Python skbio.SequenceCollection类代码示例

    def test_k_word_frequencies(self):
        expected1 = defaultdict(float)
        expected1['A'] = 3 / 7.
        expected1['C'] = 1 / 7.
        expected1['G'] = 1 / 7.
        expected1['T'] = 2 / 7.
        expected2 = defaultdict(float)
        expected2['G'] = 1 / 3.
        expected2['T'] = 2 / 3.
        self.assertEqual(self.s1.k_word_frequencies(k=1),
                         [expected1, expected2])

        expected1 = defaultdict(float)
        expected1['GAT'] = 1 / 2.
        expected1['TAC'] = 1 / 2.
        expected2 = defaultdict(float)
        expected2['TTG'] = 1 / 1.
        self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
                         [expected1, expected2])

        self.assertEqual(self.empty.k_word_frequencies(k=1), [])

        # Test to ensure floating point precision bug isn't present. See the
        # tests for BiologicalSequence.k_word_frequencies for more details.
        sc = SequenceCollection([RNA('C' * 10, id='s1'),
                                 RNA('G' * 10, id='s2')])
        self.assertEqual(sc.k_word_frequencies(1),
                         [defaultdict(float, {'C': 1.0}),
                          defaultdict(float, {'G': 1.0})])

def extract_seq_ids(data, fmt='fasta', variant=None):
    """
    Given FASTQ-format data (string), parse out only the
    sequence IDs and return.
    """
    fh = StringIO(data)
    if fmt == 'fastq':
        sc = SequenceCollection.read(fh, format=fmt, variant=variant)
    else:
        sc = SequenceCollection.read(fh, format=fmt)
    return frozenset(entry.id for entry in sc)

    def test_degap(self):
        """degap functions as expected
        """
        expected = [(id_, seq.replace(".", "").replace("-", "")) for id_, seq in self.seqs1_t]
        expected = SequenceCollection.from_fasta_records(expected, DNASequence)
        actual = self.a1.degap()
        self.assertEqual(actual, expected)

        expected = [(id_, seq.replace(".", "").replace("-", "")) for id_, seq in self.seqs2_t]
        expected = SequenceCollection.from_fasta_records(expected, RNASequence)
        actual = self.a2.degap()
        self.assertEqual(actual, expected)

    def test_distances(self):
        s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
        expected = [[0, 0.25],
                    [0.25, 0]]
        expected = DistanceMatrix(expected, ['d1', 'd2'])
        actual = s1.distances(hamming)
        self.assertEqual(actual, expected)

        # alt distance function provided
        def dumb_distance(s1, s2):
            return 42.
        expected = [[0, 42.],
                    [42., 0]]
        expected = DistanceMatrix(expected, ['d1', 'd2'])
        actual = s1.distances(dumb_distance)
        self.assertEqual(actual, expected)

def convert_phylip(infile, outfile, format):
    seqs = SequenceCollection.read(
        infile, format='phylip',
        data_parser=phylip.relaxed_ids
    )

    seqs.write(outfile, format=format)

def main():
    args = handle_program_options()

    if osp.isfile(args.out_dir):
        print("--out_dir (-o) option must be a valid directory and not a file",
              file=sys.stderr)
        sys.exit(1)

    # will fail gracefully if dir exists
    skbu.create_dir(args.out_dir)

    metagenomes = []
    if args.metagenome_id is not None:
        metagenomes.append(args.metagenome_id)
    elif args.metagenome_file is not None:
        metagenomes.extend(parse_metagenome_file(args.metagenome_file))
        
    if args.verbose:
        msg = 'Processing requested for {} metagenome(s) found in: {}'
        print(msg.format(len(metagenomes), args.metagenome_file))

