本文整理汇总了Python中skbio.util.get_data_path函数的典型用法代码示例。如果您正苦于以下问题：Python get_data_path函数的具体用法？Python get_data_path怎么用？Python get_data_path使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了get_data_path函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_any_sequence_to_fasta
def test_any_sequence_to_fasta(self):
# store writer function, sequence object to write, expected
# fasta filepath for default parameters, expected fasta filepath for
# non-defaults, and expected qual filepath for non-defaults
id_ = 'f o o'
desc = 'b\na\nr'
test_data = (
(_biological_sequence_to_fasta,
Sequence('ACGT', id=id_, description=desc,
quality=range(1, 5)),
('fasta_single_bio_seq_defaults',
'fasta_single_bio_seq_non_defaults',
'qual_single_bio_seq_non_defaults')),
(_dna_sequence_to_fasta,
DNA('TACG', id=id_, description=desc, quality=range(4)),
('fasta_single_dna_seq_defaults',
'fasta_single_dna_seq_non_defaults',
'qual_single_dna_seq_non_defaults')),
(_rna_sequence_to_fasta,
RNA('UACG', id=id_, description=desc, quality=range(2, 6)),
('fasta_single_rna_seq_defaults',
'fasta_single_rna_seq_non_defaults',
'qual_single_rna_seq_non_defaults')),
(_protein_sequence_to_fasta,
Protein('PQQ', id=id_, description=desc, quality=[42, 41, 40]),
('fasta_single_prot_seq_defaults',
'fasta_single_prot_seq_non_defaults',
'qual_single_prot_seq_non_defaults')))
for fn, obj, fps in test_data:
defaults_fp, non_defaults_fasta_fp, non_defaults_qual_fp = fps
# test writing with default parameters
fh = StringIO()
fn(obj, fh)
obs = fh.getvalue()
fh.close()
with open(get_data_path(defaults_fp), 'U') as fh:
exp = fh.read()
self.assertEqual(obs, exp)
# test writing with non-defaults
fasta_fh = StringIO()
qual_fh = StringIO()
fn(obj, fasta_fh, id_whitespace_replacement='-',
description_newline_replacement='_', max_width=1, qual=qual_fh)
obs_fasta = fasta_fh.getvalue()
obs_qual = qual_fh.getvalue()
fasta_fh.close()
qual_fh.close()
with open(get_data_path(non_defaults_fasta_fp), 'U') as fh:
exp_fasta = fh.read()
with open(get_data_path(non_defaults_qual_fp), 'U') as fh:
exp_qual = fh.read()
self.assertEqual(obs_fasta, exp_fasta)
self.assertEqual(obs_qual, exp_qual)示例2: test_any_sequences_to_fasta
def test_any_sequences_to_fasta(self):
for fn, obj in ((_sequence_collection_to_fasta, self.seq_coll),
(_alignment_to_fasta, self.align)):
# test writing with default parameters
fh = StringIO()
fn(obj, fh)
obs = fh.getvalue()
fh.close()
with open(get_data_path('fasta_3_seqs_defaults'), 'U') as fh:
exp = fh.read()
