def test_first_differences_single_sample(self):
single_sam = qiime2.Metadata(md[(md['ind'] == 1) & (md['Time'] == 1)])
with self.assertRaisesRegex(ValueError, "state_column must contain"):
first_differences(
metadata=single_sam, state_column='Time',
individual_id_column='ind',
metric='Value', replicate_handling='drop')
def test_first_differences_nonnumeric_metric_error(self):
with self.assertRaisesRegex(ValueError, "not a numeric"):
first_differences(metadata=self.md_ecam_fp,
state_column='month',
individual_id_column='studyid',
metric='delivery',
replicate_handling='drop')
def test_first_differences_baseline_invalid_baseline(self):
with self.assertRaisesRegex(ValueError, "must be a valid state"):
first_differences(
metadata=qiime2.Metadata(md_one_subject_many_times),
state_column='Time',
individual_id_column='ind',
metric='Value',
replicate_handling='drop',
baseline=27)
def test_first_differences_taxa(self):
exp = pd.read_csv(self.get_data_path(
'ecam-taxa-first-differences.tsv'),
sep='\t', squeeze=True, index_col=0)
obs = first_differences(
metadata=self.md_ecam_fp, state_column='month',
individual_id_column='studyid',
metric='e2c3ff4f647112723741aa72087f1bfa',
replicate_handling='drop', table=self.table_ecam_fp)
pdt.assert_series_equal(obs, exp)
def test_first_differences_static(self):
exp = pd.Series([0., 0., 0., 0., 0., 0.],
index=['3', '4', '5', '9', '10', '11'],
name='Difference')
exp.index.name = '#SampleID'
obs = first_differences(
metadata=qiime2.Metadata(md_static), state_column='Time',
individual_id_column='ind',
metric='Value', replicate_handling='drop')
pdt.assert_series_equal(obs.sort_index(), exp.sort_index())
def test_first_differences_single_individual(self):
exp = pd.Series([0.08], index=['3'], name='Difference')
exp.index.name = '#SampleID'
single_ind = qiime2.Metadata(md[md['ind'] == 1])
obs = first_differences(metadata=single_ind,
state_column='Time',
individual_id_column='ind',
metric='Value',
replicate_handling='drop')
pdt.assert_series_equal(obs.sort_index(), exp.sort_index())
def test_first_differences_baseline(self):
exp = pd.Series(
[-0.01, 0., 0.01, 0.07, 0.06, 0.09, 0.07, 0.1, 0.15, 0.12, 0.16],
index=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
name='Difference')
exp.index.name = '#SampleID'
obs = first_differences(
metadata=qiime2.Metadata(md_one_subject_many_times),
state_column='Time', individual_id_column='ind',
metric='Value', replicate_handling='drop', baseline=0)
pdt.assert_series_equal(obs, exp)
def test_first_distances_numeric_values_represented_as_strings(self):
numeric_values_represented_as_strings = pd.DataFrame(
[('0', '0.18', '1'), ('1', '0.21', '1')],
columns=['Time', 'Value', 'ind'],
index=['0', '1'])
exp = pd.Series([0.03], index=['1'], name='Difference')
exp.index.name = '#SampleID'
obs = first_differences(
metadata=qiime2.Metadata(numeric_values_represented_as_strings),
state_column='Time', individual_id_column='ind',
metric='Value', replicate_handling='drop')
pdt.assert_series_equal(obs, exp)
def test_first_differences_drop_duplicates(self):
obs = first_differences(
metadata=qiime2.Metadata(md_dup), state_column='Time',
individual_id_column='ind',
metric='Value', replicate_handling='random')
# The first diff of individual 2 is subject to random rep handling
mystery_number = obs.iloc[1]
if mystery_number < 0.051:
self.assertAlmostEqual(mystery_number, 0.05)
else:
self.assertAlmostEqual(mystery_number, 0.06)
# but other values are constant, so we will just drop in the mystery
# value and the exp/obs series should match.
exp = pd.Series([0.08, mystery_number, 0.12, 0.14,
0.14999999999999997],
index=['3', '4', '9', '10', '11'], name='Difference')
exp.index.name = '#SampleID'
pdt.assert_series_equal(obs.sort_index(), exp.sort_index())
def test_first_differences_empty(self):
with self.assertRaisesRegex(ValueError, "Metadata is empty"):
first_differences(
metadata=(qiime2.Metadata(pd.DataFrame({'a': [], 'b': []}))),
state_column='Time', individual_id_column='ind',
metric='Value', replicate_handling='drop')
