From 8e592739597fb8594c051fc705d092f085101968 Mon Sep 17 00:00:00 2001
From: Sven van der Burg <s.vanderburg@esciencecenter.nl>
Date: Tue, 19 Nov 2024 09:14:15 +0100
Subject: [PATCH] Fix #367: Improve test using a Mock implementation challenge

---
 _episodes/33-code-decoupling-abstractions.md | 27 +++++++++++++-------
 1 file changed, 18 insertions(+), 9 deletions(-)

diff --git a/_episodes/33-code-decoupling-abstractions.md b/_episodes/33-code-decoupling-abstractions.md
index 5db6932f..fffe2afd 100644
--- a/_episodes/33-code-decoupling-abstractions.md
+++ b/_episodes/33-code-decoupling-abstractions.md
@@ -420,38 +420,47 @@ Now whenever you call `mock_version.method_to_mock()` the return value will be `
 
 
 > ## Exercise: Test Using a Mock Implementation
+> Add a new test file called `test_compute_data.py` in the tests folder and add a test to verify 
+> whether we can successfully run `analyse_data()` when passing it a data source.
+> 
 > Complete this test for `analyse_data()`, using a mock object in place of the
 > `data_source`:
 > ```python
 > from unittest.mock import Mock
 >
-> def test_compute_data_mock_source():
+> def test_analyse_data_mock_source():
 >   from inflammation.compute_data import analyse_data
 >   data_source = Mock()
 >
 >   # TODO: configure data_source mock
 >
->   result = analyse_data(data_source)
+>   analyse_data(data_source)
 >
->   # TODO: add assert on the contents of result
 > ```
 > Create a mock that returns some fixed data and to use as the `data_source` in order to test
 > the `analyse_data` method.
-> Use this mock in a test.
->
+> Use this mock in the test.
+> 
 > Do not forget to import `Mock` from the `unittest.mock` package.
+> 
+> Note that the `analyse_data()` function visualizes the data with `views.visualize(graph_data)`.
+> You do not have to assert that this is done correctly. For now, it is fine to just check that 
+> the call to `analyse_data()` can proceed successfully. 
+> 
+> In the next episode we will adapt the `analyse_data()` function
+> so that we can write a test that asserts whether standard deviation calculations are correct.
+>
 >> ## Solution
 >> ```python
 >> from unittest.mock import Mock
 >>
->> def test_compute_data_mock_source():
+>> def test_analyse_data_mock_source():
 >>   from inflammation.compute_data import analyse_data
 >>   data_source = Mock()
 >>   data_source.load_inflammation_data.return_value = [[[0, 2, 0]],
 >>                                                      [[0, 1, 0]]]
 >>
->>   result = analyse_data(data_source)
->>   npt.assert_array_almost_equal(result, [0, math.sqrt(0.25) ,0])
+>>   analyse_data(data_source)
 >> ```
 > {: .solution}
 {: .challenge}
@@ -459,7 +468,7 @@ Now whenever you call `mock_version.method_to_mock()` the return value will be `
 ## Safe Code Structure Changes
 
 With the changes to the code structure we have done using code decoupling and abstractions we have 
-already refactored our code to a certain extent but we have not tested that the changes work as 
+already refactored our code to a certain extent, but we have not fully tested that the changes work as 
 intended. 
 We will now look into how to properly refactor code to guarantee that the code still works 
 as before any modifications.