cobraxy: COBRAxy/ras_to_bounds

comparison COBRAxy/ras_to_bounds_beta.py @ 418:919b5b71a61c draft

Uploaded

author	francesco_lapi
date	Tue, 09 Sep 2025 07:36:30 +0000
parents	e8dd8dca9618
children	0877682fff48

comparison

equal deleted inserted replaced

-:e8dd8dca9618
+:919b5b71a61c
 import sys
 import csv
 from joblib import Parallel, delayed, cpu_count
 import utils.rule_parsing  as rulesUtils
 import utils.reaction_parsing as reactionUtils
+import utils.model_utils as modelUtils
 # , medium
 ################################# process args ###############################
 def process_args(args :List[str] = None) -> argparse.Namespace:
 if upper_bound!=0 and lower_bound!=0:
 new_bounds.loc[reaction, "lower_bound"] = valMin
 new_bounds.loc[reaction, "upper_bound"] = valMax
 return new_bounds
-################################- DATA GENERATION -################################
-ReactionId = str
-def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
-"""
-Generates a dictionary mapping reaction ids to rules from the model.
-Args:
-model : the model to derive data from.
-asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
-Returns:
-Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
-Dict[ReactionId, str] : the generated dictionary of raw rules.
-"""
-# Is the below approach convoluted? yes
-# Ok but is it inefficient? probably
-# Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
-_ruleGetter   =  lambda reaction : reaction.gene_reaction_rule
-ruleExtractor = (lambda reaction :
-rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
-return {
-reaction.id : ruleExtractor(reaction)
-for reaction in model.reactions
-if reaction.gene_reaction_rule }
-def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
-"""
-Generates a dictionary mapping reaction ids to reaction formulas from the model.
-Args:
-model : the model to derive data from.
-asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
-Returns:
-Dict[ReactionId, str] : the generated dictionary.
-"""
-unparsedReactions = {
-reaction.id : reaction.reaction
-for reaction in model.reactions
-if reaction.reaction
-}
-if not asParsed: return unparsedReactions
-return reactionUtils.create_reaction_dict(unparsedReactions)
-def get_medium(model:cobra.Model) -> pd.DataFrame:
-trueMedium=[]
-for r in model.reactions:
-positiveCoeff=0
-for m in r.metabolites:
-if r.get_coefficient(m.id)>0:
-positiveCoeff=1;
-if (positiveCoeff==0 and r.lower_bound<0):
-trueMedium.append(r.id)
-df_medium = pd.DataFrame()
-df_medium["reaction"] = trueMedium
-return df_medium
-def generate_bounds(model:cobra.Model) -> pd.DataFrame:
-rxns = []
-for reaction in model.reactions:
-rxns.append(reaction.id)
-bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
-for reaction in model.reactions:
-bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
-return bounds
-def generate_compartments(model: cobra.Model) -> pd.DataFrame:
-"""
-Generates a DataFrame containing compartment information for each reaction.
-Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
-Args:
-model: the COBRA model to extract compartment data from.
-Returns:
-pd.DataFrame: DataFrame with ReactionID and compartment columns
-"""
-pathway_data = []
-# First pass: determine the maximum number of pathways any reaction has
-max_pathways = 0
-reaction_pathways = {}
-for reaction in model.reactions:
-# Get unique pathways from all metabolites in the reaction
-if type(reaction.annotation['pathways']) == list:
-reaction_pathways[reaction.id] = reaction.annotation['pathways']
-max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
-else:
-reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
-# Create column names for pathways
-pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
-# Second pass: create the data
-for reaction_id, pathways in reaction_pathways.items():
-row = {"ReactionID": reaction_id}
-# Fill pathway columns
-for i in range(max_pathways):
-col_name = pathway_columns[i]
-if i < len(pathways):
-row[col_name] = pathways[i]
-else:
-row[col_name] = None  # or "" if you prefer empty strings
-pathway_data.append(row)
-return pd.DataFrame(pathway_data)
 def save_model(model, filename, output_folder, file_format='csv'):
 """
 Save a COBRA model to file in the specified format.
 try:
 if file_format == 'tabular' or file_format == 'csv':
 # Special handling for tabular format using utils functions
 filepath = os.path.join(output_folder, f"{filename}.csv")
-rules = generate_rules(model, asParsed = False)
+rules = modelUtils.generate_rules(model, asParsed = False)
-reactions = generate_reactions(model, asParsed = False)
+reactions = modelUtils.generate_reactions(model, asParsed = False)
-bounds = generate_bounds(model)
+bounds = modelUtils.generate_bounds(model)
-medium = get_medium(model)
+medium = modelUtils.get_medium(model)
 try:
-compartments = utils.generate_compartments(model)
+compartments = modelUtils.generate_compartments(model)
 except:
 compartments = None
 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])

Mercurial > repos > bimib > cobraxy

comparison COBRAxy/ras_to_bounds_beta.py @ 418:919b5b71a61c draft