Refactor: seperate the coef_opt task into jy and pw #93
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📝 Walkthrough📝 WalkthroughWalkthroughThe changes involve updates to the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant TestScript as xtest.sh
participant API as api.py
User->>TestScript: Execute test
TestScript->>API: Run api.py with -v flag
API->>API: Initialize environment
API->>API: Optimize coefficients (jY or pw)
API-->>TestScript: Return results
TestScript-->>User: Display results
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Actionable comments posted: 8
🧹 Outside diff range and nitpick comments (1)
.github/tests/xtest.sh (1)
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1-17: Consider enhancing the script with error handling and performance improvements.While the current implementation is straightforward, consider the following enhancements:
- Add basic error handling to identify which script fails if an error occurs.
- Implement output management to make it easier to analyze the results.
- Consider adding an option for parallel execution to improve performance, especially useful when the number of scripts grows.
Here's a suggested implementation incorporating these improvements:
#!/usr/bin/bash # Function to run a single Python script run_script() { echo "Running $1..." if ! python3 "$1" -v > "$1.log" 2>&1; then echo "Error running $1. Check $1.log for details." return 1 fi } # List of scripts to run scripts=( "./SIAB/spillage/basistrans.py" "./SIAB/spillage/datparse.py" "./SIAB/spillage/index.py" "./SIAB/spillage/inputio.py" "./SIAB/spillage/jlzeros.py" "./SIAB/spillage/linalg_helper.py" "./SIAB/spillage/listmanip.py" "./SIAB/spillage/orbio.py" "./SIAB/spillage/orbscreen.py" "./SIAB/spillage/radial.py" "./SIAB/spillage/spillage.py" "./SIAB/spillage/struio.py" "./SIAB/spillage/lcao_wfc_analysis.py" "./SIAB/spillage/api.py" ) # Check for parallel execution flag if [ "$1" == "--parallel" ]; then echo "Running scripts in parallel..." for script in "${scripts[@]}"; do run_script "$script" & done wait else echo "Running scripts sequentially..." for script in "${scripts[@]}"; do run_script "$script" || exit 1 done fi echo "All scripts completed. Check individual .log files for detailed output."This implementation:
- Uses a function to run each script, capturing output and errors in separate log files.
- Allows for easy addition or removal of scripts from the list.
- Provides an option for parallel execution with the
--parallelflag.- Stops execution on the first error in sequential mode, but runs all scripts in parallel mode.
To use parallel execution, run the script with
./xtest.sh --parallel.
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📒 Files selected for processing (2)
- .github/tests/xtest.sh (1 hunks)
- SIAB/spillage/api.py (11 hunks)
🧰 Additional context used
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SIAB/spillage/api.py
268-268: Ambiguous variable name:
l(E741)
296-296: Ambiguous variable name:
l(E741)
855-855: f-string without any placeholders
Remove extraneous
fprefix(F541)
🔇 Additional comments (3)
SIAB/spillage/api.py (3)
855-855:⚠️ Potential issueRemove the unnecessary
fprefix from the string.The string in the
os.path.joinfunction does not contain any placeholders, so thefprefix is not needed. This can cause confusion and should be removed.-running = read_running_scf_log(os.path.join(outdir, f"running_{params.get('calculation', 'scf')}.log")) +running = read_running_scf_log(os.path.join(outdir, "running_{params.get('calculation', 'scf')}.log"))Alternatively, if the intention was to include the value of
params.get('calculation', 'scf')in the filename, ensure that thefprefix is used correctly:running = read_running_scf_log(os.path.join(outdir, f"running_{params.get('calculation', 'scf')}.log"))If
params.get('calculation', 'scf')should not be treated as a string literal, thefprefix is appropriate.Please confirm whether the placeholder is intended in the string. If so, the
fprefix is correct, and no changes are needed.🧰 Tools
🪛 Ruff
855-855: f-string without any placeholders
Remove extraneous
fprefix(F541)
476-476:⚠️ Potential issueRename the
iterfunction to avoid shadowing built-in names.Naming a function
itershadows the built-initer()function in Python, which can lead to unexpected behavior or confusion. It is advisable to use a different function name that reflects its purpose more precisely.Consider renaming the function to something like
iterate_rcuts,process_iterations, orrun_optimization.-def iter(siab_settings: dict, calculation_settings: list, folders: list): +def run_optimization(siab_settings: dict, calculation_settings: list, folders: list):Likely invalid or redundant comment.
