czitools
This repository provides tools for reading CZI (Carl Zeiss Image) pixel data and metadata in Python, interpreting CZI well plates as an HCS Plate → Well → Field model, and converting CZI data to OME-Zarr. It is available as a Python package on PyPI.
For full documentation see sebi06.github.io/czitools.
Installation
Basic Installation
Install the core package from PyPI:
pip install czitools
Optional Features
Install with additional functionality using optional extras:
# OME-ZARR export (conversion + validation)
pip install "czitools[omezarr]"
# OME-ZARR export with GUI converter application
pip install "czitools[omezarr-gui]"
# HCS plate analysis and visualization
pip install "czitools[analysis]"
# Everything (all optional dependencies)
pip install "czitools[all]"
Development Installation
For development or to get the latest unreleased features:
# Clone the repository
git clone https://github.com/sebi06/czitools.git
cd czitools
# Install in editable mode with all extras
pip install -e ".[all]"
Conda/Pixi Development Environment
The cloned repository includes both a conda environment file and a Pixi workspace:
# Create the provided conda environment
conda env create -f env_czitools.yml
conda activate czitools
# Or install the locked Pixi workspace
pixi install
For more details see the Installation docs.
Quick Start
from czitools.metadata_tools import CziMetadata
from czitools.read_tools import read_6darray, read_stacks_list
# Read metadata without loading pixels.
mdata = CziMetadata("path/to/file.czi")
print(mdata.image_required.SizeC)
print(mdata.scale_required.X)
# Read regular, equal-sized scenes eagerly as a labelled STCZYX(A) array.
array6d, mdata = read_6darray("path/to/file.czi", use_xarray=True)
# For true on-demand Dask reads, keep scenes as a list.
scenes, dims, scene_count, mdata = read_stacks_list(
"path/to/file.czi",
use_dask=True,
use_xarray=True,
)
first_plane = scenes[0].isel(T=0, C=0, Z=0).compute()
read_6darray(..., use_dask=True) produces a Dask-backed result but still
reads the CZI eagerly. Use read_stacks(..., use_dask=True) or
read_stacks_list(..., use_dask=True) for genuinely lazy pixel access.
For detailed usage examples see the Usage docs.
Features
CZI Well Plates and OME-Zarr HCS
from czitools.export_tools import convert_czi2hcs_ngff, validate_ome_zarr
from czitools.metadata_tools import CziMetadata
from czitools.read_tools import read_field
filepath = "path/to/plate.czi"
mdata = CziMetadata(filepath)
if mdata.hcs is None:
raise ValueError(mdata.hcs_status.reason)
well = mdata.hcs.get_well("B04")
field, _ = read_field(filepath, well="B04", field=0)
# Requires: pip install "czitools[omezarr]"
output = convert_czi2hcs_ngff(filepath, overwrite=True)
assert validate_ome_zarr(output)
Well names accept forms such as B4, b04, and B/4. Field indices are
zero-based within a well. The OME-Zarr converter writes the HCS hierarchy
plate → well → field image → multiscale level.
Analysis Tools
The analysis_tools package provides image processing and HCS plate analysis utilities:
from czitools.analysis_tools import ArrayProcessor, process_hcs_omezarr, create_well_plate_heatmap
# Process 2D images with filters and object detection
proc = ArrayProcessor(image_2d)
filtered = proc.apply_gaussian_filter(sigma=2)
binary = ArrayProcessor(filtered).apply_threshold(value=100)
labelled, count, props = ArrayProcessor(binary).label_objects(
min_size=50,
measure_params=True,
)
# Analyze HCS OME-ZARR plates
results = process_hcs_omezarr("plate.ome.zarr", channel2analyze=0)
# Visualize results as heatmap
fig = create_well_plate_heatmap(results, num_rows=8, num_cols=12)
Requires: pip install "czitools[analysis]"
CZI inside NDV
CZI inside Napari
Colab Notebooks
| Topic | Link |
|---|---|
| Read CZI metadata |  |
| Read CZI pixel data | |
| Read CZI well-plate data | |
| Process OME-Zarr HCS plate | |
| Show planetable as surface | |
| Segment with Voronoi-Otsu | |
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