self.assertEqual(obs, exp)
# test writing with non-defaults
fasta_fh = StringIO()
qual_fh = StringIO()
fn(obj, fasta_fh, id_whitespace_replacement='*',
description_newline_replacement='+', max_width=3, qual=qual_fh)
obs_fasta = fasta_fh.getvalue()
obs_qual = qual_fh.getvalue()
fasta_fh.close()
qual_fh.close()
with open(get_data_path('fasta_3_seqs_non_defaults'), 'U') as fh:
exp_fasta = fh.read()
with open(get_data_path('qual_3_seqs_non_defaults'), 'U') as fh:
exp_qual = fh.read()
self.assertEqual(obs_fasta, exp_fasta)
self.assertEqual(obs_qual, exp_qual)示例3: setUp
def setUp(self):
self.multi_fp = get_data_path('ecoli_multi.sam')
self.single_fp = get_data_path('ecoli_single.sam')
self.single_exp = \
('MANLSGYNFAYLDEQTKRMIRRAILKAVAIPGYQVPFGGREMP'
'MPYGWGTGGIQLTASVIGESDVLKVIDQGADDTTNAVSIRNFF'
'KRVTGVNTTERTDDATLIQTRHRIPETPLTEDQIIIFQVPIPE'
'PLRFIEPRETETRTMHALEEYGVMQVKLYEDIARFGHIATTYA'
'YPVKVNGRYVMDPSPIPKFDNPKMDMMPALQLFGAGREKRIYA'
'VPPFTHVESLDFDDHPFTVQQWDEPCAICGSTHSYLDEVVLDD'
'AGNRMFVCSDTDYCRQQNEAKSQ', {
'@HD': 'VN:1.5\tSO:query',
'@PG': 'PN:DIAMOND',
'@mm': 'BlastX',
'QNAME': 'WP_000002278.1',
'FLAG': 0,
'RNAME': 'UniRef100_P16688',
'POS': 1,
'MAPQ': 255,
'CIGAR': '281M',
'RNEXT': '*',
'PNEXT': 0,
'TLEN': 0,
'QUAL': '*',
'AS': 573,
'NM': 3,
'ZR': 1477,
'ZE': 5.9e-164,
'ZI': 98,
'ZL': 281,
'ZF': 1,
'ZS': 1,
'MD': '102V117R54S5'
})
self.header_fp = get_data_path('header.sam')示例4: setUp
def setUp(self):
self.methods = ('pearson', 'spearman')
self.alternatives = ('two-sided', 'greater', 'less')
# Small dataset of minimal size (3x3). Mix of floats and ints in a
# native Python nested list structure.
self.minx = [[0, 1, 2], [1, 0, 3], [2, 3, 0]]
self.miny = [[0, 2, 7], [2, 0, 6], [7, 6, 0]]
self.minz = [[0, 0.5, 0.25], [0.5, 0, 0.1], [0.25, 0.1, 0]]
# No variation in distances. Taken from Figure 10.20(b), pg. 603 in L&L
# 3rd edition. Their example is 4x4 but using 3x3 here for easy
# comparison to the minimal dataset above.
self.no_variation = [[0, 0.667, 0.667],
[0.667, 0, 0.667],
[0.667, 0.667, 0]]
# This second dataset is derived from vegan::mantel's example dataset.
# The "veg" distance matrix contains Bray-Curtis distances derived from
# the varespec data (named "veg.dist" in the example). The "env"
# distance matrix contains Euclidean distances derived from scaled
# varechem data (named "env.dist" in the example).
self.veg_dm_vegan = np.loadtxt(
get_data_path('mantel_veg_dm_vegan.txt'))
self.env_dm_vegan = np.loadtxt(
get_data_path('mantel_env_dm_vegan.txt'))