554-555: Ensure correct aggregation ofnzetavalues.In the calculation of the average
nzeta, the resizing and addition of arrays may lead to incorrect values if the shapes do not match as expected. Verify that the aggregation correctly averages thenzetavalues over all folders.Run the following script to check the aggregation logic:
Ensure that the outputted
nzetavalues are correctly combined and averaged in the main function.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (11)
SIAB/spillage/api.py (11)
52-77: LGTM! New function improves modularity.The addition of
_coef_opt_env_initfunction enhances the code's modularity by separating the initialization logic for the optimization environment. This is a good practice for maintainability.Consider enhancing the docstring by providing more details about the return value:
def _coef_opt_env_init(siab_settings: dict, folders: list): """ Initialize the optimization environment for orbital coefficient optimization. Parameters ---------- siab_settings: dict The settings for SIAB optimization, including orbital parameters. folders: list The folders where the ABACUS run information are stored. Returns ------- list[list[list[int]]] A nested list structure where: - The outer list represents different orbitals. - The middle list represents different folders or configurations. - The innermost list contains the band indexes for each configuration. """
79-147: LGTM! New function improves separation of concerns.The addition of
_coef_opt_jyfunction enhances the code structure by separating the jy-specific optimization logic. This improves readability and maintainability.Consider adding error handling for the case when no valid folders are found:
configs = list(set([folder for f in configs for folder in f])) if not configs: raise ValueError("No valid configuration folders found.")This will provide a more informative error message if the input folders are invalid or empty.
149-219: LGTM! New function handles pw-specific optimization.The addition of
_coef_opt_pwfunction improves the code structure by separating the pw-specific optimization logic. This enhances modularity and maintainability.For consistency with
_coef_opt_jy, consider adding error handling for the case when no valid folders are found:configs = list(set([folder for f in configs for folder in f])) if not configs: raise ValueError("No valid configuration folders found.")Also, consider adding a comment explaining why
nzetais not inferred in this function, unlike in_coef_opt_jy.
221-251: LGTM! Function refactored for improved flexibility.The changes to
_initguessfunction enhance its flexibility and usability. The new dictionary return value improves readability and makes it easier to extend the function in the future.Consider adding type hints to the function signature for improved code clarity:
def _initguess(nzeta: List[List[int]], folder: str, jy: bool = True, diagnosis: bool = True) -> Dict[str, Union[List[int], bool, str]]:Don't forget to import the necessary types from the
typingmodule.
253-304: LGTM! New function implements hierarchical optimization.The addition of
_do_onion_optfunction is a significant improvement, implementing a sophisticated hierarchical optimization strategy. The code is well-structured and commented, enhancing maintainability.To improve readability, consider using tuple unpacking in the for loop:
for iorb, (index_, nzeta_, iconfs_, ibands_) in enumerate(zip(deps, nzeta, iconfs, ibands)):This eliminates the need for separate indexing and makes the code more concise.
306-328: LGTM! Function refactored for improved modularity.The changes to
_coef_optfunction enhance its flexibility by handling both jy and pw cases. The delegation to specific functions improves code modularity and maintainability.Consider adding a check for unsupported basis types:
def _coef_opt(rcut: float, siab_settings: dict, folders: list, jy: bool = False): if jy: call = _coef_opt_jy elif not jy: call = _coef_opt_pw else: raise ValueError(f"Unsupported basis type: {jy}") # ... rest of the functionThis will provide a more informative error message if an unsupported basis type is passed.
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603-635: LGTM! New function improves nzeta inference.The addition of
_nzeta_mean_conffunction is a significant improvement, implementing a sophisticated strategy for inferring nzeta values across multiple configurations. The code is well-structured and commented, enhancing maintainability.To improve robustness, consider adding a check for empty folders:
def _nzeta_mean_conf(nbands, folders): # ... existing code ... if not folders: raise ValueError("No folders provided for nzeta inference.") nzeta = np.array([0]) # ... rest of the functionThis will prevent potential issues if an empty list of folders is passed to the function.
