ecoli.analysis.antibiotics_colony.plot
Script to make majority of figures in paper. Before running, extract colony_data.zip in the “data” folder. Mapping between filenames in “colony_data/sim_dfs” folder and simulation conditions in ecoli/analysis/antibiotics_colony/__init__.py. The __init__ file also constructs a dictionary of all columns in the saved CSV files (and gives their paths in the simulation store hierarchy).
- Refer to these other scripts for the code to create remaining figures:
- ecoli/analysis/antibiotics_colony/subgen_gene_plots/
- count_subgen.py: counts for Fig. 1B
Run with “–data data/colony_data/glc_10000_expressome.csv”
make_fig_1b.py: create Fig. 1B
- ecoli/analysis/antibiotics_colony/snapshot_and_hist_plot.py: Fig. 2E-F
Run with “–local data/colony_data/2022-12-08_00-35-28_562633+0000.csv”
ecoli/analysis/antibiotics_colony/tet_dry_mass.py: Fig. 3E
- ecoli/analysis/antibiotics_colony/amp_plots.py: Fig. 4D-J, L
Run with “–glc_data data/colony_data/2022-12-08_00-33-56_581605+0000.csv” and “–amp_data data/colony_data/2022-12-08_17-03-56_357734+0000.csv”
- ecoli/analysis/antibiotics_colony/spatial_autocorrelation.py: Fig. S4
Run with “data/colony_data/sim_dfs/2022-12-08_00-35-28_562633+0000.csv”
Repeat with CSV files for other two baseline glucose simulations to get all Moran’s I and p-values in Table S1
- ecoli/analysis/proteinCountsValidation.py: Fig. S2A
Run with “–avg_data data/colony_data/glc_10000_proteome_avgs.csv”
- ecoli/analysis/centralCarbonMetabolismScatter.py: Fig. S2B
Run with “–numpy_data data/colony_data/glc_10000_fluxome.csv” and “–sim_df data/colony_data/2022-12-08_00-35-28_562633+0000.csv”