# Expected test statistic when comparing x and y with method='pearson'.
self.exp_x_vs_y = 0.7559289
# Expected test statistic when comparing x and z with method='pearson'.
self.exp_x_vs_z = -0.9897433示例5: setUp
def setUp(self):
self.table1 = np.array(
[[1, 3, 0, 1, 0],
[0, 2, 0, 4, 4],
[0, 0, 6, 2, 1],
[0, 0, 1, 1, 1],
[5, 3, 5, 0, 0],
[0, 0, 0, 3, 5]])
self.sids1 = list('ABCDEF')
self.oids1 = ['OTU%d' % i for i in range(1, 6)]
self.t1 = TreeNode.read(
StringIO(u'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
u'0.75,OTU5:0.75):1.25):0.0)root;'))
self.t1_w_extra_tips = TreeNode.read(
StringIO(u'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
u'0.75,(OTU5:0.25,(OTU6:0.5,OTU7:0.5):0.5):0.5):1.25):0.0'
u')root;'))
self.t2 = TreeNode.read(
StringIO(u'((OTU1:0.1, OTU2:0.2):0.3, (OTU3:0.5, OTU4:0.7):1.1)'
u'root;'))
self.oids2 = ['OTU%d' % i for i in range(1, 5)]
# the following table and tree are derived from the QIIME 1.9.1
# "tiny-test" data
tt_table_fp = get_data_path(
os.path.join('qiime-191-tt', 'otu-table.tsv'), 'data')
tt_tree_fp = get_data_path(
os.path.join('qiime-191-tt', 'tree.nwk'), 'data')
self.q_table = pd.read_csv(tt_table_fp, sep='\t', skiprows=1,
index_col=0)
self.q_tree = TreeNode.read(tt_tree_fp)示例6: setUp
def setUp(self):
# Crawford dataset for unweighted UniFrac
fp = get_data_path('PCoA_sample_data_3')
self.ordination = pcoa(DistanceMatrix.read(fp))
fp = get_data_path('PCoA_biplot_descriptors')
self.descriptors = pd.read_table(fp, index_col='Taxon').T示例7: test_any_sequences_to_fasta
def test_any_sequences_to_fasta(self):
# test writing with default parameters
fh = io.StringIO()
_tabular_msa_to_fasta(self.msa, fh)
obs = fh.getvalue()
fh.close()
with io.open(get_data_path('fasta_3_seqs_defaults')) as fh:
exp = fh.read()
self.assertEqual(obs, exp)
# test writing with non-defaults
fasta_fh = io.StringIO()
qual_fh = io.StringIO()
_tabular_msa_to_fasta(self.msa, fasta_fh,
id_whitespace_replacement='*',
description_newline_replacement='+', max_width=3,
qual=qual_fh)
obs_fasta = fasta_fh.getvalue()
obs_qual = qual_fh.getvalue()
fasta_fh.close()
qual_fh.close()
with io.open(get_data_path('fasta_3_seqs_non_defaults')) as fh:
exp_fasta = fh.read()
with io.open(get_data_path('qual_3_seqs_non_defaults')) as fh:
exp_qual = fh.read()
self.assertEqual(obs_fasta, exp_fasta)
self.assertEqual(obs_qual, exp_qual)示例8: test_simple
def test_simple(self):
eigvals = [0.51236726, 0.30071909, 0.26791207, 0.20898868,
0.19169895, 0.16054235, 0.15017696, 0.12245775,
0.0]
proportion_explained = [0.2675738328, 0.157044696, 0.1399118638,
0.1091402725, 0.1001110485,
0.0838401162, 0.0784269939,
0.0639511764, 0.0]
sample_ids = ['PC.636', 'PC.635', 'PC.356', 'PC.481', 'PC.354',
'PC.593', 'PC.355', 'PC.607', 'PC.634']
axis_labels = ['PC%d' % i for i in range(1, 10)]
expected_results = OrdinationResults(
short_method_name='PCoA',
long_method_name='Principal Coordinate Analysis',
eigvals=pd.Series(eigvals, index=axis_labels),
samples=pd.DataFrame(
np.loadtxt(get_data_path('exp_PCoAEigenResults_site')),
index=sample_ids, columns=axis_labels),
proportion_explained=pd.Series(proportion_explained,
index=axis_labels))
dm = DistanceMatrix.read(get_data_path('PCoA_sample_data_3'))
results = pcoa(dm)
assert_ordination_results_equal(results, expected_results,
ignore_directionality=True)示例9: test_default_valid_multi_line
def test_default_valid_multi_line(self):
fp = get_data_path('blast7_default_multi_line')
df = _blast7_to_data_frame(fp)
exp = pd.DataFrame([['query1', 'subject2', 70.00, 5.0, 0.0, 0.0, 7.0,
60.0, 3.0, 100.0, 9e-05, 10.5],
['query1', 'subject2', 30.00, 8.0, 0.0, 0.0, 6.0,
15.0, 1.0, 100.0, 0.053, 12.0],
['query1', 'subject2', 90.00, 2.0, 0.0, 0.0, 9.0,
35.0, 2.0, 100.0, 0.002, 8.3]],
columns=['qseqid', 'sseqid', 'pident', 'length',
'mismatch', 'gapopen', 'qstart', 'qend',
'sstart', 'send', 'evalue', 'bitscore'])
assert_data_frame_almost_equal(df, exp)
fp = get_data_path('legacy9_multi_line')
df = _blast7_to_data_frame(fp)
exp = pd.DataFrame([['query1', 'subject1', 90.00, 7.0, 1.0, 0.0, 0.0,
8.0, 4.0, 10.0, 1e-05, 15.5],
['query1', 'subject1', 70.00, 8.0, 0.0, 1.0, 0.0,
9.0, 5.0, 7.0, 0.231, 7.8],
['query1', 'subject1', 90.00, 5.0, 1.0, 1.0, 0.0,
0.0, 2.0, 10.0, 0.022, 13.0]],
columns=['qseqid', 'sseqid', 'pident', 'length',
'mismatch', 'gapopen', 'qstart', 'qend',
'sstart', 'send', 'evalue', 'bitscore'])
assert_data_frame_almost_equal(df, exp)示例10: setUp
def setUp(self):
super(MantelTests, self).setUp()
self.methods = ('pearson', 'spearman')
self.alternatives = ('two-sided', 'greater', 'less')
