = popargs('pixel_mask', 'voxel_mask', 'image_mask', kwargs)
if image_mask is None and pixel_mask is None and voxel_mask is None:
raise ValueError("Must provide 'image_mask' and/or 'pixel_mask'")
raise ValueError("Must provide 'image_mask', 'pixel_mask', or 'voxel_mask'.")

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rkwargs = dict(kwargs)
if image_mask is not None:
rkwargs['image_mask'] = image_mask
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