Mercurial > repos > bimib > cobraxy

comparison COBRAxy/utils/model_utils.py @ 418:919b5b71a61c draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Uploaded
author francesco_lapi
date Tue, 09 Sep 2025 07:36:30 +0000
parents
children ed2c1f9e20ba
comparison
equal deleted inserted replaced
417:e8dd8dca9618 418:919b5b71a61c
1 import os
2 import csv
3 import cobra
4 import pickle
5 import argparse
6 import pandas as pd
7 from typing import Optional, Tuple, Union, List, Dict
8 import utils.general_utils as utils
9 import utils.rule_parsing as rulesUtils
10
11 ################################- DATA GENERATION -################################
12 ReactionId = str
13 def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
14 """
15 Generates a dictionary mapping reaction ids to rules from the model.
16
17 Args:
18 model : the model to derive data from.
19 asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
20
21 Returns:
22 Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
23 Dict[ReactionId, str] : the generated dictionary of raw rules.
24 """
25 # Is the below approach convoluted? yes
26 # Ok but is it inefficient? probably
27 # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
28 _ruleGetter = lambda reaction : reaction.gene_reaction_rule
29 ruleExtractor = (lambda reaction :
30 rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
31
32 return {
33 reaction.id : ruleExtractor(reaction)
34 for reaction in model.reactions
35 if reaction.gene_reaction_rule }
36
37 def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
38 """
39 Generates a dictionary mapping reaction ids to reaction formulas from the model.
40
41 Args:
42 model : the model to derive data from.
43 asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
44
45 Returns:
46 Dict[ReactionId, str] : the generated dictionary.
47 """
48
49 unparsedReactions = {
50 reaction.id : reaction.reaction
51 for reaction in model.reactions
52 if reaction.reaction
53 }
54
55 if not asParsed: return unparsedReactions
56
57 return reactionUtils.create_reaction_dict(unparsedReactions)
58
59 def get_medium(model:cobra.Model) -> pd.DataFrame:
60 trueMedium=[]
61 for r in model.reactions:
62 positiveCoeff=0
63 for m in r.metabolites:
64 if r.get_coefficient(m.id)>0:
65 positiveCoeff=1;
66 if (positiveCoeff==0 and r.lower_bound<0):
67 trueMedium.append(r.id)
68
69 df_medium = pd.DataFrame()
70 df_medium["reaction"] = trueMedium
71 return df_medium
72
73 def generate_bounds(model:cobra.Model) -> pd.DataFrame:
74
75 rxns = []
76 for reaction in model.reactions:
77 rxns.append(reaction.id)
78
79 bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
80
81 for reaction in model.reactions:
82 bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
83 return bounds
84
85
86
87 def generate_compartments(model: cobra.Model) -> pd.DataFrame:
88 """
89 Generates a DataFrame containing compartment information for each reaction.
90 Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
91
92 Args:
93 model: the COBRA model to extract compartment data from.
94
95 Returns:
96 pd.DataFrame: DataFrame with ReactionID and compartment columns
97 """
98 pathway_data = []
99
100 # First pass: determine the maximum number of pathways any reaction has
101 max_pathways = 0
102 reaction_pathways = {}
103
104 for reaction in model.reactions:
105 # Get unique pathways from all metabolites in the reaction
106 if type(reaction.annotation['pathways']) == list:
107 reaction_pathways[reaction.id] = reaction.annotation['pathways']
108 max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
109 else:
110 reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
111
112 # Create column names for pathways
113 pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
114
115 # Second pass: create the data
116 for reaction_id, pathways in reaction_pathways.items():
117 row = {"ReactionID": reaction_id}
118
119 # Fill pathway columns
120 for i in range(max_pathways):
121 col_name = pathway_columns[i]
122 if i < len(pathways):
123 row[col_name] = pathways[i]
124 else:
125 row[col_name] = None # or "" if you prefer empty strings
126
127 pathway_data.append(row)
128
129 return pd.DataFrame(pathway_data)