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.
- Large-image exports use lazy CZI ROI reads instead of loading whole scenes.
- Both export backends reuse native CZI pyramids for individual images and HCS plates, preserving their pixels and physical transforms.
- NGFF directory exports write directly to the final store; regenerated levels use their previously written predecessor.
- The GUI offers 256/512/1024-pixel output chunks and RAM/CPU-based Auto concurrency, with the selected worker count shown and logged.
Install the core package from PyPI:
pip install czitoolsInstall 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]"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]"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
python -m pip install -e ".[all]"
# Or install the locked Pixi workspace (Windows and Linux)
pixi installFor more details see the Installation docs.
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.SizeS)
print(mdata.image_required.SizeC)
print(mdata.scale_required.X)
print(mdata.scene_shape_is_consistent) # True when all scenes can be stacked
# scene_shape_tolerance (default=1) controls the maximum allowed pixel
# difference in width or height between scenes before they are considered
# inconsistent. A value of 1 absorbs the ±1-pixel rounding that commonly
# occurs with HCS plate coordinates and allows those scenes to be stacked.
mdata_plate = CziMetadata("path/to/plate.czi", scene_shape_tolerance=1)
# Read regular, equal-sized scenes eagerly as a labelled STCZYX(A) array.
array6d, mdata = read_6darray("path/to/file.czi", use_xarray=True)
# For HCS plate files whose scenes differ by ±1 pixel due to coordinate
# rounding, pass scene_stack_tolerance=1 so read_stacks crops them to a
# common shape and stacks them into one array (default=0, strict equality).
from czitools.read_tools import read_stacks
stacked, dims, n, mdata = read_stacks(
"path/to/plate.czi",
use_dask=True,
use_xarray=True,
stack_scenes=True,
scene_stack_tolerance=1,
)
# For irregular scenes, keep genuinely lazy reads 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()Image dimension sizes (SizeS, SizeT, SizeC, SizeZ, SizeY, SizeX,
and optional dimensions) describe the full-resolution data physically stored
in CZI subblocks. They do not use numeric XML Size* declarations. This is
important for split acquisitions, where every child file can retain XML for
the complete acquisition while storing only a subset of the global scene
indices. Use mdata.stored_scene_indices to inspect those physical S keys.
read_6darray(..., use_dask=True) also provides genuinely lazy pixel access
when the CZI has equal-sized scenes and consistent pixel types.
Both eager and lazy reads use each scene's full-resolution, non-pyramid
bounding rectangle, so rounded pyramid coverage cannot pad or change the
regular STCZYX(A) shape.
read_stacks(..., use_dask=True) groups up to 64 planes per task by default to
reduce file-open and scheduler overhead. Set lazy_read_strategy="plane" for
the finest-grained random access, or tune the group with planes_per_chunk.
For gigapixel CZIs (single planes over ~256 MB uncompressed) read_stacks
automatically activates spatial Y/X tiling: each dask chunk becomes one
ROI-based read via pylibCZIrw, so viewers such as napari only load the tiles
that intersect the visible viewport instead of full planes. Tune the tile edge
with tile_size (default 4096) or the trigger threshold with
chunk_memory_limit. Small planes keep the fast whole-plane path.
For interactive viewers that need a multiscale pyramid (napari's
add_image(..., multiscale=True), gigapixel whole-slide display, etc.) use
read_stacks_multiscale:
from czitools.read_tools import get_pyramid_zooms, read_stacks_multiscale
# Inspect the stored pyramid without reading pixels.
print(get_pyramid_zooms("path/to/large.czi"))
# -> [1.0, 0.5, 0.25, 0.125, 0.0625] (standard 2x pyramid)
# One lazy dask array per level, ready for napari.
levels, infos, dims, num_stacks, mdata = read_stacks_multiscale(
"path/to/large.czi",
max_coarse_edge=8192, # force coarser synthetic levels if needed
)Levels detected on disk are served directly from their subblocks (no
resampling). If the coarsest stored level is still larger than
max_coarse_edge on any edge, additional coarser levels are synthesized
via libCZI's C++ downsampler so the top of the pyramid always fits in one
GPU texture.
For detailed usage examples see the Usage docs.
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.
convert_czi2hcs_ngff() writes OME-NGFF v0.5 by default and supports v0.6
when requested. OME-NGFF v0.6 HCS output requires ngff-zarr>=0.47.0; the
validator selects the image or HCS reader from the store layout and declared
version.
Use czi_hcs_check to inspect CZI well-plate metadata from the command line
with colorized output. The inspector displays all
full-resolution, subblock-derived dimension sizes and, by default, limits the
HCS hierarchy to fields physically stored in the selected file:
# Inspect dimensions and HCS fields physically stored in this file
czi_hcs_check -f plate.czi
# Inspect a specific well
czi_hcs_check -f plate.czi --well B4
# Omit the well summary table while retaining plate, sample, and field details
czi_hcs_check -f plate.czi --no-well-table
# Show the complete acquisition model declared by the CZI XML
czi_hcs_check -f plate.czi --show-declared
# Get help
czi_hcs_check --helpUse --no-well-table for large plates when the per-well summary would make
the terminal output unnecessarily long. --show-declared is useful for split
acquisitions when you want the planned plate layout rather than only the
payload of the current file.
