def get_units_from_table_header(header_str):
(a, parens_str, comma_str) = resolve_table_header(header_str)
if parens_str:
matched_units = parse_units_from_str(parens_str)
if matched_units:
return parens_str
elif comma_str:
matched_units = parse_units_from_str(comma_str)
if matched_units:
return comma_str
else:
# split header using whitespace and check if any are units
for e in reversed(a.split()):
matched_units = parse_units_from_str(e)
if matched_units:
return e
return None
def match_ephys_header(header_str, ephys_synonym_list):
"""Given a data table header string, returns closest matching ephys prop object
or None if no ephys synonym has a high match
Args:
header_str: header string from a data table
ephys_synonym_list: the list of strings representing ephys synonyms
Returns:
An EphysProp neuroelectro.models object whose Ephys Synonym
best matches the header_str if match is higher than threshold,
or None otherwise
example:
<EphysProp: Input resistance>
"""
synapse_stop_words = get_synapse_stop_words() # a list of stop words relating to synapse terms
(normHeader, insideParens, commaStr) = resolve_table_header(header_str)
best_matching_ephys_syn = fuzzy_match_term_to_list(normHeader, ephys_synonym_list)
if best_matching_ephys_syn: # if it's not None
if any(substring in normHeader for substring in synapse_stop_words):
# if header contains a synaptic plasticity term, then dont associate it to anything
return None
# find ephys prop matching synonym term
ephysPropQuerySet = m.EphysProp.objects.filter(synonyms__term = best_matching_ephys_syn)
if ephysPropQuerySet.count() > 0:
ephysPropOb = ephysPropQuerySet[0]
if ephysPropQuerySet.count() > 1:
print 'Multiple ephys properties found matching synonym: %s' % best_matching_ephys_syn
return ephysPropOb
else:
return None
else:
return None
