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annotate scripts/outputs.py @ 17:5b85006245f3 draft

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planemo upload for repository https://github.com/usegalaxy-au/tools-au commit 2e1eba8094c506a99a844546e8343849d3cef694
author galaxy-australia
date Wed, 31 May 2023 01:30:22 +0000
parents f9eb041c518c
children 6ab1a261520a
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1 """Generate additional output files not produced by AlphaFold.
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3 Currently this is includes:
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4 - model confidence scores
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5 - per-residue confidence scores (pLDDTs - optional output)
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6 - model_*.pkl files renamed with rank order
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8 N.B. There have been issues with this script breaking between AlphaFold
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9 versions due to minor changes in the output directory structure across minor
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10 versions. It will likely need updating with future releases of AlphaFold.
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12 This code is more complex than you might expect due to the output files
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13 'moving around' considerably, depending on run parameters. You will see that
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14 several output paths are determined dynamically.
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15 """
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17 import argparse
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18 import json
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19 import os
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20 import pickle as pk
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21 import shutil
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22 from pathlib import Path
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23 from typing import List
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25 from matplotlib import pyplot as plt
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27 # Output file paths
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28 OUTPUT_DIR = 'extra'
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29 OUTPUTS = {
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30 'model_pkl': OUTPUT_DIR + '/ranked_{rank}.pkl',
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31 'model_pae': OUTPUT_DIR + '/pae_ranked_{rank}.csv',
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32 'model_plot': OUTPUT_DIR + '/ranked_{rank}.png',
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33 'model_confidence_scores': OUTPUT_DIR + '/model_confidence_scores.tsv',
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34 'plddts': OUTPUT_DIR + '/plddts.tsv',
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35 'relax': OUTPUT_DIR + '/relax_metrics_ranked.json',
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36 }
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38 # Keys for accessing confidence data from JSON/pkl files
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39 # They change depending on whether the run was monomer or multimer
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40 PLDDT_KEY = {
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41 'monomer': 'plddts',
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42 'multimer': 'iptm+ptm',
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43 }
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46 class Settings:
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47 """Parse and store settings/config."""
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48 def __init__(self):
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49 self.workdir = None
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50 self.output_confidence_scores = True
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51 self.output_residue_scores = False
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52 self.is_multimer = False
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53 self.parse()
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54
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55 def parse(self) -> None:
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56 parser = argparse.ArgumentParser()
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57 parser.add_argument(
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58 "workdir",
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59 help="alphafold output directory",
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60 type=str
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61 )
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62 parser.add_argument(
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63 "-p",
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64 "--plddts",
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65 help="output per-residue confidence scores (pLDDTs)",
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66 action="store_true"
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67 )
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68 parser.add_argument(
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69 "-m",
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70 "--multimer",
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71 help="parse output from AlphaFold multimer",
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72 action="store_true"
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73 )
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74 parser.add_argument(
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75 "--pkl",
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76 help="rename model pkl outputs with rank order",
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77 action="store_true"
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78 )
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79 parser.add_argument(
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80 "--pae",
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81 help="extract PAE from pkl files to CSV format",
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82 action="store_true"
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83 )
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84 parser.add_argument(
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85 "--plot",
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86 help="Plot pLDDT and PAE for each model",
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87 action="store_true"
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88 )
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89 args = parser.parse_args()
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90 self.workdir = Path(args.workdir.rstrip('/'))
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91 self.output_residue_scores = args.plddts
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92 self.output_model_pkls = args.pkl
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93 self.output_model_plots = args.plot
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94 self.output_pae = args.pae
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95 self.is_multimer = args.multimer
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96 self.output_dir = self.workdir / OUTPUT_DIR
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97 os.makedirs(self.output_dir, exist_ok=True)
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98
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99
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100 class ExecutionContext:
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101 """Collect file paths etc."""
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102 def __init__(self, settings: Settings):
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103 self.settings = settings
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104 if settings.is_multimer:
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105 self.plddt_key = PLDDT_KEY['multimer']
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106 else:
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107 self.plddt_key = PLDDT_KEY['monomer']
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108
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109 def get_model_key(self, ix: int) -> str:
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110 """Return json key for model index.
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111
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112 The key format changed between minor AlphaFold versions so this
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113 function determines the correct key.
