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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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418
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11 import utils.model_utils as modelUtils
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426
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12 import logging
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406
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13
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14 ARGS : argparse.Namespace
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15 def process_args(args: List[str] = None) -> argparse.Namespace:
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16 """
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17 Parse command-line arguments for CustomDataGenerator.
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18 """
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19
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20 parser = argparse.ArgumentParser(
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21 usage="%(prog)s [options]",
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22 description="Generate custom data from a given model"
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23 )
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24
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25 parser.add_argument("--out_log", type=str, required=True,
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26 help="Output log file")
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27
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28 parser.add_argument("--model", type=str,
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29 help="Built-in model identifier (e.g., ENGRO2, Recon, HMRcore)")
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30 parser.add_argument("--input", type=str,
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31 help="Custom model file (JSON or XML)")
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32 parser.add_argument("--name", type=str, required=True,
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33 help="Model name (default or custom)")
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34
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35 parser.add_argument("--medium_selector", type=str, required=True,
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36 help="Medium selection option")
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37
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38 parser.add_argument("--gene_format", type=str, default="Default",
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39 help="Gene nomenclature format: Default (original), ENSNG, HGNC_SYMBOL, HGNC_ID, ENTREZ")
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40
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41 parser.add_argument("--out_tabular", type=str,
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42 help="Output file for the merged dataset (CSV or XLSX)")
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43
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44 parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
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45 help="Tool directory (passed from Galaxy as $__tool_directory__)")
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46
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47
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48 return parser.parse_args(args)
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49
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50 ################################- INPUT DATA LOADING -################################
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51 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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52 """
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53 Loads a custom model from a file, either in JSON or XML format.
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54
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55 Args:
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56 file_path : The path to the file containing the custom model.
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57 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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58
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59 Raises:
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60 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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61
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62 Returns:
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63 cobra.Model : the model, if successfully opened.
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64 """
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65 ext = ext if ext else file_path.ext
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66 try:
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67 if ext is utils.FileFormat.XML:
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68 return cobra.io.read_sbml_model(file_path.show())
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69
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70 if ext is utils.FileFormat.JSON:
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71 return cobra.io.load_json_model(file_path.show())
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72
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73 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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74 raise utils.DataErr(file_path,
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75 f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
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76
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77
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78 ###############################- FILE SAVING -################################
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79 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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80 """
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81 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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82
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83 Args:
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84 data : the data to be written to the file.
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85 file_path : the path to the .csv file.
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86 fieldNames : the names of the fields (columns) in the .csv file.
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87
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88 Returns:
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89 None
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90 """
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91 with open(file_path.show(), 'w', newline='') as csvfile:
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92 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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93 writer.writeheader()
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94
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95 for key, value in data.items():
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96 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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97
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98 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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99 """
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100 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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101
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102 Args:
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103 data : the data to be written to the file.
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104 file_path : the path to the .csv file.
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105 fieldNames : the names of the fields (columns) in the .csv file.
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106
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107 Returns:
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108 None
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109 """
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110 with open(file_path, 'w', newline='') as csvfile:
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111 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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112 writer.writeheader()
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113
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114 for key, value in data.items():
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115 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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116
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117 def save_as_tabular_df(df: pd.DataFrame, path: str) -> None:
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118 try:
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119 os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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120 df.to_csv(path, sep="\t", index=False)
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121 except Exception as e:
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122 raise utils.DataErr(path, f"failed writing tabular output: {e}")
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123
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124
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125 ###############################- ENTRY POINT -################################
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126 def main(args:List[str] = None) -> None:
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127 """
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128 Initializes everything and sets the program in motion based on the fronted input arguments.
