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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 unittest
import tempfile
import os.path
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
import qiime2
from q2_feature_table import heatmap
from q2_feature_table._heatmap._visualizer import (
_munge_sample_metadata, _munge_feature_metadata)
class TestHeatmap(unittest.TestCase):
def setUp(self):
self.table = pd.DataFrame(data=[[0, 10], [10, 12], [10, 11]],
columns=['O1', 'O2'],
index=['S1', 'S2', 'S3'])
self.output_dir_obj = tempfile.TemporaryDirectory(
prefix='q2-feature-table-test-temp-')
self.output_dir = self.output_dir_obj.name
def tearDown(self):
self.output_dir_obj.cleanup()
def assertBasicVizValidity(self, viz_dir, normalize=True):
index_fp = os.path.join(viz_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp) as fh:
index_html = fh.read()
normalize_str = '(normalized)' if normalize else '(not normalized)'
self.assertTrue(normalize_str in index_html)
for ext in ['png', 'svg']:
fp = os.path.join(viz_dir, 'feature-table-heatmap.%s' % ext)
self.assertTrue(os.path.exists(fp))
def test_defaults(self):
heatmap(self.output_dir, self.table)
self.assertBasicVizValidity(self.output_dir)
def test_with_title(self):
heatmap(self.output_dir, self.table, title='foo')
self.assertBasicVizValidity(self.output_dir)
def test_with_metadata(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'summer', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
heatmap(self.output_dir, self.table, sample_metadata=md)
self.assertBasicVizValidity(self.output_dir)
def test_with_feature_metadata(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog'], name='species',
index=pd.Index(['O1', 'O2'], name='id')))
heatmap(self.output_dir, self.table, feature_metadata=feature_md)
self.assertBasicVizValidity(self.output_dir)
def test_with_sample_and_feature_metadata(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'summer', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog'], name='species',
index=pd.Index(['O1', 'O2'], name='id')))
heatmap(self.output_dir, self.table, sample_metadata=md,
feature_metadata=feature_md)
self.assertBasicVizValidity(self.output_dir)
def test_empty_table(self):
empty_table = pd.DataFrame([], [], [])
with self.assertRaisesRegex(ValueError, 'empty'):
heatmap(self.output_dir, empty_table)
def test_table_ids_are_subset_of_metadata_ids(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'russ'], name='pet',
index=pd.Index(['S1', 'S3'], name='id')))
with self.assertRaisesRegex(ValueError, 'not present.*S2'):
heatmap(self.output_dir, self.table, sample_metadata=md)
def test_extra_metadata_ids(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'summer', 'russ', 'peanut'], name='pet',
index=pd.Index(['S1', 'S2', 'S3', 'S4'], name='id')))
heatmap(self.output_dir, self.table, sample_metadata=md)
self.assertBasicVizValidity(self.output_dir)
def test_no_normalization(self):
heatmap(self.output_dir, self.table, normalize=False)
self.assertBasicVizValidity(self.output_dir, normalize=False)
def test_no_sample_cluster(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'summer', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
heatmap(self.output_dir, self.table, sample_metadata=md,
cluster='features')
self.assertBasicVizValidity(self.output_dir)
def test_no_cluster(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'summer', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
heatmap(self.output_dir, self.table, sample_metadata=md,
cluster='none')
self.assertBasicVizValidity(self.output_dir)
class TestPrivateHelpers(unittest.TestCase):
def setUp(self):
self.table = pd.DataFrame(data=[[0, 10], [10, 12], [10, 11]],
columns=['O1', 'O2'],
index=['S1', 'S2', 'S3'])
def test_munge_sample_metadata_simple(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'russ', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
obs = _munge_sample_metadata(md, self.table, 'both')
exp_idx = pd.Index(['milo | S1', 'russ | S2', 'russ | S3'],
name='pet | id')
