semvec/pypi-packages huggingface dataset:
napari-3d-label-eraser 0.1.3
Simple napari plugin to erase the current label value in 3D for an entire Labels volume.
License: MIT
Requires Python: >=3.9

# napari-3d-label-eraser

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A simple tool to erase a selected label across an entire 3D volume in napari.

## Overview

**napari-3d-label-eraser** provides a fast and convenient way to remove all voxels belonging to a given label in a 3D `Labels` layer.  
This is useful for cleaning segmentation results, removing misdetected objects, or performing manual corrections in volumetric datasets.

The plugin adds a dock widget featuring:
- a dropdown menu to select the target `Labels` layer,
- a button to erase the currently selected label across the entire 3D volume,
- optional usage instructions.

A keyboard shortcut (`A`) can also be used to trigger the erase operation directly from the viewer.

## Features

- Erase all voxels of the selected label throughout a 3D volume.
- Dropdown to choose the active `Labels` layer.
- Compatible with napari’s label picker (`L` key).
- Optional keyboard shortcut (`A`) for quick editing.
- Minimal, lightweight, and easy to integrate into existing workflows.

## Installation

Install with pip:

```bash
pip install napari-3d-label-eraser
```

Or install from the napari plugin manager:

```
Plugins → Install/Uninstall Plugins
```

For development installation:

```bash
git clone https://github.com/koopa31/napari-3d-label-eraser.git
cd napari-3d-label-eraser
pip install -e .
```

## Usage

1. Load a `Labels` layer in napari.
2. Open the plugin:  
   **Plugins → 3D Label Eraser → 3D Label Eraser**
3. In the widget:
   - Select your target `Labels` layer.
   - Choose the label to erase using the label picker (`L` key).
   - Click the button to erase that label across the full 3D volume.
4. Alternatively, press **A** to erase the current label directly from the viewer (focus must be on the viewer).

## Notes

- The plugin operates only on `Labels` layers.
- The erase action sets all voxels with the selected label to `0`.
- The `A` shortcut is registered automatically when the widget is opened.

## Contributing

Issues, feature requests, and pull requests are welcome.

## License

This project is released under the MIT License.
mcf 0.9.0
The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.
License: MIT
Keywords: causal machine learning, heterogeneous treatment effects, causal forests, optimal policy learning

The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.