def chanmsg(self, conn, user, channel, message):
ch = self.channels[conn.factory.network, conn.irclower(channel)]
if message.strip().lower() == "!next":
ch.nobodygotit()
elif message.strip().lower() == "!stop":
ch.on = False
ch.timer.stop()
elif message.strip().lower() == "!start":
ch.on = True
ch.doquestion()
elif message.strip().lower() == "!hint":
stars = [i for i, c in enumerate(ch.hint) if c == "*"]
n = min(3, len(stars)-3)
if n >= 1:
for i in random.sample(stars, n):
ch.hint = ch.hint[:i] + ch.answer[i] + ch.hint[i+1:]
ch.post("Hint!!!!! \x037" + ch.hint)
elif ch.on:
inp = ' '.join(message.strip().lower().split())
ans = ' '.join(ch.answer.strip().lower().split())
if inp == ans:
ch.post("%s got the answer! %s" % (irc.usersplit(user).group("nick"), ch.explanation))
ch.doquestion()
elif dameraulevenshtein.dameraulevenshtein(inp, ans) / len(ans) <= .2:
ch.post(message + "? \x033That's close!")
print "levenshtein"
elif metaphone.dm(inp) == metaphone.dm(ans):
ch.post(message + "? \x033That's close!")
print "metaphone"
def calc_dists(mine, theirs):
''' Calculates Levenshtein distance, Damerau-Levenshtein distance,
and Jaro-Winkler distance between two strings.
Returns a 3-item tuple containing results, respectively.
'''
my_str = unicode(mine)
search_str = unicode(theirs)
# find levenshtein distance
lev = distance(my_str, search_str)
# find damerau-levenshtein distance
dl = dameraulevenshtein(my_str, search_str)
# find jaro-winkler distance
jw = jarow(my_str, search_str)
return (lev, dl, jw)
def rank_candidates():
for cand in candidates:
yield dameraulevenshtein(cand, word), cand
def find_item_by_dl(self, search): results = [(dameraulevenshtein(search, name), name) for name in self.nodes_iter()] results.sort() return results
def reconcile_product(raw_product):
product_map = {u'anti-diphtheria serum': u'anti-diphtheria-serum',
u'anti-diphtheria-serum': u'anti-diphtheria-serum',
u'anti-scorpion venom': u'anti-scorpion-venom',
u'anti-scorpion-venom': u'anti-scorpion-venom',
u'anti-tetanus serum': u'anti-tetanus-serum',
u'anti-tetanus-serum': u'anti-tetanus-serum',
u'antivenin': u'snake-antivenom',
u'bcg': u'bcg-20',
u'bcg-10': u'bcg-20',
u'bcg-20': u'bcg-20',
u'bopv-20': u'bopv-20',
u'bvpo': u'bopv-20',
u'bvpo-20': u'bopv-20',
u'cholera': u'cholera-2',
u'cholera-1': u'cholera-1',
u'cholera-2': u'cholera-2',
u'dt-10': u'dt-10',
u'dt-20': u'dt-20',
u'dtc': u'dtp-20',
u'dtc-10': u'dtp-20',
u'dtc-hepb': u'dtp-hepb-10',
u'dtc-hepb-10': u'dtp-hepb-10',
u'dtc-hepb-2': u'dtp-hepb-2',
u'dtc-hepb-hib': u'dtp-hepbhib-2',
u'dtc-hepb-hib-1': u'dtp-hepbhib-1',
u'dtc-hepb-hib-2': u'dtp-hepbhib-2',
u'dtc-hib': u'dtp-hib-10',
u'dtc-hib-1 (liquide)': u'dtp-hib-1-lqd',
u'dtc-hib-1 (lyophilise)': u'dtp-hib-1-lph',
u'dtc-hib-10': u'dtp-hib-10',
u'dtp-10': u'dtp-10',
u'dtp-20': u'dtp-20',
u'dtp-hepb+hib-1': u'dtp-hepbhib-1',
u'dtp-hepb+hib-2': u'dtp-hepbhib-2',
u'dtp-hepb-10': u'dtp-hepb-10',
u'dtp-hepb-2': u'dtp-hepb-2',
u'dtp-hepb-hib-1': u'dtp-hepbhib-1',
u'dtp-hepbhib-1': u'dtp-hepbhib-1',
u'dtp-hepbhib-2': u'dtp-hepbhib-2',
u'dtp-hib-1 (lph)': u'dtp-hib-1-lph',
u'dtp-hib-1 (lqd)': u'dtp-hib-1-lqd',
u'dtp-hib-1-lph': u'dtp-hib-1-lph',
u'dtp-hib-1-lqd': u'dtp-hib-1-lqd',
u'dtp-hib-10': u'dtp-hib-10',
u'foetal bovine serum': u'foetal-bovine-serum',
u'foetal-bovine-serum': u'foetal-bovine-serum',
u'hepb': u'hepb-20',
u'hepb adulte': u'hepb-10-adult',
u'hepb-1': u'hepb-1',
u'hepb-1 adult': u'hepb-1-adult',
u'hepb-1 adulte': u'hepb-1-adult',
u'hepb-1-adult': u'hepb-1-adult',
u'hepb-10': u'hepb-10',
u'hepb-10 adult': u'hepb-10-adult',
u'hepb-10 adulte': u'hepb-10-adult',
u'hepb-10-adult': u'hepb-10-adult',
u'hepb-2': u'hepb-2',
u'hepb-20': u'hepb-20',