# No variation in distances. Taken from Figure 10.20(b), pg. 603 in L&L
# 3rd edition. Their example is 4x4 but using 3x3 here for easy
# comparison to the minimal dataset above.
self.no_variation = [[0, 0.667, 0.667],
[0.667, 0, 0.667],
[0.667, 0.667, 0]]
# This second dataset is derived from vegan::mantel's example dataset.
# The "veg" distance matrix contains Bray-Curtis distances derived from
# the varespec data (named "veg.dist" in the example). The "env"
# distance matrix contains Euclidean distances derived from scaled
# varechem data (named "env.dist" in the example).
self.veg_dm_vegan = np.loadtxt(
get_data_path('mantel_veg_dm_vegan.txt'))
self.env_dm_vegan = np.loadtxt(
get_data_path('mantel_env_dm_vegan.txt'))
# Expected test statistic when comparing x and y with method='pearson'.
self.exp_x_vs_y = 0.7559289
# Expected test statistic when comparing x and z with method='pearson'.
self.exp_x_vs_z = -0.9897433示例11: setUp
def setUp(self):
self.positives = [get_data_path(e) for e in [
'phylip_dna_3_seqs',
'phylip_single_seq_long',
'phylip_single_seq_short',
'phylip_two_chunks',
'phylip_variable_length_ids',
'phylip_varied_whitespace_in_seqs',
'phylip_whitespace_in_header_1',
'phylip_whitespace_in_header_2',
'phylip_whitespace_in_header_3',
]]
# negative tests for sniffer don't include
# phylip_invalid_empty_line_between_seqs, phylip_invalid_too_few_seqs,
# phylip_invalid_too_many_seqs - because sniffer only reads first seq
self.negatives = [get_data_path(e) for e in [
'empty',
'whitespace_only',
'phylip_invalid_empty_line_after_header',
'phylip_invalid_empty_line_before_header',
'phylip_invalid_header_too_long',
'phylip_invalid_header_too_short',
'phylip_invalid_no_header',
'phylip_invalid_seq_too_long',
'phylip_invalid_seq_too_short',
'phylip_invalid_zero_seq_len',
'phylip_invalid_zero_seqs',
]]示例12: test_confirm_betadispr_results
def test_confirm_betadispr_results(self):
mp_dm = DistanceMatrix.read(get_data_path('moving_pictures_dm.tsv'))
mp_mf = pd.read_csv(get_data_path('moving_pictures_mf.tsv'), sep='\t')
mp_mf.set_index('#SampleID', inplace=True)
obs_med_mp = permdisp(mp_dm, mp_mf,
column='BodySite')
obs_cen_mp = permdisp(mp_dm, mp_mf, column='BodySite',
test='centroid')
exp_data_m = ['PERMDISP', 'F-value', 33, 4, 10.1956, 0.001, 999]
exp_data_c = ['PERMDISP', 'F-value', 33, 4, 17.4242, 0.001, 999]
exp_ind = ['method name', 'test statistic name', 'sample size',
'number of groups', 'test statistic', 'p-value',
'number of permutations']
exp_med_mp = pd.Series(data=exp_data_m, index=exp_ind, dtype='object',
name='PERMDISP results')
exp_cen_mp = pd.Series(data=exp_data_c, index=exp_ind, dtype='object',
name='PERMDISP results')
self.assert_series_equal(exp_med_mp, obs_med_mp)