exp = pd.DataFrame([[0, 10], [10, 12], [10, 11]], columns=['O1', 'O2'],
index=exp_idx)
assert_frame_equal(exp, obs)
def test_munge_sample_metadata_ids_different_order(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['russ', 'milo', 'russ'], name='pet',
index=pd.Index(['S2', 'S1', 'S3'], name='id')))
obs = _munge_sample_metadata(md, self.table, 'both')
exp_idx = pd.Index(['milo | S1', 'russ | S2', 'russ | S3'],
name='pet | id')
exp = pd.DataFrame([[0, 10], [10, 12], [10, 11]], columns=['O1', 'O2'],
index=exp_idx)
assert_frame_equal(exp, obs)
def test_munge_sample_metadata_missing_samples(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['milo', 'russ'], name='pet',
index=pd.Index(['S1', 'S3'], name='id')))
with self.assertRaisesRegex(ValueError, 'not present.*S2'):
_munge_sample_metadata(md, self.table, 'both')
def test_munge_sample_metadata_empty_values(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series([None, 'russ', np.nan], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
obs = _munge_sample_metadata(md, self.table, 'both')
exp_idx = pd.Index(['[No Value] | S1', 'russ | S2', '[No Value] | S3'],
name='pet | id')
exp = pd.DataFrame([[0, 10], [10, 12], [10, 11]], columns=['O1', 'O2'],
index=exp_idx)
assert_frame_equal(exp, obs)
def test_munge_sample_metadata_sort_samples(self):
md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'milo', 'russ'], name='pet',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
obs = _munge_sample_metadata(md, self.table, 'features')
exp_idx = pd.Index(['milo | S2', 'peanut | S1', 'russ | S3'],
name='pet | id')
exp = pd.DataFrame([[10, 12], [0, 10], [10, 11]], columns=['O1', 'O2'],
index=exp_idx)
assert_frame_equal(exp, obs)
def test_munge_feature_metadata_simple(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog'], name='species',
index=pd.Index(['O1', 'O2'], name='id')))
obs = _munge_feature_metadata(feature_md, self.table, 'both')
exp = pd.DataFrame(
[[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
index=pd.Index(['S1', 'S2', 'S3']))
assert_frame_equal(exp, obs)
def test_munge_feature_metadata_ids_different_order(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['dog', 'peanut'], name='species',
index=pd.Index(['O2', 'O1'], name='id')))
obs = _munge_feature_metadata(feature_md, self.table, 'both')
exp = pd.DataFrame(
[[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
index=pd.Index(['S1', 'S2', 'S3']))
assert_frame_equal(exp, obs)
def test_munge_feature_metadata_missing_features(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(
['dog'], name='species', index=pd.Index(['O2'], name='id')))
with self.assertRaisesRegex(ValueError, 'not present.*O1'):
_munge_feature_metadata(feature_md, self.table, 'both')
def test_munge_feature_metadata_is_superset(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog', 'cujo'], name='species',
index=pd.Index(['O1', 'O2', 'O3'], name='id')))
obs = _munge_feature_metadata(feature_md, self.table, 'both')
exp = pd.DataFrame(
[[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
index=pd.Index(['S1', 'S2', 'S3']))
assert_frame_equal(exp, obs)
def test_munge_feature_metadata_sort_samples(self):
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog'], name='species',
index=pd.Index(['O1', 'O2'], name='id')))
obs = _munge_feature_metadata(feature_md, self.table, 'samples')
exp = pd.DataFrame(
[[10, 0], [12, 10], [11, 10]], columns=['dog', 'peanut'],
index=pd.Index(['S1', 'S2', 'S3']))
assert_frame_equal(exp, obs)
def test_munge_feature_metadata_sort_duplicate_feature_names(self):
new_tab = self.table.copy()
new_tab['O3'] = [1, 2, 3]
feature_md = qiime2.CategoricalMetadataColumn(
pd.Series(['peanut', 'dog', 'peanut'], name='species',
index=pd.Index(['O1', 'O2', 'O3'], name='id')))
obs = _munge_feature_metadata(feature_md, new_tab, 'samples')
exp = pd.DataFrame(
[[10, 0, 1], [12, 10, 2], [11, 10, 3]],
columns=['dog', 'peanut', 'peanut'],
index=pd.Index(['S1', 'S2', 'S3']))
assert_frame_equal(exp, obs)
if __name__ == "__main__":
unittest.main()
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