PyMIF — Python code for users of the Mesoscopic Imaging Facility

PyMIF (source code here) is a modular Python package to read, visualize, and write multiscale (pyramidal) microscopy image data from a variety of microscope platforms available at the Mesoscopic Imaging Facility (MIF) into the OME-NGFF (Zarr) format.

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📦 Features

• ✅ Read and parse image metadata from multiple microscope vendors and data formats:

  • Viventis (.ome + .tif)

  • Luxendo (.xml + .h5)

  • Opera PE (.ome.tiff)

  • Zeiss (.czi)

  • Generic OME-Zarr

  • Numpy or Dask array

• ✅ Abstract base class MicroscopeManager ensures uniform interface for all readers

• ✅ Lazy loading via Dask for memory-efficient processing

• ✅ Build pyramidal (multiscale) OME-Zarr archives from raw data or existing pyramids

• ✅ Write OME-Zarr with:

  • Blosc or GZIP compression

  • Nested directory layout

  • Full NGFF + OMERO metadata (channel names, colors, scales, units)

  • Optional parallelization with dask-distribute

• ✅ Visualize pyramids in Napari using napari-ome-zarr plugin:

  • Using lazy loading for fast visualization, or

  • Using in-memory loading of any resolution layer for interactivity.

• ✅ Compatible with automated workflows and interactive exploration (Jupyter + scripts)

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🗂️ Project Structure

pymif/
├── pymif
│ └── microscope_manager
│   ├── luxendo_manager.py
│   ├── viventis_manager.py
│   ├── opera_manager.py
│   ├── zeiss_manager.py
│   ├── zarr_manager.py
│   ├── array_manager.py
│   ├── microscope_manager.py
│   └── utils/
│    ├── pyramid.py
│    ├── visualize.py
│    ├── add_labels.py
│    └── write.py
│
├── examples/
| ├── example_luxendo.ipynb
| ├── example_viventis.ipynb
| ├── example_opera.ipynb
| ├── example_zeiss.ipynb
| ├── example_zarr.ipynb
│ └── example_array.ipynb
│
├── tests/
│ └── ...
│
├── requirements.txt
├── setup.py
└── README.md

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🚀 Getting Started

📥 Installation

It is recommended to install pymif in a clean conda environment:

conda create -n pymif python=3.10
conda activate pymif

Installation is then done by cloning the repository:

git clone https://github.com/grinic/pymif.git
cd pymif
python -m pip install -e .

📚 Example Usage

With the following code, we read Viventis image data and parse the corresponding metadata. Next, we build a pyramidal structure of 3 resolution layers and save it into an OME-Zarr format. Finally, we load the new dataset and visualize it in napari.

import pymif.microscope_manager as mm

dataset = mm.ViventisManager("path/to/Position_1")
dataset.build_pyramid(num_levels=3)
dataset.write("output.zarr")
dataset_zarr = mm.ZarrManager("output.zarr")
viewer = dataset_zarr.visualize(start_level=0, in_memory=False)

Demonstration of pymif usage. Data: near newborn mouse embryo (~1.5 cm long). Fluorescence signal: methylene blue + autofluorescence. Sample processed and imaged by Montserrat Coll at the Mesoscopic Imaging Facility. Video speed: 2.5X real speed.

For more examples, see examples.

🧪 Running Tests

pytest tests/

➕ Adding New Microscope Support and Contributing

Contributions/PRs are welcome! If you would like to help and add a new format:

• Subclass MicroscopeManager

• Implement read() returning:

Tuple[List[dask.array], Dict[str, Any]]

• Follow this metadata schema:

{
  "size": [... per level ...],
  "scales": [... per level ...],
  "units": (...),
  "axes": "tczyx",
  "channel_names": [...],
  "channel_colors": [...],
  "time_increment": ...,
  "time_increment_unit": ...,
  ...
}

You will automatically inherit all MicroscopeManager methods, including:

• build_pyramid(),

• write(),

• visualize(),

• reorder_channels(),

• update_metadata()