[REF] optimize get_studies_by_mask
#891
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jdkent
changed the title
get_studies_by_mask
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@sourcery-ai review |
Reviewer's Guide by SourceryThis pull request optimizes the File-Level Changes
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Hey @jdkent - I've reviewed your changes and they look great!
Here's what I looked at during the review
- 🟡 General issues: 4 issues found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟢 Complexity: all looks good
- 🟢 Documentation: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment to tell me if it was helpful.
| mask_coords = np.vstack(np.where(mask_data)).T | ||
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| # Check for presence of coordinates in mask | ||
| in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1) |
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issue (performance): Potential performance issue with nested array operations
The nested array operations (np.all and np.any) could be computationally expensive, especially for large datasets. Consider optimizing this part of the code if performance becomes an issue.
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| # Check for presence of coordinates in mask | ||
| in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1) | ||
| found_ids = list(self.coordinates.loc[in_mask, "id"].unique()) |
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issue (performance): Check for potential performance impact of unique()
Calling unique() on a potentially large dataset could have performance implications. Ensure that this operation is efficient for the expected size of self.coordinates.
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what would be your suggestion to optimize this code for a large dataset?
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To optimize the code for a large dataset, you can consider the following suggestions:
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Use a Set for Uniqueness:
Instead of usingunique(), you can use a set to collect unique IDs. This can be more efficient for large datasets. -
Vectorized Operations:
Ensure that the operations are as vectorized as possible to leverage NumPy's performance benefits. -
Efficient Data Structures:
If the dataset is extremely large, consider using more memory-efficient data structures or libraries likepandaswithdaskfor out-of-core computation.
Here's a revised version of the code incorporating the use of a set:
mask_data = mask.get_fdata()
mask_coords = np.vstack(np.where(mask_data)).T
# Check for presence of coordinates in mask
in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1)
# Use a set to collect unique IDs
found_ids = set(self.coordinates.loc[in_mask, "id"])
# Convert the set back to a list if needed
found_ids = list(found_ids)This approach should help in managing the performance impact when dealing with large datasets. If you have any specific constraints or further details about the dataset size, feel free to share, and we can refine the approach further.
| distances = cdist(mask_ijk, dset_ijk) | ||
| distances = np.any(distances == 0, axis=0) | ||
| found_ids = list(self.coordinates.loc[distances, "id"].unique()) | ||
| mask_data = mask.get_fdata() |
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suggestion: Consider renaming mask_data to mask_fdata for consistency
Since mask.get_fdata() is being used, it might be clearer to name the variable mask_fdata to indicate that it holds the floating-point data array from the mask.
| mask_data = mask.get_fdata() | |
| mask_fdata = mask.get_fdata() |
| # Check for presence of coordinates in mask | ||
| in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1) |
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suggestion: Consider adding a brief explanation for the coordinate check
While comments are generally avoided, a brief explanation here could help future developers understand the purpose and logic behind this coordinate check.
| # Check for presence of coordinates in mask | |
| in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1) | |
| # Check if each coordinate in dset_ijk is present in mask_coords | |
| in_mask = np.any(np.all(dset_ijk[:, None] == mask_coords[None, :], axis=-1), axis=-1) |
This comes from a user trying to use get_studies_by_mask, but kept running out of memory on their machine, this makes the function more efficient.
Closes # .
Changes proposed in this pull request:
Summary by Sourcery
This pull request optimizes the
get_studies_by_maskfunction innimare/dataset.pyto improve memory efficiency, addressing issues where users experienced memory exhaustion.get_studies_by_maskfunction to improve memory efficiency by replacing the use ofcdistwith a more direct coordinate comparison approach.