def setUp(self):
ref_seq = Seq.Seq('GA' * 9 + 'GC' * 6 + 'GT' * 15)
query_seq = 'AA' * 4 + 'GA' * 5 + 'AC' + 'GC' * 5 + 'GT' * 15
aln_string = '>seq1\n{0}'.format(query_seq)
self.aln = Alignment(helpers.parse_fasta(aln_string).values(),
reference_sequence=ref_seq)
self.mutation_patterns = [mut_pattern.GA, mut_pattern.GM]
class TestBasicAnalysis(unittest.TestCase):
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCACCCC
>seq2
GTCAGTCAGTCAGTCACCCC
>seq3
ATCAATCAGTCAATCACCCC
"""
self.seqs = helpers.parse_fasta(aln_string)
self.aln = Alignment(self.seqs.values())
def test_consensus_reference(self):
self.assertEqual(str(self.aln.reference_sequence), 'GTCAGTCAGTCAGTCACCCC')
def test_hm_pos(self):
for result in self.aln.analyze((old_focus_pattern, old_control_pattern)):
hm_pos = result['hm_pos']
if result['sequence'] in ['seq1', 'seq2']:
self.assertFalse(hm_pos)
else:
self.assertTrue(hm_pos)
def test_hm_pos_indices(self):
hm_pos_indices = [1, 5, 13]
for result in self.aln.analyze((old_focus_pattern, old_control_pattern)):
if result['hm_pos']:
self.assertEqual(result['mut_columns'], hm_pos_indices)
else:
self.assertEqual(result['mut_columns'], [])
class TestBasicAnalysis(unittest.TestCase):
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCACCCC
>seq2
GTCAGTCAGTCAGTCACCCC
>seq3
ATCAATCAGTCAATCACCCC
"""
self.seqs = helpers.parse_fasta(aln_string)
self.aln = Alignment(self.seqs.values())
def test_consensus_reference(self):
self.assertEqual(str(self.aln.reference_sequence),
'GTCAGTCAGTCAGTCACCCC')
def test_hm_pos(self):
for result in self.aln.analyze(
(old_focus_pattern, old_control_pattern)):
hm_pos = result['hm_pos']
if result['sequence'] in ['seq1', 'seq2']:
self.assertFalse(hm_pos)
else:
self.assertTrue(hm_pos)
def test_hm_pos_indices(self):
hm_pos_indices = [1, 5, 13]
for result in self.aln.analyze(
(old_focus_pattern, old_control_pattern)):
if result['hm_pos']:
self.assertEqual(result['mut_columns'], hm_pos_indices)
else:
self.assertEqual(result['mut_columns'], [])
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCACCCC
>seq2
GTCAGTCAGTCAGTCACCCC
>seq3
ATCAATCAGTCAATCACCCC
"""
self.seqs = helpers.parse_fasta(aln_string)
self.aln = Alignment(self.seqs.values())
def setUp(self):
aln_string = """
>seq1
GGTGACGCT
>seq2
AGTAACGCT
>seq3
GGTAACACT
"""
ref_seq = Seq.Seq('GGTGACGCT')
self.aln = Alignment(helpers.parse_fasta(aln_string).values(),
reference_sequence=ref_seq)
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq2
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq3
ATCAATCAGTCAATCG
ATCAATCAGTCAATCG"""
self.seqs = helpers.parse_fasta_list(aln_string)
self.aln = Alignment(self.seqs)
def setUp(self):
ref_seq = helpers.parse_fasta("""
>all
GGGGGGGGGTGTGTGTGT""")
self.aln = Alignment(helpers.parse_fasta("""
>seq1
GGGGGGGGGTGTGTGTGT
>seq2
AGAGAGAGGTGTGTGTGT
>seq3
GGGGGGGGGTATATATAT
""").values(),
reference_sequence=ref_seq['all'])
class TestCallPatterns(unittest.TestCase):
"""The following motivates these tests:
BetaRat(5.5, 0.5, 11.0, 16.0) cdf: 0.00392333077990058 map: 3.27321043801743 ppf: 2.47393902966
BetaRat(4.5, 1.5, 6.0, 21.0) cdf: 0.00570220397191901 map: 3.475475215347 ppf: 1.98195901549
As you can see, different methods can make different calls."""
