Run output is placed under out/:
Some of the output data is stored as .cpickle files. To observe those files, you need the original Python classes, and therefore you have to be inside Docker, from the host it won't work.
We can list all the plots that have been produced under out/ with
find -name '*.png'
Plots are also available in SVG and PDF formats, e.g.:
  • PNG: ./out/manual/plotOut/low_res_plots/massFractionSummary.png
  • SVG: ./out/manual/plotOut/svg_plots/massFractionSummary.svg The SVGs write text as polygons, see also: SVG fonts.
  • PDF: ./out/manual/plotOut/massFractionSummary.pdf
The output directory has a hierarchical structure of type:
  • wildtype_000000: variant conditions. wildtype is a human readable label, and 000000 is an index amongst the possible wildtype conditions. For example, we can have different simulations with different nutrients, or different DNA sequences. An example of this is shown at run variants.
  • 000000: initial random seed for the initial cell, likely fed to NumPy's np.random.seed
  • genereation_000000: this will increase with generations if we simulate multiple cells, which is supported by the model
  • 000000: this will presumably contain the cell index within a generation
We also understand that some of the top level directories contain summaries over all cells, e.g. the massFractionSummary.pdf plot exists at several levels of the hierarchy:
Each of thoes four levels of plotOut is generated by a different one of the analysis scripts:
  • ./out/manual/plotOut: generated by python runscripts/manual/ Contains comparisons of different variant conditions. We confirm this by looking at the results of run variants.
  • ./out/manual/wildtype_000000/plotOut: generated by python runscripts/manual/ --variant_index 0. TODO not sure how to differentiate between two different labels e.g. wildtype_000000 and somethingElse_000000. If -v is not given, a it just picks the first one alphabetically. TODO not sure how to automatically generate all of those plots without inspecting the directories.
  • ./out/manual/wildtype_000000/000000/plotOut: generated by python runscripts/manual/ --variant_index 0 --seed 0
  • ./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated by python runscripts/manual/ --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specific cell.
Let's look into a sample plot, out/manual/plotOut/svg_plots/massFractionSummary.svg, and try to understand as much as we can about what it means and how it was generated.
This plot contains how much of each type of mass is present in all cells. Since we simulated just one cell, it will be the same as the results for that cell.
We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential. We can see that all of them grow more or less linearly, perhaps as the start of an exponential.
  • total dry mass (mass excluding water)
  • protein mass
  • rRNA mass
  • mRNA mass
  • DNA mass. The last label is not very visible on the plots, but we can deduce it from the source code.
By grepping the title "Cell mass fractions" in the source code, we see the files:
which must correspond to the different massFractionSummary plots throughout different levels of the hierarchy.
By reading models/ecoli/analysis/variant/ a little bit, we see that:
  • the plotting is done with Matplotlib, hurray
  • it is reading its data from files under ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/, more precisely ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/columns/<column-name>/data. They are binary files however.
    Looking at the source for wholecell/io/ shows that those are just a standard NumPy serialization mechanism. Maybe they should have used the Hierarchical Data Format instead.
    We can also take this opportunity to try and find where the data is coming from. Mass from the ./out/manual/wildtype_000000/000000/generation_000000/000000/simOut/Mass/ looks like an ID, so we grep that and we reach models/ecoli/listeners/
    From this we understand that all data that is to be saved from a simulation must be coming from listeners: likely nothing, or not much, is dumped by default, because otherwise it would take up too much disk space. You have to explicitly say what it is that you want to save via a listener that acts on each time step.
Figure 1.
Minimal condition mass fraction plot
. Source. File name: out/manual/plotOut/svg_plots/massFractionSummary.svg
More plot types will be explored at time series run variant, where we will contrast two runs with different growth mediums.