Mercurial > repos > galaxy-australia > alphafold2

annotate gen_extra_outputs.py @ 13:c0e71cb2bd1b draft

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planemo upload for repository https://github.com/usegalaxy-au/tools-au commit 830b5bbf9c5375e714e1b7b9a3e8eec1e584e6b2
author galaxy-australia
date Wed, 12 Oct 2022 22:25:20 +0000
parents 3bd420ec162d
children
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1 """Generate additional output files not produced by AlphaFold."""
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2
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3 import json
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4 import pickle
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5 import argparse
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6 from typing import Any, Dict, List
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7
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8 # Keys for accessing confidence data from JSON/pkl files
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9 # They change depending on whether the run was monomer or multimer
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10 CONTEXT_KEY = {
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11 'monomer': 'plddts',
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12 'multimer': 'iptm+ptm',
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13 }
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0
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16 class Settings:
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17 """parses then keeps track of program settings"""
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18 def __init__(self):
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19 self.workdir = None
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20 self.output_confidence_scores = True
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21 self.output_residue_scores = False
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22 self.is_multimer = False
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23
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24 def parse_settings(self) -> None:
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25 parser = argparse.ArgumentParser()
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26 parser.add_argument(
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27 "workdir",
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28 help="alphafold output directory",
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29 type=str
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30 )
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31 parser.add_argument(
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32 "-p",
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33 "--plddts",
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34 help="output per-residue confidence scores (pLDDTs)",
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35 action="store_true"
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36 )
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37 parser.add_argument(
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38 "-m",
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39 "--multimer",
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40 help="parse output from AlphaFold multimer",
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41 action="store_true"
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42 )
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43 args = parser.parse_args()
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44 self.workdir = args.workdir.rstrip('/')
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45 self.output_residue_scores = args.plddts
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46 self.is_multimer = False
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47 self.is_multimer = args.multimer
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48
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49
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50 class ExecutionContext:
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51 """uses program settings to get paths to files etc"""
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52 def __init__(self, settings: Settings):
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53 self.settings = settings
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54
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55 def get_model_key(self, ix):
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56 """Return json key for model index."""
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57 if self.settings.is_multimer:
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58 return f'model_{ix}_multimer'
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59 return f'model_{ix}'
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60
0
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61 @property
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62 def ranking_debug(self) -> str:
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63 return f'{self.settings.workdir}/ranking_debug.json'
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64
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65 @property
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66 def model_pkls(self) -> List[str]:
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67 ext = '.pkl'
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68 if self.settings.is_multimer:
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69 ext = '_multimer.pkl'
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70 return [
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71 f'{self.settings.workdir}/result_model_{i}{ext}'
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72 for i in range(1, 6)
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73 ]
0
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75 @property
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76 def model_conf_score_output(self) -> str:
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77 return f'{self.settings.workdir}/model_confidence_scores.tsv'
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78
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79 @property
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80 def plddt_output(self) -> str:
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81 return f'{self.settings.workdir}/plddts.tsv'
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82
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83
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84 class FileLoader:
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85 """loads file data for use by other classes"""
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86
0
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87 def __init__(self, context: ExecutionContext):
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88 self.context = context
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89
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90 @property
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91 def confidence_key(self) -> str:
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92 """Return the correct key for confidence data."""
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93 if self.context.settings.is_multimer:
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94 return CONTEXT_KEY['multimer']
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95 return CONTEXT_KEY['monomer']
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96
0
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97 def get_model_mapping(self) -> Dict[str, int]:
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98 data = self.load_ranking_debug()
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99 return {name: int(rank) + 1
0
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100 for (rank, name) in enumerate(data['order'])}
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101
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102 def get_conf_scores(self) -> Dict[str, float]:
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103 data = self.load_ranking_debug()
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104 return {
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105 name: float(f'{score:.2f}')
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106 for name, score in data[self.confidence_key].items()
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107 }
0
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108
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109 def load_ranking_debug(self) -> Dict[str, Any]:
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110 with open(self.context.ranking_debug, 'r') as fp:
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111 return json.load(fp)
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112
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113 def get_model_plddts(self) -> Dict[str, List[float]]:
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114 plddts: Dict[str, List[float]] = {}
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115 model_pkls = self.context.model_pkls
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116 for i in range(len(model_pkls)):
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117 pklfile = model_pkls[i]
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118 with open(pklfile, 'rb') as fp:
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119 data = pickle.load(fp)
13
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120 plddts[self.context.get_model_key(i+1)] = [
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121 float(f'{x:.2f}')
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122 for x in data['plddt']
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123 ]
0
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124 return plddts
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125
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126
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127 class OutputGenerator:
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128 """generates the output data we are interested in creating"""
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129 def __init__(self, loader: FileLoader):
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130 self.loader = loader
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131 self.context = loader.context
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132
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133 def gen_conf_scores(self):
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134 mapping = self.loader.get_model_mapping()
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135 scores = self.loader.get_conf_scores()
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136 ranked = list(scores.items())
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137 ranked.sort(key=lambda x: x[1], reverse=True)
13
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138 return {
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139 self.context.get_model_key(mapping[name]): score
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140 for name, score in ranked
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141 }
0
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142
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143 def gen_residue_scores(self) -> Dict[str, List[float]]:
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144 mapping = self.loader.get_model_mapping()
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145 model_plddts = self.loader.get_model_plddts()
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146 return {
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147 self.context.get_model_key(mapping[name]): plddts
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148 for name, plddts in model_plddts.items()
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149 }
0
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150
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151
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152 class OutputWriter:
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153 """writes generated data to files"""
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154 def __init__(self, context: ExecutionContext):
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155 self.context = context
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156
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157 def write_conf_scores(self, data: Dict[str, float]) -> None:
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158 outfile = self.context.model_conf_score_output
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159 with open(outfile, 'w') as fp:
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160 for model, score in data.items():
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161 fp.write(f'{model}\t{score}\n')
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162
0
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163 def write_residue_scores(self, data: Dict[str, List[float]]) -> None:
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164 outfile = self.context.plddt_output
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165 model_plddts = list(data.items())
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166 model_plddts.sort()
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167
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168 with open(outfile, 'w') as fp:
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169 for model, plddts in model_plddts:
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170 plddt_str_list = [str(x) for x in plddts]
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171 plddt_str = ','.join(plddt_str_list)
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172 fp.write(f'{model}\t{plddt_str}\n')
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173
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174
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175 def main():
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176 # setup
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177 settings = Settings()
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178 settings.parse_settings()
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179 context = ExecutionContext(settings)
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180 loader = FileLoader(context)
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181
0
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182 # generate & write outputs
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183 generator = OutputGenerator(loader)
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184 writer = OutputWriter(context)
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185
0
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186 # confidence scores
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187 conf_scores = generator.gen_conf_scores()
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188 writer.write_conf_scores(conf_scores)
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189
0
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190 # per-residue plddts
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191 if settings.output_residue_scores:
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192 residue_scores = generator.gen_residue_scores()
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193 writer.write_residue_scores(residue_scores)
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194
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195
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196 if __name__ == '__main__':
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197 main()