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annotate tomo_setup.py @ 56:ca61007a60fa draft

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"planemo upload for repository https://github.com/rolfverberg/galaxytools commit fc9b26da497e467c28b0adf3f94422b4cb61f59c"
author rv43
date Tue, 02 Aug 2022 19:52:47 +0000
parents bead50a4eadc
children feb2a5fc7c76
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1 #!/usr/bin/env python3
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3 import logging
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5 import os
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6 import sys
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7 import re
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8 import yaml
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9 import argparse
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10 import numpy as np
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11 import tracemalloc
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13 from tomo import Tomo
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14
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15 #from memory_profiler import profile
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16 #@profile
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17 def __main__():
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19 # Parse command line arguments
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20 parser = argparse.ArgumentParser(
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21 description='Setup tomography reconstruction')
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22 parser.add_argument('-i', '--inputfiles',
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23 nargs='+',
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24 default='inputfiles.txt',
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25 help='Input file datasets or collections')
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26 parser.add_argument('-c', '--config',
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27 help='Input config')
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28 parser.add_argument('-l', '--log',
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29 type=argparse.FileType('w'),
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30 default=sys.stdout,
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31 help='Log file')
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32 parser.add_argument('-t', '--inputfile_types',
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33 nargs='+',
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34 default='collection',
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35 help='Input files type (collection or a list of set types: dark, bright, or data)')
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36 parser.add_argument('--theta_range',
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37 help='Theta range (lower bound, upper bound, number of angles)')
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38 parser.add_argument('--output_config',
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39 help='Output config')
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40 parser.add_argument('--output_data',
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41 help='Preprocessed tomography data')
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42 parser.add_argument('tomo_ranges', metavar='N', type=int, nargs='+')
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43 args = parser.parse_args()
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45 # Starting memory monitoring
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46 tracemalloc.start()
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47
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48 # Set basic log configuration
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49 logging_format = '%(asctime)s : %(levelname)s - %(module)s : %(funcName)s - %(message)s'
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50 log_level = 'INFO'
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51 level = getattr(logging, log_level.upper(), None)
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52 if not isinstance(level, int):
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53 raise ValueError(f'Invalid log_level: {log_level}')
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54 logging.basicConfig(format=logging_format, level=level, force=True,
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55 handlers=[logging.StreamHandler()])
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56
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57 logging.debug(f'config = {args.config}')
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58 logging.debug(f'inputfiles = {args.inputfiles}')
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59 logging.debug(f'inputfile_types = {args.inputfile_types}')
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60 logging.debug(f'log = {args.log}')
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61 logging.debug(f'is log stdout? {args.log is sys.stdout}')
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62 logging.debug(f'theta_range = {args.theta_range.split()}')
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63 logging.debug(f'output_config = {args.output_config}')
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64 logging.debug(f'output_data = {args.output_data}')
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65 logging.debug(f'tomoranges = {args.tomo_ranges}')
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66
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67 # Check input file type
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68 if isinstance(args.inputfile_types, list):
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69 if len(args.inputfile_types) == 1 and args.inputfile_types[0] == 'collection':
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70 if len(args.inputfiles) != 1 or args.inputfiles[0] != 'inputfiles.txt':
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71 raise ValueError('Inconsistent inputfiles and inputfile_types:\n'+
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72 f'inputfiles ({type(inputfiles)}):\n{inputfiles}\n'+
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73 f'inputfile_types ({type(inputfile_types)}):\n{inputfile_types}')
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74 input_as_collection = True
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75 else:
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76 if len(args.inputfiles) != len(args.inputfile_types):
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77 raise ValueError('Inconsistent inputfiles and inputfile_types:\n'+
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78 f'inputfiles ({type(inputfiles)}):\n{inputfiles}\n'+
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79 f'inputfile_types ({type(inputfile_types)}):\n{inputfile_types}')
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80 input_as_collection = False
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81 else:
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82 raise ValueError(f'Invalid args.inputfile_types: {args.inputfile_types} '+
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83 f'{type(args.inputfile_types)}')
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84
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85 datasets = []
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86 collections = []
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87 if input_as_collection:
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88 # Read input file collections and collect data files info
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89 with open(args.inputfiles[0]) as cf:
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90 for line in cf:
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91 if not line.strip() or line.startswith('#'):
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92 continue
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93 fields = [x.strip() for x in line.split('\t')]
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94 filepath = fields[0]
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95 element_identifier = fields[1] if len(fields) > 1 else fields[0].split('/')[-1]
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96 datasets.append({'element_identifier' : fields[1], 'filepath' : filepath})
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97 logging.debug(f'datasets:\n{datasets}')
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98
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99 # Read and sort data files
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100 for dataset in datasets:
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101 element_identifier = [x.strip() for x in dataset['element_identifier'].split('_')]
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102 if len(element_identifier) > 1:
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103 name = element_identifier[0]
0
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104 else:
49
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105 name = 'other'
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106 filepath = dataset['filepath']
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107 if not len(collections):
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108 collections = [{'name' : name, 'filepaths' : [filepath]}]
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109 else:
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110 collection = [c for c in collections if c['name'] == name]
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111 if len(collection):
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112 collection[0]['filepaths'].append(filepath)
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113 else:
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114 collection = {'name' : name, 'filepaths' : [filepath]}
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115 collections.append(collection)
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116 else:
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117 # Collect input file datasets info
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118 collections = [{'name' : filetype, 'filepaths' : [filepath]}
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119 for filetype, filepath in zip(args.inputfile_types, args.inputfiles)]
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120 logging.debug(f'collections:\n{collections}')
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121 if len(args.tomo_ranges) != 2*len(collections):
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122 raise ValueError('Inconsistent tomo ranges size.')
