Code

 
 
 
 
 
 
 
 
 
 

We believe in early and open sharing of code to speed up scientific progress. If you need help using our software or are interested in collaborating on further developing software with us, please get in touch!

FlyWire Codex

Interface for data sharing and analysis of the first whole-brain connectome for Drosophila

Murthy Lab Github Repository includes the following: 

SLEAP (Social LEAP Estimates Animal Pose)

Code to track the pose of multiple animals during social interactions via deep learning. See also SLEAP.ai and Pereira et al. Nature Methods 2022.

LEAP (LEAP Estimates Animal Pose)
C
ode for tracking animal body parts via deep learning: see Pereira*, Aldarondo* et al. Nature Methods 2019

Murthy Lab FlySongSegmenter
Code to combine signals from multiple microphones, and to segment courtship song into its three modes (sine, Pfast, and Pslow) - see Coen et al. Nature 2014 and Arthur et al. BMC Bio 2013

Pulse Type Classification
See Clemens et al. Current Biology 2018; code to separate courtship song pulses into Pfast and Pslow

See also: FlyCourtship GitHub for original FlySongSegmenter (written together with David Stern and Ben Arthur).

no IPI cycles
Code and data from Stern et al. PNAS 2017

Drosophila virilis song analysis
GLMvirilis
and
Virilis Song Segmenter
Code for analyzing data in LaRue et al. eLife 2015

Python Ball Motion Tracking (pybmt)

python code for tracking ball motion during head-fixed fly-on-the-ball experiments and delivering stimuli in closed-loop with ball motion - this is a python interface for running and using Richard Moore’s FicTrac

FLyTRAP

Code for tracking flies in an auditory playback assay: see Deutsch*, Clemens* et al. Current Biology 2019

GLMHMM

Code for running the Genearlized Linear Model (GLM) - Hidden Markov Model (HMM) implemented in Calhoun et al. Nature Neuroscience 2019

DeepFlyTrack

Code for tracking fly centroids using deep networks: see Calhoun et al. Nature Neuroscience 2019

FlyCaImAn

Code for analysis of volumetric calcium imaging data: see Pacheco et al. Nature Neuroscience 2020