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Images

Supported formats

Via bioio:

Format Reader
TIFF / OME-TIFF bioio-tifffile
OME-Zarr bioio-ome-zarr
Any bioio-supported format appropriate bioio-* plugin 
from bioio import BioImage
import bioio_tifffile
import bioio_ome_zarr

img = BioImage("image.tif",  reader=bioio_tifffile.Reader)
img = BioImage("image.zarr", reader=bioio_ome_zarr.Reader)

Or pass a NumPy array directly — the viewer infers the dimension order:

viewer.set_image(array)  # 2D, CYX, CZYX, or TCZYX

Multi-dimensional navigation

When the image has T > 1 or Z > 1, the widget shows time and Z sliders. Channel count is reflected in the Layers panel.

img = BioImage("image.zarr", reader=bioio_ome_zarr.Reader)
print(img.shape)  # e.g. (10, 3, 5, 2048, 2048) → T=10, C=3, Z=5

viewer.set_image(img)
# → T slider (0–9), Z slider (0–4), 3 channels in Layers panel

Multi-channel display

Channels are rendered as a composite. Each channel has:

  • Color (hex) — mapped from intensity to that hue
  • Min / Max — contrast limits (0–1 normalized)
  • Visible toggle

Controls are in the Layers dropdown in the toolbar. They can also be set programmatically:

settings = list(viewer._channel_settings)
settings[0] = {**settings[0], "name": "DAPI",  "color": "#0000ff"}
settings[1] = {**settings[1], "name": "GFP",   "color": "#00ff00"}
settings[2] = {**settings[2], "name": "mCherry","color": "#ff0000"}
viewer._channel_settings = settings

Performance

For large images, BioImage with an OME-Zarr reader enables lazy loading. The viewer uses a tile-based rendering pipeline that precomputes visible tiles across all T/Z positions in the background — T/Z scrubbing becomes instant after the initial precompute pass.

Pan and zoom send the current viewport to Python so the precompute prioritizes visible tiles.