Metadata-Version: 2.4
Name: czitools
Version: 0.10.1
Summary: Tools to simplify reading CZI (Carl Zeiss Image) meta and pixel data
Home-page: https://github.com/sebi06/czitools
Author: Sebastian Rhode
Author-email: sebrhode@gmail.com
License: GNU General Public License v3 (GPLv3)
Project-URL: Bug Tracker, https://github.com/sebi06/czitools/issues
Project-URL: Documentation, https://github.com/sebi06/czitools/#README.md
Project-URL: Source Code, https://github.com/sebi06/czitools/
Project-URL: User Support, https://github.com/sebi06/czitools//issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: Unix
Classifier: Operating System :: Microsoft :: Windows
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: <3.14,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pylibCZIrw>=5
Requires-Dist: aicspylibczi>=3.3.1
Requires-Dist: bioio
Requires-Dist: bioio-czi
Requires-Dist: tqdm
Requires-Dist: pandas
Requires-Dist: python-dateutil
Requires-Dist: python-box[all]
Requires-Dist: czifile
Requires-Dist: loguru
Requires-Dist: colorlog
Requires-Dist: pydantic
Requires-Dist: xarray
Requires-Dist: xarray[complete]
Provides-Extra: all
Requires-Dist: magicgui; extra == "all"
Requires-Dist: seaborn; extra == "all"
Requires-Dist: plotly; extra == "all"
Requires-Dist: qtpy; extra == "all"
Requires-Dist: pyqtgraph; extra == "all"
Requires-Dist: colormap; extra == "all"
Requires-Dist: ngff-zarr; extra == "all"
Requires-Dist: ngff-zarr[validate]; extra == "all"
Requires-Dist: ngff-zarr[dask-image]; extra == "all"
Requires-Dist: bioio-ome-zarr; extra == "all"
Dynamic: license-file

# czitools

[![PyPI](https://img.shields.io/pypi/v/czitools.svg?color=green)](https://pypi.org/project/czitools)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/czitools)](https://pypistats.org/packages/czitools)
[![License](https://img.shields.io/pypi/l/czitools.svg?color=green)](https://github.com/sebi06/czitools/raw/master/LICENSE)
[![codecov](https://codecov.io/github/sebi06/czitools/graph/badge.svg?token=WK1KIMZARL)](https://codecov.io/github/sebi06/czitools)
[![Python Version](https://img.shields.io/pypi/pyversions/czitools.svg?color=green)](https://python.org)
[![Development Status](https://img.shields.io/pypi/status/czitools.svg)](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)

This repository provides a collection of tools to simplify reading CZI (Carl Zeiss Image) pixel and metadata in Python. It is available as a [Python Package on PyPi](https://pypi.org/project/czitools/)

## Installation

To install czitools use (will not install Napari or plotting functionality):

```text
pip install czitools
```

To install the package with all optional dependencies use (will not install Napari):

```text
pip install czitools[all]
```

