🧪 Add unit tests for coherence_index calculation#11
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Co-authored-by: Sir-Ripley <31619989+Sir-Ripley@users.noreply.github.com>
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Reviewer's GuideAdds an inline unit-test cell in the notebook to exercise coherence_index across scalar and array inputs, and wires it to run under the existing main convention; also introduces a .gitignore file (content not shown in diff). Sequence diagram for coherence_index unit test executionsequenceDiagram
actor User
participant PythonInterpreter
participant NotebookMainCell as Notebook_main_block
participant TestFunction as test_coherence_index
participant CoherenceFunction as coherence_index
participant NumPy as numpy
User ->> PythonInterpreter: run notebook
PythonInterpreter ->> NotebookMainCell: evaluate cells
NotebookMainCell ->> NotebookMainCell: check __name__ == __main__
NotebookMainCell ->> TestFunction: test_coherence_index()
TestFunction ->> NumPy: import numpy as np
TestFunction ->> CoherenceFunction: coherence_index(0, 1.5)
CoherenceFunction -->> TestFunction: result_scalar_r0
TestFunction ->> NumPy: np.isclose(result_scalar_r0, 1.0)
NumPy -->> TestFunction: bool_r0
TestFunction ->> CoherenceFunction: coherence_index(10, 1.0)
CoherenceFunction -->> TestFunction: result_scalar_typical
TestFunction ->> NumPy: np.exp(-10 / (10 * 1.0))
NumPy -->> TestFunction: expected_scalar_typical
TestFunction ->> NumPy: np.isclose(result_scalar_typical, expected_scalar_typical)
NumPy -->> TestFunction: bool_typical
TestFunction ->> NumPy: np.array([0, 10, 20])
NumPy -->> TestFunction: r_arr
TestFunction ->> CoherenceFunction: coherence_index(r_arr, 1.0)
CoherenceFunction -->> TestFunction: result_array
TestFunction ->> NumPy: np.exp(-r_arr / (10 * 1.0))
NumPy -->> TestFunction: expected_array
TestFunction ->> NumPy: np.testing.assert_allclose(result_array, expected_array)
NumPy -->> TestFunction: assertion_ok
TestFunction -->> NotebookMainCell: tests_done
NotebookMainCell -->> PythonInterpreter: print All tests passed
PythonInterpreter -->> User: output
Flow diagram for test_coherence_index logicflowchart TD
A["Start test_coherence_index"] --> B["Import numpy as np"]
B --> C["Compute scalar case r = 0, rho_info = 1.5"]
C --> D["Call coherence_index(0, 1.5)"]
D --> E["Assert np.isclose(result, 1.0)"]
E --> F["Set r = 10, rho_info = 1.0"]
F --> G["Compute expected = np.exp(-10 / (10 * 1.0))"]
G --> H["Call coherence_index(r, rho_info)"]
H --> I["Assert np.isclose(result, expected)"]
I --> J["Create r_arr = np.array([0, 10, 20])"]
J --> K["Compute expected_arr = np.exp(-r_arr / (10 * 1.0))"]
K --> L["Call coherence_index(r_arr, 1.0)"]
L --> M["np.testing.assert_allclose(result_arr, expected_arr)"]
M --> N["Print All tests for coherence_index passed!"]
N --> O["End test_coherence_index"]
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request focuses on enhancing the reliability of the Highlights
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Code Review
This pull request adds valuable unit tests for the coherence_index function, significantly improving the robustness and maintainability of the code. The tests cover several key scenarios, including boundary conditions and array inputs. My feedback focuses on enhancing the test suite further by using pre-calculated values for assertions, which makes tests less brittle, and by adding coverage for edge cases like division by zero to make the tests more comprehensive.
| " # Test with typical values\n", | ||
| " r = 10\n", | ||
| " rho_info = 1.0\n", | ||
| " expected = np.exp(-10 / (10 * 1.0))\n", | ||
| " assert np.isclose(coherence_index(r, rho_info), expected), \"Failed on typical values\"\n", | ||
| " \n", | ||
| " # Test with array input\n", | ||
| " r_arr = np.array([0, 10, 20])\n", | ||
| " expected_arr = np.exp(-r_arr / (10 * 1.0))\n", | ||
| " np.testing.assert_allclose(coherence_index(r_arr, 1.0), expected_arr, err_msg=\"Failed on array input\")\n", |
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While these tests are a great start, they can be made more robust and maintainable. Currently, the expected values are calculated by re-implementing the logic from the coherence_index function. This makes the tests brittle; if the implementation of coherence_index changes (e.g., the constant 10 is updated), these tests might still pass if the same logic error is made in both places, or they will require updating the same logic in two places.
A better practice is to test against pre-calculated, known-good values. This verifies that the function produces the correct output for a given input, making the test's intent clearer and more resilient to implementation changes.
# Test with typical values
r = 10
rho_info = 1.0
expected = 0.36787944 # np.exp(-1)
assert np.isclose(coherence_index(r, rho_info), expected), "Failed on typical values"
# Test with array input
r_arr = np.array([0, 10, 20])
expected_arr = np.array([1.0, 0.36787944, 0.13533528]) # [np.exp(0), np.exp(-1), np.exp(-2)]
np.testing.assert_allclose(coherence_index(r_arr, 1.0), expected_arr, err_msg="Failed on array input")
| " # Test error condition (e.g. division by zero if rho_info is 0)\n", | ||
| " # Although the function doesn't explicitly handle it, it will return inf or raise warning. \n", | ||
| " # We focus on the functional math values.\n", |
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The comment here correctly identifies the division-by-zero edge case when rho_info is 0. While you've noted you're focusing on functional values, a comprehensive test suite should cover such predictable edge cases to prevent future regressions and clarify the function's behavior under these conditions.
I suggest replacing these comments with actual test assertions for rho_info = 0. For r > 0, the function should return 0.0 (and raise a RuntimeWarning). For r = 0, it should return NaN.
# Test error condition (e.g. division by zero if rho_info is 0)
# This should produce a RuntimeWarning and result in 0.0 for r > 0.
assert np.isclose(coherence_index(10, 0), 0.0), "Failed on rho_info=0"
# Test with r=0 and rho_info=0, which should result in NaN.
assert np.isnan(coherence_index(0, 0)), "Failed on r=0, rho_info=0"
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🎯 What: The
coherence_indexfunction inCopy_of_Untitled7.ipynbwas missing tests, making it susceptible to regressions. Added a new cell with a unit test to verify the function's output.📊 Coverage: The tests now cover:
✨ Result: Increased test coverage and confidence in the
coherence_indexlogic. Tests are executed under theif __name__ == "__main__":block as per existing convention.PR created automatically by Jules for task 17589903377000490864 started by @Sir-Ripley
Summary by Sourcery
Tests: