navis.read_tiff¶

navis.read_tiff(f, channel=0, threshold=None, include_subdirs=False, parallel='auto', output='voxels', errors='log', **kwargs)[source]¶

Create Neuron/List from TIFF file.

Requires tifffile library which is not automatically installed!

Parameters:

f (str | iterable) – Filename(s) or folder. If folder, will import all .tif files.
channel (int) – Which channel to import. Ignored if file has only one channel. Can use e.g. -1 to get the last channel.
threshold (int | float | None) – For output='dotprops' only: a threshold to filter low intensity voxels. If None, no threshold is applied and all values > 0 are converted to points.
include_subdirs (bool, optional) – If True and f is a folder, will also search subdirectories for .tif files.
parallel ("auto" | bool | int,) – Defaults to auto which means only use parallel processing if more than 10 TIFF files are imported. Spawning and joining processes causes overhead and is considerably slower for imports of small numbers of neurons. Integer will be interpreted as the number of cores (otherwise defaults to os.cpu_count() - 2).
output ("voxels" | "dotprops" | "raw") – Determines function’s output. See Returns for details.
errors ("raise" | "log" | "ignore") – If “log” or “ignore”, errors will not be raised but instead empty neuron will be returned.
**kwargs – Keyword arguments passed to navis.make_dotprops() if output='dotprops'. Use this to adjust e.g. the number of nearest neighbors used for calculating the tangent vector by passing e.g. k=5.

Return type:

Returns:

navis.VoxelNeuron – If output="voxels" (default): requires TIFF data to be 3-dimensional voxels. VoxelNeuron will have TIFF file info as .tiff_header attribute.
navis.Dotprops – If output="dotprops". Dotprops will contain TIFF header as .tiff_header attribute.
navis.NeuronList – If import of multiple TIFF will return NeuronList of Dotprops/VoxelNeurons.
(image, header) (np.ndarray, OrderedDict) – If output='raw' return raw data contained in TIFF file.