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Remi-Gau / nilearn

Machine learning for NeuroImaging in Python
http://nilearn.github.io
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TEST (Sourcery refactored) #21

Closed sourcery-ai[bot] closed 1 year ago

sourcery-ai[bot] commented 1 year ago

Pull Request #20 refactored by Sourcery.

If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.

NOTE: As code is pushed to the original Pull Request, Sourcery will re-run and update (force-push) this Pull Request with new refactorings as necessary. If Sourcery finds no refactorings at any point, this Pull Request will be closed automatically.

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sourcery-ai[bot] commented 1 year ago

Sourcery Code Quality Report

✅  Merging this PR will increase code quality in the affected files by 0.03%.

Quality metrics Before After Change
Complexity 4.48 ⭐ 4.22 ⭐ -0.26 👍
Method Length 213.71 ⛔ 213.59 ⛔ -0.12 👍
Working memory 11.96 😞 11.99 😞 0.03 👎
Quality 51.20% 🙂 51.23% 🙂 0.03% 👍
Other metrics Before After Change
Lines 11852 11788 -64
Changed files Quality Before Quality After Quality Change
examples/00_tutorials/plot_decoding_tutorial.py 40.11% 😞 40.10% 😞 -0.01% 👎
examples/01_plotting/plot_atlas.py 83.28% ⭐ 83.00% ⭐ -0.28% 👎
examples/01_plotting/plot_carpet.py 49.87% 😞 49.80% 😞 -0.07% 👎
examples/01_plotting/plot_colormaps.py 48.95% 😞 52.19% 🙂 3.24% 👍
examples/01_plotting/plot_haxby_masks.py 55.38% 🙂 55.28% 🙂 -0.10% 👎
examples/01_plotting/plot_visualization.py 66.39% 🙂 66.22% 🙂 -0.17% 👎
examples/02_decoding/plot_haxby_anova_svm.py 54.70% 🙂 54.61% 🙂 -0.09% 👎
examples/02_decoding/plot_haxby_different_estimators.py 30.15% 😞 30.16% 😞 0.01% 👍
examples/02_decoding/plot_haxby_full_analysis.py 37.66% 😞 37.62% 😞 -0.04% 👎
examples/02_decoding/plot_haxby_grid_search.py 33.56% 😞 33.55% 😞 -0.01% 👎
examples/02_decoding/plot_haxby_multiclass.py 45.53% 😞 45.51% 😞 -0.02% 👎
examples/02_decoding/plot_haxby_searchlight.py 44.54% 😞 44.51% 😞 -0.03% 👎
examples/02_decoding/plot_miyawaki_encoding.py 48.45% 😞 49.16% 😞 0.71% 👍
examples/02_decoding/plot_miyawaki_reconstruction.py 49.48% 😞 49.26% 😞 -0.22% 👎
examples/02_decoding/plot_oasis_vbm.py 39.20% 😞 39.19% 😞 -0.01% 👎
examples/03_connectivity/plot_compare_decomposition.py 50.42% 🙂 50.40% 🙂 -0.02% 👎
examples/03_connectivity/plot_data_driven_parcellations.py 36.40% 😞 36.40% 😞 0.00%
examples/03_connectivity/plot_inverse_covariance_connectome.py 63.09% 🙂 63.02% 🙂 -0.07% 👎
examples/03_connectivity/plot_multi_subject_connectome.py 59.92% 🙂 59.82% 🙂 -0.10% 👎
examples/03_connectivity/plot_probabilistic_atlas_extraction.py 64.16% 🙂 64.09% 🙂 -0.07% 👎
examples/03_connectivity/plot_signal_extraction.py 43.20% 😞 38.95% 😞 -4.25% 👎
examples/03_connectivity/plot_sphere_based_connectome.py 34.90% 😞 34.90% 😞 0.00%
examples/04_glm_first_level/plot_bids_features.py 48.54% 😞 48.53% 😞 -0.01% 👎
examples/04_glm_first_level/plot_fir_model.py 49.79% 😞 49.79% 😞 0.00%
examples/04_glm_first_level/plot_first_level_details.py 58.58% 🙂 58.48% 🙂 -0.10% 👎
examples/04_glm_first_level/plot_predictions_residuals.py 38.09% 😞 38.09% 😞 0.00%
examples/04_glm_first_level/plot_spm_multimodal_faces.py 41.07% 😞 41.07% 😞 0.00%
examples/04_glm_first_level/plot_write_events_file.py 61.16% 🙂 61.13% 🙂 -0.03% 👎
examples/05_glm_second_level/plot_second_level_association_test.py 49.95% 😞 49.93% 😞 -0.02% 👎
examples/05_glm_second_level/plot_second_level_design_matrix.py 70.41% 🙂 70.67% 🙂 0.26% 👍
examples/06_manipulating_images/plot_compare_mean_image.py 81.23% ⭐ 80.94% ⭐ -0.29% 👎
examples/06_manipulating_images/plot_mask_computation.py 48.34% 😞 48.32% 😞 -0.02% 👎
examples/06_manipulating_images/plot_nifti_labels_simple.py 65.65% 🙂 65.56% 🙂 -0.09% 👎
examples/06_manipulating_images/plot_nifti_simple.py 59.74% 🙂 59.67% 🙂 -0.07% 👎
examples/06_manipulating_images/plot_roi_extraction.py 36.87% 😞 36.86% 😞 -0.01% 👎
examples/06_manipulating_images/plot_smooth_mean_image.py 80.68% ⭐ 80.53% ⭐ -0.15% 👎
examples/07_advanced/plot_advanced_decoding_scikit.py 38.45% 😞 38.45% 😞 0.00%
examples/07_advanced/plot_bids_analysis.py 49.34% 😞 49.30% 😞 -0.04% 👎
examples/07_advanced/plot_haxby_mass_univariate.py 40.17% 😞 40.16% 😞 -0.01% 👎
examples/07_advanced/plot_ica_neurovault.py 32.82% 😞 31.90% 😞 -0.92% 👎
examples/07_advanced/plot_ica_resting_state.py 66.56% 🙂 66.47% 🙂 -0.09% 👎
examples/07_advanced/plot_localizer_mass_univariate_methods.py 46.40% 😞 46.39% 😞 -0.01% 👎
examples/07_advanced/plot_neurovault_meta_analysis.py 75.32% ⭐ 73.23% 🙂 -2.09% 👎
nilearn/image/image.py 56.93% 🙂 56.91% 🙂 -0.02% 👎
nilearn/image/resampling.py 28.51% 😞 29.71% 😞 1.20% 👍
nilearn/image/tests/test_image.py 64.96% 🙂 64.96% 🙂 0.00%
nilearn/image/tests/test_resampling.py 61.74% 🙂 61.74% 🙂 0.00%

Here are some functions in these files that still need a tune-up:

File Function Complexity Length Working Memory Quality Recommendation
nilearn/image/resampling.py resample_img 54 ⛔ 901 ⛔ 26 ⛔ 4.16% ⛔ Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
nilearn/image/image.py new_img_like 31 😞 253 ⛔ 12 😞 27.14% 😞 Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
nilearn/image/resampling.py reorder_img 10 🙂 409 ⛔ 15 😞 33.36% 😞 Try splitting into smaller methods. Extract out complex expressions
nilearn/image/image.py _smooth_array 18 🙂 216 ⛔ 11 😞 38.94% 😞 Try splitting into smaller methods. Extract out complex expressions
nilearn/image/image.py clean_img 7 ⭐ 197 😞 20 ⛔ 39.46% 😞 Try splitting into smaller methods. Extract out complex expressions

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