A testing pipeline that allows us to run a behavioural phenotyping of our virtual worm running the same test statistics the Schafer Lab used on their worm data.
Switch branches/tags
Nothing to show
Clone or download
Latest commit ea1d3cd Sep 14, 2018


GitHub license Travis-CI BountySource

| | Open Worm Analysis Toolbox |

The Open Worm Analysis Toolbox is a Python port of the Schafer Lab's Worm Analysis Toolbox 1.3.4.

It can be used to process videos of C. elegans into statistics so the behaviour of individual worms can be compared.

It is also the package used by the OpenWorm project to determine how closely its simulated worm behaves like real worms. It was started as a sub-project of the OpenWorm project.

OWAT is on PyPI, so to install, simply type:

pip install open_worm_analysis_toolbox

Contributors please see:

Usage Example

import open_worm_analysis_toolbox as mv

# Load a "basic" worm from a file
bw = mv.BasicWorm.from_schafer_file_factory("example_contour_and_skeleton_info.mat")
# Normalize the basic worm
nw = mv.NormalizedWorm.from_BasicWorm_factory(bw)
# Plot this normalized worm    
wp = mv.NormalizedWormPlottable(nw, interactive=False)
# Obtain features
wf = mv.WormFeatures(nw)

Later, if we have control worms, we can run statistics on our worm:

# Compute histograms
experiment_histograms = mv.HistogramManager([wf, wf])
control_histograms = mv.HistogramManager(control_worms)

# Compute statistics
stat = mv.StatisticsManager(experiment_histograms, control_histograms)

# Plot statistics for the first extended feature
stat[0].plot(ax=None, use_alternate_plot=True)

# Give an overall assessment of the worm's similarity to the control set
print("Nonparametric p and q values are %.2f and %.2f, respectively." %
      (stat.min_p_wilcoxon, stat.min_q_wilcoxon))