czitools 0.4.1

Tools to simplify reading CZI (Carl Zeiss Image) meta and pixel data

czitools

This repository provides a collection of tools to simplify reading CZI (Carl Zeiss Image) pixel and metadata in Python. In addition it also contains other useful utilities to visualize CZI images inside Napari (needs to be installed). It is also available as a Python Package on PyPi

Reading the metadata

Please check use_metadata_tools.py for some examples.

# get the metadata at once as one big class
mdata = czimd.CziMetadata(filepath)

# get only specific metadata
czi_dimensions = czimd.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 = czimd.writexml(filepath)

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

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

# get an object containing only the dimension information
czi_dimensions = czimd.CziDimensions(filepath)

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

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

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

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

# get additional metainformation
czi_addmd = czimd.CziAddMetaData(filepath)

# get the complete data about the bounding boxes
czi_bbox = czimd.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 STZCYX(A) stack. Please check use_pylibczirw_md_read.py for some examples.

# return a dask array with dimension order STZCYX(A)
array6d, mdata, dim_string6d = read_tools.read_6darray(filepath,
                                                             output_order="STCZYX",
                                                             # T=0,
                                                             # Z=0
                                                             )

# show array inside napari viewer
viewer = napari.Viewer()
layers = napari_tools.show(viewer, array6d, mdata,
                           dim_string=dim_string6d,
                           blending="additive",
                           contrast='from_czi',
                           gamma=0.85,
                           add_mdtable=True,
                           name_sliders=True)

napari.run()

Remarks

The code to read multi-dimensional with delayed reading using Dask array was heavily inspired by input from: Pradeep Rajasekhar.