remove commented code

Author: ym2877

pymgpipe/fva.py

@@ -243,138 +243,6 @@ def _optlang_worker(threshold, metabolites):
         result.append({"id": m, "min": min_sol, "max": max_sol})
     return result
 
-
-# def veryFastFVA(
-#     nCores,
-#     nThreads,
-#     path,
-#     model=None,
-#     ex_only=True,
-#     scaling=0,
-#     memAff="none",
-#     schedule="dynamic",
-#     nChunk=50,
-#     optPerc=90,
-#     reactions=None,
-#     regex=None,
-#     threshold=1e-5,
-# ):
-#     """
-#     VFFVA performs Very Fast Flux Variability Analysis (VFFVA). VFFVA is a parallel implementation of FVA that
-#     allows dynamically assigning reactions to each worker depending on their computational load
-#     Guebila, Marouen Ben. "Dynamic load balancing enables large-scale flux variability analysis." bioRxiv (2018): 440701.
-
-#     USAGE:
-#     minFlux,maxFlux=VFFVA(nCores, nThreads, model, scaling, memAff, schedule, nChunk, optPerc, ex)
-
-#     :param nCores:   Number of non-shared memory cores/machines.
-#     :param nThreads: Number of shared memory threads in each core/machine.
-#     :param model:    .mps format: path to metabolic model in .mps format.
-#     :param scaling:  CPLEX parameter. It corresponds to SCAIND parameter (Default = 0).
-#                      -1: no scaling; 0: equilibration scaling; 1: more aggressive scaling.
-#                      more information here: https://www.ibm.com/support/knowledgecenter/SSSA5P_12.7.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/ScaInd.html.
-#     :param memAff:   'none', 'core', or 'socket'. (Default = 'none'). This an OpenMPI parameter, more
-#                      information here: https://www.open-mpi.org/faq/?category=tuning#using-paffinity-v1.4.
-#     :param schedule: 'dynamic', 'static', or 'guided'. (Default = 'dynamic'). This is an OpenMP parameter, more
-#                      information here: https://software.intel.com/en-us/articles/openmp-loop-scheduling
-#     :param nChunk:   Number of reactions in each chunk (Default = 50). This is an OpenMP parameter, more
-#                      information here: https://software.intel.com/en-us/articles/openmp-loop-scheduling
-#     :param optPerc:  Percentage of the optimal objective used in FVA. Integer between 0 and 100. For example, when set to 90
-#                      FVA will be computed on 90% of the optimal objective.
-#     :param ex:      0-based indices of reactions to optimize as a numpy array.  (Default, all reactions)
-#     :return:
-#            minFlux:          (n,1) vector of minimal flux values for each reaction.
-#            maxFlux:          (n,1) vector of maximal flux values for each reaction.
-#     """
-#     status = os.system("mpirun --version")
-#     if status != 0:
-#         raise ValueError(
-#             [
-#                 "MPI and/or CPLEX nont installed, please follow the install guide"
-#                 "or use the quick install script"
-#             ]
-#         )
-
-#     # Set schedule and chunk size parameters
-#     os.environ["OMP_SCHEDUELE"] = schedule + str(nChunk)
-
-#     model = load_model(path if model is None else model)
-#     with suppress_stdout():
-#         model.optimize()
-#     if model.status == "infeasible":
-#         raise Exception("%s model is infeasible!" % model.name)
-#         return
-
-#     var_dict = {i: v.name for i, v in enumerate(model.variables)}
-#     var_dict_inv = {v: k for k, v in var_dict.items()}
-
-#     ex_indices = [
-#         var_dict_inv[r.name]
-#         for r in get_reactions(
-#             model,
-#             reactions=reactions,
-#             regex=Constants.EX_REGEX if ex_only and regex is None else regex,
-#         )
-#     ]
-#     print("Running on %s reactions!" % len(ex_indices))
-
-#     # Set reactions to optimize
-#     rxns_file = "rxns_%s.txt" % model.name
-#     with open(rxns_file, "w") as f:
-#         for num in ex_indices:
-#             f.write(str(num) + "\n")
-
-#     assert (
-#         vffva_config.path is not None
-#     ), "Please set value of `vffva_config.path` to location of VFFVA executable"
-#     try:
-#         status = os.system(
-#             "mpirun -np "
-#             + str(nCores)
-#             + " --bind-to "
-#             + str(memAff)
-#             + " -x OMP_NUM_THREADS="
-#             + str(nThreads)
-#             + f" {vffva_config.path}/lib/veryfastFVA "
-#             + path
-#             + " "
-#             + str(optPerc)
-#             + " "
-#             + str(scaling)
-#             + " "
-#             + rxns_file
-#         )
-#     except:
-#         raise Exception(
-#             "Ran into issue when submitting VFFVA mpirun, please check installation- %s"
-#             % status
-#         )
-
-#     # Fetch results
-#     resultFile = path[:-4] + "output.csv"
-#     if not os.path.exists(resultFile):
-#         raise Exception(
-#             "Ran into issue when running VFFVA, could not find results file..."
-#         )
-#     results = pd.read_csv(resultFile, header=0)
-
-#     os.system("rm " + resultFile)
-#     os.system("rm " + rxns_file)
-#     results.rename(
-#         {i: var_dict[x] for i, x in enumerate(ex_indices) if i in results.index},
-#         axis=0,
-#         inplace=True,
-#     )
-
-#     results.index.rename("id", inplace=True)
-#     if threshold is not None:
-#         results[abs(results) < threshold] = 0
-
-#     results.sort_index(inplace=True)
-#     results.columns = ["min", "max"]
-#     return results
-
-
 def _pool_init(sample_model):
     sys.stdout = open(os.devnull, "w")