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129
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130 Returns:
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131 None
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132 """
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133 # get args from frontend (related xml)
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134 global ARGS
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135 ARGS = process_args(args)
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136
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137
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138 if ARGS.input:
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139 # load custom model
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140 model = load_custom_model(
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141 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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142 else:
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143 # load built-in model
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144
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145 try:
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146 model_enum = utils.Model[ARGS.model] # e.g., Model['ENGRO2']
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147 except KeyError:
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148 raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
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149
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150 # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
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151 try:
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152 model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
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153 except Exception as e:
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154 # Wrap/normalize load errors as DataErr for consistency
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155 raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
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156
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157 # Determine final model name: explicit --name overrides, otherwise use the model id
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158
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159 model_name = ARGS.name if ARGS.name else ARGS.model
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160
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161 if ARGS.name == "ENGRO2" and ARGS.medium_selector != "Default":
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162 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
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163 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
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164 medium = df_mediums[[ARGS.medium_selector]]
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165 medium = medium[ARGS.medium_selector].to_dict()
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166
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167 # Set all reactions to zero in the medium
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168 for rxn_id, _ in model.medium.items():
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169 model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
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170
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171 # Set medium conditions
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172 for reaction, value in medium.items():
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173 if value is not None:
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174 model.reactions.get_by_id(reaction).lower_bound = -float(value)
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175
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444
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176 #if ARGS.name == "ENGRO2" and ARGS.gene_format != "Default":
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177 # logging.basicConfig(level=logging.INFO)
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178 # logger = logging.getLogger(__name__)
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179
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180 #model = modelUtils.translate_model_genes(
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181 # model=model,
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182 # mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv"), dtype={'entrez_id': str},
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183 # target_nomenclature=ARGS.gene_format.replace("HGNC_", "HGNC "),
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184 # source_nomenclature='HGNC_ID',
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185 # logger=logger
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186 #)
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187 #model = modelUtils.convert_genes(model, ARGS.gene_format.replace("HGNC_", "HGNC "))
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188
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189 if (ARGS.name == "Recon" or ARGS.name == "ENGRO2") and ARGS.gene_format != "Default":
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190 logging.basicConfig(level=logging.INFO)
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191 logger = logging.getLogger(__name__)
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406
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192
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426
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193 model = modelUtils.translate_model_genes(
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194 model=model,
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444
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195 mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
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196 target_nomenclature=ARGS.gene_format.replace("HGNC_", "HGNC "),
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197 source_nomenclature='HGNC_symbol',
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198 logger=logger
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199 )
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406
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200
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201 # generate data
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418
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202 rules = modelUtils.generate_rules(model, asParsed = False)
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203 reactions = modelUtils.generate_reactions(model, asParsed = False)
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204 bounds = modelUtils.generate_bounds(model)
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205 medium = modelUtils.get_medium(model)
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426
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206 objective_function = modelUtils.extract_objective_coefficients(model)
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207
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406
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208 if ARGS.name == "ENGRO2":
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418
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209 compartments = modelUtils.generate_compartments(model)
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210
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426
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211 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "GPR"])
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212 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Formula"])
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406
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213
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214 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
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215 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
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216 df_medium["InMedium"] = True # flag per indicare la presenza nel medium
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217
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218 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
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219 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
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426
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220 merged = merged.merge(objective_function, on = "ReactionID", how = "outer")
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406
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221 if ARGS.name == "ENGRO2":
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222 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
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223 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
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224
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225 merged["InMedium"] = merged["InMedium"].fillna(False)
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226
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227 merged = merged.sort_values(by = "InMedium", ascending = False)
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228
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229 #out_file = os.path.join(ARGS.output_path, f"{os.path.basename(ARGS.name).split('.')[0]}_custom_data")
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230
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231 #merged.to_csv(out_file, sep = '\t', index = False)
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232
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233 ####
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234
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235 if not ARGS.out_tabular:
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236 raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
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237 save_as_tabular_df(merged, ARGS.out_tabular)
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238 expected = ARGS.out_tabular
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239
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240 # verify output exists and non-empty
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241 if not expected or not os.path.exists(expected) or os.path.getsize(expected) == 0:
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242 raise utils.DataErr(expected, "Output non creato o vuoto")
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243
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244 print("CustomDataGenerator: completed successfully")
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245
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246 if __name__ == '__main__':
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247 main() |
