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1 import os
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2 import csv
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3 import cobra
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4 import pickle
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5 import argparse
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6 import pandas as pd
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7 import utils.general_utils as utils
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8 import utils.rule_parsing as rulesUtils
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9 from typing import Optional, Tuple, Union, List, Dict
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10 import utils.reaction_parsing as reactionUtils
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11
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12 ARGS : argparse.Namespace
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13 def process_args(args: List[str] = None) -> argparse.Namespace:
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14 """
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15 Parse command-line arguments for CustomDataGenerator.
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16 """
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17
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18 parser = argparse.ArgumentParser(
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19 usage="%(prog)s [options]",
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20 description="Generate custom data from a given model"
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21 )
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22
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23 parser.add_argument("--out_log", type=str, required=True,
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24 help="Output log file")
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25
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26 parser.add_argument("--model", type=str,
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27 help="Built-in model identifier (e.g., ENGRO2, Recon, HMRcore)")
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28 parser.add_argument("--input", type=str,
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29 help="Custom model file (JSON or XML)")
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30 parser.add_argument("--name", type=str, required=True,
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31 help="Model name (default or custom)")
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32
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33 parser.add_argument("--medium_selector", type=str, required=True,
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34 help="Medium selection option")
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35
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36 parser.add_argument("--gene_format", type=str, default="Default",
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37 help="Gene nomenclature format: Default (original), ENSNG, HGNC_SYMBOL, HGNC_ID, ENTREZ")
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38
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39 parser.add_argument("--out_tabular", type=str,
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40 help="Output file for the merged dataset (CSV or XLSX)")
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41
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42 parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
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43 help="Tool directory (passed from Galaxy as $__tool_directory__)")
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44
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45
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46 return parser.parse_args(args)
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47
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48 ################################- INPUT DATA LOADING -################################
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49 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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50 """
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51 Loads a custom model from a file, either in JSON or XML format.
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52
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53 Args:
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54 file_path : The path to the file containing the custom model.
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55 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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56
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57 Raises:
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58 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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59
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60 Returns:
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61 cobra.Model : the model, if successfully opened.
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62 """
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63 ext = ext if ext else file_path.ext
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64 try:
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65 if ext is utils.FileFormat.XML:
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66 return cobra.io.read_sbml_model(file_path.show())
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67
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68 if ext is utils.FileFormat.JSON:
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69 return cobra.io.load_json_model(file_path.show())
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70
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71 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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72 raise utils.DataErr(file_path,
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73 f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
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74
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414
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75 ################################- DATA GENERATION -################################
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76 ReactionId = str
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77 def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
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78 """
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79 Generates a dictionary mapping reaction ids to rules from the model.
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406
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80
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414
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81 Args:
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82 model : the model to derive data from.
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83 asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
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84
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85 Returns:
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86 Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
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87 Dict[ReactionId, str] : the generated dictionary of raw rules.
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88 """
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89 # Is the below approach convoluted? yes
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90 # Ok but is it inefficient? probably
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91 # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
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92 _ruleGetter = lambda reaction : reaction.gene_reaction_rule
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93 ruleExtractor = (lambda reaction :
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94 rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
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95
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96 return {
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97 reaction.id : ruleExtractor(reaction)
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98 for reaction in model.reactions
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99 if reaction.gene_reaction_rule }
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100
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101 def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
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102 """
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103 Generates a dictionary mapping reaction ids to reaction formulas from the model.
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104
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105 Args:
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106 model : the model to derive data from.
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107 asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
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108
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109 Returns:
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110 Dict[ReactionId, str] : the generated dictionary.
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111 """
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112
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113 unparsedReactions = {
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114 reaction.id : reaction.reaction
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115 for reaction in model.reactions
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116 if reaction.reaction
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117 }
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118
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119 if not asParsed: return unparsedReactions
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120
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121 return reactionUtils.create_reaction_dict(unparsedReactions)
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122
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123 def get_medium(model:cobra.Model) -> pd.DataFrame:
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124 trueMedium=[]
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125 for r in model.reactions:
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126 positiveCoeff=0
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127 for m in r.metabolites:
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128 if r.get_coefficient(m.id)>0:
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129 positiveCoeff=1;
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130 if (positiveCoeff==0 and r.lower_bound<0):
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131 trueMedium.append(r.id)
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132
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133 df_medium = pd.DataFrame()
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134 df_medium["reaction"] = trueMedium
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135 return df_medium
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136
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137 def generate_bounds(model:cobra.Model) -> pd.DataFrame:
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138
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139 rxns = []
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140 for reaction in model.reactions:
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141 rxns.append(reaction.id)
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142
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143 bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
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144
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145 for reaction in model.reactions:
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146 bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
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147 return bounds
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148
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149
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150
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151 def generate_compartments(model: cobra.Model) -> pd.DataFrame:
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152 """
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153 Generates a DataFrame containing compartment information for each reaction.
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154 Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
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155
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156 Args:
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157 model: the COBRA model to extract compartment data from.
