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# Copyright 2002 by Katharine Lindner. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""
This module provides code to work with html files from NDB.
http://ndbserver.rutgers.edu/NDB/structure-finder/ndb/index.html
Classes:
Record Holds NDB sequence data.
NdbParser Parses NDB sequence data into a Record object.
The algorithm is based on a state machine because the record has multiple
sections and the handling of tags varies depending on the section.
Citations have their own state machine.
"""
from types import *
import string
from Bio import File
from Bio import Index
from Bio.Crystal import Hetero
from Bio.Crystal import Chain
from Bio.Crystal import Crystal
from Bio.SeqFeature import Reference
import urllib
import sgmllib
from Bio.ParserSupport import *
from Bio.SeqFeature import Reference
class Record( dict ):
def __init__( self ):
self[ 'Id' ] = ''
self[ 'Features' ] = ''
self[ 'Name' ] = ''
self[ 'Sequence' ] = Crystal( {} )
self[ 'Citation' ] = Reference()
self[ 'Space Group' ] = ''
self[ 'Cell Constants' ] = {}
self[ 'Crystallization Conditions' ] = []
self[ 'Refinement' ] = ''
self[ 'Coordinates' ] = ''
def __str__( self ):
keys = self.keys()
keys.sort()
out = ''
for key in keys:
val = self[ key ]
if( type( val ) == type( [] ) ):
out = out + '\n%s\n' % key
for item in val:
out = out + '%s\n' % item
elif( type( val ) == type( {} ) ):
out = out + '\n%s\n' % key
subkeys = val.keys()
subkeys.sort()
for item in subkeys:
out = out + '%s : %s\n' % ( item, val[ item ] )
elif( isinstance( val, dict ) ):
out = out + '\n%s\n' % key
subkeys = val.keys()
subkeys.sort()
for item in subkeys:
out = out + '%s : %s\n' % ( item, val[ item ] )
else:
out = out + '%s: %s\n' % ( key, self[ key ] )
return out
def _parse_constants( text ):
items = text.split( '=' )
constants = {}
key = ''
for i in range( 0, ( len( items ) - 1 ) ):
item = items[ i ]
item = item.strip()
separator = item.rfind( ' ' )
if( separator < 0 ):
separator = 0
val = item[ :separator ]
val = val.strip()
if( key != '' ):
constants[ key ] = val
key = item[ separator: ]
key = key.strip()
constants[ key ] = items[ -1 ]
return constants
class NdbParser( sgmllib.SGMLParser ):
"""Parses Ndb sequence data into a Record object.
data available at: http://ndbserver.rutgers.edu/NDB/NDBATLAS/index.html
"""
def reset(self):
sgmllib.SGMLParser.reset( self )
self.ndb_dict = Record()
self.text = ''
self._space_group = ''
self._state = 'id'
self._reference_state = 'authors'
self._current_reference = Reference()
def parse(self, handle):
self.reset()
self.feed(handle)
return self.ndb_dict
def feed(self, handle):
"""feed(self, handle )
Feed in ndb data for scanning. handle is a file-like object
containing ndb data. consumer is a Consumer object that will
receive events as the ndb data is scanned.
