Description:

iohub 0.1.0

iohub



N-dimensional bioimaging produces data and metadata in various formats,
and iohub aims to become a unified Python interface to the most common formats
used at the Biohub and in the broader imaging community.
Supported formats
Read

OME-Zarr (OME-NGFF v0.4)
Micro-Manager TIFF sequence, OME-TIFF (MMStack), and NDTiff datasets
Custom data formats generated by Biohub microscopes

Supported: Falcon (PTI), Dorado (ClearControl), Dragonfly (OpenCell OME-TIFF), Mantis (NDTiff)
WIP: DaXi



Write

OME-Zarr
Multi-page TIFF stacks organized in a directory hierarchy that mimics OME-NGFF (WIP)

Quick start
Installation
Install a pre-release of iohub with pip:
pip install iohub

Or install the latest Git version:
git clone https://github.com/czbiohub-sf/iohub.git
pip install /path/to/iohub


For more details about installation, see the related section in the contribution guide.

Command-line interface
To check if iohub works for a dataset:
iohub info /path/to/data/

The CLI can show a summary of the dataset,
point to relevant Python calls,
and convert other data formats to the latest OME-Zarr.
See the full CLI help message by typing iohub or iohub [command] --help in the terminal.
Working with OME-Zarr
Load and modify an example OME-Zarr dataset:
import numpy as np
from iohub import open_ome_zarr

with open_ome_zarr(
"20200812-CardiomyocyteDifferentiation14-Cycle1.zarr",
mode="r",
layout="auto",
) as dataset:
dataset.print_tree() # prints the hierarchy of the zarr store
channel_names = dataset.channel_names
print(channel_names)
img_array = dataset[
"B/03/0/0"
] # lazy Zarr array for the raw image in the first position
raw_data = img_array.numpy() # loads a CZYX 4D array into RAM
print(raw_data.mean()) # does some analysis

with open_ome_zarr(
"max_intensity_projection.zarr",
mode="w-",
layout="hcs",
channel_names=channel_names,
) as dataset:
new_fov = dataset.create_position(
"B", "03", "0"
) # creates fov with the same path
new_fov["0"] = raw_data.max(axis=1).reshape(
(1, 1, 1, *raw_data.shape[2:])
) # max projection along Z axis and prepend dims to 5D
dataset.print_tree() # checks that new data has been written

For more about API usage, refer to the documentation
and the example scripts.
Reading Micro-Manager TIFF data
Read a directory containing a TIFF dataset:
from iohub import read_micromanager

reader = read_micromanager("/path/to/data/")
print(reader.shape)

Why iohub?
This project is inspired by the existing Python libraries for bioimaging data I/O,
including ome-zarr-py, tifffile and aicsimageio.
They support some of the most widely adopted and/or promising formats in microscopy,
such as OME-Zarr and OME-TIFF.
iohub bridges the gaps among them with the following features:

Efficient reading of data in various TIFF-based formats produced by the Micro-Manager/Pycro-Manager acquisition stack.
Efficient and customizable conversion of data and metadata from TIFF to OME-Zarr.
Pythonic and atomic access of OME-Zarr data with parallelized analysis in mind.
OME-Zarr metadata is automatically constructed and updated for writing,
and verified against the specification when reading.
Adherence to the latest OME-NGFF specification (v0.4) whenever possible.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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