def test_deprecated_io(self):
fh = StringIO()
npt.assert_warns(UserWarning, self.dm_3x3.to_file, fh)
fh.seek(0)
deserialized = npt.assert_warns(UserWarning,
DissimilarityMatrix.from_file, fh)
self.assertEqual(deserialized, self.dm_3x3)
self.assertTrue(type(deserialized) == DissimilarityMatrix)
def test_deprecated_io(self):
fh = StringIO()
npt.assert_warns(UserWarning, self.ordination_results.to_file, fh)
fh.seek(0)
deserialized = npt.assert_warns(UserWarning,
OrdinationResults.from_file, fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
def test_io(self):
# Very basic check that read/write public API is present and appears to
# be functioning. Roundtrip from memory -> disk -> memory and ensure
# results match.
fh = StringIO()
self.dm_3x3.write(fh)
fh.seek(0)
deserialized = DissimilarityMatrix.read(fh)
self.assertEqual(deserialized, self.dm_3x3)
self.assertTrue(type(deserialized) == DissimilarityMatrix)
def test_io(self):
# Very basic check that read/write public API is present and appears to
# be functioning. Roundtrip from memory -> disk -> memory and ensure
# results match.
fh = StringIO()
self.ordination_results.write(fh)
fh.seek(0)
deserialized = OrdinationResults.read(fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
def test_write(self):
for fp, obj in zip(self.valid_fps, self.ordination_results_objs):
fh = StringIO()
_ordination_results_to_ordres(obj, fh)
obs = fh.getvalue()
fh.close()
with open(fp, 'U') as fh:
exp = fh.read()
npt.assert_equal(obs, exp)
def test_make_study_from_cmd(self):
fh = StringIO(self.config1)
load_study_from_cmd('*****@*****.**', 'newstudy', fh)
sql = ("select study_id from qiita.study where email = %s and "
"study_title = %s")
study_id = self.conn_handler.execute_fetchone(sql, ('*****@*****.**',
'newstudy'))
self.assertTrue(study_id is not None)
fh2 = StringIO(self.config2)
with self.assertRaises(configparser.NoOptionError):
load_study_from_cmd('*****@*****.**', 'newstudy2', fh2)
def test_round_trip_read_write(self):
"""Test reading, writing, and reading again works as expected."""
for dm_f in self.dm_fs:
# Read.
dm1 = DissimilarityMatrix.from_file(dm_f)
# Write.
out_f = StringIO()
dm1.to_file(out_f)
out_f.seek(0)
# Read.
dm2 = DissimilarityMatrix.from_file(out_f)
self.assertEqual(dm1, dm2)
def test_round_trip_read_write(self):
"""Test reading, writing, and reading again works as expected."""
for dm_f in self.dm_fs:
# Read.
dm1 = DissimilarityMatrix.from_file(dm_f)
# Write.
out_f = StringIO()
dm1.to_file(out_f)
out_f.seek(0)
# Read.
dm2 = DissimilarityMatrix.from_file(out_f)
self.assertEqual(dm1, dm2)
def setUp(self):
self.dm = DistanceMatrix(
[[0.0, 1.0, 2.0], [1.0, 0.0, 3.0], [2.0, 3.0, 0.0]],
['a', 'b', 'c'])
self.grouping = [1, 2, 1]
# Ordering of IDs shouldn't matter, nor should extra IDs.
self.df = pd.read_csv(
StringIO('ID,Group\nb,Group1\na,Group2\nc,Group1\nd,Group3'),
index_col=0)
self.df_missing_id = pd.read_csv(
StringIO('ID,Group\nb,Group1\nc,Group1'), index_col=0)
self.categorical_stats = CategoricalStats(self.dm, self.grouping)
self.categorical_stats_from_df = CategoricalStats(self.dm,
self.df,
column='Group')
def test_roundtrip_read_write(self):
for fp in self.valid_fps:
# Read.
obj1 = _ordres_to_ordination_results(fp)
# Write.
fh = StringIO()
_ordination_results_to_ordres(obj1, fh)
fh.seek(0)
# Read.
obj2 = _ordres_to_ordination_results(fh)
fh.close()
assert_ordination_results_equal(obj1, obj2)
def test_to_file(self):
"""Should serialize a DissimilarityMatrix to file."""
for dm_f_line, dm in zip(self.dm_f_lines, self.dms):
for file_type in ('file like', 'file name'):
if file_type == 'file like':
obs_f = StringIO()
dm.to_file(obs_f)
obs = obs_f.getvalue()
obs_f.close()
elif file_type == 'file name':
with tempfile.NamedTemporaryFile('r+') as temp_file:
dm.to_file(temp_file.name)
temp_file.flush()
temp_file.seek(0)
obs = temp_file.read()
self.assertEqual(obs, dm_f_line)
def test_to_file(self):
"""Should serialize a DissimilarityMatrix to file."""
