Mercurial > repos > bimib > cobraxy

comparison COBRAxy/custom_data_generator.py @ 404:08f1ff359397 draft

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author francesco_lapi
date Mon, 08 Sep 2025 13:50:08 +0000
parents 05092b0cfca0
children 187cee1a00e2
comparison
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403:05092b0cfca0 404:08f1ff359397
157 model: the COBRA model to extract compartment data from. 157 model: the COBRA model to extract compartment data from.
158 158
159 Returns: 159 Returns:
160 pd.DataFrame: DataFrame with ReactionID and compartment columns 160 pd.DataFrame: DataFrame with ReactionID and compartment columns
161 """ 161 """
162 compartment_data = [] 162 pathway_data = []
163 163
164 # First pass: determine the maximum number of compartments any reaction has 164 # First pass: determine the maximum number of pathways any reaction has
165 max_compartments = 0 165 max_pathways = 0
166 reaction_compartments = {} 166 reaction_pathways = {}
167 167
168 for reaction in model.reactions: 168 for reaction in model.reactions:
169 # Get unique compartments from all metabolites in the reaction 169 # Get unique pathways from all metabolites in the reaction
170 if type(reaction.annotation['pathways']) == list: 170 if type(reaction.annotation['pathways']) == list:
171 reaction_compartments[reaction.id] = reaction.annotation['pathways'] 171 reaction_pathways[reaction.id] = reaction.annotation['pathways']
172 max_compartments = max(max_compartments, len(reaction.annotation['pathways'])) 172 max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
173 else: 173 else:
174 reaction_compartments[reaction.id] = [reaction.annotation['pathways']] 174 reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
175 175
176 # Create column names for compartments 176 # Create column names for pathways
177 compartment_columns = [f"Compartment_{i+1}" for i in range(max_compartments)] 177 pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
178 178
179 # Second pass: create the data 179 # Second pass: create the data
180 for reaction_id, compartments in reaction_compartments.items(): 180 for reaction_id, pathways in reaction_pathways.items():
181 row = {"ReactionID": reaction_id} 181 row = {"ReactionID": reaction_id}
182 182
183 # Fill compartment columns 183 # Fill pathway columns
184 for i in range(max_compartments): 184 for i in range(max_pathways):
185 col_name = compartment_columns[i] 185 col_name = pathway_columns[i]
186 if i < len(compartments): 186 if i < len(pathways):
187 row[col_name] = compartments[i] 187 row[col_name] = pathways[i]
188
189 else: 188 else:
190 row[col_name] = None # or "" if you prefer empty strings 189 row[col_name] = None # or "" if you prefer empty strings
191 190
192 compartment_data.append(row) 191 pathway_data.append(row)
193 192
194 return pd.DataFrame(compartment_data) 193 return pd.DataFrame(pathway_data)
195 194
196 195
197 ###############################- FILE SAVING -################################ 196 ###############################- FILE SAVING -################################
198 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None: 197 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
199 """ 198 """
299 # generate data 298 # generate data
300 rules = generate_rules(model, asParsed = False) 299 rules = generate_rules(model, asParsed = False)
301 reactions = generate_reactions(model, asParsed = False) 300 reactions = generate_reactions(model, asParsed = False)
302 bounds = generate_bounds(model) 301 bounds = generate_bounds(model)
303 medium = get_medium(model) 302 medium = get_medium(model)
304 compartments = generate_compartments(model) 303 if ARGS.name == "ENGRO2":
304 compartments = generate_compartments(model)
305 305
306 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"]) 306 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
307 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"]) 307 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
308 308
309 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"}) 309 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
310 df_medium = medium.rename(columns = {"reaction": "ReactionID"}) 310 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
311 df_medium["InMedium"] = True # flag per indicare la presenza nel medium 311 df_medium["InMedium"] = True # flag per indicare la presenza nel medium
312 312
313 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer") 313 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
314 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer") 314 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
315 merged = merged.merge(compartments, on = "ReactionID", how = "outer") 315 if ARGS.name == "ENGRO2":
316 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
316 merged = merged.merge(df_medium, on = "ReactionID", how = "left") 317 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
317 318
318 merged["InMedium"] = merged["InMedium"].fillna(False) 319 merged["InMedium"] = merged["InMedium"].fillna(False)
319 320
320 merged = merged.sort_values(by = "InMedium", ascending = False) 321 merged = merged.sort_values(by = "InMedium", ascending = False)