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
Some file types require additional dependencies:
Usage
As executable
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.
As library
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.
Other formats
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.
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 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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for smalldataviewer-0.6.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1e7b81ba15e0f11f2b078b3088c5b467921075e0db3b8ceb1a8ba0c0366fc4b3 |
|
MD5 | 963fec97fc0ad29dc2c5d9159037737a |
|
BLAKE2b-256 | e25b47de6c6f34f4fa4859fe4e2326fdae10b04b639c2c064fed76921634d319 |