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123
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124 # Instantiate Tomo object
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125 tomo = Tomo(config_file=args.config, config_out=args.output_config, log_level=log_level,
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126 log_stream=args.log, galaxy_flag=True)
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127 if not tomo.is_valid:
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128 raise ValueError('Invalid config file provided.')
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129 logging.debug(f'config:\n{tomo.config}')
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130
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131 # Set theta inputs
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132 theta_range = args.theta_range.split()
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133 config_theta_range = tomo.config.get('theta_range')
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134 if config_theta_range is None:
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135 config_tomo.config['theta_range'] = {'start' : float(theta_range[0]),
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136 'end' : float(theta_range[1]), 'num' : int(theta_range[2])}
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137 else:
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138 config_theta_range['start'] = float(theta_range[0])
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139 config_theta_range['end'] = float(theta_range[1])
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140 config_theta_range['num'] = int(theta_range[2])
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141
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142 # Find dark field files
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143 dark_field = tomo.config['dark_field']
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144 tdf_files = [c['filepaths'] for c in collections if c['name'] == 'tdf']
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145 if len(tdf_files) != 1 or len(tdf_files[0]) < 1:
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146 logging.warning('Unable to obtain dark field files')
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147 assert(dark_field['data_path'] is None)
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148 assert(dark_field['img_start'] == -1)
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149 assert(not dark_field['num'])
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150 tdf_files = [None]
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151 num_collections = 0
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152 else:
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153 dark_field['img_offset'] = args.tomo_ranges[0]
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154 dark_field['num'] = args.tomo_ranges[1]
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155 num_collections = 1
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156
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157 # Find bright field files
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158 bright_field = tomo.config['bright_field']
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159 bright_field['img_offset'] = args.tomo_ranges[2*num_collections]
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160 bright_field['num'] = args.tomo_ranges[2*num_collections+1]
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161 tbf_files = [c['filepaths'] for c in collections if c['name'] == 'tbf']
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162 if len(tbf_files) != 1 or len(tbf_files[0]) < 1:
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163 exit('Unable to obtain bright field files')
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164 num_collections += 1
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165
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166 # Find tomography files
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167 stack_info = tomo.config['stack_info']
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168 if stack_info['num'] != len(collections) - num_collections:
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169 raise ValueError('Inconsistent number of tomography data image sets')
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170 tomo_stack_files = []
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171 for stack in stack_info['stacks']:
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172 stack['img_offset'] = args.tomo_ranges[2*num_collections]
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173 stack['num'] = args.tomo_ranges[2*num_collections+1]
49
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174 if input_as_collection:
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175 tomo_files = [c['filepaths'] for c in collections
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176 if c['name'] == f'set{stack["index"]}']
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177 else:
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178 assert(collections[num_collections]['name'] == 'data')
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179 tomo_files = [collections[num_collections]['filepaths']]
0
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180 if len(tomo_files) != 1 or len(tomo_files[0]) < 1:
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181 exit(f'Unable to obtain tomography images for set {stack["index"]}')
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182 tomo_stack_files.append(tomo_files[0])
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183 num_collections += 1
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184
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185 # Preprocess the image files
15
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186 galaxy_param = {'tdf_files' : tdf_files[0], 'tbf_files' : tbf_files[0],
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187 'tomo_stack_files' : tomo_stack_files, 'output_name' : args.output_data}
15
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188 tomo.genTomoStacks(galaxy_param)
0
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189 if not tomo.is_valid:
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190 IOError('Unable to load all required image files.')
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191
15
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192 # Displaying memory usage
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193 logging.info(f'Memory usage: {tracemalloc.get_traced_memory()}')
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194
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195 # stopping memory monitoring
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196 tracemalloc.stop()
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197
0
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198 if __name__ == "__main__":
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199 __main__()
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200