637-687: LGTM! New function provides detailed nzeta inference.The addition of
_nzeta_inferfunction is a valuable improvement, implementing a detailed process for inferring nzeta values from a single structure. The code is well-structured and commented, enhancing maintainability.Consider adding error handling for file reading operations:
try: params = read_input_script(os.path.join(folder, "INPUT")) running = read_running_scf_log(os.path.join(outdir, f"running_{params.get('calculation', 'scf')}.log")) # ... other file reading operations ... except FileNotFoundError as e: raise ValueError(f"Required file not found: {e.filename}")This will provide more informative error messages if required files are missing.
689-713: LGTM! New function implements wll matrix folding.The addition of
_wll_foldfunction is a useful improvement, implementing a specific strategy for folding the wll matrix. The code is concise and focused, enhancing modularity.To improve code clarity, consider adding a brief comment explaining the folding strategy:
def _wll_fold(wll, nband): # ... existing code ... for ib in nband: wlb = np.sum(wll[ib].real, 1) # Fold by dividing by the degeneracy of each angular momentum wll_fold += wlb / degen return wll_foldThis comment helps readers understand the purpose of the division by
degen.
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715-758: LGTM! New function provides detailed nzeta analysis.The addition of
_nzeta_analysisfunction is a valuable improvement, implementing a detailed process for analyzing nzeta values from the initial guess. The code is well-structured and commented, enhancing maintainability.Consider adding a check for valid
lmaxmaxvalues:lmaxmax = len(running["nzeta"][itype]) - 1 if lmaxmax < 0: raise ValueError(f"Invalid lmaxmax value: {lmaxmax}. Check the 'nzeta' data in the running log.")This will prevent potential issues if the
nzetadata in the running log is unexpected or invalid.
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760-1195: LGTM! Comprehensive test coverage added.The addition of the
TestAPIclass significantly improves the test coverage of the module. The test cases cover various scenarios and edge cases, which is crucial for ensuring the reliability of the code.To improve the readability and maintainability of the test class, consider the following suggestions:
Split long test methods into smaller, more focused tests. For example,
test_orb_matricescould be split into separate tests for old and new file formats.Use setup and teardown methods to reduce code duplication. For example:
def setUp(self): self.test_folder = "test_orb_matrices" os.makedirs(self.test_folder, exist_ok=True) def tearDown(self): shutil.rmtree(self.test_folder)
- Use parameterized tests for similar test cases with different inputs. For example, in
test_nzeta_mean_conf:@parameterized.expand([ (4, [1, 1, 0]), (5, [2, 1, 0]), (10, [2, 1, 1]), ]) def test_nzeta_mean_conf_monomer_gamma(self, nbands, expected): fpath = os.path.join(self.here, "testfiles/Si/jy-7au/monomer-gamma/") folders = [fpath] nzeta = _nzeta_mean_conf(nbands, folders) nzeta = [int(np.round(nz)) for nz in nzeta] self.assertEqual(nzeta, expected)These changes will make the tests more modular, easier to maintain, and simpler to extend in the future.
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934-934: f-string without any placeholders
Remove extraneous
fprefix(F541)
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📒 Files selected for processing (1)
- SIAB/spillage/api.py (12 hunks)
🧰 Additional context used
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SIAB/spillage/api.py
347-347: Ambiguous variable name:
l(E741)
375-375: Ambiguous variable name:
l(E741)
934-934: f-string without any placeholders
Remove extraneous
fprefix(F541)
🔇 Additional comments (1)
SIAB/spillage/api.py (1)
Line range hint
1-1195: Overall, excellent refactoring and improvements!The changes in this file represent a significant enhancement to the codebase:
- Improved modularity with the introduction of separate functions for jy and pw basis optimizations.
- Enhanced flexibility in handling different configurations and basis types.
- Addition of new utility functions for nzeta inference and analysis.
- Comprehensive test coverage with the new TestAPI class.
These changes will greatly improve the maintainability, readability, and reliability of the code. The separation of concerns and the addition of detailed comments make the code easier to understand and modify in the future.
While minor suggestions have been made in previous comments, there are no major issues left unaddressed. Great work on this refactoring effort!
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🪛 Ruff
347-347: Ambiguous variable name:
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375-375: Ambiguous variable name:
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Summary by CodeRabbit
New Features
Bug Fixes
Refactor