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🧪 Add unit tests for coherence_index calculation #11

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33 changes: 33 additions & 0 deletions Copy_of_Untitled7.ipynb
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"metadata": {}
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# --- UNIT TESTS ---\n",
"import numpy as np\n",
"\n",
"def test_coherence_index():\n",
" # Test with r=0, should be 1.0\n",
" assert np.isclose(coherence_index(0, 1.5), 1.0), \"Failed on r=0\"\n",
" \n",
" # 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",
Comment on lines +239 to +248
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@gemini-code-assist gemini-code-assist bot Mar 14, 2026

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medium

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")

" \n",
" # 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",
Comment on lines +250 to +252
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medium

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"

" print(\"All tests for coherence_index passed!\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" test_coherence_index()\n"
]
}
]
}