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Command-line tool and python library for visualising small 3D datasets

Project description

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:

  • HDF5: h5py

    • pip install h5py

  • zarr, N5: z5py

    • conda install -c cpape -c conda-forge z5py


As executable

usage: smalldataviewer [-h] [-i INTERNAL_PATH] [-t TYPE] [-o ORDER]
                       [-f OFFSET] [-s SHAPE] [-v]

positional arguments:
  path                  Path to HDF5, N5, zarr, npy, npz or JSON 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 outerobject is a dict, and
                        internal_path is the key of the array
  -t TYPE, --type TYPE  Dataset file type. Inferred from extension if not
  -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, in the form
  -s SHAPE, --shape SHAPE
                        3D shape of ROI in pixels, in the form
  -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 and Shape options.

As library

from smalldataviewer import DataViewer, dataviewer_from_file

import numpy as np
data = np.random.random((30, 100, 100))

viewer = DataViewer(data)  # or

viewer2 = dataviewer_from_file(dataviewer, "my_data.npz", "volume")

Note: dataviewer_from_file reads the requested data into memory. DataViewer does not, by default. However, you may need to, depending on the rest of your script.

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