self.assert_series_equal(exp_cen_mp, obs_cen_mp)示例13: setUp
def setUp(self):
"""varespec and varechem from Väre etal. 1995 DOI: 10.2307/3236351"""
self.Y = pd.read_csv(get_data_path('varespec.csv'), index_col=0)
self.X = pd.read_csv(get_data_path('varechem.csv'), index_col=0)
self.Y.index.name = None
self.X.index.name = None示例14: setUp
def setUp(self):
self.jbe_con = get_data_path('test_contacts/1jbeA.psicov')
self.jbe_pdb = get_data_path('test_contacts/1jbeA_clean.pdb')
self.qjp_con = get_data_path('test_contacts/1qjpA.psicov')
self.qjp_pdb = get_data_path('test_contacts/1qjpA_clean.pdb')
self.real_n_con_qjp = {'lr': 6441,
'sr': 2337,
'all': 8778}
self.real_n_con_jbe = {'lr': 4742,
'sr': 2025,
'all': 6767}
self.positive_params = [
{'-t': 'all', '-l': 1},
{'-t': 'all', '-l': 2},
{'-t': 'all', '-l': 5},
{'-t': 'all', '-l': 10},
{'-t': 'lr', '-l': 1},
{'-t': 'lr', '-l': 2},
{'-t': 'lr', '-l': 10},
{'-t': 'sr', '-l': 2},
{'-t': 'sr', '-l': 10}]示例15: setUp
def setUp(self):
self.positives = [get_data_path(e) for e in [
'fastq_multi_seq_sanger',
'fastq_single_seq_illumina1.3',
'fastq_wrapping_as_illumina_no_description',
'fastq_wrapping_as_sanger_no_description',
'fastq_wrapping_original_sanger_no_description',
'fastq_writer_illumina1.3_defaults',
'fastq_writer_sanger_defaults',
'fastq_writer_sanger_non_defaults',
'illumina_full_range_as_illumina.fastq',
'illumina_full_range_as_sanger.fastq',
'illumina_full_range_original_illumina.fastq',
'longreads_as_illumina.fastq',
'longreads_as_sanger.fastq',
'longreads_original_sanger.fastq',
'misc_dna_as_illumina.fastq',
'misc_dna_as_sanger.fastq',
'misc_dna_original_sanger.fastq',
'misc_rna_as_illumina.fastq',
'misc_rna_as_sanger.fastq',
'misc_rna_original_sanger.fastq',
'sanger_full_range_as_illumina.fastq',
'sanger_full_range_as_sanger.fastq',
'sanger_full_range_original_sanger.fastq',
'solexa_full_range_original_solexa.fastq',
'wrapping_as_illumina.fastq',
'wrapping_as_sanger.fastq',
'wrapping_original_sanger.fastq'
]]
self.negatives = [get_data_path(e) for e in [
'empty',
'whitespace_only',
'fastq_invalid_missing_header',
'fastq_invalid_missing_seq_data',
'error_diff_ids.fastq',
'error_double_qual.fastq',
'error_double_seq.fastq',
'error_long_qual.fastq',
'error_no_qual.fastq',
'error_qual_del.fastq',
'error_qual_escape.fastq',
'error_qual_null.fastq',
'error_qual_space.fastq',
'error_qual_tab.fastq',
'error_qual_unit_sep.fastq',
'error_qual_vtab.fastq',
'error_short_qual.fastq',
'error_spaces.fastq',
'error_tabs.fastq',
'error_trunc_at_seq.fastq',
'error_trunc_at_plus.fastq',
'error_trunc_at_qual.fastq',
'error_trunc_in_title.fastq',
'error_trunc_in_seq.fastq',
'error_trunc_in_plus.fastq',
'error_trunc_in_qual.fastq',
]]