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. Supports python 2.7 and 3.4+.
Adapted from this matplotlib recipe.
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 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. If JSON, assumes the outer object is a dict, and internal_path is the key of the array -t TYPE, --type TYPE Dataset file type. Inferred from extension if not given. -o ORDER, --order ORDER Order of non-channel 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, and dimension 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, in the form "<scroll>,<vertical>,<horizontal>" -s SHAPE, --shape SHAPE 3D shape of ROI in pixels, in the form "<scroll>,<vertical>,<horizontal>" -v, --verbose Increase logging verbosity
smalldataviewer my_data.hdf5 -i /my_group/my_volume
Note: because of the circumstances under which python holds file descriptors open, and under which matplotlib blocks, the executable form reads the data into memory in its entirety. If your data are too big for this, look at small chunks with the --offset (-f) and --shape (-s) options.
from smalldataviewer import DataViewer, dataviewer_from_file import numpy as np data = np.random.random((30, 100, 100)) viewer = DataViewer(data) viewer.show() # or matplotlib.pyplot.show() viewer2 = DataViewer.from_file("my_data.npz", "volume") viewer2.show()
Note: Dataviewer.from_file reads the requested data from the file into memory. DataViewer does not, by default. However, you may need to, depending on the rest of your script.
Support for many data formats comes from the excellent library imageio. Even more formats are available with plugins: see the imageio docs for more details.
Install a development environment (not including z5py) with make install-dev
Run tests in your current python environment with make test
Run tests against all supported python versions with make test-all
If you would like to add support for a new file type:
- Add to tests/common a function which creates such a file and returns whether
it needs an internal path, and add it to file_constructors.
- Add to smalldataviewer.files.FileReader a function which reads such a file,
returning a numpy array, and add a mapping from likely file extensions to a single file type in NORMALISED_TYPES (see examples).
- Don’t forget to specify any dependencies in smalldataviewer.ext,
extras_require in setup.py, and requirements.txt
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