Supplementary files for the dingo Python library (Q6716389)

Wecompare theMultiphase Monte Carloflux Sampling (MMCS) featureof the dingo libraryagainst the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a rangingdimension(ext_data.zip). Thesimpl_transf_polytopes.zip 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 thedingo_samples_on_simpl_transf_polytopes.zipthe resultingsamples from dingo can be found using the MMCS algorithm. Similarly, polyrounded_polytopes.zipcontainsthe polytopes retrieved afterapplying simplify(), transform() andround() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking foran ESS of 1000. The hopsy_samples.zipfolder contains the resultingsamples from hopsy library, usinga thinning of 100d ; only in the case of Recon3D a thinning of 200d was used as suggested by the authors. Under thehopsy_samples_ess_1000.zipfolder, we provide thehopsy samples with an ESS of 1000. Last,the samples produced using the efficient Billiard Walk implementation of dingo can be found under the BWRsamples.zip file. In this case, 20000 points were sampled for each model. Further, the sars_samples.zipfile containsdingosamples from the solution space of the SARS-CoV-2integratedmodel of Renz et al (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 thevirus biomass objective function (VBOF) The following Python scripts to perform these experiments are included: polyround_preproces.py: runs the PolyRoundfunctions and builds the simplified and transformed polytopes thatdingowill use as well as the simplified, transformed and rounded polytopeshopsyuses hopsy_on_polyrounded_polytopes.py: performssampling with hopsy dingo_on_simpl_transf_polytopes.py: performs sampling withdingo run_bwr_exp.py computes samples using the efficient Billiard Walk of dingo binary_search.py: a function to return the index in the chain where ESS becomes 1000 compute_ess.py: based on a model'shopsy samples (under thehopsy_samples.zip folder)it retrieves the sampleswith an ESS of 1000 and the correspondingrequired time for hopsyto build them. The script requires the total time of thehopsyexperimentrecorded in the model's corresponding .txtfile (you can find this under thehopsy_samples.zip) compute_ess_psrf_per_phase.pycomputes ESS and PSRF in specific indices which correspond to those when MMCS switches from a phase a to next one A notebook is available for how the integrated model was sampled.