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Could you add an example of how to load such an image and perhaps perform smoothing?
This is how I started in the proc.py:
from lys import *
from skimage import io, data
import numpy as np
def load_img():
img = data.cells3d() #data is (60, 2, 256, 256) (Z,C,Y,X)
print(img.shape)
img = np.moveaxis(img, 0, 3)
img = np.moveaxis(img, 0, 3) # make axes to (256,256,2,60)
print(img.shape)
return img
ALso, with visualization, how do I setup multicut to toggle or visualize both channels?
i.e., in the canvas I want to visualize XY, have a line graph or slider at bottom so I can scroll through Z.
Be able to look at C dimension index 0 and index 1.
I added the example page in the document that shows how to visualize/smooth cells3d data.
I recommend you to check basic tutorial before reading the example above, if you have not read it.
Please let me know if you have any questions and comments regarding the cells3d example.
This is great. I had a brief look at the basic tutorial, but looking at your example page, I have a better idea of how to handle axes and importing data..
Hi @lys-devel
When you say multidimensional data, I'm used to working with 3D or 4D microscopy image datasets.
For example, this data from scikit-image: https://scikit-image.org/docs/stable/api/skimage.data.html#skimage.data.cells3d
Could you add an example of how to load such an image and perhaps perform smoothing?
This is how I started in the proc.py:
ALso, with visualization, how do I setup multicut to toggle or visualize both channels?
i.e., in the canvas I want to visualize XY, have a line graph or slider at bottom so I can scroll through Z.
Be able to look at C dimension index 0 and index 1.
Let me know if this isn't clear.
Cheers
Pradeep
openjournals/joss-reviews#5869
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