Napari scripts

A collection of napari related tools in various state of disrepair/functionality.

napari_colormaps_widget.py

This is a script that can be run as:
python napari_colormaps_widget.py
It will launch a viewer with an example single channel image and add widget that permits extended colormap/LUT features. There will be 3 tabs:

1. Allows access to inverted ChrisLUTs by @cleterrier (https://github.com/cleterrier/ChrisLUTs)
2. Allows one to make a colormap by specifying a name, whether it should be inverted, and the end-point color.
3. Allows for importing of ImageJ .lut files (both ascii and binary) and applying them to layers in napari.

Note: inverted LUTs are not yet supported for multichannel images in napari. Also, when you apply a colormap to an image, it will persist in the napari colormaps menu for the duration of the viewer (even with the widget closed).

napari_line_profile_widget.py

This is a module that can be imported, for example:

import napari_line_profile_widget as linepro

and then permits:

line_plot = linepro.profile_line(<insert name of napari viewer>) 

This will add a shape layer with a red line and widget at the bottom of the Napari window. The widget will display a plot of the pixel instensities along the red line, as you move the red line or change z- or t-stack slice. The top most image layer will be used for the intensity data and the x-axis of the plot should be consistent with any scale data provided to napari. If you close and open a new image, move the line/change slice to update. You can also get a nice figure (6"x3", 300 dpi) of the current viewer status and the line profile:

linepro.get_figure(line_plot, <insert name of napari viewer>)

If you don't want the viewer status and just want the plot, pass screenshot=False The figure can be saved, for example as PDF:

linepro.get_figure(line_plot, <insert name of napari viewer>, name="test_profile.pdf")

napari_measure_widget.py

This is a module that can be imported, for example:

import napari_measure_widget as nmw

and then permits:

nmw.measure_shape(<insert name of napari viewer>)

This will add a shape layer to the napari window and create a widget to display a table of results. By default, the key m will be bound to measuring:

• length of lines drawn with line or path tools
• angle between path segments, for the case of a path of two segments (3 points)
• area of shapes drawn with rectangle, polygon, or ellipse tools The last drawn shape will be measured, unless a shape is selected. The keybind can be changed, by passing a different keybind as a string. For example, to set the keybind to z:

nm.measure_shape(<insert name of napari viewer>, keybind="z")

By default, existing keybinds will not be overwritten. To overwrite an existing keybind (for example, if re-opening the widget), you must pass overwrite=True. The measurements will be displayed in a table in the widget. Make sure there is an open, visible image layer, so that the measurements can take into account any scale and unit information.

Browse_LIF_widget.py

This functionality is now part of the napari-aicsimageio plugin, which supports other file types, as well. Please test it! Note: empty/singleton dimensions may be handled differently, so leave feedback.

This module can be imported, for example:

import napari_scripts.Browse_LIF_widget as BL

it then can be used to open Napari with a LIF browser widget:

viewer = BL.lif_widget()

This Napari viewer will have a empty widget on the right, where you can drag-and-drop a LIF. Make sure you drop it on the side panel, not the main/middle Napari canvas Using aicsimageio, the widget will import the LIF and prepare a list of scenes. Clicking on a scene should load the chosen scene as an image layer. Note: the Image will have all MTCZYX channels, to permit browsing all types of scenes. The returned viewer can be used for other manipulations, such listing the selected scenes: viewer.layers