test-related updates

README.md

@@ -29,19 +29,9 @@ conda env create -n q2-boots-2024.10 -f https://raw.githubusercontent.com/qiime2
 conda activate q2-boots-2024.10
 ```
 
-## Testing and using `q2-boots`
+## Using `q2-boots`
 
-After completing the install steps above, confirm that everything is working as expected by running:
-
-```shell
-make test
-```
-
-You should get a report that tests were run, and you should see that all tests passed and none failed.
-It's usually ok if some warnings are reported.
-
-If all of the tests pass, you're ready to use the plugin.
-Start by making QIIME 2's command line interface aware of `q2-boots` by running:
+After completing the install steps above, make QIIME 2's command line interface aware of `q2-boots` by running:
 
 ```shell
 qiime dev refresh-cache

q2_boots/_normalize.py

@@ -17,11 +17,6 @@ def _bootstrap_iteration(table: biom.Table, sampling_depth: int) -> biom.Table:
     table = table.subsample(sampling_depth, axis='sample', by_id=False,
                             with_replacement=True)
 
-    if table.is_empty():
-        return ValueError('The output table contains no samples or features.'
-                          'Verify your table is valid and that you provided a '
-                          'shallow enough samplign depth')
-
     return table
 
 

q2_boots/tests/test_normalize.py

@@ -16,12 +16,42 @@
 
 class TestBootstrapIteration(TestCase):
 
-    def test_bootstrap_iteration(self):
+    def test_bootstrap_iteration_filters_samples(self):
         t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
                   ['O1', 'O2'],
                   ['S1', 'S2', 'S3'])
-        a = _bootstrap_iteration(t, 2)
-        self.assertEqual(a.shape, (2, 2))
+
+        observed = _bootstrap_iteration(t, 6)
+        self.assertTrue(observed.is_empty())
+
+        observed = _bootstrap_iteration(t, 5)
+        self.assertEqual(list(observed.ids(axis='sample')), ['S3'])
+
+        observed = _bootstrap_iteration(t, 2)
+        self.assertEqual(list(observed.ids(axis='sample')), ['S2', 'S3'])
+
+        observed = _bootstrap_iteration(t, 1)
+        self.assertEqual(list(observed.ids(axis='sample')), ['S1', 'S2', 'S3'])
+
+    def test_bootstrap_iteration_obtains_expected_counts(self):
+        t = Table(np.array([[0, 10, 30], [1, 10, 20]]),
+                  ['O1', 'O2'],
+                  ['S1', 'S2', 'S3'])
+
+        observed = _bootstrap_iteration(t, 1)
+        self.assertEqual(list(observed.sum(axis="sample")), [1., 1., 1.])
+
+        observed = _bootstrap_iteration(t, 10)
+        self.assertEqual(list(observed.sum(axis="sample")), [10., 10.])
+
+        observed = _bootstrap_iteration(t, 19)
+        self.assertEqual(list(observed.sum(axis="sample")), [19., 19.])
+
+        observed = _bootstrap_iteration(t, 25)
+        self.assertEqual(list(observed.sum(axis="sample")), [25.])
+
+        observed = _bootstrap_iteration(t, 49)
+        self.assertEqual(list(observed.sum(axis="sample")), [49.])
 
 
 if __name__ == "__main__":