def classify(token_list):
out = dict()
tokens = copy(token_list)
# from pprint import pprint
# pprint(FIELD_DMV)
# first, sanity-check the digified tokens --
# if more than one can pass for a phone number, a color,
# or a zip code (that is to say, the input is ambiguous),
# we bail:
for token in tokens:
# check each term against all test funcs --
# if more than one bucket is nonzero, it's a problem
h = Histogram()
for FieldType in FIELD_DMV:
if FieldType.check(token):
h.inc(FieldType.json_name)
if len(h) > 1:
# ERROR: couldn't distinguish one thing
# from another... BAIL
raise RDAmbiguousTerms("Token '%s' parsed ambiguously\n"
"Passed multiple field checks: %s" % (
token, SEP_WS.join(h.iterkeys())
))
# update `out` with the classified tokens
for idx, token in enumerate(copy(tokens)):
for FieldType in FIELD_DMV:
if FieldType.check(token):
field = FieldType()
out.update(field.value_for_token(token))
tokens.remove(token)
continue
# raise appropriate errors when we don't find what we need
for FieldType in FIELD_DMV:
field = FieldType()
if not out.has_key(field.name):
field.unfound(token_list) # this may raise
# what is left "should" be the pieces of the name,
# e.g. ['Washington', 'Booker T.'], ['James Murphy'], &c
first_field = FirstNameField()
last_field = LastNameField()
if len(tokens) > 2:
# ERROR: wtf is going on
pass
elif len(tokens) == 2:
out.update(first_field.value_for_token(tokens[-1]))
out.update(last_field.value_for_token(tokens[0]))
elif len(tokens) == 1:
names = tokens[0].split()
if len(names) > 1:
out.update(first_field.value_for_token(names[0]))
out.update(last_field.value_for_token(names[-1]))
else:
NameField().unfound(token_list)
else:
# WHY ARE WE HERE. No names... really??
raise RDAmbiguousNames("No names present!"
"Reconstructed original line:\n"
"\t%s" % reconstruct(token_list))
# pprint(out, indent=4)
return out
