{
    "info": {
        "author": "Chris L Barnes",
        "author_email": "barnesc@janelia.hhmi.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# smalldataviewer\n\n[![Travis](https://img.shields.io/travis/clbarnes/smalldataviewer.svg)](https://travis-ci.org/clbarnes/smalldataviewer)\n[![PyPI](https://img.shields.io/pypi/v/smalldataviewer.svg)](https://pypi.org/project/smalldataviewer/)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/smalldataviewer.svg)](https://www.python.org/)\n\nSimple matplotlib-based tool for viewing small amounts of 3D image data;\nhelpful for debugging. Supports python 2.7 and 3.4+.\n\nAdapted from [this matplotlib recipe](https://matplotlib.org/gallery/animation/image_slices_viewer.html).\n\n\n![Small ssTEM Volume](https://media.giphy.com/media/fWggPcSFuQH0ZTAHKG/giphy.gif)\n\n\n## Installation\n\n```bash\npip install smalldataviewer[all]\n```\n\nThe `all` installation includes all of these optional extras:\n\n- `hdf5`: HDF5 file support via [h5py](http://docs.h5py.org/en/latest/build.html)\n- `img`: volumetric and animated images via [imageio](https://imageio.readthedocs.io)\n- `fits`: FITS images via `imageio[fits]`, which uses [astropy](http://www.astropy.org/)\n- `itk`: ITK images via `imageio[simpleitk]`, which uses [SimpleITK](http://www.simpleitk.org/)\n\nSupport for N5 and zarr arrays is also available via [z5py](https://github.com/constantinpape/z5).\nThis must be installed with conda (`conda install -c conda-forge -c cpape z5py`).\n\n## Usage\n\nThe `DataViewer` opens a `matplotlib` figure of the data volume.\n\n-   Dimension 0 can be scrolled through with the mouse wheel\n-   Dimension 1 is shown on the vertical axis\n-   Dimension 2 is shown on the horizontal axis\n-   Dimension 3, if it exists, is a colour tuple\n\n### As executable\n\nAvailable as a command-line utility at `smalldataviewer` or `sdv`\n\n```help\nusage: smalldataviewer [-h] [--version] [-i INTERNAL_PATH] [-t TYPE]\n                       [-o ORDER] [-f OFFSET] [-s SHAPE] [-v] [-l]\n                       path\n\npositional arguments:\n  path                  Path to file containing a 3D dataset\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --version             Print version information and exit\n  -i INTERNAL_PATH, --internal_path INTERNAL_PATH\n                        Internal path of dataset inside HDF5, N5, zarr or npz\n                        file. If JSON, assumes the outer object is a dict, and\n                        internal_path is the key of the array\n  -t TYPE, --type TYPE  Dataset file type. Inferred from extension if not\n                        given.\n  -o ORDER, --order ORDER\n                        Order of non-channel axes for axis labelling purposes\n                        (data is not transposed): dimension 0 will be scrolled\n                        through, dimension 1 will be on the up-down axis,\n                        dimension 2 will be on the left-right axis, and\n                        dimension 3, if it exists, will be used as the colour\n                        channels. Default \"zyx\".\n  -f OFFSET, --offset OFFSET\n                        3D offset of ROI from (0, 0, 0) in pixels, in the form\n                        \"<scroll>,<vertical>,<horizontal>\"\n  -s SHAPE, --shape SHAPE\n                        3D shape of ROI in pixels, in the form\n                        \"<scroll>,<vertical>,<horizontal>\"\n  -v, --verbose         Increase logging verbosity\n  -l, --label           Whether to treat images as a label volume\n```\n\ne.g.\n\n```bash\nsmalldataviewer my_data.hdf5 -i /my_group/my_volume\n```\n\nNote: because of the circumstances under which python holds file\ndescriptors open, and under which matplotlib blocks, the executable form\nreads the data into memory in its entirety. If your data are too big for\nthis, look at small chunks with the `--offset` (`-f`) and `--shape`\n(`-s`) options.\n\n### As library\n\n```python\nimport smalldataviewer as sdv\n\nimport numpy as np\ndata = np.random.random((30, 100, 100))\nviewer = sdv.DataViewer(data)\nviewer.show()  # or matplotlib.pyplot.show()\n\nviewer2 = sdv.DataViewer.from_file(\n    \"my_data.npz\", offset=(10, 20, 30), shape=(256, 512, 512), internal_path=\"volume\"\n)\nviewer2.show()\n\nreader = sdv.FileReader(\"my_cat_video.gif\")\ndata2 = reader.read()  # returns a numpy array\nviewer3 = sdv.DataViewer(data2)\nviewer3.show()\n```\n\nNote: `FileReader` (and by extension `Dataviewer.from_file`) reads the requested data\nfrom the file into memory.\nPassing an indexable representation of a file, like a numpy memmap or an hdf5 dataset,\nwill not.\nHowever, you may need to copy it into memory for performance, or depending on the rest of your script.\n\n## Contributing\n\nInstall a development environment (not including z5py) with\n`make install-dev`\n\nRun tests in your current python environment with `make test`\n\nRun tests against all supported python versions with `make test-all`\n\nIf you would like to add support for a new file type:\n\n1. 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