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test_util.py
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test_util.py
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#!/usr/bin/env python
import os
import pandas as pd
from StringIO import StringIO
from unittest import TestCase, main
from numpy import array, nan, arange
from numpy.testing import assert_almost_equal
from biom import Table
from pandas.util.testing import assert_frame_equal
from americangut.util import (
slice_mapping_file, parse_mapping_file,
verify_subset, concatenate_files, trim_fasta, count_samples,
count_seqs, count_unique_participants, clean_and_reformat_mapping,
add_alpha_diversity, get_single_id_lists, collapse_taxonomy, collapse_full
)
__author__ = "Daniel McDonald"
__copyright__ = "Copyright 2013, The American Gut Project"
__credits__ = ["Daniel McDonald", "Adam Robbins-Pianka"]
__license__ = "BSD"
__version__ = "unversioned"
__maintainer__ = "Daniel McDonald"
__email__ = "mcdonadt@colorado.edu"
class UtilTests(TestCase):
def test_count_samples(self):
test_mapping = ["#SampleID\tfoo\tbar",
"A\t1\t2",
"B\t1\t3",
"C\t2\t4",
"D\t3\t5",
"E\t2\t6"]
obs = count_samples(iter(test_mapping))
exp = 5
self.assertEqual(obs, exp)
obs = count_samples(iter(test_mapping), criteria={'foo': '2'})
exp = 2
def test_count_seqs(self):
test_seqs = [">a b",
"aattggcc",
">b.xyz stuff",
"asdasd",
">c",
"qweasd",
">d.foo",
"qweasdasd"]
obs = count_seqs(iter(test_seqs))
exp = 4
self.assertEqual(obs, exp)
obs = count_seqs(iter(test_seqs), subset=['b', 'c', 'foo'])
exp = 2
self.assertEqual(obs, exp)
def test_count_unique_participants(self):
test_mapping = ["#SampleID\tfoo\tbar\tHOST_SUBJECT_ID",
"A\t1\t2\tx",
"B\t1\t3\tx",
"C\t2\t4\ty",
"D\t3\t5\tz",
"E\t2\t6\tw"]
obs = count_unique_participants(iter(test_mapping))
exp = 4
self.assertEqual(obs, exp)
obs = count_unique_participants(iter(test_mapping),
criteria={'foo': '1'})
exp = 1
self.assertEqual(obs, exp)
obs = count_unique_participants(iter(test_mapping),
criteria={'foo': '2'})
exp = 2
self.assertEqual(obs, exp)
def test_verify_subset(self):
metadata = [('a','other stuff\tfoo'), ('b', 'asdasdasd'),
('c','123123123')]
table = Table(array([[1,2,3],[4,5,6]]),
['x', 'y'],
['a', 'b', 'c'])
self.assertTrue(verify_subset(table, metadata))
table = Table(array([[1,2],[3,4]]),
['x','y'],
['a','b'])
self.assertTrue(verify_subset(table, metadata))
table = Table(array([[1,2,3],[4,5,6]]),
['x','y'],
['a','b','x'])
self.assertFalse(verify_subset(table, metadata))
def test_slice_mapping_file(self):
header, metadata = parse_mapping_file(StringIO(test_mapping))
table = Table(array([[1,2],[4,5]]),
['x','y'],
['a','c'])
exp = ["a\t1\t123123", "c\tpoop\tdoesn't matter"]
obs = slice_mapping_file(table, metadata)
self.assertEqual(obs,exp)
def test_parse_mapping_file(self):
exp = ("#SampleIDs\tfoo\tbar", [['a','1\t123123'],
['b','yy\txxx'],
['c',"poop\tdoesn't matter"]])
obs = parse_mapping_file(StringIO(test_mapping))
self.assertEqual(obs, exp)
def test_concatenate_files(self):
expected_output = concat_test_input + concat_test_input
input_files = [StringIO(concat_test_input),
StringIO(concat_test_input)]
output_file = StringIO()
concatenate_files(input_files, output_file)
output_file.seek(0)
self.assertEqual(expected_output, output_file.read())
# try again with a tiny chunk size
input_files = [StringIO(concat_test_input),
StringIO(concat_test_input)]
output_file = StringIO()
concatenate_files(input_files, output_file, 2)
output_file.seek(0)
self.assertEqual(expected_output, output_file.read())
def test_trim_fasta(self):
infasta = StringIO(test_fasta)
# Trim length 10
expected = (">seq1\n"
"0123456789\n"
">seq2\n"
"0123456789\n"
">seq3\n"
"012345\n")
