Skip to main content

A napari plugin for drawing PALM RoboSoftware elements using StarDist segmentation

Project description

🦇 Bruce

A napari plugin for drawing PALM RoboSoftware elements using StarDist segmentation


Quick start

After installation (see below), open a terminal and run:

# Launch Bruce via the command line
bruce

# List all available options 
bruce -h

Features

  • Load 2-channel images and metadata from .zvi files produced by PALM RoboSoftware 4.5
  • Perform StarDist-based cell segmentation (default or user-defined models)
  • Allow manual editing of ROIs / elements in napari
  • Perform ROI overlap analysis between 2 channels
  • Export element list as .txt file compatible with PALM RoboSoftware 4.5

System requirements

  • Conda / Mamba (recommended)
  • Java (OpenJDK) – required for Bio-Formats .zvi → OME-TIFF conversion
  • GPU (optional) – for accelerated StarDist inference

Installation

Bruce requires a platform-specific Conda environment due to differences in native dependencies and GPU support. Predefined environment files are provided in the env/ directory:

| Platform                | Environment file                     |
|-------------------------|--------------------------------------|
| Windows (native)        | `env/bruce-env_windows_native.yml`   |
| macOS (Apple Silicon)   | `env/bruce-env_macos_arm.yml`        |
| Linux                   | `env/bruce-env_linux.yml`            |

Open a terminal and run:

# Create the conda environment (replace <ENV_FILE> with the appropriate .yml file)
mamba env create -f <ENV_FILE>

# Activate the environment
mamba activate bruce-env

# Install Bruce from PyPI
pip install napari-bruce

# Launch Bruce via the command line
bruce

# Or launch Bruce directly from napari
napari --with napari-bruce

Configuration

Bruce stores its configuration in a user-specific JSON file.

Useful commands:

# Show config file path
bruce --show-config-path

# Open config in default editor
bruce --edit-config

# Reset config to defaults
bruce --reset-config

GPU support & StarDist models

Bruce runs StarDist predictions on the GPU when visible to TensorFlow, and supports user-defined StarDist models.

Useful commands:

# Check whether GPU(s) are visible to TensorFlow
bruce --gpu-status

# List available StarDist models
bruce --list-models

# Add a user-defined StarDist model (replace <MODEL_DIR> with the model directory)
bruce --add-model <MODEL_DIR>

Example images

Example .zvi files are provided in the example_images/ directory for testing and demonstration purposes.

They are NOT installed with the napari-bruce Python package.

To use them:

  1. Clone or download the repository from GitHub
  2. Launch Bruce
  3. Load the example files via the GUI

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

napari_bruce-0.1.0.tar.gz (83.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

napari_bruce-0.1.0-py3-none-any.whl (83.6 MB view details)

Uploaded Python 3

File details

Details for the file napari_bruce-0.1.0.tar.gz.

File metadata

  • Download URL: napari_bruce-0.1.0.tar.gz
  • Upload date:
  • Size: 83.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for napari_bruce-0.1.0.tar.gz
Algorithm Hash digest
SHA256 d0b155486acb79a4c58bbfde189178c9f0c1c12499639ed9f1467988b51ed3a9
MD5 584105acdb8acf9ad12391c4147751c9
BLAKE2b-256 77c19e65a8c79fbb7433e521de20eb0c9dfd2791450f5636ff9a497e90be0b13

See more details on using hashes here.

File details

Details for the file napari_bruce-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: napari_bruce-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 83.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for napari_bruce-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 63a1b6f18ef52907b21bed097fc06a968181e9f0912ffaef184d83e1ddce1f45
MD5 eebd31bbd146f49dd4fba4ef131d4d1d
BLAKE2b-256 6251d09f5cf6c1499d058088660b4338782519935f711f9ad8f50b49a9cadb26

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page