    # MG-RAST stage.file ids for downloading
    derep_passed = '150.1'
    screen_passed = '299.1'

    for mg_id in metagenomes:
        if args.verbose:
            print('Processing metagenome: {}'.format(mg_id))
            print('\tDownloading: Dereplication Passed...', end='')
            sys.stdout.flush()
        derepp_rsp = mgapi.mgrast_request('download', mg_id,
                                          {'file': derep_passed},
                                          auth_key=args.auth_key)
        derepp_sc = SequenceCollection.read(StringIO(derepp_rsp.text),
                                            format='fastq',
                                            variant='illumina1.8')
        if args.verbose:
            print('{} sequences'.format(len(derepp_sc)))
            print('\tDownloading: Screen Passed...', end='')
            sys.stdout.flush()
        screenp_rsp = mgapi.mgrast_request('download', mg_id,
                                           {'file': screen_passed},
                                           auth_key=args.auth_key)
        screenp_ids = extract_seq_ids(screenp_rsp.text, fmt='fastq',
                                      variant='illumina1.8')
        if args.verbose:
            print('{} sequences'.format(len(screenp_ids)))

        # filter dereplication passed with IDs from screen passed
        failed_screen = filter_seqs(derepp_sc, screenp_ids)
        if args.verbose:
            nsp = len(screenp_ids)
            print('\tRemoved {} sequences from Dereplication Passed'.format(nsp))
            print('\tleaving {} sequences'.format(len(failed_screen)))

        out_fp = osp.join(args.out_dir, mg_id + '_screen_failed.fastq')
        failed_screen.write(out_fp, format='fastq', variant='illumina1.8')
        if args.verbose:
            print('Sequence data written to: ' + out_fp)

 def test_make_mini_otu_files(self):
     os.system("mkdir tmp")
     self.extension_seqs = SequenceCollection.read(self.extension_seqs)
     result = _make_mini_otu_files(self.key_node,
                                   self.extension_genus_dic_few,
                                   self.extension_seqs)
     os.system("rm -r tmp")
     self.assertEqual(result, """>P1\nTTAAAAAA\n""")

    def setUp(self):
        self.d1 = DNA('GATTACA', metadata={'id': "d1"})
        self.d2 = DNA('TTG', metadata={'id': "d2"})
        self.d3 = DNA('GTATACA', metadata={'id': "d3"})
        self.r1 = RNA('GAUUACA', metadata={'id': "r1"})
        self.r2 = RNA('UUG', metadata={'id': "r2"})
        self.r3 = RNA('U-----UGCC--', metadata={'id': "r3"})

        self.seqs1 = [self.d1, self.d2]
        self.seqs2 = [self.r1, self.r2, self.r3]
        self.seqs3 = self.seqs1 + self.seqs2
        self.seqs4 = [self.d1, self.d3]

        self.s1 = SequenceCollection(self.seqs1)
        self.s2 = SequenceCollection(self.seqs2)
        self.s3 = SequenceCollection(self.seqs3)
        self.s4 = SequenceCollection(self.seqs4)
        self.empty = SequenceCollection([])

    def test_distances(self):
        s1 = SequenceCollection([DNA("ACGT", metadata={'id': "d1"}),
                                 DNA("ACGG", metadata={'id': "d2"})])
        expected = [[0, 0.25],
                    [0.25, 0]]
        expected = DistanceMatrix(expected, ['d1', 'd2'])

        def h(s1, s2):
            return hamming(s1.values, s2.values)
        actual = s1.distances(h)
        self.assertEqual(actual, expected)

        # alt distance function provided
        def dumb_distance(s1, s2):
            return 42.
        expected = [[0, 42.],
                    [42., 0]]
        expected = DistanceMatrix(expected, ['d1', 'd2'])
        actual = s1.distances(dumb_distance)
        self.assertEqual(actual, expected)

    def setUp(self):
        self.d1 = DNASequence('GATTACA', id="d1")
        self.d2 = DNASequence('TTG', id="d2")
        self.d3 = DNASequence('GTATACA', id="d3")
        self.d1_lower = DNASequence('gattaca', id="d1")
        self.d2_lower = DNASequence('ttg', id="d2")
        self.d3_lower = DNASequence('gtataca', id="d3")
        self.r1 = RNASequence('GAUUACA', id="r1")
        self.r2 = RNASequence('UUG', id="r2")
        self.r3 = RNASequence('U-----UGCC--', id="r3")

        self.i1 = DNASequence('GATXACA', id="i1")

