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Remi-Gau / nilearn

Machine learning for NeuroImaging in Python
http://nilearn.github.io
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Sourcery refactored main branch #1

Closed sourcery-ai[bot] closed 1 year ago

sourcery-ai[bot] commented 2 years ago

Branch main refactored by Sourcery.

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pep8speaks commented 2 years ago

Hello @sourcery-ai[bot]! Thanks for opening this PR. We checked the lines you've touched for PEP 8 issues, and found:

Line 41:80: E501 line too long (81 > 79 characters) Line 45:80: E501 line too long (85 > 79 characters) Line 49:80: E501 line too long (85 > 79 characters)

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Line 660:13: W503 line break before binary operator Line 1402:5: E303 too many blank lines (2) Line 1629:80: E501 line too long (145 > 79 characters)

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Line 43:21: E231 missing whitespace after ':' Line 43:21: E701 multiple statements on one line (colon) Line 43:22: E225 missing whitespace around operator Line 571:51: E127 continuation line over-indented for visual indent Line 571:80: E501 line too long (85 > 79 characters) Line 622:80: E501 line too long (94 > 79 characters) Line 883:80: E501 line too long (83 > 79 characters) Line 896:80: E501 line too long (114 > 79 characters) Line 908:80: E501 line too long (87 > 79 characters) Line 915:80: E501 line too long (210 > 79 characters)

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sourcery-ai[bot] commented 2 years ago

Sourcery Code Quality Report

✅  Merging this PR will increase code quality in the affected files by 0.25%.