def setUp(self):
ref_seq = Seq.Seq('GA' * 9 + 'GC' * 6 + 'GT' * 15)
query_seq = 'AA' * 4 + 'GA' * 5 + 'AC' + 'GC' * 5 + 'GT' * 15
aln_string = '>seq1\n{0}'.format(query_seq)
self.aln = Alignment(helpers.parse_fasta(aln_string).values(),
reference_sequence=ref_seq)
self.mutation_patterns = [mut_pattern.GA, mut_pattern.GM]
def test_map_caller(self):
for result in self.aln.multiple_context_analysis(
self.mutation_patterns, caller='map'):
call = result['call']
self.assertEqual(call['call_pattern'], 'GA')
def test_ppf_caller(self):
for result in self.aln.multiple_context_analysis(
self.mutation_patterns,
caller='q_0.05',
quants=[0.05],
pos_quants_only=False):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def test_cdf_caller(self):
"""Note that CDF should be evaluated as smaller => more extreme, whereas the other statistics are the
inverse"""
for result in self.aln.multiple_context_analysis(
self.mutation_patterns, caller='cutoff_cdf'):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def test_ppf_caller_without_all_quants(self):
"""Should raise if we don't have the given statistic to test for call comparison."""
analysis = self.aln.multiple_context_analysis(self.mutation_patterns,
caller='q_0.05')
with self.assertRaises(ValueError):
analysis.next()
def test_not_calling_negatives(self):
"""We should only call sequences when they are actually positive, even if there are non-positive
sequences with more extreme call statistics."""
for result in self.aln.multiple_context_analysis(
self.mutation_patterns, caller='map',
significance_level=0.005):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def split(args):
hm_col_reader = csv.DictReader(args.columns)
hm_columns = map(lambda x: int(x['column']), hm_col_reader)
hm_columns = list(set(hm_columns))
seq_records = SeqIO.parse(args.alignment, 'fasta')
aln = Alignment(seq_records)
aln.split_hypermuts(hm_columns = hm_columns)
fn_base = path.join(args.out_dir, args.prefix)
hm_pos_handle = open(fn_base + '.pos.fasta', 'w')
hm_neg_handle = open(fn_base + '.neg.fasta', 'w')
AlignIO.write(aln.hm_pos_aln, hm_pos_handle, 'fasta')
AlignIO.write(aln.hm_neg_aln, hm_neg_handle, 'fasta')
for handle in [args.alignment, args.columns, hm_pos_handle, hm_neg_handle]:
handle.close()
def split(args):
hm_col_reader = csv.DictReader(args.columns)
hm_columns = map(lambda x: int(x['column']), hm_col_reader)
hm_columns = list(set(hm_columns))
seq_records = SeqIO.parse(args.alignment, 'fasta')
aln = Alignment(seq_records)
aln.split_hypermuts(hm_columns=hm_columns)
fn_base = path.join(args.out_dir, args.prefix)
hm_pos_handle = open(fn_base + '.pos.fasta', 'w')
hm_neg_handle = open(fn_base + '.neg.fasta', 'w')
AlignIO.write(aln.hm_pos_aln, hm_pos_handle, 'fasta')
AlignIO.write(aln.hm_neg_aln, hm_neg_handle, 'fasta')
for handle in [args.alignment, args.columns, hm_pos_handle, hm_neg_handle]:
handle.close()
class TestCallPatterns(unittest.TestCase):
"""The following motivates these tests:
BetaRat(5.5, 0.5, 11.0, 16.0) cdf: 0.00392333077990058 map: 3.27321043801743 ppf: 2.47393902966
BetaRat(4.5, 1.5, 6.0, 21.0) cdf: 0.00570220397191901 map: 3.475475215347 ppf: 1.98195901549
As you can see, different methods can make different calls."""
def setUp(self):
ref_seq = Seq.Seq('GA'*9 + 'GC'*6 + 'GT'*15)
query_seq = 'AA'*4 + 'GA'*5 + 'AC' + 'GC'*5 + 'GT'*15
aln_string = '>seq1\n{0}'.format(query_seq)
self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq)
self.mutation_patterns=[mut_pattern.GA, mut_pattern.GM]
def test_map_caller(self):
for result in self.aln.multiple_context_analysis(self.mutation_patterns, caller='map'):
call = result['call']
self.assertEqual(call['call_pattern'], 'GA')
def test_ppf_caller(self):
for result in self.aln.multiple_context_analysis(self.mutation_patterns, caller='q_0.05',
quants=[0.05], pos_quants_only=False):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def test_cdf_caller(self):
"""Note that CDF should be evaluated as smaller => more extreme, whereas the other statistics are the
inverse"""
for result in self.aln.multiple_context_analysis(self.mutation_patterns, caller='cutoff_cdf'):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def test_ppf_caller_without_all_quants(self):
"""Should raise if we don't have the given statistic to test for call comparison."""
