def test_optimized(self):
# calling optimized faith_pd gives same results as calling unoptimized
# version
optimized = alpha_diversity('faith_pd', self.table1, tree=self.tree1,
otu_ids=self.oids1)
unoptimized = alpha_diversity(faith_pd, self.table1, tree=self.tree1,
otu_ids=self.oids1)
assert_series_almost_equal(optimized, unoptimized)
def test_single_count_vector(self):
actual = alpha_diversity('observed_otus', np.array([1, 0, 2]))
expected = pd.Series([2])
assert_series_almost_equal(actual, expected)
actual = alpha_diversity('faith_pd', np.array([1, 3, 0, 1, 0]),
tree=self.tree1, otu_ids=self.oids1)
self.assertAlmostEqual(actual[0], 4.5)
def test_single_count_vector(self):
actual = alpha_diversity('observed_otus', np.array([1, 0, 2]))
expected = pd.Series([2])
assert_series_almost_equal(actual, expected)
actual = alpha_diversity('faith_pd', np.array([1, 3, 0, 1, 0]),
tree=self.tree1, otu_ids=self.oids1)
self.assertAlmostEqual(actual[0], 4.5)
def test_optimized(self):
# calling optimized faith_pd gives same results as calling unoptimized
# version
optimized = alpha_diversity('faith_pd', self.table1, tree=self.tree1,
otu_ids=self.oids1)
unoptimized = alpha_diversity(faith_pd, self.table1, tree=self.tree1,
otu_ids=self.oids1)
assert_series_almost_equal(optimized, unoptimized)
def test_to_series_4x4(self):
dm = DistanceMatrix([[0.0, 0.2, 0.3, 0.4], [0.2, 0.0, 0.5, 0.6],
[0.3, 0.5, 0.0, 0.7], [0.4, 0.6, 0.7, 0.0]],
['a', 'b', 'c', 'd'])
series = dm.to_series()
exp = pd.Series([0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
index=pd.Index([('a', 'b'), ('a', 'c'), ('a', 'd'),
('b', 'c'), ('b', 'd'), ('c', 'd')]))
assert_series_almost_equal(series, exp)
def test_to_series_4x4(self):
dm = DistanceMatrix([
[0, 0.25, 0.75, 0.75],
[0.25, 0.0, 0.5, 0.5],
[0.75, 0.5, 0.0, 0.0],
[0.75, 0.5, 0.0, 0.0]], ['a', 'b', 'c', 'd'])
series = dm.to_series()
exp = pd.Series([0.25, 0.75, 0.75, 0.25, 0.5, 0.5, 0.75, 0.5, 0.75, 0.5],
index = [('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'a'), ('b', 'c'), ('b', 'd'),
('c', 'a'), ('c', 'b'), ('d', 'a'), ('d', 'b')])
assert_series_almost_equal(series, exp)
def test_input_types(self):
list_result = alpha_diversity('observed_otus', [1, 3, 0, 1, 0])
array_result = alpha_diversity('observed_otus',
np.array([1, 3, 0, 1, 0]))
self.assertAlmostEqual(list_result[0], 3)
assert_series_almost_equal(list_result, array_result)
list_result = alpha_diversity('faith_pd', [1, 3, 0, 1, 0],
tree=self.tree1, otu_ids=self.oids1)
array_result = alpha_diversity('faith_pd', np.array([1, 3, 0, 1, 0]),
tree=self.tree1, otu_ids=self.oids1)
self.assertAlmostEqual(list_result[0], 4.5)
assert_series_almost_equal(list_result, array_result)
def test_to_series_4x4(self):
dm = DistanceMatrix([
[0.0, 0.2, 0.3, 0.4],
[0.2, 0.0, 0.5, 0.6],
[0.3, 0.5, 0.0, 0.7],
[0.4, 0.6, 0.7, 0.0]], ['a', 'b', 'c', 'd'])
series = dm.to_series()
exp = pd.Series([0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
index=pd.Index([('a', 'b'), ('a', 'c'), ('a', 'd'),
('b', 'c'), ('b', 'd'), ('c', 'd')]))
assert_series_almost_equal(series, exp)
def test_input_types(self):
list_result = alpha_diversity('observed_otus', [1, 3, 0, 1, 0])
array_result = alpha_diversity('observed_otus',
np.array([1, 3, 0, 1, 0]))
self.assertAlmostEqual(list_result[0], 3)
assert_series_almost_equal(list_result, array_result)
list_result = alpha_diversity('faith_pd', [1, 3, 0, 1, 0],
tree=self.tree1, otu_ids=self.oids1)
array_result = alpha_diversity('faith_pd', np.array([1, 3, 0, 1, 0]),
tree=self.tree1, otu_ids=self.oids1)
self.assertAlmostEqual(list_result[0], 4.5)
assert_series_almost_equal(list_result, array_result)
def test_faith_pd(self):
# calling faith_pd through alpha_diversity gives same results as
# calling it directly
expected = []
