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# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import os
from unittest import TestCase, main
import tempfile
import skbio
import biom
import pandas as pd
import numpy as np
import qiime2
from q2_types.feature_data import DNAIterator
import csv
from q2_feature_table import tabulate_seqs, summarize
from q2_feature_table._summarize._visualizer import _compute_descriptive_stats
from q2_feature_table._summarize._visualizer import _frequencies
from q2_feature_table._summarize._vega_spec import vega_spec
class TabulateSeqsTests(TestCase):
def test_basic(self):
seqs = DNAIterator(skbio.DNA(a, metadata=b) for a, b in (
('ACGT', {'id': 'seq1'}),
('AAAA', {'id': 'seq2'})))
with tempfile.TemporaryDirectory() as output_dir:
tabulate_seqs(output_dir, seqs)
expected_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(expected_fp))
with open(expected_fp) as fh:
file_text = fh.read()
self.assertTrue('ACGT</a>' in file_text)
self.assertTrue('<td>4</td>' in file_text)
self.assertTrue('<td>seq2</td>' in file_text)
def test_descriptive_stats(self):
seq_lengths = [2, 2, 5, 6, 10]
exp_stats = {
'mean': 5.0, 'min': 2,
'seven_num_summ_values': [2.0, 2.0, 2.0, 5.0, 6.0, 8.56, 9.68],
'max': 10, 'count': 5, 'range': 8}
rendered_stats = _compute_descriptive_stats(seq_lengths)
self.assertEqual(exp_stats['count'], rendered_stats['count'])
self.assertEqual(exp_stats['min'], rendered_stats['min'])
self.assertEqual(exp_stats['max'], rendered_stats['max'])
self.assertEqual(exp_stats['range'], rendered_stats['range'])
self.assertAlmostEqual(exp_stats['mean'], rendered_stats['mean'])
for expected, rendered in zip(exp_stats['seven_num_summ_values'],
rendered_stats['seven_num_summ_values']):
self.assertAlmostEqual(expected, rendered)
def test_lengths_identical(self):
seq_lengths = [5, 5, 5, 5, 5]
exp_stats = {
'mean': 5.0, 'min': 5,
'seven_num_summ_values': [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
'max': 5, 'count': 5, 'range': 0}
rendered_stats = _compute_descriptive_stats(seq_lengths)
self.assertEqual(exp_stats['count'], rendered_stats['count'])
self.assertEqual(exp_stats['min'], rendered_stats['min'])
self.assertEqual(exp_stats['max'], rendered_stats['max'])
self.assertEqual(exp_stats['range'], rendered_stats['range'])
self.assertAlmostEqual(exp_stats['mean'], rendered_stats['mean'])
for expected, rendered in zip(exp_stats['seven_num_summ_values'],
rendered_stats['seven_num_summ_values']):
self.assertAlmostEqual(expected, rendered)
def test_no_sequences(self):
seq_lengths = []
with self.assertRaisesRegex(ValueError, 'No values provided.'):
_compute_descriptive_stats(seq_lengths)
def test_descriptive_stats_integration(self):
seqs = DNAIterator(skbio.DNA(a, metadata=b)for a, b in (
('A', {'id': 'seq01'}),
('AA', {'id': 'seq02'}),
('AAA', {'id': 'seq03'}),
('AAAA', {'id': 'seq04'}),
('AAAA', {'id': 'seq05'}),
('AAA', {'id': 'seq06'}),
('AA', {'id': 'seq07'}),
('AAAAAAAAAA', {'id': 'seq08'})))
with tempfile.TemporaryDirectory() as output_dir:
tabulate_seqs(output_dir, seqs)
expected_fp = os.path.join(output_dir, 'index.html')
# all expected values are unique. If they all render in index.html, our
# function likely worked as expected.
with open(expected_fp) as fh:
file_text = fh.read()
self.assertTrue('<td>8</td>' in file_text)
self.assertTrue('<td>1</td>' in file_text)
self.assertTrue('<td>10</td>' in file_text)
self.assertTrue('<td>3.62</td>' in file_text)
self.assertTrue('<td>9</td>' in file_text)
self.assertTrue('<td>1</td>' in file_text)
self.assertTrue('<td>1</td>' in file_text)
self.assertTrue('<td>2</td>' in file_text)
self.assertTrue('<td>3</td>' in file_text)
self.assertTrue('<td>4</td>' in file_text)
self.assertTrue('<td>6</td>' in file_text)
self.assertTrue('<td>9</td>' in file_text)
def test_tsv_builder(self):
seqs = DNAIterator(skbio.DNA(a, metadata=b)for a, b in (
('A', {'id': 'seq01'}),
('AA', {'id': 'seq02'}),
('AAA', {'id': 'seq03'}),
('AAAA', {'id': 'seq04'}),
('AAAA', {'id': 'seq05'}),
('AAA', {'id': 'seq06'}),
('AA', {'id': 'seq07'}),
('AAAAAAAAAA', {'id': 'seq08'})))
# Do the files exist?
with tempfile.TemporaryDirectory() as output_dir:
tabulate_seqs(output_dir, seqs)
expected_stats_fp = os.path.join(
output_dir, 'descriptive_stats.tsv')
expected_summary_fp = os.path.join(
output_dir, 'seven_number_summary.tsv')
self.assertTrue(os.path.exists(expected_stats_fp))
self.assertTrue(os.path.exists(expected_summary_fp))
