Collecting Data with Symbulation¶
You are able to run Symbulation with whatever workflow you prefer to run many replicates. The following assumes that you used the Symbulation Cookiecutter to get setup.
We’ve provided a short script that can be used with
screen to run several replicates and treatments.
We recommend (and have provided) a workflow where you have a
Data folder that contains subfolders for each experiment and within each of those subfolders are:
Your executable file
README.mdcontaining the date and the purpose of the experiment
Assuming that you are in the
SymbulationEmp directory and have already compiled your
symbulation_default executable, copy your executable to your
Data folder and change to that directory:
cp symbulation_default ../Data/sample_treatment cd ../Data/sample_treatment
simple_repeat.py script assumes that you already have a copy of the executable and
SymSettings.cfg in the same directory.
Within that directory, you can run
By default, this will run 5 replicates of each treatment specified in
simple_repeat.py and use the random seeds 21-25 (inclusive).
You can specify the random seeds (and therefore also the number of replicates) using command line arguments, which are optional.
The first command line argument is the start of the range of seeds (inclusive), and the second command line argument is the end of the range of seeds (inclusive).
For example, the input
python3 simple_repeat.py 10 15
will use seeds 10, 11, 12, 13, and 14.
We’ve also provided a basic analysis pipeline for visualizing your data.
Once you have let
simple_repeat.py run, you can change directory to the
and run our provided Python script:
These commands will output a file
munged_basic.dat that contains the average interaction value of hosts and symbionts over time in each of your replicates and treatments.
You can then open the R script
SampleAnalysis.R, set your working directory to the
Analysis folder and run all of the lines to see a plot of the effect of vertical transmission on the evolved interaction value for hosts and symbionts. We recommend using RStudio for running R scripts. You can find the documentation and information on how to download RStudio here.