analysis = self.aln.multiple_context_analysis(self.mutation_patterns, caller='q_0.05')
with self.assertRaises(ValueError):
analysis.next()
def test_not_calling_negatives(self):
"""We should only call sequences when they are actually positive, even if there are non-positive
sequences with more extreme call statistics."""
for result in self.aln.multiple_context_analysis(self.mutation_patterns, caller='map',
significance_level=0.005):
call = result['call']
self.assertEqual(call['call_pattern'], 'GM')
def setUp(self):
aln_string = """
>seq1
GGTGACGCT
>seq2
AGTAACGCT
>seq3
GGTAACACT
"""
ref_seq = Seq.Seq('GGTGACGCT')
self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq)
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCACCCC
>seq2
GTCAGTCAGTCAGTCACCCC
>seq3
ATCAATCAGTCAATCACCCC
"""
self.seqs = helpers.parse_fasta(aln_string)
self.aln = Alignment(self.seqs.values())
def setUp(self):
ref_seq = helpers.parse_fasta("""
>all
GGGGGGGGGTGTGTGTGT""")
self.aln = Alignment(helpers.parse_fasta("""
>seq1
GGGGGGGGGTGTGTGTGT
>seq2
AGAGAGAGGTGTGTGTGT
>seq3
GGGGGGGGGTATATATAT
""").values(), reference_sequence = ref_seq['all'])
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq2
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq3
ATCAATCAGTCAATCG
ATCAATCAGTCAATCG"""
self.seqs = helpers.parse_fasta_list(aln_string)
self.aln = Alignment(self.seqs)
class TestMutCounts(unittest.TestCase):
def setUp(self):
aln_string = """
>seq1
GGTGACGCT
>seq2
AGTAACGCT
>seq3
GGTAACACT
"""
ref_seq = Seq.Seq('GGTGACGCT')
self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq)
def __test_counts__(self, pattern, real_counts):
for result in self.aln.analyze(pattern):
seq_counts = [result[x] for x in ('focus_pos', 'control_pos', 'focus_neg', 'control_neg')]
seq_real_counts = real_counts[result['sequence']]
self.assertEqual(seq_counts, seq_real_counts)
def test_ga_counts(self):
self.__test_counts__(mut_pattern.GA, dict(
seq1=[0, 0, 1, 3],
seq2=[1, 1, 0, 2],
seq3=[1, 1, 0, 2]))
def test_gg_counts(self):
self.__test_counts__(mut_pattern.GG, dict(
seq1=[0, 0, 1, 3],
seq2=[1, 1, 0, 2],
seq3=[0, 2, 1, 1]))
def test_gr_counts(self):
self.__test_counts__(mut_pattern.GR, dict(
seq1=[0, 0, 2, 2],
seq2=[2, 0, 0, 2],
seq3=[1, 1, 1, 1]))
def test_gm_counts(self):
self.__test_counts__(mut_pattern.GM, dict(
seq1=[0, 0, 2, 2],
seq2=[1, 1, 1, 1],
seq3=[2, 0, 0, 2]))
def test_gv_counts(self):
self.__test_counts__(mut_pattern.GV, dict(
seq1=[0, 0, 3, 1],
seq2=[2, 0, 1, 1],
seq3=[2, 0, 1, 1]))
class TestMutCounts(unittest.TestCase):
def setUp(self):
aln_string = """
>seq1
GGTGACGCT
>seq2
AGTAACGCT
>seq3
GGTAACACT
"""
ref_seq = Seq.Seq('GGTGACGCT')
self.aln = Alignment(helpers.parse_fasta(aln_string).values(),
reference_sequence=ref_seq)
def __test_counts__(self, pattern, real_counts):
for result in self.aln.analyze(pattern):
seq_counts = [
result[x] for x in ('focus_pos', 'control_pos', 'focus_neg',
'control_neg')
]
seq_real_counts = real_counts[result['sequence']]
self.assertEqual(seq_counts, seq_real_counts)
def test_ga_counts(self):
self.__test_counts__(
mut_pattern.GA,
dict(seq1=[0, 0, 1, 3], seq2=[1, 1, 0, 2], seq3=[1, 1, 0, 2]))
def test_gg_counts(self):
self.__test_counts__(
mut_pattern.GG,
dict(seq1=[0, 0, 1, 3], seq2=[1, 1, 0, 2], seq3=[0, 2, 1, 1]))
def test_gr_counts(self):
self.__test_counts__(
mut_pattern.GR,
dict(seq1=[0, 0, 2, 2], seq2=[2, 0, 0, 2], seq3=[1, 1, 1, 1]))
def test_gm_counts(self):
self.__test_counts__(
mut_pattern.GM,
dict(seq1=[0, 0, 2, 2], seq2=[1, 1, 1, 1], seq3=[2, 0, 0, 2]))
def test_gv_counts(self):
self.__test_counts__(
mut_pattern.GV,
