Companion Data Page Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds Ryan T. Maloney1,2, Athena Q. Ye1, Sam-Keny Saint-Pre1,3, Tom Alisch1, David Zimmerman1, and Nicole Pittoors1, Benjamin de Bivort1,** 1 Organismic and Evolutionary Biology & Center for Brain Science, Harvard University, Cambridge, MA, USA 2 Current affiliation: Department of Psychology, Colorado College, Colorado Springs, CO, USA 3 Tufts University, Medford MA, USA * Corresponding author: rtmaloney@coloradocollege.edu
Data All data is available on Zenodo
DGRPsummerall.csv, serotonin_mutant_longform.csv, serotonin_drug_longform.csv	Handedness Data for each set of experiments (DGRP, 5HT pharmacology, and trh mutants) for each day, with number of turns and number of right turns.
Raw Circling Data/	This folder (zipped) contains the .mat files for all of the centroid data for each experiments in the circling data. This is the most compact form of the data (14.1 GB), but is transformed into analyzed data with the scripts exportdatabyfly.m
AllChunks.csv	The real world environment chunks used for real world environment data. Used in realworlddata.py
allsimresults.nc	Simulation results for all real world environmental simulations. Used in AnalyzeRealWorldDataSims.ipynb
MatureAge_linear.zip	Zipped folder with data from phase sweep (lin space) used for mature age analysis (Fig 3D. See matureage.ipynb
MatureAge_log.zip	Zipped folder with data from phase sweep (log space) used for mature age analysis. See matureage.ipynb
Analysis and Simulation Code The following scripts and notebooks (MATLAB 2023b, Python 3.1) are available on github.
Scripts
exportdatabyfly.m	script for converting matlab continuous circling data to python. Dependency: AngleArrays.m
AngleArrays.m	helper function for analyzing circling data
stanhelpers.py	Helper functions for Bayesian analysis with STAN in python. Used by SerotoninStan.ipynb and DGRPstan.ipynb
loadcontinuousmatlabfiles.py	Helper functions for analyzing continuous data and transitioning from matlab to python
continuousanalysis.py	Helper functions for analyzing continuous data
dmodel6_AR_transformed.stan	STAN code for Bayesian Autoregressive Model. Used by SerotoninStan.ipynb and DGRPStan.ipynb
simpledrift.py	Core simulation code for running individual simulations over time
matrixmaker.py	Code for running multiple parameters at once to make phase space plots of parameters
realworlddata.py	Code for running simulations on realworld data on the cluster
realworlddata.sh	Helper shell script for above to run on server
frequency_environment_server.py	Script for generating simulations on server across multiple environmental amplitudes and frequencies
frequency_environment.sh	Helper shell script for above to run on server
matureage_server.py	Script for generating simulations on server across multiple ages of reproductive maturities and frequencies
matureage.sh	Helper shell script for above to run on server
Notebooks This folder contains notebooks used for data analysis and creating figures. Note: Paths may need to be edited for depend files and folders listed, as the data has been separated due to file size restrictions for repositories.
SerotoninStan.ipynb	Notebook for analyzing data from Serotonin Experiments in STAN and generating figures in paper. Dependencies: serotonin_mutant_longform.csv, serotonin_drug_longform.csv, stanhelpers.py
angle_analysis.ipynb	Notebook used for analyzing continuous circling data and respective figures/power analysis. Dependencies:Raw Circling Data/, as processed by exportdatabyfly.m
Analyze_Centroids.ipynb	Notebook used for lowpass filtered data (Fig 1B). Dependencies:Raw Circling Data/, as processed by exportdatabyfly.m
5HT_Supplemental_Figures.ipynb	Notebook used for r values over time (Fig S1 H-M). Dependencies:Need to Download for Supplemental/
DGRPStan.ipynb	Code for analyzing DGRP data. Dependencies are stanhelpers.py and dmodel6_AR_transformed.stan, and DGRPsummerall.csv
freqvsenvmean.ipynb	Code for frequency vs envmean analysis. Note, the actual depencies (simulations based on randomly generated data) are too large (>100GB) to be included, however this file is maintained for clarity of sourcing. Data is created with frequency_environment.sh and frequency_environment_server.py
matureage.ipynb	Code for analyzing results of changing frequency and age of reproductive maturity; depencies /MatureAge_Run7_lin_matchinterestingemv/
AnalyzeRealWorldDataSims.ipynb	Code for analyzing results of real world simulation data; depencies allsimresults.nc