Supplementary files for the dingo Python library

We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (<em>ext_data.zip</em>). The <em>simpl_transf_polytopes.zip</em> contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the <em>dingo_samples_on_simpl_transf_polytopes.zip</em> the resulting samples from <em>dingo</em> can be found. Similarly, <em>polyrounded_polytopes.zip contains </em>the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The <em>hopsy_samples.zip</em> folder contains the resulting samples from hopsy<em> </em>library, using a thinning of 100<em>d </em>;<em> </em>only in the case of Recon3D a thinning of 200<em>d </em>was used as suggested by the authors. Under the <em>hopsy_samples_ess_1000.zip </em>folder, we provide the <em>hopsy</em> samples with an ESS of 1000. Further, the <em>sars_samples.zip </em>file contains <em>dingo</em> samples from the solution space of the SARS-CoV-2 integrated model of Renz et <em>al</em> (2020) for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximising for the human biomass after maximising for the virus biomass objective function (VBOF) The following Python scripts to perform these experiments are included: <em>polyround_preproces.py </em>: runs the <em>PolyRound </em>functions and builds the simplified and transformed polytopes that <em>dingo </em>will use as well as the simplified, transformed and rounded polytopes <em>hopsy</em> uses <em>hopsy_on_polyrounded_polytopes.py </em>: performs sampling with <em>hopsy </em> <em>dingo_on_simpl_transf_polytopes.py </em>: performs sampling with <em>dingo </em> <em>binary_search.py</em> : a function to return the index in the chain where ESS becomes 1000 <em>compute_ess.py: </em>based on a model's <em>hopsy</em> samples (under the <em>hopsy_samples.zip </em>folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for <em>hopsy </em>to build them. The script requires the total time of the <em>hopsy </em>experiment recorded in the model's corresponding <em>.txt </em>file (you can find this under the <em>hopsy_samples.zip) </em> A notebook is available for how the integrated model was sampled.