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annotate scripts/outputs.py @ 20:6ab1a261520a draft

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planemo upload for repository https://github.com/usegalaxy-au/tools-au commit c3a90eb12ada44d477541baa4dd6182be29cd554-dirty
author galaxy-australia
date Sun, 28 Jul 2024 20:09:55 +0000
parents f9eb041c518c
children e7f1b552a695
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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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44
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45 HTML_PATH = Path(__file__).parent / "alphafold.html"
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46 HTML_OUTPUT_FILENAME = 'alphafold.html'
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47 HTML_BUTTON_ATTR = 'class="btn" id="btn-ranked_{rank}"'
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48 HTML_BUTTON_ATTR_DISABLED = (
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49 'class="btn disabled" id="btn-ranked_{rank}" disabled')
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50
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52 class Settings:
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53 """Parse and store settings/config."""
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54 def __init__(self):
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55 self.workdir = None
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56 self.output_confidence_scores = True
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57 self.output_residue_scores = False
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58 self.is_multimer = False
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59 self.parse()
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60
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61 def parse(self) -> None:
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62 parser = argparse.ArgumentParser()
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63 parser.add_argument(
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64 "workdir",
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65 help="alphafold output directory",
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66 type=str
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67 )
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68 parser.add_argument(
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69 "-p",
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70 "--plddts",
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71 help="output per-residue confidence scores (pLDDTs)",
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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 "-m",
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76 "--multimer",
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77 help="parse output from AlphaFold multimer",
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78 action="store_true"
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79 )
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80 parser.add_argument(
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81 "--pkl",
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82 help="rename model pkl outputs with rank order",
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83 action="store_true"
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84 )
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85 parser.add_argument(
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86 "--pae",
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87 help="extract PAE from pkl files to CSV format",
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88 action="store_true"
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89 )
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90 parser.add_argument(
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91 "--plot",
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92 help="Plot pLDDT and PAE for each model",
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93 action="store_true"
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94 )
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95 args = parser.parse_args()
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96 self.workdir = Path(args.workdir.rstrip('/'))
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97 self.output_residue_scores = args.plddts
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98 self.output_model_pkls = args.pkl
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99 self.output_model_plots = args.plot
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100 self.output_pae = args.pae
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101 self.is_multimer = args.multimer
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102 self.output_dir = self.workdir / OUTPUT_DIR
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103 os.makedirs(self.output_dir, exist_ok=True)
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104
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105
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106 class ExecutionContext:
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107 """Collect file paths etc."""
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108 def __init__(self, settings: Settings):
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109 self.settings = settings
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110 if settings.is_multimer:
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111 self.plddt_key = PLDDT_KEY['multimer']
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112 else:
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113 self.plddt_key = PLDDT_KEY['monomer']
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114
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115 def get_model_key(self, ix: int) -> str:
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116 """Return json key for model index.
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117
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118 The key format changed between minor AlphaFold versions so this
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119 function determines the correct key.
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120 """
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121 with open(self.ranking_debug) as f:
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122 data = json.load(f)
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galaxy-australia
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123 model_keys = list(data[self.plddt_key].keys())
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124 for k in model_keys:
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125 if k.startswith(f"model_{ix}_"):
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126 return k
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127 return KeyError(
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128 f'Could not find key for index={ix} in'
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129 ' ranking_debug.json')
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130
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131 @property
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132 def ranking_debug(self) -> str:
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133 return self.settings.workdir / 'ranking_debug.json'
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134
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galaxy-australia
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135 @property
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136 def relax_metrics(self) -> str:
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137 return self.settings.workdir / 'relax_metrics.json'
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138
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139 @property
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140 def relax_metrics_ranked(self) -> str:
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141 return self.settings.workdir / 'relax_metrics_ranked.json'
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142
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galaxy-australia
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143 @property
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144 def model_pkl_paths(self) -> List[str]:
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galaxy-australia
parents:
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145 return sorted([
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galaxy-australia
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diff changeset
146 self.settings.workdir / f
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galaxy-australia
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147 for f in os.listdir(self.settings.workdir)
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148 if f.startswith('result_model_') and f.endswith('.pkl')
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149 ])
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150
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151
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152 class ResultModelPrediction:
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153 """Load and manipulate data from result_model_*.pkl files."""