for dm_f_line, dm in zip(self.dm_f_lines, self.dms):
for file_type in ('file like', 'file name'):
if file_type == 'file like':
obs_f = StringIO()
dm.to_file(obs_f)
obs = obs_f.getvalue()
obs_f.close()
elif file_type == 'file name':
with tempfile.NamedTemporaryFile('r+') as temp_file:
dm.to_file(temp_file.name)
temp_file.flush()
temp_file.seek(0)
obs = temp_file.read()
self.assertEqual(obs, dm_f_line)
def setUp(self):
# Distance matrices with and without ties in the ranks, with 2 groups
# of equal size.
dm_ids = ['s1', 's2', 's3', 's4']
grouping_equal = ['Control', 'Control', 'Fast', 'Fast']
df = pd.read_csv(StringIO(
'ID,Group\ns2,Control\ns3,Fast\ns4,Fast\ns5,Control\n'
's1,Control'),
index_col=0)
self.dm_ties = DistanceMatrix(
[[0, 1, 1, 4], [1, 0, 3, 2], [1, 3, 0, 3], [4, 2, 3, 0]], dm_ids)
self.dm_no_ties = DistanceMatrix(
[[0, 1, 5, 4], [1, 0, 3, 2], [5, 3, 0, 3], [4, 2, 3, 0]], dm_ids)
# Test with 3 groups of unequal size.
grouping_unequal = [
'Control', 'Treatment1', 'Treatment2', 'Treatment1', 'Control',
'Control'
]
self.dm_unequal = DistanceMatrix(
[[0.0, 1.0, 0.1, 0.5678, 1.0, 1.0],
[1.0, 0.0, 0.002, 0.42, 0.998, 0.0],
[0.1, 0.002, 0.0, 1.0, 0.123, 1.0],
[0.5678, 0.42, 1.0, 0.0, 0.123, 0.43],
[1.0, 0.998, 0.123, 0.123, 0.0, 0.5],
[1.0, 0.0, 1.0, 0.43, 0.5, 0.0]],
['s1', 's2', 's3', 's4', 's5', 's6'])
self.permanova_ties = PERMANOVA(self.dm_ties, grouping_equal)
self.permanova_no_ties = PERMANOVA(self.dm_no_ties, grouping_equal)
self.permanova_ties_df = PERMANOVA(self.dm_ties, df, column='Group')
self.permanova_unequal = PERMANOVA(self.dm_unequal, grouping_unequal)
def test_to_file(self):
for scores, test_path in zip(self.scores, self.test_paths):
for file_type in ('file like', 'file name'):
if file_type == 'file like':
obs_f = StringIO()
scores.to_file(obs_f)
obs = obs_f.getvalue()
obs_f.close()
elif file_type == 'file name':
with tempfile.NamedTemporaryFile('r+') as temp_file:
scores.to_file(temp_file.name)
temp_file.flush()
temp_file.seek(0)
obs = temp_file.read()
with open(get_data_path(test_path), 'U') as f:
exp = f.read()
yield npt.assert_equal, obs, exp
def test_to_file(self):
for scores, test_path in zip(self.scores, self.test_paths):
for file_type in ('file like', 'file name'):
if file_type == 'file like':
obs_f = StringIO()
scores.to_file(obs_f)
obs = obs_f.getvalue()
obs_f.close()
elif file_type == 'file name':
with tempfile.NamedTemporaryFile('r+') as temp_file:
scores.to_file(temp_file.name)
temp_file.flush()
temp_file.seek(0)
obs = temp_file.read()
with open(get_data_path(test_path), 'U') as f:
exp = f.read()
yield npt.assert_equal, obs, exp
def test_roundtrip_read_write(self):
for reader_fn, writer_fn, fhs in ((_dm_to_dissimilarity_matrix,
_dissimilarity_matrix_to_dm,
self.dissim_fhs),
(_dm_to_distance_matrix,
_distance_matrix_to_dm,
self.dist_fhs)):
for fh in fhs:
# Read.
dm1 = reader_fn(fh)
# Write.
out_fh = StringIO()
writer_fn(dm1, out_fh)
out_fh.seek(0)
# Read.
dm2 = reader_fn(out_fh)
out_fh.close()
self.assertEqual(dm1, dm2)
def summary(self, delimiter='\t'):
"""Return a formatted summary of results as a string.
The string is formatted as delimited text.
Parameters
----------
delimiter : str, optional
String to delimit fields by in formatted output. Default is tab
(TSV).
Returns
-------
str
Delimited-text summary of results.
"""
summary = StringIO()
csv_writer = csv.writer(summary, delimiter=delimiter,
lineterminator='\n')
csv_writer.writerow(self._format_header())
csv_writer.writerow(self._format_data())
return summary.getvalue()
def summary(self, delimiter='\t'):
"""Return a formatted summary of results as a string.