        self.seqs1 = [self.d1, self.d2]
        self.seqs1_lower = [self.d1_lower, self.d2_lower]
        self.seqs2 = [self.r1, self.r2, self.r3]
        self.seqs3 = self.seqs1 + self.seqs2
        self.seqs4 = [self.d1, self.d3]

        self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
        self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
                        ('r3', 'U-----UGCC--')]
        self.seqs3_t = self.seqs1_t + self.seqs2_t

        self.s1 = SequenceCollection(self.seqs1)
        self.s1_lower = SequenceCollection(self.seqs1_lower)
        self.s2 = SequenceCollection(self.seqs2)
        self.s3 = SequenceCollection(self.seqs3)
        self.s4 = SequenceCollection(self.seqs4)
        self.empty = SequenceCollection([])

        self.invalid_s1 = SequenceCollection([self.i1])

    def setUp(self):
        """Initialize values to be used in tests
        """
        self.d1 = DNASequence("GATTACA", id="d1")
        self.d2 = DNASequence("TTG", id="d2")
        self.d1_lower = DNASequence("gattaca", id="d1")
        self.d2_lower = DNASequence("ttg", id="d2")
        self.r1 = RNASequence("GAUUACA", id="r1")
        self.r2 = RNASequence("UUG", id="r2")
        self.r3 = RNASequence("U-----UGCC--", id="r3")

        self.i1 = DNASequence("GATXACA", id="i1")

        self.seqs1 = [self.d1, self.d2]
        self.seqs1_lower = [self.d1_lower, self.d2_lower]
        self.seqs2 = [self.r1, self.r2, self.r3]
        self.seqs3 = self.seqs1 + self.seqs2

        self.seqs1_t = [("d1", "GATTACA"), ("d2", "TTG")]
        self.seqs2_t = [("r1", "GAUUACA"), ("r2", "UUG"), ("r3", "U-----UGCC--")]
        self.seqs3_t = self.seqs1_t + self.seqs2_t

        self.s1 = SequenceCollection(self.seqs1)
        self.s1_lower = SequenceCollection(self.seqs1_lower)
        self.s2 = SequenceCollection(self.seqs2)
        self.s3 = SequenceCollection(self.seqs3)
        self.empty = SequenceCollection([])

        self.invalid_s1 = SequenceCollection([self.i1])

    def test_kmer_frequencies(self):
        expected1 = Counter({'GAT': 1, 'TAC': 1})
        expected2 = Counter({'TTG': 1})
        self.assertEqual(
            self.s1.kmer_frequencies(k=3, overlap=False, relative=False),
            [expected1, expected2])

        expected1 = defaultdict(float)
        expected1['A'] = 3 / 7.
        expected1['C'] = 1 / 7.
        expected1['G'] = 1 / 7.
        expected1['T'] = 2 / 7.
        expected2 = defaultdict(float)
        expected2['G'] = 1 / 3.
        expected2['T'] = 2 / 3.
        self.assertEqual(self.s1.kmer_frequencies(k=1, relative=True),
                         [expected1, expected2])

        expected1 = defaultdict(float)
        expected1['GAT'] = 1 / 2.
        expected1['TAC'] = 1 / 2.
        expected2 = defaultdict(float)
        expected2['TTG'] = 1 / 1.
        self.assertEqual(
            self.s1.kmer_frequencies(k=3, overlap=False, relative=True),
            [expected1, expected2])

        self.assertEqual(self.empty.kmer_frequencies(k=1, relative=True), [])

        # Test to ensure floating point precision bug isn't present. See the
        # tests for Sequence.kmer_frequencies for more details.
        sc = SequenceCollection([RNA('C' * 10, metadata={'id': 's1'}),
                                 RNA('G' * 10, metadata={'id': 's2'})])
        self.assertEqual(sc.kmer_frequencies(1, relative=True),
                         [defaultdict(float, {'C': 1.0}),
                          defaultdict(float, {'G': 1.0})])

from qiime_default_reference import get_template_alignment, get_reference_sequences

from skbio import SequenceCollection

gapped_sequences = [(s.id, str(s)) for s in SequenceCollection.read(get_template_alignment())][:500]

sequences = [(s.id, str(s)) for s in SequenceCollection.read(get_reference_sequences())][:500]

motif_1 = "GGTGCAAGCCGGTGGAAACA"

 def test_from_fasta_records(self):
     """Initialization from list of tuples functions as expected
     """
     SequenceCollection.from_fasta_records(self.seqs1_t, DNASequence)
     SequenceCollection.from_fasta_records(self.seqs2_t, RNASequence)
     SequenceCollection.from_fasta_records(self.seqs3_t, NucleotideSequence)

class SequenceCollectionTests(TestCase):

