def get_network_results(name, settings, cache): print "STARTING", name if name in cache.keys(): print "CACHE HIT" return cache[name] ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(name, settings) # Create date string to append to output_dir t = datetime.now().strftime("%Y-%m-%d_%H.%M.%S") settings["global"]["output_dir"] = settings["global"]["output_dir_save"] + "/" + \ settings["global"]["experiment_name"] + "-" + t + "-" + name + "/" os.mkdir(settings["global"]["output_dir"]) # Get a list of the multifactorial files # Read data into program # Where the format is "FILENAME" "DATATYPE" mf_storage = ReadData(mf_file[0], "multifactorial") knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") wildtype_storage = ReadData(wt_file[0], "wildtype") timeseries_storage = ReadData(ts_file[0], "timeseries") gene_list = knockout_storage.gene_list # Setup job manager jobman = JobManager(settings) # MCZ mczjob = MCZ() mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ") jobman.queueJob(mczjob) # CLR clrjob = CLR() clrjob.setup(knockout_storage, settings, "CLR", "plos", 6) jobman.queueJob(clrjob) # GENIE3 mf_storage.combine(knockout_storage) mf_storage.combine(wildtype_storage) mf_storage.combine(knockdown_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "GENIE3") jobman.queueJob(genie3job) ## TLCLR tlclrjob = TLCLR() tlclrjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "TLCLR") jobman.queueJob(tlclrjob) #if sys.argv[1] != "dream4100": #cojob = ConvexOptimization() #cojob.setup(knockout_storage, settings, "ConvOpt_T-"+ str(0.01),None, None, 0.01) #jobman.queueJob(cojob) ### DFG4GRN dfg = DFG4GRN() settings["dfg4grn"]["eta_z"] = 0.01 settings["dfg4grn"]["lambda_w"] = 0.001 settings["dfg4grn"]["tau"] = 3 dfg.setup(timeseries_storage, TFList(timeseries_storage[0].gene_list), settings, "DFG", 20) jobman.queueJob(dfg) ### Inferelator ### NIR nirjob = NIR() nirjob.setup(knockout_storage, settings, "NIR", 5, 5) jobman.queueJob(nirjob) #### TDARACNE settings = ReadConfig(settings, "./config/default_values/tdaracne.cfg") bjob = tdaracne() settings["tdaracne"]["num_bins"] = 4 bjob.setup(timeseries_storage, settings, "TDARACNE") jobman.queueJob(bjob) print jobman.queue jobman.runQueue() jobman.waitToClear(name) SaveResults(jobman.finished, goldnet, settings, name) cache[name] = jobman.finished[:] return cache[name]
settings["global"]["time_series_delta_t"] = delta_t # Read data into program timeseries_storage = ReadData(ts_filenames[0], True) # Get config file for tdaracne settings = ReadConfig(settings, "./config/default_values/tdaracne.cfg") settings = ReadConfig(settings, "./config/default_values/banjo.cfg") settings = ReadConfig(settings, "./config/default_values/dfg4grn.cfg") # settings = ReadConfig(settings, settings["tdaracne"]["config"]) # Setup job manager jobman = JobManager(settings) # Make tdaracne jobs bjob = tdaracne() bjob.setup(timeseries_storage, settings, "tdaracne-test-run-1") jobman.queueJob(bjob) trans_factors = TFList(timeseries_storage[0].gene_list) settings["dfg4grn"]["eta_z"] = 0.01 settings["dfg4grn"]["lambda_w"] = 0.001 settings["dfg4grn"]["tau"] = 2 dfg = DFG4GRN() dfg.setup(timeseries_storage, trans_factors, settings, "dfg4grn-test-run-1") jobman.queueJob(dfg) bjob = banjo() bjob.setup(timeseries_storage, settings, "banjo-test-run-1") jobman.queueJob(bjob)