Or use the utility functions in Python:
from czitools.utils import print_hcs_plate_info, print_sample_metadata, print_well_fields
from czitools.metadata_tools import CziMetadata
mdata = CziMetadata("plate.czi")
# Print plate hierarchy with well summary
print_hcs_plate_info(mdata)
# Print sample metadata and scene details
print_sample_metadata(mdata)
# Print field information for a specific well
print_well_fields(mdata, well_name="B4")The experimental converter GUI exports individual CZI images and HCS plates
using either ome-zarr-py or ngff-zarr. All conversions use Zarr v3. The GUI
loads the selected file's metadata automatically and enables Write HCS
layout when a plate layout is detected. For multi-scene, non-HCS files, the
Scene ID selector appears directly below the HCS option. A single Create
Single-File OME-ZARR (.ozx) checkbox selects archive output for both HCS and
non-HCS conversions. Additional controls configure compression and optional
napari viewing. The ngff-zarr backend uses zarrista automatically. The
metadata preview shows the detected dimensions and scenes before conversion,
while the log panel reports progress.
For individual images, Spatial chunk size (pixels) selects 256, 512, or 1024, with 512 as the default. The choice applies to both backends; larger chunks reduce Dask task overhead; 256 can be useful when smaller storage chunks are required.
Reuse CZI pyramid is enabled by default for individual images with both
ngff-zarr and ome-zarr-py. HCS conversion also reuses stored levels with
both backends. Native levels, including ZEN's approximately 3x pyramid
steps, are exported without generating replacement 2x levels. Their pixels,
dimensions, physical scales, and translations are preserved. Only a missing
coarse tail is added through the lazy CZI reader, targeting a longest edge
of 512 pixels within libCZI's supported zoom range. Files without stored
lower-resolution levels use that reader's synthetic pyramid fallback.
Reuse still requires decoding CZI data and encoding the destination arrays;
it is not a compressed-byte copy.
To regenerate an individual image's pyramid, uncheck Reuse CZI pyramid.
Pyramid levels then selects the number of additional 2x Y/X levels.
The ngff-zarr quality and fast/balanced presets select Gaussian and
bin-shrink downsampling, respectively. These regeneration controls are
disabled while reuse is enabled; compression remains independently selectable.
Python callers of write_omezarr and write_omezarr_ngff can supply lazy
levels through source_multiscales; raw-array calls retain their existing
generated-pyramid behavior.
Individual scenes are read lazily with spatial CZI ROI tiles instead of allocating entire channel planes. CZI read tiles are up to 4096 pixels with a 32 MiB per-tile budget, independent of the selected output chunk size. Larger read tiles reduce CZI file opens and ROI task overhead; the reader reduces tile dimensions when needed to stay within the budget. For individual images, Dask workers defaults to Auto. At conversion start, Auto reads currently available RAM, reserves the larger of 2 GiB or 20% of total RAM, and budgets 1 GiB per worker. It selects at most half the logical CPU count and caps automatic concurrency at 8 workers, with a minimum of one. When RAM metrics are unavailable, it uses at most two. The selected count and memory budget appear in the conversion log. Selected Dask workers also shows the resolved count beside the worker control during conversion, for example 8 (Auto). This is the selected pool size, not an instantaneous count of busy tasks. Manual selections are shown with (manual), and changing files clears the previous status.
Manual values from 1 to 32 override Auto; the Python writers accept
worker_count=0 for Auto or a positive count for a manual override. Higher
counts can overlap CZI reads, compression, and writes, but increase active
memory use and disk contention. This is a conservative starting heuristic,
not a hard memory limit or a throughput guarantee, and does not resize the
pool during a conversion. HCS retains its existing field-concurrency policy.
For ngff-zarr directory output, the base image is written directly to the
final store. Reused CZI levels are read and appended directly. When regeneration
is selected, each next level is built from the preceding written level and
appended to that store, avoiding an extra intermediate pyramid copy and
source-dependent graphs spanning the entire pyramid. Single-file .ozx
output writes supplied CZI levels directly; regenerated pyramids retain
disk staging before final archive packaging.
Export requires ngff-zarr>=0.47.0 for incremental directory writes.
The terminal and GUI log show a progress bar for each directly written directory level. Archive output shows base-image caching, each pyramid-level cache when regenerating, and final output writing. Task percentages refer to the current region, not the entire conversion. Initial graph preparation may show elapsed time before task counts become available.
Directory output does not require an intermediate cache. For regenerated
.ozx pyramids, temporary caches are created beside the output and removed on success or
failure. Allow additional free disk space for an intermediate pyramid
(approximately 1.33 times the uncompressed base image for a 2x Y/X pyramid,
before compression), as well as the final output. .ozx packaging may need
additional temporary space. These limits bound active pixel processing, not
all Python/scheduler memory; very large datasets still need sufficient RAM
for task metadata. HCS conversion uses stored levels with both backends and
adds only the missing coarse tail; the chunk selector applies to individual images.
Install and launch it with:
pip install "czitools[omezarr-gui]"
czitools-omezarr-guiFrom the repository's Pixi environment, use the equivalent task:
pixi run omezarr-gui
See the usage documentation for the workflow and Python/napari integration examples.
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
| Topic | Link |
|---|---|
| General usage czitools |  |
| 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 | |
The Pixi workspace is the recommended development setup on Windows and Linux. After cloning the repository, install the locked environment and run the local quality checks:
pixi install
pixi run lint
pixi run test-no-netPlease keep changes focused, add or update tests for behavioral changes, and open an issue before starting a large API or dependency change.