for e in self.table1:
expected.append(faith_pd(e, tree=self.tree1, otu_ids=self.oids1))
expected = pd.Series(expected)
actual = alpha_diversity('faith_pd', self.table1, tree=self.tree1,
otu_ids=self.oids1)
assert_series_almost_equal(actual, expected)
# alt input table and tree
expected = []
for e in self.table2:
expected.append(faith_pd(e, tree=self.tree2, otu_ids=self.oids2))
expected = pd.Series(expected)
actual = alpha_diversity('faith_pd', self.table2, tree=self.tree2,
otu_ids=self.oids2)
assert_series_almost_equal(actual, expected)
def test_faith_pd(self):
# calling faith_pd through alpha_diversity gives same results as
# calling it directly
expected = []
for e in self.table1:
expected.append(faith_pd(e, tree=self.tree1, otu_ids=self.oids1))
expected = pd.Series(expected)
actual = alpha_diversity('faith_pd', self.table1, tree=self.tree1,
otu_ids=self.oids1)
assert_series_almost_equal(actual, expected)
# alt input table and tree
expected = []
for e in self.table2:
expected.append(faith_pd(e, tree=self.tree2, otu_ids=self.oids2))
expected = pd.Series(expected)
actual = alpha_diversity('faith_pd', self.table2, tree=self.tree2,
otu_ids=self.oids2)
assert_series_almost_equal(actual, expected)
def test_equal(self):
s1 = pd.Series([1., 2., 3.])
s2 = pd.Series([1.000001, 2., 3.])
assert_series_almost_equal(s1, s2)
# all series should be equal to themselves and copies of themselves
for s in self.series:
assert_series_almost_equal(s, s)
assert_series_almost_equal(s, pd.Series(s, copy=True))
def test_observed_otus(self):
# expected values hand-calculated
expected = pd.Series([3, 3, 3, 3], index=self.sids1)
actual = alpha_diversity('observed_otus', self.table1, self.sids1)
assert_series_almost_equal(actual, expected)
# function passed instead of string
actual = alpha_diversity(observed_otus, self.table1, self.sids1)
assert_series_almost_equal(actual, expected)
# alt input table
expected = pd.Series([2, 1, 0], index=self.sids2)
actual = alpha_diversity('observed_otus', self.table2, self.sids2)
assert_series_almost_equal(actual, expected)
def test_observed_otus(self):
# expected values hand-calculated
expected = pd.Series([3, 3, 3, 3], index=self.sids1)
actual = alpha_diversity('observed_otus', self.table1, self.sids1)
assert_series_almost_equal(actual, expected)
# function passed instead of string
actual = alpha_diversity(observed_otus, self.table1, self.sids1)
assert_series_almost_equal(actual, expected)
# alt input table
expected = pd.Series([2, 1, 0], index=self.sids2)
actual = alpha_diversity('observed_otus', self.table2, self.sids2)
assert_series_almost_equal(actual, expected)
def test_no_ids(self):
# expected values hand-calculated
expected = pd.Series([3, 3, 3, 3])
actual = alpha_diversity('observed_otus', self.table1)
assert_series_almost_equal(actual, expected)
def test_to_series_1x1(self):
series = self.dm_1x1.to_series()
exp = pd.Series([], index=[])
assert_series_almost_equal(series, exp)
def test_to_series_2x2(self):
series = self.dm_2x2.to_series()
exp = pd.Series([0.123], index=pd.Index([('a', 'b')]))
assert_series_almost_equal(series, exp)
def test_empty(self):
# empty vector
actual = alpha_diversity('observed_otus', np.array([], dtype=np.int64))
expected = pd.Series([0])
assert_series_almost_equal(actual, expected)
# array of empty vector
actual = alpha_diversity('observed_otus',
np.array([[]], dtype=np.int64))
expected = pd.Series([0])
assert_series_almost_equal(actual, expected)
# array of empty vectors
actual = alpha_diversity('observed_otus',
np.array([[], []], dtype=np.int64))
expected = pd.Series([0, 0])
assert_series_almost_equal(actual, expected)
# empty vector
actual = alpha_diversity('faith_pd', np.array([], dtype=np.int64),
tree=self.tree1, otu_ids=[])
expected = pd.Series([0.])
