Command-line tool and python library for visualising small 3D datasets
Simple matplotlib-based tool for viewing small amounts of 3D image data; helpful for debugging.
Due to the circumstances in which matplotlib holds open figure windows, and stops updates during blocks, the entire array must be read into memory before it can be viewed. It is a smalldataviewer after all…
pip install smalldataviewer
Some file types require additional dependencies:
usage: smalldataviewer [-h] [-i INTERNAL_PATH] [-t TYPE] [-o ORDER] [-f OFFSET] [-s SHAPE] [-v] path positional arguments: path Path to HDF5, N5, zarr, npy or npz file containing a 3D dataset optional arguments: -h, --help show this help message and exit -i INTERNAL_PATH, --internal_path INTERNAL_PATH Internal path of dataset inside HDF5, N5, zarr or npz file -t TYPE, --type TYPE Dataset file type. Inferred from extension if not given. -o ORDER, --order ORDER Order of spatial axes for axis labelling purposes. Data is not transposed: dimension 0 will be scrolled through, dimension 1 will be on the up-down axis, dimension 2 will be on the left-right axis, anddimension 3, if it exists, will be used as the colour channels. Default "zyx". -f OFFSET, --offset OFFSET 3D offset of ROI from (0, 0, 0) in pixels -s SHAPE, --shape SHAPE 3D shape of ROI in pixels -v, --verbose Increase output verbosity >>> smalldataviewer.py my_data.hdf5 -i /my_group/my_volume
from smalldataviewer import DataViewer, dataviewer_from_file # data can be anything which slices like a np.ndarray (e.g. h5py dataset) import numpy as np data = np.random.random((30, 100, 100)) viewer = DataViewer(data) viewer.show() # or matplotlib.pyplot.show() viewer2 = dataviewer_from_file(dataviewer, "my_data.npz", "volume") as viewer2: viewer2.show()
Because of the circumstances under which python holds file descriptors open and matplotlib blocks, file-based volumes may need to read into numpy arrays to be scrolled through if the .show() is at the end of the script.
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