Add tests for SQS structures #218
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Actionable comments posted: 2
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- tests/test_sqs.py (1 hunks)
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tests/test_sqs.py (2)
1-3: Ensure correct imports and module usage.The imports appear to be correct and relevant to the operations performed in the test cases.
6-6: Class definition for SQS testing.The class
SQSTestCaseis appropriately named and inherits fromunittest.TestCase, which is standard for writing unit tests in Python.
| def test_sqs_structures_with_stats(self): | ||
| structures_lst, sro_breakdown, num_iterations, cycle_time = stk.build.sqs_structures( | ||
| structure=bulk("Au", cubic=True).repeat([2, 2, 2]), | ||
| mole_fractions={"Cu": 0.5, "Au": 0.5}, | ||
| weights=None, | ||
| objective=0.0, | ||
| iterations=1e6, | ||
| output_structures=10, | ||
| mode="random", | ||
| num_threads=None, | ||
| prefactors=None, | ||
| pair_weights=None, | ||
| rtol=None, | ||
| atol=None, | ||
| which=None, | ||
| shell_distances=None, | ||
| minimal=True, | ||
| similar=True, | ||
| return_statistics=True, | ||
| ) | ||
| self.assertEqual(len(structures_lst), 10) | ||
| symbols_lst = [s.get_chemical_symbols() for s in structures_lst] | ||
| for s in symbols_lst: | ||
| self.assertEqual(len(s), 32) | ||
| for el in ["Au", "Cu"]: | ||
| self.assertAlmostEqual(s.count(el)/len(s), 0.5) | ||
| for sro in sro_breakdown: | ||
| self.assertEqual(list(sro.keys()), ['Cu-Cu', 'Cu-Au', 'Au-Au']) | ||
| self.assertEqual(num_iterations, 1000000.0) | ||
| self.assertTrue(cycle_time < 10) |
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Review of test_sqs_structures_with_stats method.
This method tests the sqs_structures function with the return of statistical data. It is structured similarly to the previous test but includes additional assertions for the statistical data returned. The checks for num_iterations and cycle_time are good practices to ensure performance expectations are met.
The method could benefit from more granular tests for the statistical data to ensure that the values within sro_breakdown are within expected ranges or follow expected distributions, rather than just checking the keys.
+ # Example of a more detailed check for statistical data
+ for sro in sro_breakdown:
+ self.assertTrue(0 <= sro['Cu-Cu'] <= 1)Also, consider using a more descriptive variable name than sro to enhance readability.
Committable suggestion was skipped due to low confidence.
| def test_sqs_structures_no_stats(self): | ||
| structures_lst = stk.build.sqs_structures( | ||
| structure=bulk("Au", cubic=True).repeat([2, 2, 2]), | ||
| mole_fractions={"Cu": 0.5, "Au": 0.5}, | ||
| weights=None, | ||
| objective=0.0, | ||
| iterations=1e6, | ||
| output_structures=10, | ||
| mode="random", | ||
| num_threads=None, | ||
| prefactors=None, | ||
| pair_weights=None, | ||
| rtol=None, | ||
| atol=None, | ||
| which=None, | ||
| shell_distances=None, | ||
| minimal=True, | ||
| similar=True, | ||
| return_statistics=False, | ||
| ) | ||
| self.assertEqual(len(structures_lst), 10) | ||
| symbols_lst = [s.get_chemical_symbols() for s in structures_lst] | ||
| for s in symbols_lst: | ||
| self.assertEqual(len(s), 32) | ||
| for el in ["Au", "Cu"]: | ||
| self.assertAlmostEqual(s.count(el)/len(s), 0.5) | ||
|
|
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Review of test_sqs_structures_no_stats method.
This method tests the sqs_structures function without returning statistical data. The parameters used in the function call are well-defined, and the assertions check the length of the output list and the properties of the structures, ensuring that the elements are distributed as expected.
However, the use of 1e6 directly in the iterations parameter could be improved for readability by defining it as a constant at the beginning of the test class.
