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1 # -*- coding: utf-8 -*-
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2
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10
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3 from galaxy.datatypes.data import Text
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9
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4 from galaxy.datatypes.sniff import get_headers, get_test_fname
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5 from galaxy.datatypes.data import get_file_peek
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6 import subprocess
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7 import os
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8
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9 from galaxy.datatypes.metadata import MetadataElement
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10 from galaxy.datatypes import metadata
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11
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12 def count_special_lines( word, filename, invert = False ):
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13 """
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14 searching for special 'words' using the grep tool
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15 grep is used to speed up the searching and counting
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16 The number of hits is returned.
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17 """
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18 try:
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19 cmd = ["grep", "-c"]
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20 if invert:
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21 cmd.append('-v')
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22 cmd.extend([word, filename])
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23 out = subprocess.Popen(cmd, stdout=subprocess.PIPE)
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24 return int(out.communicate()[0].split()[0])
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25 except:
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26 pass
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27 return 0
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28
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29 def count_lines( filename, non_empty = False):
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30 """
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31 counting the number of lines from the 'filename' file
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32 """
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33 try:
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34 if non_empty:
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35 out = subprocess.Popen(['grep', '-cve', '^\s*$', filename], stdout=subprocess.PIPE)
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36 else:
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37 out = subprocess.Popen(['wc', '-l', filename], stdout=subprocess.PIPE)
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38 return int(out.communicate()[0].split()[0])
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39 except:
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40 pass
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41 return 0
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42
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43
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10
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44 class Infernal_CM_1_1( Text ):
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45 file_ext = "cm"
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46
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47 MetadataElement( name="number_of_models", default=0, desc="Number of covariance models", readonly=True, visible=True, optional=True, no_value=0 )
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48
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49 def set_peek( self, dataset, is_multi_byte=False ):
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50 if not dataset.dataset.purged:
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51 dataset.peek = get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
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52 if (dataset.metadata.number_of_models == 1):
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53 dataset.blurb = "1 model"
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54 else:
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55 dataset.blurb = "%s models" % dataset.metadata.number_of_models
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56 dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
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57 else:
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58 dataset.peek = 'file does not exist'
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59 dataset.blurb = 'file purged from disc'
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60
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61 def sniff( self, filename ):
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62 if count_special_lines("^INFERNAL1/a", filename) > 0:
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63 return True
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64 else:
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65 return False
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66
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67 def set_meta( self, dataset, **kwd ):
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68 """
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69 Set the number of models in dataset.
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70 """
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71 dataset.metadata.number_of_models = count_special_lines("^INFERNAL1/a", dataset.file_name)
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72
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73 def split( cls, input_datasets, subdir_generator_function, split_params):
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74 """
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75 Split the input files by model records.
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76 """
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77 if split_params is None:
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78 return None
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79
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80 if len(input_datasets) > 1:
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81 raise Exception("CM-file splitting does not support multiple files")
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82 input_files = [ds.file_name for ds in input_datasets]
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83
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84 chunk_size = None
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85 if split_params['split_mode'] == 'number_of_parts':
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86 raise Exception('Split mode "%s" is currently not implemented for CM-files.' % split_params['split_mode'])
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87 elif split_params['split_mode'] == 'to_size':
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88 chunk_size = int(split_params['split_size'])
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89 else:
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90 raise Exception('Unsupported split mode %s' % split_params['split_mode'])
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91
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92 def _read_cm_records( filename ):
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93 lines = []
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94 with open(filename) as handle:
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95 for line in handle:
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96 if line.startswith("INFERNAL1/a") and lines:
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97 yield lines
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98 lines = [line]
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99 else:
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100 lines.append( line )
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101 yield lines
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102
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103 def _write_part_cm_file( accumulated_lines ):
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104 part_dir = subdir_generator_function()
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105 part_path = os.path.join( part_dir, os.path.basename( input_files[0] ) )
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106 part_file = open( part_path, 'w' )
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107 part_file.writelines( accumulated_lines )
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108 part_file.close()
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109
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110 try:
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111 cm_records = _read_cm_records( input_files[0] )
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112 cm_lines_accumulated = []
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113 for counter, cm_record in enumerate( cm_records, start = 1):
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114 cm_lines_accumulated.extend( cm_record )
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115 if counter % chunk_size == 0:
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116 _write_part_cm_file( cm_lines_accumulated )
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117 cm_lines_accumulated = []
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118 if cm_lines_accumulated:
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119 _write_part_cm_file( cm_lines_accumulated )
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120 except Exception, e:
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121 log.error('Unable to split files: %s' % str(e))
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122 raise
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123 split = classmethod(split)
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124
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10
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125 if __name__ == '__main__':
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126 Infernal_CM_1_1()
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127 Stockholm_1_0()
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128
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