dict(seq1=[0, 0, 3, 1], seq2=[2, 0, 1, 1], seq3=[2, 0, 1, 1]))
class TestBasicSplit(unittest.TestCase):
def assertSeqsEqual(self, seq_record, string):
self.assertEqual(str(seq_record.seq), string)
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq2
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq3
ATCAATCAGTCAATCG
ATCAATCAGTCAATCG"""
self.seqs = helpers.parse_fasta_list(aln_string)
self.aln = Alignment(self.seqs)
def test_manual_split(self):
columns = [1, 2, 3, 5]
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 28)
self.assertEqual(pos.get_alignment_length(), 4)
self.assertEqual(neg[:, 0], "AAA")
self.assertEqual(neg[:, 11], "AAG")
self.assertSeqsEqual(neg[0, :], "ATCAGTCAGTCAGTCAGTCAGTCAGTCA")
self.assertEqual(pos[:, 1], "TTT")
def test_splitting_final_col(self):
columns = [3, 7, 32]
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 29)
self.assertEqual(pos.get_alignment_length(), 3)
self.assertEqual(neg[:, 0], "GGA")
self.assertEqual(neg[:, 12], "CCC")
self.assertSeqsEqual(neg[0, :], "GTAGTAGTCAGTCAGTCAGTCAGTCAGTC")
self.assertEqual(pos[:, 1], "CCC")
self.assertEqual(pos[:, 2], "AAG")
def test_splitting_on_no_hm(self):
columns = []
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 32)
self.assertEqual(pos.get_alignment_length(), 0)
self.assertEqual(neg[:, 0], "GGA")
self.assertSeqsEqual(neg[0, :], "GTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCA")
self.assertSeqsEqual(pos[0, :], "")
class TestBasicSplit(unittest.TestCase):
def assertSeqsEqual(self, seq_record, string):
self.assertEqual(str(seq_record.seq), string)
def setUp(self):
aln_string = """
>seq1
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq2
GTCAGTCAGTCAGTCA
GTCAGTCAGTCAGTCA
>seq3
ATCAATCAGTCAATCG
ATCAATCAGTCAATCG"""
self.seqs = helpers.parse_fasta_list(aln_string)
self.aln = Alignment(self.seqs)
def test_manual_split(self):
columns = [1, 2, 3, 5]
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 28)
self.assertEqual(pos.get_alignment_length(), 4)
self.assertEqual(neg[:, 0], 'AAA')
self.assertEqual(neg[:, 11], 'AAG')
self.assertSeqsEqual(neg[0, :], 'ATCAGTCAGTCAGTCAGTCAGTCAGTCA')
self.assertEqual(pos[:, 1], 'TTT')
def test_splitting_final_col(self):
columns = [3, 7, 32]
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 29)
self.assertEqual(pos.get_alignment_length(), 3)
self.assertEqual(neg[:, 0], 'GGA')
self.assertEqual(neg[:, 12], 'CCC')
self.assertSeqsEqual(neg[0, :], 'GTAGTAGTCAGTCAGTCAGTCAGTCAGTC')
self.assertEqual(pos[:, 1], 'CCC')
self.assertEqual(pos[:, 2], 'AAG')
def test_splitting_on_no_hm(self):
columns = []
self.aln.split_hypermuts(hm_columns=columns)
neg, pos = self.aln.hm_neg_aln, self.aln.hm_pos_aln
self.assertEqual(neg.get_alignment_length(), 32)
self.assertEqual(pos.get_alignment_length(), 0)
self.assertEqual(neg[:, 0], 'GGA')
self.assertSeqsEqual(neg[0, :], 'GTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCA')
self.assertSeqsEqual(pos[0, :], '')
class TestContextBasedEvaluation(unittest.TestCase):
def setUp(self):
ref_seq = helpers.parse_fasta("""
>all
GGGGGGGGGTGTGTGTGT""")
self.aln = Alignment(helpers.parse_fasta("""
>seq1
GGGGGGGGGTGTGTGTGT
>seq2
AGAGAGAGGTGTGTGTGT
>seq3
GGGGGGGGGTATATATAT
""").values(), reference_sequence = ref_seq['all'])
def test_gg(self):
for result in self.aln.analyze(mut_pattern.GG):
hm_pos = result['hm_pos']
if result['sequence'] == 'seq2':
self.assertTrue(hm_pos)
else:
self.assertFalse(hm_pos)
class TestContextBasedEvaluation(unittest.TestCase):
def setUp(self):
ref_seq = helpers.parse_fasta("""
>all
GGGGGGGGGTGTGTGTGT""")
self.aln = Alignment(helpers.parse_fasta("""
>seq1