outfasta = StringIO()
trim_fasta(infasta, outfasta, 10)
outfasta.seek(0)
self.assertEqual(expected, outfasta.read())
def test_clean_and_reformat_mapping(self):
"""Exercise the reformat mapping code, verify expected results"""
out = StringIO()
reformat_mapping_testdata.seek(0)
clean_and_reformat_mapping(reformat_mapping_testdata, out, 'body_site',
'test')
out.seek(0)
# verify the resulting header structure
test_mapping = [l.strip().split('\t') for l in out]
test_header = test_mapping[0]
self.assertEqual(test_header[-4:], ['SIMPLE_BODY_SITE',
'TITLE_ACRONYM', 'TITLE_BODY_SITE',
'HMP_SITE'])
self.assertEqual(test_mapping[1][:], ['A', 'w00t', '43.0',
'UBERON_mucosa_of_tongue', '5',
'ORAL', 'test', 'test-ORAL',
'ORAL'])
self.assertEqual(test_mapping[2][:], ['B', 'left', '51.0',
'UBERON:FECES', '10',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[3][:], ['C', 'right', '12.0',
'UBERON_FECES', '15',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[4][:], ['E', 'stuff', '56.0',
'UBERON:SKIN', '37',
'SKIN', 'test', 'test-SKIN',
'SKIN'])
def test_clean_and_reformat_mapping_nopgp(self):
"""Exercise the reformat mapping code, verify expected results"""
out = StringIO()
reformat_mapping_testdata.seek(0)
clean_and_reformat_mapping(reformat_mapping_testdata, out, 'body_site',
'test')
out.seek(0)
# verify the resulting header structure
test_mapping = [l.strip().split('\t') for l in out]
test_header = test_mapping[0]
self.assertEqual(test_header[-4:], ['SIMPLE_BODY_SITE',
'TITLE_ACRONYM', 'TITLE_BODY_SITE',
'HMP_SITE'])
self.assertEqual(test_mapping[1][:], ['A', 'w00t', '43.0',
'UBERON_mucosa_of_tongue', '5',
'ORAL', 'test', 'test-ORAL',
'ORAL'])
self.assertEqual(test_mapping[2][:], ['B', 'left', '51.0',
'UBERON:FECES', '10',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[3][:], ['C', 'right', '12.0',
'UBERON_FECES', '15',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[4][:], ['E', 'stuff', '56.0',
'UBERON:SKIN', '37',
'SKIN', 'test', 'test-SKIN',
'SKIN'])
def test_clean_and_reformat_mapping_allpgp(self):
"""Exercise the reformat mapping code, verify expected results"""
out = StringIO()
reformat_mapping_testdata.seek(0)
clean_and_reformat_mapping(reformat_mapping_testdata, out, 'body_site',
'test')
out.seek(0)
# verify the resulting header structure
test_mapping = [l.strip().split('\t') for l in out]
test_header = test_mapping[0]
self.assertEqual(test_header[-4:], ['SIMPLE_BODY_SITE',
'TITLE_ACRONYM', 'TITLE_BODY_SITE',
'HMP_SITE'])
self.assertEqual(test_mapping[1][:], ['A', 'w00t', '43.0',
'UBERON_mucosa_of_tongue', '5',
'ORAL', 'test', 'test-ORAL',
'ORAL'])
self.assertEqual(test_mapping[2][:], ['B', 'left', '51.0',
'UBERON:FECES', '10',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[3][:], ['C', 'right', '12.0',
'UBERON_FECES', '15',
'FECAL', 'test', 'test-FECAL',
'FECAL'])
self.assertEqual(test_mapping[4][:], ['E', 'stuff', '56.0',
'UBERON:SKIN', '37',
'SKIN', 'test', 'test-SKIN',
'SKIN'])
def test_add_alpha_diversity(self):
map_ = pd.DataFrame(
array([
['GAZ:w00t', '43.0', 'UBERON_mucosa_of_tongue', '5'],
['GAZ:left', '51.0', 'UBERON:FECES', '10'],
['GAZ:right', '12.0', 'UBERON_FECES', '15'],
['GAZ:stuff', '32.0', 'unknown', '26'],
['GAZ:stuff', '56.0', 'UBERON:SKIN', '37'],
]),
columns=['COUNTRY', 'AGE', 'BODY_SITE', 'BMI'],
index=['A', 'B', 'C', 'D', 'E']
)
alpha = {
'alpha_1': pd.DataFrame(
array([
['0', '1', '2', '3', '4'],
['100', '100', '100', '100', '100'],
[nan, nan, nan, nan, nan],
['14.5', '14.0', '15.1', '14.7', '14.4'],
['12.1', '15.2', '13.1', '14.1', '12.8'],
['16.2', '16.5', '16.9', '15.9', '16.2'],
['10.1', '9.8', '10.5', '10.0', '10.2'],
]),
columns=[
'alpha_rarefaction_100_0.txt',
'alpha_rarefaction_100_1.txt',