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154 def __init__(self, path: str, context: ExecutionContext):
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155 self.context = context
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156 self.path = path
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galaxy-australia
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157 self.name = os.path.basename(path).replace('result_', '').split('.')[0]
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158 with open(path, 'rb') as path:
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159 self.data = pk.load(path)
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160
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161 @property
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162 def plddts(self) -> List[float]:
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163 """Return pLDDT scores for each residue."""
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galaxy-australia
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164 return list(self.data['plddt'])
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165
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166
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167 class ResultRanking:
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168 """Load and manipulate data from ranking_debug.json file."""
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169
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170 def __init__(self, context: ExecutionContext):
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171 self.path = context.ranking_debug
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172 self.context = context
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173 with open(self.path, 'r') as f:
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diff changeset
174 self.data = json.load(f)
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175
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176 @property
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177 def order(self) -> List[str]:
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178 """Return ordered list of model indexes."""
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179 return self.data['order']
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180
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181 def get_plddt_for_rank(self, rank: int) -> List[float]:
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182 """Get pLDDT score for model instance."""
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galaxy-australia
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183 return self.data[self.context.plddt_key][self.data['order'][rank - 1]]
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184
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185 def get_rank_for_model(self, model_name: str) -> int:
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186 """Return 0-indexed rank for given model name.
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187
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188 Model names are expressed in result_model_*.pkl file names.
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189 """
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190 return self.data['order'].index(model_name)
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191
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192
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193 def write_confidence_scores(ranking: ResultRanking, context: ExecutionContext):
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194 """Write per-model confidence scores."""
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195 path = context.settings.workdir / OUTPUTS['model_confidence_scores']
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196 with open(path, 'w') as f:
20
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197 for rank in range(1, len(context.model_pkl_paths) + 1):
16
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198 score = ranking.get_plddt_for_rank(rank)
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199 f.write(f'ranked_{rank - 1}\t{score:.2f}\n')
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200
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201
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202 def write_per_residue_scores(
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203 ranking: ResultRanking,
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204 context: ExecutionContext,
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205 ):
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206 """Write per-residue plddts for each model.
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207
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208 A row of plddt values is written for each model in tabular format.
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209 """
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210 model_plddts = {}
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211 for i, path in enumerate(context.model_pkl_paths):
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212 model = ResultModelPrediction(path, context)
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213 rank = ranking.get_rank_for_model(model.name)
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214 model_plddts[rank] = model.plddts
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215
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216 path = context.settings.workdir / OUTPUTS['plddts']
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217 with open(path, 'w') as f:
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218 for i in sorted(list(model_plddts.keys())):
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219 row = [f'ranked_{i}'] + [
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220 str(x) for x in model_plddts[i]
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221 ]
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222 f.write('\t'.join(row) + '\n')
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223
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224
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225 def rename_model_pkls(ranking: ResultRanking, context: ExecutionContext):
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226 """Rename model.pkl files so the rank order is implicit."""
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227 for path in context.model_pkl_paths:
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228 model = ResultModelPrediction(path, context)
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229 rank = ranking.get_rank_for_model(model.name)
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230 new_path = (
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231 context.settings.workdir
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232 / OUTPUTS['model_pkl'].format(rank=rank)
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233 )
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234 shutil.copyfile(path, new_path)
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235
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236
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237 def extract_pae_to_csv(ranking: ResultRanking, context: ExecutionContext):
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238 """Extract predicted alignment error matrix from pickle files.
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239
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240 Creates a CSV file for each of five ranked models.
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241 """
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242 for path in context.model_pkl_paths:
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243 model = ResultModelPrediction(path, context)
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244 rank = ranking.get_rank_for_model(model.name)
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245 with open(path, 'rb') as f:
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246 data = pk.load(f)
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247 if 'predicted_aligned_error' not in data:
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248 print("Skipping PAE output"
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249 f" - not found in {path}."