Quality metrics Before After Change
Complexity 6.97 ⭐ 6.66 ⭐ -0.31 👍
Method Length 124.36 😞 123.69 😞 -0.67 👍
Working memory 10.51 😞 10.53 😞 0.02 👎
Quality 56.08% 🙂 56.33% 🙂 0.25% 👍
Other metrics Before After Change
Lines 28225 28307 82
Changed files Quality Before Quality After Quality Change
doc/conf.py 60.85% 🙂 61.82% 🙂 0.97% 👍
doc/sphinxext/gh_substitutions.py 83.22% ⭐ 82.87% ⭐ -0.35% 👎
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_demo_plotting.py 68.41% 🙂 68.22% 🙂 -0.19% 👎
examples/01_plotting/plot_haxby_masks.py 55.38% 🙂 55.28% 🙂 -0.10% 👎
examples/01_plotting/plot_surf_atlas.py 44.18% 😞 45.71% 😞 1.53% 👍
examples/01_plotting/plot_surf_stat_map.py 48.66% 😞 48.61% 😞 -0.05% 👎
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 37.67% 😞 37.62% 😞 -0.05% 👎
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_haxby_searchlight_surface.py 48.61% 😞 48.50% 😞 -0.11% 👎
examples/02_decoding/plot_miyawaki_encoding.py 48.46% 😞 49.16% 😞 0.70% 👍
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_atlas_comparison.py 62.80% 🙂 62.19% 🙂 -0.61% 👎
examples/03_connectivity/plot_compare_decomposition.py 50.42% 🙂 50.40% 🙂 -0.02% 👎
examples/03_connectivity/plot_data_driven_parcellations.py 36.39% 😞 36.39% 😞 0.00%
examples/03_connectivity/plot_group_level_connectivity.py 32.03% 😞 32.03% 😞 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 48.56% 😞 48.55% 😞 -0.01% 👎
examples/04_glm_first_level/plot_fiac_analysis.py 63.47% 🙂 63.38% 🙂 -0.09% 👎
examples/04_glm_first_level/plot_hrf.py 70.48% 🙂 70.14% 🙂 -0.34% 👎
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 40.98% 😞 41.00% 😞 0.02% 👍
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 49.85% 😞 49.83% 😞 -0.02% 👎
examples/05_glm_second_level/plot_second_level_design_matrix.py 70.41% 🙂 70.67% 🙂 0.26% 👍
examples/05_glm_second_level/plot_second_level_one_sample_test.py 41.55% 😞 41.55% 😞 0.00%
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 38.46% 😞 38.46% 😞 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_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% ⭐ 74.82% 🙂 -0.50% 👎
maint_tools/show-python-packages-versions.py 88.79% ⭐ 88.71% ⭐ -0.08% 👎
nilearn/masking.py 57.03% 🙂 57.30% 🙂 0.27% 👍
nilearn/signal.py 57.62% 🙂 58.38% 🙂 0.76% 👍
nilearn/version.py 77.70% ⭐ 77.69% ⭐ -0.01% 👎
nilearn/_utils/cache_mixin.py 43.70% 😞 43.68% 😞 -0.02% 👎
nilearn/_utils/class_inspect.py 70.08% 🙂 70.26% 🙂 0.18% 👍
nilearn/_utils/data_gen.py 42.02% 😞 41.48% 😞 -0.54% 👎
nilearn/_utils/docs.py 46.05% 😞 48.36% 😞 2.31% 👍
nilearn/_utils/glm.py 68.67% 🙂 68.75% 🙂 0.08% 👍
nilearn/_utils/helpers.py 83.91% ⭐ 82.54% ⭐ -1.37% 👎
nilearn/_utils/logger.py 55.68% 🙂 61.56% 🙂 5.88% 👍
nilearn/_utils/niimg.py 70.49% 🙂 70.89% 🙂 0.40% 👍
nilearn/_utils/niimg_conversions.py 34.18% 😞 34.84% 😞 0.66% 👍
nilearn/_utils/numpy_conversions.py 41.17% 😞 44.93% 😞 3.76% 👍
nilearn/_utils/path_finding.py 82.74% ⭐ 82.58% ⭐ -0.16% 👎
nilearn/_utils/segmentation.py 57.52% 🙂 57.10% 🙂 -0.42% 👎
nilearn/_utils/testing.py 62.25% 🙂 64.27% 🙂 2.02% 👍
nilearn/connectome/connectivity_matrices.py 53.07% 🙂 53.39% 🙂 0.32% 👍
nilearn/connectome/tests/test_connectivity_matrices.py 49.82% 😞 51.21% 🙂 1.39% 👍
nilearn/datasets/_testing.py 77.87% ⭐ 77.92% ⭐ 0.05% 👍
nilearn/datasets/atlas.py 52.99% 🙂 53.21% 🙂 0.22% 👍
nilearn/datasets/func.py 46.67% 😞 46.51% 😞 -0.16% 👎
nilearn/datasets/neurovault.py 71.74% 🙂 71.87% 🙂 0.13% 👍
nilearn/datasets/struct.py 58.57% 🙂 59.82% 🙂 1.25% 👍
nilearn/datasets/utils.py 41.25% 😞 41.02% 😞 -0.23% 👎
nilearn/datasets/data/convert_templates.py 48.43% 😞 48.35% 😞 -0.08% 👎
nilearn/datasets/tests/test_atlas.py 70.66% 🙂 70.84% 🙂 0.18% 👍
nilearn/datasets/tests/test_func.py 70.33% 🙂 70.14% 🙂 -0.19% 👎
nilearn/datasets/tests/test_neurovault.py 76.20% ⭐ 76.61% ⭐ 0.41% 👍
nilearn/datasets/tests/test_struct.py 73.18% 🙂 73.40% 🙂 0.22% 👍
nilearn/datasets/tests/test_testing.py 73.13% 🙂 73.16% 🙂 0.03% 👍
nilearn/datasets/tests/test_utils.py 65.55% 🙂 65.51% 🙂 -0.04% 👎
nilearn/decoding/decoder.py 41.44% 😞 41.52% 😞 0.08% 👍
nilearn/decoding/fista.py 14.28% ⛔ 14.80% ⛔ 0.52% 👍
nilearn/decoding/objective_functions.py 77.42% ⭐ 77.55% ⭐ 0.13% 👍
nilearn/decoding/proximal_operators.py 29.17% 😞 30.36% 😞 1.19% 👍

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

File Function Complexity Length Working Memory Quality Recommendation
nilearn/_utils/data_gen.py create_fake_bids_dataset 57 ⛔ 557 ⛔ 24 ⛔ 4.63% ⛔ Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
nilearn/datasets/func.py fetch_localizer_contrasts 47 ⛔ 883 ⛔ 5.64% ⛔ Refactor to reduce nesting. Try splitting into smaller methods
nilearn/decoding/proximal_operators.py _prox_tvl1 45 ⛔ 473 ⛔ 28 ⛔ 6.04% ⛔ Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
nilearn/decoding/fista.py mfista 38 ⛔ 419 ⛔ 28 ⛔ 8.48% ⛔ Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
nilearn/datasets/utils.py _fetch_file 33 ⛔ 552 ⛔ 28 ⛔ 9.58% ⛔ Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions

Legend and Explanation

The emojis denote the absolute quality of the code:

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.

Please see our documentation here for details on how these metrics are calculated.

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