def test_bad_metadata(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.Metadata(
pd.DataFrame({'value': ['a', 'b', 'c']},
index=['sample1', 'sample2', 'sample3']))
with tempfile.TemporaryDirectory() as output_dir:
with self.assertRaises(ValueError):
alpha_correlation(output_dir, alpha_div, md)
def test_bad_method(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.MetadataCategory(
pd.Series(['1.0', '2.0', '3.0'], name='value',
index=['sample1', 'sample2', 'sample3']))
with tempfile.TemporaryDirectory() as output_dir:
with self.assertRaises(ValueError):
alpha_correlation(output_dir, alpha_div, md, method='bad!')
def test_extra_alpha_div(self):
alpha_div = pd.Series([2.0, 4.0, 6.0, 8.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3',
'sample4'])
md = qiime2.Metadata(
pd.DataFrame(
{'value': [1.0, 2.0, 3.0]},
index=pd.Index(['sample1', 'sample2', 'sample3'], name='id')))
with tempfile.TemporaryDirectory() as output_dir:
with self.assertRaisesRegex(ValueError,
'not present.*metadata.*sample4'):
alpha_correlation(output_dir, alpha_div, md)
def test_all_metadata_columns_filtered(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
# Non-numeric and empty columns are filtered.
md = qiime2.Metadata(
pd.DataFrame(
{'col1': ['a', 'b', 'a'],
'col2': [np.nan, np.nan, np.nan]},
index=pd.Index(['sample1', 'sample2', 'sample3'], name='id')))
with tempfile.TemporaryDirectory() as output_dir:
with self.assertRaisesRegex(
ValueError, 'contains only non-numeric or empty columns'):
alpha_correlation(output_dir, alpha_div, md)
def test_extra_metadata(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.Metadata(
pd.DataFrame({'value': ['1.0', '2.0', '3.0', '4.0']},
index=['sample1', 'sample2', 'sample3', 'sample4']))
with tempfile.TemporaryDirectory() as output_dir:
alpha_correlation(output_dir, alpha_div, md)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
jsonp_fp = os.path.join(output_dir, 'category-value.jsonp')
self.assertTrue(os.path.exists(jsonp_fp))
self.assertTrue('"sampleSize": 3' in open(jsonp_fp).read())
def test_extra_alpha_div_intersect(self):
alpha_div = pd.Series(
[2.0, 4.0, 6.0, 8.0],
name='alpha-div',
index=['sample1', 'sample2', 'sample3', 'sample4'])
md = qiime2.Metadata(
pd.DataFrame({'value': [1.0, 2.0, 3.0]},
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
with tempfile.TemporaryDirectory() as output_dir:
alpha_correlation(output_dir, alpha_div, md, intersect_ids=True)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
jsonp_fp = os.path.join(output_dir, 'column-value.jsonp')
self.assertTrue(os.path.exists(jsonp_fp))
def test_nan_metadata(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.Metadata(
pd.DataFrame(
{'value': [1.0, 2.0, np.nan]},
index=pd.Index(['sample1', 'sample2', 'sample3'], name='id')))
with tempfile.TemporaryDirectory() as output_dir:
alpha_correlation(output_dir, alpha_div, md)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
jsonp_fp = os.path.join(output_dir, 'column-value.jsonp')
self.assertTrue(os.path.exists(jsonp_fp))
with open(jsonp_fp) as jsonp_fh:
jsonp_content = jsonp_fh.read()
self.assertTrue('"filtered": 2' in jsonp_content)
self.assertTrue('"initial": 3' in jsonp_content)
def test_pearson(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.Metadata(
pd.DataFrame(
{'value': [1.0, 2.0, 3.0]},
index=pd.Index(['sample1', 'sample2', 'sample3'], name='id')))
with tempfile.TemporaryDirectory() as output_dir:
alpha_correlation(output_dir, alpha_div, md, method='pearson')
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
jsonp_fp = os.path.join(output_dir, 'column-value.jsonp')
self.assertTrue(os.path.exists(jsonp_fp))
with open(jsonp_fp) as jsonp_fh:
jsonp_content = jsonp_fh.read()
self.assertTrue('Pearson' in jsonp_content)
self.assertTrue('"sampleSize": 3' in jsonp_content)
self.assertTrue('"data":' in jsonp_content)
self.assertFalse('filtered' in jsonp_content)
def test_non_numeric_metadata(self):
alpha_div = pd.Series([2.0, 4.0, 6.0], name='alpha-div',
index=['sample1', 'sample2', 'sample3'])
md = qiime2.Metadata(
pd.DataFrame(
{'col1': [4, 5, 6],
'col2': ['a', 'b', 'c']},
index=pd.Index(['sample1', 'sample2', 'sample3'], name='id')))
with tempfile.TemporaryDirectory() as output_dir:
alpha_correlation(output_dir, alpha_div, md)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.exists(
os.path.join(output_dir,
'column-col1.jsonp')))
self.assertFalse(os.path.exists(
os.path.join(output_dir,
'column-col2.jsonp')))
with open(index_fp) as index_fh:
index_content = index_fh.read()
self.assertTrue('contain numeric data' in index_content)
self.assertTrue('<strong>col2' in index_content)