## Reading the metadata

Please check [use_metadata_tools.py](https://github.com/sebi06/czitools/blob/main/demo/scripts/use_metadata_tools.py) for some examples.

```python
from czitools.metadata_tools.czi_metadata import CziMetadata, writexml
from czitools.metadata_tools.dimension import CziDimensions
from czitools.metadata_tools.boundingbox import CziBoundingBox
from czitools.metadata_tools.channel import CziChannelInfo
from czitools.metadata_tools.scaling import CziScaling
from czitools.metadata_tools.sample import CziSampleInfo
from czitools.metadata_tools.objective import CziObjectives
from czitools.metadata_tools.microscope import CziMicroscope
from czitools.metadata_tools.add_metadata import CziAddMetaData
from czitools.metadata_tools.detector import CziDetector
from czitools.read_tools import read_tools

try:
    import napari
    from napari.utils.colormaps import Colormap

    show_napari = True
except ImportError:
    print("Napari not installed, skipping napari import")
    show_napari = False

# get the metadata_tools at once as one big class
mdata = CziMetadata(filepath)

# get only specific metadata_tools
czi_dimensions = CziDimensions(filepath)
print("SizeS: ", czi_dimensions.SizeS)
print("SizeT: ", czi_dimensions.SizeT)
print("SizeZ: ", czi_dimensions.SizeZ)
print("SizeC: ", czi_dimensions.SizeC)
print("SizeY: ", czi_dimensions.SizeY)
print("SizeX: ", czi_dimensions.SizeX)

# try to write XML to file
xmlfile = writexml(filepath)

# get info about the channels
czi_channels = CziChannelInfo(filepath)

# get the complete metadata_tools from the CZI as one big object
czimd_complete = get_metadata_as_object(filepath)

# get an object containing only the dimension information
czi_scale = CziScaling(filepath)

# get an object containing information about the sample
czi_sample = CziSampleInfo(filepath)

# get info about the objective, the microscope and the detectors
czi_objectives = CziObjectives(filepath)
czi_detectors = CziDetector(filepath)
czi_microscope = CziMicroscope(filepath)

# get info about the sample carrier
czi_sample = CziSampleInfo(filepath)

# get additional metainformation
czi_addmd = CziAddMetaData(filepath)

# get the complete data about the bounding boxes
czi_bbox = CziBoundingBox(filepath)
```

## Reading CZI pixel data

While the [pylibCZIrw](https://pypi.org/project/pylibCZIrw/) is focussing on reading individual planes it is also helpful to read CZI pixel data as a STCZYX(A) stack. Please check [use_read_tools.py](https://github.com/sebi06/czitools/blob/main/demo/scripts/use_read_tools.py) for some examples.

```python
# return a dask or numpy array with dimension order STCZYX(A)
array6d, mdata = read_tools.read_6darray(filepath, use_xarray=True)

if show_napari:

    # show in napari (requires napari to be installed!)
    viewer = napari.Viewer()

    # loop over all channels
    for ch in range(0, array6d.sizes["C"]):

        # extract channel subarray
        sub_array = array6d.sel(C=ch)

        # get the scaling factors for that channel and adapt Z-axis scaling
        scalefactors = [1.0] * len(sub_array.shape)
        scalefactors[sub_array.get_axis_num("Z")] = mdata.scale.ratio["zx_sf"]

        # remove the last scaling factor in case of an RGB image
        if "A" in sub_array.dims:
            # remove the A axis from the scaling factors
            scalefactors.pop(sub_array.get_axis_num("A"))

        # get colors and channel name
        chname = mdata.channelinfo.names[ch]

        # inside the CZI metadata_tools colors are defined as ARGB hexstring
        rgb = "#" + mdata.channelinfo.colors[ch][3:]
        ncmap = Colormap(["#000000", rgb], name="cm_" + chname)

        # add the channel to the viewer
        viewer.add_image(
            sub_array,
            name=chname,
            colormap=ncmap,
            blending="additive",
            scale=scalefactors,
            gamma=0.85,
        )

        # set the axis labels based on the dimensions
        viewer.dims.axis_labels = sub_array.dims

    napari.run()

```


![5D CZI inside Napari](https://github.com/sebi06/czitools/raw/main/images/czi_napari2.png)

## Colab Notebooks

### Read CZI metadata

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/read_czi_metadata.ipynb)

### Read CZI pixeldata

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/read_czi_pixeldata.ipynb)

### Write OME-ZARR from 5D CZI image data

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/omezarr_from_czi_5d.ipynb)

### Write CZI using ZSTD compression

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/save_with_ZSTD_compression.ipynb)

### Show planetable of a CZI image as surface

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/show_czi_surface.ipynb)

### Read a CZI and segment using Voroni-Otsu provided by PyClesperanto GPU processing

The basic usage can be inferred from this sample notebook:&nbsp;
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sebi06/czitools/blob/main/demo/notebooks/read_czi_segment_voroni_otsu.ipynb)

## Remarks

The code to read multi-dimensional with delayed reading using Dask array was heavily inspired by input from: [Pradeep Rajasekhar](https://github.com/pr4deepr).

### Local Installation

Local installation:

```text
pip install -e .
```

Local installation (full functionality):

```text
pip install -e ".[all]"
```
