def __getitem__(self, id):
"""S.__getitem__(id) -> object
Return the Medline entry. id is either the Medline Unique ID
or the Pubmed ID of the article. Raises a KeyError if there's an
error.
"""
# First, check to see if enough time has passed since my
# last query.
self.limiter.wait()
try:
handle = NCBI.efetch(
db="pubmed", id=id, retmode='text', rettype='medlars')
except IOError, x:
# raise a KeyError instead of an IOError
# XXX I really should distinguish between a real IOError and
# if the id is not in the database.
raise KeyError, x
def download_many(ids, callback_fn, broken_fn=None, delay=120.0, faildelay=5.0,
batchsize=500, parser=None):
"""download_many(ids, callback_fn[, broken_fn][, delay][, faildelay][, batchsize])
Download many records from PubMed. ids is a list of either the
Medline Unique ID or the PubMed ID's of the articles. Each time a
record is downloaded, callback_fn is called with the text of the
record. broken_fn is an optional function that is called with the
id of records that were not able to be downloaded. delay is the
number of seconds to wait between requests. batchsize is the
number of records to request each time.
"""
# parser is an undocumented parameter that allows people to
# specify an optional parser to handle each record. This is
# dangerous because the results may be malformed, and exceptions
# in the parser may disrupt the whole download process.
if batchsize > 500 or batchsize < 1:
raise ValueError, "batchsize must be between 1 and 500"
limiter = RequestLimiter(delay)
current_batchsize = batchsize
# Loop until all the ids are processed. We want to process as
# many as possible with each request. Unfortunately, errors can
# occur. Some id may be incorrect, or the server may be
# unresponsive. In addition, one broken id out of a list of id's
# can cause a non-specific error. Thus, the strategy I'm going to
# take, is to start by downloading as many as I can. If the
# request fails, I'm going to half the number of records I try to
# get. If there's only one more record, then I'll report it as
# broken and move on. If the request succeeds, I'll double the
# number of records until I get back up to the batchsize.
nsuccesses = 0
while ids:
if current_batchsize > len(ids):
current_batchsize = len(ids)
id_str = ','.join(ids[:current_batchsize])
# Make sure enough time has passed before I do another query.
if not nsuccesses:
limiter.wait(faildelay)
else:
limiter.wait()
try:
# Query PubMed. If one or more of the id's are broken,
# this will raise an IOError.
handle = NCBI.efetch(
db="pubmed", id=id_str, retmode='text', rettype='medlars')
# I'm going to check to make sure PubMed returned the same
# number of id's as I requested. If it didn't then I'm going
# to raise an exception. This could take a lot of memory if
# the batchsize is large.
results = handle.read()
num_ids = 0
for x in Medline.Iterator(File.StringHandle(results)):
num_ids = num_ids + 1
if num_ids != current_batchsize:
raise IOError
handle = File.StringHandle(results)
except IOError: # Query did not work.
if current_batchsize == 1:
# There was only 1 id in the query. Report it as
# broken and move on.
id = ids.pop(0)
if broken_fn is not None:
broken_fn(id)
else:
# I don't know which one is broken. Try again with
# fewer id's.
current_batchsize = current_batchsize / 2
nsuccesses = 0
continue
nsuccesses = nsuccesses + 1
# Iterate through the results and pass the records to the
# callback.
idnum = 0
for rec in Medline.Iterator(handle, parser):
callback_fn(ids[idnum], rec)
idnum = idnum + 1
ids = ids[current_batchsize:]
# If I'm not downloading the maximum number of articles,
# double the number for next time.
if nsuccesses >= 2 and current_batchsize < batchsize:
current_batchsize = current_batchsize * 2
if current_batchsize > batchsize:
current_batchsize = batchsize