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158
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159 Returns:
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160 pd.DataFrame: DataFrame with ReactionID and compartment columns
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161 """
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162 pathway_data = []
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163
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164 # First pass: determine the maximum number of pathways any reaction has
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165 max_pathways = 0
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166 reaction_pathways = {}
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167
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168 for reaction in model.reactions:
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169 # Get unique pathways from all metabolites in the reaction
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170 if type(reaction.annotation['pathways']) == list:
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171 reaction_pathways[reaction.id] = reaction.annotation['pathways']
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172 max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
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173 else:
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174 reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
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175
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176 # Create column names for pathways
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177 pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
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178
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179 # Second pass: create the data
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180 for reaction_id, pathways in reaction_pathways.items():
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181 row = {"ReactionID": reaction_id}
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182
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183 # Fill pathway columns
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184 for i in range(max_pathways):
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185 col_name = pathway_columns[i]
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186 if i < len(pathways):
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187 row[col_name] = pathways[i]
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188 else:
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189 row[col_name] = None # or "" if you prefer empty strings
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190
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191 pathway_data.append(row)
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192
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193 return pd.DataFrame(pathway_data)
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406
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194
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195
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196 ###############################- FILE SAVING -################################
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197 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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198 """
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199 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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200
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201 Args:
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202 data : the data to be written to the file.
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203 file_path : the path to the .csv file.
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204 fieldNames : the names of the fields (columns) in the .csv file.
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205
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206 Returns:
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207 None
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208 """
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209 with open(file_path.show(), 'w', newline='') as csvfile:
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210 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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211 writer.writeheader()
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212
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213 for key, value in data.items():
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214 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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215
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216 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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217 """
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218 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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219
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220 Args:
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221 data : the data to be written to the file.
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222 file_path : the path to the .csv file.
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223 fieldNames : the names of the fields (columns) in the .csv file.
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224
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225 Returns:
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226 None
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227 """
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228 with open(file_path, 'w', newline='') as csvfile:
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229 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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230 writer.writeheader()
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231
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232 for key, value in data.items():
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233 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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234
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235 def save_as_tabular_df(df: pd.DataFrame, path: str) -> None:
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236 try:
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237 os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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238 df.to_csv(path, sep="\t", index=False)
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239 except Exception as e:
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240 raise utils.DataErr(path, f"failed writing tabular output: {e}")
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241
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242
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243 ###############################- ENTRY POINT -################################
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244 def main(args:List[str] = None) -> None:
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245 """
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246 Initializes everything and sets the program in motion based on the fronted input arguments.
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247
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248 Returns:
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249 None
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250 """
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251 # get args from frontend (related xml)
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252 global ARGS
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253 ARGS = process_args(args)
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254
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255
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256 if ARGS.input:
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257 # load custom model
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258 model = load_custom_model(
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259 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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260 else:
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261 # load built-in model
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262
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263 try:
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264 model_enum = utils.Model[ARGS.model] # e.g., Model['ENGRO2']
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265 except KeyError:
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266 raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
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267
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268 # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
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269 try:
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270 model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
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271 except Exception as e:
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272 # Wrap/normalize load errors as DataErr for consistency
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273 raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
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274
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275 # Determine final model name: explicit --name overrides, otherwise use the model id
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276
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277 model_name = ARGS.name if ARGS.name else ARGS.model
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278
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279 if ARGS.name == "ENGRO2" and ARGS.medium_selector != "Default":
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280 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
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281 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
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282 medium = df_mediums[[ARGS.medium_selector]]
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283 medium = medium[ARGS.medium_selector].to_dict()
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284
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285 # Set all reactions to zero in the medium
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286 for rxn_id, _ in model.medium.items():
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287 model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
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288
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289 # Set medium conditions
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290 for reaction, value in medium.items():
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291 if value is not None:
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292 model.reactions.get_by_id(reaction).lower_bound = -float(value)
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293
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294 if ARGS.name == "ENGRO2" and ARGS.gene_format != "Default":
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295
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296 model = utils.convert_genes(model, ARGS.gene_format.replace("HGNC_", "HGNC "))
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297
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298 # generate data
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414
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299 rules = generate_rules(model, asParsed = False)
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300 reactions = generate_reactions(model, asParsed = False)
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301 bounds = generate_bounds(model)
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302 medium = get_medium(model)
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406
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303 if ARGS.name == "ENGRO2":
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414
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304 compartments = generate_compartments(model)
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406
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305
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306 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
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307 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
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308
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309 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
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310 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
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311 df_medium["InMedium"] = True # flag per indicare la presenza nel medium
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312
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313 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
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314 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
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315 if ARGS.name == "ENGRO2":
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316 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
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317 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
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318
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319 merged["InMedium"] = merged["InMedium"].fillna(False)
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320
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321 merged = merged.sort_values(by = "InMedium", ascending = False)
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322
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323 #out_file = os.path.join(ARGS.output_path, f"{os.path.basename(ARGS.name).split('.')[0]}_custom_data")
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324
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325 #merged.to_csv(out_file, sep = '\t', index = False)
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326
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327 ####
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328
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329 if not ARGS.out_tabular:
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330 raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
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331 save_as_tabular_df(merged, ARGS.out_tabular)
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332 expected = ARGS.out_tabular
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333
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334 # verify output exists and non-empty
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335 if not expected or not os.path.exists(expected) or os.path.getsize(expected) == 0:
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336 raise utils.DataErr(expected, "Output non creato o vuoto")
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337
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338 print("CustomDataGenerator: completed successfully")
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339
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340 if __name__ == '__main__':
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341 main() |
