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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, 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() -> argparse.Namespace:
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14 """
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15 Interfaces the script of a module with its frontend, making the user's choices for
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16 various parameters available as values in code.
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17
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18 Args:
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19 args : Always obtained (in file) from sys.argv
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20
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21 Returns:
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22 Namespace : An object containing the parsed arguments
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23 """
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24 parser = argparse.ArgumentParser(
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25 usage = "%(prog)s [options]",
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26 description = "generate custom data from a given model")
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27
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28 parser.add_argument("-ol", "--out_log", type = str, required = True, help = "Output log")
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23
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29
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30 parser.add_argument("-orules", "--out_rules", type = str, required = True, help = "Output rules")
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31 parser.add_argument("-orxns", "--out_reactions", type = str, required = True, help = "Output reactions")
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32 parser.add_argument("-omedium", "--out_medium", type = str, required = True, help = "Output medium")
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33 parser.add_argument("-obnds", "--out_bounds", type = str, required = True, help = "Output bounds")
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34
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4
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35 parser.add_argument("-id", "--input", type = str, required = True, help = "Input model")
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36 parser.add_argument("-mn", "--name", type = str, required = True, help = "Input model name")
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37 # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
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38
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39 argsNamespace = parser.parse_args()
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40 argsNamespace.out_dir = "result"
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41 # ^ can't get this one to work from xml, there doesn't seem to be a way to get the directory attribute from the collection
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42
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43 return argsNamespace
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44
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45 ################################- INPUT DATA LOADING -################################
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46 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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47 """
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48 Loads a custom model from a file, either in JSON or XML format.
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49
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50 Args:
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51 file_path : The path to the file containing the custom model.
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52 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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53
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54 Raises:
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55 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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56
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57 Returns:
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58 cobra.Model : the model, if successfully opened.
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59 """
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60 ext = ext if ext else file_path.ext
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61 try:
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62 if ext is utils.FileFormat.XML:
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63 return cobra.io.read_sbml_model(file_path.show())
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64
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65 if ext is utils.FileFormat.JSON:
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66 return cobra.io.load_json_model(file_path.show())
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67
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68 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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69 raise utils.DataErr(file_path,
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70 f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
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71
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72 ################################- DATA GENERATION -################################
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73 ReactionId = str
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74 def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
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75 """
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76 Generates a dictionary mapping reaction ids to rules from the model.
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77
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78 Args:
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79 model : the model to derive data from.
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80 asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
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81
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82 Returns:
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83 Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
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84 Dict[ReactionId, str] : the generated dictionary of raw rules.
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85 """
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86 # Is the below approach convoluted? yes
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87 # Ok but is it inefficient? probably
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88 # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
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89 _ruleGetter = lambda reaction : reaction.gene_reaction_rule
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90 ruleExtractor = (lambda reaction :
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91 rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
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92
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93 return {
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94 reaction.id : ruleExtractor(reaction)
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95 for reaction in model.reactions
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96 if reaction.gene_reaction_rule }
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97
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98 def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
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99 """
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100 Generates a dictionary mapping reaction ids to reaction formulas from the model.
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101
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102 Args:
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103 model : the model to derive data from.
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104 asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
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105
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106 Returns:
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107 Dict[ReactionId, str] : the generated dictionary.
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108 """
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109
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110 unparsedReactions = {
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111 reaction.id : reaction.reaction
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112 for reaction in model.reactions
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113 if reaction.reaction
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114 }
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115
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116 if not asParsed: return unparsedReactions
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117
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118 return reactionUtils.create_reaction_dict(unparsedReactions)
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119
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120 def get_medium(model:cobra.Model) -> pd.DataFrame:
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121 trueMedium=[]
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122 for r in model.reactions:
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123 positiveCoeff=0
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124 for m in r.metabolites:
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125 if r.get_coefficient(m.id)>0:
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126 positiveCoeff=1;
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127 if (positiveCoeff==0 and r.lower_bound<0):
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128 trueMedium.append(r.id)
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129
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130 df_medium = pd.DataFrame()
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131 df_medium["reaction"] = trueMedium
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132 return df_medium
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133
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134 def generate_bounds(model:cobra.Model) -> pd.DataFrame:
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135
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136 rxns = []
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137 for reaction in model.reactions:
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138 rxns.append(reaction.id)
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139
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140 bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
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141
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142 for reaction in model.reactions:
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143 bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
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144 return bounds
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145
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146
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147 ###############################- FILE SAVING -################################
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26
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148 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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4
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149 """
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28
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150 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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4
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151
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152 Args:
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153 data : the data to be written to the file.
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154 file_path : the path to the .csv file.
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155 fieldNames : the names of the fields (columns) in the .csv file.
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156
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157 Returns:
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158 None
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159 """
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160 with open(file_path.show(), 'w', newline='') as csvfile:
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23
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161 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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162 writer.writeheader()
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163
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164 for key, value in data.items():
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165 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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166
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26
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167 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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168 """
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28
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169 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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26
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170
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171 Args:
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172 data : the data to be written to the file.
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173 file_path : the path to the .csv file.
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174 fieldNames : the names of the fields (columns) in the .csv file.
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175
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176 Returns:
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177 None
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178 """
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179 with open(file_path, 'w', newline='') as csvfile:
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180 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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181 writer.writeheader()
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182
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183 for key, value in data.items():
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184 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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185
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4
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186 ###############################- ENTRY POINT -################################
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187 def main() -> None:
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188 """
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189 Initializes everything and sets the program in motion based on the fronted input arguments.
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190
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191 Returns:
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192 None
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193 """
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194 # get args from frontend (related xml)
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195 global ARGS
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196 ARGS = process_args()
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197
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198 # this is the worst thing I've seen so far, congrats to the former MaREA devs for suggesting this!
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199 if os.path.isdir(ARGS.out_dir) == False: os.makedirs(ARGS.out_dir)
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200
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201 # load custom model
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202 model = load_custom_model(
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203 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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204
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205 # generate data
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206 rules = generate_rules(model, asParsed = False)
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207 reactions = generate_reactions(model, asParsed = False)
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208 bounds = generate_bounds(model)
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209 medium = get_medium(model)
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4
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210
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28
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211 # save files out of collection: path coming from xml
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212 save_as_csv(rules, ARGS.out_rules, ("ReactionID", "Rule"))
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213 save_as_csv(reactions, ARGS.out_reactions, ("ReactionID", "Reaction"))
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214 bounds.to_csv(ARGS.out_bounds, sep = '\t')
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215 medium.to_csv(ARGS.out_medium, sep = '\t')
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4
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216
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217 if __name__ == '__main__':
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218 main() |