"""
if isinstance(handle, File.UndoHandle):
uhandle = handle
else:
uhandle = File.UndoHandle(handle)
text = ''
while 1:
line = uhandle.readline()
if( not line ):
break
line = string.strip( line )
if( line[ -7: ] == '</HTML>' ):
break
text = text + ' ' + line
sgmllib.SGMLParser.feed( self, text )
def handle_data(self, newtext ):
newtext = string.strip( newtext )
self.text = self.text + newtext
def start_h1( self, attrs ):
self._flush_text()
def end_h1( self ):
text = self._flush_text()
if( self._state == 'id' ):
cols = text.split( ':' )
self.ndb_dict[ 'Id' ] = ( cols[ 1 ] ).upper()
self._state = 'id_found'
def start_h2( self, attrs ):
text = self._flush_text()
if( self._state == 'features' ):
self.ndb_dict[ 'Features' ] = text
elif( self._state == 'name' ):
self.ndb_dict[ 'Name' ] = text
elif( self._state == 'sequence' ):
pass
elif( self._state == 'citation' ):
if( self._reference_state == 'journal' ):
self._current_reference.journal = text
self.ndb_dict[ 'Citation' ] = self._current_reference
elif( self._state == 'space' ):
self._space_group = self._space_group + text
self.ndb_dict[ 'Space Group' ] = self._space_group
elif( self._state == 'constants' ):
self.ndb_dict[ 'Cell Constants' ] = _parse_constants( text )
elif( self._state == 'crystallization' ):
pass
elif( self._state == 'refinement' ):
self.ndb_dict[ 'Refinement' ] = text
elif( self._state == 'coordinates' ):
self.ndb_dict[ 'Coordinates' ] = text
def end_h2( self ):
text = self._flush_text()
text = text.lower()
if( self._state == 'id' ):
if( text.find( 'id' ) >= 0 ):
cols = text.split( ':' )
self.ndb_dict[ 'Id' ] = ( cols[ 1 ] ).upper()
self._state = 'id_found'
elif( text.find( 'feature' ) >= 0 ):
self._state = 'features'
elif( text.find( 'name' ) >= 0 ):
self._state = 'name'
elif( text.find( 'sequence' ) >= 0 ):
self._state = 'sequence'
elif( text.find( 'citation' ) >= 0 ):
self._state = 'citation'
elif( text.find( 'space' ) >= 0 ):
self._state = 'space'
elif( text.find( 'constants' ) >= 0 ):
self._state = 'constants'
elif( text.find( 'crystallization' ) >= 0 ):
self._state = 'crystallization'
elif( text.find( 'refinement' ) >= 0 ):
self._state = 'refinement'
elif( text.find( 'coordinates' ) >= 0 ):
self._state = 'coordinates'
def start_ul( self, attrs ):
if( self._state == 'sequence' ):
self._flush_text()
elif( self._state == 'crystallization' ):
self._flush_text()
def end_ul( self ):
if( self._state == 'sequence' ):
self._parse_chain()
elif( self._state == 'crystallization' ):
text = self._flush_text()
( self.ndb_dict[ 'Crystallization Conditions' ] ).append( text )
elif( self._state == 'citation' ):
if( self._reference_state == 'journal' ):
self._current_reference.journal = self._flush_text()
self._reference_state = 'done'
def start_sub( self, attrs ):
if( self._state == 'space' ):
self._space_group = self._space_group + self._flush_text()
def end_sub( self ):
if( self._state == 'space' ):
self._space_group = self._space_group + '(%s) ' % self._flush_text()
def start_li( self, attrs ):
if( self._state == 'sequence' ):
self._parse_chain()
elif( self._state == 'crystallization' ):
text = self._flush_text()
( self.ndb_dict[ 'Crystallization Conditions' ] ).append( text )
def end_li( self ):
if( self._state == 'sequence' ):
self._parse_chain()
elif( self._state == 'crystallization' ):
text = self._flush_text()
( self.ndb_dict[ 'Crystallization Conditions' ] ).append( text )
def do_br( self, attrs ):
if( self._state == 'citation' ):
if( self._reference_state == 'authors' ):
self._current_reference.authors = self._flush_text()
self._reference_state = 'title'
elif( self._reference_state == 'title' ):
self._current_reference.title = self._flush_text()
self._reference_state = 'journal'
def start_i( self, attrs ):
pass
def end_i( self ):
if( self._state == 'references' ):
if( self._reference_state == 'title' ):
text = self._flush_text()
self._current_reference.title = text
self._reference_state = 'journal'
def _parse_chain( self ):
text = self._flush_text()
text = text.strip()
if( text.lower().startswith( 'chain' ) ):
fields = text.split( ':' )
words = fields[ 0 ].split()
key = words[ 1 ]
val = fields[ 1 ]
self.ndb_dict[ 'Sequence' ][ key ] = val
def _flush_text( self ):
text = string.strip( self.text )
self.text = ''
return text[:]
if( __name__ == '__main__' ):
handle = open( 'PR0004.htm')
undo_handle = File.UndoHandle( handle )
ndb_parser = NdbParser()
record = ndb_parser.parse( handle )
print str( record )
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