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114 """
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115 with open(self.ranking_debug) as f:
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116 data = json.load(f)
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117 model_keys = list(data[self.plddt_key].keys())
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118 for k in model_keys:
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119 if k.startswith(f"model_{ix}_"):
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120 return k
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121 return KeyError(
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122 f'Could not find key for index={ix} in'
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123 ' ranking_debug.json')
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124
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125 @property
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126 def ranking_debug(self) -> str:
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127 return self.settings.workdir / 'ranking_debug.json'
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128
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129 @property
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130 def relax_metrics(self) -> str:
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131 return self.settings.workdir / 'relax_metrics.json'
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132
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133 @property
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134 def relax_metrics_ranked(self) -> str:
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135 return self.settings.workdir / 'relax_metrics_ranked.json'
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136
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137 @property
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138 def model_pkl_paths(self) -> List[str]:
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139 return sorted([
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140 self.settings.workdir / f
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141 for f in os.listdir(self.settings.workdir)
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142 if f.startswith('result_model_') and f.endswith('.pkl')
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143 ])
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144
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145
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146 class ResultModelPrediction:
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147 """Load and manipulate data from result_model_*.pkl files."""
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148 def __init__(self, path: str, context: ExecutionContext):
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149 self.context = context
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150 self.path = path
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151 self.name = os.path.basename(path).replace('result_', '').split('.')[0]
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152 with open(path, 'rb') as path:
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153 self.data = pk.load(path)
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154
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155 @property
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156 def plddts(self) -> List[float]:
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157 """Return pLDDT scores for each residue."""
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158 return list(self.data['plddt'])
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159
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160
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161 class ResultRanking:
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162 """Load and manipulate data from ranking_debug.json file."""
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163
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164 def __init__(self, context: ExecutionContext):
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165 self.path = context.ranking_debug
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166 self.context = context
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167 with open(self.path, 'r') as f:
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168 self.data = json.load(f)
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169
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170 @property
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171 def order(self) -> List[str]:
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172 """Return ordered list of model indexes."""
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173 return self.data['order']
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174
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175 def get_plddt_for_rank(self, rank: int) -> List[float]:
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176 """Get pLDDT score for model instance."""
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177 return self.data[self.context.plddt_key][self.data['order'][rank - 1]]
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178
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179 def get_rank_for_model(self, model_name: str) -> int:
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180 """Return 0-indexed rank for given model name.
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181
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182 Model names are expressed in result_model_*.pkl file names.
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183 """
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184 return self.data['order'].index(model_name)
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185
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186
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187 def write_confidence_scores(ranking: ResultRanking, context: ExecutionContext):
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188 """Write per-model confidence scores."""
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189 path = context.settings.workdir / OUTPUTS['model_confidence_scores']
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190 with open(path, 'w') as f:
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191 for rank in range(1, 6):
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192 score = ranking.get_plddt_for_rank(rank)
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193 f.write(f'ranked_{rank - 1}\t{score:.2f}\n')
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194
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195
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196 def write_per_residue_scores(
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197 ranking: ResultRanking,
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198 context: ExecutionContext,
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199 ):
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200 """Write per-residue plddts for each model.
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201
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202 A row of plddt values is written for each model in tabular format.
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203 """
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204 model_plddts = {}
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205 for i, path in enumerate(context.model_pkl_paths):
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206 model = ResultModelPrediction(path, context)
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207 rank = ranking.get_rank_for_model(model.name)
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208 model_plddts[rank] = model.plddts
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209
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210 path = context.settings.workdir / OUTPUTS['plddts']
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211 with open(path, 'w') as f:
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212 for i in sorted(list(model_plddts.keys())):
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213 row = [f'ranked_{i}'] + [
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214 str(x) for x in model_plddts[i]
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215 ]
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216 f.write('\t'.join(row) + '\n')
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217
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218
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219 def rename_model_pkls(ranking: ResultRanking, context: ExecutionContext):
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220 """Rename model.pkl files so the rank order is implicit."""
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221 for path in context.model_pkl_paths:
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222 model = ResultModelPrediction(path, context)
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223 rank = ranking.get_rank_for_model(model.name)
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galaxy-australia
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224 new_path = (
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225 context.settings.workdir
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226 / OUTPUTS['model_pkl'].format(rank=rank)
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227 )
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228 shutil.copyfile(path, new_path)
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229
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230
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231 def extract_pae_to_csv(ranking: ResultRanking, context: ExecutionContext):
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232 """Extract predicted alignment error matrix from pickle files.
