czitools 0.0.7

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

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.

Reading the metadata

Please check use_pylibczirw_metadata_class.py for some examples.

# get the metadata at once as one big class
mdata_sel = 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)

# and get more info about various aspects of the CZI
czi_scaling = czimd.CziScaling(filepath)
czi_channels = czimd.CziChannelInfo(filepath)
czi_bbox = czimd.CziBoundingBox(filepath)
czi_info = czimd.CziInfo(filepath)
czi_objectives = czimd.CziObjectives(filepath)
czi_detectors = czimd.CziDetector(filepath)
czi_microscope = czimd.CziMicroscope(filepath)
czi_sample = czimd.CziSampleInfo(filepath)

Reading CZI pixeldata

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 array with dimension order STZCYX(A)
array6d, dimstring = pylibczirw_tools.read_6darray(filepath,
                                                   dimorder="STCZYX",
                                                   output_dask=False,
                                                   remove_Adim=True)

# remove A dimension do display the array inside Napari
dim_order, dim_index, dim_valid = czimd.CziMetadata.get_dimorder(dimstring)

# show array inside napari viewer
viewer = napari.Viewer()
layers = napari_tools.show(viewer, mdarray, mdata,
                           dim_order=dim_order,
                           blending="additive",
                           contrast='napari_auto',
                           gamma=0.85,
                           add_mdtable=True,
                           name_sliders=True)

napari.run()