Mercurial > repos > bimib > cobraxy

comparison COBRAxy/ras_to_bounds.py @ 64:b10da5046afd draft

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author luca_milaz
date Sun, 13 Oct 2024 08:25:29 +0000
parents 5302258f8262
children 1f928ad6a87e
comparison
equal deleted inserted replaced
63:5302258f8262 64:b10da5046afd
121 for reaction in rxns_ids: 121 for reaction in rxns_ids:
122 if reaction in ras_row.index: 122 if reaction in ras_row.index:
123 scaling_factor = ras_row[reaction] 123 scaling_factor = ras_row[reaction]
124 lower_bound=model.reactions.get_by_id(reaction).lower_bound 124 lower_bound=model.reactions.get_by_id(reaction).lower_bound
125 upper_bound=model.reactions.get_by_id(reaction).upper_bound 125 upper_bound=model.reactions.get_by_id(reaction).upper_bound
126 warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor)) 126 #warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor))
127 valMax=float((upper_bound)*scaling_factor) 127 valMax=float((upper_bound)*scaling_factor)
128 valMin=float((lower_bound)*scaling_factor) 128 valMin=float((lower_bound)*scaling_factor)
129 if upper_bound!=0 and lower_bound==0: 129 if upper_bound!=0 and lower_bound==0:
130 model.reactions.get_by_id(reaction).upper_bound=valMax 130 model.reactions.get_by_id(reaction).upper_bound=valMax
131 if upper_bound==0 and lower_bound!=0: 131 if upper_bound==0 and lower_bound!=0:
183 rxn = model.reactions.get_by_id(reaction) 183 rxn = model.reactions.get_by_id(reaction)
184 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"]) 184 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"])
185 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"]) 185 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"])
186 186
187 if ras is not None: 187 if ras is not None:
188 #Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows()) 188 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
189 for cellName, ras_row in ras.iterrows(): 189 #for cellName, ras_row in ras.iterrows():
190 process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder) 190 #process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder)
191 else: 191 else:
192 model_new = model.copy() 192 model_new = model.copy()
193 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids) 193 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
194 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"]) 194 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
195 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True) 195 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
231 for patient_id in ras.index: 231 for patient_id in ras.index:
232 class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name] 232 class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
233 233
234 234
235 # Concatenate all ras DataFrames into a single DataFrame 235 # Concatenate all ras DataFrames into a single DataFrame
236 ras_combined = pd.concat(ras_list, axis=1) 236 ras_combined = pd.concat(ras_list, axis=0)
237 # Normalize the RAS values by max RAS 237 # Normalize the RAS values by max RAS
238 ras_combined = ras_combined.div(ras_combined.max(axis=0)) 238 ras_combined = ras_combined.div(ras_combined.max(axis=0))
239 ras_combined = ras_combined.fillna(0) 239 ras_combined = ras_combined.fillna(0)
240 240
241 241