u'hepb-6': u'hepb-6',
u'hib-1': u'hib-1',
u'hib-1 (liquide)': u'hib-1-lqd',
u'hib-1 (lph)': u'hib-1-lph',
u'hib-1 (lqd)': u'hib-1-lqd',
u'hib-1 (lyophilise)': u'hib-1-lph',
u'hib-1-lph': u'hib-1-lph',
u'hib-1-lqd': u'hib-1-lqd',
u'hib-10': u'hib-10',
u'influenza-1': u'influenza-1',
u'influenza-10': u'influenza-10',
u'ipv-1': u'ipv-1',
u'ipv-10': u'ipv-10',
u'mea-1': u'mea-1',
u'mea-10': u'mea-10',
u'mea-20': u'mea-20',
u'measles': u'measles',
u'mening a/c-10': u'mening-ac-10',
u'mening a/c-50': u'mening-ac-50',
u'mening ac&w-50': u'mening-acw-50',
u'mening acyw135-1': u'mening-acyw135-1',
u'mening acyw135-10': u'mening-acyw135-10',
u'mening-ac-10': u'mening-ac-10',
u'mening-ac-50': u'mening-ac-50',
u'mening-acw-50': u'mening-acw-50',
u'mening-acyw135-1': u'mening-acyw135-1',
u'mening-acyw135-10': u'mening-acyw135-10',
u'mmr-1': u'mmr-1',
u'mmr-10': u'mmr-10',
u'mmr-5': u'mmr-5',
u'mopv1-20': u'mopv1-20',
u'mopv3-20': u'mopv3-20',
u'mr-10': u'mr-10',
u'mr-2': u'mr-2',
u'mumps': u'mumps',
u'mvpo1': u'mopv1-20',
u'mvpo1-20': u'mopv1-20',
u'mvpo3': u'mopv3-20',
u'mvpo3-20': u'mopv3-20',
u'opv bulk': u'opv-bulk',
u'opv-50': u'opv-50',
u'opv-bulk': u'opv-bulk',
u'pcv1-7': u'pcv1-7',
u'pneumococcal serum': u'pneumococcal-serum',
u'pneumococcal-serum': u'pneumococcal-serum',
u'rabies': u'rabies',
u'rabies hdc-1': u'rabies-hdc-1',
u'rabies hri-1': u'rabies-hri-1',
u'rabies-1': u'rabies-1',
u'rabies-2': u'rabies-2',
u'rabies-5': u'rabies-5',
u'rabies-hdc-1': u'rabies-hdc-1',
u'rabies-hri-1': u'rabies-hri-1',
u'ror': u'mmr-10',
u'ror-1': u'mmr-1',
u'ror-10': u'mmr-10',
u'ror-5': u'mmr-5',
u'rubella-1': u'rubella-1',
u'rubella-10': u'rubella-10',
u'serum anti-diphterie': u'anti-diphtheria-serum',
u'serum anti-scorpion': u'anti-scorpion-venom',
u'serum anti-tetanique': u'anti-tetanus-serum',
u'snake antivenom': u'snake-antivenom',
u'snake-antivenom': u'snake-antivenom',
u'td-10': u'td-10',
u'td-20': u'td-20',
u'topv-10': u'topv-10',
u'topv-20': u'topv-20',
u'tt uniject': u'tt-uniject',
u'tt-10': u'tt-10',
u'tt-20': u'tt-20',
u'tt-uniject': u'tt-uniject',
u'tuberculin/ppd': u'tuberculinppd',
u'tuberculinppd': u'tuberculinppd',
u'tvpo': u'topv-10',
u'tvpo-10': u'topv-10',
u'tvpo-20': u'topv-20',
u'typhoid': u'typhoid',
u'vaa': u'yf-50',
u'vaa-1': u'yf-1',
u'vaa-10': u'yf-10',
u'vaa-20': u'yf-20',
u'vaa-5': u'yf-5',
u'vaa-50': u'yf-50',
u'vam ac-10': u'mening-ac-10',
u'vam ac-50': u'mening-ac-50',
u'vam w135-1': u'mening-acyw135-1',
u'vam w135-10': u'mening-acyw135-10',
u'vam w50': u'mening-acw-50',
u'vao': u'mumps',
u'var': u'measles',
u'var-1': u'mea-1',
u'var-10': u'mea-10',
u'var-20': u'mea-20',
u'vat': u'tt-20',
u'vpi-1': u'ipv-1',
u'vpi-10': u'ipv-10',
u'vpo': u'opv-50',
u'vpo-50': u'opv-50',
u'yf-1': u'yf-1',
u'yf-10': u'yf-10',
u'yf-20': u'yf-20',
u'yf-5': u'yf-5',
u'yf-50': u'yf-50'}
# check if term is a key in product_map
if raw_product.lower() in product_map:
return True, product_map[raw_product.lower()]
# list for dm distance results
# as tuples [(edit_distance, "product_map key"),...]
dl_dists = []
# find damerau-levenshtein distance between
# raw_product and all of the keys in product_map
for term in product_map.keys():
dl = dameraulevenshtein(term, raw_product)
dl_dists.append((dl, raw_product, term, product_map[term]))
# sort tuples by edit distance
dl_dists.sort(None, operator.itemgetter(0))
# edit distance threshold is half the length of the
# raw_product string (rounded up)
# TODO accept as parameter
dl_threshold = math.ceil(float(len(raw_product))/float(2))
# limit to edit distances below dl_threshold
dl_suggestions = [d for d in dl_dists if d[0] <= dl_threshold]
return False, {"damerau-levenshtein": dl_suggestions}
