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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
import io
from unittest import TestCase, main
from skbio import DistanceMatrix
from skbio.io import LSMatFormatError
from skbio.io.format.lsmat import (
_lsmat_to_dissimilarity_matrix, _lsmat_to_distance_matrix,
_dissimilarity_matrix_to_lsmat, _distance_matrix_to_lsmat, _lsmat_sniffer)
from skbio.stats.distance import DissimilarityMatrix, DistanceMatrixError
class LSMatTestData(TestCase):
def setUp(self):
self.lsmat_1x1_fh = io.StringIO(LSMat_1x1)
self.lsmat_2x2_fh = io.StringIO(LSMat_2x2)
self.lsmat_2x2_asym_fh = io.StringIO(LSMat_2x2_ASYM)
self.lsmat_3x3_fh = io.StringIO(LSMat_3x3)
self.lsmat_3x3_whitespace_fh = io.StringIO(LSMat_3x3_WHITESPACE)
self.lsmat_3x3_csv_fh = io.StringIO(LSMat_3x3_CSV)
self.valid_fhs = [
self.lsmat_1x1_fh,
self.lsmat_2x2_fh,
self.lsmat_2x2_asym_fh,
self.lsmat_3x3_fh,
self.lsmat_3x3_whitespace_fh
]
self.empty_fh = io.StringIO()
self.invalid_1_fh = io.StringIO(INVALID_1)
self.invalid_2_fh = io.StringIO(INVALID_2)
self.invalid_3_fh = io.StringIO(INVALID_3)
self.invalid_4_fh = io.StringIO(INVALID_4)
self.invalid_5_fh = io.StringIO(INVALID_5)
self.invalid_6_fh = io.StringIO(INVALID_6)
self.invalid_fhs = [
(self.empty_fh, 'empty'),
(self.invalid_1_fh, '1 value\(s\).*2.*\(2\)'),
(self.invalid_2_fh, "'b'.*'a'"),
(self.invalid_3_fh, 'extra row\(s\)'),
(self.invalid_4_fh, '2 row\(s\).*found 1'),
(self.invalid_5_fh, '2 row\(s\).*found 0'),
(self.invalid_6_fh, r"delimiter '\\t'")
]
class DissimilarityAndDistanceMatrixReaderWriterTests(LSMatTestData):
def setUp(self):
super(DissimilarityAndDistanceMatrixReaderWriterTests, self).setUp()
self.lsmat_1x1_data = [[0.0]]
self.lsmat_2x2_data = [[0.0, 0.123], [0.123, 0.0]]
self.lsmat_2x2_asym_data = [[0.0, 1.0], [-2.0, 0.0]]
self.lsmat_3x3_data = [[0.0, 0.01, 4.2], [0.01, 0.0, 12.0],
[4.2, 12.0, 0.0]]
# We repeat the 3x3 example because there are two file format
# representations of it, one that is messy and one that is not. Both
# should be read into an equivalent object and written to an equivalent
# format though, which is why we duplicate the 3x3 objects and strings.
self.dissim_objs = [
DissimilarityMatrix(self.lsmat_1x1_data, ['a']),
DissimilarityMatrix(self.lsmat_2x2_data, ['a', 'b']),
DissimilarityMatrix(self.lsmat_2x2_asym_data, ['a', 'b']),
DissimilarityMatrix(self.lsmat_3x3_data, ['a', 'b', 'c']),
DissimilarityMatrix(self.lsmat_3x3_data, ['a', 'b', 'c'])
]
self.dissim_strs = [LSMat_1x1, LSMat_2x2, LSMat_2x2_ASYM, LSMat_3x3,
LSMat_3x3]
self.dissim_fhs = [self.lsmat_1x1_fh, self.lsmat_2x2_fh,
self.lsmat_2x2_asym_fh, self.lsmat_3x3_fh,
self.lsmat_3x3_whitespace_fh]
self.dist_objs = [
DistanceMatrix(self.lsmat_1x1_data, ['a']),
DistanceMatrix(self.lsmat_2x2_data, ['a', 'b']),
DistanceMatrix(self.lsmat_3x3_data, ['a', 'b', 'c']),
DistanceMatrix(self.lsmat_3x3_data, ['a', 'b', 'c'])
]
self.dist_strs = [LSMat_1x1, LSMat_2x2, LSMat_3x3, LSMat_3x3]
self.dist_fhs = [self.lsmat_1x1_fh, self.lsmat_2x2_fh,
self.lsmat_3x3_fh, self.lsmat_3x3_whitespace_fh]
def test_read_valid_files(self):
for fn, cls, objs, fhs in ((_lsmat_to_dissimilarity_matrix,
DissimilarityMatrix, self.dissim_objs,
self.dissim_fhs),
(_lsmat_to_distance_matrix, DistanceMatrix,
self.dist_objs, self.dist_fhs)):
for fh, obj in zip(fhs, objs):
fh.seek(0)
obs = fn(fh)
self.assertEqual(obs, obj)
self.assertIsInstance(obs, cls)
