napari-label-focus 0.2.0

A configurable features table widget for Napari.

Configurable Features Table for Napari

demo.webm

This is an extended version of Napari's built-in features table widget, with extra options to:

• Sort table values
• Display a subset of table columns
• Colorize Labels based on feature values

The table is essentially a graphical view of the features attribute of a layer.

The table is optimized for usage with 2D and 3D Labels layers. It can probably be used to display features from other layer types as well, but this hasn't been tested.

Displaying the table

Open the table from Plugins > Features Table in Napari.

The table displays features from the currently selected layer in the layers list. It will automatically update when the layer selection changes. If multiple layers are selected, only features from the first selected layer will be displayed.

Computing custom features

By default, the table displays a label column for the selected Labels layer, along with any pre-existing features that can be matched with the label column (they should be in Pandas DataFrame format with at least a 'label' column).

It is possible to compute features automatically when a new Labels layer is selected, based on a provided featurizer function. Featurizer functions will receive as input the labels layer data as a Numpy array, and should return a Pandas DataFrame with at least a label column, along with any other feature columns.

For example, the following code extends the behaviour of the table to compute and display the area (or volume) of a labels layer:

import pandas as pd
from skimage.measure import regionprops_table

def area_featurizer(labels: np.ndarray) -> pd.DataFrame:
    return pd.DataFrame(regionprops_table(labels, properties=["label", "area"]))

if __name__ == "__main__":
    import napari
    from napari_label_focus import ConfigurableFeaturesTableWidget
    viewer = napari.Viewer()
    widget = ConfigurableFeaturesTableWidget(viewer, featurizers=[area_featurizer])
    viewer.window.add_dock_widget(widget)
    napari.run()

If more than one featurizer is provided, featurizers will be run one by one and the results from each will be merged into a single features DataFrame.

Controlling what clicking on a table row does

By default, clicking on a table row selects the corresponding label in the Labels layer. This behaviour can be extended by adding callback functions to the table_click_callbacks parameter of the table widget. The callback functions receive a selection context object with references to the viewer, selected layer, selected table row, and the table itself.

The following example illustrates how this works:

from napari_label_focus._context import SelectionContext

def print_selection_context(ctx: SelectionContext):
    print(f"Napari viewer: {ctx.viewer}")
    print(f"Selected layer: {ctx.selected_layer}")
    print(f"Selected table row: {ctx.selected_table_idx}")
    print(f"Features table: {ctx.features_table}")

if __name__ == "__main__":
    import napari
    from napari_label_focus import ConfigurableFeaturesTableWidget
    viewer = napari.Viewer()
    widget = ConfigurableFeaturesTableWidget(viewer, table_click_callbacks=[print_selection_context])
    viewer.window.add_dock_widget(widget)
    napari.run()

In this case, the function print_selection_context gets called whenever users click on a table row.

Installation

You can install napari-label-focus via [pip]:

pip install napari-label-focus

Contributing

Contributions are very welcome.

License

This software is distributed under the terms of the BSD-3 license.

Issues

If you encounter any problems, please file an issue along with a detailed description.