treem 1.1.0

Tree-like morphology data processing

treem - neuron morphology processing tool

Module provides data structure and command-line tools for accessing and manipulating the digital reconstructions of the neuron morphology in Stockley-Wheal-Cannon format (SWC).

Main classes

Access to morphological data from the source code is supported by 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 are possible via the swc command-line tool:

swc <command> [options] file

or sometimes more convenient as

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

List of swc commands:

• check - Test morphology reconstruction for structural consistency
• convert - Convert morphology to compliant SWC format
• find - Locate single nodes in the reconstruction
• measure - Calculate morphometric features
• modify - Manipulate morphology reconstruction
• render - Display 3D model of the reconstruction
• repair - Correct reconstruction errors
• view - Show morphology structure

Installation

Install the latest stable release:

pip3 install treem

Install a development version:

pip3 install git+https://github.com/a1eko/treem

See also pip3 documentation for installation alternatives.

Dependencies

Module treem has minimal runtime dependencies:

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

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

• sphinx with napoleon and programoutput
• pytest with pytest-cov
• coverage

Documentation

Documentation is available online at Read the Docs.

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 Swedish National Infrastructure for Computing (SNIC) at PDC KTH partially funded by the Swedish Research Council through grant agreement no. 2018-05973.