Extend symmetrizer #215
Extend symmetrizer #215
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Outside diff range, codebase verification and nitpick comments (1)
structuretoolkit/analyse/symmetry.py (1)
Line range hint
235-266: Address unused variable and verify tensor operations.The variable
vis defined but not used, which could be a mistake or leftover from a previous version of the code. Additionally, the tensor operations usingnp.einsumneed to be verified for correctness, especially since they are crucial for the new functionality.- v = np.transpose( - tensor[_get_outer_slicer(tensor.shape, self.permutations)], - _back_order(tensor.shape, len(self._structure)) - ) - return np.einsum( - _get_einsum_str(tensor.shape, len(self._structure)), - *sum([s == len(self._structure) for s in tensor.shape]) * [self.rotations], - tensor, - ) + # Assuming the intended use of `v` was in the einsum operation + return np.einsum( + _get_einsum_str(tensor.shape, len(self._structure)), + *sum([s == len(self._structure) for s in tensor.shape]) * [self.rotations], + np.transpose( + tensor[_get_outer_slicer(tensor.shape, self.permutations)], + _back_order(tensor.shape, len(self._structure)) + ), + )Tools
Ruff
257-257: Local variable
vis assigned to but never usedRemove assignment to unused variable
v(F841)
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Files selected for processing (2)
- structuretoolkit/analyse/symmetry.py (3 hunks)
- tests/test_symmetry.py (1 hunks)
Additional context used
Ruff
structuretoolkit/analyse/symmetry.py
257-257: Local variable
vis assigned to but never usedRemove assignment to unused variable
v(F841)
Additional comments not posted (5)
tests/test_symmetry.py (1)
Line range hint
1-463: Verify coverage for new tensor symmetrization functionality.The removal of the
TestSymmetrizeTensorsclass raises concerns about whether the new tensor symmetrization functionality introduced insymmetry.pyis adequately tested. It's crucial to ensure that the remaining tests or new tests cover this extended functionality.structuretoolkit/analyse/symmetry.py (4)
429-433: Review the_get_inner_slicerfunction for correctness.Ensure that the
_get_inner_slicerfunction correctly generates slicing tuples as intended. This function is critical for tensor manipulation insymmetrize_tensor.Verification successful
Verification of
_get_inner_slicerfunctionThe
_get_inner_slicerfunction correctly generates slicing tuples as intended. It initializes a list ofNonevalues of lengthn, sets the first andi-thelements toslice(None), and returns the list as a tuple. This behavior aligns with the expected functionality for tensor manipulation insymmetrize_tensor.
- The function definition is located in
structuretoolkit/analyse/symmetry.py:
- Line 429:
def _get_inner_slicer(n, i):- Line 430:
s = [None for nn in range(n)]- Line 431:
s[0] = slice(None)- Line 432:
s[i] = slice(None)- Line 433:
return tuple(s)Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correctness of the `_get_inner_slicer` function by testing various input scenarios. ast-grep --lang python --pattern $'def _get_inner_slicer($$$) { $$$ }'Length of output: 528
Script:
#!/bin/bash # Correcting the pattern to verify the `_get_inner_slicer` function by retrieving its definition. ast-grep --lang python --pattern 'def _get_inner_slicer($_$) { $$$ }'Length of output: 528
Script:
#!/bin/bash # Correcting the pattern to verify the `_get_inner_slicer` function by retrieving its definition. ast-grep --lang python --pattern 'def _get_inner_slicer($_):\n $$$'Length of output: 72
Script:
#!/bin/bash # Using `rg` to search for the `_get_inner_slicer` function definition in the codebase. rg 'def _get_inner_slicer' -A 5Length of output: 385
450-461: Review the_back_orderfunction for correctness.The
_back_orderfunction reorders tensor indices. Verify that it correctly handles the reordering based on the tensor shape.
436-447: Review the_get_outer_slicerfunction for correctness.The
_get_outer_slicerfunction is used to generate slicing tuples for tensor operations. Verify that it handles different tensor shapes correctly.
463-473: Review the_get_einsum_strfunction for correctness.The
_get_einsum_strfunction generates the string fornp.einsumoperations. Ensure that it correctly constructs the string based on the tensor shape and structure.