assert_series_almost_equal(actual, expected)
# array of empty vector
actual = alpha_diversity('faith_pd',
np.array([[]], dtype=np.int64),
tree=self.tree1, otu_ids=[])
expected = pd.Series([0.])
assert_series_almost_equal(actual, expected)
# array of empty vectors
actual = alpha_diversity('faith_pd',
np.array([[], []], dtype=np.int64),
tree=self.tree1, otu_ids=[])
expected = pd.Series([0., 0.])
assert_series_almost_equal(actual, expected)
def test_not_equal(self):
# no pair of series should compare equal
for s1, s2 in itertools.permutations(self.series, 2):
with self.assertRaises(AssertionError):
assert_series_almost_equal(s1, s2)
def test_to_series_default_ids(self):
series = DistanceMatrix(self.dm_2x2_data).to_series()
exp = pd.Series([0.123], index=pd.Index([('0', '1')]))
assert_series_almost_equal(series, exp)
def test_to_series_default_ids(self):
series = DistanceMatrix(self.dm_2x2_data).to_series()
exp = pd.Series([0.123], index=pd.Index([('0', '1')]))
assert_series_almost_equal(series, exp)
def test_to_series_2x2(self):
series = self.dm_2x2.to_series()
exp = pd.Series([0.123], index=pd.Index([('a', 'b')]))
assert_series_almost_equal(series, exp)
def test_empty(self):
# empty vector
actual = alpha_diversity('observed_otus', np.array([], dtype=np.int64))
expected = pd.Series([0])
assert_series_almost_equal(actual, expected)
# array of empty vector
actual = alpha_diversity('observed_otus', np.array([[]],
dtype=np.int64))
expected = pd.Series([0])
assert_series_almost_equal(actual, expected)
# array of empty vectors
actual = alpha_diversity('observed_otus',
np.array([[], []], dtype=np.int64))
expected = pd.Series([0, 0])
assert_series_almost_equal(actual, expected)
# empty vector
actual = alpha_diversity('faith_pd',
np.array([], dtype=np.int64),
tree=self.tree1,
otu_ids=[])
expected = pd.Series([0.])
assert_series_almost_equal(actual, expected)
# array of empty vector
actual = alpha_diversity('faith_pd',
np.array([[]], dtype=np.int64),
tree=self.tree1,
otu_ids=[])
expected = pd.Series([0.])
assert_series_almost_equal(actual, expected)
# array of empty vectors
actual = alpha_diversity('faith_pd',
np.array([[], []], dtype=np.int64),
tree=self.tree1,
otu_ids=[])
expected = pd.Series([0., 0.])
assert_series_almost_equal(actual, expected)
def test_no_ids(self):
# expected values hand-calculated
expected = pd.Series([3, 3, 3, 3])
actual = alpha_diversity('observed_otus', self.table1)
assert_series_almost_equal(actual, expected)
def test_to_series_1x1(self):
series = self.dm_1x1.to_series()
exp = pd.Series([], index=[])
assert_series_almost_equal(series, exp)
def test_to_series_3x3(self):
series = self.dm_3x3.to_series()
exp = pd.Series([0.01, 4.2, 0.01, 12.0, 4.2, 12.0],
index = [('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')])
assert_series_almost_equal(series, exp)
