# 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)

# return a array with dimension order STZCYX(A)
array6d, mdata, dim_string6d = pylibczirw_tools.read_6darray(filepath,
                                                             output_order="STZCYX",
                                                             output_dask=False,
                                                             remove_adim=True
                                                             )

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

napari.run()