    """Tests of the SequenceCollection class """

    def setUp(self):
        """Initialize values to be used in tests
        """
        self.d1 = DNASequence('GATTACA', id="d1")
        self.d2 = DNASequence('TTG', id="d2")
        self.d1_lower = DNASequence('gattaca', id="d1")
        self.d2_lower = DNASequence('ttg', id="d2")
        self.r1 = RNASequence('GAUUACA', id="r1")
        self.r2 = RNASequence('UUG', id="r2")
        self.r3 = RNASequence('U-----UGCC--', id="r3")

        self.i1 = DNASequence('GATXACA', id="i1")

        self.seqs1 = [self.d1, self.d2]
        self.seqs1_lower = [self.d1_lower, self.d2_lower]
        self.seqs2 = [self.r1, self.r2, self.r3]
        self.seqs3 = self.seqs1 + self.seqs2

        self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
        self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
                        ('r3', 'U-----UGCC--')]
        self.seqs3_t = self.seqs1_t + self.seqs2_t

        self.s1 = SequenceCollection(self.seqs1)
        self.s1_lower = SequenceCollection(self.seqs1_lower)
        self.s2 = SequenceCollection(self.seqs2)
        self.s3 = SequenceCollection(self.seqs3)
        self.empty = SequenceCollection([])

        self.invalid_s1 = SequenceCollection([self.i1])

    def test_init(self):
        """Initialization functions as expected with varied input types
        """
        SequenceCollection(self.seqs1)
        SequenceCollection(self.seqs2)
        SequenceCollection(self.seqs3)
        SequenceCollection([])

    def test_init_fail(self):
        """initialization with sequences with overlapping ids fails
        """
        s1 = [self.d1, self.d1]
        self.assertRaises(SequenceCollectionError, SequenceCollection, s1)

    def test_init_validate(self):
        """initialization with validation functions as expected
        """
        SequenceCollection(self.seqs1, validate=True)
        SequenceCollection(self.seqs1, validate=True)
        # can't validate self.seqs2 as a DNASequence
        self.assertRaises(SequenceCollectionError, SequenceCollection,
                          self.invalid_s1, validate=True)

    def test_from_fasta_records(self):
        """Initialization from list of tuples functions as expected
        """
        SequenceCollection.from_fasta_records(self.seqs1_t, DNASequence)
        SequenceCollection.from_fasta_records(self.seqs2_t, RNASequence)
        SequenceCollection.from_fasta_records(self.seqs3_t, NucleotideSequence)

    def test_contains(self):
        """in operator functions as expected
        """
        self.assertTrue('d1' in self.s1)
        self.assertTrue('r2' in self.s2)
        self.assertFalse('r2' in self.s1)

    def test_eq(self):
        """equality operator functions as expected
        """
        self.assertTrue(self.s1 == self.s1)
        self.assertFalse(self.s1 == self.s2)

        # different objects can be equal
        self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
        self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)

        # SequenceCollections with different number of sequences are not equal
        self.assertFalse(self.s1 == SequenceCollection([self.d1]))

        class FakeSequenceCollection(SequenceCollection):
            pass
        # SequenceCollections of different types are not equal
        self.assertFalse(self.s1 == FakeSequenceCollection([self.d1, self.d2]))
        self.assertFalse(self.s1 == Alignment([self.d1, self.d2]))

        # SequenceCollections with different sequences are not equal
        self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))

    def test_getitem(self):
        """getitem functions as expected
        """
        self.assertEqual(self.s1[0], self.d1)
        self.assertEqual(self.s1[1], self.d2)
        self.assertEqual(self.s2[0], self.r1)
#.........这里部分代码省略.........