nViz

This project focuses on ingesting a set of TIFF images as OME-Zarr or OME-TIFF. Each input image set^1^ are organized by channel and z-slices which form four dimensional (4D) microscopy data. These 4D microscopy data contain information for biological objects (such as organoids).

We read the output with Napari, which provides a way to analyze and understand the 3D image data.

1. Image set is loosely defined and changes depending on the context of the data. Here it represents a set of images in multiple dimensions that contain information regarding the same sample. Each image in an imageset is paired data and must be related as such.

Installation

Install nViz from PyPI or from source:

# install from pypi
pip install nviz

# install directly from source
pip install git+https://github.com/WayScience/nViz.git

Contributing, Development, and Testing

Please see our contributing documentation for more details on contributions, development, and testing.

Contents:

• Python API
  • nviz.image
    • image_set_to_arrays()
    • tiff_to_ometiff()
    • tiff_to_zarr()
  • nviz.image_meta
    • extract_z_slice_number_from_filename()
    • gather_scaling_info_from_scaninfoxml()
    • generate_ome_xml()
  • nviz.view
    • view_ometiff_with_napari()
    • view_zarr_with_napari()
• Contributing
  • Code of conduct
  • Development
    • Development setup
    • Linting
    • Testing
  • Making changes to this repository
    • Creating a pull request
    • Pull requests
  • Versioning
    • Releases
  • Documentation
• Code of Conduct
  • Our Pledge
  • Our Standards
  • Enforcement Responsibilities
  • Scope
  • Enforcement
  • Enforcement Guidelines
    • 1. Correction
    • 2. Warning
    • 3. Temporary Ban
    • 4. Permanent Ban
  • Attribution