def __init__(self, normalizer=None):

self.normalizer = normalizer or (lambda x: x)

def find_recommendations(self, name, candidates, max_matches=10):

"""Return a list of close matches to `name` from `candidates`."""

if not name or not candidates:

return []

norm_name = self.normalizer(name)

norm_candidates = self._get_normalized_candidates(candidates)

cutoff = self._calculate_cutoff(norm_name)

norm_matches = difflib.get_close_matches(norm_name,

norm_candidates,

n=max_matches,

cutoff=cutoff)

return self._get_original_candidates(norm_candidates, norm_matches)

@staticmethod

def format_recommendations(msg, recommendations):

"""Add recommendations to the given message.

if recommendations:

msg += " Did you mean:"

for rec in recommendations:

msg += "\n %s" % rec

return msg

def _get_normalized_candidates(self, candidates):

norm_candidates = {}

# sort before normalization for consistent Python/Jython ordering

for cand in sorted(candidates):

norm = self.normalizer(cand)

norm_candidates.setdefault(norm, []).append(cand)

return norm_candidates

def _get_original_candidates(self, norm_candidates, norm_matches):

candidates = []

for norm_match in norm_matches:

candidates.extend(norm_candidates[norm_match])

return candidates

def _calculate_cutoff(self, string, min_cutoff=.5, max_cutoff=.85,

step=.03):

"""Calculate a cutoff depending on string length.

cutoff = min_cutoff + len(string) * step