Figures, scripts and data for paper "Spatial resource heterogeneity creates local hotspots of evolutionary potential"
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config
data
figs
scripts
LICENSE.md
README.md
Zotero.bib
alifeconf.sty
hotspots.tex
hotspots_main.tex

README.md

This repository contains figures, scripts, and data for the paper:

Spatial resource heterogeneity creates local hotspots of evolutionary potential

By Emily Dolson and Charles Ofria

European Conference on Artificial Life, 2017

License: MIT Paper:10.7551/ecal_a_023

Contents:

  • hotspots.tex - LaTeX file for main paper
  • alifeconf.sty - Style file for ECAL
  • Zotero.bib - bibliography file for paper
  • config - contains all files used to generate daa
    • avida - executable used to generate data
    • avida-lineage - executable used to output lineage trajectories
    • avida.cfg - config file for Avida (not changed from default)
    • defaults-heads.org - starting organism (not changed from default)
    • env500XX.cfg - these files indicate how to build the 8 different environments
    • envcontrol.cfg - file to specify the homogeneous control environment
    • events.cfg - tells Avida what data to output when
    • corner_events.cfg - events file that puts starting organism in oppostie corner
    • instset-heads.cfg - tells avida what instruction set to use (not changed from default)
    • lineage_events.cfg - events file for runs where spatial lineage is being output
    • write_events_file.py - python script to make events file that only prints phenotype grid immediately before a new task is going to evolve (assumes that extract_points.py has already been called to determine when each task will evolve)
  • data - contains summary-level data files extracted from raw data (raw data would be too large too include)
    • all_task_locs.csv - the locations where each task first evolved in each run
    • control_diversity_ranks.csv - the percentile of the diversity of the cell where each task first evolved in each replicate of the control condition
    • diversity_ranks.csv - the percentile of the diversity of the cell where each task first evolved in each replicate of the experimental conditions
    • qsub_files - all of the scripts used to submit experiments to MSU's high performance compupting cluster
  • figs - figures
    • 50060_9_paths.png - all lineage paths for task 9 in environment 50060 (not very useful - there are too many)
    • 50065_all_hotspots.png - all hotspots across all environments
    • 9_50013_density.png - kernel density map for task 9 in environment 50013
    • 9_50013_hotspots.png - hotspot locations for task 9 in environment 50013
    • 9_50013_k-hat.png - Ripley's K for task 9 in environment 50013
    • 9_50013_points.png - point locations for task 9 in environment 50013
    • control_9_paths.png - 5 example lineage paths for task 9 for each environment
    • localdiversity.png - qqplot comparing diversity ranks to uniform distribution
    • statsfig.png - diagram outlining statsitics pipeline
    • statsfig_horizontal_1col.png - smaller version of the stats pipeline figure that fits in 1 column
  • scripts - Python and R scripts for processing and analyzing data
    • collect_points.py - grab data from task_locs.csv files for each rep and summarize them into all_task_locs.csv
    • draw_hotspots.py - can be used to make various figures showing points, hotspots, and lineage paths over an environment
    • env500XX.csv - files generated by extract_env_data.py that indicate distance from each cell in each environment to each resource
    • envcontrol.csv - like above, except it's all 0s
    • extract_diversity.py - generate diversity_ranks.csv file from raw phenotype grids
    • extract_env_data.py - generate csv files indicating the distance from each cell in an environment to each resource
    • extract_grids.py - grab phenotype grids from right before each task evolves out of tar archives
    • extract_paths.csv - convert sequence of ints output by avida-lineage into actual sequences of coordinates and environments that a lineage passed through
    • extract_points.csv - loop through phenotype grids to figure out when each task first appears (generates task_locs.csv)
    • kriging.R - R script that handles all of the regression that we used in an attempt to figure out what causes hotspots
    • make_diversity_maps.csv - makes heat maps of local diversity for updates immediately before tasks evolved
    • make_hotspot_figure.py - variant on draw_hotspots.py, used to make multi-panel hotspot figure in paper
    • make_path_figure.py - variant on draw_hotspots.py used to make multipanel path figure in paper