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250 " Running with model_preset=monomer?")
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251 return
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252 pae = data['predicted_aligned_error']
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253 out_path = (
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254 context.settings.workdir
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255 / OUTPUTS['model_pae'].format(rank=rank)
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256 )
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257 with open(out_path, 'w') as f:
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258 for row in pae:
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259 f.write(','.join([str(x) for x in row]) + '\n')
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260
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261
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262 def rekey_relax_metrics(ranking: ResultRanking, context: ExecutionContext):
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263 """Replace keys in relax_metrics.json with 0-indexed rank."""
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264 with open(context.relax_metrics) as f:
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265 data = json.load(f)
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266 for k in list(data.keys()):
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267 rank = ranking.get_rank_for_model(k)
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268 data[f'ranked_{rank}'] = data.pop(k)
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269 new_path = context.settings.workdir / OUTPUTS['relax']
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270 with open(new_path, 'w') as f:
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271 json.dump(data, f)
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272
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273
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274 def plddt_pae_plots(ranking: ResultRanking, context: ExecutionContext):
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275 """Generate a pLDDT + PAE plot for each model."""
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276 for path in context.model_pkl_paths:
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277 num_plots = 2
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278 model = ResultModelPrediction(path, context)
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279 rank = ranking.get_rank_for_model(model.name)
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280 png_path = (
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281 context.settings.workdir
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282 / OUTPUTS['model_plot'].format(rank=rank)
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283 )
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284 plddts = model.data['plddt']
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285 if 'predicted_aligned_error' in model.data:
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286 pae = model.data['predicted_aligned_error']
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287 max_pae = model.data['max_predicted_aligned_error']
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288 else:
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289 num_plots = 1
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290
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291 plt.figure(figsize=[8 * num_plots, 6])
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292 plt.subplot(1, num_plots, 1)
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293 plt.plot(plddts)
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294 plt.title('Predicted LDDT')
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295 plt.xlabel('Residue')
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296 plt.ylabel('pLDDT')
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297
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298 if num_plots == 2:
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299 plt.subplot(1, 2, 2)
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300 plt.imshow(pae, vmin=0., vmax=max_pae, cmap='Greens_r')
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301 plt.colorbar(fraction=0.046, pad=0.04)
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302 plt.title('Predicted Aligned Error')
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303 plt.xlabel('Scored residue')
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304 plt.ylabel('Aligned residue')
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305
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306 plt.savefig(png_path)
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307
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308
20
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309 def template_html(context: ExecutionContext):
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310 """Template HTML file.
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311
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312 Remove buttons that are redundant with limited model outputs.
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313 """
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314 print("Templating HTML file...")
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315 with open(HTML_PATH) as f:
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316 html = f.read()
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317 for i in range(len(context.model_pkl_paths), 5):
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318 btn_id = HTML_BUTTON_ATTR.format(rank=i)
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319 btn_attr_disabled = HTML_BUTTON_ATTR_DISABLED.format(rank=i)
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320 html = html.replace(btn_id, btn_attr_disabled)
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321 with open(context.settings.output_dir / HTML_OUTPUT_FILENAME, 'w') as f:
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322 f.write(html)
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323
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324
16
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325 def main():
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326 """Parse output files and generate additional output files."""
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327 settings = Settings()
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328 context = ExecutionContext(settings)
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329 ranking = ResultRanking(context)
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330 write_confidence_scores(ranking, context)
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331 rekey_relax_metrics(ranking, context)
20
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332 template_html(context)
16
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333
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334 # Optional outputs
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335 if settings.output_model_pkls:
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336 rename_model_pkls(ranking, context)
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337 if settings.output_model_plots:
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338 plddt_pae_plots(ranking, context)
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339 if settings.output_pae:
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340 # Only created by monomer_ptm and multimer models
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341 extract_pae_to_csv(ranking, context)
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342 if settings.output_residue_scores:
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343 write_per_residue_scores(ranking, context)
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344
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345
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346 if __name__ == '__main__':
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347 main()