+ ITERATIONS = 1000000
- iterations=1e6,
+ iterations=ITERATIONS,Additionally, consider adding more descriptive comments to each block of assertions to clarify what each test is specifically verifying.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def test_sqs_structures_no_stats(self): | |
| structures_lst = stk.build.sqs_structures( | |
| structure=bulk("Au", cubic=True).repeat([2, 2, 2]), | |
| mole_fractions={"Cu": 0.5, "Au": 0.5}, | |
| weights=None, | |
| objective=0.0, | |
| iterations=1e6, | |
| output_structures=10, | |
| mode="random", | |
| num_threads=None, | |
| prefactors=None, | |
| pair_weights=None, | |
| rtol=None, | |
| atol=None, | |
| which=None, | |
| shell_distances=None, | |
| minimal=True, | |
| similar=True, | |
| return_statistics=False, | |
| ) | |
| self.assertEqual(len(structures_lst), 10) | |
| symbols_lst = [s.get_chemical_symbols() for s in structures_lst] | |
| for s in symbols_lst: | |
| self.assertEqual(len(s), 32) | |
| for el in ["Au", "Cu"]: | |
| self.assertAlmostEqual(s.count(el)/len(s), 0.5) | |
| ITERATIONS = 1000000 | |
| def test_sqs_structures_no_stats(self): | |
| structures_lst = stk.build.sqs_structures( | |
| structure=bulk("Au", cubic=True).repeat([2, 2, 2]), | |
| mole_fractions={"Cu": 0.5, "Au": 0.5}, | |
| weights=None, | |
| objective=0.0, | |
| iterations=ITERATIONS, | |
| output_structures=10, | |
| mode="random", | |
| num_threads=None, | |
| prefactors=None, | |
| pair_weights=None, | |
| rtol=None, | |
| atol=None, | |
| which=None, | |
| shell_distances=None, | |
| minimal=True, | |
| similar=True, | |
| return_statistics=False, | |
| ) | |
| self.assertEqual(len(structures_lst), 10) | |
| symbols_lst = [s.get_chemical_symbols() for s in structures_lst] | |
| for s in symbols_lst: | |
| self.assertEqual(len(s), 32) | |
| for el in ["Au", "Cu"]: | |
| self.assertAlmostEqual(s.count(el)/len(s), 0.5) |
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Actionable comments posted: 1
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- tests/test_sqs.py (1 hunks)
Additional context used
Ruff
tests/test_sqs.py
7-7:
sqsgeneratorimported but unused; consider usingimportlib.util.find_specto test for availability(F401)
Additional comments not posted (2)
tests/test_sqs.py (2)
16-42: Consider further improvements totest_sqs_structures_no_stats.The existing comments about readability and adding more descriptive comments are still valid and should be implemented. Additionally, consider checking the type of elements in
structures_lstto ensure they are instances of the expected class, which would make the tests more robust.
43-72: Consider further improvements totest_sqs_structures_with_stats.The existing comments about adding more granular tests for statistical data and enhancing readability are still valid and should be implemented. Additionally, consider verifying the statistical data against known properties or expected values to ensure the accuracy of the
sqs_structuresmethod.
| try: | ||
| import sqsgenerator | ||
|
|
||
| sqsgenerator_not_available = False | ||
| except ImportError: | ||
| sqsgenerator_not_available = True |
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Refactor module availability check.
The current method of checking for sqsgenerator availability using a try-except block is functional but can be optimized. Consider using importlib.util.find_spec for a more direct and cleaner check.
- try:
- import sqsgenerator
- sqsgenerator_not_available = False
- except ImportError:
- sqsgenerator_not_available = True
+ from importlib import util
+ sqsgenerator_not_available = util.find_spec("sqsgenerator") is NoneCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| import sqsgenerator | |
| sqsgenerator_not_available = False | |
| except ImportError: | |
| sqsgenerator_not_available = True | |
| from importlib import util | |
| sqsgenerator_not_available = util.find_spec("sqsgenerator") is None |
Tools
Ruff
7-7:
sqsgeneratorimported but unused; consider usingimportlib.util.find_specto test for availability(F401)
Summary by CodeRabbit
sqs_structuresmethod, ensuring accurate generation of special quasirandom structures with specified properties.