GGGGGGGGGTGTGTGTGT
>seq2
AGAGAGAGGTGTGTGTGT
>seq3
GGGGGGGGGTATATATAT
""").values(),
reference_sequence=ref_seq['all'])
def test_gg(self):
for result in self.aln.analyze(mut_pattern.GG):
hm_pos = result['hm_pos']
if result['sequence'] == 'seq2':
self.assertTrue(hm_pos)
else:
self.assertFalse(hm_pos)
def setUp(self):
ref_seq = Seq.Seq('GA'*9 + 'GC'*6 + 'GT'*15)
query_seq = 'AA'*4 + 'GA'*5 + 'AC' + 'GC'*5 + 'GT'*15
aln_string = '>seq1\n{0}'.format(query_seq)
self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq)
self.mutation_patterns=[mut_pattern.GA, mut_pattern.GM]
def analyze(args):
import logging
logging.captureWarnings(True)
# Fetch sequence records and analysis patterns
seq_records = SeqIO.to_dict(SeqIO.parse(args.alignment, 'fasta'))
patterns = [mut_pattern.patterns[p] for p in args.patterns]
pattern_names = [p.name for p in patterns]
prefix = path.join(args.out_dir, args.prefix)
analysis_settings = dict(rpr_cutoff=args.rpr_cutoff,
significance_level=args.significance_level,
quants=args.quants,
pos_quants_only=args.pos_quants_only,
caller=args.caller,
prior=args.prior,
cdfs=args.cdfs,
quadr_maxiter=args.quadr_maxiter,
optim_maxiter=args.optim_maxiter)
# Need to think about how best to fork things here; for instance, might make sense to let the user specify
# the initial clusters for whatever reason... However, specifying the reference sequences shouldn't make
# any sense there
if args.reference_sequences:
reference_sequences = SeqIO.to_dict(
SeqIO.parse(args.reference_sequences, 'fasta'))
else:
reference_sequences = None
# This lets the cluster map be optional, so that this script can be used
# for naive hm filtering/analysis
cluster_map = load_cluster_map(
args.cluster_map,
cluster_col=args.cluster_col) if args.cluster_map else None
alignments = AlignmentSet(seq_records,
cluster_map,
consensus_threshold=args.consensus_threshold,
reference_sequences=reference_sequences)
# Create the analysis generator
analysis = alignments.multiple_context_analysis(patterns,
**analysis_settings)
if args.cluster_threshold:
for hm_it in range(args.cluster_iterations - 1):
print " ..On hm/cluster iteration", hm_it
# Grab the HM columns from the most recent analysis and split out the pos sites
hm_columns = []
for result in analysis:
hm_columns += result['call']['mut_columns']
hm_neg_aln = Alignment(
seq_records.values()).split_hypermuts(hm_columns).hm_neg_aln
# Cluster with the specified settings
clustering = alnclst.Clustering(hm_neg_aln, args.cluster_threshold,
args.consensus_threshold)
clustering = clustering.recenter(args.recentering_iterations)
clustering.merge_small_clusters(args.min_per_cluster)
cluster_map = parse_clusters(clustering.mapping_iterator(),
cluster_key=0,
sequence_key=1)
# Create the Alignment set
clustered_alignment = AlignmentSet(
seq_records,
cluster_map,
consensus_threshold=args.consensus_threshold)
analysis = clustered_alignment.multiple_context_analysis(
patterns, **analysis_settings)
# write out the final clusters
clusterout_handle = file(prefix + '.clst.csv', 'w')
clustering.write(clusterout_handle)
if args.interactive:
local = copy.copy(locals())
import hyperfreq
local.update(
dict(hyperfreq=hyperfreq,
Alignment=Alignment,
AlignmentSet=AlignmentSet,
mut_pattern=mut_pattern,
write_analysis=write_analysis))
code.interact(local=local)
# Write the final analysis to file
write_analysis(analysis,
prefix,
pattern_names,
args.quants,
args.cdfs,
call_only=args.call_only)
if args.write_references:
write_reference_seqs(alignments, prefix)
# Closing files
args.alignment.close()
if args.cluster_map:
args.cluster_map.close()