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233
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234 Creates a CSV file for each of five ranked models.
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235 """
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236 for path in context.model_pkl_paths:
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237 model = ResultModelPrediction(path, context)
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238 rank = ranking.get_rank_for_model(model.name)
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galaxy-australia
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239 with open(path, 'rb') as f:
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240 data = pk.load(f)
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241 if 'predicted_aligned_error' not in data:
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242 print("Skipping PAE output"
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galaxy-australia
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243 f" - not found in {path}."
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galaxy-australia
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diff changeset
244 " Running with model_preset=monomer?")
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galaxy-australia
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245 return
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galaxy-australia
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246 pae = data['predicted_aligned_error']
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galaxy-australia
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247 out_path = (
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diff changeset
248 context.settings.workdir
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249 / OUTPUTS['model_pae'].format(rank=rank)
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250 )
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galaxy-australia
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251 with open(out_path, 'w') as f:
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252 for row in pae:
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253 f.write(','.join([str(x) for x in row]) + '\n')
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254
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255
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256 def rekey_relax_metrics(ranking: ResultRanking, context: ExecutionContext):
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257 """Replace keys in relax_metrics.json with 0-indexed rank."""
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258 with open(context.relax_metrics) as f:
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259 data = json.load(f)
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260 for k in list(data.keys()):
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galaxy-australia
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261 rank = ranking.get_rank_for_model(k)
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262 data[f'ranked_{rank}'] = data.pop(k)
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galaxy-australia
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263 new_path = context.settings.workdir / OUTPUTS['relax']
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galaxy-australia
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264 with open(new_path, 'w') as f:
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diff changeset
265 json.dump(data, f)
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266
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267
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268 def plddt_pae_plots(ranking: ResultRanking, context: ExecutionContext):
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269 """Generate a pLDDT + PAE plot for each model."""
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270 for path in context.model_pkl_paths:
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271 num_plots = 2
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272 model = ResultModelPrediction(path, context)
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273 rank = ranking.get_rank_for_model(model.name)
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galaxy-australia
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274 png_path = (
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galaxy-australia
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diff changeset
275 context.settings.workdir
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276 / OUTPUTS['model_plot'].format(rank=rank)
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diff changeset
277 )
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galaxy-australia
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278 plddts = model.data['plddt']
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galaxy-australia
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diff changeset
279 if 'predicted_aligned_error' in model.data:
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galaxy-australia
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diff changeset
280 pae = model.data['predicted_aligned_error']
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galaxy-australia
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281 max_pae = model.data['max_predicted_aligned_error']
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diff changeset
282 else:
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galaxy-australia
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283 num_plots = 1
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284
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285 plt.figure(figsize=[8 * num_plots, 6])
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286 plt.subplot(1, num_plots, 1)
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287 plt.plot(plddts)
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288 plt.title('Predicted LDDT')
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289 plt.xlabel('Residue')
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290 plt.ylabel('pLDDT')
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291
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292 if num_plots == 2:
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293 plt.subplot(1, 2, 2)
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294 plt.imshow(pae, vmin=0., vmax=max_pae, cmap='Greens_r')
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295 plt.colorbar(fraction=0.046, pad=0.04)
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296 plt.title('Predicted Aligned Error')
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297 plt.xlabel('Scored residue')
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298 plt.ylabel('Aligned residue')
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299
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300 plt.savefig(png_path)
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301
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302
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303 def main():
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304 """Parse output files and generate additional output files."""
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305 settings = Settings()
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306 context = ExecutionContext(settings)
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307 ranking = ResultRanking(context)
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308 write_confidence_scores(ranking, context)
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309 rekey_relax_metrics(ranking, context)
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310
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311 # Optional outputs
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312 if settings.output_model_pkls:
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313 rename_model_pkls(ranking, context)
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314 if settings.output_model_plots:
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315 plddt_pae_plots(ranking, context)
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316 if settings.output_pae:
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317 # Only created by monomer_ptm and multimer models
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318 extract_pae_to_csv(ranking, context)
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319 if settings.output_residue_scores:
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320 write_per_residue_scores(ranking, context)
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321
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322
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323 if __name__ == '__main__':
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324 main()