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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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147
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9 from typing import Optional, Tuple, Union, List, Dict
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93
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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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343
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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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93
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16 """
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343
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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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343
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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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343
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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 (default/custom)")
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35 parser.add_argument("--medium", type=str,
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36 help="Custom medium file if medium_selector=Custom")
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37
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38 parser.add_argument("--output_format", type=str, choices=["tabular", "xlsx"], required=True,
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39 help="Output format: CSV (tabular) or Excel (xlsx)")
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40
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41 parser.add_argument("--out_tabular", type=str,
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363
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42 help="Output file for the merged dataset (CSV or XLSX)")
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365
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43
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44 parser.add_argument("--out_xlsx", type=str,
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45 help="Output file for the merged dataset (CSV or XLSX)")
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46
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47 parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
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363
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48 help="Tool directory (passed from Galaxy as $__tool_directory__)")
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49
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50
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343
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51 return parser.parse_args(args)
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93
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52
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53 ################################- INPUT DATA LOADING -################################
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54 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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55 """
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56 Loads a custom model from a file, either in JSON or XML format.
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57
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58 Args:
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59 file_path : The path to the file containing the custom model.
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60 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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61
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62 Raises:
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63 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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64
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65 Returns:
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66 cobra.Model : the model, if successfully opened.
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67 """
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68 ext = ext if ext else file_path.ext
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69 try:
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70 if ext is utils.FileFormat.XML:
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71 return cobra.io.read_sbml_model(file_path.show())
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72
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73 if ext is utils.FileFormat.JSON:
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74 return cobra.io.load_json_model(file_path.show())
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75
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76 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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77 raise utils.DataErr(file_path,
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78 f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
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79
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80 ################################- DATA GENERATION -################################
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81 ReactionId = str
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82 def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
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83 """
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84 Generates a dictionary mapping reaction ids to rules from the model.
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85
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86 Args:
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87 model : the model to derive data from.
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88 asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
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89
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90 Returns:
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91 Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
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92 Dict[ReactionId, str] : the generated dictionary of raw rules.
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93 """
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94 # Is the below approach convoluted? yes
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95 # Ok but is it inefficient? probably
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96 # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
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97 _ruleGetter = lambda reaction : reaction.gene_reaction_rule
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98 ruleExtractor = (lambda reaction :
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99 rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
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100
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101 return {
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102 reaction.id : ruleExtractor(reaction)
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103 for reaction in model.reactions
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104 if reaction.gene_reaction_rule }
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105
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106 def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
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107 """
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108 Generates a dictionary mapping reaction ids to reaction formulas from the model.
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109
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110 Args:
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111 model : the model to derive data from.
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112 asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
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113
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114 Returns:
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115 Dict[ReactionId, str] : the generated dictionary.
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116 """
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117
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118 unparsedReactions = {
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119 reaction.id : reaction.reaction
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120 for reaction in model.reactions
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121 if reaction.reaction
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122 }
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123
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124 if not asParsed: return unparsedReactions
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125
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126 return reactionUtils.create_reaction_dict(unparsedReactions)
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127
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128 def get_medium(model:cobra.Model) -> pd.DataFrame:
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129 trueMedium=[]
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130 for r in model.reactions:
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131 positiveCoeff=0
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132 for m in r.metabolites:
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133 if r.get_coefficient(m.id)>0:
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134 positiveCoeff=1;
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135 if (positiveCoeff==0 and r.lower_bound<0):
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136 trueMedium.append(r.id)
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137
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138 df_medium = pd.DataFrame()
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139 df_medium["reaction"] = trueMedium
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140 return df_medium
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141
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142 def generate_bounds(model:cobra.Model) -> pd.DataFrame:
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143
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144 rxns = []
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145 for reaction in model.reactions:
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146 rxns.append(reaction.id)
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147
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148 bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
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149
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150 for reaction in model.reactions:
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151 bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
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152 return bounds
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153
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154
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155 ###############################- FILE SAVING -################################
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156 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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157 """
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158 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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159
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160 Args:
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161 data : the data to be written to the file.
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162 file_path : the path to the .csv file.
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163 fieldNames : the names of the fields (columns) in the .csv file.
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164
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165 Returns:
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166 None
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167 """
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168 with open(file_path.show(), 'w', newline='') as csvfile:
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169 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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170 writer.writeheader()
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171
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172 for key, value in data.items():
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173 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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174
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175 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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176 """
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177 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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178
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179 Args:
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180 data : the data to be written to the file.
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181 file_path : the path to the .csv file.
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182 fieldNames : the names of the fields (columns) in the .csv file.
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183
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184 Returns:
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185 None
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186 """
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187 with open(file_path, 'w', newline='') as csvfile:
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188 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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189 writer.writeheader()
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190
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191 for key, value in data.items():
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192 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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193
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194 ###############################- ENTRY POINT -################################
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195 def main(args:List[str] = None) -> None:
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196 """
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197 Initializes everything and sets the program in motion based on the fronted input arguments.
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198
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199 Returns:
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200 None
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201 """
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202 # get args from frontend (related xml)
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203 global ARGS
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204 ARGS = process_args(args)
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205
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206 # this is the worst thing I've seen so far, congrats to the former MaREA devs for suggesting this!
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361
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207 #if os.path.isdir(ARGS.output_path) == False:
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208 # os.makedirs(ARGS.output_path)
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209
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350
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210 if ARGS.input:
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211 # load custom model
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212 model = load_custom_model(
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213 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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214 else:
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215 # load built-in model
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216
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217 try:
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218 model_enum = utils.Model[ARGS.model] # e.g., Model['ENGRO2']
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219 except KeyError:
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220 raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
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221
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222 # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
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223 try:
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224 model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
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225 except Exception as e:
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226 # Wrap/normalize load errors as DataErr for consistency
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227 raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
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228
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229 # Determine final model name: explicit --name overrides, otherwise use the model id
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230 model_name = ARGS.name if ARGS.name else ARGS.model
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231
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232 # generate data
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233 rules = generate_rules(model, asParsed = False)
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234 reactions = generate_reactions(model, asParsed = False)
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235 bounds = generate_bounds(model)
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236 medium = get_medium(model)
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237
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238 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
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239 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
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240
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241 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
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242 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
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243 df_medium["InMedium"] = True # flag per indicare la presenza nel medium
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244
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245 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
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246 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
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247
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248 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
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249
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250 merged["InMedium"] = merged["InMedium"].fillna(False)
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251
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252 merged = merged.sort_values(by = "InMedium", ascending = False)
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253
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359
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254 #out_file = os.path.join(ARGS.output_path, f"{os.path.basename(ARGS.name).split('.')[0]}_custom_data")
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255
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256 #merged.to_csv(out_file, sep = '\t', index = False)
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257
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258
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259 ####
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260
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261 if ARGS.output_format == "xlsx":
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262 merged.to_excel(ARGS.out_xlsx, index=False)
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263 else:
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264 merged.to_csv(ARGS.out_tabular, sep="\t", index=False)
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265
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367
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266 print("CustomDataGenerator: completed successfully")
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267
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268 if __name__ == '__main__':
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269 main() |
