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1 #!/usr/bin/env python
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2 # Retrieves data from external data source applications and stores in a dataset file.
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3 # Data source application parameters are temporarily stored in the dataset file.
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4 import socket, urllib, sys, os
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5 from galaxy import eggs #eggs needs to be imported so that galaxy.util can find docutils egg...
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6 from galaxy.util.json import from_json_string, to_json_string
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7 import galaxy.model # need to import model before sniff to resolve a circular import dependency
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8 from galaxy.datatypes import sniff
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9 from galaxy.datatypes.registry import Registry
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10 from galaxy.jobs import TOOL_PROVIDED_JOB_METADATA_FILE
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11
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12 assert sys.version_info[:2] >= ( 2, 4 )
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13
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14 def stop_err( msg ):
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15 sys.stderr.write( msg )
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16 sys.exit()
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17
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18 GALAXY_PARAM_PREFIX = 'GALAXY'
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19 GALAXY_ROOT_DIR = os.path.realpath( os.path.join( os.path.split( os.path.realpath( __file__ ) )[0], '..', '..' ) )
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20 GALAXY_DATATYPES_CONF_FILE = os.path.join( GALAXY_ROOT_DIR, 'datatypes_conf.xml' )
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21
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22 def load_input_parameters( filename, erase_file = True ):
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23 datasource_params = {}
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24 try:
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25 json_params = from_json_string( open( filename, 'r' ).read() )
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26 datasource_params = json_params.get( 'param_dict' )
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27 except:
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28 json_params = None
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29 for line in open( filename, 'r' ):
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30 try:
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31 line = line.strip()
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32 fields = line.split( '\t' )
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33 datasource_params[ fields[0] ] = fields[1]
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34 except:
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35 continue
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36 if erase_file:
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37 open( filename, 'w' ).close() #open file for writing, then close, removes params from file
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38 return json_params, datasource_params
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39
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40 def __main__():
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41 filename = sys.argv[1]
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42 try:
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43 max_file_size = int( sys.argv[2] )
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44 except:
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45 max_file_size = 0
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46
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47 job_params, params = load_input_parameters( filename )
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48 if job_params is None: #using an older tabular file
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49 enhanced_handling = False
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50 job_params = dict( param_dict = params )
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51 job_params[ 'output_data' ] = [ dict( out_data_name = 'output',
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52 ext = 'data',
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53 file_name = filename,
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54 extra_files_path = None ) ]
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55 job_params[ 'job_config' ] = dict( GALAXY_ROOT_DIR=GALAXY_ROOT_DIR, GALAXY_DATATYPES_CONF_FILE=GALAXY_DATATYPES_CONF_FILE, TOOL_PROVIDED_JOB_METADATA_FILE = TOOL_PROVIDED_JOB_METADATA_FILE )
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56 else:
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57 enhanced_handling = True
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58 json_file = open( job_params[ 'job_config' ][ 'TOOL_PROVIDED_JOB_METADATA_FILE' ], 'w' ) #specially named file for output junk to pass onto set metadata
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59
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60 datatypes_registry = Registry()
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61 datatypes_registry.load_datatypes( root_dir = job_params[ 'job_config' ][ 'GALAXY_ROOT_DIR' ], config = job_params[ 'job_config' ][ 'GALAXY_DATATYPES_CONF_FILE' ] )
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62
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63 URL = params.get( 'URL', None ) #using exactly URL indicates that only one dataset is being downloaded
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64 URL_method = params.get( 'URL_method', None )
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65 simpleD = params.get('galaxyData')
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66 # The Python support for fetching resources from the web is layered. urllib uses the httplib
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67 # library, which in turn uses the socket library. As of Python 2.3 you can specify how long
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68 # a socket should wait for a response before timing out. By default the socket module has no
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69 # timeout and can hang. Currently, the socket timeout is not exposed at the httplib or urllib2
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70 # levels. However, you can set the default timeout ( in seconds ) globally for all sockets by
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71 # doing the following.
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72 socket.setdefaulttimeout( 600 )
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73 cur_filename = params.get('output')
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74 outputfile = open( cur_filename, 'w' ).write( simpleD )
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75
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76 if __name__ == "__main__": __main__()
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77
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78
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79
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