ndv 0.5.0

Simple, fast-loading, n-dimensional array viewer, with minimal dependencies.

ndv

Simple, fast-loading, asynchronous, n-dimensional array viewer, with minimal dependencies.

Works in Qt, Jupyter, or wxPython.

import ndv

data = ndv.data.cells3d()  # or any arraylike object
ndv.imshow(data)

ndv.imshow() creates an instance of ndv.ArrayViewer, which you can also use directly:

import ndv

viewer = ndv.ArrayViewer(data)
viewer.show()
ndv.run_app()

[!TIP] To embed the viewer in a broader Qt or wxPython application, you can access the viewer's widget attribute and add it to your layout.

Documentation

For more information, and complete API reference, see the documentation.

Features

• ⚡️ fast to import, fast to show
• 🪶 minimal dependencies
• 📦 supports arbitrary number of dimensions
• 🥉 2D/3D view canvas
• 🌠 supports VisPy or pygfx backends
• 🛠️ support Qt, wx, or Jupyter GUI frontends
• 🎨 colormaps provided by cmap
• 🏷️ supports named dimensions and categorical coordinate values (WIP)
• 🦆 supports most array types, including:
  • numpy.ndarray
  • cupy.ndarray
  • dask.array.Array
  • jax.Array
  • pyopencl.array.Array
  • sparse.COO
  • tensorstore.TensorStore (supports named dimensions)
  • torch.Tensor (supports named dimensions)
  • xarray.DataArray (supports named dimensions)
  • zarr (named dimensions WIP)

See examples for each of these array types in examples

[!NOTE] You can add support for any custom storage class by subclassing ndv.DataWrapper and implementing a couple methods. (This doesn't require modifying ndv, but contributions of new wrappers are welcome!)

Installation

Because ndv supports many combinations of GUI and graphics frameworks, you must install it along with additional dependencies for your desired backend.

See the installation guide for complete details.

To just get started quickly using Qt and vispy:

pip install ndv[qt]

For Jupyter with vispy, (no Qt or wxPython):

pip install ndv[jup]