Mercurial > repos > bimib > cobraxy

comparison COBRAxy/flux_simulation_beta.py @ 469:5f02f7e4ea9f draft

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Uploaded
author luca_milaz
date Mon, 22 Sep 2025 15:14:47 +0000
parents 73f02860f7d7
children 46901af8df7c
comparison
equal deleted inserted replaced
468:77ed5c696865 469:5f02f7e4ea9f
43 43
44 parser.add_argument("-mab", "--model_and_bounds", type=str, 44 parser.add_argument("-mab", "--model_and_bounds", type=str,
45 choices=['True', 'False'], 45 choices=['True', 'False'],
46 required=True, 46 required=True,
47 help="upload mode: True for model+bounds, False for complete models") 47 help="upload mode: True for model+bounds, False for complete models")
48
49 parser.add_argument("-ens", "--sampling_enabled", type=str,
50 choices=['True', 'False'],
51 required=True,
52 help="enable sampling: True for sampling, False for no sampling")
48 53
49 parser.add_argument('-ol', '--out_log', 54 parser.add_argument('-ol', '--out_log',
50 help="Output log") 55 help="Output log")
51 56
52 parser.add_argument('-td', '--tool_dir', 57 parser.add_argument('-td', '--tool_dir',
340 cell_name (str): Name of the cell, used to generate filenames for output. 345 cell_name (str): Name of the cell, used to generate filenames for output.
341 346
342 Returns: 347 Returns:
343 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results. 348 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
344 """ 349 """
345 350
346 if ARGS.algorithm == 'OPTGP': 351 if ARGS.sampling_enabled == "True" and ARGS.n_samples > 0:
347 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed) 352
348 elif ARGS.algorithm == 'CBS': 353 if ARGS.algorithm == 'OPTGP':
349 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed) 354 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
350 355 elif ARGS.algorithm == 'CBS':
351 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types) 356 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
352 357
353 if("fluxes" not in ARGS.output_types): 358 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
354 os.remove(ARGS.output_path + "/" + cell_name + '.csv') 359
355 360 if("fluxes" not in ARGS.output_types):
356 returnList = [df_mean, df_median, df_quantiles] 361 os.remove(ARGS.output_path + "/" + cell_name + '.csv')
362
363 returnList = [df_mean, df_median, df_quantiles]
357 364
358 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis) 365 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
359 366
360 if("pFBA" in ARGS.output_type_analysis): 367 if("pFBA" in ARGS.output_type_analysis):
361 returnList.append(df_pFBA) 368 returnList.append(df_pFBA)
490 ARGS.output_types = ARGS.output_type.split(",") if ARGS.output_type else [] 497 ARGS.output_types = ARGS.output_type.split(",") if ARGS.output_type else []
491 # optional analysis output types -> list or empty 498 # optional analysis output types -> list or empty
492 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else [] 499 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else []
493 500
494 # Determine if sampling should be performed 501 # Determine if sampling should be performed
495 perform_sampling = ARGS.n_samples > 0 502 if ARGS.sampling_enabled == "True" and ARGS.n_samples > 0:
503 perform_sampling = True
496 504
497 print("=== INPUT FILES ===") 505 print("=== INPUT FILES ===")
498 print(f"{ARGS.input_files}") 506 print(f"{ARGS.input_files}")
499 print(f"{ARGS.file_names}") 507 print(f"{ARGS.file_names}")
500 print(f"{ARGS.output_type}") 508 print(f"{ARGS.output_type}")
558 if "quantiles" in ARGS.output_types: 566 if "quantiles" in ARGS.output_types:
559 all_quantiles = all_quantiles.fillna(0.0) 567 all_quantiles = all_quantiles.fillna(0.0)
560 all_quantiles = all_quantiles.sort_index() 568 all_quantiles = all_quantiles.sort_index()
561 write_to_file(all_quantiles.T, "quantiles", True) 569 write_to_file(all_quantiles.T, "quantiles", True)
562 else: 570 else:
563 print("=== SAMPLING SKIPPED (n_samples = 0) ===") 571 print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===")
564 572
565 # Handle optimization analysis outputs (always available) 573 # Handle optimization analysis outputs (always available)
566 print("=== PROCESSING OPTIMIZATION RESULTS ===") 574 print("=== PROCESSING OPTIMIZATION RESULTS ===")
567 575
568 # Determine the starting index for optimization results 576 # Determine the starting index for optimization results