czitools

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

Installation

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

pip install czitools

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

pip install czitools[all]

Reading the metadata

Please check use_metadata_tools.py for some examples.

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 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 for some examples.

# 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()