Mercurial > repos > bimib > cobraxy
comparison COBRAxy/flux_simulation.py @ 210:3ca179b83574 draft
Uploaded
| author | luca_milaz |
|---|---|
| date | Thu, 28 Nov 2024 14:07:11 +0000 |
| parents | c2aa3034aac2 |
| children | c561c060a55f |
comparison
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| 209:00a66b9bc29e | 210:3ca179b83574 |
|---|---|
| 236 bounds_df = read_dataset(bounds_path, "bounds dataset") | 236 bounds_df = read_dataset(bounds_path, "bounds dataset") |
| 237 for rxn_index, row in bounds_df.iterrows(): | 237 for rxn_index, row in bounds_df.iterrows(): |
| 238 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound | 238 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound |
| 239 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound | 239 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound |
| 240 | 240 |
| 241 #name = '.'.join(cell_name.rsplit('.', 1)[:-1]) | |
| 242 name = cell_name | |
| 243 | 241 |
| 244 if ARGS.algorithm == 'OPTGP': | 242 if ARGS.algorithm == 'OPTGP': |
| 245 OPTGP_sampler(model_input, name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed) | 243 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed) |
| 246 | 244 |
| 247 elif ARGS.algorithm == 'CBS': | 245 elif ARGS.algorithm == 'CBS': |
| 248 CBS_sampler(model_input, name, ARGS.n_samples, ARGS.n_batches, ARGS.seed) | 246 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed) |
| 249 | 247 |
| 250 df_mean, df_median, df_quantiles = fluxes_statistics(name, ARGS.output_types) | 248 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types) |
| 251 | 249 |
| 252 if("fluxes" not in ARGS.output_types): | 250 if("fluxes" not in ARGS.output_types): |
| 253 os.remove(ARGS.output_path + "/" + name + '.csv') | 251 os.remove(ARGS.output_path + "/" + cell_name + '.csv') |
| 254 | 252 |
| 255 returnList = [] | 253 returnList = [] |
| 256 returnList.append(df_mean) | 254 returnList.append(df_mean) |
| 257 returnList.append(df_median) | 255 returnList.append(df_median) |
| 258 returnList.append(df_quantiles) | 256 returnList.append(df_quantiles) |
| 259 | 257 |
| 260 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, name, ARGS.output_type_analysis) | 258 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis) |
| 261 | 259 |
| 262 if("pFBA" in ARGS.output_type_analysis): | 260 if("pFBA" in ARGS.output_type_analysis): |
| 263 returnList.append(df_pFBA) | 261 returnList.append(df_pFBA) |
| 264 if("FVA" in ARGS.output_type_analysis): | 262 if("FVA" in ARGS.output_type_analysis): |
| 265 returnList.append(df_FVA) | 263 returnList.append(df_FVA) |
