def test_heatmap_extra_tips(self):
# Adds in test scenario where there more tips than features
# in the table
np.random.seed(0)
num_otus = 11 # otus
index = np.arange(5).astype(np.str)
table = pd.DataFrame(np.random.random((len(index), num_otus)),
index=index,
columns=np.arange(num_otus).astype(np.str))
x = np.random.rand(num_otus*2)
dm = DistanceMatrix.from_iterable(x, lambda x, y: np.abs(x-y))
lm = ward(dm.condensed_form())
t = TreeNode.from_linkage_matrix(lm, np.arange(len(x)).astype(np.str))
for i, n in enumerate(t.postorder()):
if not n.is_tip():
n.name = "y%d" % i
n.length = np.random.rand()*3
md = MetadataCategory(
pd.Series(['a', 'a', 'a', 'b', 'b'], index=index))
dendrogram_heatmap(self.results, table, t, md)
index_fp = os.path.join(self.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h1>Dendrogram heatmap</h1>',
html)
def test_visualization_garbage_metadata(self):
# tests the scenario where ndim > number of tips
np.random.seed(0)
num_otus = 10 # otus
num_samples = 5
table = pd.DataFrame(np.random.random((num_samples, num_otus)),
index=np.arange(num_samples).astype(np.str),
columns=np.arange(num_otus).astype(np.str))
x = np.random.rand(num_otus)
dm = DistanceMatrix.from_iterable(x, lambda x, y: np.abs(x-y))
lm = ward(dm.condensed_form())
t = TreeNode.from_linkage_matrix(lm, np.arange(len(x)).astype(np.str))
for i, n in enumerate(t.postorder()):
if not n.is_tip():
n.name = "y%d" % i
n.length = np.random.rand()*3
md = MetadataCategory(
pd.Series(['a', 'a', 'a', 'b', 'b', 'foo', 'foo'],
index=np.arange(7).astype(np.str)))
dendrogram_heatmap(self.results, table, t, md)
index_fp = os.path.join(self.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h1>Dendrogram heatmap</h1>',
html)
def test_visualization(self):
np.random.seed(0)
num_otus = 500 # otus
index = pd.Index(np.arange(5).astype(np.str), name='id')
table = pd.DataFrame(np.random.random((len(index), num_otus)),
index=index,
columns=np.arange(num_otus).astype(np.str))
x = np.random.rand(num_otus)
dm = DistanceMatrix.from_iterable(x, lambda x, y: np.abs(x - y))
lm = ward(dm.condensed_form())
t = TreeNode.from_linkage_matrix(lm, np.arange(len(x)).astype(np.str))
for i, n in enumerate(t.postorder()):
if not n.is_tip():
n.name = "y%d" % i
n.length = np.random.rand() * 3
md = CategoricalMetadataColumn(
pd.Series(['a', 'a', 'a', 'b', 'b'],
index=index,
name='column-name'))
dendrogram_heatmap(self.results, table, t, md)
index_fp = os.path.join(self.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h1>Dendrogram heatmap</h1>', html)
