PyNeval is a Python package for evaluating the qualities of neuron reconstructions in the SWC format. It outputs quality scores of one or more test models by comparing them to a gold standard model. The scores also depend on which metric is specified because PyNeval supports several metric options.

Here is the simplest command line interface to run PyNeval:

pyneval --test <test_swc_path> --gold <gold_swc_path> --metric <metric>

test_swc_path is the file path to a test model, which is often produced by an automatic reconstruction method, and gold_swc_path is the file path to the gold standard model, which is typically created by manual editing. metric is the name of a quality metric, which can be

• length: Length metric for measuring the overlapping ratio between two models by matching line segments.
• ssd: SSD metric for measuring the overlapping ratio between two models by matching resampled nodes.
• diadem: DIADEM metric for measuring the amount of paths that contribute to topological similarity between two models.
• cn: Critical node metric for measuring the topological similarity between two models by matching topologically critical nodes.

pip install pyneval

conda install pyneval -c csdllab -c conda-forge

git clone https://github.com/bennieHan/pyNeval.git
cd PyNeval
python setup.py install

Once PyNeval is installed successfully, you can test it with the demo data included in the source code.

pyneval --gold data/demo/gold.swc --test data/demo/test.swc --metric ssd