# Was data written to the files?
with open(expected_stats_fp) as stats_tsv:
tsv_reader = csv.reader(stats_tsv, dialect="excel-tab")
tsv_text = []
for row in tsv_reader:
tsv_text.append(row)
self.assertEqual(['Statistic', 'Value'], tsv_text[0])
self.assertEqual(['count', '8'], tsv_text[1])
with open(expected_summary_fp) as summ_tsv:
tsv_reader = csv.reader(summ_tsv, dialect="excel-tab")
tsv_text = []
for row in tsv_reader:
tsv_text.append(row)
self.assertEqual(['Quantile', 'Value'], tsv_text[0])
self.assertEqual(['0.02', '1.14'], tsv_text[1])
# Does link html generate correctly?
expected_index_fp = os.path.join(output_dir, 'index.html')
with open(expected_index_fp) as fh:
self.assertTrue('href="descriptive_stats.tsv"' in fh.read())
with open(expected_index_fp) as fh:
self.assertTrue(
'href="seven_number_summary.tsv"' in fh.read())
class SummarizeTests(TestCase):
def test_basic(self):
table = biom.Table(np.array([[0, 1, 3],
[1, 1, 2],
[400, 450, 500],
[1000, 10000, 100000],
[52, 42, 99]]),
['O1', 'O2', '03', '04', 'O5'],
['S1', 'S2', 'S3'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
feature_freq_fp = os.path.join(output_dir,
'feature-frequency-detail.csv')
self.assertTrue(os.path.exists(feature_freq_fp))
self.assertTrue('O1,4' in open(feature_freq_fp).read())
sample_freq_fp = os.path.join(output_dir,
'sample-frequency-detail.csv')
self.assertTrue(os.path.exists(sample_freq_fp))
self.assertTrue('S1,1453' in open(sample_freq_fp).read())
def test_frequency_ranges_are_zero(self):
table = biom.Table(np.array([[25, 25, 25], [25, 25, 25]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
feature_freq_fp = os.path.join(output_dir,
'feature-frequency-detail.csv')
self.assertTrue(os.path.exists(feature_freq_fp))
self.assertTrue('O1,75' in open(feature_freq_fp).read())
sample_freq_fp = os.path.join(output_dir,
'sample-frequency-detail.csv')
self.assertTrue(os.path.exists(sample_freq_fp))
self.assertTrue('S1,50' in open(sample_freq_fp).read())
def test_one_sample(self):
sample_frequencies_pdf_fn = 'sample-frequencies.pdf'
# sample-frequencies.pdf should not be written when there is only
# one sample...
table = biom.Table(np.array([[0], [1]]),
['O1', 'O2'],
['S1'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
sample_frequencies_pdf_fp = \
os.path.join(output_dir, sample_frequencies_pdf_fn)
self.assertFalse(os.path.exists(sample_frequencies_pdf_fp))
# but it should be written when there is more than one sample
table = biom.Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
sample_frequencies_pdf_fp = \
os.path.join(output_dir, sample_frequencies_pdf_fn)
self.assertTrue(os.path.exists(sample_frequencies_pdf_fp))
def test_one_feature(self):
feature_frequencies_pdf_fn = 'feature-frequencies.pdf'
# feature-frequencies.pdf should not be written when there is only
# one feature...
table = biom.Table(np.array([[0, 4]]),
['O1'],
['S1', 'S2'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
feature_frequencies_pdf_fp = \
os.path.join(output_dir, feature_frequencies_pdf_fn)
self.assertFalse(os.path.exists(feature_frequencies_pdf_fp))
# but it should be written when there is more than one feature
table = biom.Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table)
feature_frequencies_pdf_fp = \
os.path.join(output_dir, feature_frequencies_pdf_fn)
self.assertTrue(os.path.exists(feature_frequencies_pdf_fp))
def test_w_sample_metadata(self):
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = biom.Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with tempfile.TemporaryDirectory() as output_dir:
summarize(output_dir, table, metadata)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
feature_freq_fp = os.path.join(output_dir,
'feature-frequency-detail.csv')
self.assertTrue(os.path.exists(feature_freq_fp))
self.assertTrue('O1,4' in open(feature_freq_fp).read())
sample_freq_fp = os.path.join(output_dir,
'sample-frequency-detail.csv')
self.assertTrue(os.path.exists(sample_freq_fp))
self.assertTrue('S1,1' in open(sample_freq_fp).read())
def test_vega_spec_data(self):
# test if metadata is converted correctly to vega compatible JSON
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = biom.Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
sample_frequencies = _frequencies(table, axis='sample')
spec = vega_spec(metadata, sample_frequencies)
self.assertTrue([{'id': 'S1', 'metadata': {'Subject': 'subject-1',
'SampleType': 'gut'}, 'frequency': 1.0},
{'id': 'S2', 'metadata': {'Subject': 'subject-1',
'SampleType': 'tongue'}, 'frequency': 2.0},
{'id': 'S3', 'metadata': {'Subject': 'subject-2',
'SampleType': 'gut'}, 'frequency': 5.0}],
spec['data'][0]['values'])
def test_vega_spec_nandling(self):
df = pd.DataFrame({'a': [0.5, float('nan')]})
df.index = df.index.map(str)
df.index.name = 'id'
md = qiime2.Metadata(df)
sample_freqs = pd.Series([10, 50])
sample_freqs.index = sample_freqs.index.map(str)
spec = vega_spec(md, sample_freqs)
exp = [{'frequency': 10, 'id': '0', 'metadata': {'a': 0.5}},
{'frequency': 50, 'id': '1', 'metadata': {'a': None}}]
self.assertEqual(spec['data'][0]['values'], exp)
if __name__ == "__main__":
main()
|
