jupyterlab-hdf 0.1.0

# jupyterlab-hdf5

Open and explore HDF5 files in JupyterLab. Can handle very large (TB) sized files. Now integrated with the standard filebrowser!


Currently in early release. Double clicking on an .hdf5 file in the file browser will open it in a special HDF browser. You can then browse through the groups and open the datasets in the .hdf5 file. All datasets will open read only.

Designed from the ground up to be as efficient as possible. Data will only be fetched as needed to create the visible display. This allows the extension to work with very large files, potentially up to the TB range.

## Installation

`bash pip install jupyterlab_hdf jupyter labextension install jupyterlab-hdf `

This will install both the server extension and the labextension needed by this plugin.

You can also install the labextension via Jupyterlab’s extension manager GUI. Keep in mind that if you use the GUI, you’ll still need to install the jupyterlab_hdf server extension via pip.

## Development

For a development install, clone the repository and then run the following in the repo dir:

`bash pip install . jlpm build:dev `

To watch for/rebuild on changes to this extension’s source code, run:

`bash jlpm run build:watch `

## What’s in this extension

This extension has two parts: an hdf5 filebrowser, and an hdf5 dataset file type.

### HDF5 Filebrowser

Allows you to navigate an .hdf5 file’s groups as though they were directories in a filesystem. Any .hdf5 file on a user’s system can be opened by entering its path (relative to the Jupyterlab home directory) in the box at the top of the browser.

### HDF5 dataset file type

When you open a dataset using the hdf5 filebrowser, a document will open that displays the contents of the dataset via a grid.

## Status of the latest version

• filebrowser

  • works well, and allows you to open datasets via a double click. Some minor UI issues remain (mostly with the breadcrumbs).

• dataset file type - works well. Data will load to the grid dynamically as needed. Read only. The next goal of development is to enable selection/copying of a dataset’s elements.