def search(self):
if self.database == 'PubMed':
from Bio import PubMed
from Bio import GenBank
searchIds = PubMed.search_for(self.searchTerm, max_ids=self.maxResults)
GBrecParser = GenBank.FeatureParser()
ncbiDict = GenBank.NCBIDictionary(self.type,
'genbank',
parser=GBrecParser)
from Bio import Medline
MLrecParser = Medline.RecordParser()
medlineDict = PubMed.Dictionary(delay=1.0, parser=MLrecParser)
for id in searchIds:
MLrecord = medlineDict[id]
GBrecord = ncbiDict[id]
newDBItem = DBItem(self.project,
seq=GBrecord.seq,
descript=GBrecord.description,
id=id,
record=MLrecord)
self.items[id] = newDBItem
"""Example script showing how to interact with PubMed."""
# standard library
import string
# biopython
from Bio import PubMed
from Bio import Medline
# do the search and get the ids
search_term = 'orchid'
orchid_ids = PubMed.search_for(search_term)
print orchid_ids
# access Medline through a dictionary interface that returns PubMed Records
rec_parser = Medline.RecordParser()
medline_dict = PubMed.Dictionary(parser=rec_parser)
for id in orchid_ids[0:5]:
cur_record = medline_dict[id]
print 'title:', string.rstrip(cur_record.title)
print 'authors:', cur_record.authors
print 'source:', string.strip(cur_record.source)
print
elif opt == '-c':
count_only = 1
elif opt == '-d':
try:
delay = float(arg)
except ValueError:
print "Delay must be a floating point value"
sys.exit(0)
if delay < 0:
print "Delay cannot be negative"
sys.exit(0)
if help:
print_usage()
sys.exit(0)
print "Doing a PubMed search for %s..." % repr(query)
ids = PubMed.search_for(query)
print "Found %d citations" % len(ids)
if count_only:
sys.exit(0)
pm = PubMed.Dictionary(delay=delay)
for id in ids:
try:
print pm[id]
except KeyError, x:
print "Couldn't download %s, %s" % (id, x)
sys.stdout.flush()
if reduce(operator.or_,
[x in chunk.lower() for x in useless_lines]): continue
if len(chunk) < 2: continue
outfile.write('%010d|%s\n' % (chunkid, chunk))
chunkmap[fakename].append(chunkid)
chunkid += 1
outfile.close()
print "Saving chunkmap"
pickle.dump(chunkmap, open(outmapname, "wb"), pickle.HIGHEST_PROTOCOL)
print "These files couldn't be processed:"
print '\n'.join(skipped)
print "Opening (or creating) cache in", sys.argv[2]
the_cache=StringDBDict(os.path.join(sys.argv[2], DEFAULT_CACHE_NAME),
file_mode='c')
PubMed.download_many([str(x) for x in known_articles if str(x) not in
the_cache.keys()], download_callback,
parser=Medline.RecordParser())
mti_filename=sys.argv[1]+'.mti'
print "Finished processing the cache. Using the cache to build", \
mti_filename
mti_file=open(mti_filename, "w")
chunkmap={}
hexfinder=re.compile(r'\\x[a-f0-9][a-f0-9]', re.IGNORECASE)
for article in known_articles:
try:
article_record=the_cache[str(article)]
except KeyError:
print "Article doesn't exist in cache. Skipping."
continue
if article_record.abstract=='':
print "Article", article, "has no abstract. Skipping."
help="output format." , action = "store_true" )
parser.add_option("-l", "--library", dest="library",
help="library to add medline entries to." , metavar="FILE" )
parser.set_defaults(
pmid = None,
format= "oo",
clipboard = False,
library = None,
)
(options, args) = Experiment.Start( parser )
if options.pmid:
ids = PubMed.search_for(options.pmid)
outlines = []
medline_parser = Medline.RecordParser()
medline_dict = PubMed.Dictionary(parser = medline_parser)
for id in ids:
this_record = medline_dict[id]
year = this_record.publication_date.split(" ")[0]
last_names = map( lambda x: x.split(" ")[0], this_record.authors)
if options.format == "oo":
elif opt == '-c':
count_only = 1
elif opt == '-d':
try:
delay = float(arg)
except ValueError:
print "Delay must be a floating point value"
sys.exit(0)
if delay < 0:
print "Delay cannot be negative"
sys.exit(0)
if help:
print_usage()
sys.exit(0)
print "Doing a PubMed search for %s..." % repr(query)
ids = PubMed.search_for(query)
print "Found %d citations" % len(ids)
if count_only:
sys.exit(0)
pm = PubMed.Dictionary(delay=delay)
for id in ids:
try:
print pm[id]
except KeyError, x:
print "Couldn't download %s, %s" % (id, x)
sys.stdout.flush()
"""Example script showing how to interact with PubMed."""
# standard library
import string
# biopython
from Bio import PubMed
from Bio import Medline
# do the search and get the ids
search_term = 'orchid'
orchid_ids = PubMed.search_for(search_term)
print orchid_ids
# access Medline through a dictionary interface that returns PubMed Records
rec_parser = Medline.RecordParser()
medline_dict = PubMed.Dictionary(parser = rec_parser)
for id in orchid_ids[0:5]:
cur_record = medline_dict[id]
print 'title:', string.rstrip(cur_record.title)
print 'authors:', cur_record.authors
print 'source:', string.strip(cur_record.source)
print