The string is formatted as delimited text.
Parameters
----------
delimiter : str, optional
String to delimit fields by in formatted output. Default is tab
(TSV).
Returns
-------
str
Delimited-text summary of results.
"""
summary = StringIO()
csv_writer = csv.writer(summary, delimiter=delimiter,
lineterminator='\n')
csv_writer.writerow(self._format_header())
csv_writer.writerow(self._format_data())
return summary.getvalue()
def test_write(self):
for fn, objs, strs in ((_dissimilarity_matrix_to_dm, self.dissim_objs,
self.dissim_strs),
(_distance_matrix_to_dm, self.dist_objs,
self.dist_strs)):
for obj, str_ in zip(objs, strs):
fh = StringIO()
fn(obj, fh)
obs = fh.getvalue()
fh.close()
self.assertEqual(obs, str_)
# Test writing CSV (TSV is written above).
for fn, cls in ((_dissimilarity_matrix_to_dm, DissimilarityMatrix),
(_distance_matrix_to_dm, DistanceMatrix)):
obj = cls(self.dm_3x3_data, ['a', 'b', 'c'])
fh = StringIO()
fn(obj, fh, delimiter=',')
obs = fh.getvalue()
fh.close()
self.assertEqual(obs, DM_3x3_CSV)
def setUp(self):
self.qseqs = [StringIO(qseq1), StringIO(qseq2)]
def setUp(self):
self.fastas = [StringIO(fasta1), StringIO(fasta2), StringIO(fasta3)]
self.quals = [StringIO(qual1), StringIO(qual2), StringIO(qual3)]
self.bad_qual_val = [StringIO(qual1), StringIO(qual_bad_val),
StringIO(qual3)]
self.bad_qual_id = [StringIO(qual1), StringIO(qual_bad_id),
StringIO(qual3)]
def test_from_file(self):
"""Parse a tree from a file"""
t_io = StringIO("((a,b)c,(d,e)f)g;")
t = TreeNode.from_file(t_io)
self.assertEqual(list('abcdefg'), [n.name for n in t.postorder()])
def setUp(self):
self.fastqs = [StringIO(fastq1), StringIO(fastq2)]
def setUp(self):
self.bad_dm_fp = get_data_path('bad_dm.txt')
self.dm_2x2_asym_fp = get_data_path('dm_2x2_asym.txt')
self.dm_3x3_fp = get_data_path('dm_3x3.txt')
fd = open(self.bad_dm_fp, 'U')
self.bad_dm_f2_lines = ''.join(fd.readlines())
fd.close()
fd = open(self.dm_2x2_asym_fp, 'U')
self.dm_2x2_asym_lines = ''.join(fd.readlines())
fd.close()
fd = open(self.dm_3x3_fp, 'U')
self.dm_3x3_lines = ''.join(fd.readlines())
fd.close()
self.dm_1x1_data = [[0.0]]
self.dm_1x1_f = StringIO(DM_1x1_F)
self.dm_2x2_data = [[0.0, 0.123], [0.123, 0.0]]
self.dm_2x2_f = StringIO(DM_2x2_F)
self.dm_2x2_asym_data = [[0.0, 1.0], [-2.0, 0.0]]
self.dm_2x2_asym_f = StringIO(self.dm_2x2_asym_lines)
self.dm_3x3_data = [[0.0, 0.01, 4.2], [0.01, 0.0, 12.0],
[4.2, 12.0, 0.0]]
self.dm_3x3_f = StringIO(self.dm_3x3_lines)
self.dm_3x3_whitespace_f = StringIO('\n'.join(DM_3x3_WHITESPACE_F))
self.bad_dm_f1 = StringIO(BAD_DM_F1)
self.bad_dm_f2 = StringIO(self.bad_dm_f2_lines)
self.bad_dm_f3 = StringIO(BAD_DM_F3)
self.bad_dm_f4 = StringIO(BAD_DM_F4)
self.bad_dm_f5 = StringIO(BAD_DM_F5)
self.bad_dm_f6 = StringIO(BAD_DM_F6)
def test_is_string_or_bytes(self):
self.assertTrue(_is_string_or_bytes('foo'))
self.assertTrue(_is_string_or_bytes(u'foo'))
self.assertTrue(_is_string_or_bytes(b'foo'))
self.assertFalse(_is_string_or_bytes(StringIO('bar')))
self.assertFalse(_is_string_or_bytes([1]))
def test_load_prep_template_from_cmd(self):
"""Correctly adds a sample template to the DB"""
fh = StringIO(self.pt_contents)
st = load_prep_template_from_cmd(fh, self.raw_data.id)
self.assertEqual(st.id, self.raw_data.id)
def test_StringIO(self):
"""StringIO (useful e.g. for testing) slips through."""
f = StringIO("File contents")
with open_file(f) as fh:
self.assertTrue(fh is f)
