nViz Quickstart Guide#
This notebook helps demonstrate how to use nViz quickly.
We use demonstrational data found within this repository
to help show how the software works.
import pathlib
import shutil
from napari.utils import nbscreenshot
import nviz
# setup paths and prepare filesystem
test_image_dir = "../../../tests/data/random_tiff_z_stacks/Z99/"
test_label_dir = "../../../tests/data/random_tiff_z_stacks/labels/"
output_ome_tiff_path = "./example.ome.tiff"
output_zarr_path = "./example.zarr"
# scaling values in micrometers for z, y, and x dimensions.
# Note: This is typically a priori knowledge that is usually
# found in raw microscopy data (for example raw TIFF files
# or other microscopy metadata files [like XML]).
scaling_values = (1.0, 0.1, 0.1)
# If we already have a demo tiff file, remove it.
# (We are demoing the full nViz process from scratch.)
if (tiff_path := pathlib.Path(output_ome_tiff_path)).is_file():
tiff_path.unlink()
# If we already have a demo zarr dir, remove it
# (We are demoing the full nViz process from scratch.)
if (zarr_path := pathlib.Path(output_zarr_path)).is_dir():
shutil.rmtree(output_zarr_path)
# show our images
list(pathlib.Path(test_image_dir).rglob("*.tif"))
[PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS006.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS002.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS008.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS004.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS005.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS002.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS009.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS001.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS008.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS007.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS000.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS009.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS000.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS001.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS002.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS003.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS008.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS006.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS004.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS005.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS002.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS007.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS001.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS008.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS002.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS007.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS000.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS001.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS000.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS004.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS003.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS000.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS004.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS008.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS007.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS003.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS001.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS007.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_111_ZS006.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS006.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS003.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS009.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS006.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS005.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_555_ZS009.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS004.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_444_ZS005.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS003.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_222_ZS009.tif'),
PosixPath('../../../tests/data/random_tiff_z_stacks/Z99/Z99_333_ZS005.tif')]
# show our label files (in this case, segmentation masks)
# Other labels might include outlines, bounding boxes,
# or similar data stored as images which provide
# information about objects of interest.
list(pathlib.Path(test_label_dir).rglob("*.tif"))
[PosixPath('../../../tests/data/random_tiff_z_stacks/labels/compartment.tif')]
# convert original raw TIFF into OME-TIFF format using nViz
nviz_ome_tiff = nviz.tiff_to_ometiff(
# specify an image directory (original images)
image_dir=test_image_dir,
# specify a label (masks or outlines) directory
label_dir=test_label_dir,
# specify an output path
output_path=output_ome_tiff_path,
# Map filename patterns to channel names.
# Note: the keys of this dictionary correspond to information
# found in the file name. The value pairs are customizable, and
# they will display as specified here inside a Napari window.
channel_map={
"111": "Channel A",
"222": "Channel B",
"333": "Channel C",
"444": "Channel D",
"555": "Channel E",
},
# set the scaling for z, y, and x
scaling_values=scaling_values,
)
# show the filepath
nviz_ome_tiff
'./example.ome.tiff'
# view the ome-tiff in napari
# (will open a new window and also returns the viewer object)
viewer = nviz.view_ometiff_with_napari(
ometiff_path=nviz_ome_tiff, scaling_values=scaling_values
)
# show the napari window within this notebook to help demonstrate output
viewer.window.resize(1000, 700)
viewer.camera.angles = (50, 50, 30)
viewer.camera.zoom = 30
nbscreenshot(viewer=viewer, canvas_only=False)
# convert original data into a Zarr which
# includes OME-Zarr images format using nViz
nviz_zarr = nviz.tiff_to_zarr(
# specify an image directory (original images)
image_dir=test_image_dir,
# specify a label (masks or outlines) directory
label_dir=test_label_dir,
# specify an output path
output_path=output_zarr_path,
# Map filename patterns to channel names.
# Note: the keys of this dictionary correspond to information
# found in the file name. The value pairs are customizable, and
# they will display as specified here inside a Napari window.
channel_map={
"111": "Channel A",
"222": "Channel B",
"333": "Channel C",
"444": "Channel D",
"555": "Channel E",
},
# set the scaling for z, y, and x
scaling_values=scaling_values,
)
# show the filepath
nviz_zarr
'./example.zarr'
# view the zarr in napari
# (will open a new window and also returns the viewer object)
viewer = nviz.view_zarr_with_napari(
zarr_dir=output_zarr_path, scaling_values=scaling_values
)
# show the napari window within this notebook to help demonstrate output
viewer.window.resize(1000, 700)
viewer.camera.angles = (50, 50, -30)
viewer.camera.zoom = 30
nbscreenshot(viewer=viewer, canvas_only=False)
