Modify defensive programming section

episodes/24-diagnosing-issues-improving-robustness.md

@@ -610,13 +610,14 @@ def test_patient_normalise(test, expected, expect_raises):
 
     if expect_raises is not None:
         with pytest.raises(expect_raises):
-            result = patient_normalise(np.array(test))
-            npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
+            patient_normalise(np.array(test))
     else:
         result = patient_normalise(np.array(test))
         npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
 ```
 
+Notice that under the `pytest.raises` context manager, it isn't necessary to perform the `npt.assert_allclose` because just the call to `patient_normalise()` will raise the `ValueError`.
+
 Be sure to commit your changes so far and push them to GitHub.
 
 :::::::::::::::::::::::::::::::::::::::  challenge
@@ -629,9 +630,17 @@ You will find the Python function
 [`isinstance`](https://docs.python.org/3/library/functions.html#isinstance)
 useful here, as well as the Python exception
 [`TypeError`](https://docs.python.org/3/library/exceptions.html#TypeError).
+
+You can take this even further if your solution code involves multiple `ValueError`s or `TypeError`'s being raised at different locations.
+In this case, you want to make sure the function is raising the precise `Exception` that occurs at a specific point in your function.
+You might have noticed that `Exception`s can take a string argument corresponding to a message / description of the exception that has occurred.
+The [`pytest.raises()`](https://docs.pytest.org/en/stable/reference/reference.html#pytest.raises) context manager can query this message with its `match=` argument.
+See if you can use that to more precisely test the exceptions raised by your function.
+
 Once you are done, commit your new files,
 and push the new commits to your remote repository on GitHub.
 
+
 :::::::::::::::  solution
 
 ## Solution
@@ -683,6 +692,11 @@ from inflammation.models import patient_normalise
             None,
             TypeError,
         ),
+        (
+            [4, 5, 6],
+            None,
+            ValueError,
+        ),
         (
             [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
             [[0.33, 0.67, 1], [0.67, 0.83, 1], [0.78, 0.89, 1]],
@@ -695,8 +709,56 @@ def test_patient_normalise(test, expected, expect_raises):
         test = np.array(test)
     if expect_raises is not None:
         with pytest.raises(expect_raises):
-          result = patient_normalise(test)
-          npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
+          patient_normalise(test)
+
+    else:
+        result = patient_normalise(test)
+        npt.assert_allclose(result, np.array(expected), rtol=1e-2, atol=1e-2)
+...
+```
+
+Or, if you decided to match the specific exceptions in `test/test_models.py`:
+
+```python
+from inflammation.models import patient_normalise
+...
+@pytest.mark.parametrize(
+    "test, expected, expect_raises",
+    [
+        ...
+        (
+            [[-1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            None,
+            ValueError('inflammation values should be non-negative'),
+        ),
+        (
+            [4, 5, 6],
+            None,
+            ValueError('inflammation array should be 2-dimensional'),
+        ),
+        (
+            'hello',
+            None,
+            TypeError('data input should be ndarray'),
+        ),
+        (
+            3,
+            None,
+            TypeError('data input should be ndarray'),
+        ),
+        (
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            [[0.33, 0.67, 1], [0.67, 0.83, 1], [0.78, 0.89, 1]],
+            None,
+        )
+    ])
+def test_patient_normalise(test, expected, expect_raises):
+    """Test normalisation works for arrays of one and positive integers."""
+    if isinstance(test, list):
+        test = np.array(test)
+    if expect_raises is not None:
+        with pytest.raises(expect_raises, match=str(expect_raises)):
+          patient_normalise(test)
 
     else:
         result = patient_normalise(test)