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