add convertion for small type_map in deepmd system #676
Conversation
WalkthroughWalkthroughThe Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant sort_atom_names
participant TypeMapHandler
participant DataUpdater
User->>sort_atom_names: Call function with data and type_map
sort_atom_names->>TypeMapHandler: Determine which types to delete based on atom numbers
TypeMapHandler-->>sort_atom_names: Return updated types
sort_atom_names->>DataUpdater: Update atom names and types
DataUpdater-->>sort_atom_names: Return updated atom names and types
sort_atom_names-->>User: Return sorted atom names and updated types
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CodSpeed Performance ReportMerging #676 will not alter performanceComparing Summary
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Actionable comments posted: 2
Outside diff range and nitpick comments (2)
dpdata/utils.py (2)
Line range hint
6-6: Remove outdated version block.The version block checking for Python 3.8 is outdated and should be removed, as the minimum supported Python version is now higher.
- if sys.version_info >= (3, 8): - from typing import Literal - else: - from typing_extensions import Literal + from typing import LiteralThis change removes unnecessary code and simplifies the import statements.
Line range hint
125-125: Rename unused loop control variable.In the
uniq_atom_namesfunction, the loop control variableidxis not used and should be renamed to_to indicate its purpose.- for idx, ii in enumerate(data["atom_names"]): + for _, ii in enumerate(data["atom_names"]):This change makes it clear that the loop index is not used within the loop body.
| if not set(data["atom_names"]).issubset(set(type_map)): | ||
| # delete the types where atom_numbs == 0 | ||
| real_index = np.array(data["atom_numbs"]) > 0 | ||
| real_map = list(np.array(data["atom_names"])[real_index]) | ||
| assert set(real_map).issubset(set(type_map)) | ||
| data["atom_numbs"] = list(np.array(data["atom_numbs"])[real_index]) | ||
| idx = np.zeros(len(data["atom_names"]), dtype=int) | ||
| idx[real_index] = np.arange(len(real_map)) | ||
| data["atom_names"] = real_map | ||
| data["atom_types"] = idx[data["atom_types"]] |
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Refactor the type filtering logic for clarity and performance.
The logic to filter out atom types where atom_numbs equals zero is complex and may be hard to follow. Consider refactoring for better clarity and potentially improved performance.
- real_index = np.array(data["atom_numbs"]) > 0
- real_map = list(np.array(data["atom_names"])[real_index])
- assert set(real_map).issubset(set(type_map))
- data["atom_numbs"] = list(np.array(data["atom_numbs"])[real_index])
- idx = np.zeros(len(data["atom_names"]), dtype=int)
- idx[real_index] = np.arange(len(real_map))
- data["atom_names"] = real_map
- data["atom_types"] = idx[data["atom_types"]]
+ filtered_atoms = [(name, num) for name, num in zip(data["atom_names"], data["atom_numbs"]) if num > 0]
+ assert all(name in type_map for name, _ in filtered_atoms)
+ data["atom_names"], data["atom_numbs"] = zip(*filtered_atoms) if filtered_atoms else ([], [])
+ data["atom_types"] = np.arange(len(data["atom_names"]))[data["atom_types"]]This refactoring uses list comprehension for filtering and asserts, making the code more Pythonic and potentially faster by reducing the need for multiple numpy array conversions.
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.
| if not set(data["atom_names"]).issubset(set(type_map)): | |
| # delete the types where atom_numbs == 0 | |
| real_index = np.array(data["atom_numbs"]) > 0 | |
| real_map = list(np.array(data["atom_names"])[real_index]) | |
| assert set(real_map).issubset(set(type_map)) | |
| data["atom_numbs"] = list(np.array(data["atom_numbs"])[real_index]) | |
| idx = np.zeros(len(data["atom_names"]), dtype=int) | |
| idx[real_index] = np.arange(len(real_map)) | |
| data["atom_names"] = real_map | |
| data["atom_types"] = idx[data["atom_types"]] | |
| if not set(data["atom_names"]).issubset(set(type_map)): | |
| # delete the types where atom_numbs == 0 | |
| filtered_atoms = [(name, num) for name, num in zip(data["atom_names"], data["atom_numbs"]) if num > 0] | |
| assert all(name in type_map for name, _ in filtered_atoms) | |
| data["atom_names"], data["atom_numbs"] = zip(*filtered_atoms) if filtered_atoms else ([], []) | |
| data["atom_types"] = np.arange(len(data["atom_names"]))[data["atom_types"]] |
Optimize the handling of new atoms.
The current method for handling new atoms is efficient but could be further optimized by directly updating data within the add_atom_names function, thus avoiding potential issues with data consistency.
- new_atoms = [e for e in type_map if e not in data["atom_names"]]
- if new_atoms:
- data = add_atom_names(data, new_atoms)
+ data = add_atom_names(data, [e for e in type_map if e not in data["atom_names"]])This change ensures that data is always updated regardless of whether new atoms are added, simplifying the control flow and potentially reducing the risk of bugs.
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.
| if not set(data["atom_names"]).issubset(set(type_map)): | |
| # delete the types where atom_numbs == 0 | |
| real_index = np.array(data["atom_numbs"]) > 0 | |
| real_map = list(np.array(data["atom_names"])[real_index]) | |
| assert set(real_map).issubset(set(type_map)) | |
| data["atom_numbs"] = list(np.array(data["atom_numbs"])[real_index]) | |
| idx = np.zeros(len(data["atom_names"]), dtype=int) | |
| idx[real_index] = np.arange(len(real_map)) | |
| data["atom_names"] = real_map | |
| data["atom_types"] = idx[data["atom_types"]] | |
| if not set(data["atom_names"]).issubset(set(type_map)): | |
| # delete the types where atom_numbs == 0 | |
| real_index = np.array(data["atom_numbs"]) > 0 | |
| real_map = list(np.array(data["atom_names"])[real_index]) | |
| assert set(real_map).issubset(set(type_map)) | |
| data["atom_numbs"] = list(np.array(data["atom_numbs"])[real_index]) | |
| idx = np.zeros(len(data["atom_names"]), dtype=int) | |
| idx[real_index] = np.arange(len(real_map)) | |
| data["atom_names"] = real_map | |
| data["atom_types"] = idx[data["atom_types"]] | |
| data = add_atom_names(data, [e for e in type_map if e not in data["atom_names"]]) |
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