treem 1.1.1

Tree-like morphology data processing

treem - Neuron Morphology Processing Tool

The treem module (pronounced "trim") provides data structures and command-line tools for accessing and manipulating digital reconstructions of neuronal morphology in the Stockley-Wheal-Cannon (SWC) format.

Main classes

Access to morphological data from the source code is provided through the classes Tree, Node, Morph, and SWC:

• Tree - Recursive tree data structure
• Node - Morphology data storage
• Morph - Neuron morphology representation
• SWC - Definitions of the data format

Commands

Common operations with SWC files can be performed using the swc command-line tool:

swc <command> [options] file

Alternatively:

swc <command> file [file ...] [options] 

List of swc commands:

• check - tests morphology reconstruction for structural consistency
• convert - converts morphology to compliant SWC format
• find - locates single nodes in the reconstruction
• measure - calculates morphometric features
• modify - manipulates morphology reconstruction
• render - displays 3D model of the reconstruction
• repair - corrects reconstruction errors
• view - shows morphology structure

Installation

Install the latest stable release:

pip install treem

Install a development version:

pip install git+https://github.com/a1eko/treem

See also pip documentation for installation alternatives.

Dependencies

The treem module has minimal runtime dependencies:

• Python >= 3.7
• matplotlib
• numpy
• PyOpenGL (optional, enables swc render command)

For testing and documentation, treem requires additional development packages with third-party extensions:

• sphinx with napoleon and programoutput extensions
• pytest with pytest-cov plugin
• sphinx-rtd-theme
• coverage

Documentation

Documentation is available online at Read the Docs.

Citation

• Hjorth JJJ, Hellgren Kotaleski J, Kozlov A (2021) Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinformatics, 19(4):685-701. DOI: 10.1007/s12021-021-09531-w.

Funding

Horizon 2020 Framework Programme (785907, HBP SGA2); Horizon 2020 Framework Programme (945539, HBP SGA3); Vetenskapsrådet (VR-M-2017-02806, VR-M-2020-01652); Swedish e-science Research Center (SeRC); KTH Digital Futures.

We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858.

The computations and testing were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725, also by the Swedish National Infrastructure for Computing (SNIC) at PDC KTH partially funded by the Swedish Research Council through grant agreement no. 2018-05973.