Command-line tool and python library for visualising small 3D datasets
Project description
smalldataviewer
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.
Installation
pip install smalldataviewer[full]
The full
installation includes all of these optional extras:
hdf5
: HDF5 file support via h5pyimg
: volumetric and animated images via imageiofits
: FITS images viaimageio[fits]
, which uses astropyitk
: ITK images viaimageio[simpleitk]
, which uses SimpleITK
Support for N5 and zarr arrays is also available via z5py.
This must be installed with conda (conda install -c conda-forge -c cpape z5py
).
Usage
The DataViewer
opens a matplotlib
figure of the data volume.
- Dimension 0 can be scrolled through with the mouse wheel
- Dimension 1 is shown on the vertical axis
- Dimension 2 is shown on the horizontal axis
- Dimension 3, if it exists, is a colour tuple
As executable
Available as a command-line utility at smalldataviewer
or sdv
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
e.g.
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.
As library
import smalldataviewer as sdv
import numpy as np
data = np.random.random((30, 100, 100))
viewer = sdv.DataViewer(data)
viewer.show() # or matplotlib.pyplot.show()
viewer2 = sdv.DataViewer.from_file(
"my_data.npz", offset=(10, 20, 30), shape=(256, 512, 512), internal_path="volume"
)
viewer2.show()
reader = sdv.FileReader("my_cat_video.gif")
data2 = reader.read() # returns a numpy array
viewer3 = sdv.DataViewer(data2)
viewer3.show()
Note: FileReader
(and by extension Dataviewer.from_file
) reads the requested data
from the file into memory.
Passing an indexable representation of a file, like a numpy memmap or an hdf5 dataset,
will not.
However, you may need to copy it into memory for performance, or depending on the rest of your script.
Contributing
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 tofile_constructors
. -
Add to
smalldataviewer.files.FileReader
a method which reads such a file, returning a numpy array, and add a mapping from likely file extensions to a single file type inNORMALISED_TYPES
(see existing methods for examples). -
Don't forget to specify any dependencies in
smalldataviewer.ext
,extras_require
insetup.py
, andrequirements.txt
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