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Files selected for processing (1)
- structuretoolkit/analyse/symmetry.py (2 hunks)
Additional context used
Ruff
structuretoolkit/analyse/symmetry.py
255-255: Local variable
vis assigned to but never usedRemove assignment to unused variable
v(F841)
Additional comments not posted (5)
structuretoolkit/analyse/symmetry.py (5)
426-430: Add comments for clarity in_get_inner_slicer.The function appears to be correctly implemented for generating slicing indices. However, adding inline comments explaining the purpose of each step would enhance readability and maintainability.
433-444: Consider optimizing the_get_outer_slicerfunction.While the function is correctly implemented, the loop and conditional checks might be optimized for better performance, especially for large tensors. Consider exploring alternatives that could simplify or speed up this slicing operation.
447-458: Refactor for simplicity in_back_order.The function's logic is correct, but its complexity could hinder readability and maintainability. Consider refactoring to simplify the operations, possibly by breaking it down into smaller, more descriptive helper functions or by using more descriptive variable names.
460-470: Add detailed comments in_get_einsum_str.This function cleverly constructs the einsum operation string dynamically. Given the complexity of the operations involved, adding detailed comments explaining how the einsum string is constructed would greatly aid in understanding and maintaining the code.
250-263: Remove unused variable and verify tensor operations.The variable
vis assigned but never used, which could be a leftover from debugging. Consider removing it to clean up the code.- v = np.transpose( + _ = np.transpose(Also, ensure the complex numpy operations in this function are correctly handling the tensor symmetrization as intended. It might be beneficial to add more detailed comments or documentation explaining each step for future maintainability.
Tools
Ruff
255-255: Local variable
vis assigned to but never usedRemove assignment to unused variable
v(F841)
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Outside diff range, codebase verification and nitpick comments (1)
tests/test_symmetry.py (1)
Line range hint
31-150: Review of New and Modified Test Methods
Class Name Change: The renaming of the test class from
TestAtomstoTestSymmetryis appropriate given the focus on symmetry operations. This change aligns the class name with its functionality, enhancing readability and maintainability.Method
test_symmetrize_tensor: This new test method effectively checks the functionality of thesymmetrize_tensorfunction by validating the properties of the symmetrized tensor:
- The peak-to-peak (ptp) values being less than a very small threshold suggests correct symmetrization of tensor components.
- The shape assertions ensure that the tensor dimensions are maintained correctly after symmetrization.
- Using random data for tensors is a good practice as it tests the function under various scenarios. However, it might be beneficial to include some deterministic tests to ensure reproducibility and easier debugging.
- The use of
assertAlmostEqualfor floating-point comparisons is appropriate, considering the precision issues inherent in such operations.Coverage and Exception Testing: The tests cover various scenarios, including error handling and boundary conditions. The use of
assertRaisesto check for expected exceptions when input conditions are not met is a good practice as it ensures robustness.Randomness in Tests: While using random numbers can simulate a wide range of inputs, it's also important to include specific, deterministic test cases that serve as clear examples of what is expected. This can aid in debugging and understanding the test suite.
# Suggestion to add deterministic test cases alongside random ones deterministic_tensor = np.array([[1, 2, 3], [2, 1, 4], [3, 4, 1]]) sym_tensor = symmetry.symmetrize_tensor(deterministic_tensor) expected_tensor = np.array([[...]]) # Expected symmetrized tensor self.assertTrue(np.allclose(sym_tensor, expected_tensor))Documentation and Comments: Adding comments within the test methods to explain the purpose of each assertion and the significance of the values being tested can greatly improve the maintainability and readability of the test code.
# Example comment addition # Check that the diagonal elements of the symmetrized tensor have minimal variance, indicating proper symmetrization self.assertLess(sym_tensor.diagonal().ptp(), 1.0e-8)Overall, the test suite modifications support the extended functionality of the
symmetrize_tensormethod effectively. The tests seem well-structured to validate the new capabilities, ensuring that the symmetrization process is both accurate and efficient.
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Files selected for processing (2)
- structuretoolkit/analyse/symmetry.py (2 hunks)
- tests/test_symmetry.py (3 hunks)
Files skipped from review as they are similar to previous changes (1)
- structuretoolkit/analyse/symmetry.py
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@samwaseda |
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? It’s all covered |
The Github action |
I extended
symmetrize_tensorto make it possible to symmetrize literally any tensor.Summary by CodeRabbit
New Features
Tests