# Above files are TSV (default delimiter). Test that CSV works too.
for fn, cls in ((_lsmat_to_dissimilarity_matrix, DissimilarityMatrix),
(_lsmat_to_distance_matrix, DistanceMatrix)):
exp = cls(self.lsmat_3x3_data, ['a', 'b', 'c'])
self.lsmat_3x3_csv_fh.seek(0)
obs = fn(self.lsmat_3x3_csv_fh, delimiter=',')
self.assertEqual(obs, exp)
self.assertIsInstance(obs, cls)
def test_read_invalid_files(self):
for fn in _lsmat_to_dissimilarity_matrix, _lsmat_to_distance_matrix:
for invalid_fh, error_msg_regexp in self.invalid_fhs:
with self.assertRaisesRegex(LSMatFormatError,
error_msg_regexp):
invalid_fh.seek(0)
fn(invalid_fh)
# Asymmetric data only raises an error for DistanceMatrix.
with self.assertRaises(DistanceMatrixError):
_lsmat_to_distance_matrix(self.lsmat_2x2_asym_fh)
def test_write(self):
for fn, objs, strs in ((_dissimilarity_matrix_to_lsmat,
self.dissim_objs, self.dissim_strs),
(_distance_matrix_to_lsmat, self.dist_objs,
self.dist_strs)):
for obj, str_ in zip(objs, strs):
fh = io.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_lsmat, DissimilarityMatrix),
(_distance_matrix_to_lsmat, DistanceMatrix)):
obj = cls(self.lsmat_3x3_data, ['a', 'b', 'c'])
fh = io.StringIO()
fn(obj, fh, delimiter=',')
obs = fh.getvalue()
fh.close()
self.assertEqual(obs, LSMat_3x3_CSV)
def test_roundtrip_read_write(self):
for reader_fn, writer_fn, fhs in ((_lsmat_to_dissimilarity_matrix,
_dissimilarity_matrix_to_lsmat,
self.dissim_fhs),
(_lsmat_to_distance_matrix,
_distance_matrix_to_lsmat,
self.dist_fhs)):
for fh in fhs:
# Read.
fh.seek(0)
lsmat1 = reader_fn(fh)
# Write.
out_fh = io.StringIO()
writer_fn(lsmat1, out_fh)
out_fh.seek(0)
# Read.
lsmat2 = reader_fn(out_fh)
out_fh.close()
self.assertEqual(lsmat1, lsmat2)
class SnifferTests(LSMatTestData):
def setUp(self):
super(SnifferTests, self).setUp()
def test_match_tsv(self):
# Sniffer should match all valid files, and will match some invalid
# ones too because it doesn't exhaustively check the entire file.
fhs = self.valid_fhs + [self.invalid_1_fh, self.invalid_3_fh,
self.invalid_4_fh]
for fh in fhs:
self.assertEqual(_lsmat_sniffer(fh), (True, {'delimiter': '\t'}))
def test_match_csv(self):
self.assertEqual(_lsmat_sniffer(self.lsmat_3x3_csv_fh),
(True, {'delimiter': ','}))
def test_no_match(self):
for fh in (self.empty_fh, self.invalid_2_fh, self.invalid_5_fh,
self.invalid_6_fh):
self.assertEqual(_lsmat_sniffer(fh), (False, {}))
LSMat_1x1 = "\ta\na\t0.0\n"
LSMat_2x2 = "\ta\tb\na\t0.0\t0.123\nb\t0.123\t0.0\n"
LSMat_2x2_ASYM = "\ta\tb\na\t0.0\t1.0\nb\t-2.0\t0.0\n"
LSMat_3x3 = ("\ta\tb\tc\na\t0.0\t0.01\t4.2\nb\t0.01\t0.0\t12.0\nc\t4.2\t12.0\t"
"0.0\n")
# Extra whitespace-only lines throughout. Also has comments before the header.
LSMat_3x3_WHITESPACE = '\n'.join(['# foo',
' \t \t ',
' #bar',
'',
'',
'\ta\t b \tc',
'a \t0.0\t0.01\t4.2',
' \t',
'b\t0.01\t0.0\t12.0',
'',
'\t \t',
'',
'c\t4.2\t12.0\t0.0',
'',
' \t ',
'\t\t\t',
' '])
# Same matrix as above, but delimited by commas instead of tabs.
LSMat_3x3_CSV = ",a,b,c\na,0.0,0.01,4.2\nb,0.01,0.0,12.0\nc,4.2,12.0,0.0\n"
# missing data
INVALID_1 = '\ta\tb\na\t0\t1\nb\t1'
# mismatched IDs
INVALID_2 = '\ta\tb\nb\t0\t1\na\t1\t0'
# extra data lines
INVALID_3 = '\ta\tb\na\t0\t1\nb\t1\t0\n \nfoo\n\n\n'
# missing data lines
INVALID_4 = '\ta\tb\na\t0\t1\n \n'
# no data lines
INVALID_5 = '\ta\tb\n'
# missing leading delimiter in header
INVALID_6 = "a\tb\na\t0.0\t0.123\nb\t0.123\t0.0\n"
if __name__ == '__main__':
main()
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