def condense_ids(df,
tax_dict,
ranks,
max_group_size,
threshold_assignments=False):
"""
Create mapping from tax_id to its condensed id. Also creates the
assignment hash on either the condensed_id or assignment_tax_id decided
by the --split-condensed-assignments switch.
By taking a hash of the set (frozenset) of ids the qseqid is given a
unique identifier (the hash). Later, we will use this hash and
assign an ssignment name based on either the set of condensed_ids or
assignment_tax_ids. The modivation for using a hash rather than
the actual assignment text for grouping is that the assignment text
can contains extra annotations that are independent of which
assignment group a qseqid belongs to such as a 100% id star.
"""
condensed = sequtils.condense_ids(df[ASSIGNMENT_TAX_ID].unique(),
tax_dict,
ranks=ranks,
max_size=max_group_size)
condensed = pd.DataFrame(condensed.items(),
columns=[ASSIGNMENT_TAX_ID, 'condensed_id'])
condensed = condensed.set_index(ASSIGNMENT_TAX_ID)
if threshold_assignments:
assignment_hash = hash(frozenset(condensed.index.unique()))
else:
assignment_hash = hash(frozenset(condensed['condensed_id'].unique()))
condensed['assignment_hash'] = assignment_hash
return df.join(condensed, on=ASSIGNMENT_TAX_ID)
def condense_ids(
df, tax_dict, ranks, max_group_size, threshold_assignments=False):
"""
Create mapping from tax_id to its condensed id. Also creates the
assignment hash on either the condensed_id or assignment_tax_id decided
by the --split-condensed-assignments switch.
By taking a hash of the set (frozenset) of ids the qseqid is given a
unique identifier (the hash). Later, we will use this hash and
assign an ssignment name based on either the set of condensed_ids or
assignment_tax_ids. The modivation for using a hash rather than
the actual assignment text for grouping is that the assignment text
can contains extra annotations that are independent of which
assignment group a qseqid belongs to such as a 100% id star.
"""
condensed = sequtils.condense_ids(
df[ASSIGNMENT_TAX_ID].unique(),
tax_dict,
ranks=ranks,
max_size=max_group_size)
condensed = pd.DataFrame(
condensed.items(),
columns=[ASSIGNMENT_TAX_ID, 'condensed_id'])
condensed = condensed.set_index(ASSIGNMENT_TAX_ID)
if threshold_assignments:
assignment_hash = hash(frozenset(condensed.index.unique()))
else:
assignment_hash = hash(frozenset(condensed['condensed_id'].unique()))
condensed['assignment_hash'] = assignment_hash
return df.join(condensed, on=ASSIGNMENT_TAX_ID)
def test03(self):
"""
test max_size = 0
"""
taxonomy = self.taxonomy
thisdatadir = self.thisdatadir
this_test = sys._getframe().f_code.co_name
condensed_assignments_ref = path.join(thisdatadir,
this_test,
'assignments.pkl.bz2')
condensed_assignments_ref = BZ2File(condensed_assignments_ref)
condensed_assignments_ref = cPickle.load(condensed_assignments_ref)
condense_assignments = lambda x: sequtils.condense_ids(
x, taxonomy, max_size=0)
condensed_assignments = map(condense_assignments, self.assignments)
self.assertEquals(condensed_assignments, condensed_assignments_ref)
