Mercurial > repos > bimib > cobraxy

comparison COBRAxy/flux_simulation.py @ 519:8f65bed8d4cc draft

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author luca_milaz
date Thu, 09 Oct 2025 10:08:20 +0000
parents c66aa96336d3
children dd159f41b94c
comparison
equal deleted inserted replaced
518:c3c1d3b3941f 519:8f65bed8d4cc
164 with open(ARGS.out_log, 'a') as log: 164 with open(ARGS.out_log, 'a') as log:
165 log.write(s + "\n\n") 165 log.write(s + "\n\n")
166 print(s) 166 print(s)
167 167
168 168
169 def write_to_file(dataset: pd.DataFrame, name: str, path: str, keep_index:bool=False)->None: 169 def write_to_file(dataset: pd.DataFrame, path: str, keep_index:bool=False, name:str=None)->None:
170 """ 170 """
171 Write a DataFrame to a TSV file under path with a given base name. 171 Write a DataFrame to a TSV file under path with a given base name.
172 172
173 Args: 173 Args:
174 dataset: The DataFrame to write. 174 dataset: The DataFrame to write.
175 name: Base file name (without extension). 175 name: Base file name (without extension). If None, 'path' is treated as the full file path.
176 path: Directory path where the file will be saved. 176 path: Directory path where the file will be saved.
177 keep_index: Whether to keep the DataFrame index in the file. 177 keep_index: Whether to keep the DataFrame index in the file.
178 178
179 Returns: 179 Returns:
180 None 180 None
181 """ 181 """
182 dataset.index.name = 'Reactions' 182 dataset.index.name = 'Reactions'
183 dataset.to_csv(os.path.join(path, name + ".csv"), sep = '\t', index = keep_index) 183 if name:
184 dataset.to_csv(os.path.join(path, name + ".csv"), sep = '\t', index = keep_index)
185 else:
186 dataset.to_csv(path, sep = '\t', index = keep_index)
184 187
185 ############################ dataset input #################################### 188 ############################ dataset input ####################################
186 def read_dataset(data :str, name :str) -> pd.DataFrame: 189 def read_dataset(data :str, name :str) -> pd.DataFrame:
187 """ 190 """
188 Read a dataset from a CSV file and return it as a pandas DataFrame. 191 Read a dataset from a CSV file and return it as a pandas DataFrame.
252 255
253 # Convert back to DataFrame with proper column names 256 # Convert back to DataFrame with proper column names
254 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids) 257 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
255 258
256 # Save the final merged result as CSV 259 # Save the final merged result as CSV
257 write_to_file(samplesTotal.T, model_name, ARGS.output_path, True) 260 write_to_file(samplesTotal.T, ARGS.output_path, True, name=model_name)
258 261
259 # Clean up temporary numpy files 262 # Clean up temporary numpy files
260 for i in range(n_batches): 263 for i in range(n_batches):
261 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy" 264 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
262 if os.path.exists(batch_filename): 265 if os.path.exists(batch_filename):
329 332
330 # Convert back to DataFrame with proper column namesq 333 # Convert back to DataFrame with proper column namesq
331 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids) 334 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
332 335
333 # Save the final merged result as CSV 336 # Save the final merged result as CSV
334 write_to_file(samplesTotal.T, model_name, ARGS.output_path, True) 337 write_to_file(samplesTotal.T, ARGS.output_path, True, name=model_name)
335 338
336 # Clean up temporary numpy files 339 # Clean up temporary numpy files
337 for i in range(n_batches): 340 for i in range(n_batches):
338 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy" 341 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
339 if os.path.exists(batch_filename): 342 if os.path.exists(batch_filename):
589 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False) 592 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
590 593
591 if "mean" in ARGS.output_types: 594 if "mean" in ARGS.output_types:
592 all_mean = all_mean.fillna(0.0) 595 all_mean = all_mean.fillna(0.0)
593 all_mean = all_mean.sort_index() 596 all_mean = all_mean.sort_index()
594 write_to_file(all_mean.T, "mean", ARGS.out_mean, True) 597 write_to_file(all_mean.T, ARGS.out_mean, True)
595 598
596 if "median" in ARGS.output_types: 599 if "median" in ARGS.output_types:
597 all_median = all_median.fillna(0.0) 600 all_median = all_median.fillna(0.0)
598 all_median = all_median.sort_index() 601 all_median = all_median.sort_index()
599 write_to_file(all_median.T, "median", ARGS.out_median, True) 602 write_to_file(all_median.T, ARGS.out_median, True)
600 603
601 if "quantiles" in ARGS.output_types: 604 if "quantiles" in ARGS.output_types:
602 all_quantiles = all_quantiles.fillna(0.0) 605 all_quantiles = all_quantiles.fillna(0.0)
603 all_quantiles = all_quantiles.sort_index() 606 all_quantiles = all_quantiles.sort_index()
604 write_to_file(all_quantiles.T, "quantiles", ARGS.out_quantiles, True) 607 write_to_file(all_quantiles.T, ARGS.out_quantiles, True)
605 else: 608 else:
606 print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===") 609 print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===")
607 610
608 # Handle optimization analysis outputs (always available) 611 # Handle optimization analysis outputs (always available)
609 print("=== PROCESSING OPTIMIZATION RESULTS ===") 612 print("=== PROCESSING OPTIMIZATION RESULTS ===")
614 index_result = 3 if perform_sampling else 0 617 index_result = 3 if perform_sampling else 0
615 618
616 if "pFBA" in ARGS.output_type_analysis: 619 if "pFBA" in ARGS.output_type_analysis:
617 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False) 620 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False)
618 all_pFBA = all_pFBA.sort_index() 621 all_pFBA = all_pFBA.sort_index()
619 write_to_file(all_pFBA.T, "pFBA", ARGS.out_pfba, True) 622 write_to_file(all_pFBA.T, ARGS.out_pfba, True)
620 index_result += 1 623 index_result += 1
621 624
622 if "FVA" in ARGS.output_type_analysis: 625 if "FVA" in ARGS.output_type_analysis:
623 all_FVA = pd.concat([result[index_result] for result in results], ignore_index=False) 626 all_FVA = pd.concat([result[index_result] for result in results], ignore_index=False)
624 all_FVA = all_FVA.sort_index() 627 all_FVA = all_FVA.sort_index()
625 write_to_file(all_FVA.T, "FVA", ARGS.out_fva, True) 628 write_to_file(all_FVA.T, ARGS.out_fva, True)
626 index_result += 1 629 index_result += 1
627 630
628 if "sensitivity" in ARGS.output_type_analysis: 631 if "sensitivity" in ARGS.output_type_analysis:
629 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False) 632 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False)
630 all_sensitivity = all_sensitivity.sort_index() 633 all_sensitivity = all_sensitivity.sort_index()
631 write_to_file(all_sensitivity.T, "sensitivity", ARGS.out_sensitivity, True) 634 write_to_file(all_sensitivity.T, ARGS.out_sensitivity, True)
632 635
633 return 636 return
634 637
635 ############################################################################## 638 ##############################################################################
636 if __name__ == "__main__": 639 if __name__ == "__main__":