Napari.jl

This project is a Julia language wrapper around Napari, a multi-dimensional image viewer for Python. The project is undergoing rapid development and is at a prerelease stage.

First, we recommend that you configure PyCall.jl and install Napari into the Python environment used by PyCall.jl. To install Napari, you can try either the Napari.install_with_pip() or Napari.install_with_conda() methods although these are not tested.

This package is currently not in the Julia registry. To install, add it directly from Github as follows:

using Pkg
Pkg.add("Napari")
# Alternatively, use the following to get the development version
# Pkg.add("https://github.com/mkitti/Napari.jl.git")
using Napari # Will use pyimport_conda("Napari") unless parse(Bool, ENV["NAPARI_JL_USE_CONDA"]) == false

using Napari
@view_image Napari.astronaut()

Napari.jl exports napari which is a PyObject referring to the napari module as imported by PyCall.jl. This means that you can use the Pythonic syntax as provided by PyCall.

using Napari

random_noise = rand(UInt8, 512,512)
napari.view_image( random_noise )

using PyCall

astronaut = pyimport("skimage.data").astronaut()
viewer = napari.view_image(astronaut)
viewer.add_image(astronaut[:,:,1])

Compare this to using Napari from Python via this Julia script:

using PyCall

py"""
from skimage import data
import napari

astronaut = data.astronaut()
napari.view_image( astronaut )
"""

You may notice that the label of the image layer is "astronaut" while using Python but is just "Image" when using Julia. This is because the magic naming of the layer is dependent on Python's inspect package, which does not extend into Julia.

To improve the situation, Napari.jl provides a set of macros that provides similar magic naming functionality.

The first macro is @namekw which just adds a name keyword argument to any function.

using PyCall
astronaut = pyimport("skimage.data").astronaut()

viewer = @namekw napari.view_image( astronaut ) # Adds name = "astronaut" keyword argument
@namekw viewer.add_labels( astronaut[:,:,1] .> 100 ) # Adds name = "astronaut[:,:,1] .> 100" keyword argument

For convenience, this package also provides and exports the following macros.

@view_*:

@view_image(expr; kwargs...)   # Equivalent to napari.view_image(expr; name = string( :(expr) ), kwargs... )
@view_points(expr; kwargs...)  # Equivalent to napari.view_points(expr; name = string( :(expr) ), kwargs... )
@view_labels(expr; kwargs...)  # Equivalent to napari.view_labels(expr; name = string( :(expr) ), kwargs... )
@view_shapes(expr; kwargs...)  # Equivalent to napari.view_shapes(expr; name = string( :(expr) ), kwargs... )
@view_surface(expr; kwargs...) # Equivalent to napari.view_surface(expr; name = string( :(expr) ), kwargs... )
@view_vectors(expr; kwargs...) # Equivalent to napari.view_vectors(expr; name = string( :(expr) ), kwargs... )
@view_tracks(expr; kwargs...)  # Equivalent to napari.view_tracks(expr; name = string( :(expr) ), kwargs... )

@add_*:

@add_image(viewer, expr; kwargs...)   # Equvivalent to `viewer.add_image(expr; kwargs...)
@add_points(viewer, expr; kwargs...)  # Equvivalent to `viewer.add_points(expr; kwargs...)
@add_labels(viewer, expr; kwargs...)  # Equvivalent to `viewer.add_labels(expr; kwargs...)
@add_shapes(viewer, expr; kwargs...)  # Equvivalent to `viewer.add_shapes(expr; kwargs...)
@add_surface(viewer, expr; kwargs...) # Equvivalent to `viewer.add_surface(expr; kwargs...)
@add_vectors(viewer, expr; kwargs...) # Equvivalent to `viewer.add_vectors(expr; kwargs...)
@add_tracks(viewer, expr; kwargs...)  # Equvivalent to `viewer.add_tracks(expr; kwargs...)

You can then use these macros to view images in Napari, and the expressions will be used as labels for the image layers.

using PyCall
astronaut = pyimport("skimage.data").astronaut()

using Napari
viewer = @view_image(astronaut) # The macros can be used with parentheses
@add_labels viewer astronaut[:,:,1] .> 100 # They macros can also be used without parentheses and commas

The macros above actually call Napari.view_[layer](data, ... ) and Napari.add_[layer](viewer, data, ...).

These functions may be overloaded for specific types of data and to take advantage of multiple dispatch.

Currently, this package overloads Napari.view_image for the following types:

• ImageMeta from ImageMetadata
• AxisArray from AxisArrays
• AbstractArray{C} where C <: Colorant{T,3} where T <: FixedPoint from Images
• AbstractArray{C} where C <: Colorant{T,4} where T <: FixedPoint from Images
• AbstractArray{C} where C <: Colorant{T,1} where T <: FixedPoint from Images
• AbstractArray{C} where C <: TransparentColor{Gray} from Images
• PermutedDimsArray, wrapped using NumPyArrays
• SubArray, wrapped using NumPyArrays
• Base.ReshapedArray, wrapped using NumPyArrays
• Base.ReinterpretedArray, wrapped using NumPyArrays

For other Julia types, you can overload these methods by importing the appropriate methods:

import Napari: view_image, add_image

This package has been tested against Napari release versions 0.3.9 and 0.4.6.

This package defaults to using pyqt5 and uses PyCall.pygui_start(:qt5) to initialize the the QT event loop. Set environmental variable NAPARI_JL_QT to "false" to disable this. In that case, the GUI event loop must be initialized manually.

It is based around an earlier script, napari.jl, by Mark Kittisopikul that was posted as a gist in Janaruy 2020.

This package is licensed under the 3-Clause "Revised" BSD License.