'alpha_rarefaction_100_2.txt',
'alpha_rarefaction_100_3.txt',
'alpha_rarefaction_100_4.txt',
],
index=['sequences per sample', 'iteration',
'A', 'B', 'C', 'D', 'E']
)
}
expected = pd.DataFrame(
array([
['GAZ:left', '51.0', 'UBERON:FECES', '10', 14.54],
['GAZ:right', '12.0', 'UBERON_FECES', '15', 13.46],
['GAZ:stuff', '32.0', 'unknown', '26', 16.34],
['GAZ:stuff', '56.0', 'UBERON:SKIN', '37', 10.12]
]),
index=['B', 'C', 'D', 'E'],
columns=['COUNTRY', 'AGE', 'BODY_SITE', 'BMI', 'alpha_1']
)
expected['alpha_1'] = expected['alpha_1'].astype(float)
test = add_alpha_diversity(map_, alpha)
assert_frame_equal(expected, test)
def test_get_single_id_list(self):
map_ = pd.DataFrame(
array([
['GAZ:w00t', '43.0', 'UBERON_mucosa_of_tongue', '5', 'A',
'12'],
['GAZ:left', '51.0', 'UBERON:FECES', '10', 'B', '1500'],
['GAZ:right', '12.0', 'UBERON_FECES', '15', 'C', '121'],
['GAZ:stuff', '32.0', 'unknown', '26', 'D', '150'],
['GAZ:stuff', '56.0', 'UBERON:SKIN', '37', 'E', '201'],
]),
columns=['COUNTRY', 'AGE', 'BODY_SITE', 'BMI', 'HOST_SUBJECT_ID',
'depth'],
index=['A', 'B', 'C', 'D', 'E']
)
depths = [100]
test = get_single_id_lists(map_, depths)
known = {100: ['B', 'C', 'D', 'E'],
'unrare': ['A', 'B', 'C', 'D', 'E']}
self.assertEqual(test, known)
def test_collapse_taxonomy(self):
obs = collapse_taxonomy(table)
exp = Table(array([[100.0, 105.0, 110.0, 115.0],
[44.0, 46.0, 48.0, 50.0],
[36.0, 39.0, 42.0, 45.0]]),
['Bacteria; Firmicutes', 'Bacteria; Bacteroidetes',
'Bacteria; Proteobacteria'], sample_ids,
sample_metadata=sample_metadata, observation_metadata=[
{'collapsed_ids': ['O0', 'O1', 'O7', 'O8', 'O9']},
{'collapsed_ids': ['O5', 'O6']},
{'collapsed_ids': ['O2', 'O3', 'O4']}])
self.assertEqual(obs, exp)
def test_collapse_full(self):
obs = collapse_full(table)
exp = Table(array([[0.00769230769231], [0.0282051282051],
[0.0487179487179], [0.0692307692308],
[0.0897435897436], [0.110256410256],
[0.130769230769], [0.151282051282],
[0.171794871795], [0.192307692308]]),
observ_ids, ['average'],
observation_metadata=observ_metadata)
for r in range(10):
assert_almost_equal(obs[r, 0], exp[r, 0])
self.assertEqual(obs.ids(), exp.ids())
self.assertItemsEqual(obs.ids('observation'), exp.ids('observation'))
obs_meta = []
for _, _, m in obs.iter(axis='observation'):
obs_meta.append(m)
self.assertItemsEqual(obs_meta, observ_metadata)
test_mapping = """#SampleIDs\tfoo\tbar
a\t1\t123123
b\tyy\txxx
c\tpoop\tdoesn't matter
"""
concat_test_input="""This is
a
test file that is used
in the concatenation test. The file will be concatenated to itself."""
test_fasta = """>seq1
0123456789
>seq2
0123456789AB
>seq3
012345"""
reformat_mapping_testdata = StringIO(
"""#SampleID COUNTRY AGE BODY_SITE BMI
A GAZ:w00t 43.0 UBERON_mucosa_of_tongue 5
B GAZ:left 51.0 UBERON:FECES 10
C GAZ:right 12.0 UBERON_FECES 15
D GAZ:stuff 32.0 unknown 26
E GAZ:stuff 56.0 UBERON:SKIN 37
""")
data = arange(40).reshape(10, 4)
sample_ids = ['S%d' % i for i in range(4)]
observ_ids = ['O%d' % i for i in range(10)]
sample_metadata = [{'environment': 'A'}, {'environment': 'B'},
{'environment': 'A'}, {'environment': 'B'}]
observ_metadata = [{'taxonomy': ['Bacteria', 'Firmicutes']},
{'taxonomy': ['Bacteria', 'Firmicutes']},
{'taxonomy': ['Bacteria', 'Proteobacteria']},
{'taxonomy': ['Bacteria', 'Proteobacteria']},
{'taxonomy': ['Bacteria', 'Proteobacteria']},
{'taxonomy': ['Bacteria', 'Bacteroidetes']},
{'taxonomy': ['Bacteria', 'Bacteroidetes']},
{'taxonomy': ['Bacteria', 'Firmicutes']},
{'taxonomy': ['Bacteria', 'Firmicutes']},
{'taxonomy': ['Bacteria', 'Firmicutes']}]
table = Table(data, observ_ids, sample_ids, observ_metadata,
sample_metadata, table_id='Example Table')
if __name__ == '__main__':
main()